.*\\\")\\n for match in results_re.finditer(dat):\\n res_tab = match.group(0)\\n parser.feed(res_tab)\\n parser.close()\\n break\\n if len(results) <= 0:\\n break\\n page += 1\",\n \"def parse_category(self, response):\\n detail_links = response.css(\\\"a.detail-url-click::attr(href)\\\").extract()\\n meta = response.meta\\n self.logger.info(\\\"found %s products from page %s of category %s\\\",\\n len(detail_links), meta[\\\"page_num\\\"], meta[\\\"cate_name\\\"])\\n for link in detail_links:\\n if not link:\\n continue\\n yield response.follow(link, callback=self.parse_detail, meta=meta)\\n if detail_links:\\n meta = dict(meta)\\n meta[\\\"page_num\\\"] = int(meta[\\\"page_num\\\"]) + 1\\n base_url = meta[\\\"base_url\\\"]\\n link = \\\"{}---hot-{}.html\\\".format(base_url, meta[\\\"page_num\\\"])\\n self.logger.info(\\\"scheduled scraping request for page %s of category %s\\\",\\n meta[\\\"page_num\\\"], meta[\\\"cate_name\\\"])\\n yield response.follow(link, callback=self.parse_category, meta=meta)\",\n \"def search_multiple_words(words):\\n # YOUR CODE HERE #\\n pass # delete this when you write your code\",\n \"def browse_categories():\\n print(\\\"***** Find Businesses by Categories *****\\\")\\n while True:\\n print()\\n category = input(\\n 'Please enter a type of business (category) or type \\\"back\\\" or \\\"quit\\\": ')\\n print()\\n if category == \\\"quit\\\":\\n print(\\\"Goodbye!\\\")\\n sys.exit()\\n if category == \\\"back\\\":\\n return\\n\\n # create a regex pattern for business name\\n pattern = r\\\".*\\\" + re.escape(category) + r\\\".*\\\"\\n regx = re.compile(pattern, re.IGNORECASE)\\n\\n cursor = business_col.find({\\\"categories\\\": regx})\\n\\n business_objects = cursor.limit(10)\\n \\n if cursor.count() == 0:\\n print(\\\"No businesses found with given category.\\\")\\n continue\\n for business_object in business_objects:\\n print_business(business_object)\",\n \"def cat_results():\\n\\n cats = petfinder.search_data_map()\\n cats = list(cats.values())\\n\\n return render_template('search_results.html',\\n cats=cats)\",\n \"async def search(self, ctx: Context, category: str, *, query: str) -> None:\\n if category not in config.basic_search_categories:\\n await ctx.send(f\\\"Invalid Category! ```Available Categories : {', '.join(config.basic_search_categories)}```\\\")\\n return\\n await self._basic_search(ctx, query, category)\",\n \"def search():\\r\\n\\r\\n # determines the limit and the offset for the pagination\\r\\n if 'limit' in request.args:\\r\\n try:\\r\\n limit = int(request.args['limit'])\\r\\n\\r\\n except:\\r\\n # default limit on error\\r\\n limit = 4\\r\\n\\r\\n else:\\r\\n # default limit\\r\\n limit = 4\\r\\n\\r\\n if 'offset' in request.args:\\r\\n try:\\r\\n offset = int(request.args['offset'])\\r\\n\\r\\n except:\\r\\n # default offset on error\\r\\n offset = 0\\r\\n\\r\\n else:\\r\\n # default offset\\r\\n offset = 0\\r\\n\\r\\n # searches for all the recipes\\r\\n if request.args['collection'] == 'recipes':\\r\\n if request.args['find'] == 'all':\\r\\n # initializes page title and header\\r\\n page_title = 'All recipes'\\r\\n page_header = 'All recipes search results:'\\r\\n\\r\\n # determines all recipe titles in ascendent order\\r\\n get_titles = mongo.db.recipes.find().sort('title')\\r\\n\\r\\n try:\\r\\n # determines the title at the offset\\r\\n start = get_titles[offset]['title']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='recipes',\\r\\n find='all',\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n # determines the next link for the template\\r\\n if offset + limit >= get_titles.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='recipes',\\r\\n find='all',\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n recipes = mongo.db.recipes.find(\\r\\n {'title': {'$gte': start}}).sort('title').limit(limit)\\r\\n\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n recipes=recipes,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n # searches for all recipes in the selected category\\r\\n else:\\r\\n # initializes page title and header\\r\\n page_title = 'Recipes by category'\\r\\n page_header = (\\r\\n 'Recipes in the \\\"{}\\\" category search results:'.format(\\r\\n request.args['find'])\\r\\n )\\r\\n\\r\\n # determines all recipe titles in ascendent order\\r\\n get_titles = mongo.db.recipes.find(\\r\\n {'category': request.args['find']}).sort('title')\\r\\n\\r\\n try:\\r\\n # determines the title at the offset\\r\\n start = get_titles[offset]['title']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='recipes',\\r\\n find=request.args['find'],\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n if offset + limit >= get_titles.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='recipes',\\r\\n find=request.args['find'],\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n recipes = mongo.db.recipes.find(\\r\\n {'category': request.args['find'], 'title': {'$gte': start}}\\r\\n ).sort('title').limit(limit)\\r\\n\\r\\n if recipes.count() > 0:\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n recipes=recipes,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n else:\\r\\n # returns an error message if there is no result\\r\\n return render_template('error.html', msg='No results found!')\\r\\n\\r\\n # searches for the recipe categories\\r\\n elif request.args['collection'] == 'recipe_categories':\\r\\n\\r\\n # initializes page title and header\\r\\n page_title = 'Recipe categories'\\r\\n page_header = 'Recipe category search results:'\\r\\n\\r\\n # determines all category names in ascendent order\\r\\n get_names = mongo.db.recipe_categories.find().sort('name')\\r\\n\\r\\n try:\\r\\n # determines the name at the offset\\r\\n start = get_names[offset]['name']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='recipe_categories',\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n # determines the next link for the template\\r\\n if offset + limit >= get_names.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='recipe_categories',\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n recipe_categories = mongo.db.recipe_categories.find(\\r\\n {'name': {'$gte': start}}).sort('name').limit(limit)\\r\\n\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n recipe_categories=recipe_categories,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n # searches for all the appliances\\r\\n elif request.args['collection'] == 'appliances':\\r\\n if request.args['find'] == 'all':\\r\\n # initializes page title and header\\r\\n page_title = 'All appliances'\\r\\n page_header = 'All appliances search results:'\\r\\n\\r\\n # determines all appliance brands in ascendent order\\r\\n get_brands = mongo.db.appliances.find().sort('brand')\\r\\n\\r\\n try:\\r\\n # determines the brand at the offset\\r\\n start = get_brands[offset]['brand']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='appliances',\\r\\n find='all',\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n # determines the next link for the template\\r\\n if offset + limit >= get_brands.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='appliances',\\r\\n find='all',\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n appliances = mongo.db.appliances.find(\\r\\n {'brand': {'$gte': start}}).sort('brand').limit(limit)\\r\\n\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n appliances=appliances,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n # searches for all appliances in the selected category\\r\\n else:\\r\\n # initializes page title and header\\r\\n page_title = 'Appliances by category'\\r\\n page_header = (\\r\\n 'Appliances in the \\\"{}\\\" category search results:'.format(\\r\\n request.args['find'])\\r\\n )\\r\\n\\r\\n # determines all appliance brands in ascendent order\\r\\n get_brands = mongo.db.appliances.find(\\r\\n {'type': request.args['find']}).sort('brand')\\r\\n\\r\\n try:\\r\\n # determines the brand at the offset\\r\\n start = get_brands[offset]['brand']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='appliances',\\r\\n find=request.args['find'],\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n # determines the next link for the template\\r\\n if offset + limit >= get_brands.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='appliances',\\r\\n find=request.args['find'],\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n appliances = mongo.db.appliances.find(\\r\\n {'type': request.args['find'], 'brand': {'$gte': start}}\\r\\n ).sort('brand').limit(limit)\\r\\n\\r\\n if appliances.count() > 0:\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n appliances=appliances,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n else:\\r\\n # returns an error message if there is no result\\r\\n return render_template('error.html', msg='No results found!')\\r\\n\\r\\n # searches for the appliance categories\\r\\n elif request.args['collection'] == 'appliance_categories':\\r\\n # initializes page title and header\\r\\n page_title = 'Appliance categories'\\r\\n page_header = 'Appliance category search results:'\\r\\n\\r\\n # determines all category names in ascendent order\\r\\n get_names = mongo.db.appliance_categories.find().sort('name')\\r\\n\\r\\n try:\\r\\n # determines the name at the offset\\r\\n start = get_names[offset]['name']\\r\\n\\r\\n except IndexError:\\r\\n # returns an error page on IndexError\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Invalid index error! (/search)'\\r\\n )\\r\\n\\r\\n # determines the previous link for the template\\r\\n if offset - limit < 0:\\r\\n prev_link = ''\\r\\n\\r\\n else:\\r\\n prev_link = url_for(\\r\\n 'search',\\r\\n collection='appliance_categories',\\r\\n limit=str(limit),\\r\\n offset=str(offset - limit)\\r\\n )\\r\\n\\r\\n # determines the next link for the template\\r\\n if offset + limit >= get_names.count():\\r\\n next_link = ''\\r\\n\\r\\n else:\\r\\n next_link = url_for(\\r\\n 'search',\\r\\n collection='appliance_categories',\\r\\n limit=str(limit),\\r\\n offset=str(offset + limit)\\r\\n )\\r\\n\\r\\n # determines the subset\\r\\n appliance_categories = mongo.db.appliance_categories.find(\\r\\n {'name': {'$gte': start}}).sort('name').limit(limit)\\r\\n\\r\\n # returns the subset\\r\\n return render_template(\\r\\n 'search.html',\\r\\n appliance_categories=appliance_categories,\\r\\n page_title=page_title,\\r\\n page_header=page_header,\\r\\n prev=prev_link,\\r\\n next=next_link\\r\\n )\\r\\n\\r\\n else:\\r\\n # returns an error message on incorrect argument\\r\\n return render_template(\\r\\n 'error.html',\\r\\n msg='Bad argument error! (/search)'\\r\\n )\",\n \"def starwars_search(self, category, attribute, filters):\\n self.load_response(category)\\n while self.counter != int(self.response_info['count']):\\n self.attribute_search(attribute, filters)\\n self.load_next_response()\",\n \"def category_multi_match(self,word_list, input_dataframe):\\n temp_set = set()\\n for word in word_list:\\n # initial union with empty set\\n if len(temp_set) == 0:\\n temp_set = temp_set.union(self.index_dict[word])\\n # filtered unnecessary index\\n else:\\n temp_set = temp_set.intersection(self.index_dict[word])\\n # final list return\\n category_list = list(temp_set)\\n # filtered dataframe return\\n filtered_df = input_dataframe.loc[category_list]\\n return filtered_df\",\n \"def main():\\n domain_list = []\\n base_url = \\\"http://localhost:9200/latest-tor/_search?pretty&size=9000&_source=title,domain\\\"\\n keywords_list = ['preteen', 'loli', 'lolita', 'jailbait', 'pthc', 'best cp',\\n '\\\"child porn\\\"', '\\\"kid porn\\\"', '\\\"child sex\\\"', '\\\"cp video\\\"',\\n '\\\"nude children\\\"', '\\\"cp porn\\\"', '\\\"free child porn\\\"', 'kinderporn',\\n '\\\"child rape\\\"', '\\\"toddler porn\\\"', '\\\"kids videos\\\"', '\\\"cp videos\\\"',\\n 'lolilust', '\\\"pedo porno\\\"', '\\\"pedo content\\\"', 'underage', '\\\"cp pack\\\"',\\n 'loliporn', 'pedofamily', '\\\"cp database\\\"', '\\\"pedo webcams\\\"', 'lolitacity']\\n '\\\"xxx child\\\"', '\\\"xxx underage\\\"', '\\\"young forbidden\\\"']\\n search_terms = []\\n for index, term in enumerate(keywords_list):\\n search_terms.append(term)\\n if len(search_terms) >= 10 or index + 1 == len(keywords_list):\\n url = base_url + \\\"&q=(\\\" + \\\" OR \\\".join(search_terms).replace(\\\" \\\", \\\"%20\\\") + \\\")\\\"\\n search(url, domain_list)\\n search_terms = []\",\n \"def search():\\n query = request.form.get(\\\"query\\\")\\n category = list(mongo.db.tips.find({\\\"$text\\\": {\\\"$search\\\": query}}))\\n return render_template(\\\"tips.html\\\", category=category)\",\n \"def search(query):\\n r = requests.get(BASE_URL + str(query))\\n page_body = r.text\\n # Hand the page source to Beautiful Soup\\n\\n soup = BeautifulSoup(page_body, 'html.parser')\\n \\n product_item = soup.select('div.product-info-item')\\n if(len(product_item)==0):\\n product_item = soup.select('div.cat')\\n #get the cateegory\\n product_item = product_item[0]\\n category = str(product_item.find_all('a'))\\n category = category[category.find(\\\">\\\")+1:-5]\\n \\n url = str(soup.findAll('meta',property=\\\"og:url\\\"))\\n url_splitted = url.split('/')\\n print(url)\\n #parent_category = url_splitted[4]\\n if(len(url)>20):\\n parent_category = url_splitted[4]\\n else:\\n parent_category = None\\n\\n \\n return category, parent_category\",\n \"def start_requests(self):\\n\\n with open(os.path.join(os.path.dirname(__file__), \\\"../resources/mapemall_categories.csv\\\")) as categories:\\n for category in csv.DictReader(categories):\\n category_text=category[\\\"category\\\"]\\n url=str(MapemallCrawlerSpider.start_urls[0])+category_text\\n # The meta is used to send our search text into the parser as metadata\\n yield scrapy.Request(url, callback = self.parse, meta = {\\\"category_text\\\": category_text})\",\n \"def searchItems(request, keywords):\\n if not request.is_ajax():\\n raise Http404\\n\\n hashValue = hashlib.md5(keywords.encode()).hexdigest()\\n items = cache.get(\\\"search_%s\\\" % hashValue)\\n\\n if items is None:\\n # List wasn't in cache, let's fetch it from the database.\\n itemQuery = Item.objects.filter(typeName__icontains=keywords, published=True,\\n categoryID_id__in=CATEGORIES_ITEMS)\\n\\n # Augment with CPU, PG and slot info.\\n items = getCPUandPGandSlot(itemQuery)\\n\\n # Now store it in cache.\\n cache.set(\\\"search_%s\\\" % hashValue, items)\\n\\n return render_to_response(\\\"items.html\\\", locals())\",\n \"def search_categorie(input) :\\n j = _jpdb()\\n _input = _process_search_input(input)\\n if not _input : return None\\n f = j.base_format\\n q = Query().select(f.categories, f.categories.id, f.categories.name)\\n q.where().equal(f.categories.name, _input)\\n categorie_data = j.executeQuery(q)\\n\\n if categorie_data: \\n cat_id, cat_name = categorie_data[0]\\n examples = _create_examples(j.list_word_by_categorie, cat_name)\\n return SelectorResult('categorie', cat_id, cat_name, *examples)\",\n \"def add_aliments(self):\\n for cat in Category.objects.all():\\n print(CAT_SEARCH.format(cat.name))\\n n_page = 1\\n counter = 0\\n while counter < MAX_PRODUCTS_KEEPED:\\n imported_aliments = self._scratch_category(cat.name, n_page)\\n if not imported_aliments:\\n print(CAT_FINDING_FAIL.format(counter, cat.name))\\n break\\n\\n for aliment in imported_aliments:\\n if self._no_full_info(aliment):\\n continue\\n if self._redundant_info(aliment, cat):\\n continue\\n\\n counter = self._save(aliment, cat, counter)\\n n_page += 1\\n\\n print(CAT_FINDING_SUCCESS.format(cat))\",\n \"def doSearch(self):\\n r = \\\"https://www.gumtree.pl/s-%s/%s/v1c9000l3200008p1?sort=dt&order=desc\\\" % (self.category, self.location)\\n print (r)\\n request = requests.get(\\\"https://www.gumtree.pl/s-%s/%s/v1c9000l3200008p1?sort=dt&order=desc\\\" % (self.category, self.location), headers=REQUEST_HEADERS)\\n\\n\\n if request.status_code == 200:\\n # Got a valid response\\n\\n listingResult = []\\n\\n souped = BeautifulSoup(request.text, \\\"html5lib\\\")\\n for listings_wrapper in souped.find_all(\\\"li\\\", class_=\\\"result pictures\\\"):\\n title = listings_wrapper.find(\\\"a\\\", class_=\\\"href-link\\\").string\\n url = \\\"https://www.gumtree.pl\\\" + listings_wrapper.find(class_=\\\"href-link\\\").get(\\\"href\\\")\\n price = listings_wrapper.find(\\\"span\\\", class_=\\\"amount\\\").string\\n description = listings_wrapper.find(class_=\\\"description hidden\\\").string\\n creationDate = listings_wrapper.find(class_=\\\"creation-date\\\").find_all(\\\"span\\\")[1].string\\n category = listings_wrapper.find(class_=\\\"category-location\\\").find(\\\"span\\\").string\\n print(title)\\n searchResultItem = GTRoomItemMain(creationDate, title, category, url, price, description)\\n listingResult.append(searchResultItem)\\n return listingResult\\n else:\\n # TODO: Add error handling\\n print (\\\"Server returned code: \\\" + request.status_code)\\n return []\",\n \"def search_item(search_term, next=False, page=0, board=0):\\n if next == False:\\n page = requests.get(\\\"https://www.nairaland.com/search?q=\\\" + urllib.parse.quote_plus(str(search_term)) + \\\"&board=\\\"+str(board))\\n else:\\n page = requests.get(\\\"https://www.nairaland.com/search/\\\"\\n + str(search_term) + \\\"/0/\\\"+str(board)+\\\"/0/1\\\" + str(page))\\n soup = BeautifulSoup(page.content, 'html.parser')\\n\\n comments = soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"narrow\\\"})\\n\\n return comments\",\n \"def get_items_from_category(save_db=False):\\n query_result = pd.read_sql_query(\\\"\\\"\\\"SELECT c1.id, c1.name, c1.parent_id, c1.url\\n FROM categories c1 LEFT OUTER JOIN categories c2\\n ON c1.id = c2.parent_id\\n WHERE c2.parent_id IS NULL\\n LIMIT 1400 OFFSET 1300\\\"\\\"\\\", conn)\\n for i in query_result.itertuples():\\n name = i.name[:-10].strip()\\n cat_url = i.url\\n cat_id = i.id\\n quantity = i.name[-10:].strip()\\n \\n for i in range(100):\\n url = cat_url + f'&page={i+1}'\\n print(url)\\n soup = get_url(url)\\n \\n result = []\\n \\\"\\\"\\\" item: div 'product-item' > div 'content'\\n img: img 'product-imgage'\\n title: p 'title'\\n price: span 'price-regular'\\n sale-tag: span 'sale-tag'\\n final-price: span 'final-price'\\n \\\"\\\"\\\"\\n try:\\n div_container = soup.find_all('div', {'class': 'product-item'})\\n except Exception as err:\\n print('ERROR BY DIV FINDALL: ', err)\\n if div_container:\\n for div in div_container:\\n # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\\n item_id = None\\n item_path = div['data-category']\\n item_name = div.a['title']\\n brand = div['data-brand']\\n item_url = div.a['href']\\n img_url = div.img['src']\\n regular_price = div.find('span', {'class': 'price-regular'}).text\\n sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\\n final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\\n\\n item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\\n img_url, regular_price, sale_tag, final_price)\\n if save_db:\\n item.save_into_db()\\n print(f'SAVE {item_name} INTO DTB')\\n result.append(item)\\n else:\\n break\",\n \"def main():\\n entries = get_feed_entries()\\n \\n entries.sort(key=lambda x: x.date)\\n while True:\\n term = input(\\\"Search Term? \\\")\\n if term == '':\\n print('Please provide a search term')\\n continue\\n if term == 'q':\\n print('Bye')\\n break\\n \\n \\n matches = 0\\n for entry in entries:\\n found = filter_entries_by_tag(term,entry)\\n if found:\\n print(entry.title)\\n matches += 1\\n\\n \\n if matches == 1:\\n print('1 entry matched')\\n else:\\n print(f\\\"{matches} entries matched\\\")\",\n \"def get_category_links(self):\\n self.logger.info(\\\"Gathering categories links...\\\")\\n try:\\n category_link = \\\"services-catalog__column-title ui-link _t37mbJS _2fIr6we _2l1CpUa\\\"\\n self.driver.get(self.MAIN_URL)\\n categories = self.driver.find_elements_by_class_name(\\\"services-catalog__content\\\")\\n elems = categories[0].find_elements_by_xpath(\\\"//a[@class]\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured! Cannot gather category list!\\\")\\n return\\n search_cat = True\\n # Compose two links lists: for subject categories, and for generic categories\\n for i, elem in enumerate(elems):\\n if elem.get_attribute(\\\"class\\\") == category_link:\\n if elem.get_attribute(\\\"href\\\")[-1] == \\\"#\\\":\\n subject = \\\" \\\".join(elem.text.split()[:-1])\\n self.others_links.append([self.other_links_dict[subject]])\\n search_cat = False\\n else:\\n self.link_list.append(elem.get_attribute(\\\"href\\\"))\\n search_cat = True\\n elif not search_cat:\\n self.others_links[-1].append(elem.text)\\n # Move english category to the end because it is the largest one\\n self.link_list.append(self.link_list.pop(self.link_list.index('https://profi.ru/repetitor/english/')))\\n self.logger.info(f'Found {len(self.link_list) + len(self.others_links)} categories')\",\n \"def suggest_categories(self, category, max_results=10, blogid=1):\\n return self.execute('wp.suggestCategories', blogid, self.username, self.password, category, max_results)\",\n \"def find_categories_used(request):\\n categories_used = []\\n\\n for item_index, item in enumerate(all_shopping_items(request)):\\n if item_index == 0:\\n categories_used.append(item.category)\\n else:\\n add_category = True\\n\\n for list_item in categories_used:\\n if list_item.category == item.category.category:\\n add_category = False\\n \\n if add_category:\\n categories_used.append(item.category)\\n\\n return categories_used\",\n \"def search_recipes():\\r\\n cuisine, course, allergens = Helpers.dropdowns(coll_cuisines, coll_courses, coll_allergens)\\r\\n args = request.args.get\\r\\n args_list = request.args.getlist\\r\\n\\r\\n # Get Search and Pagination arguments from URL\\r\\n keyword_args = (\\r\\n args(\\\"search_keys\\\") if args(\\\"search_keys\\\") is not None else \\\"\\\")\\r\\n cuisineFilter_args = (\\r\\n args(\\\"cuisine_filter\\\") if args(\\\"cuisine_filter\\\") is not None else \\\"\\\")\\r\\n courseFilter_args = (\\r\\n args(\\\"course_filter\\\") if args(\\\"course_filter\\\") is not None else \\\"\\\")\\r\\n allergenFilter_args = (\\r\\n args_list(\\\"allergen_filter\\\") if args_list(\\r\\n \\\"allergen_filter\\\") is not None else [])\\r\\n page_args = int(args(\\\"page\\\")) if args(\\\"page\\\") is not None else 1\\r\\n\\r\\n # Set search variables\\r\\n search_keywords = (\\r\\n keyword_args.split() if keyword_args is not None else \\\"\\\")\\r\\n search_cuisine = (\\r\\n cuisineFilter_args if cuisineFilter_args is not None else \\\"\\\")\\r\\n search_course = (\\r\\n courseFilter_args if courseFilter_args is not None else \\\"\\\")\\r\\n search_allergens = (\\r\\n allergenFilter_args if allergenFilter_args != [] else \\\"\\\")\\r\\n\\r\\n # Join search variables and perform search\\r\\n search = (\\r\\n '\\\"' + '\\\" \\\"'.join(search_keywords) +\\r\\n '\\\" \\\"' + ''.join(search_cuisine) +\\r\\n '\\\" \\\"' + ''.join(search_course) +\\r\\n '\\\"' + ' -' + ' -'.join(search_allergens))\\r\\n search_results = coll_recipes.find(\\r\\n {\\\"$text\\\": {\\\"$search\\\": search}}).skip((page_args * 8) - 8)\\\\\\r\\n .limit(8).sort([(\\\"views\\\", -1)])\\r\\n\\r\\n # Pagination\\r\\n (\\r\\n pages, previous_page, next_page, count,\\r\\n total_recipes, results_count) = Helpers.pagination(\\r\\n search_results, page_args, coll_recipes)\\r\\n\\r\\n return render_template(\\r\\n \\\"searchrecipes.html\\\",\\r\\n recipes=search_results,\\r\\n cuisine=sorted(cuisine),\\r\\n course=course,\\r\\n allergens=allergens,\\r\\n keywords=keyword_args,\\r\\n f_cuisine=cuisineFilter_args,\\r\\n f_course=courseFilter_args,\\r\\n f_allergen=allergenFilter_args,\\r\\n pages=pages,\\r\\n results_count=results_count,\\r\\n total_recipes=total_recipes,\\r\\n count=count,\\r\\n page=page_args,\\r\\n next_page=next_page,\\r\\n previous_page=previous_page)\",\n \"def collect_city_items(self, samples: int, city: str, country:str) -> None:\\n time_start = time.time()\\n url = self.get_city_url(city,country)\\n samples_taken = 0\\n while url != None:\\n\\n page_source = self.get_page_source(url, \\\"_1g5ss3l\\\")\\n soup = BeautifulSoup(page_source, \\\"html.parser\\\") \\n for item in soup.find_all(\\\"div\\\", class_=\\\"_fhph4u\\\"): \\n if samples_taken == samples:\\n self.__driver.quit()\\n print(\\n f\\\"{city} scraping is done!{samples_taken} samples was taken.Time elapsed: {time.time()-time_start} seconds.\\\"\\n )\\n return\\n else:\\n self.__collected_dic[\\\"city\\\"].append(city)\\n\\n item_url = self.get_item_url(item)\\n self.get_item_title(item)\\n self.get_item_property_type(item)\\n self.get_item_location(item)\\n self.get_item_rating(item)\\n self.get_item_reviews(item)\\n self.get_item_price(item)\\n self.get_item_guests(item)\\n self.get_item_bedrooms(item)\\n self.get_item_beds(item)\\n self.get_item_baths(item)\\n\\n self.collect_amenities(item_url)\\n\\n samples_taken = samples_taken + 1\\n url = self.find_next_page(soup)\\n print(\\n f\\\"{city} scraping is done!{samples_taken} samples was taken.Time elapsed: {time.time()-time_start} seconds.\\\"\\n )\",\n \"def search_prod(self):\\n\\n # Connection to the SQL database\\n self.db_connect = pymysql.connect(\\\"localhost\\\",\\\"root\\\",\\\"\\\",\\\"mydb\\\")\\n db = self.db_connect.cursor()\\n \\n # Category selection display loop\\n choice = 0\\n while choice is 0:\\n choice = input(\\\"\\\\nVoulez vous afficher la liste de catégories ? O/n \\\")\\n if choice.upper() == \\\"N\\\":\\n print(\\\"\\\\nRetour au menu principal\\\")\\n break\\n elif choice.upper() == \\\"O\\\":\\n choice_cat = True\\n print(\\\"\\\\n Liste des catégories:\\\\n\\\")\\n # Defining the query to return the list of categories\\n show_list_cat = \\\"SELECT category_id, category_name \\\\\\n FROM `mydb`.`Categories`\\\"\\n # Command to execute the request\\n db.execute(show_list_cat)\\n # Variable assignment loop\\n for ligne in db.fetchall():\\n list_id_cat = ligne[0]\\n list_name_cat = ligne[1]\\n # View category list\\n print(list_id_cat,'-', list_name_cat)\\n\\n # Product Selection Loop\\n choice_cat = 0\\n while choice_cat == 0:\\n choice_cat = input(\\\"\\\\nSélectionner une catégorie de produit (Q pour annuler): \\\")\\n\\n if choice_cat.upper() == \\\"Q\\\":\\n choice = False\\n break\\n\\n elif choice_cat.isdigit() == False or int(choice_cat) >= 29 \\\\\\n or int(choice_cat) == 0:\\n print(\\\"\\\\nMerci de bien vouloir entrer un chiffre compris entre 1 et 28\\\\n\\\")\\n time.sleep(2)\\n choice_cat = 0\\n \\n # Definition of the request to return \\n # the list of products of the selected category\\n show_list_prod_select = f\\\"SELECT product_id, \\\\\\n product_name, \\\\\\n product_nutri \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n \\n # Execution command of the SQL query\\n db.execute(show_list_prod_select)\\n for ligne in db.fetchall():\\n list_id_prod = ligne[0]\\n list_name_prod = ligne[1]\\n nutriscore_prod = ligne[2]\\n # Uppercase display\\n nutriscore_prod = nutriscore_prod.upper()\\n # Display of the list of products and nutriscores\\n print(list_id_prod,\\\"-\\\", list_name_prod, \\\\\\n \\\"- Nutriscore:\\\",nutriscore_prod)\\n \\n # SQL query to determine the maximum and minimum value\\n # the product ID for the selected category\\n vmax = f\\\"SELECT MAX(product_id) \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n db.execute(vmax)\\n for maxv in db.fetchall():\\n max_id_prod = maxv[0]\\n\\n vmin = f\\\"SELECT MIN(product_id) \\\\\\n FROM `mydb`.`Products` \\\\\\n WHERE Categories_category_id = {choice_cat}\\\"\\n db.execute(vmin)\\n for minv in db.fetchall():\\n min_id_prod = minv[0]\\n \\n # Display loop for selecting the product to substitute\\n choice_sub = 0\\n while choice_sub == 0:\\n choice_sub = input(\\\"\\\\nSélectionner un produit à substituer (Q pour annuler): \\\")\\n\\n if choice_sub.upper() == \\\"Q\\\":\\n choice = 0\\n break\\n # Selects the product selection choice for the selected category\\n elif choice_sub.isdigit() == False or \\\\\\n int(choice_sub) > max_id_prod or \\\\\\n int(choice_sub) < min_id_prod or int(choice_sub) == 0:\\n print(\\\"\\\\nMerci de bien vouloir entrer un chiffre compris dans la liste des produits\\\")\\n time.sleep(2)\\n choice_sub = 0\\n\\n # Definition of the request to return the list of\\n # Substitute products of the selected category\\n show_list_sub_select = f\\\"SELECT * FROM `mydb`.`Products`\\\\\\n WHERE product_id = {choice_sub}\\\"\\n \\n # Execution command of the SQL query\\n db.execute(show_list_sub_select)\\n for ligne_sub in db.fetchall():\\n sub_name = ligne_sub[1]\\n sub_nutri = ligne_sub[3]\\n # Uppercase display\\n sub_nutri = sub_nutri.upper()\\n \\n # Definition of the request to return the product \\n # with the highest nutriscores of the selected category\\n list_best_nutri = f\\\"SELECT * FROM `mydb`.`Products`\\\\\\n WHERE Categories_category_id = {choice_cat} \\\\\\n AND product_nutri <= '{sub_nutri}' \\\\\\n ORDER BY product_nutri ASC\\\"\\n \\n db.execute(list_best_nutri)\\n result = db.fetchall()\\n best = (result)[0]\\n bestnutri = (best)[3]\\n # Uppercase display\\n bestnutri = bestnutri.upper()\\n # Case where the user chooses one of the products \\n # with the highest nutriscore of the category\\n if sub_nutri == bestnutri:\\n print(\\\"\\\\nLe produit sélectionné a le meilleur nutriscore de cette catégorie\\\")\\n print(\\\"Veuillez sélectionner un autre produit dans la liste\\\")\\n choice_sub = 0\\n # Case where the user selects a substitutable product \\n else:\\n best_id = (best)[0]\\n best_name = (best)[1]\\n best_nutriscore = (best)[3]\\n best_nutriscore = best_nutriscore.upper()\\n best_shop = (best)[2]\\n best_url = (best)[4]\\n # Returns the selected product and its substitute\\n print(\\\"\\\\nProduit sélectionné:\\\",sub_name,\\\\\\n \\\". Nutriscore:\\\",sub_nutri)\\n print(\\\"Substitue:\\\",best_name,\\\". Nutriscore:\\\",\\\\\\n best_nutriscore)\\n print(\\\"Magasin(s):\\\",best_shop)\\n print(\\\"Infos produit:\\\",best_url)\\n # Backup loop in the SQL database\\n save_choice1 = 0\\n while save_choice1 is 0:\\n save_choice1 = input(\\\"\\\\nSouhaitez-vous sauvegarder le produit dans la base ? O/n \\\")\\n if save_choice1.upper() == \\\"N\\\":\\n print(\\\"\\\\nRetour au menu principal\\\")\\n time.sleep(2)\\n break\\n elif save_choice1.upper() == \\\"O\\\":\\n # Insertion request in the \\\"Substitute\\\" table\\n save_sub = \\\"INSERT IGNORE INTO \\\\\\n `mydb`.`Substitute` \\\\\\n (save_product_id, save_product_sub_name) \\\\\\n VALUES ('{}','{}')\\\".format(best_id, sub_name)\\n db.execute(save_sub)\\n self.db_connect.commit()\\n print(\\\"\\\\nLe produit a bien été sauvegardé dans vos favoris\\\")\\n print(\\\"Retour au menu principal\\\")\\n time.sleep(2)\\n break\",\n \"def get_categories(searched_nouns, keywords, n_categories=5):\\n\\n matches = []\\n\\n for keyword in keywords.itertuples():\\n if len(matches) == n_categories:\\n break\\n\\n try:\\n if keyword.Index in searched_nouns:\\n matches.append(keyword.Index)\\n except:\\n continue\\n\\n missing_count = abs(len(matches) - n_categories)\\n\\n for _ in range(missing_count):\\n matches.append(None)\\n\\n return matches\",\n \"def OLD_get_items_from_category(save_db=False):\\n # for i in range(16):\\n # name = pd.read_sql_query(\\n # f'SELECT name FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # cat_url = pd.read_sql_query(\\n # f'SELECT url FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # # cat_id = pd.read_sql_query(\\n # # f'SELECT id FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # cat_id = i + 1\\n # print(cat_id)\\n # \\\"\\\"\\\" pandas return DataFrame, use DataFrame.values to return list of list value in that DataFrame\\n # url: https://tiki.vn/dien-thoai-may-tinh-bang/c1789?src=c.1789.hamburger_menu_fly_out_banner&page=2\\n # \\\"\\\"\\\"\\n # for i in range(100):\\n # url = cat_url + f'&page={i+1}'\\n # print(url)\\n # soup = get_url(url)\\n\\n # result = []\\n # \\\"\\\"\\\" item: div 'product-item' > div 'content'\\n # img: img 'product-imgage'\\n # title: p 'title'\\n # price: span 'price-regular'\\n # sale-tag: span 'sale-tag'\\n # final-price: span 'final-price'\\n # \\\"\\\"\\\"\\n # div_container = soup.find_all('div', {'class': 'product-item'})\\n # item_path = div_container['data-category']\\n # if div_container:\\n # for div in div_container:\\n # # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\\n # item_id = None\\n # item_name = div.a['title']\\n # brand = div['data-brand']\\n # item_url = div.a['href']\\n # img_url = div.img['src']\\n # regular_price = div.find('span', {'class': 'price-regular'}).text\\n # sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\\n # final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\\n\\n # item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\\n # img_url, regular_price, sale_tag, final_price)\\n # if save_db:\\n # item.save_into_db()\\n # print(f'SAVE {item_name} INTO DTB')\\n # result.append(item)\\n # else:\\n # break\",\n \"def search(self, query, maxhits=100):\",\n \"def _traverse_categories():\\n \\n csv_output_dict = _get_csv_data()\\n print csv_output_dict.keys()\\n #~ if csv_output_dict:\\n #~ return csv_output_dict\\n #~ else:\\n final_output_url_list = []\\n traverse_cat_output = {}\\n # brand_seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-brand-all.w'\\n seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-category-all.w'\\n \\n browser = utils.create_browser(SLEEP_MIN, SLEEP_MAX)\\n browser.open(seed_page)\\n html_response = browser.response().read()\\n \\n soup = BeautifulSoup(html_response)\\n \\n all_category_names = soup.findAll('div', {'class': 'brwsBrndLogoClip'})\\n for category_names in all_category_names:\\n temp_category_url = str(category_names.a['href'])\\n category_url_name = temp_category_url.split('&catname=')[-1]\\n category_url = 'https://www.broderbros.com/cgi-bin/online/webshr/search-result.w?nResults=5000&mc=&RequestAction=advisor&RequestData=CA_CategoryExpand&bpath=c&CatPath=All Products////BRO-Categories////'+str(category_url_name)\\n category_url = category_url.replace(' ', '%20')\\n category_names = category_names.a.img['alt'].lower()\\n returned_url_list = _get_product_urls(category_url, category_names)\\n final_output_url_list.extend(returned_url_list)\\n \\n for productsurl in final_output_url_list:\\n if productsurl['id'] in traverse_cat_output.keys() and productsurl['category_list'] != traverse_cat_output[productsurl['id']]['category_list']:\\n traverse_cat_output[productsurl['id']]['category_list'].extend(productsurl['category_list'])\\n else:\\n traverse_cat_output[productsurl['id']] = productsurl\\n \\n brand_output_categories = _traverse_categories_brand()\\n for brand_url in brand_output_categories:\\n if brand_url['id'] in traverse_cat_output.keys():\\n traverse_cat_output[brand_url['id']]['brand'] = brand_url['brand']\\n else:\\n traverse_cat_output['id'] = brand_url\\n \\n if TESTRUN:\\n for k,v in traverse_cat_output.items():\\n print v\\n print '*'*78\\n for item_id in traverse_cat_output.keys():\\n print item_id\\n csv_data = csv_output_dict.get(item_id)\\n if csv_data:\\n traverse_cat_output[item_id]['brand'] = csv_data['brand']\\n if not traverse_cat_output[item_id].get('brand'):\\n del traverse_cat_output[item_id]\\n \\n return traverse_cat_output\",\n \"def search(self, text: str, category: str = None):\\n if category:\\n # ensuring that it will be in lowercase\\n category = category.lower()\\n\\n if not category or not category in self.search_categories:\\n category = \\\"all\\\"\\n\\n search_url = f\\\"{SITE_URL}/{self.search_categories[category]}/?adb.search={text}\\\"\\n\\n # return answer.text\\n return self.fetch_url(search_url)\",\n \"def search_books_body(collection_of_books: tuple) -> list:\\r\\n search_tag, search_keyword = search_info()\\r\\n try:\\r\\n found_books = search(collection_of_books, search_tag, search_keyword)\\r\\n except KeyError as err:\\r\\n print(f\\\"Invalid input: {err}\\\\n\\\"\\r\\n f\\\"{err} raised KeyError. Please follow the instruction carefully.\\\\n\\\")\\r\\n else:\\r\\n print_as_ordered_list(found_books)\\r\\n\\r\\n return found_books\",\n \"def parse(self, response):\\n self.driver.get(response.url)\\n product_category=response.meta[\\\"category_text\\\"]\\n products=response.xpath(\\\"//*[(@class='list-item')]\\\")\\n \\n # item containers for storing product\\n items = CrawlingECommerceItem()\\n \\n # iterating over search results\\n # for product in products:\\n # # Defining the XPaths\\n # XPATH_PRODUCT_LINK=\\\".//*[contains(concat( ' ', @class, ' ' ), concat( ' ', 'goods-tit', ' ' ))]//a\\\"\\n # XPATH_PRODUCT_NAME=\\\".//div[@class='goods-introudce']//a/@href\\\"\\n # XPATH_PRODUCT_PRICE=\\\".//div[@class='catalog-detail']//div[@class='detail-right']//p/text()\\\"\\n # XPATH_PRODUCT_IMAGE_LINK=\\\".//img\\\"\\n\\n # raw_product_name=product.xpath(XPATH_PRODUCT_NAME).get()\\n # raw_product_price=product.xpath(XPATH_PRODUCT_PRICE).get()\\n # raw_product_image_link=product.xpath(XPATH_PRODUCT_IMAGE_LINK).extract()\\n # raw_product_link=product.xpath(XPATH_PRODUCT_LINK).get()\\n\\n # # cleaning the data\\n # product_name=''.join(raw_product_name).strip(\\n # ) if raw_product_name else None\\n # product_price=''.join(raw_product_price).strip(\\n # ) if raw_product_price else None\\n # product_image_link=''.join(raw_product_image_link).strip(\\n # ) if raw_product_image_link else None\\n # product_link=''.join(raw_product_link).strip(\\n # ) if raw_product_link else None\\n\\n # # storing item\\n # yield CrawlingECommerceItem (\\n # product_name=product_name,\\n # product_price=product_price,\\n # product_url=product_link,\\n # product_category=product_category,\\n # image_urls=raw_product_image_link\\n # )\\n\\n # # yield items\\n \\n # XPATH_PRAGINATION_LINK=\\\"//*[(@class='next right')]/a/@href\\\"\\n\\n yield response.follow(str(response.request.url), callback = self.parse, meta = {\\\"category_text\\\": product_category})\",\n \"def main():\\n entries = get_feed_entries()\\n\\n while True:\\n response = input(\\\"What you you like to search for? \\\")\\n\\n if not response:\\n print(\\\"Please provide a search term\\\")\\n continue\\n\\n if response.lower() == \\\"q\\\":\\n print(\\\"Bye\\\")\\n break\\n\\n matches = [\\n entry\\n for entry in list(\\n filter(lambda x: filter_entries_by_tag(response.lower(), x), entries)\\n )\\n ]\\n\\n if matches:\\n for entry in sorted(matches, key=lambda x: x.date):\\n print(entry.title)\\n\\n print(f\\\"{len(matches)} {'entry' if len(matches) == 1 else 'entries'} matched\\\")\",\n \"def keyword_search(self, *args, batch_func=None, batch_number=10):\\n batch_func_ = batch_func\\n keywords = args\\n request_url = self._assemble_kw_url(keywords)\\n # print(request_url)\\n\\n # xml_files = self._send_request_xml(request_url, batch_func=None, batch_number=1000)\\n # data = self._construct_data_xml(xml_files)\\n\\n data = self._send_request_xml(request_url, batch_func=batch_func, batch_number=batch_number)\\n\\n return data\",\n \"def shop_items(request):\\n\\n items = Item.objects.all()\\n\\n query = None\\n\\n \\\"\\\"\\\" Used Code Institute Search logic from Tutorial \\\"\\\"\\\"\\n if 'query' in request.GET:\\n query = request.GET['query']\\n if not query:\\n messages.error(request, \\\"Please enter your search\\\")\\n return redirect(reverse('items'))\\n \\n queries = Q(name__icontains=query) | Q(item_description__icontains=query)\\n items = items.filter(queries)\\n\\n context = {\\n 'items': items,\\n 'search_term': query,\\n }\\n\\n return render(request, 'items/items.html', context)\",\n \"def search(category, keywords, maximum_price,\\n minimum_price, sort, page):\\n params = dict(\\n genreId=category,\\n keyword=keywords\\n )\\n if maximum_price is not None:\\n params['maxPrice'] = int(float(maximum_price))\\n if minimum_price is not None:\\n params['minPrice'] = int(float(minimum_price))\\n if sort is not None:\\n params['sort'] = '+itemPrice' if sort == 'price' else '-itemPrice'\\n\\n # we threat 100 results as 1 page which equals to 4 pages\\n # on current API search request\\n page = page * 4\\n\\n # lets load 100 search results\\n results = []\\n # pylint: disable=bad-builtin\\n for i in range(page, page + 4):\\n result = RAKUTEN.item_search(hits=25, page=i+1, **params)\\n result = map(extract_data, result.get('Items', []))\\n results.append(result)\\n responce = list(filter(IS_DATA_VALID, chain(*results)))\\n return responce\",\n \"def main(url):\\n \\n words = fetch_words(url)\\n print_items(words)\",\n \"def search(collection_of_books: tuple, search_tag: str, search_keyword: str) -> list:\\r\\n found_books = []\\r\\n\\r\\n if search_tag == \\\"Shelf\\\" and search_keyword.isnumeric():\\r\\n found_books = [book for book in collection_of_books if search_keyword == book[\\\"Shelf\\\"]]\\r\\n\\r\\n else:\\r\\n for book in collection_of_books:\\r\\n if search_keyword.lower() in book[search_tag].lower():\\r\\n found_books.append(book)\\r\\n\\r\\n return found_books\",\n \"def get_word_frequency_per_year(client, search_term='', pos='', item_list_name=''):\\n\\t\\n\\tif not search_term:\\n\\t\\tprint \\\"Enter a word to count its frequency:\\\"\\n\\t\\tsearch_term = raw_input()\\n\\n\\tlists = client.get_item_lists()\\n\\n\\tprimary_text = ''\\n\\ttemp_result = []\\n\\tresult = {}\\n\\titems = []\\n\\ttotal_sum = 0\\n\\n\\tif item_list_name:\\n\\t\\titem_list = client.get_item_list_by_name(item_list_name)\\n\\t\\titems = item_list.get_all()\\n\\telse:\\n\\t\\titem_lists = client.get_item_lists()\\n\\n\\t\\tfor l in item_lists['own']:\\n\\t\\t\\titems.append(client.get_item_list(l['item_list_url']))\\n\\n\\n\\tfor item in items:\\n\\t\\tprimary_text = item.get_primary_text()\\n\\t\\tyear = item.item_metadata['alveo:metadata']['dc:created']\\n\\t\\twords = word_tokenize(primary_text.lower())\\n\\t\\tfiltered_words = []\\n\\t\\t\\n\\t\\tif pos:\\n\\t\\t\\tfor i in range(len(words)):\\n\\t\\t\\t\\tif words[i] == search_term:\\n\\t\\t\\t\\t\\tfiltered_words.append(words[i-1])\\n\\t\\t\\t\\t\\tfiltered_words.append(words[i])\\n\\t\\t\\t\\t\\tfiltered_words.append(words[i+1])\\n\\n\\t\\t\\ttagged_words = nltk.pos_tag(filtered_words)\\n\\t\\t\\tf = nltk.FreqDist(tagged_words)\\n\\n\\t\\t\\tword_pos = (search_term, pos)\\n\\t\\t\\n\\t\\t\\n\\t\\t\\tif word_pos in f.keys():\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tprint word_pos\\n\\n\\t\\t\\t\\tword_freq = f[word_pos]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tword_freq = words.count(search_term)\\n\\t\\telse:\\n\\t\\t\\tword_freq = words.count(search_term)\\n\\t\\t\\tprint word_freq, search_term\\n\\t\\t\\ttotal_sum = total_sum + word_freq\\n\\t\\n\\t\\ttemp_result.append((year, word_freq))\\n\\n\\n\\tprint \\\"total sum: \\\" + str(total_sum)\\n\\n\\n\\tfor i in temp_result:\\n\\t\\tif i[0] in result.keys():\\n\\t\\t\\tresult[i[0]] = result[i[0]] + i[1]\\n\\t\\telse:\\n\\t\\t\\tresult[i[0]] = i[1]\\n\\n\\n\\treturn result\",\n \"def get_categories():\\n bu = 'http://www.watchonlinemovies.com.pk'\\n r = requests.get(bu, headers=mozhdr)\\n if r.url != bu:\\n bu = r.url\\n items = {'ARecently Uploaded Movies': bu,\\n 'B2018 Movies': bu + 'category/indian-movies/2018-full-movies/',\\n 'C2018 English Movies': bu + 'category/hollywood-movies/2018-movies-hollywood/',\\n 'D[COLOR yellow]** Search **[/COLOR]': bu + '?s=',\\n 'Z[COLOR red]Note: This addon is no longer supported, please install WatchOnlineMovies-New from ReasonsRepository [/COLOR]': 'book'}\\n \\n return items\",\n \"def crawl(self):\\n retrievedTweets = []\\n\\n count = 1\\n \\n today = datetime.datetime.now()\\n today = today.replace(hour=23, minute=59, second=59, microsecond=999999)\\n gap = 1\\n yesterday = today - datetime.timedelta(gap) \\n nextDay = yesterday + datetime.timedelta(gap)\\n \\n while True:\\n try:\\n lst = tweepy.Cursor(self.api.search, lang='en', q=self.keyword, count=50, until=nextDay.date(), result_type='popular').items(50)\\n for tweet in lst:\\n self.data = [tweet.created_at, tweet.id, tweet.text,\\n tweet.user._json['screen_name'], tweet.user._json['name'], \\n tweet.favorite_count, tweet.retweet_count, tweet.user.location]\\n self.data = tuple(self.data)\\n retrievedTweets.append(self.data)\\n break\\n except tweepy.TweepError as e:\\n print(e.reason)\\n continue\\n except StopIteration: \\n break\\n\\n return retrievedTweets\",\n \"def search(words):\\n\\tglobal books\\n\\tglobal word_count\\n\\tglobal titles\\n\\n\\ttry:\\n\\t\\t#index for titles and dictionary of books\\n\\t\\ti = 0\\n\\t\\t#condition for word not found\\n\\t\\tif word_count.get(words) == None:\\n\\t\\t\\tprint(\\\"The word {} does not appear in any books in the library.\\\".format(words))\\n\\t\\t\\treturn\\n\\n\\t\\tfor vals in word_count[words]:\\n\\t\\t\\t# #if book index was empty - case where url was not active - index was removed - increase index\\n\\t\\t\\t# while(books.get(j) == None):\\n\\t\\t\\t# \\tj += 1\\n\\t\\t\\t#print the count\\n\\t\\t\\tif(vals == 1):\\n\\t\\t\\t\\tprint(\\\"The word {} appears {} time in {} (link: {})\\\".format(words,vals,titles[i],books[i]))\\n\\t\\t\\telif(vals == 0):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"The word {} appears {} times in {} (link: {})\\\".format(words,vals,titles[i],books[i]))\\n\\t\\t\\t#increase index values\\n\\t\\t\\ti += 1\\n\\texcept:\\n\\t\\tprint(\\\"Error while searching for word.\\\")\",\n \"def parse_search_results(data, category):\\n paginate(data[\\\"pagination\\\"], category)\\n for data in data[\\\"results\\\"]:\\n add_menu_item(play_film,\\n data.get(\\\"title\\\").title(),\\n {\\\"href\\\": \\\"{}?fo=json\\\".format(data.get(\\\"url\\\"))},\\n locs.get_art(data),\\n locs.get_info(data),\\n False)\\n xbmcplugin.addSortMethod(plugin.handle, xbmcplugin.SORT_METHOD_LABEL_IGNORE_THE)\\n xbmcplugin.addSortMethod(plugin.handle, xbmcplugin.SORT_METHOD_GENRE)\\n xbmcplugin.addSortMethod(plugin.handle, xbmcplugin.SORT_METHOD_VIDEO_YEAR)\",\n \"def search_categories(self):\\n with Transaction().start(DBNAME, 1):\\n categorieslist = self.Category.search(['parent', '=', 'Ingredients'])\\n return tuple(i.name for i in categorieslist)\",\n \"def main(url):\\n words = fetch_words(url)\\n print_items(words)\",\n \"def getCategories(URIList, annotatedWords):\\n \\n L=[]\\n wordByCategory=dict()\\n i=0\\n for URI in URIList:\\n sparql = SPARQLWrapper(\\\"http://dbpedia.org/sparql\\\")\\n sparql.setQuery(\\\"\\\"\\\"\\n PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\\n PREFIX dc: <http://purl.org/dc/terms/>\\n SELECT ?label\\n WHERE { \\\"\\\"\\\"+ \\\"<\\\"+ URI + \\\"> dc:subject ?label }\\\"\\n )\\n sparql.setReturnFormat(JSON)\\n results = sparql.query().convert()\\n for result in results[\\\"results\\\"][\\\"bindings\\\"]:\\n category=result[\\\"label\\\"][\\\"value\\\"].encode(\\\"UTF-8\\\").split(\\\"/\\\")[-1].replace(\\\"_\\\",\\\" \\\").replace(\\\"Category:\\\",\\\"\\\")\\n L.append(category)\\n if category in wordByCategory:\\n if i>= len(annotatedWords):\\n print \\\"getCategories is computing URI=\\\",URI\\n print \\\"Trying to append element number\\\",i,\\n print \\\"from a list having\\\",len(annotatedWords),\\\"elements.\\\"\\n wordByCategory[category].append(annotatedWords[i])\\n else:\\n wordByCategory[category]=[annotatedWords[i]]\\n i+=1\\n return L, wordByCategory\",\n \"def search():\\n if 'query' in request.vars:\\n result = []\\n\\n for db_result in db((db.bootables.title.lower().contains(request.vars.query.lower())\\n | db.bootables.intro.lower().contains(request.vars.query.lower()))\\n & (db.bootables.status != NOT_AVAILABLE))\\\\\\n .select(limitby=(request.args[0] * 10, (request.args[0] + 1) * 10) if request.args(0) else None):\\n bootable = extract_bootable_short_form(db_result)\\n\\n # Post process the category search\\n if 'category' in request.vars and request.vars.category.lower() in bootable['category'].lower():\\n result.append(bootable)\\n elif 'category' not in request.vars:\\n result.append(bootable)\\n\\n # Report the user's search query\\n search_string = request.vars.query\\n if 'category' in request.vars:\\n search_string += ' @' + request.vars.category\\n if request.args(0):\\n search_string += '(' + str(request.args[0] * 10) + '-' + str((request.args[0] + 1) * 10) + ')'\\n\\n else:\\n session.flash = 'Please pass in a search query'\\n redirect(URL('default', 'index'))\\n\\n return dict(result=result, search_string=search_string)\",\n \"def main(url):\\n words = fetch_words(url)\\n\\n print_items(words)\",\n \"def import_categories(self, all_categories):\\n\\n i = 0\\n while (\\n i < len(all_categories['tags'])\\n and all_categories['tags'][i]['products'] > 1000\\n ):\\n category = all_categories['tags'][i]\\n if self.test_category(category):\\n self.create_category(category)\\n i += 1\",\n \"def getproductbycategory(self):\\n\\n eight = self.geteightcategories()\\n productsliste = []\\n for category in eight:\\n for page in tqdm(range(1, 5)):\\n r2 = requests.get(\\n \\\"https://fr.openfoodfacts.org/cgi/search.pl?action=process\\\"\\n + \\\"&tagtype_0=categories&tag_contains_0=contains&tag_0={}\\\".format(\\n category\\n )\\n + \\\"&tag_contains_1=contains&tag_1=france&page_size=500\\\"\\n + \\\"&fields=url,categories_tags_fr,product_name,stores_tags\\\"\\n + \\\",nutriscore_grade&tagtype_1=purchase_places&sort_by=\\\"\\n + \\\"unique_scans_n&json=1&page={}\\\".format(page)\\n )\\n dataproducts = r2.json()\\n if dataproducts[\\\"page_count\\\"] is None:\\n continue\\n else:\\n for items in dataproducts[\\\"products\\\"]:\\n productsliste.append(items)\\n\\n self.rawproductdata = productsliste\",\n \"def movie_spider(self, movieTag):\\n index = 0\\n logging.info(\\\"Start crawling tag: %s\\\" % movieTag)\\n while index < self.MAX_NUM:\\n root = \\\"https://movie.douban.com/tag/%s?start=%d&type=T\\\" % (movieTag, index)\\n result = {}\\n try:\\n html = requests.get(root, headers=random.choice(self.headers)).content\\n tree = etree.HTML(html.decode('utf-8'))\\n items = tree.xpath(\\\"//table/tr[@class='item']\\\")\\n if len(items) == 0:\\n break\\n index += len(items)\\n for item in items:\\n itemURL = item.xpath(\\\"td/a[@class='nbg']/@href\\\")[0].strip()\\n itemHTML = requests.get(itemURL, headers=random.choice(self.headers)).content\\n itemTree = etree.HTML(itemHTML.decode('utf-8'))\\n title = itemTree.xpath(\\\"//h1/span[@property='v:itemreviewed']/text()\\\")[0].strip()\\n info = itemTree.xpath(\\\"//div[@class='subject clearfix']/div[@id='info']\\\")[0]\\n director = info.xpath(\\\".//a[@rel='v:directedBy']/text()\\\")\\n scriptor = info.xpath(\\\"span\\\")[1].xpath(\\\"span/a/text()\\\") # scriptor is not well formatted\\n actors = info.xpath(\\\".//a[@rel='v:starring']/text()\\\")\\n genre = info.xpath(\\\".//span[@property='v:genre']/text()\\\")\\n initDate = info.xpath(\\\".//span[@property='v:initialReleaseDate']/text()\\\")\\n runtime = info.xpath(\\\".//span[@property='v:runtime']/text()\\\")\\n rating = itemTree.xpath(\\\"//strong[@property='v:average']/text()\\\")[0].strip()\\n \\n result['title'] = title\\n result['director'] = '/'.join(director[:])\\n result['scriptor'] = '/'.join(scriptor[:])\\n result['actors'] = '/'.join(actors[:])\\n result['genre'] = '/'.join(genre[:])\\n result['initDate'] = '/'.join(initDate[:])\\n result['runtime'] = '/'.join(runtime[:])\\n result['rating'] = rating\\n\\n self._movie_list.append(result)\\n result = {}\\n\\n except Exception as e:\\n logging.exception(\\\"Error while crawling tag: %s\\\" % movieTag)\",\n \"def redditLiker(driver, searchTerms):\\n\\n for _searchTerm in searchTerms:\\n _searchString = \\\"https://new.reddit.com/search?q=\\\" + _searchTerm + \\\"&t=all&sort=new\\\"\\n try:\\n driver.get(_searchString)\\n time.sleep(2)\\n print (\\\"Page is ready!\\\")\\n except TimeoutException:\\n print (\\\"Loading took too much time!\\\")\\n\\n _subscribeButtons = driver.find_elements_by_xpath(\\\"/html/body/div[1]/div/div[2]/div/div/div[1]/div[2]/div/div/div/div[2]/div[2]/div[1]/div/div[2]/div/div[2]/div/div/a/div[3]/button\\\")\\n print(len(_subscribeButtons))\\n _randomNumber = random.sample(range(0, 3), 2)\\n _randomNumber.sort()\\n for _ran in _randomNumber:\\n try:\\n _subscribeButtons[_ran].click()\\n time.sleep(1)\\n except Exception:\\n print (\\\"SubscribeButtons could not be scrolled into view\\\")\\n\\n _likeButtons = driver.find_elements_by_xpath('/html/body/div[1]/div/div[2]/div/div/div[1]/div/div/div/div/div[2]/div[2]/div[1]/div/div[3]/div[1]/div/div/div/div[1]/div/button[1]')\\n _lmt = len(_likeButtons)\\n _smallLmt = int(_lmt/3)\\n _randomNumber = random.sample(range(0, _lmt), _smallLmt)\\n _randomNumber.sort()\\n for i in range (0, _smallLmt):\\n try:\\n _likeButtons[_randomNumber[i]].click()\\n except Exception:\\n print (\\\"Like could not be scrolled into view\\\")\",\n \"def search(self, term):\",\n \"def call(self):\\r\\n clean_products = []\\r\\n\\r\\n for category in CATEGORIES:\\r\\n print(f\\\"Chargement des produits de type {category}\\\")\\r\\n api_url = SEARCH_API_URL + \\\\\\r\\n (f\\\"?search_terms={category}\\\"\\r\\n \\\"&search_tag=category&sort_by=unique_scans_n\\\"\\r\\n \\\"&page_size=1000&json=1\\\")\\r\\n json_response = requests.get(api_url).json()\\r\\n products = json_response[\\\"products\\\"]\\r\\n\\r\\n for product in products:\\r\\n clean_product = {\\r\\n k: v for k, v in product.items()\\r\\n if k in FIELD_NEEDED and v != ''}\\r\\n clean_products.append(clean_product)\\r\\n\\r\\n return clean_products\",\n \"def search_thru_comments(urls, listOfKWs):\\n browser = webdriver.Chrome('/Users/sophie/documents/chromedriverCurrent')\\n\\n listKWs = []\\n for KW in listOfKWs:\\n listKWs.append([KW])\\n # ex: listKWs=[['poverty'], ['inequality'], ['aids'], ['hiv']]\\n # list where list[something]=name of KW. append after that the urls.\\n global listKWsDate\\n listKWsDate = []\\n for KW in listOfKWs:\\n listKWsDate.append([KW])\\n print(listKWs == listKWsDate)\\n\\n for link in urls:\\n browser.get(link)\\n\\n source = browser.page_source\\n data = bs(source, 'html.parser')\\n body = data.find('body')\\n script = body.find('script',\\n text=lambda t: t.startswith('window._sharedData'))\\n #print(script)\\n scriptStr = str(script)\\n scriptStr.replace(\\\"'\\\",\\\"\\\")\\n #scriptSplit=script.split('shortcode')\\n #print(scriptSplit)\\n\\n #pass to searchForEach which will check the indiv posts for all KWs\\n # and will then add them to the appropriate spread sheet\\n for KW in listOfKWs:\\n searchForEachKW(KW, scriptStr, listKWs, listKWsDate)\\n\\n #need to change so that calls search for each KW here. so that\\n # searching each link for all the hashtags, and then add link to\\n # appropriatre kw spreadsheet\\n\\n return listKWs\",\n \"def get_collocation_frequency(client, item_list_name='', search_words=[]):\\n\\t#print search_words\\n\\t#print item_list_name\\n\\n\\tprimary_text = ''\\n\\t\\n\\tif not item_list_name:\\n\\t\\tlists = client.get_item_lists()\\n\\n\\t\\t\\n\\t\\tfor l in lists['own']:\\n\\t\\t\\titem_list = client.get_item_list(l['item_list_url'])\\n\\n\\t\\t\\tfor i in item_list:\\n\\t\\t\\t\\t# get item object from the item_url\\n\\t\\t\\t\\titem = client.get_item(i)\\n\\t\\t\\t\\tprimary_text = primary_text + item.get_primary_text()\\n\\telse:\\n\\t\\titem_list = client.get_item_list_by_name(item_list_name)\\n\\t\\tfor i in item_list:\\n\\t\\t\\t\\t# get item object from the item_url\\n\\t\\t\\t\\titem = client.get_item(i)\\n\\t\\t\\t\\tprimary_text = primary_text + item.get_primary_text()\\n\\n\\twords = word_tokenize(primary_text)\\n\\tunique_words = list(set(words))\\n\\trow_unique = []\\n\\tcol_unique = []\\n\\t#Create your bigrams\\n\\tbgs = nltk.bigrams(words)\\n\\n\\t#compute frequency distribution for all the bigrams in the text\\n\\tfdist = nltk.FreqDist(bgs)\\n\\tfile = open(\\\"./static/data.tsv\\\", \\\"w\\\")\\n\\tfile.write(\\\"day\\\\thour\\\\tvalue\\\\n\\\")\\n\\t# Calculates for only 50 items in the frequency distribution\\n\\tif not search_words:\\n\\t\\tlimit = 50\\n\\telse:\\n\\t\\tlimit = None\\n\\n\\tfor k,v in fdist.items()[1:limit]:\\n\\t if ((not search_words) or (k[0] in search_words and k[1] in search_words)):\\n\\t\\t if k[0] not in row_unique:\\n\\t\\t \\trow_unique.append(k[0])\\n\\t\\t if k[1] not in col_unique:\\n\\t\\t \\tcol_unique.append(k[1])\\n\\n\\t\\t \\n\\t\\t file.write(str(row_unique.index(k[0]) + 1) + '\\\\t' + str(col_unique.index(k[1]) + 1) + '\\\\t' + str(v) + '\\\\n')\\n\\n\\tfor search_word in search_words:\\n\\t\\tif ((search_word not in row_unique) and (search_word not in col_unique)):\\n\\t\\t\\trow_unique.append(search_word)\\n\\t\\t\\t\\n\\t\\t\\n\\t\\t\\tfile.write(str(row_unique.index(search_word)) + '\\\\t' + str(row_unique.index(search_word)) + '\\\\t0')\\n\\n\\treturn [row_unique, col_unique]\",\n \"def get_items_to_find(self):\\n self.items_to_find = ['sole', 'farina', 'innaffiatoio']\",\n \"def search_recipes(\\n *,\\n keyword: Optional[str] = Query(None, min_length=3, example=\\\"chicken\\\"),\\n max_results: Optional[int] = 10,\\n) -> dict:\\n if not keyword:\\n # we use Python list slicing to limit results\\n # based on the max_results query parameter\\n return {\\\"results\\\": RECIPES[:max_results]}\\n\\n results = filter(lambda recipe: keyword.lower() in recipe[\\\"label\\\"].lower(), RECIPES)\\n return {\\\"results\\\": list(results)[:max_results]}\",\n \"def processSearchResult(self):\",\n \"def get_new_keywords(self, web_driver, goto):\\n # while requests.get(goto).status_code > 499:\\n # print(requests.get(goto).status_code)\\n # web_driver.quit()\\n # web_driver = self.create_driver(random.choices(self.proxies, k=2), login=False, adsblock=False)\\n\\n web_driver.get(goto)\\n words = web_driver.find_elements_by_css_selector('#wordListContainer > li')\\n while len(words) == 0:\\n web_driver.refresh()\\n # web_driver = self.create_driver(web_driver, login=False, adsblock=False)\\n # web_driver.get(goto)\\n words = web_driver.find_elements_by_css_selector('#wordListContainer > li')\\n added = 0\\n for word in words:\\n try:\\n key_word = word.find_element_by_css_selector('a').get_attribute('href')\\n if key_word not in self.existed_url and key_word not in self.crawled_url:\\n added += 1\\n self.existed_url.append(key_word)\\n # open(save_to + str(key_word).replace('https://glosbe.com/en/vi/', '') + '.url', 'a+', encoding='utf-8')\\n except Exception as e:\\n print('Check keyword in list urls', e)\\n pass\\n # print('Added ' + str(added) + '/' + str(len(words)) + ' words')\\n time.sleep(0.1)\",\n \"async def filter_categories_and_products(categories: list[str], products: list[str]) -> None:\\n for url in categories:\\n if url not in db['urls_to_crawl']:\\n db['urls_to_crawl'].append(url)\\n\\n if url not in db['category_urls']:\\n db['category_urls'].append(url)\\n\\n for url in products:\\n if url not in db['product_urls']:\\n db['product_urls'].append(url)\\n\\n update_json_database()\",\n \"def createSpecificWordList(catsString):\\n wordList = specificWordList(catsString)\\n returnList = []\\n ids = []\\n while len(ids) < 5:\\n foo = randomInt(catsString)\\n if len(ids) < 5:\\n randInt = randomInt(catsString)\\n if randInt not in ids:\\n ids.append(randInt)\\n i = 0\\n while i < 5:\\n for j in ids:\\n word = wordList[j]\\n if word not in returnList:\\n returnList.append(word)\\n word.times_appeared += 1\\n word.save()\\n i += 1\\n return returnList\",\n \"def all_nutrition(request):\\n\\n foods = Nutrition.objects.all()\\n all_categories = Category.objects.all()\\n query = None\\n categories = None\\n\\n if request.GET:\\n\\n if 'category' in request.GET:\\n categories = request.GET['category'].split(',')\\n foods = foods.filter(category__name__in=categories)\\n categories = Category.objects.filter(name__in=categories)\\n\\n if 'q' in request.GET:\\n query = request.GET['q']\\n if not query:\\n messages.error(request, \\\"Oops, looks like you didn't search for anything!\\\")\\n return redirect(reverse('nutrition'))\\n\\n queries = Q(Food__icontains=query) | Q(category__friendly_name__icontains=query)\\n foods = foods.filter(queries)\\n\\n context = {\\n 'foods': foods,\\n 'search_term': query,\\n 'current_categories': categories,\\n 'categories': all_categories,\\n }\\n\\n return render(request, 'nutrition/nutrition.html', context)\",\n \"def scrape(url_to_scrape=None, cached_data=None):\\n all_items = []\\n \\n if cached_data:\\n cat_dict = cached_data['categories_dict']\\n else:\\n cat_dict = _traverse_categories() # {item_id: { 'id': item_id, 'category_list': [categories], 'url': item_url } }\\n \\n category_list = None\\n brand = None\\n if url_to_scrape:\\n for scrape_cat_data in cat_dict.values():\\n if scrape_cat_data['url'] == url_to_scrape:\\n if scrape_cat_data.has_key('category_list'):\\n category_list = scrape_cat_data['category_list']\\n else:\\n category_list = None\\n \\n if scrape_cat_data.has_key('brand'):\\n brand = scrape_cat_data['brand']\\n else:\\n brand = None\\n break\\n # Only include item to scrape\\n if category_list and brand:\\n scrape_data_output = _get_item_attributes(url_to_scrape, category_list, brand)\\n elif category_list:\\n scrape_data_output = _get_item_attributes(url_to_scrape, category_list)\\n else:\\n scrape_data_output = _get_item_attributes(url_to_scrape)\\n return scrape_data_output\\n \\n else:\\n for scrape_cat_data in cat_dict.values():\\n if scrape_cat_data.has_key('category_list'):\\n category_list = scrape_cat_data['category_list']\\n else:\\n category_list = None\\n \\n if scrape_cat_data.has_key('brand'):\\n brand = scrape_cat_data['brand']\\n else:\\n brand = None\\n \\n if category_list and brand:\\n get_all_data_points = _get_item_attributes(scrape_cat_data['url'], category_list, brand)\\n elif category_list:\\n get_all_data_points = _get_item_attributes(scrape_cat_data['url'], category_list)\\n else:\\n get_all_data_points = _get_item_attributes(scrape_cat_data['url'])\\n \\n if TESTRUN:\\n print \\\"URL is not set so scrapping data from :- \\\", scrape_cat_data['url']\\n print '-'*78\\n print get_all_data_points\\n print '-'*78\\n all_items.append(get_all_data_points)\\n return all_items\",\n \"def get_recipes_by_meal_item(self, item_type, number_of_results):\\n\\n # Make sure that query is in the right format\\n item_type_formatted = item_type[0].replace(\\\" \\\", \\\"+\\\")\\n url = \\\"{}/recipes/search?number={}&offset=0&type={}\\\".format(\\n self.base_url, number_of_results, item_type_formatted\\n )\\n\\n recipe_ids = self._get_recipe_ids(url)\\n return [self.get_recipe_by_id(recipe_id) for recipe_id in recipe_ids]\",\n \"def lots_of_items():\\n categorySoccer = Category(name=\\\"Soccer\\\")\\n categoryBasketball = Category(name=\\\"Basketball\\\")\\n categoryBaseball = Category(name=\\\"Baseball\\\")\\n categoryFrisbee = Category(name=\\\"Frisbee\\\")\\n categorySnowboarding = Category(name=\\\"Snowboarding\\\")\\n categoryRockClimbing = Category(name=\\\"Rock Climbing\\\")\\n categoryFoosball = Category(name=\\\"Foosball\\\")\\n categorySkating = Category(name=\\\"Skating\\\")\\n categoryHockey = Category(name=\\\"Hockey\\\")\\n\\n\\n categorySoccer.save()\\n categoryBasketball.save()\\n categoryBaseball.save()\\n categoryFrisbee.save()\\n categorySnowboarding.save()\\n categoryRockClimbing.save()\\n categoryFoosball.save()\\n categorySkating.save()\\n categoryHockey.save()\\n\\n item = Item(\\n title=\\\"Stick\\\",\\n description=\\\"A hockey stick\\\",\\n category=categoryHockey)\\n item.save()\\n\\n item = Item(\\n title=\\\"Goggles\\\",\\n description=\\\"Keep the snow out of your eyes\\\",\\n category=categorySnowboarding)\\n item.save()\\n\\n item = Item(\\n title=\\\"Snowboard\\\",\\n description=\\\"Type-A vintage\\\",\\n category=categorySnowboarding)\\n item.save()\\n\\n item = Item(\\n title=\\\"Two shinguards\\\",\\n description=\\\"Prevent injuries resulting from kicks to the shin\\\",\\n category=categorySoccer)\\n item.save()\\n\\n item = Item(\\n title=\\\"Shinguards\\\",\\n description=\\\"Prevent injuries resulting from kicks to the shin\\\",\\n category=categorySoccer)\\n item.save()\\n\\n item = Item(\\n title=\\\"Frisbee\\\",\\n description=\\\"A flying disc\\\",\\n category=categoryFrisbee)\\n item.save()\\n\\n item = Item(\\n title=\\\"Bat\\\",\\n description=\\\"Louisville slugger\\\",\\n category=categoryBaseball)\\n item.save()\\n\\n item = Item(\\n title=\\\"Jersey\\\",\\n description=\\\"World Cup 2014 commemorative jersy\\\",\\n category=categorySoccer)\\n item.save()\\n\\n item = Item(\\n title=\\\"Soccer Cleats\\\",\\n description=\\\"Nike cleats\\\",\\n category=categorySoccer)\\n item.save()\",\n \"def keyword_classifier(utterance):\\n categories = {\\n 'hello': ['hi ', 'greetings', 'hello', 'what\\\\'s up', 'hey ', 'how are you?', 'good morning', 'good night',\\n 'good evening', 'good day', 'howdy', 'hi-ya', 'hey ya'],\\n 'bye': ['bye', 'cheerio', 'adios', 'sayonara', 'peace out', 'see ya', 'see you', 'c ya', 'c you', 'ciao'],\\n 'ack': ['okay', 'whatever', 'ok ', 'o.k. ', 'kay ', 'fine '],\\n 'confirm': ['is it', 'is that', 'make sure', 'confirm', 'double check', 'check again', 'does it'],\\n 'deny': ['dont want', 'don\\\\'t want', 'wrong', 'dont like', 'don\\\\'t like'],\\n 'inform': ['dont care', 'don\\\\'t care', 'whatever', 'bakery', 'bar', 'cafe', 'coffeeshop', 'pub', 'restaurants',\\n 'roadhouse', 'african',\\n 'american', 'arabian', 'asian', 'international', 'european', 'central american', 'middle eastern',\\n 'world', 'vegan', 'vegetarian', 'free', 'kosher', 'traditional', 'fusion', 'modern', 'afghan',\\n 'algerian', 'angolan', 'argentine',\\n 'austrian', 'australian', 'bangladeshi', 'belarusian', 'belgian', 'bolivian', 'bosnian',\\n 'herzegovinian', 'brazilian', 'british', 'bulgarian', 'cambodian',\\n 'cameroonian', 'canadian', 'cantonese', 'catalan', 'caribbean', 'chadian', 'chinese', 'colombian',\\n 'costa rican', 'czech', 'congolese', 'cuban', 'danish', 'ecuadorian', 'salvadoran', 'emirati',\\n 'english', 'eritrean',\\n 'estonian',\\n 'ethiopian', 'finnish', 'french', 'german', 'ghanaian', 'greek', 'guatemalan', 'dutch', 'honduran',\\n 'hungarian', 'icelandic',\\n 'indian', 'indonesian', 'iranian', 'iraqi', 'irish', 'israeli', 'italian', 'ivorian', 'jamaican',\\n 'japanese',\\n 'jordanian', 'kazakh', 'kenyan', 'korean', 'lao', 'latvian', 'lebanese', 'libyan', 'lithuanian',\\n 'malagasy', 'malaysian',\\n 'mali', 'mauritanian', 'mediterranean', 'mexican', 'moroccan', 'namibian', 'new zealand',\\n 'nicaraguan',\\n 'nigerien', 'nigerian', 'norwegian', 'omani', 'pakistani', 'panamanian', 'paraguayan', 'peruvian',\\n 'persian', 'philippine', 'polynesian', 'polish', 'portuguese', 'romanian', 'russian', 'scottish',\\n 'senegalese', 'serbian',\\n 'singaporean', 'slovak', 'somalian', 'spanish', 'sudanese', 'swedish', 'swiss', 'syrian', 'thai',\\n 'tunisian', 'turkish',\\n 'ukranian', 'uruguayan', 'vietnamese', 'welsh', 'zambian', 'zimbabwean', 'west', 'north', 'south',\\n 'east', 'part of town', 'moderate', 'expensive', 'cheap', 'any ', 'priced', 'barbecue', 'burger',\\n 'chicken',\\n 'doughnut', 'fast food',\\n 'fish and chips', 'hamburger', 'hot dog', 'ice cream', 'noodles', 'pasta', 'pancake', 'pizza',\\n 'ramen', 'restaurant', 'seafood', 'steak',\\n 'sandwich', 'sushi'],\\n 'negate': ['no ', 'false', 'nope'],\\n 'repeat': ['repeat', 'say again', 'what was that'],\\n 'reqalts': ['how about', 'what about', 'anything else'],\\n 'reqmore': ['more', 'additional information'],\\n 'request': ['what', 'whats' 'what\\\\'s', 'why', 'where', 'when', 'how much', 'may', 'address', 'post code',\\n 'location', 'phone number'],\\n 'restart': ['reset', 'start over', 'restart'],\\n 'thankyou': ['thank you', 'cheers', 'thanks'],\\n 'affirm': ['ye ', 'yes', 'right ']\\n }\\n\\n classification = []\\n sentence_to_classify = utterance.lower()\\n for category, keywords in categories.items():\\n keywords_found = [keyword for keyword in keywords if keyword in sentence_to_classify]\\n if len(keywords_found) > 0: classification.append(category)\\n\\n return classification if len(classification) > 0 else ['null']\",\n \"def runSearch():\\n\\tglobal processLanguageOn\\n\\tdataToFind=getDataFromWidget(podSearchEntry)\\n\\t#Search through the keys otherwise data changes\\n\\tdataSource=podListbox.data.keys()\\n\\t#Store the results of the search\\n\\tresults=[]\\n\\t#Search the data source\\n\\tfor item in dataSource:\\n\\t\\tif searchDataSource(dataToFind,[item],capital=True,full=False):\\n\\t\\t\\tresults.append(item)\\n\\n\\t#Add the results to screen\\n\\tpodListbox.delete(0,END)\\n\\tfor item in results:\\n\\t\\tpodListbox.addExisting(item)\\n\\n\\tif processLanguageOn:\\n\\t\\tprocessSearchLanguage(podSearchEntry)\",\n \"def get_items_for_word(query, word: str) -> list:\\n # TODO Do these in parallel\\n outputs = {}\\n with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:\\n futures = {\\n executor.submit(pd.meaning, word): \\\"meanings\\\",\\n executor.submit(pd.synonym, word): \\\"synonyms\\\",\\n executor.submit(pd.antonym, word): \\\"antonyms\\\",\\n }\\n for future in concurrent.futures.as_completed(futures):\\n key = futures[future]\\n try:\\n outputs[key] = future.result()\\n except Exception as exc:\\n print(f\\\"[W] Getting the word {key} generated an exception: {exc}\\\")\\n\\n meanings = outputs[\\\"meanings\\\"]\\n synonyms = outputs[\\\"synonyms\\\"]\\n antonyms = outputs[\\\"antonyms\\\"]\\n\\n # meaning\\n items = []\\n if meanings:\\n for k, v in meanings.items():\\n for vi in v:\\n items.append(\\n v0.Item(\\n id=md_name,\\n icon=[icon_path],\\n text=vi,\\n subtext=k,\\n completion=f\\\"{query.trigger} {word}\\\",\\n actions=[\\n ClipAction(\\\"Copy\\\", vi),\\n ],\\n )\\n )\\n\\n # synonyms\\n if synonyms:\\n items.append(\\n v0.Item(\\n id=\\\"{md_name}_g\\\",\\n icon=[icon_path_g],\\n text=\\\"Synonyms\\\",\\n subtext=\\\"|\\\".join(synonyms),\\n completion=synonyms[0],\\n actions=[ClipAction(a, a) for a in synonyms],\\n )\\n )\\n\\n # antonym\\n if antonyms:\\n items.append(\\n v0.Item(\\n id=\\\"{md_name}_r\\\",\\n icon=[icon_path_r],\\n text=\\\"Antonyms\\\",\\n subtext=\\\"|\\\".join(antonyms),\\n completion=antonyms[0],\\n actions=[ClipAction(a, a) for a in antonyms],\\n )\\n )\\n\\n return items\",\n \"def crawl(self, keyword, since=None, to=None):\\n no_of_pages = self.get_no_of_pages(keyword, since, to)\\n print('pages: ' + str(no_of_pages))\\n self.keyword = keyword\\n self.since = since\\n self.to = to\\n p = Pool()\\n p.map(self.distribute, range(1, no_of_pages+1))\",\n \"def __filter_itunes_results(data: List[Dict[str, str]], query: str) -> int:\\n # Split the search query into words.\\n words: List[str] = re.findall(r'[\\\\w\\\\']+', query.lower())\\n max_score: int = 0\\n min_length: int = 0\\n index: int = 0\\n i: int = 0\\n query_ci: str = query.lower()\\n # Check each result.\\n for result in data:\\n track_name_ci: str = result['trackName'].lower()\\n artist_name_ci: str = result['artistName'].lower()\\n if query_ci == track_name_ci:\\n # This result matches the search query, use it.\\n break\\n if query_ci == (track_name_ci + ' ' + artist_name_ci) or query_ci == (artist_name_ci + ' ' + track_name_ci):\\n # This result's track name and artist name values combined together matches the search query, use it.\\n break\\n # Get all the words contained within the title according to iTunes.\\n title_words: List[str] = re.findall(r'[\\\\w\\\\']+', track_name_ci)\\n title_length: int = len(title_words)\\n # Find the common words between the words contained in the query and the words contained in this title.\\n score: int = len(list(set(title_words) & set(words)))\\n if score > max_score and (min_length == 0 or title_length < min_length):\\n # Find the element having the highest number of matching words but the lowest length.\\n index = i\\n max_score = score\\n min_length = title_length\\n i += 1\\n return index\",\n \"def suggestCategories(self, prefix: str, page: int = 1, count: int = 20, returnInfo: ReturnInfo = ReturnInfo(), **kwargs):\\n assert page > 0, \\\"page parameter should be above 0\\\"\\n params = { \\\"prefix\\\": prefix, \\\"page\\\": page, \\\"count\\\": count }\\n params.update(returnInfo.getParams())\\n params.update(kwargs)\\n return self.jsonRequest(\\\"/api/v1/suggestCategoriesFast\\\", params)\",\n \"def search_items(self):\\n urls = []\\n prices = []\\n names = []\\n for item in self.items:\\n print(f\\\"Searching for {item}...\\\")\\n\\n self.driver.get(self.amazon_url)\\n #select = Select(self.driver.find_element_by_id(\\\"searchDropdownDescription\\\"))\\n #select.select_by_visible_text('All Departments')\\n\\n search_input = self.driver.find_element_by_id(\\\"twotabsearchtextbox\\\")\\n search_input.send_keys(item)\\n\\n time.sleep(2)\\n #wait = WebDriverWait(self.driver, self.explicit_wait)\\n #wait.until(EC.presence_of_all_elements_located((By.ID, \\\"twotabsearchtextbox\\\")))\\n\\n search_button = self.driver.find_element_by_xpath('//*[@id=\\\"nav-search\\\"]/form/div[2]/div/input')\\n search_button.click()\\n\\n time.sleep(2)\\n\\n t = self.driver.find_element_by_id(\\\"result_0\\\")\\n asin = t.get_attribute(\\\"data-asin\\\")\\n url = \\\"https://www.amazon.ca/dp/\\\" + asin\\n price = self.get_product_price(url)\\n name = self.get_product_name(url)\\n \\n prices.append(price)\\n urls.append(url)\\n names.append(name)\\n\\n print(name)\\n print(price)\\n print(url)\\n\\n time.sleep(2)\\n\\n return prices, urls, names\",\n \"def distribute(self, page):\\n self.crawl_page(self.keyword, self.since, self.to, page)\",\n \"def __get_data_from_store(term):\\n url_search = PLAY_STORE_URL + \\\"/search\\\"\\n response = requests.get(url_search, {'c': 'apps', 'q': term})\\n soup = BeautifulSoup(response.content, \\\"html.parser\\\")\\n apps = soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"card no-rationale square-cover apps small\\\"})\\n\\n result = []\\n print(result)\\n for i, app in enumerate(apps):\\n app_details_basic = app.find(\\\"div\\\", {\\\"class\\\": \\\"details\\\"})\\n app_id = app['data-docid']\\n app_data = {\\n 'uid': app_id,\\n 'name': app_details_basic.find(\\\"a\\\", {\\\"class\\\": \\\"title\\\"})['title'].strip().encode('utf-8'),\\n 'dev_name': app_details_basic.find(\\\"a\\\", {\\\"class\\\": \\\"subtitle\\\"})['title'].strip(),\\n 'icon_url': \\\"http://\\\" + app.find(\\n \\\"div\\\", {\\\"class\\\": \\\"cover-inner-align\\\"}).img['data-cover-large'].strip(\\\"//\\\")\\n }\\n\\n url_app_detail = PLAY_STORE_URL + \\\"/apps/details\\\"\\n response = requests.get(url_app_detail, {'id': app_id})\\n soup = BeautifulSoup(response.content, \\\"html.parser\\\")\\n\\n app_data.update({\\n 'category': soup.find(\\\"a\\\", {\\\"itemprop\\\": \\\"genre\\\"}).text,\\n 'description': soup.find(\\\"div\\\", {\\\"itemprop\\\": \\\"description\\\"}).text.strip().encode('utf-8'),\\n \\n })\\n\\n \\n dev_links = soup.find_all(\\\"a\\\", {\\\"class\\\": \\\"dev-link\\\", \\\"rel\\\": \\\"nofollow\\\"})\\n if dev_links:\\n for dev_link in dev_links:\\n if \\\"mailto\\\" in dev_link['href']:\\n app_data['dev_email'] = dev_link['href'].replace(\\\"mailto:\\\", \\\"\\\")\\n break\\n\\n result.append(app_data)\\n\\n if i + 1 == SEARCH_RESULT_COUNT:\\n break\\n print(result)\\n return result\",\n \"def pass_category(url, name):\\n print(f\\\"Passage de la catégorie {name}...\\\")\\n # Initialize list of books\\n books = []\\n while url != '':\\n response = requests.get(url)\\n if response.status_code == 200:\\n soup = BeautifulSoup(response.content, 'html.parser')\\n # Books are notify with article balise\\n articles = soup.findAll('article', {'class': 'product_pod'})\\n for article in tqdm(articles):\\n # Get the book's link\\n link = url + \\\"/../\\\" + article.find('h3').find('a')['href']\\n # Run the function to get book's info\\n dict_product = get_book_infos(link)\\n books.append(dict_product)\\n # Searching for a next page\\n next_page = soup.find('li', {'class': 'next'})\\n if not next_page:\\n url = ''\\n else:\\n url = url + \\\"/../\\\" + soup.find('li', {'class': 'next'}).find('a')['href']\\n write_category(name, books)\",\n \"def SearchForCategories(self, searchterm, domain='US'):\\n # Check if valid domain\\n if domain not in dcodes:\\n raise Exception('Invalid domain code')\\n\\n payload = {'key': self.accesskey,\\n 'domain': dcodes.index(domain),\\n 'type': 'category',\\n 'term': searchterm}\\n\\n r = requests.get('https://api.keepa.com/search/?', params=payload)\\n response = r.json()\\n\\n if response['categories'] == {}:\\n log.info('Categories search results not yet available or no ' +\\n 'search terms found.')\\n else:\\n return response['categories']\",\n \"def _request_search(self, query, topics, pages=1):\\n\\n query = query.replace(' ', '+')\\n url = 'https://google.com/search?q={}'.format(query)\\n\\n user_agent = \\\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/81.0.4044.122 Safari/537.36\\\"\\n\\n headers = {'user-agent': user_agent}\\n resp = requests.get(url, headers=headers)\\n\\n if resp.status_code == 200:\\n soup = BeautifulSoup(resp.content, \\\"html.parser\\\")\\n else:\\n raise Exception(\\\"Something's fucky\\\")\\n\\n items = []\\n\\n for result in soup.find_all('div', class_='rc'):\\n anchors = result.find_all('a')\\n if anchors:\\n link = anchors[0]['href']\\n title = result.find('h3').text\\n\\n topic_words = list({topic.text.lower() for topic in result.find_all('em') if topic.text.lower() in topics})\\n\\n item = {\\n 'title': title,\\n 'url': link,\\n 'topics': topic_words\\n }\\n items.append(item)\\n else:\\n raise Exception('No anchor to extract text from')\\n\\n return items\",\n \"def find_categories_used_dict(request):\\n categories_used = []\\n\\n for item_index, item in enumerate(all_shopping_items(request)):\\n category_dict = {\\n 'category': item.category.category,\\n }\\n if item_index == 0:\\n categories_used.append(category_dict)\\n else:\\n add_category = True\\n\\n for list_item in categories_used:\\n \\n if list_item['category'] == item.category.category:\\n add_category = False\\n \\n if add_category:\\n categories_used.append(category_dict)\\n\\n return categories_used\",\n \"def searchItems(name, allPages = False):\\n return Gw2Spidy._paginatedRequest(allPages, 'item-search', name)\",\n \"def test_get_categories(self, mocker):\\n mock = mocker.patch(\\\"requests_html.HTMLSession\\\")\\n mock.return_value.get.return_value.html.find.return_value = iter(\\n [\\n mocker.Mock(text=\\\"Ammo\\\", attrs={\\\"href\\\": \\\"catalogue?cat=1\\\"}),\\n mocker.Mock(text=\\\"Food\\\", attrs={\\\"href\\\": \\\"catalogue?cat=2\\\"}),\\n mocker.Mock(text=\\\"Armour\\\", attrs={\\\"href\\\": \\\"catalogue?cat=3\\\"}),\\n mocker.Mock(text=\\\"Weapons\\\", attrs={\\\"href\\\": \\\"catalogue?cat=4\\\"}),\\n ]\\n )\\n\\n result = resources.get_categories()\\n assert list(result) == [\\n (1, \\\"Ammo\\\"),\\n (2, \\\"Food\\\"),\\n (3, \\\"Armour\\\"),\\n (4, \\\"Weapons\\\"),\\n ]\",\n \"def get_listings(drug, page):\\n file_number = (page-1) * 25\\n html = open('%s_data/%s.html' % (drug, file_number)).read()\\n soup = BeautifulSoup(html, \\\"lxml\\\")\\n\\n container = soup.findAll(\\\"div\\\", id=\\\"cat_item\\\")\\n \\n items = []\\n for item in container:\\n title_elem = item.find(class_=\\\"h2\\\")\\n title = title_elem.text\\n if \\\"lottery\\\" in title.lower():\\n # ignore lottery listings because they mess up the average price\\n continue\\n try:\\n quantity = guess_quantity(title)\\n except:\\n print \\\"failed:\\\", title \\n \\n if not quantity:\\n continue\\n\\n url = title_elem.attrs['href']\\n price = item.find(class_=\\\"price_big\\\").text[1:] # remove bitcoin glyph\\n price = float(price.replace(',', ''))\\n seller = item.find(\\\"a\\\", href=re.compile(\\\"/silkroad/user/\\\")).text\\n thumb_e = item.find(\\\"img\\\", src=re.compile(\\\"^data:\\\"))\\n thumb = (thumb_e and thumb_e.attrs['src']) or \\\"\\\"\\n ships_from = [x for x in item.find(id=\\\"cat_item_description\\\").children][8]\\n country = ships_from[12:]\\n\\n items.append({\\n 'title': title,\\n \\\"seller\\\": seller,\\n \\\"price\\\": price,\\n \\\"quantity\\\": quantity,\\n \\\"url\\\": url,\\n \\\"country\\\": silkroad_country_to_iso(country),\\n \\\"thumb\\\": uri_to_file(thumb),\\n })\\n\\n print \\\"parsed page: %s (%s.html)\\\" % (page, file_number)\\n return items\",\n \"def problem1() -> Dict[str, List[req.Response]]:\\n keywords = collect_keywords(KEYWORDS)\\n if len(keywords) == 0:\\n raise Exception(\\\"[ERROR] No keywords were found in the keyword file!\\\")\\n web_scraper = WebScraper(keywords) \\n web_pages = web_scraper.crawl()\\n return web_pages\",\n \"def read_all(search):\\n if search == \\\"\\\":\\n print(\\\"No searching for items\\\")\\n # Create the list of trays from our data\\n return [TRAYS[key] for key in sorted(TRAYS.keys())] # return this sorted by tray name i.e. A, B, C ,... or 1, 2, 3, ...\\n else:\\n search_words = search.split() # get a list of individual words\\n\\n # TODO: probably want something which removes any basic word ('a', 'the', ...) from search_words\\n # since these might give loads of unwanted matches in our search\\n\\n # get a list of tuples: tray names paired with the number of word matches. Then sort these tuples by the number of matches. \\n # We can then use the order of the names\\n # lol Haskell says hello:\\n search_ordered_pairs = sorted([(name, num_word_matches(search_words, TRAYS[name][\\\"info\\\"].split())) for name in TRAYS.keys()], key=lambda x:x[1], reverse=True)\\n print(search_ordered_pairs)\\n return [TRAYS[pair[0]] for pair in search_ordered_pairs] # trays are ordered appropriately\",\n \"def _perform_search(self, query, root, ignore_case, search_targets):\\n ret = []\\n buffer = [root]\\n processed = 0\\n occurrences = 0\\n while buffer:\\n current = buffer.pop(0) # XXX or do we want depth first?\\n if IContainer.providedBy(current):\\n buffer += current.objectValues()\\n elif IContent.providedBy(current):\\n content_objects = self._get_content_objects(current)\\n processed += len(content_objects)\\n for state, url, obj in content_objects:\\n content = self._get_silvaxml_content(obj)\\n if content is None:\\n continue\\n num = self._count_or_replace(\\n content, query, None, ignore_case, search_targets)\\n occurrences += num\\n if num > 0:\\n ret.append({\\n 'url': url,\\n 'path': '/'.join(obj.getPhysicalPath()),\\n 'title': obj.get_title(),\\n 'occurrences': num,\\n 'state': state,\\n })\\n return processed, occurrences, ret\",\n \"def scrapeCategories():\\n page = requests.get(\\\"http://books.toscrape.com/index.html\\\")\\n soup = BeautifulSoup(page.content, 'html.parser')\\n content = soup.find(\\\"div\\\", {\\\"class\\\": \\\"side_categories\\\"}).findChild().find(\\\"ul\\\")\\n categories = content.find_all(\\\"a\\\")\\n for category in categories:\\n Categories.objects.create(name=category.text.strip())\\n return HttpResponse(status=204)\",\n \"def main():\\n grid = make_grid(3, 3) # Takes a long time for a grid of 4 , 4\\n dictionary = get_dictionary('words.txt')\\n words = search(grid, dictionary)\\n for word in words:\\n print(word)\\n print(\\\"Found %s words\\\" % len(words))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5936122","0.5875122","0.58417684","0.58384866","0.5813017","0.5788929","0.5743952","0.574124","0.5724943","0.5716384","0.57140964","0.5701603","0.5691337","0.56734043","0.5648546","0.5612714","0.5611656","0.55976135","0.55937296","0.55912596","0.55780137","0.55677557","0.555618","0.55243796","0.55204064","0.5502625","0.54932857","0.5479509","0.5452323","0.54511434","0.54328865","0.54309404","0.5422614","0.54070616","0.53985417","0.53913337","0.53822523","0.534689","0.53466505","0.53410274","0.5325792","0.53212065","0.5317001","0.53167766","0.5314879","0.5310083","0.53026444","0.5297641","0.5290973","0.5283995","0.5281385","0.52777475","0.52761745","0.52618355","0.5258849","0.5253908","0.5249762","0.5249426","0.52485293","0.5244611","0.52303135","0.5229046","0.5223783","0.52220035","0.52100176","0.52066356","0.5204697","0.520144","0.52008283","0.5194078","0.5191179","0.5189409","0.5185832","0.5180091","0.5179776","0.5177901","0.5171435","0.5168298","0.51664805","0.516234","0.5160365","0.5159326","0.51523566","0.5149292","0.51418227","0.51372117","0.51360774","0.51357865","0.5129671","0.51295805","0.5124702","0.51179326","0.5108607","0.51082134","0.51027966","0.5092436","0.50879663","0.5083791","0.50820345","0.50816864"],"string":"[\n \"0.5936122\",\n \"0.5875122\",\n \"0.58417684\",\n \"0.58384866\",\n \"0.5813017\",\n \"0.5788929\",\n \"0.5743952\",\n \"0.574124\",\n \"0.5724943\",\n \"0.5716384\",\n \"0.57140964\",\n \"0.5701603\",\n \"0.5691337\",\n \"0.56734043\",\n \"0.5648546\",\n \"0.5612714\",\n \"0.5611656\",\n \"0.55976135\",\n \"0.55937296\",\n \"0.55912596\",\n \"0.55780137\",\n \"0.55677557\",\n \"0.555618\",\n \"0.55243796\",\n \"0.55204064\",\n \"0.5502625\",\n \"0.54932857\",\n \"0.5479509\",\n \"0.5452323\",\n \"0.54511434\",\n \"0.54328865\",\n \"0.54309404\",\n \"0.5422614\",\n \"0.54070616\",\n \"0.53985417\",\n \"0.53913337\",\n \"0.53822523\",\n \"0.534689\",\n \"0.53466505\",\n \"0.53410274\",\n \"0.5325792\",\n \"0.53212065\",\n \"0.5317001\",\n \"0.53167766\",\n \"0.5314879\",\n \"0.5310083\",\n \"0.53026444\",\n \"0.5297641\",\n \"0.5290973\",\n \"0.5283995\",\n \"0.5281385\",\n \"0.52777475\",\n \"0.52761745\",\n \"0.52618355\",\n \"0.5258849\",\n \"0.5253908\",\n \"0.5249762\",\n \"0.5249426\",\n \"0.52485293\",\n \"0.5244611\",\n \"0.52303135\",\n \"0.5229046\",\n \"0.5223783\",\n \"0.52220035\",\n \"0.52100176\",\n \"0.52066356\",\n \"0.5204697\",\n \"0.520144\",\n \"0.52008283\",\n \"0.5194078\",\n \"0.5191179\",\n \"0.5189409\",\n \"0.5185832\",\n \"0.5180091\",\n \"0.5179776\",\n \"0.5177901\",\n \"0.5171435\",\n \"0.5168298\",\n \"0.51664805\",\n \"0.516234\",\n \"0.5160365\",\n \"0.5159326\",\n \"0.51523566\",\n \"0.5149292\",\n \"0.51418227\",\n \"0.51372117\",\n \"0.51360774\",\n \"0.51357865\",\n \"0.5129671\",\n \"0.51295805\",\n \"0.5124702\",\n \"0.51179326\",\n \"0.5108607\",\n \"0.51082134\",\n \"0.51027966\",\n \"0.5092436\",\n \"0.50879663\",\n \"0.5083791\",\n \"0.50820345\",\n \"0.50816864\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58419687"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":9984,"cells":{"query":{"kind":"string","value":"Converts string to URL compatible."},"document":{"kind":"string","value":"def make_url_compatible(category: str) -> str:\n return urllib.parse.quote(category)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def text_to_url(self, text):\r\n return QUrl(text)","def url(value: Any) -> str:\n url_in = str(value)\n\n if urlparse(url_in).scheme in [\"http\", \"https\"]:\n return cast(str, vol.Schema(vol.Url())(url_in))\n\n raise vol.Invalid(\"Invalid URL\")","def url_fix(s, charset='utf-8'):\n if isinstance(s, unicode):\n s = s.encode(charset, 'ignore')\n scheme, netloc, path, qs, anchor = urlparse.urlsplit(s)\n path = urllib.quote(path, '/%')\n qs = urllib.quote_plus(qs, ':&=')\n return urlparse.urlunsplit((scheme, netloc, path, qs, anchor))","def correct_url(self, url: str) -> str:\n # check if url has \"http://\" prefix\n if \"http://\" not in url:\n if \"https://\" not in url:\n url = \"http://\" + url\n url_split = url.split(\"/\")\n # correct URL as needed for script\n if url_split[4] == '':\n raise URLError('No Story ID given')\n if len(url_split) == 5:\n url_split.append('')\n else:\n raise URLError('Unknown URL format')\n url = '/'.join(url_split)\n url = urljoin(url, ' ')[0:-2]\n return url","def format_url(url):\n if not (url.startswith(\"//\") or url.startswith(\"http\")):\n url = \"http://\" + url\n return url","def _get_converted_url(self):\n new_parsed_url = ParseResult(\n scheme=\"https\",\n netloc=self.parsed_url.netloc,\n path=self.parsed_url.path,\n params=self.parsed_url.params,\n query=self.parsed_url.query,\n fragment=self.parsed_url.fragment,\n )\n\n return urlunparse(new_parsed_url)","def path_to_url(path):\r\n if os.sep == '/':\r\n return path\r\n else:\r\n return '/'.join(split_all(path))","def _format_url(s):\n return u'%s%s\\n' % (BASE_URL, s.get_absolute_url())","def _proper_url(self, url):\n if self.base_url not in url:\n url = self.base_url + url\n url = re.sub(r'(?<!https:)//', '/', url)\n if not url.endswith('/') and '?' not in url:\n url = url + '/'\n if url.endswith('?'):\n url = url[:-1]\n return url","def str2url(str):\n try:\n str = str.encode('utf-8')\n except:\n pass\n mfrom = \"ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝßàáâãäåæçèéêëìíîï\"\n to = \"AAAAAAECEEEEIIIIDNOOOOOOUUUUYSaaaaaaaceeeeiiii\"\n mfrom += \"ñòóôõöøùúûüýÿĀāĂ㥹ĆćĈĉĊċČčĎďĐđĒēĔĕĖėĘęĚěĜĝĞğĠġĢģ\"\n to += \"noooooouuuuyyaaaaaaccccccccddddeeeeeeeeeegggggggg\"\n mfrom += \"ĤĥĦħĨĩĪīĬĭĮįİıĴĵĶķĸĹĺĻļĽľĿŀŁłŃńŅņŇňʼnŊŋŌōŎŏŐőŒœŔŕŖŗŘř\"\n to += \"hhhhiiiiiiiiiijjkkkllllllllllnnnnnnnnnoooooooorrrrrr\"\n mfrom += \"ŚśŜŝŞşŠšŢţŤťŦŧŨũŪūŬŭŮůŰűŲųŴŵŶŷŸŹźŻżŽžſƀƂƃƄƅƇƈƉƊƐƑƒƓƔ\"\n to += \"ssssssssttttttuuuuuuuuuuuuwwyyyzzzzzzfbbbbbccddeffgv\"\n mfrom += \"ƖƗƘƙƚƝƞƟƠƤƦƫƬƭƮƯưƱƲƳƴƵƶǍǎǏǐǑǒǓǔǕǖǗǘǙǚǛǜǝǞǟǠǡǢǣǤǥǦǧǨǩ\"\n to += \"likklnnoopettttuuuuyyzzaaiioouuuuuuuuuueaaaaeeggggkk\"\n mfrom += \"ǪǫǬǭǰǴǵǷǸǹǺǻǼǽǾǿȀȁȂȃȄȅȆȇȈȉȊȋȌȍȎȏȐȑȒȓȔȕȖȗȘșȚțȞȟȤȥȦȧȨȩ\"\n to += \"oooojggpnnaaeeooaaaaeeeeiiiioooorrrruuuusstthhzzaaee\"\n mfrom += \"ȪȫȬȭȮȯȰȱȲȳḀḁḂḃḄḅḆḇḈḉḊḋḌḍḎḏḐḑḒḓḔḕḖḗḘḙḚḛḜḝḞḟḠḡḢḣḤḥḦḧḨḩḪḫ\"\n to += \"ooooooooyyaabbbbbbccddddddddddeeeeeeeeeeffgghhhhhhhhhh\"\n mfrom += \"ḬḭḮḯḰḱḲḳḴḵḶḷḸḹḺḻḼḽḾḿṀṁṂṃṄṅṆṇṈṉṊṋṌṍṎṏṐṑṒṓṔṕṖṗṘṙṚṛṜṝṞṟ\"\n to += \"iiiikkkkkkllllllllmmmmmmnnnnnnnnoooooooopppprrrrrrrr\"\n mfrom += \"ṠṡṢṣṤṥṦṧṨṩṪṫṬṭṮṯṰṱṲṳṴṵṶṷṸṹṺṻṼṽṾṿẀẁẂẃẄẅẆẇẈẉẊẋẌẍẎẏẐẑẒẓẔẕ\"\n to += \"ssssssssssttttttttuuuuuuuuuuvvvvwwwwwwwwwwxxxxxyzzzzzz\"\n mfrom += \"ẖẗẘẙẚẛẠạẢảẤấẦầẨẩẪẫẬậẮắẰằẲẳẴẵẶặẸẹẺẻẼẽẾếỀềỂểỄễỆệỈỉỊị\"\n to += \"htwyafaaaaaaaaaaaaaaaaaaaaaaaaeeeeeeeeeeeeeeeeiiii\"\n mfrom += \"ỌọỎỏỐốỒồỔổỖỗỘộỚớỜờỞởỠỡỢợỤụỦủỨứỪừỬửỮữỰựỲỳỴỵỶỷỸỹ\"\n to += \"oooooooooooooooooooooooouuuuuuuuuuuuuuyyyyyyyy\"\n for i in zip(mfrom, to):\n str = str.replace(*i)\n return str","def make_url_safe(self, url):\n return url.replace(' ', '%20')\\\n .replace('(', '%28')\\\n .replace(')', '%29')\\\n .replace('\"', '%22')","def url_fix(s, charset='utf-8'):\n # First step is to convert backslashes (which are invalid in URLs anyways)\n # to slashes. This is consistent with what Chrome does.\n s = s.replace('\\\\', '/')\n\n # For the specific case that we look like a malformed windows URL\n # we want to fix this up manually:\n if (\n s.startswith('file://') and\n s[7:8].isalpha() and\n s[8:10] in (':/', '|/')\n ):\n s = 'file:///' + s[7:]\n\n url = urlsplit(s)\n\n netloc = _encode_netloc(url)\n\n path = urlquote(\n url.path, encoding=charset, safe='/%+$!*\\'(),'\n )\n qs = urlquote_plus(\n url.query, encoding=charset, safe=':&%=+$!*\\'(),'\n )\n anchor = urlquote_plus(\n url.fragment, encoding=charset, safe=':&%=+$!*\\'(),'\n )\n\n return urlunsplit(\n (url.scheme, netloc, path, qs, anchor)\n )","def fix_url(cls, url: str):\r\n ...","def _override_tourl(self):\n base_url = urlparse.urlparse(self.url)\n try:\n query = base_url.query\n except AttributeError:\n # must be python <2.5\n query = base_url[4]\n query = parse_qs(query)\n for k, v in self.items():\n query.setdefault(k, []).append(v)\n\n try:\n scheme = base_url.scheme\n netloc = base_url.netloc\n path = base_url.path\n params = base_url.params\n fragment = base_url.fragment\n except AttributeError:\n # must be python <2.5\n scheme = base_url[0]\n netloc = base_url[1]\n path = base_url[2]\n params = base_url[3]\n fragment = base_url[5]\n\n url = (scheme, netloc, path, params,\n urllib.urlencode(query, True), fragment)\n return urlparse.urlunparse(url)","def to_url(request):\r\n scheme, netloc, path, query, fragment = urlsplit(to_utf8(request.url))\r\n query = parse_qs(query)\r\n\r\n for key, value in request.data_and_params.iteritems():\r\n query.setdefault(key, []).append(value)\r\n\r\n query = urllib.urlencode(query, True)\r\n return urlunsplit((scheme, netloc, path, query, fragment))","def _make_url(self, path):\n if not self.base_location:\n raise ValueError(\"No base_location set. Cannot construct url.\")\n\n if path:\n path = self._normalise_last_slashes(path)\n path = self._normalise_head_slashes(path)\n\n return \"\".join((self.base_location, self.endpoint, path))","def reformat_weburl(s):\n s = (s or '').strip()\n\n if s and '.' in s and 'notavailable' not in s:\n match = re.match(r'^http(s)?://', s)\n if not match:\n s = 'http://' + s\n\n return s","def format_url(url):\n no_scheme = url.split('://', 1)[-1]\n return '[{0}]({1})'.format(no_scheme, url)","def format_url(url: str) -> str:\n return urljoin(url.replace('https://app', 'https://api'), '')","def normalize_url(self, url):\n pass","def normalize_url(url):\n # print(url)\n if not url.startswith('http://') and not url.startswith('https://'):\n return 'https://{}/{}'.format(zone_name, url.replace('//', '/'))\n return url","def _full_url(url, _params={}):\n\n # Support for unicode domain names and paths.\n scheme, netloc, path, params, query, fragment = urlparse.urlparse(url)\n\n if not scheme:\n raise ValueError(\"Invalid URL %r: No schema supplied\" % url)\n\n netloc = netloc.encode('idna')\n\n if isinstance(path, unicode):\n path = path.encode('utf-8')\n\n path = requote_path(path)\n\n url = str(urlparse.urlunparse([scheme, netloc, path, params, query,\n fragment]))\n\n if _params:\n if urlparse.urlparse(url).query:\n return '%s&%s' % (url, _params)\n else:\n return '%s?%s' % (url, _params)\n else:\n return url","def _convert_url(url, website):\n\n if website == 'xinhua':\n page_url = url.replace('\"', '')\n page_url = page_url.encode('ascii')\n elif website == 'upi':\n page_url = url.encode('ascii')\n elif website == 'zaman':\n # Find the weird thing. They tend to be ap or reuters, but generalized\n # just in case\n com = url.find('.com')\n slash = url[com + 4:].find('/')\n replaced_url = url.replace(url[com + 4:com + slash + 4], '')\n split = replaced_url.split('/')\n # This is nasty and hackish but it gets the jobs done.\n page_url = '/'.join(['/'.join(split[0:3]), 'world_' + split[-1]])\n else:\n page_url = url.encode('utf-8')\n\n return page_url","def encode_url(self, url):\n # turn string into unicode\n if not isinstance(url, unicode):\n url = url.decode('utf8')\n\n # parse it\n parsed = urlsplit(url)\n\n # divide the netloc further\n netloc_pattern = re.compile(r\"\"\"\n (?:(?P<user>[^:@]+)(?::(?P<password>[^:@]+))?@)?\n (?P<host>[^:]+)\n (?::(?P<port>[0-9]+))?\n \"\"\", re.X | re.U)\n netloc_parsed = netloc_pattern.match(parsed.netloc).groupdict()\n\n # encode each component\n scheme = parsed.scheme\n user = netloc_parsed['user'] and quote(netloc_parsed['user'])\n password = (netloc_parsed['password'] and\n quote(netloc_parsed['password']))\n host = netloc_parsed['host']\n port = netloc_parsed['port'] and netloc_parsed['port']\n path = '/'.join( # could be encoded slashes!\n quote(unquote(pce).encode('utf8'), '')\n for pce in parsed.path.split('/')\n )\n query = quote(unquote(parsed.query), '=&?/')\n fragment = quote(unquote(parsed.fragment))\n\n # put it back together\n netloc = ''\n if user:\n netloc += user\n if password:\n netloc += ':' + password\n netloc += '@'\n netloc += host\n if port:\n netloc += ':'+port\n return urlunsplit((scheme, netloc, path, query, fragment))","def my_url(url):\n if USE_HTTPS:\n return url_for(url, _scheme=\"https\", _external=True)\n else:\n return url_for(url)","def _fix_url(url):\n\n if not url.startswith('http'):\n url = 'http://' + url\n\n return url","def convertToURL( cPathname ):\n if len( cPathname ) > 1:\n if cPathname[1:2] == \":\":\n cPathname = \"/\" + cPathname[0] + \"|\" + cPathname[2:]\n cPathname = string.replace( cPathname, \"\\\\\", \"/\" )\n cPathname = \"file://\" + cPathname\n return cPathname","def norm(url):\n url = _unicode(url) # operate on unicode strings\n url_tuple = urlparse(url)\n normalized_tuple = norm_tuple(*url_tuple)\n return urlunparse(normalized_tuple).replace(' ','%20')","def normalize_url(url: str) -> str:\n parts = urlparse(url)\n\n path = quote(parts.path)\n while '//' in path:\n path = path.replace(\"//\", \"/\")\n\n return urlunparse(parts._replace(path=path))","def transform_url(result):\n import re\n result = re.sub('//', '/', result)\n result = re.sub('/', '//', result, count=1)\n return encode_url_path(result)","def make_url_safe(url):\n if not urlparse(url).scheme:\n url = \"http://\" + url\n\n parsed_url = urlparse(url)\n\n safe_path = urls.url_fix(parsed_url.path)\n\n return parsed_url.scheme + '://' + parsed_url.netloc + safe_path","def URIEscape(str):\n ret = libxml2mod.xmlURIEscape(str)\n return ret","def _encode_url(full_url):\n return urllib.parse.quote(full_url, safe=\"%/:=&?~#+!$,;'@()*[]|\")","def convertToURL( cPathname ):\n if len( cPathname ) > 1:\n if cPathname[1:2] == \":\":\n cPathname = \"/\" + cPathname[0] + \"|\" + cPathname[2:]\n cPathname = cPathname.replace( \"\\\\\", \"/\" )\n cPathname = \"file://\" + cPathname\n return cPathname","def b2_url_decode(s):\n return unquote_plus(s)","def URLify(s):\n\n # Without any additional libraries and by doing it in an array as intended by the writer.\n return ''.join('%20' if c == ' ' else c for c in s.strip())\n\n # Time Complexity: O(n)\n # Space Complexity: O(1)","def format_internal_url(url):\n\n url = url.split('\"')[-2]\n\n if not url.startswith('https:'):\n url = (\n 'https://medium.com{}'.format(url) if not url.startswith('//medium.com')\n else 'https:{}'.format(url))\n\n return url","def from_url(cls, url, **kwargs):\n kwargs = dict(kwargs)\n kwargs['format'] = 'url'\n # for now we only support ascii\n return cls(value=url.encode('ascii'), **kwargs)","def _convert_file_to_url(filename, no_file_check = False):\n if no_file_check: # We already a priori know that the path is\n # correct and in its final form.\n return filename\n relpath = os.path.relpath(filename, settings.SENDFILE_ROOT)\n\n url = [settings.SENDFILE_URL]\n\n while relpath:\n relpath, head = os.path.split(relpath)\n url.insert(1, head)\n\n return u'/'.join(url) # Note: xlates from os.path.sep to '/'","def _transform_gdrive_url(self):\n fileid = self.parsed.path.replace('/file/d/', '').split('/')[0]\n self.url = self.GDRIVE_LINK_TEMPLATE.format(fileid=fileid)","def make_safe_url(self, url):\n\n # Split the URL into scheme, netloc, path, query and fragment\n parts = list(urlsplit(url))\n\n # Clear scheme and netloc and rebuild URL\n parts[0] = '' # Empty scheme\n parts[1] = '' # Empty netloc (hostname:port)\n safe_url = urlunsplit(parts)\n return safe_url","def format_url(url, msg):\n return url+\"?str={}\".format(urllib.parse.quote(msg))","def to_url(val, scheme, url_scheme=\"http\"):\n pid = normalize_pid(val, scheme)\n if scheme in LANDING_URLS:\n if scheme == \"gnd\" and pid.startswith(\"gnd:\"):\n pid = pid[len(\"gnd:\") :]\n if scheme == \"urn\" and not pid.lower().startswith(\"urn:nbn:\"):\n return \"\"\n if scheme == \"ascl\":\n pid = val.split(\":\")[1]\n if scheme == \"viaf\" and pid.startswith(\"viaf:\"):\n pid = pid[len(\"viaf:\") :]\n url_scheme = \"https\"\n return LANDING_URLS[scheme].format(scheme=url_scheme, pid=pid)\n elif scheme in [\"purl\", \"url\"]:\n return pid\n\n return \"\"","def urlify(board):\n return(board.replace(\" \",\"%20\"))","def safe_url_string(url, encoding='utf8', path_encoding='utf8'):\n # Python3's urlsplit() chokes on bytes input with non-ASCII chars,\n # so let's decode (to Unicode) using page encoding:\n # - it is assumed that a raw bytes input comes from a document\n # encoded with the supplied encoding (or UTF8 by default)\n # - if the supplied (or default) encoding chokes,\n # percent-encode offending bytes\n decoded = to_unicode(url, encoding=encoding, errors='percentencode')\n parts = urlsplit(_ascii_tab_newline_re.sub('', decoded))\n\n # IDNA encoding can fail for too long labels (>63 characters)\n # or missing labels (e.g. http://.example.com)\n try:\n netloc = parts.netloc.encode('idna')\n except UnicodeError:\n netloc = parts.netloc\n\n # quote() in Python2 return type follows input type;\n # quote() in Python3 always returns Unicode (native str)\n return urlunsplit((\n to_native_str(parts.scheme),\n to_native_str(netloc).rstrip(':'),\n\n # default encoding for path component SHOULD be UTF-8\n quote(to_bytes(parts.path, path_encoding), _safe_chars),\n\n # encoding of query and fragment follows page encoding\n # or form-charset (if known and passed)\n quote(to_bytes(parts.query, encoding), _safe_chars),\n quote(to_bytes(parts.fragment, encoding), _safe_chars),\n ))","def _transform_dropbox_url(self):\n self.url = requests.utils.urlunparse(\n self.parsed._replace(query='dl=1'))","def _encode_url(data: str) -> str:\n return urllib.parse.quote(data, safe=\"\")","def enforce_url(value: Union[\"URL\", bytes, str], *, name: str) -> \"URL\":\n if isinstance(value, (bytes, str)):\n return URL(value)\n elif isinstance(value, URL):\n return value\n\n seen_type = type(value).__name__\n raise TypeError(f\"{name} must be a URL, bytes, or str, but got {seen_type}.\")","def format_url(self, url, text):\r\n return u'<a href=\"%s\">%s</a>' % (escape(url), text)","def sanitize_url(self, url):\r\n if not self.markdown.safeMode:\r\n # Return immediately bipassing parsing.\r\n return url\r\n \r\n try:\r\n scheme, netloc, path, params, query, fragment = url = urlparse(url)\r\n except ValueError:\r\n # Bad url - so bad it couldn't be parsed.\r\n return ''\r\n \r\n locless_schemes = ['', 'mailto', 'news']\r\n if netloc == '' and scheme not in locless_schemes:\r\n # This fails regardless of anything else. \r\n # Return immediately to save additional proccessing\r\n return ''\r\n\r\n for part in url[2:]:\r\n if \":\" in part:\r\n # Not a safe url\r\n return ''\r\n\r\n # Url passes all tests. Return url as-is.\r\n return urlunparse(url)","def fullUrl(self, resource):\n\n res = urlparse(str(resource))\n\n if res.scheme == 'https':\n return resource\n else:\n return self.url + resource","def _make_url(self):\n ...","def normalize_url(node):\n if not node:\n node = DEFAULT_NODE\n elif '://' not in node:\n node = '//{}'.format(node)\n parts = urlparse(node, scheme='http', allow_fragments=False)\n port = parts.port if parts.port else _get_default_port(parts.scheme)\n netloc = '{}:{}'.format(parts.hostname, port)\n return urlunparse((parts.scheme, netloc, parts.path, '', '', ''))","def normalize_for_url(text: str) -> str:\n\n # German is our main language, so we are extra considerate about it\n # (unidecode turns ü into u)\n text = text.replace(\"ü\", \"ue\")\n text = text.replace(\"ä\", \"ae\")\n text = text.replace(\"ö\", \"oe\")\n clean = _unwanted_url_chars.sub('-', unidecode(text).strip(' ').lower())\n clean = _double_dash.sub('-', clean)\n clean = clean.strip('-')\n\n return clean","def encode_url(url):\n\treturn url.replace(' ', '_')","def clean_url(url):\r\n s = url\r\n url = url.encode('utf8')\r\n url = ''.join([urllib.quote(c) if ord(c) >= 127 else c for c in url])\r\n return url","def b2_url_encode(s):\n return quote(s.encode('utf-8'))","def sanitize_url(url: str, protocol: str = 'https://') -> str:\n sanitized = url[0:-1] if url[-1] == '/' else url\n with_protocol = sanitized if sanitized.startswith('http') else f'{protocol}{sanitized}'\n return with_protocol","def compile_route_to_url(self):\n\n if 'http' in self.redirect_url:\n return self.redirect_url\n\n # Split the url into a list\n split_url = self.redirect_url.split('/')\n\n # Start beginning of the new compiled url\n compiled_url = '/'\n\n # Iterate over the list\n for url in split_url:\n\n # if the url contains a parameter variable like @id:int\n if '@' in url:\n url = url.replace('@', '').replace(\n ':int', '').replace(':string', '')\n compiled_url += str(self.param(url)) + '/'\n else:\n compiled_url += url + '/'\n\n # The loop isn't perfect and may have an unwanted trailing slash\n if compiled_url.endswith('/') and not self.redirect_url.endswith('/'):\n compiled_url = compiled_url[:-1]\n\n # The loop isn't perfect and may have 2 slashes next to eachother\n if '//' in compiled_url:\n compiled_url = compiled_url.replace('//', '/')\n\n return compiled_url","def _transform_github_url(self):\n self.url = (\n self.url\n .replace('/blob/', '/')\n .replace(self.GITHUB_NETLOC, self.GITHUB_RAW_NETLOC)\n )","def handle_url(self, url):\n parse = urlparse.urlparse(url, \"http\")\n # relative url path\n if not parse.netloc:\n parse = urlparse.urlparse(\n urlparse.urljoin(\n self.source_url,\n parse.path))\n return urlparse.urlunparse(parse)","def urlize(string):\n return _quote('_'.join(string.split()))","def urlQuote(string):\r\n return quote(string.encode(\"utf-8\"))","def youtube_fix_url(url):\n p = urlparse.urlparse(url)\n path = p.path\n if '&' in p.path:\n # sign of a malformed path\n path = re.sub('\\&.+', '', p.path)\n return urlparse.urlunparse((p.scheme, p.netloc, path, p.params, p.query, p.fragment))","def absolute_url(device_url: str, url: str) -> str:\n if url.startswith('http:') or \\\n url.startswith('https:'):\n return url\n\n return urljoin(device_url, url)","def _url(self, path):\n \n return self.url + path","def url_python(name):\n\n return reverse(name)","def transform_url_without_encode(result):\n import re\n result = re.sub('//', '/', result)\n result = re.sub('/', '//', result, count=1)\n return result","def TOURL(self) -> str:\n\t\treturn \"%s://%s:%d/\" % (\"https\" if self.useSSL else \"https\", self.toHost, self.toPort)","def urldecode(val):\n return urllib.unquote_plus(val)","def purl2url(purl):\n if purl:\n try:\n return router.process(purl)\n except NoRouteAvailable:\n return","def urlify_pythonic(text, length):\n return text.rstrip().replace(\" \", \"%20\")","def CanonicalUrl(self, u:str)->str:\n return u","def absolute_url(device_url: str, url: str) -> str:\n if url.startswith(\"http:\") or url.startswith(\"https:\"):\n return url\n\n return urljoin(device_url, url)","def url_prepare(url):\n if 'http://' in url or 'https://' in url:\n return url\n try:\n if requests.get('https://' + url):\n return 'https://' + url\n except Exception as ex:\n pprint(ex)\n return 'http://' + url","def get_normalized_url(url):\r\n scheme, netloc, path, params, query, fragment = urlparse(url)\r\n\r\n # Exclude default port numbers.\r\n if scheme == 'http' and netloc[-3:] == ':80':\r\n netloc = netloc[:-3]\r\n elif scheme == 'https' and netloc[-4:] == ':443':\r\n netloc = netloc[:-4]\r\n if scheme not in ('http', 'https'):\r\n raise ValueError(\"Unsupported URL %s (%s).\" % (url, scheme))\r\n\r\n # Normalized URL excludes params, query, and fragment.\r\n return urlunparse((scheme, netloc, path, None, None, None))","def sanitize_url(url, require_scheme = False):\r\n if not url or ' ' in url:\r\n return\r\n\r\n url = url.strip()\r\n if url.lower() == 'self':\r\n return url\r\n\r\n u = urlparse(url)\r\n # first pass: make sure a scheme has been specified\r\n if not require_scheme and not u.scheme:\r\n url = 'http://' + url\r\n u = urlparse(url)\r\n\r\n if (u.scheme and u.scheme in valid_schemes\r\n and u.hostname and len(u.hostname) < 255\r\n and '%' not in u.netloc):\r\n return url","def urlpath(self, url):\n\t\t# remove schema + hostname\n\t\turl = re.sub('^[^:]*://[^/]+', '/', url)\n\n\t\treturn self.canonicalize(url)","def validate_url(ctx, param, value):\n try:\n return URL(request.urlopen(value).read())\n except ValueError:\n raise click.BadParameter('url need to be a correct URL string')","def _format_url(self, hostname):\n share_name = self.share_name\n if isinstance(share_name, six.text_type):\n share_name = share_name.encode('UTF-8')\n return \"{}://{}/{}/{}{}\".format(\n self.scheme,\n hostname,\n quote(share_name),\n \"/\".join([quote(p, safe='~') for p in self.file_path]),\n self._query_str)","def parseUrl(self, resource, type):\n try:\n rId = int(resource)\n resource = \"%s/%s\" % (type, rId)\n except:\n pass\n assert type in resource\n return self.fullUrl(resource)","def _make_url(self, url_part, blueprint_prefix):\n parts = (blueprint_prefix, self.prefix, url_part)\n return ''.join(_ for _ in parts if _)","def contract_url(full_url: str) -> str:\n url_lst = list(urlparse(full_url))\n # delete params, query and fragment\n for i in [3, 4, 5]:\n url_lst[i] = ''\n # reduce url : path parts\n path_parts = url_lst[2].split('/')\n url_lst[2] = '/'.join((path_parts[0], '...', path_parts[-2], path_parts[-1]))\n contracted_url = urlunparse(url_lst)\n\n return contracted_url","def _url(path, **kwargs):\n if kwargs:\n encoded_parameters = urllib.urlencode(kwargs)\n if path.endswith('?'):\n # Trailing ? on path. Append parameters to end.\n return '%s%s' % (path, encoded_parameters)\n elif '?' in path:\n # Append additional parameters to existing query parameters.\n return '%s&%s' % (path, encoded_parameters)\n else:\n # Add query parameters to path with no query parameters.\n return '%s?%s' % (path, encoded_parameters)\n else:\n return path","def cleanUri(uri):\n if not uri.startswith(\"/\") and not uri.startswith('http'):\n uri = \"/\" + uri\n\n if 'http://' in uri or 'https://' in uri:\n uri = uri.split('://')[0] + '://' + \\\n uri.split('://')[1].replace(\"//\", \"/\")\n else:\n uri = uri.replace(\"//\", \"/\")\n\n if uri.endswith(\"/\"):\n uri = uri[:-1]\n\n return uri","def category2url(cat):\n return remove_diacritics(cat).replace(\" \", \"_\")","def as_url(cls, api=None, name_prefix='', url_prefix=''):\r\n url_prefix = url_prefix and \"%s/\" % url_prefix\r\n name_prefix = name_prefix and \"%s-\" % name_prefix\r\n\r\n url_regex = '^%s%s/?$' % (\r\n url_prefix, cls._meta.url_regex.lstrip('^').rstrip('/$'))\r\n url_regex = url_regex.replace('//', '/')\r\n url_name = '%s%s' % (name_prefix, cls._meta.url_name)\r\n\r\n return url(url_regex, cls.as_view(api=api), name=url_name)","def format_url(self, data):\n git_url = urlparse(data[\"git_url\"])\n\n url = \"oauth2:{0}@{1}\".format(data[\"token\"], git_url.netloc)\n return git_url._replace(netloc=url).geturl()","def url_norm(url, encoding=None, strip=False, lowercase_path=False, remove_fragment=False):\n\n if strip:\n url = url.strip()\n\n if isinstance(url, unicode):\n # try to decode the URL to ascii since urllib.unquote()\n # handles non-unicode strings differently\n try:\n url = url.encode('ascii')\n except UnicodeEncodeError:\n pass\n encode_unicode = True\n else:\n encode_unicode = False\n urlparts = list(urlparse.urlsplit(url))\n\n #fix missing scheme\n if not urlparts[0] or not urlparts[1]:\n urlparts = list(fix_missing_scheme(url, urlparts))\n elif urlparts[0] not in default_scheme_for_port:\n # Todo: find the scheme with the min edit distance\n pass\n\n # scheme\n if not http_scheme_pattern.match(urlparts[0]):\n raise InvalidUrl(url)\n\n urlparts[0] = urllib.unquote(urlparts[0]).lower()\n # host (with path or query side effects)\n is_idn = url_fix_host(urlparts)\n # query\n urlparts[3] = url_parse_query(urlparts[3], encoding=encoding)\n if urlparts[0] in urlparse.uses_relative:\n # URL has a hierarchical path we should norm\n if not urlparts[2]:\n # Empty path is allowed if both query and fragment are also empty.\n # Note that in relative links, urlparts[0] might be empty.\n # In this case, do not make any assumptions.\n if urlparts[0] and (urlparts[3] or urlparts[4]):\n urlparts[2] = '/'\n else:\n # fix redundant path parts\n urlparts[2] = collapse_segments(urlparts[2])\n if not remove_fragment:\n # anchor\n urlparts[4] = urllib.unquote(urlparts[4])\n # quote parts again\n urlparts[0] = url_quote_part(urlparts[0], encoding=encoding) # scheme\n urlparts[1] = url_quote_part(urlparts[1], safechars='@:', encoding=encoding) # host\n urlparts[2] = url_quote_part(urlparts[2], safechars=_nopathquote_chars, encoding=encoding) # path\n\n if lowercase_path:\n urlparts[2] = urlparts[2].lower()\n\n if remove_fragment:\n urlparts[4] = ''\n else:\n urlparts[4] = url_quote_part(urlparts[4], encoding=encoding) # anchor\n\n if not urlparts[2]:\n urlparts[2] = '/'\n\n res = urlunsplit(urlparts)\n\n if encode_unicode:\n res = unicode(res)\n return res, is_idn","def sanitize_url(url: str) -> Optional[str]:\n try:\n parts = urllib.parse.urlparse(url.replace(\" \", \"%20\"))\n scheme, netloc, path, params, query, fragment = parts\n except ValueError:\n # Bad URL - so bad it couldn't be parsed.\n return \"\"\n\n # If there is no scheme or netloc and there is a '@' in the path,\n # treat it as a mailto: and set the appropriate scheme\n if scheme == \"\" and netloc == \"\" and \"@\" in path:\n scheme = \"mailto\"\n elif scheme == \"\" and netloc == \"\" and len(path) > 0 and path[0] == \"/\":\n # Allow domain-relative links\n return urllib.parse.urlunparse((\"\", \"\", path, params, query, fragment))\n elif (scheme, netloc, path, params, query) == (\"\", \"\", \"\", \"\", \"\") and len(fragment) > 0:\n # Allow fragment links\n return urllib.parse.urlunparse((\"\", \"\", \"\", \"\", \"\", fragment))\n\n # Zulip modification: If scheme is not specified, assume http://\n # We re-enter sanitize_url because netloc etc. need to be re-parsed.\n if not scheme:\n return sanitize_url(\"http://\" + url)\n\n # Upstream code will accept a URL like javascript://foo because it\n # appears to have a netloc. Additionally there are plenty of other\n # schemes that do weird things like launch external programs. To be\n # on the safe side, we allow a fixed set of schemes.\n if scheme not in allowed_schemes:\n return None\n\n # Upstream code scans path, parameters, and query for colon characters\n # because\n #\n # some aliases [for javascript:] will appear to urllib.parse to have\n # no scheme. On top of that relative links (i.e.: \"foo/bar.html\")\n # have no scheme.\n #\n # We already converted an empty scheme to http:// above, so we skip\n # the colon check, which would also forbid a lot of legitimate URLs.\n\n # URL passes all tests. Return URL as-is.\n return urllib.parse.urlunparse((scheme, netloc, path, params, query, fragment))","def urlify2(w, length):\n chars = []\n while i < len(w):\n c = w[i]\n if c == ' ':\n chars.append('%20') \n else:\n chars.append(c)\n i += 1\n url_w = build_string(chars)\n return url_w","def hostify_url(url):\n\tif url[0] == '/':\n\t\treturn HOST + url\n\telse:\n\t\treturn url","def doi_to_url(doi, plos_network=False):\n URL_TMP = INT_URL_TMP if plos_network else EXT_URL_TMP\n return URL_TMP.format(doi)","def makeXnatUrl(host, _url):\n\n if isinstance(_url, bytes):\n _url = _url.decode(sys.getdefaultencoding())\n \n if _url.startswith('/'):\n _url = _url[1:]\n\n if not _url.startswith(host):\n if _url.startswith('data/'):\n _url = requests.compat.urljoin(host, _url)\n else:\n prefixUri = requests.compat.urljoin(host, 'data/archive/')\n _url = requests.compat.urljoin(prefixUri, _url)\n\n\n #--------------------\n # Remove double slashes\n #--------------------\n _url = _url.replace('//', '/')\n if 'http:/' in _url:\n _url = _url.replace('http:/', 'http://')\n elif 'https:/' in _url:\n _url = _url.replace('https:/', 'https://')\n\n return _url","def urldecode(val):\n return urllib2.unquote(val)","def quote_uri(uri):\n import urlparse\n import urllib\n\n up=urlparse.urlparse(uri)\n np=urllib.quote(up[2])\n return urlparse.urlunparse((up[0],up[1],np,up[3],up[4],up[5]))","def url(self, path: str) -> str:\n return url_util.join(self.base_url, path)","def url(self) -> str:\n return self.url_as()","def formatURL(self, url):\n pattern = r'(imdb\\.com\\/title\\/(.*/))'\n urls = re.findall(pattern, url)\n urls = urls[0]\n new_url = urls[0]\n new_url = \"https://www.\" + new_url\n title_code = urls[1].replace(\"/\", \"\")\n return new_url","def url_to_string(cls, v, values, **kwargs):\n return str(v)"],"string":"[\n \"def text_to_url(self, text):\\r\\n return QUrl(text)\",\n \"def url(value: Any) -> str:\\n url_in = str(value)\\n\\n if urlparse(url_in).scheme in [\\\"http\\\", \\\"https\\\"]:\\n return cast(str, vol.Schema(vol.Url())(url_in))\\n\\n raise vol.Invalid(\\\"Invalid URL\\\")\",\n \"def url_fix(s, charset='utf-8'):\\n if isinstance(s, unicode):\\n s = s.encode(charset, 'ignore')\\n scheme, netloc, path, qs, anchor = urlparse.urlsplit(s)\\n path = urllib.quote(path, '/%')\\n qs = urllib.quote_plus(qs, ':&=')\\n return urlparse.urlunsplit((scheme, netloc, path, qs, anchor))\",\n \"def correct_url(self, url: str) -> str:\\n # check if url has \\\"http://\\\" prefix\\n if \\\"http://\\\" not in url:\\n if \\\"https://\\\" not in url:\\n url = \\\"http://\\\" + url\\n url_split = url.split(\\\"/\\\")\\n # correct URL as needed for script\\n if url_split[4] == '':\\n raise URLError('No Story ID given')\\n if len(url_split) == 5:\\n url_split.append('')\\n else:\\n raise URLError('Unknown URL format')\\n url = '/'.join(url_split)\\n url = urljoin(url, ' ')[0:-2]\\n return url\",\n \"def format_url(url):\\n if not (url.startswith(\\\"//\\\") or url.startswith(\\\"http\\\")):\\n url = \\\"http://\\\" + url\\n return url\",\n \"def _get_converted_url(self):\\n new_parsed_url = ParseResult(\\n scheme=\\\"https\\\",\\n netloc=self.parsed_url.netloc,\\n path=self.parsed_url.path,\\n params=self.parsed_url.params,\\n query=self.parsed_url.query,\\n fragment=self.parsed_url.fragment,\\n )\\n\\n return urlunparse(new_parsed_url)\",\n \"def path_to_url(path):\\r\\n if os.sep == '/':\\r\\n return path\\r\\n else:\\r\\n return '/'.join(split_all(path))\",\n \"def _format_url(s):\\n return u'%s%s\\\\n' % (BASE_URL, s.get_absolute_url())\",\n \"def _proper_url(self, url):\\n if self.base_url not in url:\\n url = self.base_url + url\\n url = re.sub(r'(?<!https:)//', '/', url)\\n if not url.endswith('/') and '?' not in url:\\n url = url + '/'\\n if url.endswith('?'):\\n url = url[:-1]\\n return url\",\n \"def str2url(str):\\n try:\\n str = str.encode('utf-8')\\n except:\\n pass\\n mfrom = \\\"ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖØÙÚÛÜÝßàáâãäåæçèéêëìíîï\\\"\\n to = \\\"AAAAAAECEEEEIIIIDNOOOOOOUUUUYSaaaaaaaceeeeiiii\\\"\\n mfrom += \\\"ñòóôõöøùúûüýÿĀāĂ㥹ĆćĈĉĊċČčĎďĐđĒēĔĕĖėĘęĚěĜĝĞğĠġĢģ\\\"\\n to += \\\"noooooouuuuyyaaaaaaccccccccddddeeeeeeeeeegggggggg\\\"\\n mfrom += \\\"ĤĥĦħĨĩĪīĬĭĮįİıĴĵĶķĸĹĺĻļĽľĿŀŁłŃńŅņŇňʼnŊŋŌōŎŏŐőŒœŔŕŖŗŘř\\\"\\n to += \\\"hhhhiiiiiiiiiijjkkkllllllllllnnnnnnnnnoooooooorrrrrr\\\"\\n mfrom += \\\"ŚśŜŝŞşŠšŢţŤťŦŧŨũŪūŬŭŮůŰűŲųŴŵŶŷŸŹźŻżŽžſƀƂƃƄƅƇƈƉƊƐƑƒƓƔ\\\"\\n to += \\\"ssssssssttttttuuuuuuuuuuuuwwyyyzzzzzzfbbbbbccddeffgv\\\"\\n mfrom += \\\"ƖƗƘƙƚƝƞƟƠƤƦƫƬƭƮƯưƱƲƳƴƵƶǍǎǏǐǑǒǓǔǕǖǗǘǙǚǛǜǝǞǟǠǡǢǣǤǥǦǧǨǩ\\\"\\n to += \\\"likklnnoopettttuuuuyyzzaaiioouuuuuuuuuueaaaaeeggggkk\\\"\\n mfrom += \\\"ǪǫǬǭǰǴǵǷǸǹǺǻǼǽǾǿȀȁȂȃȄȅȆȇȈȉȊȋȌȍȎȏȐȑȒȓȔȕȖȗȘșȚțȞȟȤȥȦȧȨȩ\\\"\\n to += \\\"oooojggpnnaaeeooaaaaeeeeiiiioooorrrruuuusstthhzzaaee\\\"\\n mfrom += \\\"ȪȫȬȭȮȯȰȱȲȳḀḁḂḃḄḅḆḇḈḉḊḋḌḍḎḏḐḑḒḓḔḕḖḗḘḙḚḛḜḝḞḟḠḡḢḣḤḥḦḧḨḩḪḫ\\\"\\n to += \\\"ooooooooyyaabbbbbbccddddddddddeeeeeeeeeeffgghhhhhhhhhh\\\"\\n mfrom += \\\"ḬḭḮḯḰḱḲḳḴḵḶḷḸḹḺḻḼḽḾḿṀṁṂṃṄṅṆṇṈṉṊṋṌṍṎṏṐṑṒṓṔṕṖṗṘṙṚṛṜṝṞṟ\\\"\\n to += \\\"iiiikkkkkkllllllllmmmmmmnnnnnnnnoooooooopppprrrrrrrr\\\"\\n mfrom += \\\"ṠṡṢṣṤṥṦṧṨṩṪṫṬṭṮṯṰṱṲṳṴṵṶṷṸṹṺṻṼṽṾṿẀẁẂẃẄẅẆẇẈẉẊẋẌẍẎẏẐẑẒẓẔẕ\\\"\\n to += \\\"ssssssssssttttttttuuuuuuuuuuvvvvwwwwwwwwwwxxxxxyzzzzzz\\\"\\n mfrom += \\\"ẖẗẘẙẚẛẠạẢảẤấẦầẨẩẪẫẬậẮắẰằẲẳẴẵẶặẸẹẺẻẼẽẾếỀềỂểỄễỆệỈỉỊị\\\"\\n to += \\\"htwyafaaaaaaaaaaaaaaaaaaaaaaaaeeeeeeeeeeeeeeeeiiii\\\"\\n mfrom += \\\"ỌọỎỏỐốỒồỔổỖỗỘộỚớỜờỞởỠỡỢợỤụỦủỨứỪừỬửỮữỰựỲỳỴỵỶỷỸỹ\\\"\\n to += \\\"oooooooooooooooooooooooouuuuuuuuuuuuuuyyyyyyyy\\\"\\n for i in zip(mfrom, to):\\n str = str.replace(*i)\\n return str\",\n \"def make_url_safe(self, url):\\n return url.replace(' ', '%20')\\\\\\n .replace('(', '%28')\\\\\\n .replace(')', '%29')\\\\\\n .replace('\\\"', '%22')\",\n \"def url_fix(s, charset='utf-8'):\\n # First step is to convert backslashes (which are invalid in URLs anyways)\\n # to slashes. This is consistent with what Chrome does.\\n s = s.replace('\\\\\\\\', '/')\\n\\n # For the specific case that we look like a malformed windows URL\\n # we want to fix this up manually:\\n if (\\n s.startswith('file://') and\\n s[7:8].isalpha() and\\n s[8:10] in (':/', '|/')\\n ):\\n s = 'file:///' + s[7:]\\n\\n url = urlsplit(s)\\n\\n netloc = _encode_netloc(url)\\n\\n path = urlquote(\\n url.path, encoding=charset, safe='/%+$!*\\\\'(),'\\n )\\n qs = urlquote_plus(\\n url.query, encoding=charset, safe=':&%=+$!*\\\\'(),'\\n )\\n anchor = urlquote_plus(\\n url.fragment, encoding=charset, safe=':&%=+$!*\\\\'(),'\\n )\\n\\n return urlunsplit(\\n (url.scheme, netloc, path, qs, anchor)\\n )\",\n \"def fix_url(cls, url: str):\\r\\n ...\",\n \"def _override_tourl(self):\\n base_url = urlparse.urlparse(self.url)\\n try:\\n query = base_url.query\\n except AttributeError:\\n # must be python <2.5\\n query = base_url[4]\\n query = parse_qs(query)\\n for k, v in self.items():\\n query.setdefault(k, []).append(v)\\n\\n try:\\n scheme = base_url.scheme\\n netloc = base_url.netloc\\n path = base_url.path\\n params = base_url.params\\n fragment = base_url.fragment\\n except AttributeError:\\n # must be python <2.5\\n scheme = base_url[0]\\n netloc = base_url[1]\\n path = base_url[2]\\n params = base_url[3]\\n fragment = base_url[5]\\n\\n url = (scheme, netloc, path, params,\\n urllib.urlencode(query, True), fragment)\\n return urlparse.urlunparse(url)\",\n \"def to_url(request):\\r\\n scheme, netloc, path, query, fragment = urlsplit(to_utf8(request.url))\\r\\n query = parse_qs(query)\\r\\n\\r\\n for key, value in request.data_and_params.iteritems():\\r\\n query.setdefault(key, []).append(value)\\r\\n\\r\\n query = urllib.urlencode(query, True)\\r\\n return urlunsplit((scheme, netloc, path, query, fragment))\",\n \"def _make_url(self, path):\\n if not self.base_location:\\n raise ValueError(\\\"No base_location set. Cannot construct url.\\\")\\n\\n if path:\\n path = self._normalise_last_slashes(path)\\n path = self._normalise_head_slashes(path)\\n\\n return \\\"\\\".join((self.base_location, self.endpoint, path))\",\n \"def reformat_weburl(s):\\n s = (s or '').strip()\\n\\n if s and '.' in s and 'notavailable' not in s:\\n match = re.match(r'^http(s)?://', s)\\n if not match:\\n s = 'http://' + s\\n\\n return s\",\n \"def format_url(url):\\n no_scheme = url.split('://', 1)[-1]\\n return '[{0}]({1})'.format(no_scheme, url)\",\n \"def format_url(url: str) -> str:\\n return urljoin(url.replace('https://app', 'https://api'), '')\",\n \"def normalize_url(self, url):\\n pass\",\n \"def normalize_url(url):\\n # print(url)\\n if not url.startswith('http://') and not url.startswith('https://'):\\n return 'https://{}/{}'.format(zone_name, url.replace('//', '/'))\\n return url\",\n \"def _full_url(url, _params={}):\\n\\n # Support for unicode domain names and paths.\\n scheme, netloc, path, params, query, fragment = urlparse.urlparse(url)\\n\\n if not scheme:\\n raise ValueError(\\\"Invalid URL %r: No schema supplied\\\" % url)\\n\\n netloc = netloc.encode('idna')\\n\\n if isinstance(path, unicode):\\n path = path.encode('utf-8')\\n\\n path = requote_path(path)\\n\\n url = str(urlparse.urlunparse([scheme, netloc, path, params, query,\\n fragment]))\\n\\n if _params:\\n if urlparse.urlparse(url).query:\\n return '%s&%s' % (url, _params)\\n else:\\n return '%s?%s' % (url, _params)\\n else:\\n return url\",\n \"def _convert_url(url, website):\\n\\n if website == 'xinhua':\\n page_url = url.replace('\\\"', '')\\n page_url = page_url.encode('ascii')\\n elif website == 'upi':\\n page_url = url.encode('ascii')\\n elif website == 'zaman':\\n # Find the weird thing. They tend to be ap or reuters, but generalized\\n # just in case\\n com = url.find('.com')\\n slash = url[com + 4:].find('/')\\n replaced_url = url.replace(url[com + 4:com + slash + 4], '')\\n split = replaced_url.split('/')\\n # This is nasty and hackish but it gets the jobs done.\\n page_url = '/'.join(['/'.join(split[0:3]), 'world_' + split[-1]])\\n else:\\n page_url = url.encode('utf-8')\\n\\n return page_url\",\n \"def encode_url(self, url):\\n # turn string into unicode\\n if not isinstance(url, unicode):\\n url = url.decode('utf8')\\n\\n # parse it\\n parsed = urlsplit(url)\\n\\n # divide the netloc further\\n netloc_pattern = re.compile(r\\\"\\\"\\\"\\n (?:(?P<user>[^:@]+)(?::(?P<password>[^:@]+))?@)?\\n (?P<host>[^:]+)\\n (?::(?P<port>[0-9]+))?\\n \\\"\\\"\\\", re.X | re.U)\\n netloc_parsed = netloc_pattern.match(parsed.netloc).groupdict()\\n\\n # encode each component\\n scheme = parsed.scheme\\n user = netloc_parsed['user'] and quote(netloc_parsed['user'])\\n password = (netloc_parsed['password'] and\\n quote(netloc_parsed['password']))\\n host = netloc_parsed['host']\\n port = netloc_parsed['port'] and netloc_parsed['port']\\n path = '/'.join( # could be encoded slashes!\\n quote(unquote(pce).encode('utf8'), '')\\n for pce in parsed.path.split('/')\\n )\\n query = quote(unquote(parsed.query), '=&?/')\\n fragment = quote(unquote(parsed.fragment))\\n\\n # put it back together\\n netloc = ''\\n if user:\\n netloc += user\\n if password:\\n netloc += ':' + password\\n netloc += '@'\\n netloc += host\\n if port:\\n netloc += ':'+port\\n return urlunsplit((scheme, netloc, path, query, fragment))\",\n \"def my_url(url):\\n if USE_HTTPS:\\n return url_for(url, _scheme=\\\"https\\\", _external=True)\\n else:\\n return url_for(url)\",\n \"def _fix_url(url):\\n\\n if not url.startswith('http'):\\n url = 'http://' + url\\n\\n return url\",\n \"def convertToURL( cPathname ):\\n if len( cPathname ) > 1:\\n if cPathname[1:2] == \\\":\\\":\\n cPathname = \\\"/\\\" + cPathname[0] + \\\"|\\\" + cPathname[2:]\\n cPathname = string.replace( cPathname, \\\"\\\\\\\\\\\", \\\"/\\\" )\\n cPathname = \\\"file://\\\" + cPathname\\n return cPathname\",\n \"def norm(url):\\n url = _unicode(url) # operate on unicode strings\\n url_tuple = urlparse(url)\\n normalized_tuple = norm_tuple(*url_tuple)\\n return urlunparse(normalized_tuple).replace(' ','%20')\",\n \"def normalize_url(url: str) -> str:\\n parts = urlparse(url)\\n\\n path = quote(parts.path)\\n while '//' in path:\\n path = path.replace(\\\"//\\\", \\\"/\\\")\\n\\n return urlunparse(parts._replace(path=path))\",\n \"def transform_url(result):\\n import re\\n result = re.sub('//', '/', result)\\n result = re.sub('/', '//', result, count=1)\\n return encode_url_path(result)\",\n \"def make_url_safe(url):\\n if not urlparse(url).scheme:\\n url = \\\"http://\\\" + url\\n\\n parsed_url = urlparse(url)\\n\\n safe_path = urls.url_fix(parsed_url.path)\\n\\n return parsed_url.scheme + '://' + parsed_url.netloc + safe_path\",\n \"def URIEscape(str):\\n ret = libxml2mod.xmlURIEscape(str)\\n return ret\",\n \"def _encode_url(full_url):\\n return urllib.parse.quote(full_url, safe=\\\"%/:=&?~#+!$,;'@()*[]|\\\")\",\n \"def convertToURL( cPathname ):\\n if len( cPathname ) > 1:\\n if cPathname[1:2] == \\\":\\\":\\n cPathname = \\\"/\\\" + cPathname[0] + \\\"|\\\" + cPathname[2:]\\n cPathname = cPathname.replace( \\\"\\\\\\\\\\\", \\\"/\\\" )\\n cPathname = \\\"file://\\\" + cPathname\\n return cPathname\",\n \"def b2_url_decode(s):\\n return unquote_plus(s)\",\n \"def URLify(s):\\n\\n # Without any additional libraries and by doing it in an array as intended by the writer.\\n return ''.join('%20' if c == ' ' else c for c in s.strip())\\n\\n # Time Complexity: O(n)\\n # Space Complexity: O(1)\",\n \"def format_internal_url(url):\\n\\n url = url.split('\\\"')[-2]\\n\\n if not url.startswith('https:'):\\n url = (\\n 'https://medium.com{}'.format(url) if not url.startswith('//medium.com')\\n else 'https:{}'.format(url))\\n\\n return url\",\n \"def from_url(cls, url, **kwargs):\\n kwargs = dict(kwargs)\\n kwargs['format'] = 'url'\\n # for now we only support ascii\\n return cls(value=url.encode('ascii'), **kwargs)\",\n \"def _convert_file_to_url(filename, no_file_check = False):\\n if no_file_check: # We already a priori know that the path is\\n # correct and in its final form.\\n return filename\\n relpath = os.path.relpath(filename, settings.SENDFILE_ROOT)\\n\\n url = [settings.SENDFILE_URL]\\n\\n while relpath:\\n relpath, head = os.path.split(relpath)\\n url.insert(1, head)\\n\\n return u'/'.join(url) # Note: xlates from os.path.sep to '/'\",\n \"def _transform_gdrive_url(self):\\n fileid = self.parsed.path.replace('/file/d/', '').split('/')[0]\\n self.url = self.GDRIVE_LINK_TEMPLATE.format(fileid=fileid)\",\n \"def make_safe_url(self, url):\\n\\n # Split the URL into scheme, netloc, path, query and fragment\\n parts = list(urlsplit(url))\\n\\n # Clear scheme and netloc and rebuild URL\\n parts[0] = '' # Empty scheme\\n parts[1] = '' # Empty netloc (hostname:port)\\n safe_url = urlunsplit(parts)\\n return safe_url\",\n \"def format_url(url, msg):\\n return url+\\\"?str={}\\\".format(urllib.parse.quote(msg))\",\n \"def to_url(val, scheme, url_scheme=\\\"http\\\"):\\n pid = normalize_pid(val, scheme)\\n if scheme in LANDING_URLS:\\n if scheme == \\\"gnd\\\" and pid.startswith(\\\"gnd:\\\"):\\n pid = pid[len(\\\"gnd:\\\") :]\\n if scheme == \\\"urn\\\" and not pid.lower().startswith(\\\"urn:nbn:\\\"):\\n return \\\"\\\"\\n if scheme == \\\"ascl\\\":\\n pid = val.split(\\\":\\\")[1]\\n if scheme == \\\"viaf\\\" and pid.startswith(\\\"viaf:\\\"):\\n pid = pid[len(\\\"viaf:\\\") :]\\n url_scheme = \\\"https\\\"\\n return LANDING_URLS[scheme].format(scheme=url_scheme, pid=pid)\\n elif scheme in [\\\"purl\\\", \\\"url\\\"]:\\n return pid\\n\\n return \\\"\\\"\",\n \"def urlify(board):\\n return(board.replace(\\\" \\\",\\\"%20\\\"))\",\n \"def safe_url_string(url, encoding='utf8', path_encoding='utf8'):\\n # Python3's urlsplit() chokes on bytes input with non-ASCII chars,\\n # so let's decode (to Unicode) using page encoding:\\n # - it is assumed that a raw bytes input comes from a document\\n # encoded with the supplied encoding (or UTF8 by default)\\n # - if the supplied (or default) encoding chokes,\\n # percent-encode offending bytes\\n decoded = to_unicode(url, encoding=encoding, errors='percentencode')\\n parts = urlsplit(_ascii_tab_newline_re.sub('', decoded))\\n\\n # IDNA encoding can fail for too long labels (>63 characters)\\n # or missing labels (e.g. http://.example.com)\\n try:\\n netloc = parts.netloc.encode('idna')\\n except UnicodeError:\\n netloc = parts.netloc\\n\\n # quote() in Python2 return type follows input type;\\n # quote() in Python3 always returns Unicode (native str)\\n return urlunsplit((\\n to_native_str(parts.scheme),\\n to_native_str(netloc).rstrip(':'),\\n\\n # default encoding for path component SHOULD be UTF-8\\n quote(to_bytes(parts.path, path_encoding), _safe_chars),\\n\\n # encoding of query and fragment follows page encoding\\n # or form-charset (if known and passed)\\n quote(to_bytes(parts.query, encoding), _safe_chars),\\n quote(to_bytes(parts.fragment, encoding), _safe_chars),\\n ))\",\n \"def _transform_dropbox_url(self):\\n self.url = requests.utils.urlunparse(\\n self.parsed._replace(query='dl=1'))\",\n \"def _encode_url(data: str) -> str:\\n return urllib.parse.quote(data, safe=\\\"\\\")\",\n \"def enforce_url(value: Union[\\\"URL\\\", bytes, str], *, name: str) -> \\\"URL\\\":\\n if isinstance(value, (bytes, str)):\\n return URL(value)\\n elif isinstance(value, URL):\\n return value\\n\\n seen_type = type(value).__name__\\n raise TypeError(f\\\"{name} must be a URL, bytes, or str, but got {seen_type}.\\\")\",\n \"def format_url(self, url, text):\\r\\n return u'<a href=\\\"%s\\\">%s</a>' % (escape(url), text)\",\n \"def sanitize_url(self, url):\\r\\n if not self.markdown.safeMode:\\r\\n # Return immediately bipassing parsing.\\r\\n return url\\r\\n \\r\\n try:\\r\\n scheme, netloc, path, params, query, fragment = url = urlparse(url)\\r\\n except ValueError:\\r\\n # Bad url - so bad it couldn't be parsed.\\r\\n return ''\\r\\n \\r\\n locless_schemes = ['', 'mailto', 'news']\\r\\n if netloc == '' and scheme not in locless_schemes:\\r\\n # This fails regardless of anything else. \\r\\n # Return immediately to save additional proccessing\\r\\n return ''\\r\\n\\r\\n for part in url[2:]:\\r\\n if \\\":\\\" in part:\\r\\n # Not a safe url\\r\\n return ''\\r\\n\\r\\n # Url passes all tests. Return url as-is.\\r\\n return urlunparse(url)\",\n \"def fullUrl(self, resource):\\n\\n res = urlparse(str(resource))\\n\\n if res.scheme == 'https':\\n return resource\\n else:\\n return self.url + resource\",\n \"def _make_url(self):\\n ...\",\n \"def normalize_url(node):\\n if not node:\\n node = DEFAULT_NODE\\n elif '://' not in node:\\n node = '//{}'.format(node)\\n parts = urlparse(node, scheme='http', allow_fragments=False)\\n port = parts.port if parts.port else _get_default_port(parts.scheme)\\n netloc = '{}:{}'.format(parts.hostname, port)\\n return urlunparse((parts.scheme, netloc, parts.path, '', '', ''))\",\n \"def normalize_for_url(text: str) -> str:\\n\\n # German is our main language, so we are extra considerate about it\\n # (unidecode turns ü into u)\\n text = text.replace(\\\"ü\\\", \\\"ue\\\")\\n text = text.replace(\\\"ä\\\", \\\"ae\\\")\\n text = text.replace(\\\"ö\\\", \\\"oe\\\")\\n clean = _unwanted_url_chars.sub('-', unidecode(text).strip(' ').lower())\\n clean = _double_dash.sub('-', clean)\\n clean = clean.strip('-')\\n\\n return clean\",\n \"def encode_url(url):\\n\\treturn url.replace(' ', '_')\",\n \"def clean_url(url):\\r\\n s = url\\r\\n url = url.encode('utf8')\\r\\n url = ''.join([urllib.quote(c) if ord(c) >= 127 else c for c in url])\\r\\n return url\",\n \"def b2_url_encode(s):\\n return quote(s.encode('utf-8'))\",\n \"def sanitize_url(url: str, protocol: str = 'https://') -> str:\\n sanitized = url[0:-1] if url[-1] == '/' else url\\n with_protocol = sanitized if sanitized.startswith('http') else f'{protocol}{sanitized}'\\n return with_protocol\",\n \"def compile_route_to_url(self):\\n\\n if 'http' in self.redirect_url:\\n return self.redirect_url\\n\\n # Split the url into a list\\n split_url = self.redirect_url.split('/')\\n\\n # Start beginning of the new compiled url\\n compiled_url = '/'\\n\\n # Iterate over the list\\n for url in split_url:\\n\\n # if the url contains a parameter variable like @id:int\\n if '@' in url:\\n url = url.replace('@', '').replace(\\n ':int', '').replace(':string', '')\\n compiled_url += str(self.param(url)) + '/'\\n else:\\n compiled_url += url + '/'\\n\\n # The loop isn't perfect and may have an unwanted trailing slash\\n if compiled_url.endswith('/') and not self.redirect_url.endswith('/'):\\n compiled_url = compiled_url[:-1]\\n\\n # The loop isn't perfect and may have 2 slashes next to eachother\\n if '//' in compiled_url:\\n compiled_url = compiled_url.replace('//', '/')\\n\\n return compiled_url\",\n \"def _transform_github_url(self):\\n self.url = (\\n self.url\\n .replace('/blob/', '/')\\n .replace(self.GITHUB_NETLOC, self.GITHUB_RAW_NETLOC)\\n )\",\n \"def handle_url(self, url):\\n parse = urlparse.urlparse(url, \\\"http\\\")\\n # relative url path\\n if not parse.netloc:\\n parse = urlparse.urlparse(\\n urlparse.urljoin(\\n self.source_url,\\n parse.path))\\n return urlparse.urlunparse(parse)\",\n \"def urlize(string):\\n return _quote('_'.join(string.split()))\",\n \"def urlQuote(string):\\r\\n return quote(string.encode(\\\"utf-8\\\"))\",\n \"def youtube_fix_url(url):\\n p = urlparse.urlparse(url)\\n path = p.path\\n if '&' in p.path:\\n # sign of a malformed path\\n path = re.sub('\\\\&.+', '', p.path)\\n return urlparse.urlunparse((p.scheme, p.netloc, path, p.params, p.query, p.fragment))\",\n \"def absolute_url(device_url: str, url: str) -> str:\\n if url.startswith('http:') or \\\\\\n url.startswith('https:'):\\n return url\\n\\n return urljoin(device_url, url)\",\n \"def _url(self, path):\\n \\n return self.url + path\",\n \"def url_python(name):\\n\\n return reverse(name)\",\n \"def transform_url_without_encode(result):\\n import re\\n result = re.sub('//', '/', result)\\n result = re.sub('/', '//', result, count=1)\\n return result\",\n \"def TOURL(self) -> str:\\n\\t\\treturn \\\"%s://%s:%d/\\\" % (\\\"https\\\" if self.useSSL else \\\"https\\\", self.toHost, self.toPort)\",\n \"def urldecode(val):\\n return urllib.unquote_plus(val)\",\n \"def purl2url(purl):\\n if purl:\\n try:\\n return router.process(purl)\\n except NoRouteAvailable:\\n return\",\n \"def urlify_pythonic(text, length):\\n return text.rstrip().replace(\\\" \\\", \\\"%20\\\")\",\n \"def CanonicalUrl(self, u:str)->str:\\n return u\",\n \"def absolute_url(device_url: str, url: str) -> str:\\n if url.startswith(\\\"http:\\\") or url.startswith(\\\"https:\\\"):\\n return url\\n\\n return urljoin(device_url, url)\",\n \"def url_prepare(url):\\n if 'http://' in url or 'https://' in url:\\n return url\\n try:\\n if requests.get('https://' + url):\\n return 'https://' + url\\n except Exception as ex:\\n pprint(ex)\\n return 'http://' + url\",\n \"def get_normalized_url(url):\\r\\n scheme, netloc, path, params, query, fragment = urlparse(url)\\r\\n\\r\\n # Exclude default port numbers.\\r\\n if scheme == 'http' and netloc[-3:] == ':80':\\r\\n netloc = netloc[:-3]\\r\\n elif scheme == 'https' and netloc[-4:] == ':443':\\r\\n netloc = netloc[:-4]\\r\\n if scheme not in ('http', 'https'):\\r\\n raise ValueError(\\\"Unsupported URL %s (%s).\\\" % (url, scheme))\\r\\n\\r\\n # Normalized URL excludes params, query, and fragment.\\r\\n return urlunparse((scheme, netloc, path, None, None, None))\",\n \"def sanitize_url(url, require_scheme = False):\\r\\n if not url or ' ' in url:\\r\\n return\\r\\n\\r\\n url = url.strip()\\r\\n if url.lower() == 'self':\\r\\n return url\\r\\n\\r\\n u = urlparse(url)\\r\\n # first pass: make sure a scheme has been specified\\r\\n if not require_scheme and not u.scheme:\\r\\n url = 'http://' + url\\r\\n u = urlparse(url)\\r\\n\\r\\n if (u.scheme and u.scheme in valid_schemes\\r\\n and u.hostname and len(u.hostname) < 255\\r\\n and '%' not in u.netloc):\\r\\n return url\",\n \"def urlpath(self, url):\\n\\t\\t# remove schema + hostname\\n\\t\\turl = re.sub('^[^:]*://[^/]+', '/', url)\\n\\n\\t\\treturn self.canonicalize(url)\",\n \"def validate_url(ctx, param, value):\\n try:\\n return URL(request.urlopen(value).read())\\n except ValueError:\\n raise click.BadParameter('url need to be a correct URL string')\",\n \"def _format_url(self, hostname):\\n share_name = self.share_name\\n if isinstance(share_name, six.text_type):\\n share_name = share_name.encode('UTF-8')\\n return \\\"{}://{}/{}/{}{}\\\".format(\\n self.scheme,\\n hostname,\\n quote(share_name),\\n \\\"/\\\".join([quote(p, safe='~') for p in self.file_path]),\\n self._query_str)\",\n \"def parseUrl(self, resource, type):\\n try:\\n rId = int(resource)\\n resource = \\\"%s/%s\\\" % (type, rId)\\n except:\\n pass\\n assert type in resource\\n return self.fullUrl(resource)\",\n \"def _make_url(self, url_part, blueprint_prefix):\\n parts = (blueprint_prefix, self.prefix, url_part)\\n return ''.join(_ for _ in parts if _)\",\n \"def contract_url(full_url: str) -> str:\\n url_lst = list(urlparse(full_url))\\n # delete params, query and fragment\\n for i in [3, 4, 5]:\\n url_lst[i] = ''\\n # reduce url : path parts\\n path_parts = url_lst[2].split('/')\\n url_lst[2] = '/'.join((path_parts[0], '...', path_parts[-2], path_parts[-1]))\\n contracted_url = urlunparse(url_lst)\\n\\n return contracted_url\",\n \"def _url(path, **kwargs):\\n if kwargs:\\n encoded_parameters = urllib.urlencode(kwargs)\\n if path.endswith('?'):\\n # Trailing ? on path. Append parameters to end.\\n return '%s%s' % (path, encoded_parameters)\\n elif '?' in path:\\n # Append additional parameters to existing query parameters.\\n return '%s&%s' % (path, encoded_parameters)\\n else:\\n # Add query parameters to path with no query parameters.\\n return '%s?%s' % (path, encoded_parameters)\\n else:\\n return path\",\n \"def cleanUri(uri):\\n if not uri.startswith(\\\"/\\\") and not uri.startswith('http'):\\n uri = \\\"/\\\" + uri\\n\\n if 'http://' in uri or 'https://' in uri:\\n uri = uri.split('://')[0] + '://' + \\\\\\n uri.split('://')[1].replace(\\\"//\\\", \\\"/\\\")\\n else:\\n uri = uri.replace(\\\"//\\\", \\\"/\\\")\\n\\n if uri.endswith(\\\"/\\\"):\\n uri = uri[:-1]\\n\\n return uri\",\n \"def category2url(cat):\\n return remove_diacritics(cat).replace(\\\" \\\", \\\"_\\\")\",\n \"def as_url(cls, api=None, name_prefix='', url_prefix=''):\\r\\n url_prefix = url_prefix and \\\"%s/\\\" % url_prefix\\r\\n name_prefix = name_prefix and \\\"%s-\\\" % name_prefix\\r\\n\\r\\n url_regex = '^%s%s/?$' % (\\r\\n url_prefix, cls._meta.url_regex.lstrip('^').rstrip('/$'))\\r\\n url_regex = url_regex.replace('//', '/')\\r\\n url_name = '%s%s' % (name_prefix, cls._meta.url_name)\\r\\n\\r\\n return url(url_regex, cls.as_view(api=api), name=url_name)\",\n \"def format_url(self, data):\\n git_url = urlparse(data[\\\"git_url\\\"])\\n\\n url = \\\"oauth2:{0}@{1}\\\".format(data[\\\"token\\\"], git_url.netloc)\\n return git_url._replace(netloc=url).geturl()\",\n \"def url_norm(url, encoding=None, strip=False, lowercase_path=False, remove_fragment=False):\\n\\n if strip:\\n url = url.strip()\\n\\n if isinstance(url, unicode):\\n # try to decode the URL to ascii since urllib.unquote()\\n # handles non-unicode strings differently\\n try:\\n url = url.encode('ascii')\\n except UnicodeEncodeError:\\n pass\\n encode_unicode = True\\n else:\\n encode_unicode = False\\n urlparts = list(urlparse.urlsplit(url))\\n\\n #fix missing scheme\\n if not urlparts[0] or not urlparts[1]:\\n urlparts = list(fix_missing_scheme(url, urlparts))\\n elif urlparts[0] not in default_scheme_for_port:\\n # Todo: find the scheme with the min edit distance\\n pass\\n\\n # scheme\\n if not http_scheme_pattern.match(urlparts[0]):\\n raise InvalidUrl(url)\\n\\n urlparts[0] = urllib.unquote(urlparts[0]).lower()\\n # host (with path or query side effects)\\n is_idn = url_fix_host(urlparts)\\n # query\\n urlparts[3] = url_parse_query(urlparts[3], encoding=encoding)\\n if urlparts[0] in urlparse.uses_relative:\\n # URL has a hierarchical path we should norm\\n if not urlparts[2]:\\n # Empty path is allowed if both query and fragment are also empty.\\n # Note that in relative links, urlparts[0] might be empty.\\n # In this case, do not make any assumptions.\\n if urlparts[0] and (urlparts[3] or urlparts[4]):\\n urlparts[2] = '/'\\n else:\\n # fix redundant path parts\\n urlparts[2] = collapse_segments(urlparts[2])\\n if not remove_fragment:\\n # anchor\\n urlparts[4] = urllib.unquote(urlparts[4])\\n # quote parts again\\n urlparts[0] = url_quote_part(urlparts[0], encoding=encoding) # scheme\\n urlparts[1] = url_quote_part(urlparts[1], safechars='@:', encoding=encoding) # host\\n urlparts[2] = url_quote_part(urlparts[2], safechars=_nopathquote_chars, encoding=encoding) # path\\n\\n if lowercase_path:\\n urlparts[2] = urlparts[2].lower()\\n\\n if remove_fragment:\\n urlparts[4] = ''\\n else:\\n urlparts[4] = url_quote_part(urlparts[4], encoding=encoding) # anchor\\n\\n if not urlparts[2]:\\n urlparts[2] = '/'\\n\\n res = urlunsplit(urlparts)\\n\\n if encode_unicode:\\n res = unicode(res)\\n return res, is_idn\",\n \"def sanitize_url(url: str) -> Optional[str]:\\n try:\\n parts = urllib.parse.urlparse(url.replace(\\\" \\\", \\\"%20\\\"))\\n scheme, netloc, path, params, query, fragment = parts\\n except ValueError:\\n # Bad URL - so bad it couldn't be parsed.\\n return \\\"\\\"\\n\\n # If there is no scheme or netloc and there is a '@' in the path,\\n # treat it as a mailto: and set the appropriate scheme\\n if scheme == \\\"\\\" and netloc == \\\"\\\" and \\\"@\\\" in path:\\n scheme = \\\"mailto\\\"\\n elif scheme == \\\"\\\" and netloc == \\\"\\\" and len(path) > 0 and path[0] == \\\"/\\\":\\n # Allow domain-relative links\\n return urllib.parse.urlunparse((\\\"\\\", \\\"\\\", path, params, query, fragment))\\n elif (scheme, netloc, path, params, query) == (\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\") and len(fragment) > 0:\\n # Allow fragment links\\n return urllib.parse.urlunparse((\\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", \\\"\\\", fragment))\\n\\n # Zulip modification: If scheme is not specified, assume http://\\n # We re-enter sanitize_url because netloc etc. need to be re-parsed.\\n if not scheme:\\n return sanitize_url(\\\"http://\\\" + url)\\n\\n # Upstream code will accept a URL like javascript://foo because it\\n # appears to have a netloc. Additionally there are plenty of other\\n # schemes that do weird things like launch external programs. To be\\n # on the safe side, we allow a fixed set of schemes.\\n if scheme not in allowed_schemes:\\n return None\\n\\n # Upstream code scans path, parameters, and query for colon characters\\n # because\\n #\\n # some aliases [for javascript:] will appear to urllib.parse to have\\n # no scheme. On top of that relative links (i.e.: \\\"foo/bar.html\\\")\\n # have no scheme.\\n #\\n # We already converted an empty scheme to http:// above, so we skip\\n # the colon check, which would also forbid a lot of legitimate URLs.\\n\\n # URL passes all tests. Return URL as-is.\\n return urllib.parse.urlunparse((scheme, netloc, path, params, query, fragment))\",\n \"def urlify2(w, length):\\n chars = []\\n while i < len(w):\\n c = w[i]\\n if c == ' ':\\n chars.append('%20') \\n else:\\n chars.append(c)\\n i += 1\\n url_w = build_string(chars)\\n return url_w\",\n \"def hostify_url(url):\\n\\tif url[0] == '/':\\n\\t\\treturn HOST + url\\n\\telse:\\n\\t\\treturn url\",\n \"def doi_to_url(doi, plos_network=False):\\n URL_TMP = INT_URL_TMP if plos_network else EXT_URL_TMP\\n return URL_TMP.format(doi)\",\n \"def makeXnatUrl(host, _url):\\n\\n if isinstance(_url, bytes):\\n _url = _url.decode(sys.getdefaultencoding())\\n \\n if _url.startswith('/'):\\n _url = _url[1:]\\n\\n if not _url.startswith(host):\\n if _url.startswith('data/'):\\n _url = requests.compat.urljoin(host, _url)\\n else:\\n prefixUri = requests.compat.urljoin(host, 'data/archive/')\\n _url = requests.compat.urljoin(prefixUri, _url)\\n\\n\\n #--------------------\\n # Remove double slashes\\n #--------------------\\n _url = _url.replace('//', '/')\\n if 'http:/' in _url:\\n _url = _url.replace('http:/', 'http://')\\n elif 'https:/' in _url:\\n _url = _url.replace('https:/', 'https://')\\n\\n return _url\",\n \"def urldecode(val):\\n return urllib2.unquote(val)\",\n \"def quote_uri(uri):\\n import urlparse\\n import urllib\\n\\n up=urlparse.urlparse(uri)\\n np=urllib.quote(up[2])\\n return urlparse.urlunparse((up[0],up[1],np,up[3],up[4],up[5]))\",\n \"def url(self, path: str) -> str:\\n return url_util.join(self.base_url, path)\",\n \"def url(self) -> str:\\n return self.url_as()\",\n \"def formatURL(self, url):\\n pattern = r'(imdb\\\\.com\\\\/title\\\\/(.*/))'\\n urls = re.findall(pattern, url)\\n urls = urls[0]\\n new_url = urls[0]\\n new_url = \\\"https://www.\\\" + new_url\\n title_code = urls[1].replace(\\\"/\\\", \\\"\\\")\\n return new_url\",\n \"def url_to_string(cls, v, values, **kwargs):\\n return str(v)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6706412","0.6616591","0.6346535","0.63381904","0.6220913","0.6202903","0.6197797","0.6176937","0.6167852","0.6164724","0.61420554","0.61230433","0.6118814","0.61146235","0.6109616","0.60942715","0.60720885","0.6057571","0.6017928","0.59949386","0.59596443","0.59356004","0.5923229","0.5907076","0.5900714","0.5879596","0.58781475","0.58744574","0.5874004","0.587237","0.5868473","0.5866336","0.58484113","0.58437926","0.5841141","0.58319676","0.58281183","0.5823462","0.5823372","0.5807873","0.57987016","0.5788315","0.5779046","0.5775179","0.5762354","0.57391375","0.57173485","0.56668764","0.5663504","0.5657203","0.5649449","0.5647847","0.5646688","0.564259","0.5627243","0.56206584","0.56159127","0.5609477","0.5606667","0.5590863","0.55819714","0.55757135","0.55751854","0.55724776","0.5571228","0.55708826","0.55604327","0.556002","0.55546695","0.555202","0.5550776","0.55479234","0.5538971","0.5538883","0.55310756","0.552123","0.5510356","0.5509271","0.55053806","0.55035794","0.54854566","0.54831743","0.5480471","0.547584","0.54732215","0.54670227","0.5466824","0.5465655","0.5457498","0.54529995","0.54414034","0.54408836","0.54389113","0.5431572","0.5423347","0.54230654","0.5421304","0.5418315","0.54109144","0.54099965"],"string":"[\n \"0.6706412\",\n \"0.6616591\",\n \"0.6346535\",\n \"0.63381904\",\n \"0.6220913\",\n \"0.6202903\",\n \"0.6197797\",\n \"0.6176937\",\n \"0.6167852\",\n \"0.6164724\",\n \"0.61420554\",\n \"0.61230433\",\n \"0.6118814\",\n \"0.61146235\",\n \"0.6109616\",\n \"0.60942715\",\n \"0.60720885\",\n \"0.6057571\",\n \"0.6017928\",\n \"0.59949386\",\n \"0.59596443\",\n \"0.59356004\",\n \"0.5923229\",\n \"0.5907076\",\n \"0.5900714\",\n \"0.5879596\",\n \"0.58781475\",\n \"0.58744574\",\n \"0.5874004\",\n \"0.587237\",\n \"0.5868473\",\n \"0.5866336\",\n \"0.58484113\",\n \"0.58437926\",\n \"0.5841141\",\n \"0.58319676\",\n \"0.58281183\",\n \"0.5823462\",\n \"0.5823372\",\n \"0.5807873\",\n \"0.57987016\",\n \"0.5788315\",\n \"0.5779046\",\n \"0.5775179\",\n \"0.5762354\",\n \"0.57391375\",\n \"0.57173485\",\n \"0.56668764\",\n \"0.5663504\",\n \"0.5657203\",\n \"0.5649449\",\n \"0.5647847\",\n \"0.5646688\",\n \"0.564259\",\n \"0.5627243\",\n \"0.56206584\",\n \"0.56159127\",\n \"0.5609477\",\n \"0.5606667\",\n \"0.5590863\",\n \"0.55819714\",\n \"0.55757135\",\n \"0.55751854\",\n \"0.55724776\",\n \"0.5571228\",\n \"0.55708826\",\n \"0.55604327\",\n \"0.556002\",\n \"0.55546695\",\n \"0.555202\",\n \"0.5550776\",\n \"0.55479234\",\n \"0.5538971\",\n \"0.5538883\",\n \"0.55310756\",\n \"0.552123\",\n \"0.5510356\",\n \"0.5509271\",\n \"0.55053806\",\n \"0.55035794\",\n \"0.54854566\",\n \"0.54831743\",\n \"0.5480471\",\n \"0.547584\",\n \"0.54732215\",\n \"0.54670227\",\n \"0.5466824\",\n \"0.5465655\",\n \"0.5457498\",\n \"0.54529995\",\n \"0.54414034\",\n \"0.54408836\",\n \"0.54389113\",\n \"0.5431572\",\n \"0.5423347\",\n \"0.54230654\",\n \"0.5421304\",\n \"0.5418315\",\n \"0.54109144\",\n \"0.54099965\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5838849"},"document_rank":{"kind":"string","value":"35"}}},{"rowIdx":9985,"cells":{"query":{"kind":"string","value":"Normalizes USD price with thousand separator into float value"},"document":{"kind":"string","value":"def normalize_price(price: str) -> float:\n return float(price.strip().replace(',', ''))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_price(str_val):\n return float(str_val.replace('.', '').replace(',', '.'))","def clean_dollar_to_float(value):\n return (value.replace('$', '').replace(',', ''))","def convert_currency(val):\n new_val = val.replace(',','').replace('$', '')\n return float(new_val)","def clean_value(self, value):\n return float(value.replace('.', '').replace(',', '.'))","def to_usd(price):\n return \"${0:,.2f}\".format(price)","def to_usd(my_price):\n return \"${0:,.2f}\".format(my_price)","def to_usd(my_price):\n return f\"${my_price:,.2f}\"","def normalisePrice(raw):\n if raw:\n return Decimal(str(raw)).to_eng_string()\n else:\n return None","def to_usd(my_price):\n return f\"${my_price:,.2f}\" #> $12,000.71","def to_usd(my_price):\n return f\"${my_price:,.2f}\" #> $12,000.71","def to_usd(my_price):\n return f\"${my_price:,.2f}\" #> $12,000.71","def to_usd(my_price):\n return f\"${my_price:,.2f}\" #> $12,000.71","def to_usd(my_price):\n return f\"${my_price:,.2f}\" #> $12,000.71","def normalize_loan_amount(value):\n normalized_value = value.lower()\n if 'k' in normalized_value:\n normalized_value = normalized_value.replace('k', '000')\n normalized_value = normalized_value.replace('.', '')\n\n normalized_value = normalized_value.replace('$', '')\n normalized_value = normalized_value.replace(',', '')\n\n try: \n return Decimal(normalized_value)\n except: InvalidOperation\n \n return None","def check_price(URL, headers):\n page = requests.get(URL, headers=headers)\n soup = BeautifulSoup(page.content, 'html.parser')\n price = soup.find(id=\"priceblock_ourprice\").get_text()\n converted_price = price[:-3]# -3 removes the .99 pence value from product\n float_price = ''\n for c in converted_price:\n if c.isdigit():\n float_price = float_price + c\n #loop that removes the £$,. from product so the string can convert to float correctly\n return float(float_price)","def eur(value):\n float(value)\n return f\"€{value:,.2f}\"","def to_us_number(number):\n number = number.replace('.', '')\n number = number.replace(',', '.')\n\n return float(number)","def get_num_str(price):\n try:\n price = float(price.replace(u',', u'.'))\n except:\n price = float('nan')\n return price","def format_usd(my_price):\n return f\"${my_price:,.2f}\"","def clean_currency(x):\n \n if isinstance(x, str):\n x=x.replace(\"*\",\"\")\n x=x.replace(\",\",\"\")\n if x=='':\n return(0)\n elif x[0]!='$':\n return(0)\n else:\n x=x.split(' ')[0]\n x=x.replace('$',\"\")\n return float(x)\n return(x)","def clean_currency(x: str):\n # cprint(f\"### Function Name:-> {inspect.stack()[0][3]} ###\", 'yellow', 'on_grey', attrs=['bold'])\n try:\n # x = str(x)\n if isinstance(x, str):\n if x.startswith(\"$\"):\n return x.replace('$', '').replace(',', '')\n # return float(x)\n return x\n except Exception as ex:\n cprint(traceback.format_exc(), 'red')\n log_exception(traceback.format_exc())","def _fix_balance(self, balance):\n\n return float(balance.replace(',', '.').replace(' ', ''))","def usd(value):\r\n return f\"${Decimal(value):,.2f}\"","def format_as_usd(value):\n return f\"${value:,.2f}\"","def convert_dollars(df,col_name):\n df[col_name] = df[col_name].replace('[^.0-9]','',regex=True).astype(float)\n return df","def thousands(value):\n try:\n value = float(value)\n except ValueError:\n return value\n return f\"{value:,}\".replace(',',' ')","def int2float(value_int, currency):\r\n if currency in \"BTC LTC NMC\":\r\n return value_int / 100000000.0\r\n elif currency in \"JPY SEK\":\r\n return value_int / 1000.0\r\n else:\r\n return value_int / 100000.0","def test_normal_decimal_input(self):\r\n ws_leader = \"S. O'Neal (14.9)\"\r\n res = treat_input(ws_leader, type=\"float\")\r\n assert res == 14.9","def obtain_amount(cls, amount_string):\n return float(string.replace(amount_string, ',', '.'))","def convert_prices(price):\n return int(price.replace(\"$\", \"\").replace(\",\", \"\"))","def convert_to_float(series):\n series = series.str[:-4].str.replace('.', '').str.replace(',', '.').astype(float)\n return series","def make_float(value):\n return float(value[0])","def format_price(self, price):\n precision = self._price_limits[3] or 8\n tick_size = self._price_limits[2] or 0.00000001\n\n adjusted_price = truncate(round(price / tick_size) * tick_size, precision)\n formatted_price = \"{:0.0{}f}\".format(adjusted_price, precision)\n\n # remove tailing 0s and dot\n if '.' in formatted_price:\n formatted_price = formatted_price.rstrip('0').rstrip('.')\n\n return formatted_price","def stof(fstr):\n return float(fstr.replace(',', '.'))","def string_to_float(value):\n # if no periods (.) then assume commas are decimal separators\n if '.' not in value:\n value = value.replace(',', '.')\n # if decimals exist then simply remove commas\n else:\n value = value.replace(',', '')\n\n return float(value)","def __parse_float(str):\n return float(str.strip().replace(',','.'))","def normalize_interest_rate(value):\n if '%' in value:\n value = value.replace('%', '')\n\n try : \n return Decimal(value)/100\n except: InvalidOperation\n\n return None","def clean(amount):\n # Return empty input immediately.\n if not amount:\n return amount\n\n if re.search(r'[\\. ][0-9]{3},[0-9]{1,2}$', amount):\n # Assume amount is in 1.123,12 or 1 123,12 format (Dutch).\n return amount.replace('.', '').replace(' ', '').replace(',', '.')\n\n if re.search(r'[, ][0-9]{3}\\.[0-9]{1,2}$', amount):\n # Assume amount is in 1,123.12 format (Engels).\n return amount.replace(',', '').replace(' ', '')\n\n if re.search(r'[0-9](,[0-9]{1,2}$)', amount):\n # Assume amount is in 123,12 or in 123,1 format (Dutch).\n return amount.replace(',', '.')\n\n # Format of amount is not recognized. Return amount.\n return amount","def _strip_currency(amount):\n if amount[0] == '$':\n amount = amount[1:]\n if amount.find(\".\") != -1:\n return amount.replace(\".\", \"\")\n return amount","def format_tuition(self, data):\n d = u'$%.2f' % data\n return d.replace('.00','')","def parse_dollars_to_float(x):\n import locale\n locale.setlocale( locale.LC_ALL, 'en_US.UTF-8' )\n try:\n if x.startswith('('):\n #print(x)\n y = -1.0*locale.atof(x[2:-1])\n #print(x,y)\n elif x.startswith('$'):\n y = locale.atof(x[1:])\n except AttributeError:\n y = np.nan\n return y","def usd(value):\r\n try:\r\n value = float(value)\r\n except:\r\n return value\r\n if value >= 0:\r\n return \"${:,.2f}\".format(value)\r\n value *= (-1)\r\n return \"(\" + \"${:,.2f}\".format(value) + \")\"","def normalize_decimal(d):\n return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()","def parse_float(s):\n return float(s.replace(',', '.'))","def get_price(item):\n return float(item[1])","def convert_price_to_und_or_strike_quotation(ins, price,\n convert_to_und_quotation):\n if ins.record_type == 'Instrument' and ins.und_insaddr:\n if (ins.und_insaddr.quotation_seqnbr and\n ins.strike_quotation_seqnbr and (ins.strike_quotation_seqnbr\n != ins.und_insaddr.quotation_seqnbr)):\n instr = acm.FInstrument[ins.insaddr]\n und_instr = acm.FInstrument[ins.und_insaddr.insaddr]\n date_today = acm.Time().DateToday()\n # doubleCast = acm.GetFunction('double', 1)\n denom_func = acm.GetFunction('denominatedvalue', 4)\n denom_val = denom_func(price, ins.und_insaddr.insid, None,\n ael.date_today())\n if (convert_to_und_quotation == 1):\n to_quotation = und_instr.Quotation()\n from_quotation = instr.StrikeQuotation()\n else:\n from_quotation = und_instr.Quotation()\n to_quotation = instr.StrikeQuotation()\n\n lprice_denom_val = instr.QuoteToQuote(denom_val, date_today,\n None, None, from_quotation, to_quotation)\n lprice = lprice_denom_val.Number()\n\n return lprice\n return price","def clean_dollar(df, col):\n \n df[col] = df[col].apply(lambda s: s.strip('$')).astype(float)\n \n return df","def _fs (v):\r\n try : \r\n v = float(v)\r\n except : \r\n v = tuple([float (ss) for ss in \r\n v.replace('(', '').replace(')', '').split(',')])\r\n return v","def priceGetDeal(soup):\n price = soup.find('td', id='priceblock_dealprice', class_='a-color-price a-size-medium')\n price = price.text\n priceList = price.split()\n price = priceList[0]\n price = price.strip()\n price = price.lstrip('$')\n price = float(price)\n\n return price","def getPrice(self):\n priceElem = self.driver.find_element_by_xpath(self.priceXPath)\n price = priceElem.text.replace(\"€\", \"\").replace(\" \", \"\").replace(\",\", \".\")\n return float(price)","def SI_string_to_float(inStr, debug = False):\n func_name = \"SI_string_to_float\"\n \n # Debug print incoming string. \n if debug: print(\"DEBUG: (Func = %s): Input-str: %s\" %( func_name, inStr ))\n \n #Remove all spaces from incoming string. \n inStr = inStr.replace(\" \", \"\"); \n if debug: print(\"DEBUG: (Func = %s): Removed spaces: %s\" %( func_name, inStr ))\n \n # Allocate return value, and search in\n result = None\n letters = re.search( r'([\\d\\.]+)([a-z A-Z]+)', inStr)\n \n # Query if match was found. If not, print warning then try to directly convert incoming string.\n if letters:\n try:\n value = float(letters.group(1))\n scale = float(SI_UNITS[letters.group(2)])\n result = value * scale\n if debug: print(\"DEBUG: (Func = %s): Value: %f, scale: %f, result: %f\"%(func_name, value,scale,result))\n except:\n print(\"ERROR: (Func = %s): Couldn't extract value and SI-Unit.\"%func_name)\n print(\" Possible issue with seaching 'SI_UNITS for (%s)\"% scale)\n else:\n print(\"WARNING: (Function = %s) Couldn't extract value and SI-Unit. Will attempt direct float conversion... \"%func_name)\n #print(\" Used the following regex: '([\\d\\.]+)([a-z A-Z]+)'\")\n result = float(inStr) # TODO : Insert try catch \n \n return result","def convertRate(row):\n if pd.isnull(row):\n return 1.0\n elif ':' in str(row):\n rows = row.split(':')\n return 1.0 - float(rows[1]) / float(rows[0])\n else:\n return float(row)","def to_float(x, key):\n x = x.strip()\n if not x or x in ('NA', 'n/a'):\n return None\n if '.' in x:\n # There are '.'s, so commas are placeholders\n x = x.replace(',', '') \n if x.endswith('ft'):\n scale = 0.3048\n x = x[:-2].strip()\n else:\n scale = 1 \n try:\n return scale * float(x)\n except:\n logging.warn('Could not convert %s value %s to float', key, x)\n return None","def to_units(self, amount):\n return int(amount / self.price)","def normalize_price_values(df):\r\n\r\n\tdf_normalize_dict = dict()\r\n\r\n\tfor i, col in enumerate(df.columns):\r\n\t\tcol_array = np.array(df[col])\r\n\t\tdf_normalize_dict[\"Normalized\" + col] = preprocessing.normalize([col_array])[0]\r\n\r\n\tdf_normalize = pd.DataFrame(df_normalize_dict, index=df.index)\r\n\tpd.set_option('precision', 2) # set output display precision in 2 decimal places\r\n\r\n\treturn df_normalize","def convertRate(row):\n if row == 'null':\n return 1.0\n elif ':' in row:\n rows = row.split(':')\n return 1.0 - float(rows[1])/float(rows[0])\n else:\n return float(row)","def convert_qty (qty,unit,ing) :\n portion_presence = False\n try :\n div = re.search(r\"[^ \\w]\", qty).start()\n portion = float(qty[div-1]) / float(qty[div+1])\n qty_float=portion\n portion_presence = True\n qty = qty[:div-1]\n except :\n try : \n qty_float = float(qty)\n except :\n qty_float = 10\n\n if portion_presence == True :\n if len(qty) > 0 :\n qty_float += float(qty[:div-2])\n \n #use the unit to have in ml\n #qty_float*=conversion_unit[unit]\n \n #convert in grammes with the database of density\n #qty_float*=density[ing]\n \n return qty_float","def format_price(value: Decimal, order: Order, request: HttpRequest) -> str:\n context = {\n \"request\": request,\n \"order\": order,\n \"admin\": True,\n }\n return app_settings.SALESMAN_PRICE_FORMATTER(value, context=context)","def convert_value_to_standard_unit(value: str, symbol: str = 'i') -> float:\n try:\n # Get input value\n value_tuple = value.split()\n amount = float(value_tuple[0])\n except (ValueError, IndexError, AttributeError):\n raise with_context(\n ValueError('Value to convert is not valid.'),\n\n context={\n 'value': value,\n },\n )\n\n try:\n # Set unit symbols and find factor/multiplier.\n unit_symbol_from = value_tuple[1]\n unit_factor_from = float(STANDARD_UNITS[unit_symbol_from])\n unit_factor_to = float(STANDARD_UNITS[symbol])\n except (KeyError, IndexError):\n # Invalid symbol or no factor\n raise with_context(\n ValueError('Invalid IOTA unit.'),\n\n context={\n 'value': value,\n 'symbol': symbol,\n },\n )\n\n return amount * (unit_factor_from / unit_factor_to)","def local_price(amount, currency):\n amt = convert(amount, currency)\n sym = symbol(currency)\n return f'{sym}{amt}'","def parse_float(val, fn):\n return float(val)","def ParseAmount(am):\n\n ParseAmount.valid = True\n # filter\n am = re.sub('[^0-9,.-]','',am)\n # integers(any number).fraction(0..2) \n # find decimal point\n frac1 =len(am)-am.find('.')\n frac2 =len(am)-am.find(',')\n # No grouping & No fraction / decimal-point\n if (frac1 == frac2):\n am = '%s.00'% am\n # xxx,xxx,xxx.xx comma-grouping, dot-decimal\n elif (frac1 < 4) and (frac1 > 0): \n am = am.replace(',','')\n # xxx.xxx.xxx,xx dot-grouping, comma-decimal\n elif (frac2 < 4) and (frac2 > 0):\n am = am.replace('.','')\n am = am.replace(',','.') # harmonize decimal-point\n # grouping & No fraction / decimal-point\n else:\n am = am.replace(',','')\n am = am.replace('.','')\n am = '%s.00'% am\n # check validity result\n if (len(am) - am.find('.')) != 3:\n ParseAmount.valid = False\n return am","def _to_float(self, s: str) -> float:\n return int(s[:-1]) / 1e9 if s.endswith('n') else float(s[:-1])","def format_float(value):\n if isinstance(value, float):\n value = '{:.3f}'.format(value * 1e-3)\n return value","def find_float(input: str) -> float:\n str_split = input.split('<@')\n if (len(str_split) == 0):\n raise AmountMissingException(\"amount_not_found\")\n input_text = str_split[0]\n regex = r'(?:^|\\s)(\\d*\\.?\\d+)(?=$|\\s)'\n matches = re.findall(regex, input_text, re.IGNORECASE)\n if len(matches) >= 1:\n return abs(float(matches[0].strip()))\n raise AmountMissingException(\"amount_not_found\")","def price_parse(pstring):\n if pstring.startswith('$'):\n return locale.atof(pstring[1:])\n else:\n return np.nan","def parse_float_value(self, value: str):\n value, power = self.parse_value_ending(value)\n try:\n value = float(value)\n return value * 10 ** power\n except:\n raise Exception(\"Failed to parse the __value.\")","def str_to_decimal_price(str_val):\n result = None\n\n try:\n val = Decimal(str_val)\n except (InvalidOperation, TypeError):\n result = None\n else:\n if val >= 0.0:\n result = val.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\n\n return result","def convert_to_cents(price: str) -> int:\n price = price.replace(\"$\", \"\").replace(\",\", \".\")\n converted_price: float = float(price) * 100\n return int(converted_price)","def short_float_fmt(x):\n return '{0:f}'.format(x).rstrip('0').rstrip('.')","def money_format(ammount):\n\td = Decimal(ammount) / Decimal(\"100\")\n\treturn u'£%s' % d.quantize(Decimal(\"0.01\"))","def percentage_to_float(self, val):\n return float(val.strip('%'))/100","def quote2float(self, int_number):\r\n return float(int_number) / self.mult_quote","def price_pounds(self):\n price = '{0:03d}'.format(self.price)\n return price[:-2] + '.' + price[-2:]","def convert_to_floating(string):\n return \"\".join([\"\\u00b7\" if char==\".\" else char for char in string])","def force_float(element, surpress_error = False):\n \n if isinstance(element, float):\n # element is a float, return it\n return element\n else:\n try:\n # try if the element is a number\n return float(element)\n except (ValueError, TypeError):\n # replace all non-digit characters\n element = str(element)\n matches = convert_pattern.match(element)\n \n if matches != None:\n element = matches.group(0)\n \n try:\n return float(element)\n except (ValueError, TypeError):\n if surpress_error:\n return 0\n else:\n raise","def normalize(amount, min=2, max=4):\n if not amount:\n return amount\n\n # To Decimal, round to highest desired precision\n d = round(Decimal(amount), max)\n s = str(d)\n\n # Truncate as many extra zeros as we are allowed to\n for i in range(max-min):\n if s[-1] == '0':\n s = s[:-1]\n\n if s[-1] == '.':\n s = s[:-1]\n\n return s","def to_btc(value):\n value = float(value) / 100000000\n value = format(value, \".9f\")\n return value","def replace_comma(data):\n try:\n aux = np.float32(data.replace(',', '.'))\n except AttributeError as err:\n print('No string. Convert to numeric')\n aux = np.float32(data)\n return aux","def super_float(text):\r\n if text[-1] in SUFFIXES:\r\n return float(text[:-1]) * SUFFIXES[text[-1]]\r\n else:\r\n return float(text)","def pretty_float(x):\n return ('%f' % x).rstrip('0').rstrip('.')","def parseFloat(s, ret=0.0):\n if not isinstance(s, str):\n return float(s)\n elif s:\n if s[0] in \"+-\":\n ts = s[1:]\n else:\n ts = s\n\n if ts and ts.count(\".\") <= 1 and all([_ in \".0123456789\" for _ in ts]):\n return float(s)\n\n return ret","def priceGetSome(soup):\n price = soup.find('span', id='priceblock_ourprice', class_='a-size-medium a-color-price')\n price = price.text\n price = price.strip()\n price = price.lstrip('$')\n price = float(price)\n\n return price","def currency(self, commas=True):\n sign, digits, exp = self.quantize(Decimal('0.01')).as_tuple()\n digits = list(map(unicode, digits))\n result = []\n for i in range(2):\n result.append(digits.pop() if digits else u'0')\n result.append(u'.')\n if not digits:\n result.append(u'0')\n count = 0\n while digits:\n result.append(digits.pop())\n count += 1\n if count == 3 and digits and commas:\n count = 0\n result.append(u',')\n result.append(u'-' if sign else u'')\n return u''.join(reversed(result))","def getFactor(currency):","def reformat(number):\n if number.find('E') == -1:\n exponent = \"-101\"\n mantissa = number.split(exponent)\n return float(mantissa[0])*10**float(exponent)\n else:\n mantissa, exponent = number.split('E')\n\n return float(mantissa)*10**float(exponent)","def _parse_price_original(self, response, add_xpath=None):\n xpathes = '//*[@id=\"price\"]/.//*[contains(@class, \"a-text-strike\")]' \\\n '/text()'\n\n if add_xpath:\n xpathes += ' |' + add_xpath\n\n price_original = response.xpath(xpathes).re(FLOATING_POINT_RGEX)\n if price_original:\n return float(price_original[0].replace(',', ''))","def get_product_price(self):\n\n price = \"0.0000\"\n\n try:\n price = self.trees.get_element_by_id(\"priceblock_ourprice\").text\n except:\n try:\n price = self.trees.get_element_by_id(\n \"price_inside_buybox\").text\n except:\n try:\n price = self.trees.get_element_by_id(\n \"priceblock_dealprice\").text\n except:\n try:\n price = self.trees.xpath(\n \"//span[@class='a-color-price']/text()\")[0]\n except:\n try:\n price = self.trees.xpath(\n \"//span[@class='a-size-base a-color-price']/text()\")[0]\n except:\n pass\n\n non_decimal = re.compile(r'[^\\d.]+')\n price = non_decimal.sub('', price)\n\n return round(float(price[0:5]), 2)","def conv_num_with_commas(number_text):\n number=number_text.split(',')\n item=''\n for numero in number:\n item+=numero\n final_number=float(item)\n return final_number","def clean_pcts(x):\n # if not enough data, will be '-' with investing.com\n if x == '-' or pd.isnull(x):\n return np.nan\n elif x == 'unch':\n return float(0)\n elif type(x) == float:\n return x\n\n new_x = x.replace('+', '')\n new_x = new_x.replace('%', '')\n new_x = float(new_x) / 100\n return new_x","def real_value(val):\n\t\treturn round(val/100*sum(euros), 1)","def _serialize_decimal(val):\n return float(val)","def _convert_to_float(s):\n try:\n return float(s)\n except:\n return s","def round_float(cls, value: float) -> Dec:\n # This check for numbers which are smaller than the precision allows will\n # be commented out for now as it seems to kill economic activity.\n # if value < 1E-8:\n # return Dec(0)\n return round(Dec(value), cls.currency_precision)","def convert_to_float(word: str) -> float:\n return round(float(word), 2)","def decode(val):\n if isinstance(val, Decimal):\n return float(val)\n return val","def string_chopped_to_float(input_string, chop_up, chop_low):\n input_string = str(input_string)\n return float(string_chop_up(string_chop_low(input_string, chop_up), chop_low))","def Float(val):\n try:\n return float(val)\n except ValueError:\n return ''","def floatify(string):\n temporary_string = \"0.\" + string[:-1]\n return float(temporary_string)","def money_digitalization(raw_data, target_columns=['First Place Prize', 'Total Prize']):\r\n output = raw_data.copy()\r\n\r\n for column in target_columns:\r\n for i in range(len(raw_data)):\r\n money = raw_data.loc[i, column].replace('(', '').replace(')', '')\r\n if money[0] == '$':\r\n output.loc[i, column] = float(money[1:].replace('.', '').replace(',', '.'))\r\n elif money[:3] == 'US$':\r\n output.loc[i, column] = float(money[3:].replace('.', '').replace(',', '.'))\r\n else:\r\n print('money data error')\r\n exit(1)\r\n\r\n return output"],"string":"[\n \"def get_price(str_val):\\n return float(str_val.replace('.', '').replace(',', '.'))\",\n \"def clean_dollar_to_float(value):\\n return (value.replace('$', '').replace(',', ''))\",\n \"def convert_currency(val):\\n new_val = val.replace(',','').replace('$', '')\\n return float(new_val)\",\n \"def clean_value(self, value):\\n return float(value.replace('.', '').replace(',', '.'))\",\n \"def to_usd(price):\\n return \\\"${0:,.2f}\\\".format(price)\",\n \"def to_usd(my_price):\\n return \\\"${0:,.2f}\\\".format(my_price)\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\"\",\n \"def normalisePrice(raw):\\n if raw:\\n return Decimal(str(raw)).to_eng_string()\\n else:\\n return None\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\" #> $12,000.71\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\" #> $12,000.71\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\" #> $12,000.71\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\" #> $12,000.71\",\n \"def to_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\" #> $12,000.71\",\n \"def normalize_loan_amount(value):\\n normalized_value = value.lower()\\n if 'k' in normalized_value:\\n normalized_value = normalized_value.replace('k', '000')\\n normalized_value = normalized_value.replace('.', '')\\n\\n normalized_value = normalized_value.replace('$', '')\\n normalized_value = normalized_value.replace(',', '')\\n\\n try: \\n return Decimal(normalized_value)\\n except: InvalidOperation\\n \\n return None\",\n \"def check_price(URL, headers):\\n page = requests.get(URL, headers=headers)\\n soup = BeautifulSoup(page.content, 'html.parser')\\n price = soup.find(id=\\\"priceblock_ourprice\\\").get_text()\\n converted_price = price[:-3]# -3 removes the .99 pence value from product\\n float_price = ''\\n for c in converted_price:\\n if c.isdigit():\\n float_price = float_price + c\\n #loop that removes the £$,. from product so the string can convert to float correctly\\n return float(float_price)\",\n \"def eur(value):\\n float(value)\\n return f\\\"€{value:,.2f}\\\"\",\n \"def to_us_number(number):\\n number = number.replace('.', '')\\n number = number.replace(',', '.')\\n\\n return float(number)\",\n \"def get_num_str(price):\\n try:\\n price = float(price.replace(u',', u'.'))\\n except:\\n price = float('nan')\\n return price\",\n \"def format_usd(my_price):\\n return f\\\"${my_price:,.2f}\\\"\",\n \"def clean_currency(x):\\n \\n if isinstance(x, str):\\n x=x.replace(\\\"*\\\",\\\"\\\")\\n x=x.replace(\\\",\\\",\\\"\\\")\\n if x=='':\\n return(0)\\n elif x[0]!='$':\\n return(0)\\n else:\\n x=x.split(' ')[0]\\n x=x.replace('$',\\\"\\\")\\n return float(x)\\n return(x)\",\n \"def clean_currency(x: str):\\n # cprint(f\\\"### Function Name:-> {inspect.stack()[0][3]} ###\\\", 'yellow', 'on_grey', attrs=['bold'])\\n try:\\n # x = str(x)\\n if isinstance(x, str):\\n if x.startswith(\\\"$\\\"):\\n return x.replace('$', '').replace(',', '')\\n # return float(x)\\n return x\\n except Exception as ex:\\n cprint(traceback.format_exc(), 'red')\\n log_exception(traceback.format_exc())\",\n \"def _fix_balance(self, balance):\\n\\n return float(balance.replace(',', '.').replace(' ', ''))\",\n \"def usd(value):\\r\\n return f\\\"${Decimal(value):,.2f}\\\"\",\n \"def format_as_usd(value):\\n return f\\\"${value:,.2f}\\\"\",\n \"def convert_dollars(df,col_name):\\n df[col_name] = df[col_name].replace('[^.0-9]','',regex=True).astype(float)\\n return df\",\n \"def thousands(value):\\n try:\\n value = float(value)\\n except ValueError:\\n return value\\n return f\\\"{value:,}\\\".replace(',',' ')\",\n \"def int2float(value_int, currency):\\r\\n if currency in \\\"BTC LTC NMC\\\":\\r\\n return value_int / 100000000.0\\r\\n elif currency in \\\"JPY SEK\\\":\\r\\n return value_int / 1000.0\\r\\n else:\\r\\n return value_int / 100000.0\",\n \"def test_normal_decimal_input(self):\\r\\n ws_leader = \\\"S. O'Neal (14.9)\\\"\\r\\n res = treat_input(ws_leader, type=\\\"float\\\")\\r\\n assert res == 14.9\",\n \"def obtain_amount(cls, amount_string):\\n return float(string.replace(amount_string, ',', '.'))\",\n \"def convert_prices(price):\\n return int(price.replace(\\\"$\\\", \\\"\\\").replace(\\\",\\\", \\\"\\\"))\",\n \"def convert_to_float(series):\\n series = series.str[:-4].str.replace('.', '').str.replace(',', '.').astype(float)\\n return series\",\n \"def make_float(value):\\n return float(value[0])\",\n \"def format_price(self, price):\\n precision = self._price_limits[3] or 8\\n tick_size = self._price_limits[2] or 0.00000001\\n\\n adjusted_price = truncate(round(price / tick_size) * tick_size, precision)\\n formatted_price = \\\"{:0.0{}f}\\\".format(adjusted_price, precision)\\n\\n # remove tailing 0s and dot\\n if '.' in formatted_price:\\n formatted_price = formatted_price.rstrip('0').rstrip('.')\\n\\n return formatted_price\",\n \"def stof(fstr):\\n return float(fstr.replace(',', '.'))\",\n \"def string_to_float(value):\\n # if no periods (.) then assume commas are decimal separators\\n if '.' not in value:\\n value = value.replace(',', '.')\\n # if decimals exist then simply remove commas\\n else:\\n value = value.replace(',', '')\\n\\n return float(value)\",\n \"def __parse_float(str):\\n return float(str.strip().replace(',','.'))\",\n \"def normalize_interest_rate(value):\\n if '%' in value:\\n value = value.replace('%', '')\\n\\n try : \\n return Decimal(value)/100\\n except: InvalidOperation\\n\\n return None\",\n \"def clean(amount):\\n # Return empty input immediately.\\n if not amount:\\n return amount\\n\\n if re.search(r'[\\\\. ][0-9]{3},[0-9]{1,2}$', amount):\\n # Assume amount is in 1.123,12 or 1 123,12 format (Dutch).\\n return amount.replace('.', '').replace(' ', '').replace(',', '.')\\n\\n if re.search(r'[, ][0-9]{3}\\\\.[0-9]{1,2}$', amount):\\n # Assume amount is in 1,123.12 format (Engels).\\n return amount.replace(',', '').replace(' ', '')\\n\\n if re.search(r'[0-9](,[0-9]{1,2}$)', amount):\\n # Assume amount is in 123,12 or in 123,1 format (Dutch).\\n return amount.replace(',', '.')\\n\\n # Format of amount is not recognized. Return amount.\\n return amount\",\n \"def _strip_currency(amount):\\n if amount[0] == '$':\\n amount = amount[1:]\\n if amount.find(\\\".\\\") != -1:\\n return amount.replace(\\\".\\\", \\\"\\\")\\n return amount\",\n \"def format_tuition(self, data):\\n d = u'$%.2f' % data\\n return d.replace('.00','')\",\n \"def parse_dollars_to_float(x):\\n import locale\\n locale.setlocale( locale.LC_ALL, 'en_US.UTF-8' )\\n try:\\n if x.startswith('('):\\n #print(x)\\n y = -1.0*locale.atof(x[2:-1])\\n #print(x,y)\\n elif x.startswith('$'):\\n y = locale.atof(x[1:])\\n except AttributeError:\\n y = np.nan\\n return y\",\n \"def usd(value):\\r\\n try:\\r\\n value = float(value)\\r\\n except:\\r\\n return value\\r\\n if value >= 0:\\r\\n return \\\"${:,.2f}\\\".format(value)\\r\\n value *= (-1)\\r\\n return \\\"(\\\" + \\\"${:,.2f}\\\".format(value) + \\\")\\\"\",\n \"def normalize_decimal(d):\\n return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()\",\n \"def parse_float(s):\\n return float(s.replace(',', '.'))\",\n \"def get_price(item):\\n return float(item[1])\",\n \"def convert_price_to_und_or_strike_quotation(ins, price,\\n convert_to_und_quotation):\\n if ins.record_type == 'Instrument' and ins.und_insaddr:\\n if (ins.und_insaddr.quotation_seqnbr and\\n ins.strike_quotation_seqnbr and (ins.strike_quotation_seqnbr\\n != ins.und_insaddr.quotation_seqnbr)):\\n instr = acm.FInstrument[ins.insaddr]\\n und_instr = acm.FInstrument[ins.und_insaddr.insaddr]\\n date_today = acm.Time().DateToday()\\n # doubleCast = acm.GetFunction('double', 1)\\n denom_func = acm.GetFunction('denominatedvalue', 4)\\n denom_val = denom_func(price, ins.und_insaddr.insid, None,\\n ael.date_today())\\n if (convert_to_und_quotation == 1):\\n to_quotation = und_instr.Quotation()\\n from_quotation = instr.StrikeQuotation()\\n else:\\n from_quotation = und_instr.Quotation()\\n to_quotation = instr.StrikeQuotation()\\n\\n lprice_denom_val = instr.QuoteToQuote(denom_val, date_today,\\n None, None, from_quotation, to_quotation)\\n lprice = lprice_denom_val.Number()\\n\\n return lprice\\n return price\",\n \"def clean_dollar(df, col):\\n \\n df[col] = df[col].apply(lambda s: s.strip('$')).astype(float)\\n \\n return df\",\n \"def _fs (v):\\r\\n try : \\r\\n v = float(v)\\r\\n except : \\r\\n v = tuple([float (ss) for ss in \\r\\n v.replace('(', '').replace(')', '').split(',')])\\r\\n return v\",\n \"def priceGetDeal(soup):\\n price = soup.find('td', id='priceblock_dealprice', class_='a-color-price a-size-medium')\\n price = price.text\\n priceList = price.split()\\n price = priceList[0]\\n price = price.strip()\\n price = price.lstrip('$')\\n price = float(price)\\n\\n return price\",\n \"def getPrice(self):\\n priceElem = self.driver.find_element_by_xpath(self.priceXPath)\\n price = priceElem.text.replace(\\\"€\\\", \\\"\\\").replace(\\\" \\\", \\\"\\\").replace(\\\",\\\", \\\".\\\")\\n return float(price)\",\n \"def SI_string_to_float(inStr, debug = False):\\n func_name = \\\"SI_string_to_float\\\"\\n \\n # Debug print incoming string. \\n if debug: print(\\\"DEBUG: (Func = %s): Input-str: %s\\\" %( func_name, inStr ))\\n \\n #Remove all spaces from incoming string. \\n inStr = inStr.replace(\\\" \\\", \\\"\\\"); \\n if debug: print(\\\"DEBUG: (Func = %s): Removed spaces: %s\\\" %( func_name, inStr ))\\n \\n # Allocate return value, and search in\\n result = None\\n letters = re.search( r'([\\\\d\\\\.]+)([a-z A-Z]+)', inStr)\\n \\n # Query if match was found. If not, print warning then try to directly convert incoming string.\\n if letters:\\n try:\\n value = float(letters.group(1))\\n scale = float(SI_UNITS[letters.group(2)])\\n result = value * scale\\n if debug: print(\\\"DEBUG: (Func = %s): Value: %f, scale: %f, result: %f\\\"%(func_name, value,scale,result))\\n except:\\n print(\\\"ERROR: (Func = %s): Couldn't extract value and SI-Unit.\\\"%func_name)\\n print(\\\" Possible issue with seaching 'SI_UNITS for (%s)\\\"% scale)\\n else:\\n print(\\\"WARNING: (Function = %s) Couldn't extract value and SI-Unit. Will attempt direct float conversion... \\\"%func_name)\\n #print(\\\" Used the following regex: '([\\\\d\\\\.]+)([a-z A-Z]+)'\\\")\\n result = float(inStr) # TODO : Insert try catch \\n \\n return result\",\n \"def convertRate(row):\\n if pd.isnull(row):\\n return 1.0\\n elif ':' in str(row):\\n rows = row.split(':')\\n return 1.0 - float(rows[1]) / float(rows[0])\\n else:\\n return float(row)\",\n \"def to_float(x, key):\\n x = x.strip()\\n if not x or x in ('NA', 'n/a'):\\n return None\\n if '.' in x:\\n # There are '.'s, so commas are placeholders\\n x = x.replace(',', '') \\n if x.endswith('ft'):\\n scale = 0.3048\\n x = x[:-2].strip()\\n else:\\n scale = 1 \\n try:\\n return scale * float(x)\\n except:\\n logging.warn('Could not convert %s value %s to float', key, x)\\n return None\",\n \"def to_units(self, amount):\\n return int(amount / self.price)\",\n \"def normalize_price_values(df):\\r\\n\\r\\n\\tdf_normalize_dict = dict()\\r\\n\\r\\n\\tfor i, col in enumerate(df.columns):\\r\\n\\t\\tcol_array = np.array(df[col])\\r\\n\\t\\tdf_normalize_dict[\\\"Normalized\\\" + col] = preprocessing.normalize([col_array])[0]\\r\\n\\r\\n\\tdf_normalize = pd.DataFrame(df_normalize_dict, index=df.index)\\r\\n\\tpd.set_option('precision', 2) # set output display precision in 2 decimal places\\r\\n\\r\\n\\treturn df_normalize\",\n \"def convertRate(row):\\n if row == 'null':\\n return 1.0\\n elif ':' in row:\\n rows = row.split(':')\\n return 1.0 - float(rows[1])/float(rows[0])\\n else:\\n return float(row)\",\n \"def convert_qty (qty,unit,ing) :\\n portion_presence = False\\n try :\\n div = re.search(r\\\"[^ \\\\w]\\\", qty).start()\\n portion = float(qty[div-1]) / float(qty[div+1])\\n qty_float=portion\\n portion_presence = True\\n qty = qty[:div-1]\\n except :\\n try : \\n qty_float = float(qty)\\n except :\\n qty_float = 10\\n\\n if portion_presence == True :\\n if len(qty) > 0 :\\n qty_float += float(qty[:div-2])\\n \\n #use the unit to have in ml\\n #qty_float*=conversion_unit[unit]\\n \\n #convert in grammes with the database of density\\n #qty_float*=density[ing]\\n \\n return qty_float\",\n \"def format_price(value: Decimal, order: Order, request: HttpRequest) -> str:\\n context = {\\n \\\"request\\\": request,\\n \\\"order\\\": order,\\n \\\"admin\\\": True,\\n }\\n return app_settings.SALESMAN_PRICE_FORMATTER(value, context=context)\",\n \"def convert_value_to_standard_unit(value: str, symbol: str = 'i') -> float:\\n try:\\n # Get input value\\n value_tuple = value.split()\\n amount = float(value_tuple[0])\\n except (ValueError, IndexError, AttributeError):\\n raise with_context(\\n ValueError('Value to convert is not valid.'),\\n\\n context={\\n 'value': value,\\n },\\n )\\n\\n try:\\n # Set unit symbols and find factor/multiplier.\\n unit_symbol_from = value_tuple[1]\\n unit_factor_from = float(STANDARD_UNITS[unit_symbol_from])\\n unit_factor_to = float(STANDARD_UNITS[symbol])\\n except (KeyError, IndexError):\\n # Invalid symbol or no factor\\n raise with_context(\\n ValueError('Invalid IOTA unit.'),\\n\\n context={\\n 'value': value,\\n 'symbol': symbol,\\n },\\n )\\n\\n return amount * (unit_factor_from / unit_factor_to)\",\n \"def local_price(amount, currency):\\n amt = convert(amount, currency)\\n sym = symbol(currency)\\n return f'{sym}{amt}'\",\n \"def parse_float(val, fn):\\n return float(val)\",\n \"def ParseAmount(am):\\n\\n ParseAmount.valid = True\\n # filter\\n am = re.sub('[^0-9,.-]','',am)\\n # integers(any number).fraction(0..2) \\n # find decimal point\\n frac1 =len(am)-am.find('.')\\n frac2 =len(am)-am.find(',')\\n # No grouping & No fraction / decimal-point\\n if (frac1 == frac2):\\n am = '%s.00'% am\\n # xxx,xxx,xxx.xx comma-grouping, dot-decimal\\n elif (frac1 < 4) and (frac1 > 0): \\n am = am.replace(',','')\\n # xxx.xxx.xxx,xx dot-grouping, comma-decimal\\n elif (frac2 < 4) and (frac2 > 0):\\n am = am.replace('.','')\\n am = am.replace(',','.') # harmonize decimal-point\\n # grouping & No fraction / decimal-point\\n else:\\n am = am.replace(',','')\\n am = am.replace('.','')\\n am = '%s.00'% am\\n # check validity result\\n if (len(am) - am.find('.')) != 3:\\n ParseAmount.valid = False\\n return am\",\n \"def _to_float(self, s: str) -> float:\\n return int(s[:-1]) / 1e9 if s.endswith('n') else float(s[:-1])\",\n \"def format_float(value):\\n if isinstance(value, float):\\n value = '{:.3f}'.format(value * 1e-3)\\n return value\",\n \"def find_float(input: str) -> float:\\n str_split = input.split('<@')\\n if (len(str_split) == 0):\\n raise AmountMissingException(\\\"amount_not_found\\\")\\n input_text = str_split[0]\\n regex = r'(?:^|\\\\s)(\\\\d*\\\\.?\\\\d+)(?=$|\\\\s)'\\n matches = re.findall(regex, input_text, re.IGNORECASE)\\n if len(matches) >= 1:\\n return abs(float(matches[0].strip()))\\n raise AmountMissingException(\\\"amount_not_found\\\")\",\n \"def price_parse(pstring):\\n if pstring.startswith('$'):\\n return locale.atof(pstring[1:])\\n else:\\n return np.nan\",\n \"def parse_float_value(self, value: str):\\n value, power = self.parse_value_ending(value)\\n try:\\n value = float(value)\\n return value * 10 ** power\\n except:\\n raise Exception(\\\"Failed to parse the __value.\\\")\",\n \"def str_to_decimal_price(str_val):\\n result = None\\n\\n try:\\n val = Decimal(str_val)\\n except (InvalidOperation, TypeError):\\n result = None\\n else:\\n if val >= 0.0:\\n result = val.quantize(Decimal('0.01'), rounding=ROUND_HALF_UP)\\n\\n return result\",\n \"def convert_to_cents(price: str) -> int:\\n price = price.replace(\\\"$\\\", \\\"\\\").replace(\\\",\\\", \\\".\\\")\\n converted_price: float = float(price) * 100\\n return int(converted_price)\",\n \"def short_float_fmt(x):\\n return '{0:f}'.format(x).rstrip('0').rstrip('.')\",\n \"def money_format(ammount):\\n\\td = Decimal(ammount) / Decimal(\\\"100\\\")\\n\\treturn u'£%s' % d.quantize(Decimal(\\\"0.01\\\"))\",\n \"def percentage_to_float(self, val):\\n return float(val.strip('%'))/100\",\n \"def quote2float(self, int_number):\\r\\n return float(int_number) / self.mult_quote\",\n \"def price_pounds(self):\\n price = '{0:03d}'.format(self.price)\\n return price[:-2] + '.' + price[-2:]\",\n \"def convert_to_floating(string):\\n return \\\"\\\".join([\\\"\\\\u00b7\\\" if char==\\\".\\\" else char for char in string])\",\n \"def force_float(element, surpress_error = False):\\n \\n if isinstance(element, float):\\n # element is a float, return it\\n return element\\n else:\\n try:\\n # try if the element is a number\\n return float(element)\\n except (ValueError, TypeError):\\n # replace all non-digit characters\\n element = str(element)\\n matches = convert_pattern.match(element)\\n \\n if matches != None:\\n element = matches.group(0)\\n \\n try:\\n return float(element)\\n except (ValueError, TypeError):\\n if surpress_error:\\n return 0\\n else:\\n raise\",\n \"def normalize(amount, min=2, max=4):\\n if not amount:\\n return amount\\n\\n # To Decimal, round to highest desired precision\\n d = round(Decimal(amount), max)\\n s = str(d)\\n\\n # Truncate as many extra zeros as we are allowed to\\n for i in range(max-min):\\n if s[-1] == '0':\\n s = s[:-1]\\n\\n if s[-1] == '.':\\n s = s[:-1]\\n\\n return s\",\n \"def to_btc(value):\\n value = float(value) / 100000000\\n value = format(value, \\\".9f\\\")\\n return value\",\n \"def replace_comma(data):\\n try:\\n aux = np.float32(data.replace(',', '.'))\\n except AttributeError as err:\\n print('No string. Convert to numeric')\\n aux = np.float32(data)\\n return aux\",\n \"def super_float(text):\\r\\n if text[-1] in SUFFIXES:\\r\\n return float(text[:-1]) * SUFFIXES[text[-1]]\\r\\n else:\\r\\n return float(text)\",\n \"def pretty_float(x):\\n return ('%f' % x).rstrip('0').rstrip('.')\",\n \"def parseFloat(s, ret=0.0):\\n if not isinstance(s, str):\\n return float(s)\\n elif s:\\n if s[0] in \\\"+-\\\":\\n ts = s[1:]\\n else:\\n ts = s\\n\\n if ts and ts.count(\\\".\\\") <= 1 and all([_ in \\\".0123456789\\\" for _ in ts]):\\n return float(s)\\n\\n return ret\",\n \"def priceGetSome(soup):\\n price = soup.find('span', id='priceblock_ourprice', class_='a-size-medium a-color-price')\\n price = price.text\\n price = price.strip()\\n price = price.lstrip('$')\\n price = float(price)\\n\\n return price\",\n \"def currency(self, commas=True):\\n sign, digits, exp = self.quantize(Decimal('0.01')).as_tuple()\\n digits = list(map(unicode, digits))\\n result = []\\n for i in range(2):\\n result.append(digits.pop() if digits else u'0')\\n result.append(u'.')\\n if not digits:\\n result.append(u'0')\\n count = 0\\n while digits:\\n result.append(digits.pop())\\n count += 1\\n if count == 3 and digits and commas:\\n count = 0\\n result.append(u',')\\n result.append(u'-' if sign else u'')\\n return u''.join(reversed(result))\",\n \"def getFactor(currency):\",\n \"def reformat(number):\\n if number.find('E') == -1:\\n exponent = \\\"-101\\\"\\n mantissa = number.split(exponent)\\n return float(mantissa[0])*10**float(exponent)\\n else:\\n mantissa, exponent = number.split('E')\\n\\n return float(mantissa)*10**float(exponent)\",\n \"def _parse_price_original(self, response, add_xpath=None):\\n xpathes = '//*[@id=\\\"price\\\"]/.//*[contains(@class, \\\"a-text-strike\\\")]' \\\\\\n '/text()'\\n\\n if add_xpath:\\n xpathes += ' |' + add_xpath\\n\\n price_original = response.xpath(xpathes).re(FLOATING_POINT_RGEX)\\n if price_original:\\n return float(price_original[0].replace(',', ''))\",\n \"def get_product_price(self):\\n\\n price = \\\"0.0000\\\"\\n\\n try:\\n price = self.trees.get_element_by_id(\\\"priceblock_ourprice\\\").text\\n except:\\n try:\\n price = self.trees.get_element_by_id(\\n \\\"price_inside_buybox\\\").text\\n except:\\n try:\\n price = self.trees.get_element_by_id(\\n \\\"priceblock_dealprice\\\").text\\n except:\\n try:\\n price = self.trees.xpath(\\n \\\"//span[@class='a-color-price']/text()\\\")[0]\\n except:\\n try:\\n price = self.trees.xpath(\\n \\\"//span[@class='a-size-base a-color-price']/text()\\\")[0]\\n except:\\n pass\\n\\n non_decimal = re.compile(r'[^\\\\d.]+')\\n price = non_decimal.sub('', price)\\n\\n return round(float(price[0:5]), 2)\",\n \"def conv_num_with_commas(number_text):\\n number=number_text.split(',')\\n item=''\\n for numero in number:\\n item+=numero\\n final_number=float(item)\\n return final_number\",\n \"def clean_pcts(x):\\n # if not enough data, will be '-' with investing.com\\n if x == '-' or pd.isnull(x):\\n return np.nan\\n elif x == 'unch':\\n return float(0)\\n elif type(x) == float:\\n return x\\n\\n new_x = x.replace('+', '')\\n new_x = new_x.replace('%', '')\\n new_x = float(new_x) / 100\\n return new_x\",\n \"def real_value(val):\\n\\t\\treturn round(val/100*sum(euros), 1)\",\n \"def _serialize_decimal(val):\\n return float(val)\",\n \"def _convert_to_float(s):\\n try:\\n return float(s)\\n except:\\n return s\",\n \"def round_float(cls, value: float) -> Dec:\\n # This check for numbers which are smaller than the precision allows will\\n # be commented out for now as it seems to kill economic activity.\\n # if value < 1E-8:\\n # return Dec(0)\\n return round(Dec(value), cls.currency_precision)\",\n \"def convert_to_float(word: str) -> float:\\n return round(float(word), 2)\",\n \"def decode(val):\\n if isinstance(val, Decimal):\\n return float(val)\\n return val\",\n \"def string_chopped_to_float(input_string, chop_up, chop_low):\\n input_string = str(input_string)\\n return float(string_chop_up(string_chop_low(input_string, chop_up), chop_low))\",\n \"def Float(val):\\n try:\\n return float(val)\\n except ValueError:\\n return ''\",\n \"def floatify(string):\\n temporary_string = \\\"0.\\\" + string[:-1]\\n return float(temporary_string)\",\n \"def money_digitalization(raw_data, target_columns=['First Place Prize', 'Total Prize']):\\r\\n output = raw_data.copy()\\r\\n\\r\\n for column in target_columns:\\r\\n for i in range(len(raw_data)):\\r\\n money = raw_data.loc[i, column].replace('(', '').replace(')', '')\\r\\n if money[0] == '$':\\r\\n output.loc[i, column] = float(money[1:].replace('.', '').replace(',', '.'))\\r\\n elif money[:3] == 'US$':\\r\\n output.loc[i, column] = float(money[3:].replace('.', '').replace(',', '.'))\\r\\n else:\\r\\n print('money data error')\\r\\n exit(1)\\r\\n\\r\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72919416","0.7071461","0.6978202","0.6896954","0.68778527","0.6861502","0.668687","0.66791004","0.6679073","0.6679073","0.6679073","0.6679073","0.6679073","0.6636291","0.6459509","0.64089173","0.64083546","0.63997513","0.6395582","0.6393109","0.6388328","0.63158053","0.6277507","0.6270148","0.6220546","0.61902374","0.6186327","0.6169518","0.6137453","0.60829353","0.60719204","0.60623896","0.60509425","0.6024461","0.6006062","0.599524","0.5981105","0.59764326","0.5911266","0.58965874","0.58941966","0.58620274","0.585213","0.58372825","0.58259475","0.5801416","0.5783773","0.5763022","0.57577914","0.57324725","0.5732233","0.5730502","0.5726484","0.5721564","0.5719327","0.5719103","0.56996024","0.569775","0.5679637","0.56599635","0.5659235","0.5647825","0.56350803","0.5622138","0.5618159","0.56079787","0.55943656","0.5574867","0.5560457","0.5558547","0.5546709","0.5532052","0.55286556","0.5498941","0.5497623","0.5480561","0.54791796","0.5473866","0.5472644","0.5461526","0.5454107","0.54529905","0.54528975","0.5450168","0.54484946","0.5448191","0.5441223","0.54408586","0.5440139","0.5434738","0.5416804","0.541014","0.5408265","0.54067796","0.5395362","0.5376403","0.5375816","0.5374572","0.53721315","0.5369483"],"string":"[\n \"0.72919416\",\n \"0.7071461\",\n \"0.6978202\",\n \"0.6896954\",\n \"0.68778527\",\n \"0.6861502\",\n \"0.668687\",\n \"0.66791004\",\n \"0.6679073\",\n \"0.6679073\",\n \"0.6679073\",\n \"0.6679073\",\n \"0.6679073\",\n \"0.6636291\",\n \"0.6459509\",\n \"0.64089173\",\n \"0.64083546\",\n \"0.63997513\",\n \"0.6395582\",\n \"0.6393109\",\n \"0.6388328\",\n \"0.63158053\",\n \"0.6277507\",\n \"0.6270148\",\n \"0.6220546\",\n \"0.61902374\",\n \"0.6186327\",\n \"0.6169518\",\n \"0.6137453\",\n \"0.60829353\",\n \"0.60719204\",\n \"0.60623896\",\n \"0.60509425\",\n \"0.6024461\",\n \"0.6006062\",\n \"0.599524\",\n \"0.5981105\",\n \"0.59764326\",\n \"0.5911266\",\n \"0.58965874\",\n \"0.58941966\",\n \"0.58620274\",\n \"0.585213\",\n \"0.58372825\",\n \"0.58259475\",\n \"0.5801416\",\n \"0.5783773\",\n \"0.5763022\",\n \"0.57577914\",\n \"0.57324725\",\n \"0.5732233\",\n \"0.5730502\",\n \"0.5726484\",\n \"0.5721564\",\n \"0.5719327\",\n \"0.5719103\",\n \"0.56996024\",\n \"0.569775\",\n \"0.5679637\",\n \"0.56599635\",\n \"0.5659235\",\n \"0.5647825\",\n \"0.56350803\",\n \"0.5622138\",\n \"0.5618159\",\n \"0.56079787\",\n \"0.55943656\",\n \"0.5574867\",\n \"0.5560457\",\n \"0.5558547\",\n \"0.5546709\",\n \"0.5532052\",\n \"0.55286556\",\n \"0.5498941\",\n \"0.5497623\",\n \"0.5480561\",\n \"0.54791796\",\n \"0.5473866\",\n \"0.5472644\",\n \"0.5461526\",\n \"0.5454107\",\n \"0.54529905\",\n \"0.54528975\",\n \"0.5450168\",\n \"0.54484946\",\n \"0.5448191\",\n \"0.5441223\",\n \"0.54408586\",\n \"0.5440139\",\n \"0.5434738\",\n \"0.5416804\",\n \"0.541014\",\n \"0.5408265\",\n \"0.54067796\",\n \"0.5395362\",\n \"0.5376403\",\n \"0.5375816\",\n \"0.5374572\",\n \"0.53721315\",\n \"0.5369483\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80026025"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":9986,"cells":{"query":{"kind":"string","value":"Take a list of categories from categories table in database. Scrapes website for all of them. Prepare data for inserting to the database."},"document":{"kind":"string","value":"def get_all_categories_items(number_of_items: int) -> pd.DataFrame:\n all_items = pd.DataFrame()\n for (category_id, category_name) in get_categories():\n items = extract_etsy_items(number_of_items, category_name)\n items['categoryId'] = category_id\n items = items[['categoryId', 'title', 'price', 'itemURL', 'imgURL']]\n items.set_index('categoryId', inplace=True)\n all_items = all_items.append(items)\n return all_items"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def scrapeCategories():\n page = requests.get(\"http://books.toscrape.com/index.html\")\n soup = BeautifulSoup(page.content, 'html.parser')\n content = soup.find(\"div\", {\"class\": \"side_categories\"}).findChild().find(\"ul\")\n categories = content.find_all(\"a\")\n for category in categories:\n Categories.objects.create(name=category.text.strip())\n return HttpResponse(status=204)","def fill_category(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\n for element in result['tags']:\n try:\n cursor.execute(\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\",\n (element[\"id\"], element[\"name\"], element[\"url\"]))\n query_result = cursor.fetchone()\n categories.__setitem__(query_result[1], query_result[0])\n except self.conn.OperationalError:\n print(\"operation Error\")\n except self.conn.DataError:\n print(\"Data Error\")\n self.conn.commit()\n cursor.close()\n return categories","def fill_products(self):\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\n categories = dict()\n for page in range(1, 2):\n result = requests.get(\n 'https://fr.openfoodfacts.org/cgi/search.pl?page_size=1000&page={}&action=process&json=1'.format(\n page)).json()\n for element in result['products']:\n try:\n cursor.execute(\n \"INSERT INTO product (name, store, nutrition_grade, url) VALUES (%s, %s, %s, %s) RETURNING \"\n \"id, name\",\n (element[\"product_name\"], element[\"stores\"], element[\"nutrition_grade_fr\"], element[\"url\"]))\n # un except pour éviter les erreurs de clés\n query_result = cursor.fetchone()\n for category in element[\"categories_tags\"]:\n try:\n cursor.execute(\"INSERT INTO product_category(product_id, category_id) VALUES (%s, %s)\",\n (query_result[0], self.categories[category]))\n except KeyError:\n print(\"Categorie insertion failed\")\n\n print(element[\"product_name\"])\n except KeyError:\n print(f'product insertion failed:')\n\n self.conn.commit()\n cursor.close()","def OLD_get_items_from_category(save_db=False):\n # for i in range(16):\n # name = pd.read_sql_query(\n # f'SELECT name FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\n # cat_url = pd.read_sql_query(\n # f'SELECT url FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\n # # cat_id = pd.read_sql_query(\n # # f'SELECT id FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\n # cat_id = i + 1\n # print(cat_id)\n # \"\"\" pandas return DataFrame, use DataFrame.values to return list of list value in that DataFrame\n # url: https://tiki.vn/dien-thoai-may-tinh-bang/c1789?src=c.1789.hamburger_menu_fly_out_banner&page=2\n # \"\"\"\n # for i in range(100):\n # url = cat_url + f'&page={i+1}'\n # print(url)\n # soup = get_url(url)\n\n # result = []\n # \"\"\" item: div 'product-item' > div 'content'\n # img: img 'product-imgage'\n # title: p 'title'\n # price: span 'price-regular'\n # sale-tag: span 'sale-tag'\n # final-price: span 'final-price'\n # \"\"\"\n # div_container = soup.find_all('div', {'class': 'product-item'})\n # item_path = div_container['data-category']\n # if div_container:\n # for div in div_container:\n # # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\n # item_id = None\n # item_name = div.a['title']\n # brand = div['data-brand']\n # item_url = div.a['href']\n # img_url = div.img['src']\n # regular_price = div.find('span', {'class': 'price-regular'}).text\n # sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\n # final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\n\n # item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\n # img_url, regular_price, sale_tag, final_price)\n # if save_db:\n # item.save_into_db()\n # print(f'SAVE {item_name} INTO DTB')\n # result.append(item)\n # else:\n # break","def get_items_from_category(save_db=False):\n query_result = pd.read_sql_query(\"\"\"SELECT c1.id, c1.name, c1.parent_id, c1.url\n FROM categories c1 LEFT OUTER JOIN categories c2\n ON c1.id = c2.parent_id\n WHERE c2.parent_id IS NULL\n LIMIT 1400 OFFSET 1300\"\"\", conn)\n for i in query_result.itertuples():\n name = i.name[:-10].strip()\n cat_url = i.url\n cat_id = i.id\n quantity = i.name[-10:].strip()\n \n for i in range(100):\n url = cat_url + f'&page={i+1}'\n print(url)\n soup = get_url(url)\n \n result = []\n \"\"\" item: div 'product-item' > div 'content'\n img: img 'product-imgage'\n title: p 'title'\n price: span 'price-regular'\n sale-tag: span 'sale-tag'\n final-price: span 'final-price'\n \"\"\"\n try:\n div_container = soup.find_all('div', {'class': 'product-item'})\n except Exception as err:\n print('ERROR BY DIV FINDALL: ', err)\n if div_container:\n for div in div_container:\n # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\n item_id = None\n item_path = div['data-category']\n item_name = div.a['title']\n brand = div['data-brand']\n item_url = div.a['href']\n img_url = div.img['src']\n regular_price = div.find('span', {'class': 'price-regular'}).text\n sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\n final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\n\n item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\n img_url, regular_price, sale_tag, final_price)\n if save_db:\n item.save_into_db()\n print(f'SAVE {item_name} INTO DTB')\n result.append(item)\n else:\n break","def _traverse_categories():\n \n csv_output_dict = _get_csv_data()\n print csv_output_dict.keys()\n #~ if csv_output_dict:\n #~ return csv_output_dict\n #~ else:\n final_output_url_list = []\n traverse_cat_output = {}\n # brand_seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-brand-all.w'\n seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-category-all.w'\n \n browser = utils.create_browser(SLEEP_MIN, SLEEP_MAX)\n browser.open(seed_page)\n html_response = browser.response().read()\n \n soup = BeautifulSoup(html_response)\n \n all_category_names = soup.findAll('div', {'class': 'brwsBrndLogoClip'})\n for category_names in all_category_names:\n temp_category_url = str(category_names.a['href'])\n category_url_name = temp_category_url.split('&catname=')[-1]\n category_url = 'https://www.broderbros.com/cgi-bin/online/webshr/search-result.w?nResults=5000&mc=&RequestAction=advisor&RequestData=CA_CategoryExpand&bpath=c&CatPath=All Products////BRO-Categories////'+str(category_url_name)\n category_url = category_url.replace(' ', '%20')\n category_names = category_names.a.img['alt'].lower()\n returned_url_list = _get_product_urls(category_url, category_names)\n final_output_url_list.extend(returned_url_list)\n \n for productsurl in final_output_url_list:\n if productsurl['id'] in traverse_cat_output.keys() and productsurl['category_list'] != traverse_cat_output[productsurl['id']]['category_list']:\n traverse_cat_output[productsurl['id']]['category_list'].extend(productsurl['category_list'])\n else:\n traverse_cat_output[productsurl['id']] = productsurl\n \n brand_output_categories = _traverse_categories_brand()\n for brand_url in brand_output_categories:\n if brand_url['id'] in traverse_cat_output.keys():\n traverse_cat_output[brand_url['id']]['brand'] = brand_url['brand']\n else:\n traverse_cat_output['id'] = brand_url\n \n if TESTRUN:\n for k,v in traverse_cat_output.items():\n print v\n print '*'*78\n for item_id in traverse_cat_output.keys():\n print item_id\n csv_data = csv_output_dict.get(item_id)\n if csv_data:\n traverse_cat_output[item_id]['brand'] = csv_data['brand']\n if not traverse_cat_output[item_id].get('brand'):\n del traverse_cat_output[item_id]\n \n return traverse_cat_output","async def bulk_crawl_and_store(db_cfg, categories, **kwargs):\n\n async with ClientSession() as session:\n tasks = []\n for cat in categories:\n tasks.append(\n store_to_db(db_cfg=db_cfg, category=cat, session=session, **kwargs)\n )\n await asyncio.gather(*tasks)","def import_categories(self, all_categories):\n\n i = 0\n while (\n i < len(all_categories['tags'])\n and all_categories['tags'][i]['products'] > 1000\n ):\n category = all_categories['tags'][i]\n if self.test_category(category):\n self.create_category(category)\n i += 1","def categories(data):\n if data:\n for i in data:\n category = CategoriesModel(categories=i['categories'])\n category.save()","def insert_data_category_into_bdd(self):\n for category in constant.LIST_CATEGORIES:\n data = Category(name=category)\n data.save()\n print(\"the category \" + str(category) + \" has been created\")","def insert_categories(self):\n logic = CategoryLogic()\n \n # We create the list of category objects\n categories = self.objects_factory.create_category_object_list()\n categories = set(categories)\n \n for category in categories:\n logic.insert(category)","def get_data(self):\n products_list = []\n for category in CATEGORIES:\n json_data = self.url_to_json(category)\n pages_nb = self.retrieve_cat_pages_nb(json_data)\n for page in range(pages_nb):\n page_json_data = self.page_to_json(category, page+1)\n products = page_json_data[\"products\"]\n for p in products:\n params = {\n 'brands': \"\",\n 'product_name_fr': \"\",\n 'nutrition_grades': \"\",\n 'stores': \"\",\n 'url': \"\",\n 'categories': \"\"\n }\n for key in params:\n try:\n params[key] = p[key]\n except KeyError:\n continue\n if params['product_name_fr'] != \"\" and params['nutrition_grades'] != \"\" and params['url'] != \"\" and params['categories'] != \"\":\n product = Product(brand=params['brands'],\n name=params['product_name_fr'],\n nutrition_grade=params['nutrition_grades'],\n stores=params['stores'], url=params['url'],\n category=params['categories'])\n products_list.append(product)\n try:\n self.manager.save_all(self.clean_data(products_list))\n print(f\"\\n La base de données |{DB_NAME}| a été peuplée \\n\")\n except:\n print(\"\\n Une erreur s'est produite lors \"\n \"du peuplement de la base de données \\n\")","async def filter_categories_and_products(categories: list[str], products: list[str]) -> None:\n for url in categories:\n if url not in db['urls_to_crawl']:\n db['urls_to_crawl'].append(url)\n\n if url not in db['category_urls']:\n db['category_urls'].append(url)\n\n for url in products:\n if url not in db['product_urls']:\n db['product_urls'].append(url)\n\n update_json_database()","def load_categories():\n\n print('load_categories')\n\n Category.query.delete()\n\n for row in open(\"seed_data/categories.csv\"):\n name = row.rstrip()\n\n cat = Category(name=name)\n\n db.session.add(cat)\n\n db.session.commit()","def main():\n category_list = []\n h = httplib2.Http('.cache')\n websites = [\"http://www.goodreads.com/genres/list?page=1\", \"http://www.goodreads.com/genres/list?page=2\",\n \"http://www.goodreads.com/genres/list?page=3\"]\n for website in websites:\n response, content = h.request(website)\n if response.status != 200:\n print(\"Status code \", response.status)\n return\n soup = BeautifulSoup(content, 'html.parser')\n data = soup.find_all(\"a\", class_=\"mediumText actionLinkLite\")\n for x in data:\n category_list.append(str(x.string))\n\n data = \"category_list = \" + str(category_list)\n\n with open(\"InitialDataExtraction/category_list.py\", mode='w', encoding=\"utf-8\") as a_file:\n a_file.write(data)\n print(len(category_list))","def start_parsing_category(self, response):\n # ### First thing to do is to get the # of pages in the category.\n # Note: suppose a category has 189 entries. There are 48 entries per page.\n # 189 / 48 = 3.93. There are 4 pages. Therefore, deploy a ceil() function.\n #\n path_to_total_entries = \"//span[@class='paging-information-items']/text()\"\n total_entries_in_category = response.xpath(path_to_total_entries).extract()[0].strip(\"\\n\").split(\" \")[0]\n # print(\"T:\", total_entries_in_category)\n pages = ceil(int(total_entries_in_category) / 48)\n # assemble list of links for spider to visit\n base_url_for_category = response.meta[\"link_to_category\"]\n links_to_pages_in_this_category = []\n for page_number in range(1, pages + 1):\n start_value = page_number * 48\n paginated_links = base_url_for_category + \"?start=\" + str(start_value) + \"&sz=48\"\n links_to_pages_in_this_category.append(paginated_links)\n\n # ### Handle the first page in the category, which we are already on.\n # \"product_divs\"\n tile_path = \"//div[@class='product-tile']\"\n # gets between 1 and 48 SelectorLists, depending on how many products are on the page.\n product_tiles_from_the_page = response.xpath(tile_path)\n # FIXME: this naming seems wrong. it's feeding pages into the for loop, so its name should be \"pages\" ... and yet i was expecting tiles?\n for page in product_tiles_from_the_page[0:1]: # TODO: remove 0:3 when done developing. its just there to make things run faster\n self.convert_product_tiles_from_this_page_to_items(page, product_category=response.meta[\"category_name\"])\n # FIXME: this is currently printing an item that contains the name of every product on the page.\n\n # ### send Scrapy to handle the rest of the pages in the category, sans the first page, which is done\n page_num = 2\n for remaining_link in links_to_pages_in_this_category[1:]:\n # print(remaining_link)\n yield scrapy.Request(remaining_link, self.parse_further_pages, dont_filter=True,\n meta={\"page_number\": page_num})\n page_num = page_num + 1\n\n return None","def fill_tab_categories(self):\n self.category.fill_tab_categories(self.list_categories, self.mycursor, self.my_database)","def start_requests(self):\n\n with open(os.path.join(os.path.dirname(__file__), \"../resources/mapemall_categories.csv\")) as categories:\n for category in csv.DictReader(categories):\n category_text=category[\"category\"]\n url=str(MapemallCrawlerSpider.start_urls[0])+category_text\n # The meta is used to send our search text into the parser as metadata\n yield scrapy.Request(url, callback = self.parse, meta = {\"category_text\": category_text})","def main():\n\n conn = psycopg2.connect(**env.DATABASE)\n cursor = conn.cursor()\n\n for file, city in env.supported_cities().items():\n try:\n data = add_metadata(parse_html(city, get_html(city)))\n save_data_to_db(cursor, data, file.title())\n except Exception as e:\n print(\"Failed to scrape '%s': %s\" %(city, e))\n print(traceback.format_exc())\n\n conn.commit()\n conn.close()","def get_categories(self):\n\n self.search([]).unlink()\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\n domain = \"http://localhost:8888\"\n webservice_url = \"/webservice/rest/server.php?\"\n parameters = {\n \"wstoken\":token,\n 'wsfunction': 'core_course_get_categories',\n 'moodlewsrestformat': 'json'\n }\n request = requests.get(url=domain+webservice_url, params=parameters)\n request = request.json()\n for req in request:\n try:\n self.create({\n 'category_id': req['id'],\n 'name': req['name'],\n 'description': req['description'],\n 'category_parent': req['parent'],\n })\n except Exception:\n print('Category not created')","def get_all_categories(self):\n\t\turl = self.base_url\n\t\tcategories, code = self.get_categories(url, level = 0)\n\t\tcategories = self.databaseHelper.save_categories(categories)\n\n\t\t# Categories is a list of main categories\n\t\t# Cycle through these categories and keep going until level 3\n\n\t\tfor i in xrange(0, len(categories)):\n\t\t\tmain_category = categories[i]\n\n\t\t\turl = main_category['url']\n\t\t\tsub_categories, code = self.get_categories(url, level = 1, id_parent_category = main_category['id'])\n\n\t\t\t# Getting level 2 categories\n\t\t\tfor j in xrange(0,len(sub_categories)):\n\t\t\t\tsub_category = sub_categories[j]\n\t\t\t\turl = sub_category['url']\n\t\t\t\tsub_categories_level_2, code = self.get_categories(url, level = 2, id_parent_category = sub_category['id'])\n\n\t\t\t\t# Getting sub categories level 3\n\t\t\t\tfor k in xrange(0, len(sub_categories_level_2)):\n\t\t\t\t\tsub_category_level_2 = sub_categories_level_2[k]\n\t\t\t\t\turl = sub_category_level_2['url']\n\t\t\t\t\tsub_categories_level_3, code = self.get_categories(url, level = 3, id_parent_category = sub_category_level_2['id'] )\n\n\t\t\t\t\t# Updating sub categories level 2\n\t\t\t\t\tsub_category_level_2['sub_categories'] = sub_categories_level_3\n\t\t\t\t\tsub_categories_level_2[k] = sub_category_level_2\n\n\n\t\t\t\t# Updating sub category level 1\n\t\t\t\tsub_category['sub_categories'] = sub_categories_level_2\n\t\t\t\tsub_categories[j] = sub_category\n\n\t\t\t# Updating main category\n\t\t\tmain_category['sub_categories'] = sub_categories\n\t\t\tcategories[i] = main_category\n\n\t\treturn categories","def insert_data(self):\n self.connection_database()\n\n # no insertion if the table Food already contains any data\n self.cursor.execute(\"SELECT * FROM Food\")\n data_table_food = self.cursor.fetchall()\n if not data_table_food:\n\n # instantiate the class Call_api\n new_call_api = ca.CallApi()\n new_call_api.load_data()\n categories = new_call_api.categories\n list_data = new_call_api.list_data\n\n for elt, element in zip(categories, list_data):\n\n # inserting data into Category table\n insert_data_categories = (\"\"\"INSERT IGNORE INTO Category (categories)\n VALUES({0})\"\"\"\n .format(\"\\'\"+elt+\"\\'\"))\n self.cursor.execute(insert_data_categories)\n self.data_base.commit()\n\n # inserting data into Food table\n for value in element['products']:\n if element['products'].index(value) < 100:\n try:\n product_name = \"\\'\"+value['product_name_fr'].replace(\"'\", \"\")+\"\\'\"\n nutrition_grade = \"\\'\"+value['nutrition_grade_fr'].replace(\"'\", \"\")+\"\\'\"\n ingredients = \"\\'\"+value['ingredients_text'].replace(\"'\", \"\")+\"\\'\"\n store_tags = \"\\'\"+\", \".join(value['stores_tags']).replace(\"'\", \"\")+\"\\'\"\n url = \"\\'\"+value['url'].replace(\"'\", \"\")+\"\\'\"\n\n insert_data_food = (\"\"\"INSERT IGNORE INTO Food (name_food, category_id,\n nutriscore, description, store, link) \n VALUES({0}, \n (SELECT id FROM Category WHERE categories = {1}),\n {2}, {3}, {4}, {5})\"\"\"\n .format(product_name, \"\\'\"+elt+\"\\'\",\n nutrition_grade, ingredients,\n store_tags, url))\n self.cursor.execute(insert_data_food)\n self.data_base.commit()\n\n # if errors\n except KeyError:\n continue","def get_category_links(self):\n self.logger.info(\"Gathering categories links...\")\n try:\n category_link = \"services-catalog__column-title ui-link _t37mbJS _2fIr6we _2l1CpUa\"\n self.driver.get(self.MAIN_URL)\n categories = self.driver.find_elements_by_class_name(\"services-catalog__content\")\n elems = categories[0].find_elements_by_xpath(\"//a[@class]\")\n except:\n self.logger.critical(\"Problems with Internet connection or Web driver occured! Cannot gather category list!\")\n return\n search_cat = True\n # Compose two links lists: for subject categories, and for generic categories\n for i, elem in enumerate(elems):\n if elem.get_attribute(\"class\") == category_link:\n if elem.get_attribute(\"href\")[-1] == \"#\":\n subject = \" \".join(elem.text.split()[:-1])\n self.others_links.append([self.other_links_dict[subject]])\n search_cat = False\n else:\n self.link_list.append(elem.get_attribute(\"href\"))\n search_cat = True\n elif not search_cat:\n self.others_links[-1].append(elem.text)\n # Move english category to the end because it is the largest one\n self.link_list.append(self.link_list.pop(self.link_list.index('https://profi.ru/repetitor/english/')))\n self.logger.info(f'Found {len(self.link_list) + len(self.others_links)} categories')","def save_category(self):\n self.start_connection()\n for element in config.CATEGORIES:\n self.cursor.execute(f\"\"\"\n INSERT INTO category (name) VALUES (\"{element}\")\"\"\")\n self.mysql_connection.commit()\n self.close_connection()","def get_category_data_off(self):\n list_categories_name=[]\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\n cat_data = cat.json()\n tags_list = cat_data['tags']\n print (len(tags_list))\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\n\n for category in list_of_random_tags_list:\n try :\n category_name = category['name']\n print(category_name)\n list_categories_name.append(category_name)\n print (list_categories_name)\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\n except KeyError:\n pass\n except UnicodeEncodeError:\n pass","def get_categories(self, categories):\r\n category, created = Categories.objects.get_or_create(name=categories)\r\n category.save()","def _get_data(self, category):\n url = self._get_url(category)\n # request page\n page = get(url)\n # print page.content\n # exit()\n # raise an error\n # page.raise_for_status()\n # parse html markup\n html = html5parser.fragment_fromstring(page.content, True)\n divs = html.findall(\".//{http://www.w3.org/1999/xhtml}div\")\n json_string = \"\"\n # find the div that contains our catalog json file\n # TODO: everything request seems to have diffent html structure then specials find a method that works for both of them\n for i in range(0, len(divs)):\n d = divs[i]\n if d.text != None and \"COLRSCatalogEntryList\" in d.text:\n json_string = d.text\n break\n # parse the json\n # print \"json string is:\\n{}\".format(json_string)\n data_json = json.loads(json_string)\n self._search_data = data_json['searchInfo']\n self._product_data = data_json['products']\n # we return true on success\n return True","def get_categories(self, url, level = 0, id_parent_category = None):\n\t\tcategories = []\n\n\t\t# Getting main categories\n\t\thtml, code = self.crawler.get(url)\n\t\tif code == 200:\n\t\t\tself.parser.set_html(html)\n\t\t\tcategories = self.parser.get_categories(level = level)\n\t\t\t# Setting proper urls\n\t\t\t[ cat.update({'url': self.properurl(cat['url'])}) for cat in categories]\n\t\t\t# Save categories\n\t\t\tcategories = self.databaseHelper.save_categories(categories, id_parent_category)\n\t\telse:\n\t\t\tprint \"Something went wrong when fetching main categories for Monoprix : code %d\"%(code)\n\n\t\treturn categories, code","def load_categories():\n\n Category.query.delete()\n\n with open(category_file) as f:\n for _ in range(1):\n next(f)\n \n for row in f:\n row = row.rstrip()\n categories_data = row.split(\",\")\n\n id = int(categories_data[0])\n category = categories_data[1]\n\n category_model = Category(id=id, category=category)\n db.session.add(category_model)\n db.session.commit()","def populate_index(db):\n\tfor url in URL:\n\t\tprint url\n\t\trequest = urllib2.Request(url)\n\t\ttry :\n\t\t\tresponse = urllib2.urlopen(request)\n\t\texcept urllib2.URLError:\n\t\t\tprint \"Network Unreachable \"\n\t\t\tsys.exit()\t\n\t\ttext = html2text(response.read())\n\t\tdb.generate_index(text,url)","def parse(self):\n database = WriteToDatabase(\"config_profi_ru.conf\")\n database.create_base()\n # Start Webdriver with supressed logging\n options = webdriver.ChromeOptions() \n options.add_experimental_option(\"excludeSwitches\", [\"enable-logging\"])\n self.driver = webdriver.Chrome(options=options)\n # Get categories\n self.get_category_links()\n # Treat general categories\n for category in self.link_list:\n cat_name = category.split('/')[-2]\n # Check if category is already parsed\n if(os.path.exists(f\"profi_ru_json_data\\{cat_name}_data_file.json\")):\n self.logger.warning(f\"'{cat_name}' category is already parsed!\")\n continue\n self.logger.info(f\"Treating '{cat_name}' category\")\n counter = 0\n person_info = []\n try:\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\n self.logger.info(f\"Found {len(category_profiles)} profiles in '{cat_name}' category\")\n self.logger.info(f\"Start of parsing '{cat_name}' category...\")\n for person_link in category_profiles:\n person_info.append(self.get_person_info(person_link))\n counter+=1\n if(counter == len(category_profiles) - 1):\n self.logger.info(f\"End of parsing '{cat_name}' category...\")\n except:\n self.logger.critical(\"Problems with Internet connection or Web driver occured!\")\n self.logger.exception(f\"Only {counter} profiles of '{cat_name}' category were parsed\")\n self.cat_profiles_dict[cat_name] = person_info\n self.logger.info(f\"'{cat_name}' category was parsed successfully!\")\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\n database.create_and_write_table(f'profi_ru_json_data\\{cat_name}_data_file.json')\n # Treat generic categories\n for cat_list in self.others_links:\n cat_name = cat_list[0]\n # Check if categroy is already parsed\n if(os.path.exists(f\"profi_ru_json_data\\{cat_name}_data_file.json\")):\n self.logger.warning(f\"'{cat_name}' category is already parsed!\")\n continue\n self.logger.info(f\"Treating '{cat_name}' category\")\n counter = 0\n person_info = []\n try:\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\n self.logger.info(f\"Found {len(category_profiles)} profiles in '{cat_name}' category\")\n for person_link in category_profiles:\n person_info.append(self.get_person_info(person_link))\n counter+=1\n if(counter == len(category_profiles) - 1):\n self.logger.info(f\"End of parsing {cat_name} category\")\n except:\n self.logger.critical(\"Problems with Internet connection or Web driver occured!\")\n self.logger.exception(f\"Only {counter} profiles of '{cat_name}' category were parsed\")\n self.cat_profiles_dict[cat_name] = person_info\n self.logger.info(f\"'{cat_name}' category was parsed successfully!\")\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\n database.create_and_write_table(f'profi_ru_json_data\\{cat_name}_data_file.json')\n self.driver.quit()","def harvest_urls():\n manifest = []\n category = {}\n subcategory = {}\n directoryfiles = \"%s/directory_listing/\" % config['PREFIX']\n # ^^ the directory containing the HTML from the Technorati site.\n\n #Set up directory for intermediate data: MANIFEST\n #MANIFEST contains: Category, Subcategory, Title and URL.\n #and is a roster of URLs of blogs to autodiscover.\n if not os.path.exists(prefix + \"meta\"):\n os.mkdir(prefix + \"meta\")\n else:\n #TO DO: What if meta exists but MANIFEST got deleted?\n logging.info(\"Blog URLs already harvested. Skipping...\")\n return\n\n #Iterate through each file in the directory and extract blog URLs.\n for infile in glob.glob(os.path.join(directoryfiles, '*.html')):\n logging.info(\"Harvesting blog URLs from %s.\" % infile)\n dirpage = file(infile)\n root = parse(dirpage).getroot()\n #Rather than infer the category from the filename, just extract\n #it from the file. Not the best way to do this, hit is minimal.\n\tpieces = infile.split('/')[-1].split('_')\n\tcat = pieces[1]\n\tsubcat = None\n\tif len(pieces) == 4:\n\t\tsubcat = pieces[2]\n blogs = root.xpath(\"//td[@class='site-details']\")\n #Iterate through all of the blogs listed on the page.\n for blog in blogs:\n url = blog.xpath(\"a[@class='offsite']\")[0].text\n title = blog.xpath('h3/a')[0].text\n OUT = open(prefix + \"meta/MANIFEST\", \"a\")\n #Store the category of the blog.\n category[url] = cat\n if subcat:\n output = [cat, subcat, title.encode('utf-8').replace(' ', ' '), url]\n subcategory[url] = subcat\n print >> OUT, ' '.join(output)\n else:\n output = [cat, \"NA\", title.encode('utf-8').replace(' ', ' '), url]\n print >> OUT, '\\t'.join(output)\n manifest.append(output)\n OUT.close()\n # This is a hack to get around having to use a database.\n # TODO: Reimplement using a database.\n BLOGCATS = open(prefix + \"blogcats.pickle\", \"w\")\n cPickle.dump(category, BLOGCATS)\n BLOGCATS.close()\n return manifest","def scrape_categories():\n category_urls = []\n url = homepage + '/alle-categorieen/'\n response = rq.get(url, timeout=5)\n soup = BeautifulSoup(response.content, 'html.parser')\n main_soup = soup.find('main')\n\n for category in main_soup.find_all('a', {'href': re.compile(r'/overzicht/')}):\n category_urls.append(category['href'])\n\n return category_urls","def get_categories(request):\n try:\n categories = []\n for category in Category.objects.all():\n categories.append({\"title\": category.title, \"id\": category.pk})\n\n return format_ajax_response(True, \"Knowledgebase categories retrieved successfully.\", {\"categories\": categories})\n except Exception as ex:\n logger.error(\"Failed to get_categories: %s\" % ex)\n return format_ajax_response(False, \"There was an error retreiving the knowledgebase categories.\")","def _get_all_categories() -> dict:\n categories = database.fetchall(\"Categories\", \"id\", \"name_ua\")\n return categories","def scrap_data(cat):\n global OBSERVATION\n # Click on \"View more\" until it disapears\n # is_next_button = True\n # while is_next_button:\n # try:\n # viewmore_button = BROWSER.find_element_by_css_selector(\n # \"li.nextPaging > a.viewmore\")\n # # BROWSER.execute_script(\"arguments[0].scrollIntoView();\",\n # # viewmore_button)\n # time.sleep(5)\n # viewmore_button.click()\n # except NoSuchElementException:\n # is_next_button = False\n # Get html source\n soup = BeautifulSoup(BROWSER.page_source, 'lxml')\n cat_div = soup.find(\"div\", {\"class\": \"boxproduct\"})\n cat_div = cat_div.findAll(\"li\", {\"class\": \"product\"}) if cat_div else None\n if cat_div is None:\n cat_div = []\n for item in cat_div:\n row = {}\n good_name = item.find(\"div\", {\"class\": \"product-name\"})\n row['good_name'] = good_name.text.strip() if good_name else None\n price = item.find('div', {\"class\": \"price\"})\n row['price'] = price.strong.text.strip() if price else None\n old_price = price.find('span') if price else None\n row['old_price'] = old_price.text.strip() if old_price else None\n id1 = item.get('data-product')\n row['id'] = id1\n row['category'] = cat['name']\n row['category_label'] = cat['label']\n row['date'] = DATE\n OBSERVATION += 1\n write_data(row)","def crawler():\n job_entries = []\n for job in job_info(URL):\n labels = \"\"\n if job[\"labels\"]:\n for label in job[\"labels\"]:\n labels += label[\"name\"]\n if job[\"labels\"].index(label) != len(job[\"labels\"]) - 1:\n labels += \",\"\n job_entries.append((job[\"number\"], job[\"id\"],\n job[\"title\"], job[\"html_url\"], labels))\n\n conn = sqlite3.connect('jobber/jobber.db')\n c = conn.cursor()\n c.executemany(('INSERT OR IGNORE INTO job_entries '\n 'VALUES (?,?,?,?,?)'), job_entries)\n conn.commit()\n conn.close()","def _create_links_and_track(self, page_name, category_list):\n env = self.state.document.settings.env\n if not hasattr(env, \"categories\"):\n env.categories = {}\n\n link_rst = \"\"\n ncategs = 0\n for item in category_list:\n has_subcat = False\n if r\"\\\\\" in item: \n categs = item.split(r\"\\\\\")\n has_subcat = True\n else:\n categs = [item]\n # endif\n\n print \n for index, categ_name in enumerate(categs):\n if categ_name not in env.categories:\n category = Category(categ_name)\n env.categories[categ_name] = category\n else:\n category = env.categories[categ_name]\n #endif\n category.pages.append(PageRef(page_name))\n if has_subcat and index > 0:\n category.subcategories.append(PageRef(categ_name))\n #endif\n link_rst += \":ref:`%s` | \" % categ_name\n ncategs += 1\n # endfor\n # endfor\n\n link_rst = \"`%s: <categories.html>`_ \" + link_rst\n if ncategs == 1:\n link_rst = link_rst % \"Category\"\n else:\n link_rst = link_rst % \"Categories\"\n #endif\n\n return link_rst","def get_all() -> list:\n categorias = []\n conn = GenericDao.connect()\n cursor = conn.execute(\"SELECT * FROM categorias\")\n for row in cursor:\n categoria = Categoria(row[1], row[0])\n categorias.append(categoria)\n if debug:\n print(str(categoria))\n\n conn.close()\n return categorias","def get_all_category_urls():\n\treturn execute_sql(\"SELECT category_url FROM categories\").fetchall()","def get_disease_category_url():\n base_url = 'http://www.mayoclinic.org' \n task = load_jt('task.json') \n spider = Crawler()\n \n for url in task:\n html = spider.html(url)\n if html:\n soup = BS4(html)\n div = soup.find_all('div', id = 'main_0_left1_0_tertiarynav')[0]\n for a in div.find_all('a'):\n task[url].setdefault(base_url + a['href'], {'data': {'category': a.text.strip()}})\n dump_jt(task, 'task.json', replace = True)","def fetch_data():\n for category in CHEATSHEETS.items():\n subprocess.call(f'curl -o {PATH}{category[0] + \".csv\"} {category[1]}', shell=True)\n\n index = -1\n for filename in os.listdir(PATH):\n for idx, row in pd.read_csv(PATH + filename, on_bad_lines='skip').replace(np.nan, '').iterrows():\n name = row['Model']\n url = REDIRECT_URL + name.lower()\n category = filename.split('.')[0]\n featurizers = row['Acceptable Featurizers'].split(' ') if row['Acceptable Featurizers'] != '' else []\n backends = ['PyTorch' if item in {\"PTorch\", \"Torch\", \"PyTorch \"} else item for item in row['Backend'].split('/')]\n types = row['Type'] if filename != 'general.csv' else row['Classifier/Regressor']\n types = types.split('/') if filename == 'material.csv' else types.split('/ ')\n index += 1\n\n backend_list.append(backends)\n type_list.append(types)\n featurizer_list.append(featurizers)\n model_list.append(Model(name, url, category, featurizers, backends, types, index))","def __init__(self,url):\n self.base_url = url\n content = self._get_page_content()\n json_data = self._get_data_json(content)\n self._categories = self._get_categories(json_data)","def html_collect_pages(app):\n if not hasattr(app.builder.env, \"categories\"):\n return # nothing to do\n\n for name, context, template in create_category_pages(app):\n yield (name, context, template)","def scrap_site_category_links(base_url):\n response = requests.get(base_url)\n page = response.content\n soup = BeautifulSoup(page, \"html.parser\")\n category_links = []\n for category_link in soup.find('ul', class_='nav nav-list').find('li').find('ul').find_all('li'):\n href = category_link.a.get('href')\n # url of the category\n url = \"https://books.toscrape.com/{}\".format(href)\n category_links.append(url)\n return category_links","def html_collect_pages(app):\n\n if not hasattr(app.builder.env, \"categories\"):\n return # nothing to do\n\n for name, context, template in create_category_pages(app):\n yield (name, context, template)","def load_subcategories():\n\n print('load_subcategories')\n\n Category_Subcategory.query.delete()\n\n for row in open(\"seed_data/subcategories.csv\"):\n row = row.rstrip()\n category_id, name = row.split(',')\n\n\n sub = Category_Subcategory(main_category=category_id,\n name=name)\n\n db.session.add(sub)\n\n db.session.commit()","def browse_categories():\n print(\"***** Find Businesses by Categories *****\")\n while True:\n print()\n category = input(\n 'Please enter a type of business (category) or type \"back\" or \"quit\": ')\n print()\n if category == \"quit\":\n print(\"Goodbye!\")\n sys.exit()\n if category == \"back\":\n return\n\n # create a regex pattern for business name\n pattern = r\".*\" + re.escape(category) + r\".*\"\n regx = re.compile(pattern, re.IGNORECASE)\n\n cursor = business_col.find({\"categories\": regx})\n\n business_objects = cursor.limit(10)\n \n if cursor.count() == 0:\n print(\"No businesses found with given category.\")\n continue\n for business_object in business_objects:\n print_business(business_object)","def _create_links_and_track(self, page_name, category_list):\n env = self.state.document.settings.env\n if not hasattr(env, \"categories\"):\n env.categories = {}\n\n link_rst = \"\"\n ncategs = 0\n for categ_name in category_list:\n #categ_name is the full category name - register that\n category = self.register_category(categ_name, env)\n category.pages.add(PageRef(page_name, env.docname))\n\n #now step up a step up each time the category hierarchy\n parent_category = categ_name\n while True:\n if r\"\\\\\" in parent_category:\n categs = parent_category.split(r\"\\\\\")\n else:\n break\n # remove the last item\n subcat = Category(categ_name, env.docname) #create the category with the full name\n subcat.name=categs.pop() # and then replace it with the last token of the name\n parent_category = r\"\\\\\".join(categs)\n\n #register the parent category\n parent = self.register_category(parent_category, env)\n parent.subcategories.add(subcat)\n\n # endwhile\n\n #category should be the last subcategory by this point\n link_rst += \"`%s <%s>`_ | \" % (categ_name, category.link(env.docname))\n ncategs += 1\n # endfor\n\n link_rst = \"**%s**: \" + link_rst.rstrip(\" | \") # remove final separator\n if ncategs == 1:\n link_rst = link_rst % \"Category\"\n else:\n link_rst = link_rst % \"Categories\"\n #endif\n\n return link_rst","def crawler(self):\n\n\t\tfor page in range(self.first_page, self.last_page+1):\n\t\t\tprint(\"\\nCrawling Page \" + str(page))\n\t\t\tpage_url = self.site_url + \"?page=\" + str(page) +\\\n\t\t\t \"&index=prod_all_products_term_optimization\"\n\t\t\t\n\t\t\tself.scrape_features(page_url)","def parse_category(self, response):\n detail_links = response.css(\"a.detail-url-click::attr(href)\").extract()\n meta = response.meta\n self.logger.info(\"found %s products from page %s of category %s\",\n len(detail_links), meta[\"page_num\"], meta[\"cate_name\"])\n for link in detail_links:\n if not link:\n continue\n yield response.follow(link, callback=self.parse_detail, meta=meta)\n if detail_links:\n meta = dict(meta)\n meta[\"page_num\"] = int(meta[\"page_num\"]) + 1\n base_url = meta[\"base_url\"]\n link = \"{}---hot-{}.html\".format(base_url, meta[\"page_num\"])\n self.logger.info(\"scheduled scraping request for page %s of category %s\",\n meta[\"page_num\"], meta[\"cate_name\"])\n yield response.follow(link, callback=self.parse_category, meta=meta)","def create_category_pages(app):\n env = app.builder.env\n # jinja2 html template\n template = CATEGORY_PAGE_TEMPLATE\n\n categories = env.categories\n for name, category in categories.iteritems():\n context = {}\n # First write out the named page\n context[\"title\"] = category.name\n\n #get parent category\n if \"\\\\\" in category.name:\n categs = category.name.split(\"\\\\\")\n categs.pop()\n parent_category = r\"\\\\\".join(categs)\n parent_category_link = \"../\" + categs[-1] + \".html\"\n parent_category = \"<b>Category:</b> <a href='{0}'>{1}</a>\"\\\n .format(parent_category_link,parent_category)\n context[\"parentcategory\"] = parent_category\n\n # sort subcategories & pages alphabetically\n context[\"subcategories\"] = sorted(category.subcategories, key = lambda x: x.name)\n context[\"pages\"] = sorted(category.pages, key = lambda x: x.name)\n context[\"outpath\"] = category.html_path\n\n #jinja appends .html to output name\n category_html_path_noext = os.path.splitext(category.html_path)[0]\n yield (category_html_path_noext, context, template)\n\n # Now any additional index pages if required\n if category.name in INDEX_CATEGORIES:\n # index in categories directory\n category_html_dir = os.path.dirname(category.html_path)\n category_html_path_noext = category_html_dir + \"/index\"\n yield (category_html_path_noext, context, template)\n\n # index in document directory\n document_dir = os.path.dirname(category_html_dir)\n category_html_path_noext = document_dir + \"/index\"\n context[\"outpath\"] = category_html_path_noext + \".html\"\n yield (category_html_path_noext, context, template)","def scrapeAndSaveNewsArticles():\n http = urllib3.PoolManager()\n\n # change output document name if needed so already collected articles are not overwritten\n with open('documents/news_documents7.csv', 'w') as fout:\n writer = csv.writer(fout)\n writer.writerow(['category', 'headline', 'authors', 'link', 'short_description', 'date', 'body'])\n\n with open('documents/News_Category_Dataset_v2.json') as f:\n line = f.readline()\n cnt = 0\n while line:\n line = f.readline()\n # if cnt <= 99872:\n # cnt += 1\n # continue\n data = json.loads(line)\n link = data['link']\n try:\n resp = http.request('GET', link)\n # sleep(0.1)\n except urllib3.exceptions.MaxRetryError:\n cnt += 1\n print(\"Bad Link:\", link)\n continue\n\n try:\n textBody = extract(str(resp.data.decode('utf-8')))\n except UnicodeDecodeError:\n cnt += 1\n print(\"Non-HTML link\")\n continue\n\n data[\"body\"] = textBody\n writer.writerow(data.values())\n cnt += 1\n print(cnt)","def insert_products_categories(self):\n logic = ProductCategoryLogic()\n \n # We create the list of product_category objects\n products_categories = self.objects_factory.create_product_category_object_list()\n products_categories = set(products_categories)\n\n for p_c in products_categories:\n logic.insert(p_c)","def get_all_pages_of_category(category_url):\n\n def extract_number(text):\n \"\"\" Return number of pages \"\"\"\n # logging.debug('Paging text: ' + text.strip())\n nums = re.findall('共(\\d+)页', text)\n # logging.debug('nums=' + repr(nums))\n if len(nums) == 0:\n raise Exception('There is no number!')\n return int(nums[0])\n\n ###############################################\n\n soup = get_soup(category_url)\n num_pages = extract_number(soup.find('span', {'class': 'paging'}).text)\n current_page = int(soup.find('span', {'class': 'selpage'}).text)\n logging.debug('This category has %s page(s)' % num_pages)\n logging.debug('You are in page %s' % current_page)\n\n all_pages_url = list()\n splited_url = category_url.split(',')\n part_one = ','.join(splited_url[0:2])\n part_two = ','.join(splited_url[3:])\n url_pattern = part_one + ',%s,' + part_two\n for p_number in range(1, num_pages + 1):\n page_url = url_pattern % p_number\n all_pages_url.append(page_url)\n return all_pages_url","def pass_category(url, name):\n print(f\"Passage de la catégorie {name}...\")\n # Initialize list of books\n books = []\n while url != '':\n response = requests.get(url)\n if response.status_code == 200:\n soup = BeautifulSoup(response.content, 'html.parser')\n # Books are notify with article balise\n articles = soup.findAll('article', {'class': 'product_pod'})\n for article in tqdm(articles):\n # Get the book's link\n link = url + \"/../\" + article.find('h3').find('a')['href']\n # Run the function to get book's info\n dict_product = get_book_infos(link)\n books.append(dict_product)\n # Searching for a next page\n next_page = soup.find('li', {'class': 'next'})\n if not next_page:\n url = ''\n else:\n url = url + \"/../\" + soup.find('li', {'class': 'next'}).find('a')['href']\n write_category(name, books)","def daily_task():\n global CATEGORIES_PAGES\n global BROWSER\n global DATE\n global OBSERVATION\n log.info('Scraper started')\n # Refresh date\n DATE = str(datetime.date.today())\n OBSERVATION = 0\n # Initiate headless web browser\n log.info('Initializing browser')\n BROWSER = webdriver.Chrome(executable_path=PROJECT_PATH + \"/bin/chromedriver\",\n options=OPTIONS)\n # Download topsite and get categories directories\n base_file_name = \"All_cat_\" + DATE + \".html\"\n fetch_html(BASE_URL, base_file_name, PATH_HTML, attempts_limit=1000)\n html_file = open(PATH_HTML + base_file_name).read()\n CATEGORIES_PAGES = get_category_list(html_file)\n log.info('Found ' + str(len(CATEGORIES_PAGES)) + ' categories')\n # Read each categories pages and scrape for data\n for cat in track(CATEGORIES_PAGES,\n description = \"[green]Scraping...\",\n total = len(CATEGORIES_PAGES)):\n cat_file = \"cat_\" + cat['name'] + \"_\" + DATE + \".html\"\n download = fetch_html(cat['directlink'], cat_file, PATH_HTML)\n if download:\n scrap_data(cat)\n # Close browser\n BROWSER.close()\n BROWSER.service.process.send_signal(signal.SIGTERM)\n BROWSER.quit()","def crawler(website_url):\n\n try:\n\n # open and read the website\n pageFile = urllib2.urlopen(website_url)\n pageHtml = pageFile.read()\n pageFile.close()\n\n # call BeautifulSoup on an array of lines in string format\n soup = BeautifulSoup(\"\".join(pageHtml), \"html.parser\")\n # print soup.prettify()[0:1000]\n\n # find all links with hashtag cat, limit to 100 results\n # FIXME add hashtag cat requirement (string = \"#cat\")\n pageLinks = soup.findAll(\"a\", {\"href\": True}, limit=100)\n # import pdb; pdb.set_trace()\n page_URLs = []\n\n for pageLink in pageLinks:\n pageLink = pageLink['href']\n\n # if URL does not have a domain, add the main page's domain'\n if pageLink[0] == '/' and pageLink[:1] != '//':\n pageLink = website_url + pageLink\n\n # check if item in db, if not - add to db and commit\n existing_page = session.query(Page).filter_by(page_URL=pageLink).first()\n\n # add to array of link strings\n page_URLs.append(pageLink)\n\n if not existing_page:\n page_URL = Page(page_URL=pageLink)\n session.add(page_URL)\n session.commit()\n\n # import pdb; pdb.set_trace()\n return page_URLs\n\n except urllib2.URLError as e:\n # exception handling for URLError\n if hasattr(e, 'reason'):\n print \"We failed to reach a server.\"\n print \"Reason: \", e.reason\n # exception handling for HTTPError\n elif hasattr(e, 'code'):\n print 'The server couldn\\'t fulfill the request.'\n print 'Error code; ', e.code\n else:\n print 'Everything is fine.'","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def get_products(categories_info):\n\n for name, urlnames in categories_info.items():\n for i in range(1, (NB_PAGES+1)):\n url = 'https://world.openfoodfacts.org/cgi/search.pl?\\\n search_tag=categories&search_terms={}&\\\n purchase_places=France&page_size={}&page={}&json=1'.format(\n urlnames, str(PRODUCTS_PER_PAGE), str(i))\n data = requests.get(url).json()\n name_file = 'data/Products_' + name + str(i) + '.json'\n with open(name_file, 'w') as file:\n file.write(json.dumps(data, indent=4))","def features_from_category(cat_id):\n\n try:\n playlists_uris = get_playlists_from(cat_id)\n artist_uris_total = []\n for playlist_uri in playlists_uris:\n artist_uris = get_artists_from(playlist_uri)\n artist_uris_total.append(artist for artist in artist_uris)\n albums_uris_total = []\n for artist_uri in artist_uris_total:\n album_uris = get_albums_from(artist_uri)\n albums_uris_total.append(album for album in album_uris)\n # from a list of albums get tracks\n track_uris_total = []\n for albums_uri in albums_uris_total:\n tracks_uris = get_tracks_from(albums_uri)\n track_uris_total.append(track for track in tracks_uris)\n for track_uri in track_uris_total:\n features_to_db(track_uri)\n\n except Exception as e:\n print(\"Error processing {}: {}\".format(cat_id, e))\n raise e\n\n else:\n DB.session.commit()","def get_list_products_for_category(self, category_url, location = {}, save = False):\n\t\t# Setting location, getting category form datastore & initializing products\n\t\tis_LAD, code = self.set_location(location)\n\n\t\tif is_LAD and code == 200:\n\t\t\tstore = self.databaseHelper.get_store(location)\n\t\telse:\n\t\t\tstore = None\n\n\t\t# Category\n\t\tcategories = self.databaseHelper.get_categories({'url': category_url})\n\t\tif len(categories)>0:\n\t\t\tcategory = categories[0]['db_entity']\n\t\telse:\n\t\t\tcategory = None\n\t\tproducts = []\n\n\t\t# Getting html\n\t\thtml, code = self.crawler.get(category_url)\n\t\t\n\n\t\tif code == 200:\n\t\t\tself.parser.set_html(html)\n\t\t\ttry:\n\t\t\t\tbrands = self.parser.get_brands_values_in_category_page()\n\n\t\t\t\tfor i in xrange(0,len(brands)):\n\t\t\t\t\tbrand = brands[i]\n\t\t\t\t\tfetched_products = []\n\t\t\t\t\thtml, code = self.crawler.brand_filter(brand['value'])\n\n\t\t\t\t\tif code == 200:\n\t\t\t\t\t\tself.parser.set_html(html)\n\t\t\t\t\t\t# are all the products on in this page ?\n\t\t\t\t\t\tcurrent_page_all_products, url_all_products = self.parser.pagination_link_all()\n\t\t\t\t\t\tif not current_page_all_products:\n\t\t\t\t\t\t\tfetched_products = self.parser.get_products()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\thtml, code = self.crawler.get(url_all_products)\n\t\t\t\t\t\t\tif code == 200:\n\t\t\t\t\t\t\t\thtml, code = self.crawler.brand_filter(brand['value'])\n\t\t\t\t\t\t\t\tif code == 200:\n\t\t\t\t\t\t\t\t\tself.parser.set_html(html)\n\t\t\t\t\t\t\t\t\tfetched_products = self.parser.get_products()\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tprint \"Something went wrong when fetching category page for Monoprix : code %d\"%(code)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tprint \"Something went wrong when fetching category page for Monoprix : code %d\"%(code)\n\t\t\t\t\telse:\n\t\t\t\t\t\tprint \"Something went wrong when fetching category page (%d) for Monoprix : code %d\"%(i+1,code)\n\t\t\t\t\t# Setting brand name to products\n\t\t\t\t\tfor j in xrange(0, len(fetched_products)):\n\t\t\t\t\t\tfetched_products[j]['parent_brand'] = brand['name']\n\t\t\t\t\tproducts = products + fetched_products\n\t\t\texcept Exception, e:\n\t\t\t\tprint e\n\t\telse:\n\t\t\tprint \"Something went wrong when fetching category page for Monoprix : code %d\"%(code)\n\n\t\t# Cleaning urls\n\t\tproducts = self.clean_urls_in_products(products)\n\n\t\t# Saing products\n\t\tif save:\n\t\t\tif category and len(products) == 0:\n\t\t\t\t# Category does not exist anymore\n\t\t\t\tself.databaseHelper.shut_down_category(category)\n\t\t\telif category:\n\t\t\t\tself.databaseHelper.save_products(products, category.id, store)\n\t\t\telse:\n\t\t\t\tself.databaseHelper.save_products(products, None, store)\n\t\telse:\n\t\t\treturn products","def generate_courses():\r\n for category in CourseCategory.objects.all():\r\n Course.objects.create(name=category.name, category=category, is_active=True,\r\n is_featured=True)","def add_aliments(self):\n for cat in Category.objects.all():\n print(CAT_SEARCH.format(cat.name))\n n_page = 1\n counter = 0\n while counter < MAX_PRODUCTS_KEEPED:\n imported_aliments = self._scratch_category(cat.name, n_page)\n if not imported_aliments:\n print(CAT_FINDING_FAIL.format(counter, cat.name))\n break\n\n for aliment in imported_aliments:\n if self._no_full_info(aliment):\n continue\n if self._redundant_info(aliment, cat):\n continue\n\n counter = self._save(aliment, cat, counter)\n n_page += 1\n\n print(CAT_FINDING_SUCCESS.format(cat))","def get_all_categories_from_collection():\n api_endpoint = URL\n response = requests.get(api_endpoint)\n return response","def crawl_data(self, data_type):\n i = 0\n cat = {}\n prod = {}\n for term in self.search_response:\n if data_type == 'category' and term['products'] > 6000:\n i += 1\n cat[i] = {\"name\": term['name']}\n self.categories.append(cat[i])\n elif data_type == 'product':\n nutrigrade = \"\".join(term[\"nutrition_grades_tags\"])\n if nutrigrade in (\"a\", \"b\", \"c\", \"d\", \"e\"):\n i += 1\n prod[i] = {\"name\": term['product_name_fr'], \"url\": term['url'], \"desc\": term['generic_name_fr'],\n \"brand\": term['brands'], \"categories\": term['categories'], \"store\": term['stores'],\n \"nutriscore\": nutrigrade}\n self.products.append(prod[i])\n else:\n pass","def load_categories(request: HttpRequest) -> JsonResponse:\n result = {'categories': []}\n categories = Category.objects.all().order_by('name')\n\n for category in categories:\n sources = category.source_set.all().order_by('name')\n result['categories'].append({\n 'name': category.name,\n 'sources': []\n })\n for source in sources:\n unread_count = source.feed_set.filter(checked=False).count()\n result['categories'][-1]['sources'].append({\n 'name': source.name,\n 'unread_count': unread_count\n })\n\n result['status_code'] = 200\n return JsonResponse(result)","def run(self):\n\n # creation of directory named after the category name if it doesn't already exist\n if not os.path.exists(\"results/{}/\".format(self.category_name)):\n os.mkdir(\"results/{}\".format(self.category_name))\n\n # creation of books_img directory if it doesn't already exist\n if not os.path.exists(\"results/{}/books_img/\".format(self.category_name)):\n os.mkdir(\"results/{}/books_img\".format(self.category_name))\n\n with open(\"results/{}/{}.csv\".format(self.category_name, self.category_name), \"w\") as file:\n file.write(self.columns_headers)\n\n for book_page_url in self.url_list:\n\n # data extraction from books pages\n\n book_request = requests.get(book_page_url)\n book_request.encoding = \"utf-8\"\n\n if book_request.ok:\n book_soup = BeautifulSoup(book_request.text, \"html.parser\")\n book_title = book_soup.find(\"h1\").get_text()\n rate_and_product_description = book_soup.findAll(\"p\")\n rate = rate_and_product_description[2][\"class\"][1]\n rate = self.rate_dict[rate]\n product_description = rate_and_product_description[3].get_text()\n # We replace \";\" with \":\" in product_description\n product_description = self.regex_wrong_separator.sub(\":\", product_description)\n img_url = book_soup.find(\"img\")\n img_url = img_url[\"src\"].replace(\"../../\", self.target_url)\n information_table = book_soup.findAll(\"td\")\n\n # list which contains all data about the book (sorted in order)\n info_list = [book_page_url]\n\n # loop which gets data from the information tab and adds them in the info_list\n for information_line in information_table:\n information_line = information_line.get_text()\n info_list.append(information_line)\n # remove the field \"Books\"\n del info_list[2]\n # remove the field \"tax\"\n del info_list[4]\n info_list.insert(2, \"{}\".format(book_title))\n info_list.insert(6, product_description)\n info_list.insert(7, self.category_name)\n info_list[8] = rate\n info_list.append(img_url)\n\n # extraction of the number of books available\n info_list[5] = self.regex_extract_number.findall(info_list[5])[0]\n # convert info_list in str using \";\" as separator before writing it in cvs file\n info = \";\".join(info_list)\n\n file.write(\"\\n\" + info)\n\n # We replace \"/\" by \"-\" in book title\n book_title = self.regex_title_correction.sub(\"-\", book_title)\n urllib.request.urlretrieve(img_url, \"results/{}/books_img/{}.png\".format(\n self.category_name, book_title))","def getproductbycategory(self):\n\n eight = self.geteightcategories()\n productsliste = []\n for category in eight:\n for page in tqdm(range(1, 5)):\n r2 = requests.get(\n \"https://fr.openfoodfacts.org/cgi/search.pl?action=process\"\n + \"&tagtype_0=categories&tag_contains_0=contains&tag_0={}\".format(\n category\n )\n + \"&tag_contains_1=contains&tag_1=france&page_size=500\"\n + \"&fields=url,categories_tags_fr,product_name,stores_tags\"\n + \",nutriscore_grade&tagtype_1=purchase_places&sort_by=\"\n + \"unique_scans_n&json=1&page={}\".format(page)\n )\n dataproducts = r2.json()\n if dataproducts[\"page_count\"] is None:\n continue\n else:\n for items in dataproducts[\"products\"]:\n productsliste.append(items)\n\n self.rawproductdata = productsliste","def get_all_news_articles(categories):\n all_inshorts = []\n\n for category in categories:\n all_category_articles = get_articles(category)\n all_inshorts = all_inshorts + all_category_articles\n\n df = pd.DataFrame(all_inshorts)\n return df","def crawl(self):\n try:\n self.crawl_pages()\n self.crawl_posts()\n self.crawl_comments()\n except Exception as exception:\n self.handle_request_limit(exception)","async def initialiser_crawler(self) -> Dict[str, List[req.Response]]:\n web_pages = {}\n with ThreadPoolExecutor(max_workers=NUM_WORKERS) as exe:\n try:\n loop = asyncio.get_event_loop()\n tasks = [\n loop.run_in_executor(exe, self.collect_webpages, keyword)\n for keyword in self.keywords \n ]\n for res in await asyncio.gather(*tasks):\n web_pages.update(res)\n except KeyboardInterrupt:\n loop.close()\n raise KeyboardInterrupt\n return web_pages","def reagent_label_categories(ctx: click.Context, categories: list = None):\n adapter = ctx.obj[\"adapter\"]\n lims = ctx.obj[\"lims\"]\n if not lims:\n LOG.warning(\"Lims connection failed.\")\n raise click.Abort()\n lims = lims\n load_all(adapter=adapter, lims=lims, categories=categories)","def parse_categories(categories, categories_list, reports):\n remove_list = [\"overview\", \"configuration\", \"use_cases\", \"content\"]\n for distinct_category in categories:\n report_categories = []\n category = {}\n for report_doc in reports:\n if distinct_category in report_doc[\"report-categories\"]:\n category[\"category\"] = distinct_category\n category[\"description\"] = \"\"\n for x in remove_list:\n if x in report_doc:\n del report_doc[x]\n report_categories.append(report_doc)\n category[\"reports\"] = report_categories\n categories_list.append(category)","def extract_etsy_items(number_of_items: int, category: str) -> pd.DataFrame:\n print(f\"Scrapping category \\\"{category}\\\"\")\n result = []\n page_number = 1\n headers = {\"User-Agent\": \"Mozilla/5.0\"}\n\n while len(result) < number_of_items:\n time.sleep(2)\n url = f\"https://www.etsy.com/search?q={make_url_compatible(category)}\" \\\n f\"&ref=pagination&page={page_number} \"\n\n source = requests.get(url, headers=headers)\n soup = BeautifulSoup(source.content, \"html.parser\")\n\n items = soup.select(\"li.wt-list-unstyled\")\n if len(items) == 0:\n print(f'Total items of {category} was found less than requested: '\n f'{len(result)}')\n break\n\n for item in items:\n if len(result) >= number_of_items:\n break\n\n record = extract_item_data(item)\n if record:\n result.append(record)\n\n page_number += 1\n\n return pd.DataFrame(result)","def main():\n base_url = \"https://books.toscrape.com/index.html\"\n category_links = scrap_site_category_links(base_url)\n\n\n for category_link in category_links:\n book_links = scrap_book_links(category_link)\n\n headings = [\"product_page_url\", \"upc\", \"title\", \"price_including_tax\", \"price_excluding_tax\",\n \"number_available\", \"product_description\", \"category\", \"review_rating\", \"image_url\"]\n\n with open(get_category_name(category_link) + \".csv\", 'w', encoding=\"utf-8-sig\") as csv_file:\n writer = csv.DictWriter(csv_file, delimiter=';', fieldnames=headings)\n writer.writeheader()\n\n for book_link in book_links:\n book = scrap_book_info(book_link)\n writer.writerow(book)\n\n image_downloader(book[\"image_url\"], \"./Books_to_Scrape_Images\", book[\"upc\"] + \".png\")","def insert_data(self, data):\n category_name = data[\"substitutes\"][0][\"categories\"][0]\n products = data[\"substitutes\"]\n if len(products) != 0:\n i = 0\n Category.objects.get_or_create(name=category_name)\n category = Category.objects.get(name=category_name)\n for product in products:\n i += 1\n if Product.objects.filter(name=product[\"name\"]).exists():\n pass\n else:\n product[i] = Product.objects.get_or_create(name=product[\"name\"],\n desc=product[\"desc\"],\n photo=product[\"photo\"],\n API_link=product[\"API_link\"],\n nutriscore=product[\"nutriscore\"],\n nutrient_100g=product[\"nutrient_100g\"],\n )\n\n Product.objects.get(name=product[\"name\"]).categories.add(category.id)\n else:\n return None","def loadData(catalog):\n loadVideos(catalog)\n loadCategories(catalog)","def _scratch_category(self, cat, n_page):\n payload = {\n \"action\": \"process\",\n \"tagtype_0\": \"categories\",\n \"tag_contains_0\": \"contains\",\n \"tag_0\": cat,\n \"tagtype_1\": \"nutrition_grade\",\n \"tag_contains_1\": \"contains\",\n \"fields\": \",\".join(PRODUCTS_FIELDS),\n \"page_size\": PAGE_SIZE,\n \"page\": n_page,\n \"json\": \"true\",\n }\n\n al_list = get(OFF_URL, params=payload).json()\n return al_list[\"products\"]","def test_get_categories(self, mocker):\n mock = mocker.patch(\"requests_html.HTMLSession\")\n mock.return_value.get.return_value.html.find.return_value = iter(\n [\n mocker.Mock(text=\"Ammo\", attrs={\"href\": \"catalogue?cat=1\"}),\n mocker.Mock(text=\"Food\", attrs={\"href\": \"catalogue?cat=2\"}),\n mocker.Mock(text=\"Armour\", attrs={\"href\": \"catalogue?cat=3\"}),\n mocker.Mock(text=\"Weapons\", attrs={\"href\": \"catalogue?cat=4\"}),\n ]\n )\n\n result = resources.get_categories()\n assert list(result) == [\n (1, \"Ammo\"),\n (2, \"Food\"),\n (3, \"Armour\"),\n (4, \"Weapons\"),\n ]","async def seed_db():\n LOGGER.debug(\"Started script for seeding database...\")\n conn = await _get_db_connection()\n\n categories_status, categories = await conn.status(conn.text(insert_mcc_categories))\n LOGGER.debug(\"Status of inserting mcc categories: %s:\", categories_status)\n for category in categories:\n LOGGER.debug(\"\\t- %s\", category)\n\n mcc_status, mccs = await conn.status(conn.text(insert_mccs))\n LOGGER.debug(\"Status of inserting mccs: %s.\", mcc_status)\n for mcc in mccs:\n LOGGER.debug(\"\\t- %s\", mcc)\n\n LOGGER.debug(\"Finished script for seeding database.\")","def GetCategories():\n return GetDataFromCsvFile('categories.csv')","def load_data(self):\n if not os.path.isfile(\"{}/OFF_data.json\".format(settings.DIR_PATH)):\n self.request_constructor(settings.R_COLLECTION['category'], 'NULL', 'tags')\n self.crawl_data('category')\n i = 0\n for item in self.categories:\n i += 1\n cat = item.get(\"name\")\n self.request_constructor(settings.R_COLLECTION['product'], cat, 'products')\n self.crawl_data('product')\n\n self.data = {\"categories\": self.categories, \"products\": self.products}\n self.save_data('OFF_data.json')\n else:\n with open(\"{}/OFF_data.json\".format(settings.DIR_PATH), 'r') as f:\n self.data = json.load(f)\n self.categories = self.data[\"categories\"]\n self.products = self.data[\"products\"]\n return self.categories, self.products","def get_used():\r\n sql = text('''\r\n SELECT category.* FROM category, app\r\n WHERE app.category_id=category.id GROUP BY category.id\r\n ''')\r\n results = db.engine.execute(sql)\r\n categories = []\r\n for row in results:\r\n category = dict(id=row.id, name=row.name, short_name=row.short_name,\r\n description=row.description)\r\n categories.append(category)\r\n return categories","def mor_prepare_data():\n prices, locations, areas, links = [], [], [], []\n for i in range(START_PAGE, SEARCHING_DEPTH+1):\n handler = requests.get(main_url, params={\"page\": str(i)})\n soup = bs4.BeautifulSoup(handler.text, 'lxml')\n heads = soup.find_all(\"header\")\n once = True\n for head in heads:\n if head.find(\"meta\", {\"itemprop\": \"category\"}) and once:\n\n raw_price = head.find(\"meta\", {\"itemprop\": \"price\"})\n price = int(float(raw_price[\"content\"]) if raw_price else \"\")\n\n raw_loc_list = head.find(\"h2\",\n {\"class\": \"single-result__title\"}).getText().strip().split(\n \", \")\n found = False\n for loc in raw_loc_list:\n if location_mapper[CITY].get(loc.lower(), 0):\n location = location_mapper[CITY][loc.lower()]\n\n found = True\n break\n if not found:\n location = \"\"\n if DEBUG_MODE:\n print(raw_loc_list)\n\n raw_area = head.find(\"p\", {\n \"class\": \"single-result__price single-result__price--currency\"}).getText().strip().split()\n if price and location:\n square_price = raw_area[0] if len(raw_area) == 2 else \"\".join(\n (raw_area[0], raw_area[1]))\n\n area = int(price / float(square_price.replace(\",\", \".\")))\n link_url = head.find('a')['href']\n\n if location and area and link_url:\n prices.append(price) if price < PRICE_UPPER_LIMIT else prices.append(\n PRICE_UPPER_LIMIT)\n locations.append(location)\n areas.append(area) if area < AREA_UPPER_LIMIT else areas.append(\n AREA_UPPER_LIMIT)\n links.append(link_url)\n\n return prices, locations, areas, links","def get_categories():\n\n url = 'https://fr.openfoodfacts.org/categories.json'\n data = requests.get(url).json()\n with open('data/categories.json', 'w') as file:\n file.write(json.dumps(data, indent=4))","def get_course_all_slugs(self):\n\n unit_lessons_counter = 0\n # Unit Page -> Subunit Header + Subunit Block -> Lesson Block -> Lesson Title\n for course_unit_url, course_unit_slug in zip(\n self.course_unit_urls, self.course_unit_slugs\n ):\n\n unit_lessons_counter = 0\n # -> Unit Page\n try:\n course_unit_page = BeautifulSoup(\n requests.get(ROOT_URL + course_unit_url).text, \"lxml\"\n )\n except requests.ConnectionError as e:\n print(\"Error Connecting!\\n\", e)\n sys.exit(1)\n except requests.exceptions.HTTPError as errh:\n print(\"Http Error:\", errh)\n sys.exit(1)\n except requests.exceptions.ConnectionError as errc:\n print(\"Error Connecting:\", errc)\n sys.exit(1)\n except requests.exceptions.Timeout as errt:\n print(\"Timeout Error:\", errt)\n sys.exit(1)\n except requests.exceptions.RequestException as err:\n print(\"OOps: Something Else\", err)\n sys.exit(1)\n\n subunit_couter = 0\n\n # -> Subunit Header -> Subunit Block\n for course_subunit_title, course_subunit_body in zip(\n course_unit_page.find_all(attrs=COURSE_SUBUNIT_TITLE_ATTRS),\n course_unit_page.find_all(\n COURSE_SUBUNIT_BODY[\"tag\"], class_=COURSE_SUBUNIT_BODY[\"class\"]\n ),\n ):\n\n logging.debug(\"course_subunit_title:{}\".format(course_subunit_title))\n lesson_counter = 0\n # -> Lesson Block\n for course_lesson_body in course_subunit_body.find_all(\n COURSE_LESSON_BODY[\"tag\"],\n {\n \"class\": [\n COURSE_LESSON_BODY[\"class_i\"],\n COURSE_LESSON_BODY[\"class_ii\"],\n ]\n },\n ):\n course_lesson_span = course_lesson_body.find_all(\n COURSE_LESSON_SPAN[\"tag\"], class_=COURSE_LESSON_SPAN[\"class\"]\n )\n course_lesson_aria_label = course_lesson_span[0][\n COURSE_LESSON_LABEL\n ]\n logging.debug(\n \"course_lesson_aria_label:{}\".format(course_lesson_aria_label)\n )\n # -> Lesson Title\n # Check whether lesson block is a video\n if course_lesson_aria_label == \"Video\":\n lesson_title = course_lesson_body.find(\n COURSE_LESSON_TITLE[\"tag\"],\n class_=COURSE_LESSON_TITLE[\"class\"],\n )\n\n logging.debug(\n \"course_lesson_title:{}\".format(lesson_title.text)\n )\n self.lesson_titles.append(lesson_title.text)\n self.course_all_slugs.append(\n self.output_rel_path\n + course_unit_slug\n + \"/\"\n + str(subunit_couter)\n + \"_\"\n + course_subunit_title.text.replace(\" \", \"_\")\n + \"/\"\n + str(lesson_counter)\n + \"_\"\n + lesson_title.text.replace(\" \", \"_\")\n )\n\n lesson_counter += 1\n unit_lessons_counter += lesson_counter\n subunit_couter += 1\n self.unit_slugs_counter[course_unit_url] = unit_lessons_counter\n logging.info(\"Course - All slugs generated\")","def get_categories():\n return session.query(Category)","def get_category_list(top_html):\n page_list = []\n toppage_soup = BeautifulSoup(top_html, \"lxml\")\n categories = toppage_soup.find('ul', {'class': 'colmenu-ul'})\n categories = categories.findAll(\"li\")\n categories_tag = [cat.findAll('a') for cat in categories]\n categories_tag = [item for sublist in categories_tag for item in sublist]\n for cat in categories_tag:\n page = {}\n link = re.sub(\".+bachhoaxanh\\\\.com/\", \"\", cat['href'])\n page['relativelink'] = link\n page['directlink'] = BASE_URL + link\n page['name'] = re.sub(\"/|\\\\?.=\", \"_\", link)\n page['label'] = cat.text.strip()\n page_list.append(page)\n # Remove duplicates\n page_list = [dict(t) for t in set(tuple(i.items()) for i in page_list)]\n return(page_list)","def get_category_recipe(filters, db_conn, host_url):\n try:\n result = db_conn[\"recipes\"].find(\n filters).sort(\"createdOn\", -1).limit(9)\n recipe_list = map_response(result, host_url)\n\n return recipe_list\n\n except Exception as e:\n print(e)\n return {\"success\": False, \"message\": \"Error in api: \" + str(e)}","def process_data(message_file_name, category_file_name, db_file_name):\n # step 1: merge two csv files into one data frame\n messages = pd.read_csv(message_file_name)\n categories = pd.read_csv(category_file_name)\n df = messages.merge(categories, on='id')\n\n # step 2: reorganize the categories column\n categories = df['categories'].str.split(pat=';', expand=True)\n row = categories.iloc[0]\n category_colnames = [category_name.split('-')[0] for category_name in row]\n categories.columns = category_colnames\n for column in categories:\n categories[column] = categories[column].astype(str).str[-1]\n categories[column] = categories[column].astype(int)\n\n for index, row in categories.iterrows():\n if row['related']>1:\n row['related']=1\n\n new_df = df.drop(['categories'], axis=1)\n df = pd.concat([new_df, categories], axis=1)\n\n # step 3: remove duplicate records\n duplicated_record_num = df.duplicated().sum()\n cleaned_df = df.drop_duplicates()\n assert cleaned_df.duplicated().sum() == 0\n\n # step 4: generate the database\n table_name = \"disaster_response_table\"\n engine = create_engine('sqlite:///' + db_file_name)\n cleaned_df.to_sql(table_name, engine, index=False, if_exists='replace')","def get_all_categories(type=None):\n \n categories = None\n if type is None:\n categories = get_db().execute('SELECT * FROM categories').fetchall()\n else:\n categories = get_db().execute('SELECT * FROM categories WHERE type = ?', (type,)).fetchall()\n\n return categories","def categories(self, categories: List[str]):\n\n self._categories = categories","def fill_in(self,category):\r\n api_json = JsonFromApi(category)\r\n extracted_data = ExtractFromJson(api_json.get_json())\r\n self.fill_in_db = DatabaseUpdator(\r\n extracted_data.extract_json(), self.mydb)\r\n self.fill_in_db.table_product_update()\r\n self.fill_in_db.table_category_update()","def _verify_basic_categories(self):\n for cat in CATEGORIES:\n if not Category.objects.filter(name=cat).exists():\n self.add_category(cat)","def parse(self, response):\n s1 = set(response.xpath('//div[@id=\"leftNav\"]//a/@href').extract())\n s2 = set(response.xpath('//div[@class=\"a-section acs_dNav__carousels-container\"]//a/@href').extract())\n s3 = set(response.xpath('//span[@class=\"category-link__text\"]/../@href').extract())\n subcategories = s1 | s2 | s3\n for subcat in subcategories:\n url = format_url(self.domain + subcat)\n self.conn.sadd(self.urlFrontierKey, url)\n if subcat in s3:\n yield scrapy.Request(url, callback=self.parse)\n self.logger.info(\"category link {} yielded {} sub categories\".format(response.url, len(subcategories)))","def insert_or_refresh_categories(conn: sqlite3.Connection, api_auth,\n refresh: bool = True):\n categories = youtube.get_categories(api_auth)\n query_string = generate_insert_query('categories',\n columns=CATEGORIES_COLUMNS,\n on_conflict_ignore=True)\n if refresh or not execute_query(conn, 'SELECT * FROM categories'):\n if execute_query(conn, 'DELETE FROM categories WHERE id IS NOT NULL;'):\n for category_dict in categories['items']:\n id_ = category_dict['id']\n channel_id = category_dict['snippet']['channelId']\n title = category_dict['snippet']['title']\n assignable = str(category_dict['snippet']['assignable'])\n etag = category_dict['etag']\n execute_query(conn, query_string, (id_, channel_id, title,\n assignable, etag))\n conn.commit()","def cat_parser(self, response):\n categories = []\n for product in response:\n # crawling categories of each product\n prod_cat = product[\"categories\"].split(\", \")\n for cat in prod_cat:\n if cat not in categories:\n categories.append(cat)\n return categories","def collect_all(self, samples: int, cities: list, country:str) -> None:\n\n time_start = time.time()\n for city in cities:\n self.collect_city_items(samples,city,country)\n print(f\"All scraping is done! Time elapsed: {time.time()-time_start} seconds.\")","def create_category_pages(app):\n env = app.builder.env\n\n template = \"category.html\"\n\n categories = env.categories\n for name, category in categories.iteritems():\n context = {}\n context[\"title\"] = category.name\n context[\"subcategories\"] = category.subcategories\n context[\"pages\"] = category.pages\n\n yield (name, context, template)"],"string":"[\n \"def scrapeCategories():\\n page = requests.get(\\\"http://books.toscrape.com/index.html\\\")\\n soup = BeautifulSoup(page.content, 'html.parser')\\n content = soup.find(\\\"div\\\", {\\\"class\\\": \\\"side_categories\\\"}).findChild().find(\\\"ul\\\")\\n categories = content.find_all(\\\"a\\\")\\n for category in categories:\\n Categories.objects.create(name=category.text.strip())\\n return HttpResponse(status=204)\",\n \"def fill_category(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n result = requests.get('https://fr.openfoodfacts.org/categories.json').json()\\n for element in result['tags']:\\n try:\\n cursor.execute(\\\"INSERT INTO category (tag, name, url) VALUES (%s, %s, %s) RETURNING id, tag\\\",\\n (element[\\\"id\\\"], element[\\\"name\\\"], element[\\\"url\\\"]))\\n query_result = cursor.fetchone()\\n categories.__setitem__(query_result[1], query_result[0])\\n except self.conn.OperationalError:\\n print(\\\"operation Error\\\")\\n except self.conn.DataError:\\n print(\\\"Data Error\\\")\\n self.conn.commit()\\n cursor.close()\\n return categories\",\n \"def fill_products(self):\\n cursor = self.conn.cursor(cursor_factory=psycopg2.extras.DictCursor)\\n categories = dict()\\n for page in range(1, 2):\\n result = requests.get(\\n 'https://fr.openfoodfacts.org/cgi/search.pl?page_size=1000&page={}&action=process&json=1'.format(\\n page)).json()\\n for element in result['products']:\\n try:\\n cursor.execute(\\n \\\"INSERT INTO product (name, store, nutrition_grade, url) VALUES (%s, %s, %s, %s) RETURNING \\\"\\n \\\"id, name\\\",\\n (element[\\\"product_name\\\"], element[\\\"stores\\\"], element[\\\"nutrition_grade_fr\\\"], element[\\\"url\\\"]))\\n # un except pour éviter les erreurs de clés\\n query_result = cursor.fetchone()\\n for category in element[\\\"categories_tags\\\"]:\\n try:\\n cursor.execute(\\\"INSERT INTO product_category(product_id, category_id) VALUES (%s, %s)\\\",\\n (query_result[0], self.categories[category]))\\n except KeyError:\\n print(\\\"Categorie insertion failed\\\")\\n\\n print(element[\\\"product_name\\\"])\\n except KeyError:\\n print(f'product insertion failed:')\\n\\n self.conn.commit()\\n cursor.close()\",\n \"def OLD_get_items_from_category(save_db=False):\\n # for i in range(16):\\n # name = pd.read_sql_query(\\n # f'SELECT name FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # cat_url = pd.read_sql_query(\\n # f'SELECT url FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # # cat_id = pd.read_sql_query(\\n # # f'SELECT id FROM categories LIMIT 1 OFFSET {i}', conn).values[0][0]\\n # cat_id = i + 1\\n # print(cat_id)\\n # \\\"\\\"\\\" pandas return DataFrame, use DataFrame.values to return list of list value in that DataFrame\\n # url: https://tiki.vn/dien-thoai-may-tinh-bang/c1789?src=c.1789.hamburger_menu_fly_out_banner&page=2\\n # \\\"\\\"\\\"\\n # for i in range(100):\\n # url = cat_url + f'&page={i+1}'\\n # print(url)\\n # soup = get_url(url)\\n\\n # result = []\\n # \\\"\\\"\\\" item: div 'product-item' > div 'content'\\n # img: img 'product-imgage'\\n # title: p 'title'\\n # price: span 'price-regular'\\n # sale-tag: span 'sale-tag'\\n # final-price: span 'final-price'\\n # \\\"\\\"\\\"\\n # div_container = soup.find_all('div', {'class': 'product-item'})\\n # item_path = div_container['data-category']\\n # if div_container:\\n # for div in div_container:\\n # # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\\n # item_id = None\\n # item_name = div.a['title']\\n # brand = div['data-brand']\\n # item_url = div.a['href']\\n # img_url = div.img['src']\\n # regular_price = div.find('span', {'class': 'price-regular'}).text\\n # sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\\n # final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\\n\\n # item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\\n # img_url, regular_price, sale_tag, final_price)\\n # if save_db:\\n # item.save_into_db()\\n # print(f'SAVE {item_name} INTO DTB')\\n # result.append(item)\\n # else:\\n # break\",\n \"def get_items_from_category(save_db=False):\\n query_result = pd.read_sql_query(\\\"\\\"\\\"SELECT c1.id, c1.name, c1.parent_id, c1.url\\n FROM categories c1 LEFT OUTER JOIN categories c2\\n ON c1.id = c2.parent_id\\n WHERE c2.parent_id IS NULL\\n LIMIT 1400 OFFSET 1300\\\"\\\"\\\", conn)\\n for i in query_result.itertuples():\\n name = i.name[:-10].strip()\\n cat_url = i.url\\n cat_id = i.id\\n quantity = i.name[-10:].strip()\\n \\n for i in range(100):\\n url = cat_url + f'&page={i+1}'\\n print(url)\\n soup = get_url(url)\\n \\n result = []\\n \\\"\\\"\\\" item: div 'product-item' > div 'content'\\n img: img 'product-imgage'\\n title: p 'title'\\n price: span 'price-regular'\\n sale-tag: span 'sale-tag'\\n final-price: span 'final-price'\\n \\\"\\\"\\\"\\n try:\\n div_container = soup.find_all('div', {'class': 'product-item'})\\n except Exception as err:\\n print('ERROR BY DIV FINDALL: ', err)\\n if div_container:\\n for div in div_container:\\n # it = {'item_id':'','name':'', 'brand':'', 'url':'', 'img_url':'', 'price':'', 'sale-tag':'', 'final-price':''}\\n item_id = None\\n item_path = div['data-category']\\n item_name = div.a['title']\\n brand = div['data-brand']\\n item_url = div.a['href']\\n img_url = div.img['src']\\n regular_price = div.find('span', {'class': 'price-regular'}).text\\n sale_tag = div.find('span', {'class': 'final-price'}).text[-5:-1]\\n final_price = div.find('span', {'class': 'final-price'}).text[:-5].strip()\\n\\n item = Items(item_id, item_path, cat_id, item_name, brand, item_url,\\n img_url, regular_price, sale_tag, final_price)\\n if save_db:\\n item.save_into_db()\\n print(f'SAVE {item_name} INTO DTB')\\n result.append(item)\\n else:\\n break\",\n \"def _traverse_categories():\\n \\n csv_output_dict = _get_csv_data()\\n print csv_output_dict.keys()\\n #~ if csv_output_dict:\\n #~ return csv_output_dict\\n #~ else:\\n final_output_url_list = []\\n traverse_cat_output = {}\\n # brand_seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-brand-all.w'\\n seed_page = 'https://www.broderbros.com/cgi-bin/online/webshr/browse-category-all.w'\\n \\n browser = utils.create_browser(SLEEP_MIN, SLEEP_MAX)\\n browser.open(seed_page)\\n html_response = browser.response().read()\\n \\n soup = BeautifulSoup(html_response)\\n \\n all_category_names = soup.findAll('div', {'class': 'brwsBrndLogoClip'})\\n for category_names in all_category_names:\\n temp_category_url = str(category_names.a['href'])\\n category_url_name = temp_category_url.split('&catname=')[-1]\\n category_url = 'https://www.broderbros.com/cgi-bin/online/webshr/search-result.w?nResults=5000&mc=&RequestAction=advisor&RequestData=CA_CategoryExpand&bpath=c&CatPath=All Products////BRO-Categories////'+str(category_url_name)\\n category_url = category_url.replace(' ', '%20')\\n category_names = category_names.a.img['alt'].lower()\\n returned_url_list = _get_product_urls(category_url, category_names)\\n final_output_url_list.extend(returned_url_list)\\n \\n for productsurl in final_output_url_list:\\n if productsurl['id'] in traverse_cat_output.keys() and productsurl['category_list'] != traverse_cat_output[productsurl['id']]['category_list']:\\n traverse_cat_output[productsurl['id']]['category_list'].extend(productsurl['category_list'])\\n else:\\n traverse_cat_output[productsurl['id']] = productsurl\\n \\n brand_output_categories = _traverse_categories_brand()\\n for brand_url in brand_output_categories:\\n if brand_url['id'] in traverse_cat_output.keys():\\n traverse_cat_output[brand_url['id']]['brand'] = brand_url['brand']\\n else:\\n traverse_cat_output['id'] = brand_url\\n \\n if TESTRUN:\\n for k,v in traverse_cat_output.items():\\n print v\\n print '*'*78\\n for item_id in traverse_cat_output.keys():\\n print item_id\\n csv_data = csv_output_dict.get(item_id)\\n if csv_data:\\n traverse_cat_output[item_id]['brand'] = csv_data['brand']\\n if not traverse_cat_output[item_id].get('brand'):\\n del traverse_cat_output[item_id]\\n \\n return traverse_cat_output\",\n \"async def bulk_crawl_and_store(db_cfg, categories, **kwargs):\\n\\n async with ClientSession() as session:\\n tasks = []\\n for cat in categories:\\n tasks.append(\\n store_to_db(db_cfg=db_cfg, category=cat, session=session, **kwargs)\\n )\\n await asyncio.gather(*tasks)\",\n \"def import_categories(self, all_categories):\\n\\n i = 0\\n while (\\n i < len(all_categories['tags'])\\n and all_categories['tags'][i]['products'] > 1000\\n ):\\n category = all_categories['tags'][i]\\n if self.test_category(category):\\n self.create_category(category)\\n i += 1\",\n \"def categories(data):\\n if data:\\n for i in data:\\n category = CategoriesModel(categories=i['categories'])\\n category.save()\",\n \"def insert_data_category_into_bdd(self):\\n for category in constant.LIST_CATEGORIES:\\n data = Category(name=category)\\n data.save()\\n print(\\\"the category \\\" + str(category) + \\\" has been created\\\")\",\n \"def insert_categories(self):\\n logic = CategoryLogic()\\n \\n # We create the list of category objects\\n categories = self.objects_factory.create_category_object_list()\\n categories = set(categories)\\n \\n for category in categories:\\n logic.insert(category)\",\n \"def get_data(self):\\n products_list = []\\n for category in CATEGORIES:\\n json_data = self.url_to_json(category)\\n pages_nb = self.retrieve_cat_pages_nb(json_data)\\n for page in range(pages_nb):\\n page_json_data = self.page_to_json(category, page+1)\\n products = page_json_data[\\\"products\\\"]\\n for p in products:\\n params = {\\n 'brands': \\\"\\\",\\n 'product_name_fr': \\\"\\\",\\n 'nutrition_grades': \\\"\\\",\\n 'stores': \\\"\\\",\\n 'url': \\\"\\\",\\n 'categories': \\\"\\\"\\n }\\n for key in params:\\n try:\\n params[key] = p[key]\\n except KeyError:\\n continue\\n if params['product_name_fr'] != \\\"\\\" and params['nutrition_grades'] != \\\"\\\" and params['url'] != \\\"\\\" and params['categories'] != \\\"\\\":\\n product = Product(brand=params['brands'],\\n name=params['product_name_fr'],\\n nutrition_grade=params['nutrition_grades'],\\n stores=params['stores'], url=params['url'],\\n category=params['categories'])\\n products_list.append(product)\\n try:\\n self.manager.save_all(self.clean_data(products_list))\\n print(f\\\"\\\\n La base de données |{DB_NAME}| a été peuplée \\\\n\\\")\\n except:\\n print(\\\"\\\\n Une erreur s'est produite lors \\\"\\n \\\"du peuplement de la base de données \\\\n\\\")\",\n \"async def filter_categories_and_products(categories: list[str], products: list[str]) -> None:\\n for url in categories:\\n if url not in db['urls_to_crawl']:\\n db['urls_to_crawl'].append(url)\\n\\n if url not in db['category_urls']:\\n db['category_urls'].append(url)\\n\\n for url in products:\\n if url not in db['product_urls']:\\n db['product_urls'].append(url)\\n\\n update_json_database()\",\n \"def load_categories():\\n\\n print('load_categories')\\n\\n Category.query.delete()\\n\\n for row in open(\\\"seed_data/categories.csv\\\"):\\n name = row.rstrip()\\n\\n cat = Category(name=name)\\n\\n db.session.add(cat)\\n\\n db.session.commit()\",\n \"def main():\\n category_list = []\\n h = httplib2.Http('.cache')\\n websites = [\\\"http://www.goodreads.com/genres/list?page=1\\\", \\\"http://www.goodreads.com/genres/list?page=2\\\",\\n \\\"http://www.goodreads.com/genres/list?page=3\\\"]\\n for website in websites:\\n response, content = h.request(website)\\n if response.status != 200:\\n print(\\\"Status code \\\", response.status)\\n return\\n soup = BeautifulSoup(content, 'html.parser')\\n data = soup.find_all(\\\"a\\\", class_=\\\"mediumText actionLinkLite\\\")\\n for x in data:\\n category_list.append(str(x.string))\\n\\n data = \\\"category_list = \\\" + str(category_list)\\n\\n with open(\\\"InitialDataExtraction/category_list.py\\\", mode='w', encoding=\\\"utf-8\\\") as a_file:\\n a_file.write(data)\\n print(len(category_list))\",\n \"def start_parsing_category(self, response):\\n # ### First thing to do is to get the # of pages in the category.\\n # Note: suppose a category has 189 entries. There are 48 entries per page.\\n # 189 / 48 = 3.93. There are 4 pages. Therefore, deploy a ceil() function.\\n #\\n path_to_total_entries = \\\"//span[@class='paging-information-items']/text()\\\"\\n total_entries_in_category = response.xpath(path_to_total_entries).extract()[0].strip(\\\"\\\\n\\\").split(\\\" \\\")[0]\\n # print(\\\"T:\\\", total_entries_in_category)\\n pages = ceil(int(total_entries_in_category) / 48)\\n # assemble list of links for spider to visit\\n base_url_for_category = response.meta[\\\"link_to_category\\\"]\\n links_to_pages_in_this_category = []\\n for page_number in range(1, pages + 1):\\n start_value = page_number * 48\\n paginated_links = base_url_for_category + \\\"?start=\\\" + str(start_value) + \\\"&sz=48\\\"\\n links_to_pages_in_this_category.append(paginated_links)\\n\\n # ### Handle the first page in the category, which we are already on.\\n # \\\"product_divs\\\"\\n tile_path = \\\"//div[@class='product-tile']\\\"\\n # gets between 1 and 48 SelectorLists, depending on how many products are on the page.\\n product_tiles_from_the_page = response.xpath(tile_path)\\n # FIXME: this naming seems wrong. it's feeding pages into the for loop, so its name should be \\\"pages\\\" ... and yet i was expecting tiles?\\n for page in product_tiles_from_the_page[0:1]: # TODO: remove 0:3 when done developing. its just there to make things run faster\\n self.convert_product_tiles_from_this_page_to_items(page, product_category=response.meta[\\\"category_name\\\"])\\n # FIXME: this is currently printing an item that contains the name of every product on the page.\\n\\n # ### send Scrapy to handle the rest of the pages in the category, sans the first page, which is done\\n page_num = 2\\n for remaining_link in links_to_pages_in_this_category[1:]:\\n # print(remaining_link)\\n yield scrapy.Request(remaining_link, self.parse_further_pages, dont_filter=True,\\n meta={\\\"page_number\\\": page_num})\\n page_num = page_num + 1\\n\\n return None\",\n \"def fill_tab_categories(self):\\n self.category.fill_tab_categories(self.list_categories, self.mycursor, self.my_database)\",\n \"def start_requests(self):\\n\\n with open(os.path.join(os.path.dirname(__file__), \\\"../resources/mapemall_categories.csv\\\")) as categories:\\n for category in csv.DictReader(categories):\\n category_text=category[\\\"category\\\"]\\n url=str(MapemallCrawlerSpider.start_urls[0])+category_text\\n # The meta is used to send our search text into the parser as metadata\\n yield scrapy.Request(url, callback = self.parse, meta = {\\\"category_text\\\": category_text})\",\n \"def main():\\n\\n conn = psycopg2.connect(**env.DATABASE)\\n cursor = conn.cursor()\\n\\n for file, city in env.supported_cities().items():\\n try:\\n data = add_metadata(parse_html(city, get_html(city)))\\n save_data_to_db(cursor, data, file.title())\\n except Exception as e:\\n print(\\\"Failed to scrape '%s': %s\\\" %(city, e))\\n print(traceback.format_exc())\\n\\n conn.commit()\\n conn.close()\",\n \"def get_categories(self):\\n\\n self.search([]).unlink()\\n token = self.env['odoo.moodle'].search([('create_uid', '=', self.env.user.id)]).token\\n domain = \\\"http://localhost:8888\\\"\\n webservice_url = \\\"/webservice/rest/server.php?\\\"\\n parameters = {\\n \\\"wstoken\\\":token,\\n 'wsfunction': 'core_course_get_categories',\\n 'moodlewsrestformat': 'json'\\n }\\n request = requests.get(url=domain+webservice_url, params=parameters)\\n request = request.json()\\n for req in request:\\n try:\\n self.create({\\n 'category_id': req['id'],\\n 'name': req['name'],\\n 'description': req['description'],\\n 'category_parent': req['parent'],\\n })\\n except Exception:\\n print('Category not created')\",\n \"def get_all_categories(self):\\n\\t\\turl = self.base_url\\n\\t\\tcategories, code = self.get_categories(url, level = 0)\\n\\t\\tcategories = self.databaseHelper.save_categories(categories)\\n\\n\\t\\t# Categories is a list of main categories\\n\\t\\t# Cycle through these categories and keep going until level 3\\n\\n\\t\\tfor i in xrange(0, len(categories)):\\n\\t\\t\\tmain_category = categories[i]\\n\\n\\t\\t\\turl = main_category['url']\\n\\t\\t\\tsub_categories, code = self.get_categories(url, level = 1, id_parent_category = main_category['id'])\\n\\n\\t\\t\\t# Getting level 2 categories\\n\\t\\t\\tfor j in xrange(0,len(sub_categories)):\\n\\t\\t\\t\\tsub_category = sub_categories[j]\\n\\t\\t\\t\\turl = sub_category['url']\\n\\t\\t\\t\\tsub_categories_level_2, code = self.get_categories(url, level = 2, id_parent_category = sub_category['id'])\\n\\n\\t\\t\\t\\t# Getting sub categories level 3\\n\\t\\t\\t\\tfor k in xrange(0, len(sub_categories_level_2)):\\n\\t\\t\\t\\t\\tsub_category_level_2 = sub_categories_level_2[k]\\n\\t\\t\\t\\t\\turl = sub_category_level_2['url']\\n\\t\\t\\t\\t\\tsub_categories_level_3, code = self.get_categories(url, level = 3, id_parent_category = sub_category_level_2['id'] )\\n\\n\\t\\t\\t\\t\\t# Updating sub categories level 2\\n\\t\\t\\t\\t\\tsub_category_level_2['sub_categories'] = sub_categories_level_3\\n\\t\\t\\t\\t\\tsub_categories_level_2[k] = sub_category_level_2\\n\\n\\n\\t\\t\\t\\t# Updating sub category level 1\\n\\t\\t\\t\\tsub_category['sub_categories'] = sub_categories_level_2\\n\\t\\t\\t\\tsub_categories[j] = sub_category\\n\\n\\t\\t\\t# Updating main category\\n\\t\\t\\tmain_category['sub_categories'] = sub_categories\\n\\t\\t\\tcategories[i] = main_category\\n\\n\\t\\treturn categories\",\n \"def insert_data(self):\\n self.connection_database()\\n\\n # no insertion if the table Food already contains any data\\n self.cursor.execute(\\\"SELECT * FROM Food\\\")\\n data_table_food = self.cursor.fetchall()\\n if not data_table_food:\\n\\n # instantiate the class Call_api\\n new_call_api = ca.CallApi()\\n new_call_api.load_data()\\n categories = new_call_api.categories\\n list_data = new_call_api.list_data\\n\\n for elt, element in zip(categories, list_data):\\n\\n # inserting data into Category table\\n insert_data_categories = (\\\"\\\"\\\"INSERT IGNORE INTO Category (categories)\\n VALUES({0})\\\"\\\"\\\"\\n .format(\\\"\\\\'\\\"+elt+\\\"\\\\'\\\"))\\n self.cursor.execute(insert_data_categories)\\n self.data_base.commit()\\n\\n # inserting data into Food table\\n for value in element['products']:\\n if element['products'].index(value) < 100:\\n try:\\n product_name = \\\"\\\\'\\\"+value['product_name_fr'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n nutrition_grade = \\\"\\\\'\\\"+value['nutrition_grade_fr'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n ingredients = \\\"\\\\'\\\"+value['ingredients_text'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n store_tags = \\\"\\\\'\\\"+\\\", \\\".join(value['stores_tags']).replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n url = \\\"\\\\'\\\"+value['url'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n\\n insert_data_food = (\\\"\\\"\\\"INSERT IGNORE INTO Food (name_food, category_id,\\n nutriscore, description, store, link) \\n VALUES({0}, \\n (SELECT id FROM Category WHERE categories = {1}),\\n {2}, {3}, {4}, {5})\\\"\\\"\\\"\\n .format(product_name, \\\"\\\\'\\\"+elt+\\\"\\\\'\\\",\\n nutrition_grade, ingredients,\\n store_tags, url))\\n self.cursor.execute(insert_data_food)\\n self.data_base.commit()\\n\\n # if errors\\n except KeyError:\\n continue\",\n \"def get_category_links(self):\\n self.logger.info(\\\"Gathering categories links...\\\")\\n try:\\n category_link = \\\"services-catalog__column-title ui-link _t37mbJS _2fIr6we _2l1CpUa\\\"\\n self.driver.get(self.MAIN_URL)\\n categories = self.driver.find_elements_by_class_name(\\\"services-catalog__content\\\")\\n elems = categories[0].find_elements_by_xpath(\\\"//a[@class]\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured! Cannot gather category list!\\\")\\n return\\n search_cat = True\\n # Compose two links lists: for subject categories, and for generic categories\\n for i, elem in enumerate(elems):\\n if elem.get_attribute(\\\"class\\\") == category_link:\\n if elem.get_attribute(\\\"href\\\")[-1] == \\\"#\\\":\\n subject = \\\" \\\".join(elem.text.split()[:-1])\\n self.others_links.append([self.other_links_dict[subject]])\\n search_cat = False\\n else:\\n self.link_list.append(elem.get_attribute(\\\"href\\\"))\\n search_cat = True\\n elif not search_cat:\\n self.others_links[-1].append(elem.text)\\n # Move english category to the end because it is the largest one\\n self.link_list.append(self.link_list.pop(self.link_list.index('https://profi.ru/repetitor/english/')))\\n self.logger.info(f'Found {len(self.link_list) + len(self.others_links)} categories')\",\n \"def save_category(self):\\n self.start_connection()\\n for element in config.CATEGORIES:\\n self.cursor.execute(f\\\"\\\"\\\"\\n INSERT INTO category (name) VALUES (\\\"{element}\\\")\\\"\\\"\\\")\\n self.mysql_connection.commit()\\n self.close_connection()\",\n \"def get_category_data_off(self):\\n list_categories_name=[]\\n cat = requests.get('https://fr.openfoodfacts.org/categories?json=true')\\n cat_data = cat.json()\\n tags_list = cat_data['tags']\\n print (len(tags_list))\\n list_of_random_tags_list = random.sample(tags_list, k=self.view.num_to_select)\\n\\n for category in list_of_random_tags_list:\\n try :\\n category_name = category['name']\\n print(category_name)\\n list_categories_name.append(category_name)\\n print (list_categories_name)\\n self.list_categories = list_categories_name # list_categories_name is passed in the instance property\\n except KeyError:\\n pass\\n except UnicodeEncodeError:\\n pass\",\n \"def get_categories(self, categories):\\r\\n category, created = Categories.objects.get_or_create(name=categories)\\r\\n category.save()\",\n \"def _get_data(self, category):\\n url = self._get_url(category)\\n # request page\\n page = get(url)\\n # print page.content\\n # exit()\\n # raise an error\\n # page.raise_for_status()\\n # parse html markup\\n html = html5parser.fragment_fromstring(page.content, True)\\n divs = html.findall(\\\".//{http://www.w3.org/1999/xhtml}div\\\")\\n json_string = \\\"\\\"\\n # find the div that contains our catalog json file\\n # TODO: everything request seems to have diffent html structure then specials find a method that works for both of them\\n for i in range(0, len(divs)):\\n d = divs[i]\\n if d.text != None and \\\"COLRSCatalogEntryList\\\" in d.text:\\n json_string = d.text\\n break\\n # parse the json\\n # print \\\"json string is:\\\\n{}\\\".format(json_string)\\n data_json = json.loads(json_string)\\n self._search_data = data_json['searchInfo']\\n self._product_data = data_json['products']\\n # we return true on success\\n return True\",\n \"def get_categories(self, url, level = 0, id_parent_category = None):\\n\\t\\tcategories = []\\n\\n\\t\\t# Getting main categories\\n\\t\\thtml, code = self.crawler.get(url)\\n\\t\\tif code == 200:\\n\\t\\t\\tself.parser.set_html(html)\\n\\t\\t\\tcategories = self.parser.get_categories(level = level)\\n\\t\\t\\t# Setting proper urls\\n\\t\\t\\t[ cat.update({'url': self.properurl(cat['url'])}) for cat in categories]\\n\\t\\t\\t# Save categories\\n\\t\\t\\tcategories = self.databaseHelper.save_categories(categories, id_parent_category)\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Something went wrong when fetching main categories for Monoprix : code %d\\\"%(code)\\n\\n\\t\\treturn categories, code\",\n \"def load_categories():\\n\\n Category.query.delete()\\n\\n with open(category_file) as f:\\n for _ in range(1):\\n next(f)\\n \\n for row in f:\\n row = row.rstrip()\\n categories_data = row.split(\\\",\\\")\\n\\n id = int(categories_data[0])\\n category = categories_data[1]\\n\\n category_model = Category(id=id, category=category)\\n db.session.add(category_model)\\n db.session.commit()\",\n \"def populate_index(db):\\n\\tfor url in URL:\\n\\t\\tprint url\\n\\t\\trequest = urllib2.Request(url)\\n\\t\\ttry :\\n\\t\\t\\tresponse = urllib2.urlopen(request)\\n\\t\\texcept urllib2.URLError:\\n\\t\\t\\tprint \\\"Network Unreachable \\\"\\n\\t\\t\\tsys.exit()\\t\\n\\t\\ttext = html2text(response.read())\\n\\t\\tdb.generate_index(text,url)\",\n \"def parse(self):\\n database = WriteToDatabase(\\\"config_profi_ru.conf\\\")\\n database.create_base()\\n # Start Webdriver with supressed logging\\n options = webdriver.ChromeOptions() \\n options.add_experimental_option(\\\"excludeSwitches\\\", [\\\"enable-logging\\\"])\\n self.driver = webdriver.Chrome(options=options)\\n # Get categories\\n self.get_category_links()\\n # Treat general categories\\n for category in self.link_list:\\n cat_name = category.split('/')[-2]\\n # Check if category is already parsed\\n if(os.path.exists(f\\\"profi_ru_json_data\\\\{cat_name}_data_file.json\\\")):\\n self.logger.warning(f\\\"'{cat_name}' category is already parsed!\\\")\\n continue\\n self.logger.info(f\\\"Treating '{cat_name}' category\\\")\\n counter = 0\\n person_info = []\\n try:\\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\\n self.logger.info(f\\\"Found {len(category_profiles)} profiles in '{cat_name}' category\\\")\\n self.logger.info(f\\\"Start of parsing '{cat_name}' category...\\\")\\n for person_link in category_profiles:\\n person_info.append(self.get_person_info(person_link))\\n counter+=1\\n if(counter == len(category_profiles) - 1):\\n self.logger.info(f\\\"End of parsing '{cat_name}' category...\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured!\\\")\\n self.logger.exception(f\\\"Only {counter} profiles of '{cat_name}' category were parsed\\\")\\n self.cat_profiles_dict[cat_name] = person_info\\n self.logger.info(f\\\"'{cat_name}' category was parsed successfully!\\\")\\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\\n database.create_and_write_table(f'profi_ru_json_data\\\\{cat_name}_data_file.json')\\n # Treat generic categories\\n for cat_list in self.others_links:\\n cat_name = cat_list[0]\\n # Check if categroy is already parsed\\n if(os.path.exists(f\\\"profi_ru_json_data\\\\{cat_name}_data_file.json\\\")):\\n self.logger.warning(f\\\"'{cat_name}' category is already parsed!\\\")\\n continue\\n self.logger.info(f\\\"Treating '{cat_name}' category\\\")\\n counter = 0\\n person_info = []\\n try:\\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\\n self.logger.info(f\\\"Found {len(category_profiles)} profiles in '{cat_name}' category\\\")\\n for person_link in category_profiles:\\n person_info.append(self.get_person_info(person_link))\\n counter+=1\\n if(counter == len(category_profiles) - 1):\\n self.logger.info(f\\\"End of parsing {cat_name} category\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured!\\\")\\n self.logger.exception(f\\\"Only {counter} profiles of '{cat_name}' category were parsed\\\")\\n self.cat_profiles_dict[cat_name] = person_info\\n self.logger.info(f\\\"'{cat_name}' category was parsed successfully!\\\")\\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\\n database.create_and_write_table(f'profi_ru_json_data\\\\{cat_name}_data_file.json')\\n self.driver.quit()\",\n \"def harvest_urls():\\n manifest = []\\n category = {}\\n subcategory = {}\\n directoryfiles = \\\"%s/directory_listing/\\\" % config['PREFIX']\\n # ^^ the directory containing the HTML from the Technorati site.\\n\\n #Set up directory for intermediate data: MANIFEST\\n #MANIFEST contains: Category, Subcategory, Title and URL.\\n #and is a roster of URLs of blogs to autodiscover.\\n if not os.path.exists(prefix + \\\"meta\\\"):\\n os.mkdir(prefix + \\\"meta\\\")\\n else:\\n #TO DO: What if meta exists but MANIFEST got deleted?\\n logging.info(\\\"Blog URLs already harvested. Skipping...\\\")\\n return\\n\\n #Iterate through each file in the directory and extract blog URLs.\\n for infile in glob.glob(os.path.join(directoryfiles, '*.html')):\\n logging.info(\\\"Harvesting blog URLs from %s.\\\" % infile)\\n dirpage = file(infile)\\n root = parse(dirpage).getroot()\\n #Rather than infer the category from the filename, just extract\\n #it from the file. Not the best way to do this, hit is minimal.\\n\\tpieces = infile.split('/')[-1].split('_')\\n\\tcat = pieces[1]\\n\\tsubcat = None\\n\\tif len(pieces) == 4:\\n\\t\\tsubcat = pieces[2]\\n blogs = root.xpath(\\\"//td[@class='site-details']\\\")\\n #Iterate through all of the blogs listed on the page.\\n for blog in blogs:\\n url = blog.xpath(\\\"a[@class='offsite']\\\")[0].text\\n title = blog.xpath('h3/a')[0].text\\n OUT = open(prefix + \\\"meta/MANIFEST\\\", \\\"a\\\")\\n #Store the category of the blog.\\n category[url] = cat\\n if subcat:\\n output = [cat, subcat, title.encode('utf-8').replace(' ', ' '), url]\\n subcategory[url] = subcat\\n print >> OUT, ' '.join(output)\\n else:\\n output = [cat, \\\"NA\\\", title.encode('utf-8').replace(' ', ' '), url]\\n print >> OUT, '\\\\t'.join(output)\\n manifest.append(output)\\n OUT.close()\\n # This is a hack to get around having to use a database.\\n # TODO: Reimplement using a database.\\n BLOGCATS = open(prefix + \\\"blogcats.pickle\\\", \\\"w\\\")\\n cPickle.dump(category, BLOGCATS)\\n BLOGCATS.close()\\n return manifest\",\n \"def scrape_categories():\\n category_urls = []\\n url = homepage + '/alle-categorieen/'\\n response = rq.get(url, timeout=5)\\n soup = BeautifulSoup(response.content, 'html.parser')\\n main_soup = soup.find('main')\\n\\n for category in main_soup.find_all('a', {'href': re.compile(r'/overzicht/')}):\\n category_urls.append(category['href'])\\n\\n return category_urls\",\n \"def get_categories(request):\\n try:\\n categories = []\\n for category in Category.objects.all():\\n categories.append({\\\"title\\\": category.title, \\\"id\\\": category.pk})\\n\\n return format_ajax_response(True, \\\"Knowledgebase categories retrieved successfully.\\\", {\\\"categories\\\": categories})\\n except Exception as ex:\\n logger.error(\\\"Failed to get_categories: %s\\\" % ex)\\n return format_ajax_response(False, \\\"There was an error retreiving the knowledgebase categories.\\\")\",\n \"def _get_all_categories() -> dict:\\n categories = database.fetchall(\\\"Categories\\\", \\\"id\\\", \\\"name_ua\\\")\\n return categories\",\n \"def scrap_data(cat):\\n global OBSERVATION\\n # Click on \\\"View more\\\" until it disapears\\n # is_next_button = True\\n # while is_next_button:\\n # try:\\n # viewmore_button = BROWSER.find_element_by_css_selector(\\n # \\\"li.nextPaging > a.viewmore\\\")\\n # # BROWSER.execute_script(\\\"arguments[0].scrollIntoView();\\\",\\n # # viewmore_button)\\n # time.sleep(5)\\n # viewmore_button.click()\\n # except NoSuchElementException:\\n # is_next_button = False\\n # Get html source\\n soup = BeautifulSoup(BROWSER.page_source, 'lxml')\\n cat_div = soup.find(\\\"div\\\", {\\\"class\\\": \\\"boxproduct\\\"})\\n cat_div = cat_div.findAll(\\\"li\\\", {\\\"class\\\": \\\"product\\\"}) if cat_div else None\\n if cat_div is None:\\n cat_div = []\\n for item in cat_div:\\n row = {}\\n good_name = item.find(\\\"div\\\", {\\\"class\\\": \\\"product-name\\\"})\\n row['good_name'] = good_name.text.strip() if good_name else None\\n price = item.find('div', {\\\"class\\\": \\\"price\\\"})\\n row['price'] = price.strong.text.strip() if price else None\\n old_price = price.find('span') if price else None\\n row['old_price'] = old_price.text.strip() if old_price else None\\n id1 = item.get('data-product')\\n row['id'] = id1\\n row['category'] = cat['name']\\n row['category_label'] = cat['label']\\n row['date'] = DATE\\n OBSERVATION += 1\\n write_data(row)\",\n \"def crawler():\\n job_entries = []\\n for job in job_info(URL):\\n labels = \\\"\\\"\\n if job[\\\"labels\\\"]:\\n for label in job[\\\"labels\\\"]:\\n labels += label[\\\"name\\\"]\\n if job[\\\"labels\\\"].index(label) != len(job[\\\"labels\\\"]) - 1:\\n labels += \\\",\\\"\\n job_entries.append((job[\\\"number\\\"], job[\\\"id\\\"],\\n job[\\\"title\\\"], job[\\\"html_url\\\"], labels))\\n\\n conn = sqlite3.connect('jobber/jobber.db')\\n c = conn.cursor()\\n c.executemany(('INSERT OR IGNORE INTO job_entries '\\n 'VALUES (?,?,?,?,?)'), job_entries)\\n conn.commit()\\n conn.close()\",\n \"def _create_links_and_track(self, page_name, category_list):\\n env = self.state.document.settings.env\\n if not hasattr(env, \\\"categories\\\"):\\n env.categories = {}\\n\\n link_rst = \\\"\\\"\\n ncategs = 0\\n for item in category_list:\\n has_subcat = False\\n if r\\\"\\\\\\\\\\\" in item: \\n categs = item.split(r\\\"\\\\\\\\\\\")\\n has_subcat = True\\n else:\\n categs = [item]\\n # endif\\n\\n print \\n for index, categ_name in enumerate(categs):\\n if categ_name not in env.categories:\\n category = Category(categ_name)\\n env.categories[categ_name] = category\\n else:\\n category = env.categories[categ_name]\\n #endif\\n category.pages.append(PageRef(page_name))\\n if has_subcat and index > 0:\\n category.subcategories.append(PageRef(categ_name))\\n #endif\\n link_rst += \\\":ref:`%s` | \\\" % categ_name\\n ncategs += 1\\n # endfor\\n # endfor\\n\\n link_rst = \\\"`%s: <categories.html>`_ \\\" + link_rst\\n if ncategs == 1:\\n link_rst = link_rst % \\\"Category\\\"\\n else:\\n link_rst = link_rst % \\\"Categories\\\"\\n #endif\\n\\n return link_rst\",\n \"def get_all() -> list:\\n categorias = []\\n conn = GenericDao.connect()\\n cursor = conn.execute(\\\"SELECT * FROM categorias\\\")\\n for row in cursor:\\n categoria = Categoria(row[1], row[0])\\n categorias.append(categoria)\\n if debug:\\n print(str(categoria))\\n\\n conn.close()\\n return categorias\",\n \"def get_all_category_urls():\\n\\treturn execute_sql(\\\"SELECT category_url FROM categories\\\").fetchall()\",\n \"def get_disease_category_url():\\n base_url = 'http://www.mayoclinic.org' \\n task = load_jt('task.json') \\n spider = Crawler()\\n \\n for url in task:\\n html = spider.html(url)\\n if html:\\n soup = BS4(html)\\n div = soup.find_all('div', id = 'main_0_left1_0_tertiarynav')[0]\\n for a in div.find_all('a'):\\n task[url].setdefault(base_url + a['href'], {'data': {'category': a.text.strip()}})\\n dump_jt(task, 'task.json', replace = True)\",\n \"def fetch_data():\\n for category in CHEATSHEETS.items():\\n subprocess.call(f'curl -o {PATH}{category[0] + \\\".csv\\\"} {category[1]}', shell=True)\\n\\n index = -1\\n for filename in os.listdir(PATH):\\n for idx, row in pd.read_csv(PATH + filename, on_bad_lines='skip').replace(np.nan, '').iterrows():\\n name = row['Model']\\n url = REDIRECT_URL + name.lower()\\n category = filename.split('.')[0]\\n featurizers = row['Acceptable Featurizers'].split(' ') if row['Acceptable Featurizers'] != '' else []\\n backends = ['PyTorch' if item in {\\\"PTorch\\\", \\\"Torch\\\", \\\"PyTorch \\\"} else item for item in row['Backend'].split('/')]\\n types = row['Type'] if filename != 'general.csv' else row['Classifier/Regressor']\\n types = types.split('/') if filename == 'material.csv' else types.split('/ ')\\n index += 1\\n\\n backend_list.append(backends)\\n type_list.append(types)\\n featurizer_list.append(featurizers)\\n model_list.append(Model(name, url, category, featurizers, backends, types, index))\",\n \"def __init__(self,url):\\n self.base_url = url\\n content = self._get_page_content()\\n json_data = self._get_data_json(content)\\n self._categories = self._get_categories(json_data)\",\n \"def html_collect_pages(app):\\n if not hasattr(app.builder.env, \\\"categories\\\"):\\n return # nothing to do\\n\\n for name, context, template in create_category_pages(app):\\n yield (name, context, template)\",\n \"def scrap_site_category_links(base_url):\\n response = requests.get(base_url)\\n page = response.content\\n soup = BeautifulSoup(page, \\\"html.parser\\\")\\n category_links = []\\n for category_link in soup.find('ul', class_='nav nav-list').find('li').find('ul').find_all('li'):\\n href = category_link.a.get('href')\\n # url of the category\\n url = \\\"https://books.toscrape.com/{}\\\".format(href)\\n category_links.append(url)\\n return category_links\",\n \"def html_collect_pages(app):\\n\\n if not hasattr(app.builder.env, \\\"categories\\\"):\\n return # nothing to do\\n\\n for name, context, template in create_category_pages(app):\\n yield (name, context, template)\",\n \"def load_subcategories():\\n\\n print('load_subcategories')\\n\\n Category_Subcategory.query.delete()\\n\\n for row in open(\\\"seed_data/subcategories.csv\\\"):\\n row = row.rstrip()\\n category_id, name = row.split(',')\\n\\n\\n sub = Category_Subcategory(main_category=category_id,\\n name=name)\\n\\n db.session.add(sub)\\n\\n db.session.commit()\",\n \"def browse_categories():\\n print(\\\"***** Find Businesses by Categories *****\\\")\\n while True:\\n print()\\n category = input(\\n 'Please enter a type of business (category) or type \\\"back\\\" or \\\"quit\\\": ')\\n print()\\n if category == \\\"quit\\\":\\n print(\\\"Goodbye!\\\")\\n sys.exit()\\n if category == \\\"back\\\":\\n return\\n\\n # create a regex pattern for business name\\n pattern = r\\\".*\\\" + re.escape(category) + r\\\".*\\\"\\n regx = re.compile(pattern, re.IGNORECASE)\\n\\n cursor = business_col.find({\\\"categories\\\": regx})\\n\\n business_objects = cursor.limit(10)\\n \\n if cursor.count() == 0:\\n print(\\\"No businesses found with given category.\\\")\\n continue\\n for business_object in business_objects:\\n print_business(business_object)\",\n \"def _create_links_and_track(self, page_name, category_list):\\n env = self.state.document.settings.env\\n if not hasattr(env, \\\"categories\\\"):\\n env.categories = {}\\n\\n link_rst = \\\"\\\"\\n ncategs = 0\\n for categ_name in category_list:\\n #categ_name is the full category name - register that\\n category = self.register_category(categ_name, env)\\n category.pages.add(PageRef(page_name, env.docname))\\n\\n #now step up a step up each time the category hierarchy\\n parent_category = categ_name\\n while True:\\n if r\\\"\\\\\\\\\\\" in parent_category:\\n categs = parent_category.split(r\\\"\\\\\\\\\\\")\\n else:\\n break\\n # remove the last item\\n subcat = Category(categ_name, env.docname) #create the category with the full name\\n subcat.name=categs.pop() # and then replace it with the last token of the name\\n parent_category = r\\\"\\\\\\\\\\\".join(categs)\\n\\n #register the parent category\\n parent = self.register_category(parent_category, env)\\n parent.subcategories.add(subcat)\\n\\n # endwhile\\n\\n #category should be the last subcategory by this point\\n link_rst += \\\"`%s <%s>`_ | \\\" % (categ_name, category.link(env.docname))\\n ncategs += 1\\n # endfor\\n\\n link_rst = \\\"**%s**: \\\" + link_rst.rstrip(\\\" | \\\") # remove final separator\\n if ncategs == 1:\\n link_rst = link_rst % \\\"Category\\\"\\n else:\\n link_rst = link_rst % \\\"Categories\\\"\\n #endif\\n\\n return link_rst\",\n \"def crawler(self):\\n\\n\\t\\tfor page in range(self.first_page, self.last_page+1):\\n\\t\\t\\tprint(\\\"\\\\nCrawling Page \\\" + str(page))\\n\\t\\t\\tpage_url = self.site_url + \\\"?page=\\\" + str(page) +\\\\\\n\\t\\t\\t \\\"&index=prod_all_products_term_optimization\\\"\\n\\t\\t\\t\\n\\t\\t\\tself.scrape_features(page_url)\",\n \"def parse_category(self, response):\\n detail_links = response.css(\\\"a.detail-url-click::attr(href)\\\").extract()\\n meta = response.meta\\n self.logger.info(\\\"found %s products from page %s of category %s\\\",\\n len(detail_links), meta[\\\"page_num\\\"], meta[\\\"cate_name\\\"])\\n for link in detail_links:\\n if not link:\\n continue\\n yield response.follow(link, callback=self.parse_detail, meta=meta)\\n if detail_links:\\n meta = dict(meta)\\n meta[\\\"page_num\\\"] = int(meta[\\\"page_num\\\"]) + 1\\n base_url = meta[\\\"base_url\\\"]\\n link = \\\"{}---hot-{}.html\\\".format(base_url, meta[\\\"page_num\\\"])\\n self.logger.info(\\\"scheduled scraping request for page %s of category %s\\\",\\n meta[\\\"page_num\\\"], meta[\\\"cate_name\\\"])\\n yield response.follow(link, callback=self.parse_category, meta=meta)\",\n \"def create_category_pages(app):\\n env = app.builder.env\\n # jinja2 html template\\n template = CATEGORY_PAGE_TEMPLATE\\n\\n categories = env.categories\\n for name, category in categories.iteritems():\\n context = {}\\n # First write out the named page\\n context[\\\"title\\\"] = category.name\\n\\n #get parent category\\n if \\\"\\\\\\\\\\\" in category.name:\\n categs = category.name.split(\\\"\\\\\\\\\\\")\\n categs.pop()\\n parent_category = r\\\"\\\\\\\\\\\".join(categs)\\n parent_category_link = \\\"../\\\" + categs[-1] + \\\".html\\\"\\n parent_category = \\\"<b>Category:</b> <a href='{0}'>{1}</a>\\\"\\\\\\n .format(parent_category_link,parent_category)\\n context[\\\"parentcategory\\\"] = parent_category\\n\\n # sort subcategories & pages alphabetically\\n context[\\\"subcategories\\\"] = sorted(category.subcategories, key = lambda x: x.name)\\n context[\\\"pages\\\"] = sorted(category.pages, key = lambda x: x.name)\\n context[\\\"outpath\\\"] = category.html_path\\n\\n #jinja appends .html to output name\\n category_html_path_noext = os.path.splitext(category.html_path)[0]\\n yield (category_html_path_noext, context, template)\\n\\n # Now any additional index pages if required\\n if category.name in INDEX_CATEGORIES:\\n # index in categories directory\\n category_html_dir = os.path.dirname(category.html_path)\\n category_html_path_noext = category_html_dir + \\\"/index\\\"\\n yield (category_html_path_noext, context, template)\\n\\n # index in document directory\\n document_dir = os.path.dirname(category_html_dir)\\n category_html_path_noext = document_dir + \\\"/index\\\"\\n context[\\\"outpath\\\"] = category_html_path_noext + \\\".html\\\"\\n yield (category_html_path_noext, context, template)\",\n \"def scrapeAndSaveNewsArticles():\\n http = urllib3.PoolManager()\\n\\n # change output document name if needed so already collected articles are not overwritten\\n with open('documents/news_documents7.csv', 'w') as fout:\\n writer = csv.writer(fout)\\n writer.writerow(['category', 'headline', 'authors', 'link', 'short_description', 'date', 'body'])\\n\\n with open('documents/News_Category_Dataset_v2.json') as f:\\n line = f.readline()\\n cnt = 0\\n while line:\\n line = f.readline()\\n # if cnt <= 99872:\\n # cnt += 1\\n # continue\\n data = json.loads(line)\\n link = data['link']\\n try:\\n resp = http.request('GET', link)\\n # sleep(0.1)\\n except urllib3.exceptions.MaxRetryError:\\n cnt += 1\\n print(\\\"Bad Link:\\\", link)\\n continue\\n\\n try:\\n textBody = extract(str(resp.data.decode('utf-8')))\\n except UnicodeDecodeError:\\n cnt += 1\\n print(\\\"Non-HTML link\\\")\\n continue\\n\\n data[\\\"body\\\"] = textBody\\n writer.writerow(data.values())\\n cnt += 1\\n print(cnt)\",\n \"def insert_products_categories(self):\\n logic = ProductCategoryLogic()\\n \\n # We create the list of product_category objects\\n products_categories = self.objects_factory.create_product_category_object_list()\\n products_categories = set(products_categories)\\n\\n for p_c in products_categories:\\n logic.insert(p_c)\",\n \"def get_all_pages_of_category(category_url):\\n\\n def extract_number(text):\\n \\\"\\\"\\\" Return number of pages \\\"\\\"\\\"\\n # logging.debug('Paging text: ' + text.strip())\\n nums = re.findall('共(\\\\d+)页', text)\\n # logging.debug('nums=' + repr(nums))\\n if len(nums) == 0:\\n raise Exception('There is no number!')\\n return int(nums[0])\\n\\n ###############################################\\n\\n soup = get_soup(category_url)\\n num_pages = extract_number(soup.find('span', {'class': 'paging'}).text)\\n current_page = int(soup.find('span', {'class': 'selpage'}).text)\\n logging.debug('This category has %s page(s)' % num_pages)\\n logging.debug('You are in page %s' % current_page)\\n\\n all_pages_url = list()\\n splited_url = category_url.split(',')\\n part_one = ','.join(splited_url[0:2])\\n part_two = ','.join(splited_url[3:])\\n url_pattern = part_one + ',%s,' + part_two\\n for p_number in range(1, num_pages + 1):\\n page_url = url_pattern % p_number\\n all_pages_url.append(page_url)\\n return all_pages_url\",\n \"def pass_category(url, name):\\n print(f\\\"Passage de la catégorie {name}...\\\")\\n # Initialize list of books\\n books = []\\n while url != '':\\n response = requests.get(url)\\n if response.status_code == 200:\\n soup = BeautifulSoup(response.content, 'html.parser')\\n # Books are notify with article balise\\n articles = soup.findAll('article', {'class': 'product_pod'})\\n for article in tqdm(articles):\\n # Get the book's link\\n link = url + \\\"/../\\\" + article.find('h3').find('a')['href']\\n # Run the function to get book's info\\n dict_product = get_book_infos(link)\\n books.append(dict_product)\\n # Searching for a next page\\n next_page = soup.find('li', {'class': 'next'})\\n if not next_page:\\n url = ''\\n else:\\n url = url + \\\"/../\\\" + soup.find('li', {'class': 'next'}).find('a')['href']\\n write_category(name, books)\",\n \"def daily_task():\\n global CATEGORIES_PAGES\\n global BROWSER\\n global DATE\\n global OBSERVATION\\n log.info('Scraper started')\\n # Refresh date\\n DATE = str(datetime.date.today())\\n OBSERVATION = 0\\n # Initiate headless web browser\\n log.info('Initializing browser')\\n BROWSER = webdriver.Chrome(executable_path=PROJECT_PATH + \\\"/bin/chromedriver\\\",\\n options=OPTIONS)\\n # Download topsite and get categories directories\\n base_file_name = \\\"All_cat_\\\" + DATE + \\\".html\\\"\\n fetch_html(BASE_URL, base_file_name, PATH_HTML, attempts_limit=1000)\\n html_file = open(PATH_HTML + base_file_name).read()\\n CATEGORIES_PAGES = get_category_list(html_file)\\n log.info('Found ' + str(len(CATEGORIES_PAGES)) + ' categories')\\n # Read each categories pages and scrape for data\\n for cat in track(CATEGORIES_PAGES,\\n description = \\\"[green]Scraping...\\\",\\n total = len(CATEGORIES_PAGES)):\\n cat_file = \\\"cat_\\\" + cat['name'] + \\\"_\\\" + DATE + \\\".html\\\"\\n download = fetch_html(cat['directlink'], cat_file, PATH_HTML)\\n if download:\\n scrap_data(cat)\\n # Close browser\\n BROWSER.close()\\n BROWSER.service.process.send_signal(signal.SIGTERM)\\n BROWSER.quit()\",\n \"def crawler(website_url):\\n\\n try:\\n\\n # open and read the website\\n pageFile = urllib2.urlopen(website_url)\\n pageHtml = pageFile.read()\\n pageFile.close()\\n\\n # call BeautifulSoup on an array of lines in string format\\n soup = BeautifulSoup(\\\"\\\".join(pageHtml), \\\"html.parser\\\")\\n # print soup.prettify()[0:1000]\\n\\n # find all links with hashtag cat, limit to 100 results\\n # FIXME add hashtag cat requirement (string = \\\"#cat\\\")\\n pageLinks = soup.findAll(\\\"a\\\", {\\\"href\\\": True}, limit=100)\\n # import pdb; pdb.set_trace()\\n page_URLs = []\\n\\n for pageLink in pageLinks:\\n pageLink = pageLink['href']\\n\\n # if URL does not have a domain, add the main page's domain'\\n if pageLink[0] == '/' and pageLink[:1] != '//':\\n pageLink = website_url + pageLink\\n\\n # check if item in db, if not - add to db and commit\\n existing_page = session.query(Page).filter_by(page_URL=pageLink).first()\\n\\n # add to array of link strings\\n page_URLs.append(pageLink)\\n\\n if not existing_page:\\n page_URL = Page(page_URL=pageLink)\\n session.add(page_URL)\\n session.commit()\\n\\n # import pdb; pdb.set_trace()\\n return page_URLs\\n\\n except urllib2.URLError as e:\\n # exception handling for URLError\\n if hasattr(e, 'reason'):\\n print \\\"We failed to reach a server.\\\"\\n print \\\"Reason: \\\", e.reason\\n # exception handling for HTTPError\\n elif hasattr(e, 'code'):\\n print 'The server couldn\\\\'t fulfill the request.'\\n print 'Error code; ', e.code\\n else:\\n print 'Everything is fine.'\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def get_products(categories_info):\\n\\n for name, urlnames in categories_info.items():\\n for i in range(1, (NB_PAGES+1)):\\n url = 'https://world.openfoodfacts.org/cgi/search.pl?\\\\\\n search_tag=categories&search_terms={}&\\\\\\n purchase_places=France&page_size={}&page={}&json=1'.format(\\n urlnames, str(PRODUCTS_PER_PAGE), str(i))\\n data = requests.get(url).json()\\n name_file = 'data/Products_' + name + str(i) + '.json'\\n with open(name_file, 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def features_from_category(cat_id):\\n\\n try:\\n playlists_uris = get_playlists_from(cat_id)\\n artist_uris_total = []\\n for playlist_uri in playlists_uris:\\n artist_uris = get_artists_from(playlist_uri)\\n artist_uris_total.append(artist for artist in artist_uris)\\n albums_uris_total = []\\n for artist_uri in artist_uris_total:\\n album_uris = get_albums_from(artist_uri)\\n albums_uris_total.append(album for album in album_uris)\\n # from a list of albums get tracks\\n track_uris_total = []\\n for albums_uri in albums_uris_total:\\n tracks_uris = get_tracks_from(albums_uri)\\n track_uris_total.append(track for track in tracks_uris)\\n for track_uri in track_uris_total:\\n features_to_db(track_uri)\\n\\n except Exception as e:\\n print(\\\"Error processing {}: {}\\\".format(cat_id, e))\\n raise e\\n\\n else:\\n DB.session.commit()\",\n \"def get_list_products_for_category(self, category_url, location = {}, save = False):\\n\\t\\t# Setting location, getting category form datastore & initializing products\\n\\t\\tis_LAD, code = self.set_location(location)\\n\\n\\t\\tif is_LAD and code == 200:\\n\\t\\t\\tstore = self.databaseHelper.get_store(location)\\n\\t\\telse:\\n\\t\\t\\tstore = None\\n\\n\\t\\t# Category\\n\\t\\tcategories = self.databaseHelper.get_categories({'url': category_url})\\n\\t\\tif len(categories)>0:\\n\\t\\t\\tcategory = categories[0]['db_entity']\\n\\t\\telse:\\n\\t\\t\\tcategory = None\\n\\t\\tproducts = []\\n\\n\\t\\t# Getting html\\n\\t\\thtml, code = self.crawler.get(category_url)\\n\\t\\t\\n\\n\\t\\tif code == 200:\\n\\t\\t\\tself.parser.set_html(html)\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tbrands = self.parser.get_brands_values_in_category_page()\\n\\n\\t\\t\\t\\tfor i in xrange(0,len(brands)):\\n\\t\\t\\t\\t\\tbrand = brands[i]\\n\\t\\t\\t\\t\\tfetched_products = []\\n\\t\\t\\t\\t\\thtml, code = self.crawler.brand_filter(brand['value'])\\n\\n\\t\\t\\t\\t\\tif code == 200:\\n\\t\\t\\t\\t\\t\\tself.parser.set_html(html)\\n\\t\\t\\t\\t\\t\\t# are all the products on in this page ?\\n\\t\\t\\t\\t\\t\\tcurrent_page_all_products, url_all_products = self.parser.pagination_link_all()\\n\\t\\t\\t\\t\\t\\tif not current_page_all_products:\\n\\t\\t\\t\\t\\t\\t\\tfetched_products = self.parser.get_products()\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\thtml, code = self.crawler.get(url_all_products)\\n\\t\\t\\t\\t\\t\\t\\tif code == 200:\\n\\t\\t\\t\\t\\t\\t\\t\\thtml, code = self.crawler.brand_filter(brand['value'])\\n\\t\\t\\t\\t\\t\\t\\t\\tif code == 200:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.parser.set_html(html)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tfetched_products = self.parser.get_products()\\n\\t\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"Something went wrong when fetching category page for Monoprix : code %d\\\"%(code)\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tprint \\\"Something went wrong when fetching category page for Monoprix : code %d\\\"%(code)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tprint \\\"Something went wrong when fetching category page (%d) for Monoprix : code %d\\\"%(i+1,code)\\n\\t\\t\\t\\t\\t# Setting brand name to products\\n\\t\\t\\t\\t\\tfor j in xrange(0, len(fetched_products)):\\n\\t\\t\\t\\t\\t\\tfetched_products[j]['parent_brand'] = brand['name']\\n\\t\\t\\t\\t\\tproducts = products + fetched_products\\n\\t\\t\\texcept Exception, e:\\n\\t\\t\\t\\tprint e\\n\\t\\telse:\\n\\t\\t\\tprint \\\"Something went wrong when fetching category page for Monoprix : code %d\\\"%(code)\\n\\n\\t\\t# Cleaning urls\\n\\t\\tproducts = self.clean_urls_in_products(products)\\n\\n\\t\\t# Saing products\\n\\t\\tif save:\\n\\t\\t\\tif category and len(products) == 0:\\n\\t\\t\\t\\t# Category does not exist anymore\\n\\t\\t\\t\\tself.databaseHelper.shut_down_category(category)\\n\\t\\t\\telif category:\\n\\t\\t\\t\\tself.databaseHelper.save_products(products, category.id, store)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.databaseHelper.save_products(products, None, store)\\n\\t\\telse:\\n\\t\\t\\treturn products\",\n \"def generate_courses():\\r\\n for category in CourseCategory.objects.all():\\r\\n Course.objects.create(name=category.name, category=category, is_active=True,\\r\\n is_featured=True)\",\n \"def add_aliments(self):\\n for cat in Category.objects.all():\\n print(CAT_SEARCH.format(cat.name))\\n n_page = 1\\n counter = 0\\n while counter < MAX_PRODUCTS_KEEPED:\\n imported_aliments = self._scratch_category(cat.name, n_page)\\n if not imported_aliments:\\n print(CAT_FINDING_FAIL.format(counter, cat.name))\\n break\\n\\n for aliment in imported_aliments:\\n if self._no_full_info(aliment):\\n continue\\n if self._redundant_info(aliment, cat):\\n continue\\n\\n counter = self._save(aliment, cat, counter)\\n n_page += 1\\n\\n print(CAT_FINDING_SUCCESS.format(cat))\",\n \"def get_all_categories_from_collection():\\n api_endpoint = URL\\n response = requests.get(api_endpoint)\\n return response\",\n \"def crawl_data(self, data_type):\\n i = 0\\n cat = {}\\n prod = {}\\n for term in self.search_response:\\n if data_type == 'category' and term['products'] > 6000:\\n i += 1\\n cat[i] = {\\\"name\\\": term['name']}\\n self.categories.append(cat[i])\\n elif data_type == 'product':\\n nutrigrade = \\\"\\\".join(term[\\\"nutrition_grades_tags\\\"])\\n if nutrigrade in (\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"):\\n i += 1\\n prod[i] = {\\\"name\\\": term['product_name_fr'], \\\"url\\\": term['url'], \\\"desc\\\": term['generic_name_fr'],\\n \\\"brand\\\": term['brands'], \\\"categories\\\": term['categories'], \\\"store\\\": term['stores'],\\n \\\"nutriscore\\\": nutrigrade}\\n self.products.append(prod[i])\\n else:\\n pass\",\n \"def load_categories(request: HttpRequest) -> JsonResponse:\\n result = {'categories': []}\\n categories = Category.objects.all().order_by('name')\\n\\n for category in categories:\\n sources = category.source_set.all().order_by('name')\\n result['categories'].append({\\n 'name': category.name,\\n 'sources': []\\n })\\n for source in sources:\\n unread_count = source.feed_set.filter(checked=False).count()\\n result['categories'][-1]['sources'].append({\\n 'name': source.name,\\n 'unread_count': unread_count\\n })\\n\\n result['status_code'] = 200\\n return JsonResponse(result)\",\n \"def run(self):\\n\\n # creation of directory named after the category name if it doesn't already exist\\n if not os.path.exists(\\\"results/{}/\\\".format(self.category_name)):\\n os.mkdir(\\\"results/{}\\\".format(self.category_name))\\n\\n # creation of books_img directory if it doesn't already exist\\n if not os.path.exists(\\\"results/{}/books_img/\\\".format(self.category_name)):\\n os.mkdir(\\\"results/{}/books_img\\\".format(self.category_name))\\n\\n with open(\\\"results/{}/{}.csv\\\".format(self.category_name, self.category_name), \\\"w\\\") as file:\\n file.write(self.columns_headers)\\n\\n for book_page_url in self.url_list:\\n\\n # data extraction from books pages\\n\\n book_request = requests.get(book_page_url)\\n book_request.encoding = \\\"utf-8\\\"\\n\\n if book_request.ok:\\n book_soup = BeautifulSoup(book_request.text, \\\"html.parser\\\")\\n book_title = book_soup.find(\\\"h1\\\").get_text()\\n rate_and_product_description = book_soup.findAll(\\\"p\\\")\\n rate = rate_and_product_description[2][\\\"class\\\"][1]\\n rate = self.rate_dict[rate]\\n product_description = rate_and_product_description[3].get_text()\\n # We replace \\\";\\\" with \\\":\\\" in product_description\\n product_description = self.regex_wrong_separator.sub(\\\":\\\", product_description)\\n img_url = book_soup.find(\\\"img\\\")\\n img_url = img_url[\\\"src\\\"].replace(\\\"../../\\\", self.target_url)\\n information_table = book_soup.findAll(\\\"td\\\")\\n\\n # list which contains all data about the book (sorted in order)\\n info_list = [book_page_url]\\n\\n # loop which gets data from the information tab and adds them in the info_list\\n for information_line in information_table:\\n information_line = information_line.get_text()\\n info_list.append(information_line)\\n # remove the field \\\"Books\\\"\\n del info_list[2]\\n # remove the field \\\"tax\\\"\\n del info_list[4]\\n info_list.insert(2, \\\"{}\\\".format(book_title))\\n info_list.insert(6, product_description)\\n info_list.insert(7, self.category_name)\\n info_list[8] = rate\\n info_list.append(img_url)\\n\\n # extraction of the number of books available\\n info_list[5] = self.regex_extract_number.findall(info_list[5])[0]\\n # convert info_list in str using \\\";\\\" as separator before writing it in cvs file\\n info = \\\";\\\".join(info_list)\\n\\n file.write(\\\"\\\\n\\\" + info)\\n\\n # We replace \\\"/\\\" by \\\"-\\\" in book title\\n book_title = self.regex_title_correction.sub(\\\"-\\\", book_title)\\n urllib.request.urlretrieve(img_url, \\\"results/{}/books_img/{}.png\\\".format(\\n self.category_name, book_title))\",\n \"def getproductbycategory(self):\\n\\n eight = self.geteightcategories()\\n productsliste = []\\n for category in eight:\\n for page in tqdm(range(1, 5)):\\n r2 = requests.get(\\n \\\"https://fr.openfoodfacts.org/cgi/search.pl?action=process\\\"\\n + \\\"&tagtype_0=categories&tag_contains_0=contains&tag_0={}\\\".format(\\n category\\n )\\n + \\\"&tag_contains_1=contains&tag_1=france&page_size=500\\\"\\n + \\\"&fields=url,categories_tags_fr,product_name,stores_tags\\\"\\n + \\\",nutriscore_grade&tagtype_1=purchase_places&sort_by=\\\"\\n + \\\"unique_scans_n&json=1&page={}\\\".format(page)\\n )\\n dataproducts = r2.json()\\n if dataproducts[\\\"page_count\\\"] is None:\\n continue\\n else:\\n for items in dataproducts[\\\"products\\\"]:\\n productsliste.append(items)\\n\\n self.rawproductdata = productsliste\",\n \"def get_all_news_articles(categories):\\n all_inshorts = []\\n\\n for category in categories:\\n all_category_articles = get_articles(category)\\n all_inshorts = all_inshorts + all_category_articles\\n\\n df = pd.DataFrame(all_inshorts)\\n return df\",\n \"def crawl(self):\\n try:\\n self.crawl_pages()\\n self.crawl_posts()\\n self.crawl_comments()\\n except Exception as exception:\\n self.handle_request_limit(exception)\",\n \"async def initialiser_crawler(self) -> Dict[str, List[req.Response]]:\\n web_pages = {}\\n with ThreadPoolExecutor(max_workers=NUM_WORKERS) as exe:\\n try:\\n loop = asyncio.get_event_loop()\\n tasks = [\\n loop.run_in_executor(exe, self.collect_webpages, keyword)\\n for keyword in self.keywords \\n ]\\n for res in await asyncio.gather(*tasks):\\n web_pages.update(res)\\n except KeyboardInterrupt:\\n loop.close()\\n raise KeyboardInterrupt\\n return web_pages\",\n \"def reagent_label_categories(ctx: click.Context, categories: list = None):\\n adapter = ctx.obj[\\\"adapter\\\"]\\n lims = ctx.obj[\\\"lims\\\"]\\n if not lims:\\n LOG.warning(\\\"Lims connection failed.\\\")\\n raise click.Abort()\\n lims = lims\\n load_all(adapter=adapter, lims=lims, categories=categories)\",\n \"def parse_categories(categories, categories_list, reports):\\n remove_list = [\\\"overview\\\", \\\"configuration\\\", \\\"use_cases\\\", \\\"content\\\"]\\n for distinct_category in categories:\\n report_categories = []\\n category = {}\\n for report_doc in reports:\\n if distinct_category in report_doc[\\\"report-categories\\\"]:\\n category[\\\"category\\\"] = distinct_category\\n category[\\\"description\\\"] = \\\"\\\"\\n for x in remove_list:\\n if x in report_doc:\\n del report_doc[x]\\n report_categories.append(report_doc)\\n category[\\\"reports\\\"] = report_categories\\n categories_list.append(category)\",\n \"def extract_etsy_items(number_of_items: int, category: str) -> pd.DataFrame:\\n print(f\\\"Scrapping category \\\\\\\"{category}\\\\\\\"\\\")\\n result = []\\n page_number = 1\\n headers = {\\\"User-Agent\\\": \\\"Mozilla/5.0\\\"}\\n\\n while len(result) < number_of_items:\\n time.sleep(2)\\n url = f\\\"https://www.etsy.com/search?q={make_url_compatible(category)}\\\" \\\\\\n f\\\"&ref=pagination&page={page_number} \\\"\\n\\n source = requests.get(url, headers=headers)\\n soup = BeautifulSoup(source.content, \\\"html.parser\\\")\\n\\n items = soup.select(\\\"li.wt-list-unstyled\\\")\\n if len(items) == 0:\\n print(f'Total items of {category} was found less than requested: '\\n f'{len(result)}')\\n break\\n\\n for item in items:\\n if len(result) >= number_of_items:\\n break\\n\\n record = extract_item_data(item)\\n if record:\\n result.append(record)\\n\\n page_number += 1\\n\\n return pd.DataFrame(result)\",\n \"def main():\\n base_url = \\\"https://books.toscrape.com/index.html\\\"\\n category_links = scrap_site_category_links(base_url)\\n\\n\\n for category_link in category_links:\\n book_links = scrap_book_links(category_link)\\n\\n headings = [\\\"product_page_url\\\", \\\"upc\\\", \\\"title\\\", \\\"price_including_tax\\\", \\\"price_excluding_tax\\\",\\n \\\"number_available\\\", \\\"product_description\\\", \\\"category\\\", \\\"review_rating\\\", \\\"image_url\\\"]\\n\\n with open(get_category_name(category_link) + \\\".csv\\\", 'w', encoding=\\\"utf-8-sig\\\") as csv_file:\\n writer = csv.DictWriter(csv_file, delimiter=';', fieldnames=headings)\\n writer.writeheader()\\n\\n for book_link in book_links:\\n book = scrap_book_info(book_link)\\n writer.writerow(book)\\n\\n image_downloader(book[\\\"image_url\\\"], \\\"./Books_to_Scrape_Images\\\", book[\\\"upc\\\"] + \\\".png\\\")\",\n \"def insert_data(self, data):\\n category_name = data[\\\"substitutes\\\"][0][\\\"categories\\\"][0]\\n products = data[\\\"substitutes\\\"]\\n if len(products) != 0:\\n i = 0\\n Category.objects.get_or_create(name=category_name)\\n category = Category.objects.get(name=category_name)\\n for product in products:\\n i += 1\\n if Product.objects.filter(name=product[\\\"name\\\"]).exists():\\n pass\\n else:\\n product[i] = Product.objects.get_or_create(name=product[\\\"name\\\"],\\n desc=product[\\\"desc\\\"],\\n photo=product[\\\"photo\\\"],\\n API_link=product[\\\"API_link\\\"],\\n nutriscore=product[\\\"nutriscore\\\"],\\n nutrient_100g=product[\\\"nutrient_100g\\\"],\\n )\\n\\n Product.objects.get(name=product[\\\"name\\\"]).categories.add(category.id)\\n else:\\n return None\",\n \"def loadData(catalog):\\n loadVideos(catalog)\\n loadCategories(catalog)\",\n \"def _scratch_category(self, cat, n_page):\\n payload = {\\n \\\"action\\\": \\\"process\\\",\\n \\\"tagtype_0\\\": \\\"categories\\\",\\n \\\"tag_contains_0\\\": \\\"contains\\\",\\n \\\"tag_0\\\": cat,\\n \\\"tagtype_1\\\": \\\"nutrition_grade\\\",\\n \\\"tag_contains_1\\\": \\\"contains\\\",\\n \\\"fields\\\": \\\",\\\".join(PRODUCTS_FIELDS),\\n \\\"page_size\\\": PAGE_SIZE,\\n \\\"page\\\": n_page,\\n \\\"json\\\": \\\"true\\\",\\n }\\n\\n al_list = get(OFF_URL, params=payload).json()\\n return al_list[\\\"products\\\"]\",\n \"def test_get_categories(self, mocker):\\n mock = mocker.patch(\\\"requests_html.HTMLSession\\\")\\n mock.return_value.get.return_value.html.find.return_value = iter(\\n [\\n mocker.Mock(text=\\\"Ammo\\\", attrs={\\\"href\\\": \\\"catalogue?cat=1\\\"}),\\n mocker.Mock(text=\\\"Food\\\", attrs={\\\"href\\\": \\\"catalogue?cat=2\\\"}),\\n mocker.Mock(text=\\\"Armour\\\", attrs={\\\"href\\\": \\\"catalogue?cat=3\\\"}),\\n mocker.Mock(text=\\\"Weapons\\\", attrs={\\\"href\\\": \\\"catalogue?cat=4\\\"}),\\n ]\\n )\\n\\n result = resources.get_categories()\\n assert list(result) == [\\n (1, \\\"Ammo\\\"),\\n (2, \\\"Food\\\"),\\n (3, \\\"Armour\\\"),\\n (4, \\\"Weapons\\\"),\\n ]\",\n \"async def seed_db():\\n LOGGER.debug(\\\"Started script for seeding database...\\\")\\n conn = await _get_db_connection()\\n\\n categories_status, categories = await conn.status(conn.text(insert_mcc_categories))\\n LOGGER.debug(\\\"Status of inserting mcc categories: %s:\\\", categories_status)\\n for category in categories:\\n LOGGER.debug(\\\"\\\\t- %s\\\", category)\\n\\n mcc_status, mccs = await conn.status(conn.text(insert_mccs))\\n LOGGER.debug(\\\"Status of inserting mccs: %s.\\\", mcc_status)\\n for mcc in mccs:\\n LOGGER.debug(\\\"\\\\t- %s\\\", mcc)\\n\\n LOGGER.debug(\\\"Finished script for seeding database.\\\")\",\n \"def GetCategories():\\n return GetDataFromCsvFile('categories.csv')\",\n \"def load_data(self):\\n if not os.path.isfile(\\\"{}/OFF_data.json\\\".format(settings.DIR_PATH)):\\n self.request_constructor(settings.R_COLLECTION['category'], 'NULL', 'tags')\\n self.crawl_data('category')\\n i = 0\\n for item in self.categories:\\n i += 1\\n cat = item.get(\\\"name\\\")\\n self.request_constructor(settings.R_COLLECTION['product'], cat, 'products')\\n self.crawl_data('product')\\n\\n self.data = {\\\"categories\\\": self.categories, \\\"products\\\": self.products}\\n self.save_data('OFF_data.json')\\n else:\\n with open(\\\"{}/OFF_data.json\\\".format(settings.DIR_PATH), 'r') as f:\\n self.data = json.load(f)\\n self.categories = self.data[\\\"categories\\\"]\\n self.products = self.data[\\\"products\\\"]\\n return self.categories, self.products\",\n \"def get_used():\\r\\n sql = text('''\\r\\n SELECT category.* FROM category, app\\r\\n WHERE app.category_id=category.id GROUP BY category.id\\r\\n ''')\\r\\n results = db.engine.execute(sql)\\r\\n categories = []\\r\\n for row in results:\\r\\n category = dict(id=row.id, name=row.name, short_name=row.short_name,\\r\\n description=row.description)\\r\\n categories.append(category)\\r\\n return categories\",\n \"def mor_prepare_data():\\n prices, locations, areas, links = [], [], [], []\\n for i in range(START_PAGE, SEARCHING_DEPTH+1):\\n handler = requests.get(main_url, params={\\\"page\\\": str(i)})\\n soup = bs4.BeautifulSoup(handler.text, 'lxml')\\n heads = soup.find_all(\\\"header\\\")\\n once = True\\n for head in heads:\\n if head.find(\\\"meta\\\", {\\\"itemprop\\\": \\\"category\\\"}) and once:\\n\\n raw_price = head.find(\\\"meta\\\", {\\\"itemprop\\\": \\\"price\\\"})\\n price = int(float(raw_price[\\\"content\\\"]) if raw_price else \\\"\\\")\\n\\n raw_loc_list = head.find(\\\"h2\\\",\\n {\\\"class\\\": \\\"single-result__title\\\"}).getText().strip().split(\\n \\\", \\\")\\n found = False\\n for loc in raw_loc_list:\\n if location_mapper[CITY].get(loc.lower(), 0):\\n location = location_mapper[CITY][loc.lower()]\\n\\n found = True\\n break\\n if not found:\\n location = \\\"\\\"\\n if DEBUG_MODE:\\n print(raw_loc_list)\\n\\n raw_area = head.find(\\\"p\\\", {\\n \\\"class\\\": \\\"single-result__price single-result__price--currency\\\"}).getText().strip().split()\\n if price and location:\\n square_price = raw_area[0] if len(raw_area) == 2 else \\\"\\\".join(\\n (raw_area[0], raw_area[1]))\\n\\n area = int(price / float(square_price.replace(\\\",\\\", \\\".\\\")))\\n link_url = head.find('a')['href']\\n\\n if location and area and link_url:\\n prices.append(price) if price < PRICE_UPPER_LIMIT else prices.append(\\n PRICE_UPPER_LIMIT)\\n locations.append(location)\\n areas.append(area) if area < AREA_UPPER_LIMIT else areas.append(\\n AREA_UPPER_LIMIT)\\n links.append(link_url)\\n\\n return prices, locations, areas, links\",\n \"def get_categories():\\n\\n url = 'https://fr.openfoodfacts.org/categories.json'\\n data = requests.get(url).json()\\n with open('data/categories.json', 'w') as file:\\n file.write(json.dumps(data, indent=4))\",\n \"def get_course_all_slugs(self):\\n\\n unit_lessons_counter = 0\\n # Unit Page -> Subunit Header + Subunit Block -> Lesson Block -> Lesson Title\\n for course_unit_url, course_unit_slug in zip(\\n self.course_unit_urls, self.course_unit_slugs\\n ):\\n\\n unit_lessons_counter = 0\\n # -> Unit Page\\n try:\\n course_unit_page = BeautifulSoup(\\n requests.get(ROOT_URL + course_unit_url).text, \\\"lxml\\\"\\n )\\n except requests.ConnectionError as e:\\n print(\\\"Error Connecting!\\\\n\\\", e)\\n sys.exit(1)\\n except requests.exceptions.HTTPError as errh:\\n print(\\\"Http Error:\\\", errh)\\n sys.exit(1)\\n except requests.exceptions.ConnectionError as errc:\\n print(\\\"Error Connecting:\\\", errc)\\n sys.exit(1)\\n except requests.exceptions.Timeout as errt:\\n print(\\\"Timeout Error:\\\", errt)\\n sys.exit(1)\\n except requests.exceptions.RequestException as err:\\n print(\\\"OOps: Something Else\\\", err)\\n sys.exit(1)\\n\\n subunit_couter = 0\\n\\n # -> Subunit Header -> Subunit Block\\n for course_subunit_title, course_subunit_body in zip(\\n course_unit_page.find_all(attrs=COURSE_SUBUNIT_TITLE_ATTRS),\\n course_unit_page.find_all(\\n COURSE_SUBUNIT_BODY[\\\"tag\\\"], class_=COURSE_SUBUNIT_BODY[\\\"class\\\"]\\n ),\\n ):\\n\\n logging.debug(\\\"course_subunit_title:{}\\\".format(course_subunit_title))\\n lesson_counter = 0\\n # -> Lesson Block\\n for course_lesson_body in course_subunit_body.find_all(\\n COURSE_LESSON_BODY[\\\"tag\\\"],\\n {\\n \\\"class\\\": [\\n COURSE_LESSON_BODY[\\\"class_i\\\"],\\n COURSE_LESSON_BODY[\\\"class_ii\\\"],\\n ]\\n },\\n ):\\n course_lesson_span = course_lesson_body.find_all(\\n COURSE_LESSON_SPAN[\\\"tag\\\"], class_=COURSE_LESSON_SPAN[\\\"class\\\"]\\n )\\n course_lesson_aria_label = course_lesson_span[0][\\n COURSE_LESSON_LABEL\\n ]\\n logging.debug(\\n \\\"course_lesson_aria_label:{}\\\".format(course_lesson_aria_label)\\n )\\n # -> Lesson Title\\n # Check whether lesson block is a video\\n if course_lesson_aria_label == \\\"Video\\\":\\n lesson_title = course_lesson_body.find(\\n COURSE_LESSON_TITLE[\\\"tag\\\"],\\n class_=COURSE_LESSON_TITLE[\\\"class\\\"],\\n )\\n\\n logging.debug(\\n \\\"course_lesson_title:{}\\\".format(lesson_title.text)\\n )\\n self.lesson_titles.append(lesson_title.text)\\n self.course_all_slugs.append(\\n self.output_rel_path\\n + course_unit_slug\\n + \\\"/\\\"\\n + str(subunit_couter)\\n + \\\"_\\\"\\n + course_subunit_title.text.replace(\\\" \\\", \\\"_\\\")\\n + \\\"/\\\"\\n + str(lesson_counter)\\n + \\\"_\\\"\\n + lesson_title.text.replace(\\\" \\\", \\\"_\\\")\\n )\\n\\n lesson_counter += 1\\n unit_lessons_counter += lesson_counter\\n subunit_couter += 1\\n self.unit_slugs_counter[course_unit_url] = unit_lessons_counter\\n logging.info(\\\"Course - All slugs generated\\\")\",\n \"def get_categories():\\n return session.query(Category)\",\n \"def get_category_list(top_html):\\n page_list = []\\n toppage_soup = BeautifulSoup(top_html, \\\"lxml\\\")\\n categories = toppage_soup.find('ul', {'class': 'colmenu-ul'})\\n categories = categories.findAll(\\\"li\\\")\\n categories_tag = [cat.findAll('a') for cat in categories]\\n categories_tag = [item for sublist in categories_tag for item in sublist]\\n for cat in categories_tag:\\n page = {}\\n link = re.sub(\\\".+bachhoaxanh\\\\\\\\.com/\\\", \\\"\\\", cat['href'])\\n page['relativelink'] = link\\n page['directlink'] = BASE_URL + link\\n page['name'] = re.sub(\\\"/|\\\\\\\\?.=\\\", \\\"_\\\", link)\\n page['label'] = cat.text.strip()\\n page_list.append(page)\\n # Remove duplicates\\n page_list = [dict(t) for t in set(tuple(i.items()) for i in page_list)]\\n return(page_list)\",\n \"def get_category_recipe(filters, db_conn, host_url):\\n try:\\n result = db_conn[\\\"recipes\\\"].find(\\n filters).sort(\\\"createdOn\\\", -1).limit(9)\\n recipe_list = map_response(result, host_url)\\n\\n return recipe_list\\n\\n except Exception as e:\\n print(e)\\n return {\\\"success\\\": False, \\\"message\\\": \\\"Error in api: \\\" + str(e)}\",\n \"def process_data(message_file_name, category_file_name, db_file_name):\\n # step 1: merge two csv files into one data frame\\n messages = pd.read_csv(message_file_name)\\n categories = pd.read_csv(category_file_name)\\n df = messages.merge(categories, on='id')\\n\\n # step 2: reorganize the categories column\\n categories = df['categories'].str.split(pat=';', expand=True)\\n row = categories.iloc[0]\\n category_colnames = [category_name.split('-')[0] for category_name in row]\\n categories.columns = category_colnames\\n for column in categories:\\n categories[column] = categories[column].astype(str).str[-1]\\n categories[column] = categories[column].astype(int)\\n\\n for index, row in categories.iterrows():\\n if row['related']>1:\\n row['related']=1\\n\\n new_df = df.drop(['categories'], axis=1)\\n df = pd.concat([new_df, categories], axis=1)\\n\\n # step 3: remove duplicate records\\n duplicated_record_num = df.duplicated().sum()\\n cleaned_df = df.drop_duplicates()\\n assert cleaned_df.duplicated().sum() == 0\\n\\n # step 4: generate the database\\n table_name = \\\"disaster_response_table\\\"\\n engine = create_engine('sqlite:///' + db_file_name)\\n cleaned_df.to_sql(table_name, engine, index=False, if_exists='replace')\",\n \"def get_all_categories(type=None):\\n \\n categories = None\\n if type is None:\\n categories = get_db().execute('SELECT * FROM categories').fetchall()\\n else:\\n categories = get_db().execute('SELECT * FROM categories WHERE type = ?', (type,)).fetchall()\\n\\n return categories\",\n \"def categories(self, categories: List[str]):\\n\\n self._categories = categories\",\n \"def fill_in(self,category):\\r\\n api_json = JsonFromApi(category)\\r\\n extracted_data = ExtractFromJson(api_json.get_json())\\r\\n self.fill_in_db = DatabaseUpdator(\\r\\n extracted_data.extract_json(), self.mydb)\\r\\n self.fill_in_db.table_product_update()\\r\\n self.fill_in_db.table_category_update()\",\n \"def _verify_basic_categories(self):\\n for cat in CATEGORIES:\\n if not Category.objects.filter(name=cat).exists():\\n self.add_category(cat)\",\n \"def parse(self, response):\\n s1 = set(response.xpath('//div[@id=\\\"leftNav\\\"]//a/@href').extract())\\n s2 = set(response.xpath('//div[@class=\\\"a-section acs_dNav__carousels-container\\\"]//a/@href').extract())\\n s3 = set(response.xpath('//span[@class=\\\"category-link__text\\\"]/../@href').extract())\\n subcategories = s1 | s2 | s3\\n for subcat in subcategories:\\n url = format_url(self.domain + subcat)\\n self.conn.sadd(self.urlFrontierKey, url)\\n if subcat in s3:\\n yield scrapy.Request(url, callback=self.parse)\\n self.logger.info(\\\"category link {} yielded {} sub categories\\\".format(response.url, len(subcategories)))\",\n \"def insert_or_refresh_categories(conn: sqlite3.Connection, api_auth,\\n refresh: bool = True):\\n categories = youtube.get_categories(api_auth)\\n query_string = generate_insert_query('categories',\\n columns=CATEGORIES_COLUMNS,\\n on_conflict_ignore=True)\\n if refresh or not execute_query(conn, 'SELECT * FROM categories'):\\n if execute_query(conn, 'DELETE FROM categories WHERE id IS NOT NULL;'):\\n for category_dict in categories['items']:\\n id_ = category_dict['id']\\n channel_id = category_dict['snippet']['channelId']\\n title = category_dict['snippet']['title']\\n assignable = str(category_dict['snippet']['assignable'])\\n etag = category_dict['etag']\\n execute_query(conn, query_string, (id_, channel_id, title,\\n assignable, etag))\\n conn.commit()\",\n \"def cat_parser(self, response):\\n categories = []\\n for product in response:\\n # crawling categories of each product\\n prod_cat = product[\\\"categories\\\"].split(\\\", \\\")\\n for cat in prod_cat:\\n if cat not in categories:\\n categories.append(cat)\\n return categories\",\n \"def collect_all(self, samples: int, cities: list, country:str) -> None:\\n\\n time_start = time.time()\\n for city in cities:\\n self.collect_city_items(samples,city,country)\\n print(f\\\"All scraping is done! Time elapsed: {time.time()-time_start} seconds.\\\")\",\n \"def create_category_pages(app):\\n env = app.builder.env\\n\\n template = \\\"category.html\\\"\\n\\n categories = env.categories\\n for name, category in categories.iteritems():\\n context = {}\\n context[\\\"title\\\"] = category.name\\n context[\\\"subcategories\\\"] = category.subcategories\\n context[\\\"pages\\\"] = category.pages\\n\\n yield (name, context, template)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69870496","0.68652314","0.65082896","0.6431102","0.63733405","0.63554925","0.633605","0.632757","0.6321157","0.62610203","0.6253101","0.6226809","0.6174908","0.61444","0.6128838","0.6115741","0.6043684","0.6017082","0.5986148","0.5977991","0.5969803","0.59479606","0.5942565","0.59323895","0.5926729","0.59142035","0.58461785","0.5841466","0.5817998","0.57876456","0.5766096","0.5756542","0.57469213","0.57359487","0.5713712","0.56919765","0.5671287","0.5661696","0.5649604","0.563371","0.5608512","0.55995864","0.55867916","0.55824035","0.55750126","0.55679274","0.55585366","0.5554289","0.55479133","0.5541881","0.55351883","0.5528506","0.55232334","0.54993904","0.5497855","0.5492197","0.54896325","0.5487626","0.54875845","0.5477558","0.5461926","0.54604864","0.54475236","0.5426486","0.54136425","0.5405924","0.54048336","0.5402145","0.5395051","0.53819066","0.5375384","0.53749704","0.5354532","0.53524804","0.53416073","0.5341442","0.5340772","0.5337122","0.5336064","0.5329447","0.5316111","0.53143907","0.5309883","0.530548","0.52962285","0.529326","0.5290721","0.52851325","0.52851176","0.52828765","0.5282478","0.5279152","0.52788824","0.52717054","0.5266812","0.5262697","0.5261115","0.52552193","0.52527446","0.52514255"],"string":"[\n \"0.69870496\",\n \"0.68652314\",\n \"0.65082896\",\n \"0.6431102\",\n \"0.63733405\",\n \"0.63554925\",\n \"0.633605\",\n \"0.632757\",\n \"0.6321157\",\n \"0.62610203\",\n \"0.6253101\",\n \"0.6226809\",\n \"0.6174908\",\n \"0.61444\",\n \"0.6128838\",\n \"0.6115741\",\n \"0.6043684\",\n \"0.6017082\",\n \"0.5986148\",\n \"0.5977991\",\n \"0.5969803\",\n \"0.59479606\",\n \"0.5942565\",\n \"0.59323895\",\n \"0.5926729\",\n \"0.59142035\",\n \"0.58461785\",\n \"0.5841466\",\n \"0.5817998\",\n \"0.57876456\",\n \"0.5766096\",\n \"0.5756542\",\n \"0.57469213\",\n \"0.57359487\",\n \"0.5713712\",\n \"0.56919765\",\n \"0.5671287\",\n \"0.5661696\",\n \"0.5649604\",\n \"0.563371\",\n \"0.5608512\",\n \"0.55995864\",\n \"0.55867916\",\n \"0.55824035\",\n \"0.55750126\",\n \"0.55679274\",\n \"0.55585366\",\n \"0.5554289\",\n \"0.55479133\",\n \"0.5541881\",\n \"0.55351883\",\n \"0.5528506\",\n \"0.55232334\",\n \"0.54993904\",\n \"0.5497855\",\n \"0.5492197\",\n \"0.54896325\",\n \"0.5487626\",\n \"0.54875845\",\n \"0.5477558\",\n \"0.5461926\",\n \"0.54604864\",\n \"0.54475236\",\n \"0.5426486\",\n \"0.54136425\",\n \"0.5405924\",\n \"0.54048336\",\n \"0.5402145\",\n \"0.5395051\",\n \"0.53819066\",\n \"0.5375384\",\n \"0.53749704\",\n \"0.5354532\",\n \"0.53524804\",\n \"0.53416073\",\n \"0.5341442\",\n \"0.5340772\",\n \"0.5337122\",\n \"0.5336064\",\n \"0.5329447\",\n \"0.5316111\",\n \"0.53143907\",\n \"0.5309883\",\n \"0.530548\",\n \"0.52962285\",\n \"0.529326\",\n \"0.5290721\",\n \"0.52851325\",\n \"0.52851176\",\n \"0.52828765\",\n \"0.5282478\",\n \"0.5279152\",\n \"0.52788824\",\n \"0.52717054\",\n \"0.5266812\",\n \"0.5262697\",\n \"0.5261115\",\n \"0.52552193\",\n \"0.52527446\",\n \"0.52514255\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5311808"},"document_rank":{"kind":"string","value":"82"}}},{"rowIdx":9987,"cells":{"query":{"kind":"string","value":"Insert all items into the database."},"document":{"kind":"string","value":"def save_items_to_database(items: pd.DataFrame) -> None:\n for item in items.itertuples():\n insert_item(item)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def insert(self, items=''):\n cur = self.conn.cursor()\n\n format_args = {'table': self.__name__,\n 'items': ', '.join(items.keys()),\n 'values': ', '.join([':'+key for key in items.keys()])}\n \n insert_query = \"\"\"\n INSERT INTO {table} ({items}) VALUES ({values})\n \"\"\".format(**format_args)\n\n cur.execute(insert_query, items)","def _insert_bulk(self, iterable):\n self.cursor.executemany(self.INSERT, iterable)\n self.conn.commit()","def populate_database(self):\n self.insert_products()\n self.insert_categories()\n self.insert_products_categories()\n self.insert_stores()\n self.insert_products_stores()","def ingest(self, items: list):\n if not items:\n logging.info(f\"{self.__class__.__name__} : no `items` to store into database.\")\n return\n\n threads = list()\n\n for index, item in enumerate(items):\n logging.info(f\"{self.__class__.__name__} : create and start thread {index}.\")\n x = threading.Thread(target=self.validate_and_insert, args=(index,))\n threads.append(x)\n x.start()\n\n for index, thread in enumerate(threads):\n logging.info(f\"{self.__class__.__name__} : before joining thread {index}.\")\n thread.join()\n logging.info(f\"{self.__class__.__name__} : thread {index} done\")","async def insert_many(self, models):\n\n pass","def insert_db():\n populate_tables()","def insert_data(stored_data, table_name):\r\n for item in stored_data:\r\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)","def fill_db(qty):\n qty = int(qty)\n click.echo('Wiping old db')\n init_db()\n click.echo('Initialized the db.')\n click.echo('Inserting {} random tasks into db.'.format(qty))\n conn = get_db()\n c = conn.cursor()\n for t in create_n_tasks(qty):\n c.execute('''\n INSERT INTO task (task_title, task_description, task_complete, task_archived)\n VALUES ('{title}', '{desc}', {complete}, {archived})'''.format(\n title=t[\"task_title\"],\n desc=t[\"task_description\"],\n complete=t[\"task_complete\"],\n archived=t[\"task_archived\"])\n )\n conn.commit()\n click.echo('Tasks inserted.')","def chunked_insert(model, items, chunk_size=150):\n # https://www.sqlite.org/limits.html#max_compound_select\n with db.atomic():\n for idx in range(0, len(items), chunk_size):\n model.insert_many(items[idx:idx+chunk_size]).execute()","def insert_data(self):\n # Make a connexion with a mock database\n self.generate_data_collection()","def insert_many_execute(self) -> None:\n self.connection.isolation_level = None\n self.cursor.execute('BEGIN TRANSACTION')\n for i in self.__sql_buffer.split(';'):\n self.cursor.execute(i)\n self.__sql_buffer = \"\"\n self.cursor.execute('COMMIT')","def insert_data():\n\tBase.metadata.drop_all(engine)\n\tBase.metadata.create_all(engine)\n\tu1 = insert_user(\"233@B.com\", \"/static/image/avatar.JPG\")\n\tu2 = insert_user(\"fy@B.com\", \"/static/image/avatar.JPG\")\n\tc = insert_catalog(u1.id, \"Sichuan Dish\")\n\tinsert_catalog(u1.id, \"Fujian Dish\")\n\tinsert_catalog(u1.id, \"Guangdong Dish\")\n\tinsert_catalog(u2.id, \"Zhejiang Dish\")\n\tinsert_catalog(u2.id, \"Beijing Dish\")\n\tinsert_item(u1.id, \"Iphone 6 plus\", c, 'Is a phone', None)\n\tinsert_item(u1.id, \"Hot pot\", c, \"Hot hot hot\", None)\n\tinsert_item(u2.id, \"Kong Bao Chicken\", c, \"Classic\", None)","def commit(self):\n for db in self.values():\n db.commit()","def insert(self):\n self.getDbRecord().insert()\n\n return","def insert_data(self) -> None:\n if self.min_insert_size > self.insert_count:\n LOG.debug(\"Not enough data for insert....\")\n return\n LOG.debug(f'Inserting {self.insert_count} records...')\n self.insert.write(self.copy_trailer)\n self.insert.seek(0)\n conn = pg.connect(self.dsn)\n with conn.cursor() as cur:\n cur.copy_expert(self.cmd, self.insert)\n conn.commit()\n conn.close()\n self.insert.close()\n self.create_byte_buffer()","def insert_rows(items):\n # Initialize key variables\n _rows = []\n uniques = {}\n all_kvs = []\n\n # Make list if not so\n if isinstance(items, list) is False:\n items = [items]\n\n # Create a single list of key-value pairs.\n # Add them to a dict to make the pairs unique.\n for item in items:\n if isinstance(item, list):\n all_kvs.extend(item)\n else:\n all_kvs.append(item)\n for _kv in all_kvs:\n uniques[_kv] = None\n\n # Insert the key-value pairs into the database\n for (key, value), _ in uniques.items():\n # Skip pre-existing pairs\n if bool(pair_exists(key, value)) is True:\n continue\n\n # Add values to list for future insertion\n _row = Pair(key=key.encode(), value=value.encode())\n _rows.append(_row)\n\n if bool(_rows) is True:\n with db.db_modify(20007, die=True) as session:\n session.add_all(_rows)","def insertmany(self, table, values):\n col_name = self.table_cols[table][1:]\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\n Log.debug('DB -> %s' % sql)\n self.execute(sql, values)","def insert_values():\n pass","def insertmany(cls, *args):\n return InsertQuery(cls).bulk(True).set(*args)","def insert_data(self, table_name, data):\n for data_point in data:\n query = \"INSERT INTO %s(%s) VALUES (%s)\"\n\n fields = \", \".join(data_point.keys())\n values = \", \".join([self.pack_data(value) for value in data_point.values()])\n self.cursor.execute(query % (table_name, fields, values))\n self.db_connection.commit()","def put(self,items,priority=0):\n with self.pdq:\n self.pdq.cursor().executemany('insert into pdq values (?,?)',[(item,priority) for item in self._toiter(items)])","def insert(self, conn, matches: List[Dict], verbose: bool) -> None:\n for i in range(0, len(matches), self.batch_size):\n batch = self._get_batch(i, matches)\n self._insert_json_to_sql(conn, batch, verbose)","def insert(cls, values):\n result = yield InsertQuery(cls, values).execute()\n returnValue(result)","def save_all(self, objects):\n self.session.add_all(objects)\n self.session.commit()","def fillTweetInDB(self):\n sqlInsertTweets = \"INSERT INTO tweet content VALUES %s\"\n mycursor.executemany(sqlInsertTweets,self.content)\n mydb.commit()","def bulk_insert(self, data, insert_option):\n try:\n if (insert_option == 'distelec'):\n self.db[self.distelect_table_or_collection].insert_many(data)\n\n elif (insert_option == 'padron'):\n self.db[self.elector_table_or_collection].insert_many(data)\n\n elif (insert_option == 'province_stats'):\n self.db[self.province_stats_table_or_collection].insert_many(\n data)\n\n elif (insert_option == 'canton_stats'):\n self.db[self.canton_stats_table_or_collection].insert_many(\n data)\n\n elif (insert_option == 'district_stats'):\n self.db[self.district_stats_table_or_collection].insert_many(\n data)\n\n print(f\"--- The data was inserted ---\")\n\n except Exception as error:\n print(error)","def insert_into_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def db_insertmany(table: str, columns: list, values: list):\n\n db, c = start_db()\n\n cols = ', '.join(columns)\n vals = ', '.join(['?' for _ in values[0]])\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\n\n c.executemany(query, values)\n db.commit()\n db.close()","def _bulk_add_rows(self, converted) :\n\n insert_sql = 'INSERT INTO \"%s\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\n cur = self.con.cursor()\n cur.executemany(insert_sql, converted)","def insert_tables(cur, conn):\n for query in insert_table_queries:\n print('insert', query)\n cur.execute(query)\n conn.commit()","def insert_multiple_db_entries(self, _collection=None, _db_entries=None):\n assert _collection is not None, 'Must specify collection'\n assert _db_entries is not None, 'Must specify documents'\n try:\n # ordered=False ensures that every insert operation will be attempted\n # so that if, e.g., a document already exists, it will be simply skipped\n self.db['db'][_collection].insert_many(_db_entries, ordered=False)\n except pymongo.errors.BulkWriteError as bwe:\n print(*time_stamps(), bwe.details)\n except Exception as _e:\n traceback.print_exc()\n print(_e)","def add_all(self, objects):\n self.lock.acquire()\n self.__Session.add_all(objects)\n self.__Session.commit()\n self.lock.release()","def Insert(self):\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\n self.table_name,\n ', '.join(self.values),\n ', '.join(['?' for _ in self.values])\n )\n return Database().Execute(sql, tuple(self.values.values()))","def insert_records(self, insert_query, insert_query_columns, wiki_data, table_name):\n print(\"Inserting {} rows into {}\".format(len(wiki_data), table_name))\n for index, item in enumerate(wiki_data):\n values_to_insert = [item[column]['value'] for column in insert_query_columns]\n try:\n self.cur.execute(insert_query, values_to_insert)\n except ValueError as ve:\n print(\"Could not execute query : {} with values\".format(insert_query, values_to_insert))\n raise ve\n\n if index % 1000 == 0:\n print(\"Inserted {} rows\".format(index))\n print(\"Inserted {} rows\".format(len(wiki_data)))\n print(\"Finished inserting {}\".format(table_name))","def bulkInsert(self, url, values):\n pass","def do_insert_data(self, *args):\n print(\"Provide data to insert\")\n self.connection_obj.insert_into_table(**self.__class__.populate_data())\n print(\"Data Insertion Successful\")","async def add_items_into_database(pool):\n await pool.execute(\";\".join(f\"ALTER TABLE items ADD COLUMN IF NOT EXISTS \\\"{_.id}\\\" bigint NOT NULL DEFAULT {_.default}\" for _ in Items.all()))","def insert_many(cursor, data):\n query = \"\"\"INSERT INTO hasil(label, arti, query_id)\n VALUES(?, ?, (\n SELECT id FROM pencarian\n WHERE query=?))\"\"\"\n\n cursor.executemany(query, data)","def db_create_all(self):\n with self.app_context():\n self.db.create_all()","def bulk_insert(engine, model, entries):\n with session_scope(engine) as session:\n session.bulk_insert_mappings(model, entries)\n session.commit()","def commit(self):\n self._check_connection()\n\n for s in self._buffer_insert:\n self._connection.execute(s)\n del self._buffer_insert[:]\n\n self._connection.commit()","def insertall(self, rows):\n method = 'tabledata().insertAll().execute()'\n body = {}\n body['rows'] = [{'json': row} for row in rows]\n body[\"kind\"] = \"bigquery#tableDataInsertAllRequest\"\n return self._make_request(method, body)","def insert(self,table,values):\n self.connect.execute(self.insert_disc[table],values)\n self.connect.commit()","def insert_data_category_into_bdd(self):\n for category in constant.LIST_CATEGORIES:\n data = Category(name=category)\n data.save()\n print(\"the category \" + str(category) + \" has been created\")","def bulk_insert(cls, path=\"data.json\"):\n from json import load\n from codecs import open\n \n lists = load(open(path, \"r\", \"utf8\"))\n for lst in lists:\n ing = cls(content = lst)\n ing.put()","async def _insert_games(self, list_of_games):\n\n await self.bot.db.executemany(\n \"\"\"\n INSERT OR IGNORE INTO giveaways_game(key, title, url)\n VALUES (:key,\n :title,\n :url)\n \"\"\",\n list_of_games\n )\n\n await self.bot.db.commit()","def insert(self, sql):\n try:\n # Execute the SQL command\n self.cursor.execute(sql)\n # Commit your changes in the database\n self.db.commit()\n except:\n # Rollback in case there is any error\n self.db.rollback()","async def insert(self, args: Dict[str, Any]):\n keys = \", \".join(args.keys())\n values = \", \".join([f\"${i + 1}\" for i in range(len(args))])\n\n conn: Connection\n async with self.db_pool.acquire() as conn:\n await conn.execute(\n f\"INSERT INTO {self.table_name} \"\n f\"({keys}) VALUES ({values})\",\n *args.values(),\n )","def load_data(connection, insert_sql, data):\n cur = connection.cursor()\n for d in data:\n cur.execute(insert_sql, d)\n connection.commit()","async def seed_db():\n LOGGER.debug(\"Started script for seeding database...\")\n conn = await _get_db_connection()\n\n categories_status, categories = await conn.status(conn.text(insert_mcc_categories))\n LOGGER.debug(\"Status of inserting mcc categories: %s:\", categories_status)\n for category in categories:\n LOGGER.debug(\"\\t- %s\", category)\n\n mcc_status, mccs = await conn.status(conn.text(insert_mccs))\n LOGGER.debug(\"Status of inserting mccs: %s.\", mcc_status)\n for mcc in mccs:\n LOGGER.debug(\"\\t- %s\", mcc)\n\n LOGGER.debug(\"Finished script for seeding database.\")","def save(self):\r\n if self.table == \"Category\":\r\n Category(self.db).insert_query(self.elements)\r\n elif self.table == \"Brand\":\r\n Brand(self.db).insert_query(self.elements)\r\n elif self.table == \"Store\":\r\n Store(self.db).insert_query(self.elements)\r\n elif self.table == \"Product\":\r\n Product(self.db).insert_query(self.elements)","def import_data(self):\n\t\tif not self.log_files or len(self.log_files) ==0:\n\t\t\tprint \"There is no log files need to import into database\"\n\t\telse:\n\t\t\tfor log_file in self.log_files:\n\t\t\t\tdata = self.read_file(log_file)\n\t\t\t\tself.conn.insert(data)","def store(self, item):\n cursor = self.conn.cursor()\n # Store the item\n if item:\n cursor.execute(*self._build_insert(item, 'items'))\n for file_ in item.files:\n cursor.execute(\"\"\"insert into files (filename, item_id)\n values (?, ?)\"\"\", (file_, item.kg_id))\n self.conn.commit()\n self.logger.info(\"Succesfully stored item %d\" % item.kg_id)","def insert_tables(cur, conn):\n for query in insert_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n \n except psycopg2.Error as e:\n print(e)","def all_time_player_save():\n try:\n all_players = players.get_players()\n ap = db.all_time_players\n ap.insert_many(all_players)\n except OperationFailure as e:\n print(e)","def insert_tables(cur, conn):\n for query in insert_table_queries:\n try:\n cur.execute(query)\n conn.commit()\n except Exception as e:\n print(e)","def bulk_insert(cls, device_id, imeis):\n insertion_object = []\n for imei in imeis:\n insertion_object.append({'imei': imei, 'normalized_imei': imei[0:14], 'device_id': device_id})\n res = db.engine.execute(ImeiDevice.__table__.insert(), insertion_object)\n res.close()","def insert_mass(self, table_name, data, ignore=False):\n fields = []\n to_insert = []\n for row in data:\n if not len(fields):\n fields = map((lambda s: \"`\" + str(s) + \"`\"), row.keys())\n values = map(self.quote, row.values())\n to_insert.append(\"({0})\".format(\", \".join(values)))\n\n if len(to_insert)%50 == 0:\n self.q(\n \"INSERT \" + (\"IGNORE\" if ignore else \"\") + \" INTO `{0}` ({1}) VALUES {2}\"\n .format(table_name, \", \".join(fields), \", \".join(to_insert))\n )\n to_insert = []\n\n if len(to_insert):\n self.q(\n \"INSERT \" + (\"IGNORE\" if ignore else \"\") + \" INTO `{0}` ({1}) VALUES {2}\"\n .format(table_name, \", \".join(fields), \", \".join(to_insert))\n )","def insert(self, db):\n if not self.content:\n raise ValueError(\"SequenceList content property must be filled in before calling save.\")\n if not self.title:\n raise ValueError(\"SequenceList title property must be filled in before calling save.\")\n seq_item_list = SequenceListItems(self.content)\n cur = db.cursor()\n self._insert_data(cur, seq_item_list, self.title)\n cur.close()\n db.commit()","def insert_data(self):\n\n pass","def insert_products(self):\n logic = ProductLogic()\n \n try:\n # We create the list of product objects\n products = self.objects_factory.create_product_object_list()\n products = set(products)\n\n for product in products:\n logic.insert(product)\n except:\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')","def save_all(objs: List[ModelBase], DataClass: ModelBase):\n dicts = map(lambda x: x.to_dict(), objs)\n with db.atomic():\n if driver is Driver.POSTGRESQL:\n for bar in dicts:\n DataClass.insert(bar).on_conflict(\n update=bar,\n conflict_target=(\n DataClass.stock_id,\n DataClass.date,\n ),\n ).execute()\n else:\n i = 1\n num = 5000\n for c in chunked(dicts, num):\n sta = time.time()\n print(f\"Insert data to database {DataClass.__name__}: {i}-{i + num - 1}\")\n DataClass.insert_many(c).on_conflict_replace().execute()\n print(time.time() - sta)\n i += num","def addAll(self, objs):\n self.getSession().add_all(objs)\n self.commit() # paranoially\n return objs","def bulk_copy_to_db(self):\n database = PostgreSQLCommon()\n\n try:\n file = open(self.file_name_hash)\n database.bulk_copy(file, self.storage_table)\n\n m.info('Bulk insert from %s has been successfully completed!'\n % self.file_name_hash)\n except Exception as err:\n m.error('OOps! Bulk insert operation FAILED! Reason: %s' % str(err))\n finally:\n database.close()\n\n if os.path.exists(self.file_name_hash):\n os.remove(self.file_name_hash)","def syncdb(self, dropfirst=False):\n for db in self._db_tree:\n for collection in db['collections']:\n if dropfirst:\n self._datastore[db['database']][collection['name']].drop()\n\n with io.open(os.path.join(self._db_path, db['database'],\n collection['file']), encoding='utf-8') as coll_json:\n for record in coll_json.readlines():\n record = json_util.loads(record)\n self._datastore[db['database']][collection['name']].insert(record, check_keys=False)","def _insert_data(cur, data):\n try:\n _insert_users(data['users'], cur)\n logger.info('Users inserted to database')\n except KeyError as e:\n logger.error(f\"Failed to insert users to database: {e}\")\n try:\n things = [thing for thing in data['things'].values() if thing['remix'] is None]\n _insert_things(things, cur)\n logger.info('Things inserted to database')\n except KeyError as e:\n logger.error(f\"Failed to insert things to database: {e}\")\n try:\n remixes = [thing for thing in data['things'].values() if thing['remix'] is not None]\n _insert_things(remixes, cur)\n logger.info('Remixes inserted to database')\n except KeyError as e:\n logger.error(f\"Failed to insert remixes to database: {e}\")\n try:\n _insert_makes(data['makes'], cur)\n logger.info('Makes inserted to database')\n except KeyError as e:\n logger.error(f\"Failed to insert makes to database: {e}\")","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_tables(cur, conn):\n for query in insert_table_queries:\n cur.execute(query)\n conn.commit()","def insert_execute(self, insert_data):\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)","def test_orm_bulk_insert(n):\n session = Session(bind=engine)\n session.execute(\n insert(Customer),\n [\n {\n \"name\": \"customer name %d\" % i,\n \"description\": \"customer description %d\" % i,\n }\n for i in range(n)\n ],\n )\n session.commit()","def insert(self):\n db.session.add(self)\n db.session.commit()","def insert(self):\n db.session.add(self)\n db.session.commit()","def insert(self):\n db.session.add(self)\n db.session.commit()","def add_to_database(session, objects):\n if isinstance(objects, list):\n session.add_all(objects)\n else:\n session.add(objects)\n session.commit()","def insert_into_db(self, database):\n\n # insert person\n keys = \"\"\n values = \"\"\n for key, value in self.person.items():\n # location\n if key == \"location\":\n # ensure location is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \n VALUES((SELECT location_id \n FROM p21_cdm.location\n WHERE city='{self.person['location']['city']}' \n and zip='{self.person['location']['zip']}'), \n {values[:-1]})\"\"\")\n\n # insert visits\n for visit in self.visits:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n for key, value in visit.items():\n if key == \"care_site_name\":\n # ensure care site is in table\n database.select(f\"\"\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \n FROM p21_cdm.care_site \n WHERE care_site_name='{value}') \n THEN INSERT INTO p21_cdm.care_site (care_site_name) \n VALUES ('{value}'); END IF; END; $do$\"\"\")\n continue\n\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n database.select(f\"\"\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \n VALUES((SELECT care_site_id\n FROM p21_cdm.care_site\n WHERE care_site_name='{visit['care_site_name']}'),\n {values[:-1]}) \n RETURNING visit_occurrence_id\"\"\")\n\n # insert measurements, observations, conditions & procedures\n for data, tablename in [(self.measurements, \"measurement\"),\n (self.observations, \"observation\"),\n (self.conditions, \"condition_occurrence\"),\n (self.procedures, \"procedure_occurrence\")]:\n for entry in data:\n keys = \"person_id,\"\n values = f\"'{self.person['person_id']}',\"\n\n for key, value in entry.items():\n keys += f\"{key},\"\n values += f\"'{value}',\"\n\n entry[\"sql_id\"] = database.select(f\"\"\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\n VALUES({values[:-1]}) RETURNING {tablename}_id\"\"\")[0][0]\n\n # insert fact_relationships in both directions\n for table1, entry1, table2, entry2 in self.fact_relations:\n # 44818890 = Finding associated with (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\"\"\")\n # 44818792 = Associated with finding (SNOMED)\n database.select(f\"\"\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \n domain_concept_id_2, fact_id_2, \n relationship_concept_id)\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\"\"\")\n\n # make transactions persistent\n database.commit()","def insert_to_db(self) -> None:\n query = '''INSERT INTO ESLReceipts(Transaction_Number, Date, Description, Memo,\n Amount_Debit, Amount_Credit, Balance, Check_Number, \n Fees, Card_Type, Is_Payment, Is_Transaction, User_id)\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?);'''\n self.db.commit(query, values=self.to_tuple())\n\n if self.is_transaction \\\n and self.transaction is not None \\\n and not self.transaction.exists_in_db():\n self.transaction.insert_to_db()","def insert_data_bulk(self, table_name, data):\n if len(data) == 0:\n return\n\n fields = \", \".join(data[0].keys())\n value_placeholders = \", \".join([\"%s\" for f in data[0].keys()])\n query = \"INSERT INTO %s(%s) VALUES (%s)\" % (table_name, fields, value_placeholders)\n\n data = [tuple(self.pack(data_point.values())) for data_point in data]\n\n chunk_size = 50000\n data_chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\n for chunk in data_chunks:\n self.cursor.executemany(query, chunk)\n self.db_connection.commit()","def save_all(self):\r\n for index in range(self.count()):\r\n self.save(index)","def insertionGenero (cur, conn, genre_list):\n for genre in genre_list :\n idGenero = genre[0]\n genero=genre[1]\n # print(generoInsert.format(idTitulo,genero))\n # REGISTER DATA IN GENERO TABLE\n cur.execute(generoInsert.format(idGenero,genero))\n conn.commit()","def _batch_write(self):\n if self.to_put:\n db.put(self.to_put)\n self.to_put = []\n if self.to_delete:\n db.delete(self.to_delete)\n self.to_delete = []","def insert(self, media):\n insert_query = \"\"\"INSERT INTO %s VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n \"\"\" % MediaCollection.COLLECTIONS_TABLE\n self.cursor.execute(insert_query, media.totuple())\n self.connection.commit()","def insert_many_rows(self, row_count=10000):\n values = [\"('name_{}')\".format(i) for i in range(row_count)]\n values_string = \",\".join(values)\n self.run_sync(f\"INSERT INTO manager (name) VALUES {values_string};\")","def insert(self, fields, values):\n sql = self.generate_insert_sql(fields, values)\n self.sqlhistory.append(sql)\n return self.sql_insert(sql)","def insert_test_data(client):\n businesses = get_businesses()\n for business_dict in businesses:\n save_business_to_db(business_dict)\n\n collection = Business.objects()\n assert len(collection) == 12","def ingest(cls):\n for html in cls.query.all():\n session.add(html.parse())\n\n session.commit()","def save_in_db(self):\n self.sql_database.table_name = self.table_db\n self.sql_database.db_name = self.db\n if self.sql_database.insert_item(text_path=self.path, word_first=self.word_1.get(),\n word_second=self.word_2.get(),\n word_third=self.word_3.get(), word_fourth=self.word_4.get(),\n word_fifth=self.word_5.get()):\n msg.showinfo(message=\"Done\")","def db_insert(self, table_name: str, data: dict):\n \n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\n self.db.execute(query, list(data.values()))","async def test_valid_insert(database, valid_data):\n await database.setup_database(reset=True)\n for id ,user_id,embeddings,batch_id in valid_data:\n await database.insert_user(user_id=user_id)\n await database.insert(id=id,\n user_id=user_id,\n embeddings=embeddings,\n batch_id=batch_id)\n await database.close_pool()","def insert_data(self):\n self.connection_database()\n\n # no insertion if the table Food already contains any data\n self.cursor.execute(\"SELECT * FROM Food\")\n data_table_food = self.cursor.fetchall()\n if not data_table_food:\n\n # instantiate the class Call_api\n new_call_api = ca.CallApi()\n new_call_api.load_data()\n categories = new_call_api.categories\n list_data = new_call_api.list_data\n\n for elt, element in zip(categories, list_data):\n\n # inserting data into Category table\n insert_data_categories = (\"\"\"INSERT IGNORE INTO Category (categories)\n VALUES({0})\"\"\"\n .format(\"\\'\"+elt+\"\\'\"))\n self.cursor.execute(insert_data_categories)\n self.data_base.commit()\n\n # inserting data into Food table\n for value in element['products']:\n if element['products'].index(value) < 100:\n try:\n product_name = \"\\'\"+value['product_name_fr'].replace(\"'\", \"\")+\"\\'\"\n nutrition_grade = \"\\'\"+value['nutrition_grade_fr'].replace(\"'\", \"\")+\"\\'\"\n ingredients = \"\\'\"+value['ingredients_text'].replace(\"'\", \"\")+\"\\'\"\n store_tags = \"\\'\"+\", \".join(value['stores_tags']).replace(\"'\", \"\")+\"\\'\"\n url = \"\\'\"+value['url'].replace(\"'\", \"\")+\"\\'\"\n\n insert_data_food = (\"\"\"INSERT IGNORE INTO Food (name_food, category_id,\n nutriscore, description, store, link) \n VALUES({0}, \n (SELECT id FROM Category WHERE categories = {1}),\n {2}, {3}, {4}, {5})\"\"\"\n .format(product_name, \"\\'\"+elt+\"\\'\",\n nutrition_grade, ingredients,\n store_tags, url))\n self.cursor.execute(insert_data_food)\n self.data_base.commit()\n\n # if errors\n except KeyError:\n continue","def fill_the_db(testapp):\n SessionFactory = testapp.app.registry[\"dbsession_factory\"]\n with transaction.manager:\n dbsession = get_tm_session(SessionFactory, transaction.manager)\n if len(dbsession.query(Entries).all()) == 0:\n for entry in ENTRIES:\n row = Entries(title=entry[\"title\"], creation_date=entry[\"creation_date\"], body=entry[\"body\"])\n dbsession.add(row)","def insert(self, item, **kwargs) -> None:\n for entry in item.get_entries():\n self.client.insert(\n entry[\"feature\"],\n entry[\"label\"].encode(),\n group_label=entry[\"group_label\"].encode(),\n **kwargs\n )","def fill_the_db(testapp):\n session_factory = testapp.app.registry[\"dbsession_factory\"]\n with transaction.manager:\n dbsession = get_tm_session(session_factory, transaction.manager)\n for entry in ENTRIES:\n row = Entry(title=entry[\"title\"],\n creation_date=entry[\"creation_date\"],\n body=entry[\"body\"])\n dbsession.add(row)","def insertMasivo(self, objects):\n retorno = 1\n \n try:\n n = len(objects)\n for i in xrange(n):\n self.session.add(objects[i])\n if i % 100 == 0:\n self.session.flush()\n #for object in objects:\n # self.session.add(object)\n self.session.commit()\n except sqlalchemy.exc.DBAPIError, e:\n if self.session is not None:\n self.session.rollback()\n retorno = -1\n print(\"Error!\", e)\n finally:\n if self._DAOAlchemy__cerrarSesion:\n self.session.close()\n return retorno","def insert_many(self, frame, table_name, conn=None, index=False, index_label=None, \n schema=None, chunksize=None, copy=True, auto_adjust=True):\n \n table = SQLTable(table_name, self, frame=frame, table_setup=False, index=index,\n if_exists='append', index_label=index_label, schema=schema)\n \n table.insert(conn=conn, bulk=False, chunksize=chunksize, copy=copy, \n auto_adjust=auto_adjust)","def insert(cls, *lst, **dct):\n cls.runtime.set_set(lst, dct)\n return InsertQuery(cls.runtime)","def insert_tables(cur, conn):\n logger.debug(\"Insert tables ...\")\n for query in insert_table_queries:\n logger.debug(f'RUN query: {query}')\n cur.execute(query)\n conn.commit()\n\n print('All files INSERTED OK.')","def put_it_in_tables(self):\n my_connection = mysql.connector.connect(user=self.user, password=self.password, database='openfoodfacts')\n cursor = my_connection.cursor(buffered=True)\n for i in self.my_data:\n prod_name = i['product_name']\n try:\n add_aliment = (\"INSERT INTO aliment \"\n \"(product_name, product_description, barcode, nutritional_score, stores, product_category) \"\n \"VALUES (%s, %s, %s, %s, %s, %s)\")\n data_aliment = (i['product_name'].replace(\"'\", \"''\"), i['product_description'].replace(\"'\", \"''\"), i['barcode'].replace(\"'\", \"''\"), i['nutritional_score'].replace(\"'\", \"''\"), i['stores'].replace(\"'\", \"''\"), i['product_category'].replace(\"'\", \"''\"))\n cursor.execute(add_aliment, data_aliment)\n except mysql.connector.IntegrityError:\n pass \n my_connection.commit()\n cursor.close()\n my_connection.close()\n print(\"ok c'est fait\")","def execute_many(self, query: str, args):\n return self.insert_many(query, args)"],"string":"[\n \"def insert(self, items=''):\\n cur = self.conn.cursor()\\n\\n format_args = {'table': self.__name__,\\n 'items': ', '.join(items.keys()),\\n 'values': ', '.join([':'+key for key in items.keys()])}\\n \\n insert_query = \\\"\\\"\\\"\\n INSERT INTO {table} ({items}) VALUES ({values})\\n \\\"\\\"\\\".format(**format_args)\\n\\n cur.execute(insert_query, items)\",\n \"def _insert_bulk(self, iterable):\\n self.cursor.executemany(self.INSERT, iterable)\\n self.conn.commit()\",\n \"def populate_database(self):\\n self.insert_products()\\n self.insert_categories()\\n self.insert_products_categories()\\n self.insert_stores()\\n self.insert_products_stores()\",\n \"def ingest(self, items: list):\\n if not items:\\n logging.info(f\\\"{self.__class__.__name__} : no `items` to store into database.\\\")\\n return\\n\\n threads = list()\\n\\n for index, item in enumerate(items):\\n logging.info(f\\\"{self.__class__.__name__} : create and start thread {index}.\\\")\\n x = threading.Thread(target=self.validate_and_insert, args=(index,))\\n threads.append(x)\\n x.start()\\n\\n for index, thread in enumerate(threads):\\n logging.info(f\\\"{self.__class__.__name__} : before joining thread {index}.\\\")\\n thread.join()\\n logging.info(f\\\"{self.__class__.__name__} : thread {index} done\\\")\",\n \"async def insert_many(self, models):\\n\\n pass\",\n \"def insert_db():\\n populate_tables()\",\n \"def insert_data(stored_data, table_name):\\r\\n for item in stored_data:\\r\\n cursor.execute('''INSERT INTO {} VALUES(?, ?, ?, ?)'''.format(table_name), item)\",\n \"def fill_db(qty):\\n qty = int(qty)\\n click.echo('Wiping old db')\\n init_db()\\n click.echo('Initialized the db.')\\n click.echo('Inserting {} random tasks into db.'.format(qty))\\n conn = get_db()\\n c = conn.cursor()\\n for t in create_n_tasks(qty):\\n c.execute('''\\n INSERT INTO task (task_title, task_description, task_complete, task_archived)\\n VALUES ('{title}', '{desc}', {complete}, {archived})'''.format(\\n title=t[\\\"task_title\\\"],\\n desc=t[\\\"task_description\\\"],\\n complete=t[\\\"task_complete\\\"],\\n archived=t[\\\"task_archived\\\"])\\n )\\n conn.commit()\\n click.echo('Tasks inserted.')\",\n \"def chunked_insert(model, items, chunk_size=150):\\n # https://www.sqlite.org/limits.html#max_compound_select\\n with db.atomic():\\n for idx in range(0, len(items), chunk_size):\\n model.insert_many(items[idx:idx+chunk_size]).execute()\",\n \"def insert_data(self):\\n # Make a connexion with a mock database\\n self.generate_data_collection()\",\n \"def insert_many_execute(self) -> None:\\n self.connection.isolation_level = None\\n self.cursor.execute('BEGIN TRANSACTION')\\n for i in self.__sql_buffer.split(';'):\\n self.cursor.execute(i)\\n self.__sql_buffer = \\\"\\\"\\n self.cursor.execute('COMMIT')\",\n \"def insert_data():\\n\\tBase.metadata.drop_all(engine)\\n\\tBase.metadata.create_all(engine)\\n\\tu1 = insert_user(\\\"233@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tu2 = insert_user(\\\"fy@B.com\\\", \\\"/static/image/avatar.JPG\\\")\\n\\tc = insert_catalog(u1.id, \\\"Sichuan Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Fujian Dish\\\")\\n\\tinsert_catalog(u1.id, \\\"Guangdong Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Zhejiang Dish\\\")\\n\\tinsert_catalog(u2.id, \\\"Beijing Dish\\\")\\n\\tinsert_item(u1.id, \\\"Iphone 6 plus\\\", c, 'Is a phone', None)\\n\\tinsert_item(u1.id, \\\"Hot pot\\\", c, \\\"Hot hot hot\\\", None)\\n\\tinsert_item(u2.id, \\\"Kong Bao Chicken\\\", c, \\\"Classic\\\", None)\",\n \"def commit(self):\\n for db in self.values():\\n db.commit()\",\n \"def insert(self):\\n self.getDbRecord().insert()\\n\\n return\",\n \"def insert_data(self) -> None:\\n if self.min_insert_size > self.insert_count:\\n LOG.debug(\\\"Not enough data for insert....\\\")\\n return\\n LOG.debug(f'Inserting {self.insert_count} records...')\\n self.insert.write(self.copy_trailer)\\n self.insert.seek(0)\\n conn = pg.connect(self.dsn)\\n with conn.cursor() as cur:\\n cur.copy_expert(self.cmd, self.insert)\\n conn.commit()\\n conn.close()\\n self.insert.close()\\n self.create_byte_buffer()\",\n \"def insert_rows(items):\\n # Initialize key variables\\n _rows = []\\n uniques = {}\\n all_kvs = []\\n\\n # Make list if not so\\n if isinstance(items, list) is False:\\n items = [items]\\n\\n # Create a single list of key-value pairs.\\n # Add them to a dict to make the pairs unique.\\n for item in items:\\n if isinstance(item, list):\\n all_kvs.extend(item)\\n else:\\n all_kvs.append(item)\\n for _kv in all_kvs:\\n uniques[_kv] = None\\n\\n # Insert the key-value pairs into the database\\n for (key, value), _ in uniques.items():\\n # Skip pre-existing pairs\\n if bool(pair_exists(key, value)) is True:\\n continue\\n\\n # Add values to list for future insertion\\n _row = Pair(key=key.encode(), value=value.encode())\\n _rows.append(_row)\\n\\n if bool(_rows) is True:\\n with db.db_modify(20007, die=True) as session:\\n session.add_all(_rows)\",\n \"def insertmany(self, table, values):\\n col_name = self.table_cols[table][1:]\\n sql = 'INSERT INTO %s(%s) VALUES (%s)' % (table, ','.join(col_name), ','.join(['%s'] * len(values[0])))\\n Log.debug('DB -> %s' % sql)\\n self.execute(sql, values)\",\n \"def insert_values():\\n pass\",\n \"def insertmany(cls, *args):\\n return InsertQuery(cls).bulk(True).set(*args)\",\n \"def insert_data(self, table_name, data):\\n for data_point in data:\\n query = \\\"INSERT INTO %s(%s) VALUES (%s)\\\"\\n\\n fields = \\\", \\\".join(data_point.keys())\\n values = \\\", \\\".join([self.pack_data(value) for value in data_point.values()])\\n self.cursor.execute(query % (table_name, fields, values))\\n self.db_connection.commit()\",\n \"def put(self,items,priority=0):\\n with self.pdq:\\n self.pdq.cursor().executemany('insert into pdq values (?,?)',[(item,priority) for item in self._toiter(items)])\",\n \"def insert(self, conn, matches: List[Dict], verbose: bool) -> None:\\n for i in range(0, len(matches), self.batch_size):\\n batch = self._get_batch(i, matches)\\n self._insert_json_to_sql(conn, batch, verbose)\",\n \"def insert(cls, values):\\n result = yield InsertQuery(cls, values).execute()\\n returnValue(result)\",\n \"def save_all(self, objects):\\n self.session.add_all(objects)\\n self.session.commit()\",\n \"def fillTweetInDB(self):\\n sqlInsertTweets = \\\"INSERT INTO tweet content VALUES %s\\\"\\n mycursor.executemany(sqlInsertTweets,self.content)\\n mydb.commit()\",\n \"def bulk_insert(self, data, insert_option):\\n try:\\n if (insert_option == 'distelec'):\\n self.db[self.distelect_table_or_collection].insert_many(data)\\n\\n elif (insert_option == 'padron'):\\n self.db[self.elector_table_or_collection].insert_many(data)\\n\\n elif (insert_option == 'province_stats'):\\n self.db[self.province_stats_table_or_collection].insert_many(\\n data)\\n\\n elif (insert_option == 'canton_stats'):\\n self.db[self.canton_stats_table_or_collection].insert_many(\\n data)\\n\\n elif (insert_option == 'district_stats'):\\n self.db[self.district_stats_table_or_collection].insert_many(\\n data)\\n\\n print(f\\\"--- The data was inserted ---\\\")\\n\\n except Exception as error:\\n print(error)\",\n \"def insert_into_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def db_insertmany(table: str, columns: list, values: list):\\n\\n db, c = start_db()\\n\\n cols = ', '.join(columns)\\n vals = ', '.join(['?' for _ in values[0]])\\n query = f'INSERT INTO {table} ({cols}) VALUES ({vals})'\\n\\n c.executemany(query, values)\\n db.commit()\\n db.close()\",\n \"def _bulk_add_rows(self, converted) :\\n\\n insert_sql = 'INSERT INTO \\\"%s\\\" VALUES (%s)' % (self.name, ','.join(['?'] * len(self.cols)))\\n cur = self.con.cursor()\\n cur.executemany(insert_sql, converted)\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n print('insert', query)\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_multiple_db_entries(self, _collection=None, _db_entries=None):\\n assert _collection is not None, 'Must specify collection'\\n assert _db_entries is not None, 'Must specify documents'\\n try:\\n # ordered=False ensures that every insert operation will be attempted\\n # so that if, e.g., a document already exists, it will be simply skipped\\n self.db['db'][_collection].insert_many(_db_entries, ordered=False)\\n except pymongo.errors.BulkWriteError as bwe:\\n print(*time_stamps(), bwe.details)\\n except Exception as _e:\\n traceback.print_exc()\\n print(_e)\",\n \"def add_all(self, objects):\\n self.lock.acquire()\\n self.__Session.add_all(objects)\\n self.__Session.commit()\\n self.lock.release()\",\n \"def Insert(self):\\n sql = 'INSERT INTO %s ( %s ) VALUES ( %s )' % (\\n self.table_name,\\n ', '.join(self.values),\\n ', '.join(['?' for _ in self.values])\\n )\\n return Database().Execute(sql, tuple(self.values.values()))\",\n \"def insert_records(self, insert_query, insert_query_columns, wiki_data, table_name):\\n print(\\\"Inserting {} rows into {}\\\".format(len(wiki_data), table_name))\\n for index, item in enumerate(wiki_data):\\n values_to_insert = [item[column]['value'] for column in insert_query_columns]\\n try:\\n self.cur.execute(insert_query, values_to_insert)\\n except ValueError as ve:\\n print(\\\"Could not execute query : {} with values\\\".format(insert_query, values_to_insert))\\n raise ve\\n\\n if index % 1000 == 0:\\n print(\\\"Inserted {} rows\\\".format(index))\\n print(\\\"Inserted {} rows\\\".format(len(wiki_data)))\\n print(\\\"Finished inserting {}\\\".format(table_name))\",\n \"def bulkInsert(self, url, values):\\n pass\",\n \"def do_insert_data(self, *args):\\n print(\\\"Provide data to insert\\\")\\n self.connection_obj.insert_into_table(**self.__class__.populate_data())\\n print(\\\"Data Insertion Successful\\\")\",\n \"async def add_items_into_database(pool):\\n await pool.execute(\\\";\\\".join(f\\\"ALTER TABLE items ADD COLUMN IF NOT EXISTS \\\\\\\"{_.id}\\\\\\\" bigint NOT NULL DEFAULT {_.default}\\\" for _ in Items.all()))\",\n \"def insert_many(cursor, data):\\n query = \\\"\\\"\\\"INSERT INTO hasil(label, arti, query_id)\\n VALUES(?, ?, (\\n SELECT id FROM pencarian\\n WHERE query=?))\\\"\\\"\\\"\\n\\n cursor.executemany(query, data)\",\n \"def db_create_all(self):\\n with self.app_context():\\n self.db.create_all()\",\n \"def bulk_insert(engine, model, entries):\\n with session_scope(engine) as session:\\n session.bulk_insert_mappings(model, entries)\\n session.commit()\",\n \"def commit(self):\\n self._check_connection()\\n\\n for s in self._buffer_insert:\\n self._connection.execute(s)\\n del self._buffer_insert[:]\\n\\n self._connection.commit()\",\n \"def insertall(self, rows):\\n method = 'tabledata().insertAll().execute()'\\n body = {}\\n body['rows'] = [{'json': row} for row in rows]\\n body[\\\"kind\\\"] = \\\"bigquery#tableDataInsertAllRequest\\\"\\n return self._make_request(method, body)\",\n \"def insert(self,table,values):\\n self.connect.execute(self.insert_disc[table],values)\\n self.connect.commit()\",\n \"def insert_data_category_into_bdd(self):\\n for category in constant.LIST_CATEGORIES:\\n data = Category(name=category)\\n data.save()\\n print(\\\"the category \\\" + str(category) + \\\" has been created\\\")\",\n \"def bulk_insert(cls, path=\\\"data.json\\\"):\\n from json import load\\n from codecs import open\\n \\n lists = load(open(path, \\\"r\\\", \\\"utf8\\\"))\\n for lst in lists:\\n ing = cls(content = lst)\\n ing.put()\",\n \"async def _insert_games(self, list_of_games):\\n\\n await self.bot.db.executemany(\\n \\\"\\\"\\\"\\n INSERT OR IGNORE INTO giveaways_game(key, title, url)\\n VALUES (:key,\\n :title,\\n :url)\\n \\\"\\\"\\\",\\n list_of_games\\n )\\n\\n await self.bot.db.commit()\",\n \"def insert(self, sql):\\n try:\\n # Execute the SQL command\\n self.cursor.execute(sql)\\n # Commit your changes in the database\\n self.db.commit()\\n except:\\n # Rollback in case there is any error\\n self.db.rollback()\",\n \"async def insert(self, args: Dict[str, Any]):\\n keys = \\\", \\\".join(args.keys())\\n values = \\\", \\\".join([f\\\"${i + 1}\\\" for i in range(len(args))])\\n\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n await conn.execute(\\n f\\\"INSERT INTO {self.table_name} \\\"\\n f\\\"({keys}) VALUES ({values})\\\",\\n *args.values(),\\n )\",\n \"def load_data(connection, insert_sql, data):\\n cur = connection.cursor()\\n for d in data:\\n cur.execute(insert_sql, d)\\n connection.commit()\",\n \"async def seed_db():\\n LOGGER.debug(\\\"Started script for seeding database...\\\")\\n conn = await _get_db_connection()\\n\\n categories_status, categories = await conn.status(conn.text(insert_mcc_categories))\\n LOGGER.debug(\\\"Status of inserting mcc categories: %s:\\\", categories_status)\\n for category in categories:\\n LOGGER.debug(\\\"\\\\t- %s\\\", category)\\n\\n mcc_status, mccs = await conn.status(conn.text(insert_mccs))\\n LOGGER.debug(\\\"Status of inserting mccs: %s.\\\", mcc_status)\\n for mcc in mccs:\\n LOGGER.debug(\\\"\\\\t- %s\\\", mcc)\\n\\n LOGGER.debug(\\\"Finished script for seeding database.\\\")\",\n \"def save(self):\\r\\n if self.table == \\\"Category\\\":\\r\\n Category(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Brand\\\":\\r\\n Brand(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Store\\\":\\r\\n Store(self.db).insert_query(self.elements)\\r\\n elif self.table == \\\"Product\\\":\\r\\n Product(self.db).insert_query(self.elements)\",\n \"def import_data(self):\\n\\t\\tif not self.log_files or len(self.log_files) ==0:\\n\\t\\t\\tprint \\\"There is no log files need to import into database\\\"\\n\\t\\telse:\\n\\t\\t\\tfor log_file in self.log_files:\\n\\t\\t\\t\\tdata = self.read_file(log_file)\\n\\t\\t\\t\\tself.conn.insert(data)\",\n \"def store(self, item):\\n cursor = self.conn.cursor()\\n # Store the item\\n if item:\\n cursor.execute(*self._build_insert(item, 'items'))\\n for file_ in item.files:\\n cursor.execute(\\\"\\\"\\\"insert into files (filename, item_id)\\n values (?, ?)\\\"\\\"\\\", (file_, item.kg_id))\\n self.conn.commit()\\n self.logger.info(\\\"Succesfully stored item %d\\\" % item.kg_id)\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n \\n except psycopg2.Error as e:\\n print(e)\",\n \"def all_time_player_save():\\n try:\\n all_players = players.get_players()\\n ap = db.all_time_players\\n ap.insert_many(all_players)\\n except OperationFailure as e:\\n print(e)\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n try:\\n cur.execute(query)\\n conn.commit()\\n except Exception as e:\\n print(e)\",\n \"def bulk_insert(cls, device_id, imeis):\\n insertion_object = []\\n for imei in imeis:\\n insertion_object.append({'imei': imei, 'normalized_imei': imei[0:14], 'device_id': device_id})\\n res = db.engine.execute(ImeiDevice.__table__.insert(), insertion_object)\\n res.close()\",\n \"def insert_mass(self, table_name, data, ignore=False):\\n fields = []\\n to_insert = []\\n for row in data:\\n if not len(fields):\\n fields = map((lambda s: \\\"`\\\" + str(s) + \\\"`\\\"), row.keys())\\n values = map(self.quote, row.values())\\n to_insert.append(\\\"({0})\\\".format(\\\", \\\".join(values)))\\n\\n if len(to_insert)%50 == 0:\\n self.q(\\n \\\"INSERT \\\" + (\\\"IGNORE\\\" if ignore else \\\"\\\") + \\\" INTO `{0}` ({1}) VALUES {2}\\\"\\n .format(table_name, \\\", \\\".join(fields), \\\", \\\".join(to_insert))\\n )\\n to_insert = []\\n\\n if len(to_insert):\\n self.q(\\n \\\"INSERT \\\" + (\\\"IGNORE\\\" if ignore else \\\"\\\") + \\\" INTO `{0}` ({1}) VALUES {2}\\\"\\n .format(table_name, \\\", \\\".join(fields), \\\", \\\".join(to_insert))\\n )\",\n \"def insert(self, db):\\n if not self.content:\\n raise ValueError(\\\"SequenceList content property must be filled in before calling save.\\\")\\n if not self.title:\\n raise ValueError(\\\"SequenceList title property must be filled in before calling save.\\\")\\n seq_item_list = SequenceListItems(self.content)\\n cur = db.cursor()\\n self._insert_data(cur, seq_item_list, self.title)\\n cur.close()\\n db.commit()\",\n \"def insert_data(self):\\n\\n pass\",\n \"def insert_products(self):\\n logic = ProductLogic()\\n \\n try:\\n # We create the list of product objects\\n products = self.objects_factory.create_product_object_list()\\n products = set(products)\\n\\n for product in products:\\n logic.insert(product)\\n except:\\n print('Il y a eu un problème lors de la récupération des données, veuillez rééssayer')\",\n \"def save_all(objs: List[ModelBase], DataClass: ModelBase):\\n dicts = map(lambda x: x.to_dict(), objs)\\n with db.atomic():\\n if driver is Driver.POSTGRESQL:\\n for bar in dicts:\\n DataClass.insert(bar).on_conflict(\\n update=bar,\\n conflict_target=(\\n DataClass.stock_id,\\n DataClass.date,\\n ),\\n ).execute()\\n else:\\n i = 1\\n num = 5000\\n for c in chunked(dicts, num):\\n sta = time.time()\\n print(f\\\"Insert data to database {DataClass.__name__}: {i}-{i + num - 1}\\\")\\n DataClass.insert_many(c).on_conflict_replace().execute()\\n print(time.time() - sta)\\n i += num\",\n \"def addAll(self, objs):\\n self.getSession().add_all(objs)\\n self.commit() # paranoially\\n return objs\",\n \"def bulk_copy_to_db(self):\\n database = PostgreSQLCommon()\\n\\n try:\\n file = open(self.file_name_hash)\\n database.bulk_copy(file, self.storage_table)\\n\\n m.info('Bulk insert from %s has been successfully completed!'\\n % self.file_name_hash)\\n except Exception as err:\\n m.error('OOps! Bulk insert operation FAILED! Reason: %s' % str(err))\\n finally:\\n database.close()\\n\\n if os.path.exists(self.file_name_hash):\\n os.remove(self.file_name_hash)\",\n \"def syncdb(self, dropfirst=False):\\n for db in self._db_tree:\\n for collection in db['collections']:\\n if dropfirst:\\n self._datastore[db['database']][collection['name']].drop()\\n\\n with io.open(os.path.join(self._db_path, db['database'],\\n collection['file']), encoding='utf-8') as coll_json:\\n for record in coll_json.readlines():\\n record = json_util.loads(record)\\n self._datastore[db['database']][collection['name']].insert(record, check_keys=False)\",\n \"def _insert_data(cur, data):\\n try:\\n _insert_users(data['users'], cur)\\n logger.info('Users inserted to database')\\n except KeyError as e:\\n logger.error(f\\\"Failed to insert users to database: {e}\\\")\\n try:\\n things = [thing for thing in data['things'].values() if thing['remix'] is None]\\n _insert_things(things, cur)\\n logger.info('Things inserted to database')\\n except KeyError as e:\\n logger.error(f\\\"Failed to insert things to database: {e}\\\")\\n try:\\n remixes = [thing for thing in data['things'].values() if thing['remix'] is not None]\\n _insert_things(remixes, cur)\\n logger.info('Remixes inserted to database')\\n except KeyError as e:\\n logger.error(f\\\"Failed to insert remixes to database: {e}\\\")\\n try:\\n _insert_makes(data['makes'], cur)\\n logger.info('Makes inserted to database')\\n except KeyError as e:\\n logger.error(f\\\"Failed to insert makes to database: {e}\\\")\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_tables(cur, conn):\\n for query in insert_table_queries:\\n cur.execute(query)\\n conn.commit()\",\n \"def insert_execute(self, insert_data):\\n self.execute(query=self.db_insert_schema.format(self.table_name), data=insert_data)\",\n \"def test_orm_bulk_insert(n):\\n session = Session(bind=engine)\\n session.execute(\\n insert(Customer),\\n [\\n {\\n \\\"name\\\": \\\"customer name %d\\\" % i,\\n \\\"description\\\": \\\"customer description %d\\\" % i,\\n }\\n for i in range(n)\\n ],\\n )\\n session.commit()\",\n \"def insert(self):\\n db.session.add(self)\\n db.session.commit()\",\n \"def insert(self):\\n db.session.add(self)\\n db.session.commit()\",\n \"def insert(self):\\n db.session.add(self)\\n db.session.commit()\",\n \"def add_to_database(session, objects):\\n if isinstance(objects, list):\\n session.add_all(objects)\\n else:\\n session.add(objects)\\n session.commit()\",\n \"def insert_into_db(self, database):\\n\\n # insert person\\n keys = \\\"\\\"\\n values = \\\"\\\"\\n for key, value in self.person.items():\\n # location\\n if key == \\\"location\\\":\\n # ensure location is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * FROM p21_cdm.location WHERE city='{value['city']}' \\n AND zip='{value['zip']}') THEN INSERT INTO p21_cdm.location (city, zip) \\n VALUES ('{value['city']}', '{value['zip']}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.person (location_id, {keys[:-1]}) \\n VALUES((SELECT location_id \\n FROM p21_cdm.location\\n WHERE city='{self.person['location']['city']}' \\n and zip='{self.person['location']['zip']}'), \\n {values[:-1]})\\\"\\\"\\\")\\n\\n # insert visits\\n for visit in self.visits:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n for key, value in visit.items():\\n if key == \\\"care_site_name\\\":\\n # ensure care site is in table\\n database.select(f\\\"\\\"\\\"DO $do$ BEGIN IF NOT EXISTS (SELECT * \\n FROM p21_cdm.care_site \\n WHERE care_site_name='{value}') \\n THEN INSERT INTO p21_cdm.care_site (care_site_name) \\n VALUES ('{value}'); END IF; END; $do$\\\"\\\"\\\")\\n continue\\n\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.visit_occurrence (care_site_id, {keys[:-1]}) \\n VALUES((SELECT care_site_id\\n FROM p21_cdm.care_site\\n WHERE care_site_name='{visit['care_site_name']}'),\\n {values[:-1]}) \\n RETURNING visit_occurrence_id\\\"\\\"\\\")\\n\\n # insert measurements, observations, conditions & procedures\\n for data, tablename in [(self.measurements, \\\"measurement\\\"),\\n (self.observations, \\\"observation\\\"),\\n (self.conditions, \\\"condition_occurrence\\\"),\\n (self.procedures, \\\"procedure_occurrence\\\")]:\\n for entry in data:\\n keys = \\\"person_id,\\\"\\n values = f\\\"'{self.person['person_id']}',\\\"\\n\\n for key, value in entry.items():\\n keys += f\\\"{key},\\\"\\n values += f\\\"'{value}',\\\"\\n\\n entry[\\\"sql_id\\\"] = database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.{tablename}({keys[:-1]})\\n VALUES({values[:-1]}) RETURNING {tablename}_id\\\"\\\"\\\")[0][0]\\n\\n # insert fact_relationships in both directions\\n for table1, entry1, table2, entry2 in self.fact_relations:\\n # 44818890 = Finding associated with (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table1}','{entry1['sql_id']}','{table2}','{entry2['sql_id']}','44818890')\\\"\\\"\\\")\\n # 44818792 = Associated with finding (SNOMED)\\n database.select(f\\\"\\\"\\\"INSERT INTO p21_cdm.fact_relationship(domain_concept_id_1, fact_id_1, \\n domain_concept_id_2, fact_id_2, \\n relationship_concept_id)\\n VALUES('{table2}','{entry2['sql_id']}','{table1}','{entry1['sql_id']}','44818792')\\\"\\\"\\\")\\n\\n # make transactions persistent\\n database.commit()\",\n \"def insert_to_db(self) -> None:\\n query = '''INSERT INTO ESLReceipts(Transaction_Number, Date, Description, Memo,\\n Amount_Debit, Amount_Credit, Balance, Check_Number, \\n Fees, Card_Type, Is_Payment, Is_Transaction, User_id)\\n VALUES(?,?,?,?,?,?,?,?,?,?,?,?,?);'''\\n self.db.commit(query, values=self.to_tuple())\\n\\n if self.is_transaction \\\\\\n and self.transaction is not None \\\\\\n and not self.transaction.exists_in_db():\\n self.transaction.insert_to_db()\",\n \"def insert_data_bulk(self, table_name, data):\\n if len(data) == 0:\\n return\\n\\n fields = \\\", \\\".join(data[0].keys())\\n value_placeholders = \\\", \\\".join([\\\"%s\\\" for f in data[0].keys()])\\n query = \\\"INSERT INTO %s(%s) VALUES (%s)\\\" % (table_name, fields, value_placeholders)\\n\\n data = [tuple(self.pack(data_point.values())) for data_point in data]\\n\\n chunk_size = 50000\\n data_chunks = [data[i:i + chunk_size] for i in range(0, len(data), chunk_size)]\\n for chunk in data_chunks:\\n self.cursor.executemany(query, chunk)\\n self.db_connection.commit()\",\n \"def save_all(self):\\r\\n for index in range(self.count()):\\r\\n self.save(index)\",\n \"def insertionGenero (cur, conn, genre_list):\\n for genre in genre_list :\\n idGenero = genre[0]\\n genero=genre[1]\\n # print(generoInsert.format(idTitulo,genero))\\n # REGISTER DATA IN GENERO TABLE\\n cur.execute(generoInsert.format(idGenero,genero))\\n conn.commit()\",\n \"def _batch_write(self):\\n if self.to_put:\\n db.put(self.to_put)\\n self.to_put = []\\n if self.to_delete:\\n db.delete(self.to_delete)\\n self.to_delete = []\",\n \"def insert(self, media):\\n insert_query = \\\"\\\"\\\"INSERT INTO %s VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\\n \\\"\\\"\\\" % MediaCollection.COLLECTIONS_TABLE\\n self.cursor.execute(insert_query, media.totuple())\\n self.connection.commit()\",\n \"def insert_many_rows(self, row_count=10000):\\n values = [\\\"('name_{}')\\\".format(i) for i in range(row_count)]\\n values_string = \\\",\\\".join(values)\\n self.run_sync(f\\\"INSERT INTO manager (name) VALUES {values_string};\\\")\",\n \"def insert(self, fields, values):\\n sql = self.generate_insert_sql(fields, values)\\n self.sqlhistory.append(sql)\\n return self.sql_insert(sql)\",\n \"def insert_test_data(client):\\n businesses = get_businesses()\\n for business_dict in businesses:\\n save_business_to_db(business_dict)\\n\\n collection = Business.objects()\\n assert len(collection) == 12\",\n \"def ingest(cls):\\n for html in cls.query.all():\\n session.add(html.parse())\\n\\n session.commit()\",\n \"def save_in_db(self):\\n self.sql_database.table_name = self.table_db\\n self.sql_database.db_name = self.db\\n if self.sql_database.insert_item(text_path=self.path, word_first=self.word_1.get(),\\n word_second=self.word_2.get(),\\n word_third=self.word_3.get(), word_fourth=self.word_4.get(),\\n word_fifth=self.word_5.get()):\\n msg.showinfo(message=\\\"Done\\\")\",\n \"def db_insert(self, table_name: str, data: dict):\\n \\n query = 'INSERT OR IGNORE INTO {0} ({1}) VALUES ({2})'.format(\\n table_name, ', '.join(data.keys()), ', '.join('?'*len(data)))\\n self.db.execute(query, list(data.values()))\",\n \"async def test_valid_insert(database, valid_data):\\n await database.setup_database(reset=True)\\n for id ,user_id,embeddings,batch_id in valid_data:\\n await database.insert_user(user_id=user_id)\\n await database.insert(id=id,\\n user_id=user_id,\\n embeddings=embeddings,\\n batch_id=batch_id)\\n await database.close_pool()\",\n \"def insert_data(self):\\n self.connection_database()\\n\\n # no insertion if the table Food already contains any data\\n self.cursor.execute(\\\"SELECT * FROM Food\\\")\\n data_table_food = self.cursor.fetchall()\\n if not data_table_food:\\n\\n # instantiate the class Call_api\\n new_call_api = ca.CallApi()\\n new_call_api.load_data()\\n categories = new_call_api.categories\\n list_data = new_call_api.list_data\\n\\n for elt, element in zip(categories, list_data):\\n\\n # inserting data into Category table\\n insert_data_categories = (\\\"\\\"\\\"INSERT IGNORE INTO Category (categories)\\n VALUES({0})\\\"\\\"\\\"\\n .format(\\\"\\\\'\\\"+elt+\\\"\\\\'\\\"))\\n self.cursor.execute(insert_data_categories)\\n self.data_base.commit()\\n\\n # inserting data into Food table\\n for value in element['products']:\\n if element['products'].index(value) < 100:\\n try:\\n product_name = \\\"\\\\'\\\"+value['product_name_fr'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n nutrition_grade = \\\"\\\\'\\\"+value['nutrition_grade_fr'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n ingredients = \\\"\\\\'\\\"+value['ingredients_text'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n store_tags = \\\"\\\\'\\\"+\\\", \\\".join(value['stores_tags']).replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n url = \\\"\\\\'\\\"+value['url'].replace(\\\"'\\\", \\\"\\\")+\\\"\\\\'\\\"\\n\\n insert_data_food = (\\\"\\\"\\\"INSERT IGNORE INTO Food (name_food, category_id,\\n nutriscore, description, store, link) \\n VALUES({0}, \\n (SELECT id FROM Category WHERE categories = {1}),\\n {2}, {3}, {4}, {5})\\\"\\\"\\\"\\n .format(product_name, \\\"\\\\'\\\"+elt+\\\"\\\\'\\\",\\n nutrition_grade, ingredients,\\n store_tags, url))\\n self.cursor.execute(insert_data_food)\\n self.data_base.commit()\\n\\n # if errors\\n except KeyError:\\n continue\",\n \"def fill_the_db(testapp):\\n SessionFactory = testapp.app.registry[\\\"dbsession_factory\\\"]\\n with transaction.manager:\\n dbsession = get_tm_session(SessionFactory, transaction.manager)\\n if len(dbsession.query(Entries).all()) == 0:\\n for entry in ENTRIES:\\n row = Entries(title=entry[\\\"title\\\"], creation_date=entry[\\\"creation_date\\\"], body=entry[\\\"body\\\"])\\n dbsession.add(row)\",\n \"def insert(self, item, **kwargs) -> None:\\n for entry in item.get_entries():\\n self.client.insert(\\n entry[\\\"feature\\\"],\\n entry[\\\"label\\\"].encode(),\\n group_label=entry[\\\"group_label\\\"].encode(),\\n **kwargs\\n )\",\n \"def fill_the_db(testapp):\\n session_factory = testapp.app.registry[\\\"dbsession_factory\\\"]\\n with transaction.manager:\\n dbsession = get_tm_session(session_factory, transaction.manager)\\n for entry in ENTRIES:\\n row = Entry(title=entry[\\\"title\\\"],\\n creation_date=entry[\\\"creation_date\\\"],\\n body=entry[\\\"body\\\"])\\n dbsession.add(row)\",\n \"def insertMasivo(self, objects):\\n retorno = 1\\n \\n try:\\n n = len(objects)\\n for i in xrange(n):\\n self.session.add(objects[i])\\n if i % 100 == 0:\\n self.session.flush()\\n #for object in objects:\\n # self.session.add(object)\\n self.session.commit()\\n except sqlalchemy.exc.DBAPIError, e:\\n if self.session is not None:\\n self.session.rollback()\\n retorno = -1\\n print(\\\"Error!\\\", e)\\n finally:\\n if self._DAOAlchemy__cerrarSesion:\\n self.session.close()\\n return retorno\",\n \"def insert_many(self, frame, table_name, conn=None, index=False, index_label=None, \\n schema=None, chunksize=None, copy=True, auto_adjust=True):\\n \\n table = SQLTable(table_name, self, frame=frame, table_setup=False, index=index,\\n if_exists='append', index_label=index_label, schema=schema)\\n \\n table.insert(conn=conn, bulk=False, chunksize=chunksize, copy=copy, \\n auto_adjust=auto_adjust)\",\n \"def insert(cls, *lst, **dct):\\n cls.runtime.set_set(lst, dct)\\n return InsertQuery(cls.runtime)\",\n \"def insert_tables(cur, conn):\\n logger.debug(\\\"Insert tables ...\\\")\\n for query in insert_table_queries:\\n logger.debug(f'RUN query: {query}')\\n cur.execute(query)\\n conn.commit()\\n\\n print('All files INSERTED OK.')\",\n \"def put_it_in_tables(self):\\n my_connection = mysql.connector.connect(user=self.user, password=self.password, database='openfoodfacts')\\n cursor = my_connection.cursor(buffered=True)\\n for i in self.my_data:\\n prod_name = i['product_name']\\n try:\\n add_aliment = (\\\"INSERT INTO aliment \\\"\\n \\\"(product_name, product_description, barcode, nutritional_score, stores, product_category) \\\"\\n \\\"VALUES (%s, %s, %s, %s, %s, %s)\\\")\\n data_aliment = (i['product_name'].replace(\\\"'\\\", \\\"''\\\"), i['product_description'].replace(\\\"'\\\", \\\"''\\\"), i['barcode'].replace(\\\"'\\\", \\\"''\\\"), i['nutritional_score'].replace(\\\"'\\\", \\\"''\\\"), i['stores'].replace(\\\"'\\\", \\\"''\\\"), i['product_category'].replace(\\\"'\\\", \\\"''\\\"))\\n cursor.execute(add_aliment, data_aliment)\\n except mysql.connector.IntegrityError:\\n pass \\n my_connection.commit()\\n cursor.close()\\n my_connection.close()\\n print(\\\"ok c'est fait\\\")\",\n \"def execute_many(self, query: str, args):\\n return self.insert_many(query, args)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.738048","0.728863","0.7119373","0.69322","0.69140184","0.6894494","0.6738854","0.6694093","0.66766566","0.6634999","0.65382606","0.653699","0.6525922","0.6473785","0.6451338","0.6418449","0.63942665","0.63700026","0.63535374","0.6332911","0.62997335","0.62511104","0.62441254","0.6236722","0.62132716","0.6199585","0.61965007","0.6183691","0.61669886","0.6161343","0.61522895","0.6145156","0.6141686","0.6135644","0.61273503","0.61205536","0.61184067","0.610309","0.60818017","0.60610545","0.6041756","0.6035117","0.6031466","0.60281855","0.602118","0.6014758","0.6012589","0.6010059","0.60037315","0.5993839","0.59715503","0.5971421","0.59651697","0.5964663","0.5960652","0.595858","0.59578633","0.5955173","0.5951333","0.5943984","0.5941087","0.59384245","0.5924677","0.59245795","0.5920689","0.59194595","0.5918996","0.5918996","0.5918996","0.5918996","0.59158474","0.5914426","0.59094656","0.59094656","0.59094656","0.59084964","0.59059817","0.5905944","0.5905019","0.5904605","0.5897821","0.58973885","0.5896833","0.58952034","0.58948267","0.58896184","0.58809084","0.5876239","0.5870116","0.586656","0.5863649","0.5863304","0.58610624","0.5859252","0.58564305","0.584401","0.58438677","0.58398813","0.5835422","0.58340836"],"string":"[\n \"0.738048\",\n \"0.728863\",\n \"0.7119373\",\n \"0.69322\",\n \"0.69140184\",\n \"0.6894494\",\n \"0.6738854\",\n \"0.6694093\",\n \"0.66766566\",\n \"0.6634999\",\n \"0.65382606\",\n \"0.653699\",\n \"0.6525922\",\n \"0.6473785\",\n \"0.6451338\",\n \"0.6418449\",\n \"0.63942665\",\n \"0.63700026\",\n \"0.63535374\",\n \"0.6332911\",\n \"0.62997335\",\n \"0.62511104\",\n \"0.62441254\",\n \"0.6236722\",\n \"0.62132716\",\n \"0.6199585\",\n \"0.61965007\",\n \"0.6183691\",\n \"0.61669886\",\n \"0.6161343\",\n \"0.61522895\",\n \"0.6145156\",\n \"0.6141686\",\n \"0.6135644\",\n \"0.61273503\",\n \"0.61205536\",\n \"0.61184067\",\n \"0.610309\",\n \"0.60818017\",\n \"0.60610545\",\n \"0.6041756\",\n \"0.6035117\",\n \"0.6031466\",\n \"0.60281855\",\n \"0.602118\",\n \"0.6014758\",\n \"0.6012589\",\n \"0.6010059\",\n \"0.60037315\",\n \"0.5993839\",\n \"0.59715503\",\n \"0.5971421\",\n \"0.59651697\",\n \"0.5964663\",\n \"0.5960652\",\n \"0.595858\",\n \"0.59578633\",\n \"0.5955173\",\n \"0.5951333\",\n \"0.5943984\",\n \"0.5941087\",\n \"0.59384245\",\n \"0.5924677\",\n \"0.59245795\",\n \"0.5920689\",\n \"0.59194595\",\n \"0.5918996\",\n \"0.5918996\",\n \"0.5918996\",\n \"0.5918996\",\n \"0.59158474\",\n \"0.5914426\",\n \"0.59094656\",\n \"0.59094656\",\n \"0.59094656\",\n \"0.59084964\",\n \"0.59059817\",\n \"0.5905944\",\n \"0.5905019\",\n \"0.5904605\",\n \"0.5897821\",\n \"0.58973885\",\n \"0.5896833\",\n \"0.58952034\",\n \"0.58948267\",\n \"0.58896184\",\n \"0.58809084\",\n \"0.5876239\",\n \"0.5870116\",\n \"0.586656\",\n \"0.5863649\",\n \"0.5863304\",\n \"0.58610624\",\n \"0.5859252\",\n \"0.58564305\",\n \"0.584401\",\n \"0.58438677\",\n \"0.58398813\",\n \"0.5835422\",\n \"0.58340836\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7253259"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":9988,"cells":{"query":{"kind":"string","value":"Exports given file to csv."},"document":{"kind":"string","value":"def save_items_to_csv(items_data: pd.DataFrame):\n with open('etsy_items.csv', 'w') as f:\n writer = csv.writer(f)\n writer.writerows(items_data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def export_csv(self):\n outputfile = tkinter.filedialog.asksaveasfilename(\n defaultextension=\".csv\",\n filetypes=((\"comma seperated values\", \"*.csv\"),\n (\"All Files\", \"*.*\")))\n if outputfile:\n tabledata = self.tabs.window.aistracker.create_table_data()\n export.write_csv_file(tabledata, outputfile)\n else:\n raise ExportAborted('Export cancelled by user.')","def export_csv(self, df, filename):\n df.to_csv(str(pathlib.PurePosixPath(self.config.csv_path, filename)) , sep=\";\")","def write_file(file):\n file.to_csv('data_set.csv', encoding='utf-8', index=False)","def save_csv(self, filename): # DONE\n self.data.to_csv(filename)","def to_csv(self, filename):\n self.data.to_csv(filename)","def to_csv(self, filename):\n self.data.to_csv(filename)","def export_csv(self, outpath):\n\n\t\tself.df.to_csv(outpath)","def to_csv(self, filename, **kwargs):\n self.data.to_csv(filename, **kwargs)","def export2csv(filename, headers, data):\n click.secho('Do you really want to export file ([y]/n)? ',\n nl=False,\n fg='red')\n\n c = click.getchar()\n click.echo()\n\n if str.upper(c) != 'Y':\n sys.exit()\n\n today = datetime.now().strftime(\"%y%m%d%H%M%S\")\n filename = f'{DOWNLOAD_DIR}/{filename}_{today}.csv'\n\n click.echo(filename)\n\n with open(filename, 'w', newline='') as csvfile:\n write = csv.DictWriter(csvfile, fieldnames=headers)\n write.writeheader()\n\n with click.progressbar(data, label='exporing {}'.format(filename)) as items: # noqa\n for item in items:\n write.writerow(item)","def export_csv(state, out_file=None):\n\n if out_file is None:\n csvfile = sys.stdout\n else:\n csvfile = open(out_file, 'w')\n\n try:\n writer = csv.writer(csvfile)\n for grade in state.grades:\n writer.writerow([grade.student_name(), grade.score(),\n grade.breakdown(state.user_name)])\n finally:\n if out_file is not None:\n csvfile.close()","def export_to_csv(self, file_name):\n \n with open(file_name, 'w', newline='') as csvDataFile:\n csvWriter = csv.writer(csvDataFile, delimiter = ',')\n\n for i in range(0,self.sample_num):\n data = list()\n data.append(self.sample[i].simulation_name)\n data.append(self.sample[i].result_name)\n data.extend(self.sample[i].parameters.tolist())\n data.extend(self.sample[i].result) \n csvWriter.writerow(data)","def abs2csv(self, file_name, file_path, export_index = True, export_header = True):\n\n self.abs_data.to_csv(os.path.join(file_path,file_name), index = export_index, header = export_header)\n print('Absorbance data saved to .csv succesfully. \\n')","def preproc2csv(self, file_path, export_index = True, export_header = True):\n\n self.pre_proc_data.to_csv(file_path+'.csv', index = export_index, header = export_header)\n print('Pre-processed data saved to .csv succesfully. \\n')","def _export_csv(x, y, export_to):\r\n\r\n with open(export_to, 'w', newline='') as e:\r\n writer = csv.writer(e, delimiter=',')\r\n for i in range (0, len(x)):\r\n writer.writerow([x[i], y[i]])","def write_csv(self, filename, cutoff=2):\n f = csv.writer(open(filename, 'wb'))\n for row in self.rows(cutoff=cutoff):\n f.writerow(row)","def ExportAsCSV(csv_out_path, data):\n with open(csv_out_path, \"w\", newline=\"\") as f:\n writer = csv.writer(f, delimiter=',')\n writer.writerows(data)\n f.close()\n return","def writefile(data, filepath, encoding='utf-8', header=False):\n data.to_csv(filepath, encoding=encoding, header=header)","def export(\n self, output_fp: str, *args, _add_ext: bool = False, sep: str = \",\", **kwargs\n ) -> None:\n tmp_export, rkwarg = self._export(*args, **kwargs)\n if _add_ext:\n tmp_export.to_csv(\"{}.csv\".format(output_fp), sep=sep)\n else:\n tmp_export.to_csv(output_fp, sep=sep)","def to_csv(self, filename, **csv_args):\n dataframe = pd.DataFrame.from_dict(self.to_dict())\n return dataframe.to_csv(filename, **csv_args)","def to_csv(self, file: str = \"output_file.csv\"):\n if self._AccessPoint not in self.valid_access_points:\n raise InvalidFetcherAccessPoint(\n \" Initialize an access point (%s) first.\"\n % \",\".join(self.Fetchers.keys())\n )\n return self.load().index.to_csv(file)","def to_csv(self, path, sep=';', **kwargs):\n df = self.get_as_pandas_dataframe()\n df.to_csv(path, sep=sep, index=False, **kwargs)","def write_tocsv(file_name, dataframe) :\n print(\"\\nSaved result to {}\\n\".format(file_name))\n dataframe.to_csv(file_name, mode='a', header=False,index=False)","def export_csv(self, path):\r\n\r\n with open(path, 'w') as f:\r\n f.write('# h,hr,m')\r\n\r\n if self.rho is not None:\r\n f.write(',rho')\r\n if self.temperature is not None:\r\n f.write(',temperature')\r\n\r\n f.write('\\n')\r\n for i in range(self.shape[0]):\r\n for j in range(self.shape[1]):\r\n f.write(f'{self.h[i, j]},{self.hr[i, j]},{self.m[i, j]}')\r\n if self.rho is not None:\r\n f.write(f',{self.rho[i, j]}')\r\n if self.temperature is not None:\r\n f.write(f',{self.temperature[i, j]}')\r\n f.write('\\n')\r\n return","def write_csv(self, out_file_name, header):\n\n with open(out_file_name, 'wb') as outf:\n writer = csv.writer(outf, quoting=csv.QUOTE_ALL)\n writer.writerow(header)\n writer.writerows(self.records)","def export_csv(data: dict, file_path: str):\n fieldnames = [\n 'briefy_id', 'number_required_assets', 'number_submissions', 'total_submissions_images',\n 'total_submissions_videos', 'total_submissions_others', 'total_archive_images',\n 'total_archive_videos', 'total_archive_others', 'total_delivery_images',\n 'total_delivery_videos', 'total_delivery_others', 'submission_links', 'archive_link',\n 'delivery_link', 'order_link'\n ]\n\n with open(file_path, 'w') as fout:\n writer = csv.DictWriter(fout, fieldnames)\n writer.writeheader()\n for key, value in data.items():\n writer.writerow(value)","def export_data_to_csv(data,filename):\n data.to_csv(filename)\n logger.info(f'Data frame exported to CSV format {filename}')\n return True","def saveCSV(self, filename='cbpGetHistoricRates.csv'):\n\n p = re.compile(r\"^[\\w\\-. ]+$\")\n if not p.match(filename):\n raise TypeError('Filename required.')\n\n if not isinstance(self.df, pd.DataFrame):\n raise TypeError('Pandas DataFrame required.')\n\n try:\n self.df.to_csv(filename)\n except OSError:\n print('Unable to save: ', filename)","def exportToCSV(subdomain,sub,file_name):\n\ttry:\n\t\twith open(file_name,\"w\") as file:\n\t\t\tfnames = ['subdomain', 'saved_date']\n\t\t\twriter = csv.DictWriter(file, fieldnames=fnames)\n\t\t\twriter.writeheader()\n\t\t\tfor _ in sub:\n\t\t\t\twriter.writerow({'subdomain' : _.subdomain, 'saved_date': _.created_date})\n\t\t\tprint(f'[+] {len(sub)} results of {subdomain} exported successfully.\\n[+] Path {result_save_dir}/{subdomain}_{datetime.now().date()}.csv')\n\texcept IOError as error:\n\t\tlogger.log(\"ERROR\",\"[+] Fails to open or create file! Please check your permissions\")","def exportCSV(self, log, csvFile):\n return 0","def make_csv(file_of_data):\n with open(file_of_data, 'w') as f:\n writer = csv.writer(f)\n header = (\"Counter\", \"Date/time\", \"Latitude\", \"Longitude\", \"Temperature\", \"Humidity\")\n writer.writerow(header)","def export_to_csv(self, outputfilepath, separator):\n\n con = fdb.connect(\n database=self.db_filepath,\n # dsn='localhost:~/test/CGI.vvv', #localhost:3050\n user='sysdba', password='masterkey'\n #charset='UTF8' # specify a character set for the connection\n )\n cur = con.cursor()\n statement = \"select * from FILES\"\n cur.execute(statement)\n # Retrieve all rows as a sequence and print that sequence:\n print(cur.fetchall())\n\n # VVV export format: Volume,Path,Name,Size,Ext,Last modified,Description\n\n with open(outputfilepath, 'w') as csvfile:\n writer = csv.writer(csvfile)\n writer.writerow([ i[0] for i in cur.description ]) \n writer.writerows(cur.fetchall())","def exportToCsv(self, filepath):\n table = list()\n table.append(list(self.__header))\n for a in self.__assays:\n table.append(\n [unicode(a.timestamp.isoformat()),\n unicode(a.dab_cell_count),\n unicode(a.hem_cell_count),\n unicode(a.dab_dabhemfraction),\n unicode(a.img_path)])\n # File encoding will be same as it expected by Excel on machine where\n # this file was created.\n encoding = locale.getpreferredencoding()\n with open(filepath, mode='wb') as f:\n writer = UnicodeWriter(f, encoding=encoding, delimiter=';')\n writer.writerows(table)","def to_csv(self, filename: str):\n\n if filename and filename.endswith(\".csv\"):\n filename = filename[:-4]\n\n if len(self.analysis) > 0:\n export_to_csv(\n [\n \"ticker\",\n \"date\",\n \"category\",\n \"analyst\",\n \"rating\",\n \"price_from\",\n \"price_to\",\n ],\n self.analysis,\n f\"{filename}-analysts.csv\",\n )\n\n return export_to_csv(self.headers, self.data, f\"{filename}.csv\")","def write_df_tocsv(df, file_name):\n try:\n df.to_csv(os.path.join(script_dir, file_name))\n except:\n pass","def write_csv(self, outfile, collapse_orders=False, show_age=False):\r\n # Write header row\r\n outfile.write(self.get_csv_header(collapse_orders, show_age).encode())\r\n\r\n # Write content\r\n for x in self.records:\r\n x.write_csv(outfile, collapse_orders, show_age)","def toCsv(self, csv_path):\n ser = pd.Series(self)\n ser.to_csv(csv_path)","def _csv_export(self, exppath):\n with open(exppath, 'w') as csvfile:\n csvwriter = csv.writer(csvfile, delimiter=',', skipinitialspace=True)\n csvwriter.writerow(['hexstr','dmc','name'])\n for clr in self.lookup_table:\n csvwriter.writerow([clr.hex.to_str(), clr.id, clr.name])","def _export_data_to_csv(self, source, target):\n self.log.info(f\"Dumping data into {target}\")\n source.to_csv(target, index=False)","def write_csv(self, file):\n # Write header row\n file.write('Timestamp,MessageType,Queue,Price,Volume,OrderID\\n')\n # Write content\n for x in self.records:\n row = (str(x[0]) + ',' + x[1][\"MessageType\"] + ',' +\n x[1][\"Queue\"] + ',' + str(x[1][\"Price\"]) + ',' +\n str(x[1][\"Volume\"]) + ',' + str(x[1][\"OrderID\"]) + '\\n')\n file.write(row)","def export_csv(table_name, out_file):\n con = sqlite3.connect(dbname)\n import pandas as pd\n\n df = pd.read_sql_query(f\"SELECT * FROM {table_name}\", con)\n df.to_csv(out_file, index=False)","def to_csv(self, path):\n results = self.all()\n if self.stop_check is not None and self.stop_check():\n return\n results.to_csv(path)","def give_data_to_csv(file_of_data, data):\n with open(file_of_data, 'a') as f:\n writer = csv.writer(f)\n writer.writerow(data)","def write_output(basis, filename):\n\n logging.info('Writing output to {}'.format(filename))\n\n basis.to_csv(filename)","def to_csv_files(self, path):\n self._to_dict_tree().to_csv_files(path)","def info2csv(df, csv_path):\n df.to_csv(csv_path, index=False)","def write_csv(fn, toCSV):\n keys = toCSV[0].keys()\n with open(fn, 'w') as output_file:\n dict_writer = csv.DictWriter(output_file, keys)\n dict_writer.writeheader()\n dict_writer.writerows(toCSV)","def writeToCSV(self, filepath):\r\n\t\twith open(filepath, 'w') as outputFile:\r\n\t\t\toutputFile.write(str(self))","def createFileCSV(table, path=\"./prediction\"):\t\n\tif len(table) < 1:\n\t\traise NameError('Empty Table!')\n\telse:\n\t\tfile = open(path + '.csv', 'w+')\n\n\t\tfile.write(table[0].toStringHeaders() + \"\\n\")\n\n\t\tfor row in table:\n\t\t\tfile.write(row.toStringCSV() + '\\n')\n\t\tfile.close()","def write_csv(data_frame, file_name):\n data_frame.coalesce(1).write \\\n .option('header', True).mode('overwrite') \\\n .save(f'outputs/{file_name}', format('csv'))","def save_csv_file():\n global output_on_display, import_lst, column_names, data\n if data_base == '':\n mistake_load_table()\n else:\n column_names = data[0]\n save_name = asksaveasfilename(title=\"Select file\", filetypes=((\"CSV\", \"*.csv\"), (\"all files\", \"*.*\")),\n confirmoverwrite=True, defaultextension='.csv')\n step = len(column_names)\n data_csv = import_lst\n if len(data_csv[0]) == step:\n pass\n else:\n data_csv = import_lst[step::]\n\n with open(save_name, 'w+') as csv_file:\n csv_writer = csv.writer(csv_file)\n csv_writer.writerow(column_names)\n csv_writer.writerows(data_csv)","def write_csv(list_file, path):\n\n\twith open(path, 'w') as f:\n\t\twriter = csv.writer(f, delimiter=',')\n\t\tfor i in list_file:\n\t\t\twriter.writerow(i)","def export2csv(data, input_filename, output_filename):\n assert input_filename\n assert os.path.exists(output_filename) and os.path.isfile(output_filename)\n str2write_xyz = input_filename + \",\"\n for i in data:\n for j in i:\n str2write_xyz += str(j) + \",\"\n filen = input_filename.split(\".\")[0]\n str2write_xyz += filen[filen.find(os.sep) + 1:].replace(\"_\", \",\").replace(\"-\", \".\").replace(\"/\", \",\").replace(\"\\\\\", \",\") + \"\\n\"\n with open(output_filename, \"a\") as filep:\n filep.write(str2write_xyz)","def write_csv(self):\n self.tableView.df.to_csv('Data export.csv', index=False)\n print('CSV file exported')","def write_csv_file(filepath, fieldnames, rows):\n headers = [{'label': field} for field in fieldnames]\n with open(filepath, 'w') as f_buf:\n outfile = CsvWriter()\n outfile.set_headers(headers)\n outfile._datas = rows\n outfile.render(f_buf)","def write_csv(file_name, data):\n\n with open(file_name, \"w\") as fp:\n\n writer = RiscvInstructionTraceCsv(fp)\n writer.start_new_trace()\n\n for entry in data:\n writer.write_trace_entry(entry)","def export_csv(df, season_id, league_name, league_id):\r\n file_name = '{}_{}_{}.csv'.format(league_name,league_id,season_id)\r\n current_dir = os.path.dirname(__file__)\r\n path_name = os.path.join(current_dir, '../{}'.format(season_id))\r\n if not os.path.exists(path_name):\r\n os.mkdir(path_name)\r\n\r\n export_name = os.path.join(path_name, file_name)\r\n df.to_csv(export_name, index=False, encoding='utf-8')","def to_csv(self, file_path: str) -> None:\n with open(file_path, \"w\") as f:\n writer = csv.writer(f)\n writer.writerow([\"parameter\", \"value\"])\n writer.writerow([\"render\", str(self.render)])\n writer.writerow([\"sleep\", str(self.sleep)])\n writer.writerow([\"log_frequency\", str(self.log_frequency)])\n writer.writerow([\"video\", str(self.video)])\n writer.writerow([\"video_fps\", str(self.video_fps)])\n writer.writerow([\"video_dir\", str(self.video_dir)])\n writer.writerow([\"num_episodes\", str(self.num_episodes)])\n writer.writerow([\"gifs\", str(self.gifs)])\n writer.writerow([\"gifdir\", str(self.gif_dir)])\n writer.writerow([\"video_frequency\", str(self.video_frequency)])","def save_csv(self, filename: str, type='n', **args):\n if type == 'n':\n df = self.export_nodes()\n else:\n df = self.export_edges()\n df.to_csv(filename, index=False)","def save_csv(outputfile):\n with open(outputfile, 'w', newline='') as outfile:\n writer = csv.writer(outfile)\n writer.writerow(DATA_KEYS)\n\n # Add data to csv-file\n for data in data_list:\n writer.writerow(data)","def write_csv(self, filelike):\r\n items = self.rows()\r\n writer = unicodecsv.writer(filelike, encoding=\"utf-8\")\r\n writer.writerow(self.header())\r\n for item in items:\r\n writer.writerow(item)","def write_csv(self, file: str, table: str, libref: str =\"\", nosub: bool =False, dsopts: dict = None, opts: dict = None) -> 'The LOG showing the results of the step':\n dsopts = dsopts if dsopts is not None else {}\n opts = opts if opts is not None else {}\n\n code = \"filename x \\\"\"+file+\"\\\";\\n\"\n code += \"options nosource;\\n\"\n code += \"proc export data=\"\n\n if len(libref):\n code += libref+\".\"\n\n code += \"'\"+table.strip()+\"'n \"+self._sb._dsopts(dsopts)+\" outfile=x dbms=csv replace; \"\n code += self._sb._expopts(opts)+\" run\\n;\"\n code += \"options source;\\n\"\n\n if nosub:\n print(code)\n else:\n ll = self.submit(code, \"text\")\n return ll['LOG']","def write_csv(self, file, collapse=False):\n # Write header row\n file.write('Timestamp')\n for x in self.measures:\n file.write(',' + x)\n file.write('\\n')\n\n if collapse:\n last_ts = None\n next_write = None\n # Write content\n for x in self.records:\n if last_ts is None:\n last_ts = x[0]\n next_write = x\n elif x[0] == last_ts:\n next_write = x\n else:\n if next_write is not None:\n self.__write_record(file, next_write)\n last_ts = x[0]\n next_write = x\n if next_write is not None:\n self.__write_record(file, next_write)\n else:\n # Write content\n for x in self.records:\n self.__write_record(file, x)","def output_csv(df):\n # remove existing plot\n if os.path.exists(\"files/converted.csv\"):\n os.remove(\"files/converted.csv\")\n # save csv\n df.to_csv('files/converted.csv')","def to_csv_file(self, records):\n self._file_manager.make_dir_when_no_dir(self._directory)\n csv_file = os.path.join(self._directory, self._file_name + \".csv\")\n record_lines = [rec.to_csv_string() + \"\\n\" for rec in records]\n self._file_manager.write_lines(csv_file, record_lines)","def csv(self, destination_path):\n # todo - test for single and duplicate base cases\n to_csv(self._axl_data, destination_path)","def csv_file(data,output_dir,filename,order = [],head = True):\n with open(output_dir + filename + '.csv', 'w') as f:\n write = csv.writer(f)\n write.writerows(manip.dic_to_list(data,order,head),)\n return None","def writecsvfile(filename, columnnames, data):\n with open(filename, 'w', newline='') as csv_file:\n writer = csv.writer(csv_file, delimiter=',')\n writer.writerow(columnnames) # header row\n for row in data:\n writer.writerow(row[:])","def write_to_csv(agents, filename):\n log.info(\"Writing CSV file '%s'...\" % filename)\n with open(filename, 'w', newline='') as csvfile:\n writer = csv.DictWriter(csvfile, fieldnames=place_detail_keys)\n writer.writeheader()\n writer.writerows(agents)","def write_csv(self, outputfile):\n d = csv.writer(outputfile, quoting=csv.QUOTE_ALL)\n for row in self.translations.iteritems():\n d.writerow(row)","def write_csv(self, filename: str, author_data: list):\n split_file = filename.split('/')\n new_filename = \"\"\n for index, component in enumerate(split_file):\n if index == len(split_file) - 1:\n break\n new_filename += component + \"/\"\n new_filename += \"results_\" + split_file[-1]\n with open(new_filename, 'w') as csvfile:\n result_writer = csv.writer(csvfile)\n result_writer.writerow(self._csv_headings)\n for author in author_data:\n result_writer.writerow(self.author_data_to_str(author))\n csvfile.close()","def prepare_out_csv(output_dir, filename):\n out_columns_pi = ['fasta_file', 'acc.code',\n 'organism', 'EC.code', 'species',\n 'note', 'pi', 'modification', 'category']\n string = ''\n for i in out_columns_pi:\n if i == out_columns_pi[-1]:\n string += i\n else:\n string += i+','\n string += '\\n'\n with open(output_dir+filename, 'w') as f:\n f.write(string)","def write_csv(path: str, df_players: pd.DataFrame) -> None:\n df_players.to_csv(path, index=False, header=True)","def to_csv(self, path):\n for table in ['datasets', 'dataruns', 'hyperpartitions', 'classifiers']:\n df = pd.read_sql('SELECT * FROM %s' % table, self.session.bind)\n df.to_csv(os.path.join(path, '%s.csv' % table), index=False)","def write_csv(header_row, data_rows, filename, course_id):\n shared.ensure_directory_exists(utils.ANSWERS_DISTRIBUTION_REPORTS_DIRECTORY,\n course_id.org, course_id.course)\n\n\n path = shared.get_safe_file_path(utils.ANSWERS_DISTRIBUTION_REPORTS_DIRECTORY,\n course_id.org, course_id.course,\n filename)\n ## need to encode the unico path in order to open the file in prod env\n path = path.encode('utf-8')\n\n with open(path, \"wb\") as ofile:\n writer = csv.writer(ofile, quoting=csv.QUOTE_ALL)\n writer.writerow(header_row)\n for datarow in data_rows:\n encoded_row = [cleanup_newlines(unicode(s).encode('utf-8'))\n for s in datarow]\n writer.writerow(encoded_row)","def write_csv(filename, **values):\n writeheader = not os.path.isfile(filename)\n fieldnames = sorted(values.keys())\n\n with open(filename, 'a') as f:\n writer = csv.DictWriter(f, fieldnames, dialect='excel-tab')\n if writeheader:\n writer.writeheader()\n writer.writerow(values)","def write_file(data,file_name):\r\n\twith open(file_name,'wb') as new_csv_file:\r\n\t\twrtr = writer(new_csv_file)\r\n\t\tfor row in data:\r\n\t\t\twrtr.writerow(row)","def convert_to_csv(cls, input_file: str) -> None:\r\n parser = cls(file_path=input_file)\r\n\r\n output_dir = pathlib.Path(pathlib.Path(input_file).parent, \"results\")\r\n os.makedirs(output_dir, exist_ok=True)\r\n for statement_period, statement_activities in parser._statements.items():\r\n path = pathlib.Path(output_dir, statement_period)\r\n statement_activities.to_csv(path)","def toCSV(self, outFileName):\n with open(outFileName, 'wb') as outFile:\n writer = csv.writer(outFile)\n writer.writerow(self.getHeaderList())\n for name, (results, params) in self.result.iteritems():\n writer.writerow([name] + list(results) + list(params))","def createcsv(fileName):\n fileName = os.path.join(pathtofolder(), 'datas', fileName)\n fileFormat = '.csv'\n file = f'{fileName + fileFormat}'\n\n csvKeys = [\"product_page_url\", \"universal_product_code\", \"title\",\n \"price_including_tax\", \"price_excluding_tax\", \"number_available\",\n \"product_description\", \"category\", \"review_rating\", \"image_url\"]\n\n addon = excelExport('excel')\n\n with open(file, 'w', newline=\"\", encoding='utf-8') as csvFile:\n csvFile.write(addon) # Define the separator as <\">.\n resultWriter = csv.writer(csvFile, delimiter = '|', dialect = \"excel\")\n resultWriter.writerow(csvKeys)\n pass","def write_to_csv(self, output_dir, delimiter, include_language, filename=None):\n\n if filename is not None:\n self.filename = filename\n\n if len(self.values) == 0:\n logger.info(\"Nothing to export.\")\n return\n\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n\n file_path = os.path.join(output_dir, self.filename)\n\n # write search results to UTF8-encoded CSV file (see also http://stackoverflow.com/a/844443)\n with codecs.open(file_path, 'w', encoding='utf8') as fp:\n logger.info('Exporting search results to ' + file_path + '...')\n writer = csv.writer(fp, delimiter=delimiter)\n\n column_names = SearchResult.get_column_names(include_language)\n\n # write header of CSV file\n writer.writerow(column_names)\n\n count = 0\n try:\n for row in self.get_rows(include_language):\n if len(row) == len(column_names):\n writer.writerow(row)\n count = count + 1\n else:\n raise IllegalArgumentError(\n str(abs(len(column_names) - len(row))) + ' parameter(s) is/are missing for \"'\n + str(row) + '\"')\n\n except UnicodeEncodeError:\n logger.error('Encoding error while writing data for: ' + str(row))\n\n logger.info(str(count) + ' search results have been exported.')","def writeCSV(self, outfilename, delimiter=';'):\n\n input = open(self.filename)\n dialect = csv.Sniffer().sniff(input.read(1024))\n input.seek(0)\n reader = csv.reader(input, dialect)\n out = open(outfilename, \"w\")\n writer = csv.writer(out, delimiter=delimiter, quotechar='\"', quoting=csv.QUOTE_ALL)\n\n for row in reader:\n writer.writerow(row)\n\n input.close()\n out.close()","def export(self, directory, fname=None):\r\n if not fname:\r\n try:\r\n fname = self.dt_0.strftime('%Y.%m.%d_%H.%M.%S') + '.txt'\r\n except AttributeError:\r\n fname = self.fname\r\n\r\n self.data.to_csv(Path(directory, fname), header=None, float_format='%.7g')\r\n logger.info(f'{self} export to folder {directory}.')","def write_output_csv(filename, **kwargs):\n import csv\n import time\n\n intermediate = kwargs.pop(\"intermediate\", False)\n\n keys = sorted(kwargs.keys())\n num_vars = len(keys)\n\n if intermediate:\n full_filename = filename + \"_interm\"\n else:\n dot_index = filename.rfind('.')\n if dot_index != -1:\n full_filename = (filename[:dot_index]\n + time.strftime(\"%Y-%m-%d-%H.%M.%S\")\n + filename[dot_index:])\n else:\n full_filename = filename + time.strftime(\"%Y-%m-%d-%H.%M.%S\")\n\n # add current time to filename as an identifier\n with open(full_filename, 'w', newline='') as csvfile:\n\n writer = csv.writer(csvfile)\n\n # write header\n writer.writerow(keys)\n\n num_entries = len(kwargs[keys[0]])\n for i in range(num_entries):\n writer.writerow(kwargs[keys[j]][i] for j in range(num_vars))","def write_csv(file_names: list, csv_file_path: str):\n with open(csv_file_path, mode='w') as csv_file:\n writer = csv.writer(csv_file)\n writer.writerows(enumerate(file_names))","def create_csv_file(self):\r\n # Create a new csv-file\r\n with open(self.fname, 'w') as f:\r\n writer = csv.writer(f, dialect='excel')\r\n writer.writerow(['set_time',\r\n 'read_time_P_ac',\r\n 'read_time_P_bat',\r\n 'soc',\r\n 'set_value',\r\n 'P_ac',\r\n 'P_bat'])","def writeCSV(filename):\n if not filename.endswith('.csv'):\n filename += '.csv'\n with open(filename, 'wb') as csvfile:\n filewriter = csv.writer(csvfile, delimiter=',',\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for x in range(numRows):\n scores = quizScores()\n types = getTypes(quizScores())\n extra = []\n filewriter.writerow([x] + types + scores + extra)","def to_csv(self, path: pathlib.Path):\n combined = self.combined_df\n assert combined[CommonFields.LOCATION_ID].notna().all()\n common_df.write_csv(\n combined, path, structlog.get_logger(), [CommonFields.LOCATION_ID, CommonFields.DATE]\n )\n if self.provenance is not None:\n provenance_path = str(path).replace(\".csv\", \"-provenance.csv\")\n self.provenance.sort_index().to_csv(provenance_path)","def dump_csv(f, rra, out):\n w = writer(out)\n for row in dump(f, rra):\n w.writerow([s.strip() for s in row])","def write_csv(path, lines, headers):\n print \"Opening %s for score output\" % base_name(path)\n\n try:\n f = open(path, 'wb')\n writer = csv.writer(f)\n writer.writerow(headers)\n writer.writerows(lines)\n except IOError:\n print \"Cannot open %s\" % path\n else:\n print \"Scores successfully written to %s\" % path\n f.close()","def export_table(path, path_out):\n table = rb.get_table(path)\n table.to_csv(path_out, index=False)\n return","def file(self):\n result = []\n completePath = CompletePath(self.path, self.filename) \n with open(completePath.path(), 'w', newline='') as csvfile:\n fieldnames = ['Activity', 'Points']\n writer = csv.DictWriter(csvfile, fieldnames = fieldnames)\n writer.writeheader()\n for i in range ( len( self.groupPriority.rows() ) ):\n tmp = self.groupPriority.rows()[i]\n self.log.info ( \"FinalCSV\", \"file\",\"data {0},{1}\".format( tmp.activity(), tmp.points() ) )\n writer.writerow({'Activity': tmp.activity(), 'Points': tmp.points()})\n self.log.info(\"FinalCSV\", \"file\", \"Elaborated file: {0}\".format ( completePath.path() ) )","def _file_writer(file, extension, output_data, headers):\n if extension == 'csv':\n separator = ','\n else:\n separator = ' '\n writer = csv.writer(file, delimiter=separator, quotechar='|')\n if headers is True:\n writer.writerow(['Login', 'P/N', 'Password', 'P/N'])\n writer.writerows(output_data)","def to_file(self, target_filename, **to_csv_kwargs):\n # TODO: to_file saves will need trigger log file in future versions\n self.df.to_csv(target_filename, index=False, **to_csv_kwargs)","def to_csv(self, filename, delimiter=\",\", recommended_only=False, include_io=True):\n df = self.to_df(recommended_only, include_io)\n df.to_csv(filename, index=False, sep=delimiter)","def outputapidata_csv(filename, data, headers=None):\n with open(filename,'w',encoding='utf-8',newline = \"\", ) as f:\n if headers:\n writer = csv.DictWriter(f,fieldnames = headers)\n writer.writeheader()\n else:\n writer = csv.DictWriter(f)\n writer.writerows(out)","def write_table_to_csv(table: List[List], filename: str):\n with open(filename, 'w') as csvfile:\n writer = csv.writer(csvfile, delimiter='\\t')\n for row in table:\n writer.writerow(row)","def save_csv(df, fp):\n if not os.path.isdir(os.path.split(fp)[0]):\n os.makedirs(os.path.split(fp)[0])\n\n df.to_csv(fp, index=False)","def exportCSV(self, fields=None, coding=None, delimiter='semicolon'):\n\n result = self.generateCSV(fields=fields, delimiter=delimiter)\n\n # encode the result\n charset = self._site_encoding()\n if coding:\n result = result.decode(charset).encode(coding)\n else:\n coding = charset\n\n # set headers and return\n setheader = self.request.RESPONSE.setHeader\n setheader('Content-Length', len(result))\n setheader('Content-Type',\n 'text/x-comma-separated-values; charset=%s' % coding)\n setheader('Content-Disposition',\n 'attachment; filename=%s.csv' % self.context.getId())\n return result","def write_csv(self, directory = None):\n if ((directory is None) and\n (self._session.config.folder_basename is not None)):\n directory = self._session.results._full_path\n else:\n return\n \n file = CSV_file(self, directory)\n file.write()\n return file","def to_csv(self, filename='coins.csv', header=True):\n if filename:\n self.coins_history.to_csv(path_or_buf=filename, index=False, header=header)","def write_data_to_csv(data, csv_file=\"\"):\n with open(csv_file, \"w\", encoding='utf-8-sig', newline='') as csvfile:\n if data:\n writer = csv.writer(csvfile, dialect=\"excel\")\n writer.writerows(data)\n return csv_file"],"string":"[\n \"def export_csv(self):\\n outputfile = tkinter.filedialog.asksaveasfilename(\\n defaultextension=\\\".csv\\\",\\n filetypes=((\\\"comma seperated values\\\", \\\"*.csv\\\"),\\n (\\\"All Files\\\", \\\"*.*\\\")))\\n if outputfile:\\n tabledata = self.tabs.window.aistracker.create_table_data()\\n export.write_csv_file(tabledata, outputfile)\\n else:\\n raise ExportAborted('Export cancelled by user.')\",\n \"def export_csv(self, df, filename):\\n df.to_csv(str(pathlib.PurePosixPath(self.config.csv_path, filename)) , sep=\\\";\\\")\",\n \"def write_file(file):\\n file.to_csv('data_set.csv', encoding='utf-8', index=False)\",\n \"def save_csv(self, filename): # DONE\\n self.data.to_csv(filename)\",\n \"def to_csv(self, filename):\\n self.data.to_csv(filename)\",\n \"def to_csv(self, filename):\\n self.data.to_csv(filename)\",\n \"def export_csv(self, outpath):\\n\\n\\t\\tself.df.to_csv(outpath)\",\n \"def to_csv(self, filename, **kwargs):\\n self.data.to_csv(filename, **kwargs)\",\n \"def export2csv(filename, headers, data):\\n click.secho('Do you really want to export file ([y]/n)? ',\\n nl=False,\\n fg='red')\\n\\n c = click.getchar()\\n click.echo()\\n\\n if str.upper(c) != 'Y':\\n sys.exit()\\n\\n today = datetime.now().strftime(\\\"%y%m%d%H%M%S\\\")\\n filename = f'{DOWNLOAD_DIR}/{filename}_{today}.csv'\\n\\n click.echo(filename)\\n\\n with open(filename, 'w', newline='') as csvfile:\\n write = csv.DictWriter(csvfile, fieldnames=headers)\\n write.writeheader()\\n\\n with click.progressbar(data, label='exporing {}'.format(filename)) as items: # noqa\\n for item in items:\\n write.writerow(item)\",\n \"def export_csv(state, out_file=None):\\n\\n if out_file is None:\\n csvfile = sys.stdout\\n else:\\n csvfile = open(out_file, 'w')\\n\\n try:\\n writer = csv.writer(csvfile)\\n for grade in state.grades:\\n writer.writerow([grade.student_name(), grade.score(),\\n grade.breakdown(state.user_name)])\\n finally:\\n if out_file is not None:\\n csvfile.close()\",\n \"def export_to_csv(self, file_name):\\n \\n with open(file_name, 'w', newline='') as csvDataFile:\\n csvWriter = csv.writer(csvDataFile, delimiter = ',')\\n\\n for i in range(0,self.sample_num):\\n data = list()\\n data.append(self.sample[i].simulation_name)\\n data.append(self.sample[i].result_name)\\n data.extend(self.sample[i].parameters.tolist())\\n data.extend(self.sample[i].result) \\n csvWriter.writerow(data)\",\n \"def abs2csv(self, file_name, file_path, export_index = True, export_header = True):\\n\\n self.abs_data.to_csv(os.path.join(file_path,file_name), index = export_index, header = export_header)\\n print('Absorbance data saved to .csv succesfully. \\\\n')\",\n \"def preproc2csv(self, file_path, export_index = True, export_header = True):\\n\\n self.pre_proc_data.to_csv(file_path+'.csv', index = export_index, header = export_header)\\n print('Pre-processed data saved to .csv succesfully. \\\\n')\",\n \"def _export_csv(x, y, export_to):\\r\\n\\r\\n with open(export_to, 'w', newline='') as e:\\r\\n writer = csv.writer(e, delimiter=',')\\r\\n for i in range (0, len(x)):\\r\\n writer.writerow([x[i], y[i]])\",\n \"def write_csv(self, filename, cutoff=2):\\n f = csv.writer(open(filename, 'wb'))\\n for row in self.rows(cutoff=cutoff):\\n f.writerow(row)\",\n \"def ExportAsCSV(csv_out_path, data):\\n with open(csv_out_path, \\\"w\\\", newline=\\\"\\\") as f:\\n writer = csv.writer(f, delimiter=',')\\n writer.writerows(data)\\n f.close()\\n return\",\n \"def writefile(data, filepath, encoding='utf-8', header=False):\\n data.to_csv(filepath, encoding=encoding, header=header)\",\n \"def export(\\n self, output_fp: str, *args, _add_ext: bool = False, sep: str = \\\",\\\", **kwargs\\n ) -> None:\\n tmp_export, rkwarg = self._export(*args, **kwargs)\\n if _add_ext:\\n tmp_export.to_csv(\\\"{}.csv\\\".format(output_fp), sep=sep)\\n else:\\n tmp_export.to_csv(output_fp, sep=sep)\",\n \"def to_csv(self, filename, **csv_args):\\n dataframe = pd.DataFrame.from_dict(self.to_dict())\\n return dataframe.to_csv(filename, **csv_args)\",\n \"def to_csv(self, file: str = \\\"output_file.csv\\\"):\\n if self._AccessPoint not in self.valid_access_points:\\n raise InvalidFetcherAccessPoint(\\n \\\" Initialize an access point (%s) first.\\\"\\n % \\\",\\\".join(self.Fetchers.keys())\\n )\\n return self.load().index.to_csv(file)\",\n \"def to_csv(self, path, sep=';', **kwargs):\\n df = self.get_as_pandas_dataframe()\\n df.to_csv(path, sep=sep, index=False, **kwargs)\",\n \"def write_tocsv(file_name, dataframe) :\\n print(\\\"\\\\nSaved result to {}\\\\n\\\".format(file_name))\\n dataframe.to_csv(file_name, mode='a', header=False,index=False)\",\n \"def export_csv(self, path):\\r\\n\\r\\n with open(path, 'w') as f:\\r\\n f.write('# h,hr,m')\\r\\n\\r\\n if self.rho is not None:\\r\\n f.write(',rho')\\r\\n if self.temperature is not None:\\r\\n f.write(',temperature')\\r\\n\\r\\n f.write('\\\\n')\\r\\n for i in range(self.shape[0]):\\r\\n for j in range(self.shape[1]):\\r\\n f.write(f'{self.h[i, j]},{self.hr[i, j]},{self.m[i, j]}')\\r\\n if self.rho is not None:\\r\\n f.write(f',{self.rho[i, j]}')\\r\\n if self.temperature is not None:\\r\\n f.write(f',{self.temperature[i, j]}')\\r\\n f.write('\\\\n')\\r\\n return\",\n \"def write_csv(self, out_file_name, header):\\n\\n with open(out_file_name, 'wb') as outf:\\n writer = csv.writer(outf, quoting=csv.QUOTE_ALL)\\n writer.writerow(header)\\n writer.writerows(self.records)\",\n \"def export_csv(data: dict, file_path: str):\\n fieldnames = [\\n 'briefy_id', 'number_required_assets', 'number_submissions', 'total_submissions_images',\\n 'total_submissions_videos', 'total_submissions_others', 'total_archive_images',\\n 'total_archive_videos', 'total_archive_others', 'total_delivery_images',\\n 'total_delivery_videos', 'total_delivery_others', 'submission_links', 'archive_link',\\n 'delivery_link', 'order_link'\\n ]\\n\\n with open(file_path, 'w') as fout:\\n writer = csv.DictWriter(fout, fieldnames)\\n writer.writeheader()\\n for key, value in data.items():\\n writer.writerow(value)\",\n \"def export_data_to_csv(data,filename):\\n data.to_csv(filename)\\n logger.info(f'Data frame exported to CSV format {filename}')\\n return True\",\n \"def saveCSV(self, filename='cbpGetHistoricRates.csv'):\\n\\n p = re.compile(r\\\"^[\\\\w\\\\-. ]+$\\\")\\n if not p.match(filename):\\n raise TypeError('Filename required.')\\n\\n if not isinstance(self.df, pd.DataFrame):\\n raise TypeError('Pandas DataFrame required.')\\n\\n try:\\n self.df.to_csv(filename)\\n except OSError:\\n print('Unable to save: ', filename)\",\n \"def exportToCSV(subdomain,sub,file_name):\\n\\ttry:\\n\\t\\twith open(file_name,\\\"w\\\") as file:\\n\\t\\t\\tfnames = ['subdomain', 'saved_date']\\n\\t\\t\\twriter = csv.DictWriter(file, fieldnames=fnames)\\n\\t\\t\\twriter.writeheader()\\n\\t\\t\\tfor _ in sub:\\n\\t\\t\\t\\twriter.writerow({'subdomain' : _.subdomain, 'saved_date': _.created_date})\\n\\t\\t\\tprint(f'[+] {len(sub)} results of {subdomain} exported successfully.\\\\n[+] Path {result_save_dir}/{subdomain}_{datetime.now().date()}.csv')\\n\\texcept IOError as error:\\n\\t\\tlogger.log(\\\"ERROR\\\",\\\"[+] Fails to open or create file! Please check your permissions\\\")\",\n \"def exportCSV(self, log, csvFile):\\n return 0\",\n \"def make_csv(file_of_data):\\n with open(file_of_data, 'w') as f:\\n writer = csv.writer(f)\\n header = (\\\"Counter\\\", \\\"Date/time\\\", \\\"Latitude\\\", \\\"Longitude\\\", \\\"Temperature\\\", \\\"Humidity\\\")\\n writer.writerow(header)\",\n \"def export_to_csv(self, outputfilepath, separator):\\n\\n con = fdb.connect(\\n database=self.db_filepath,\\n # dsn='localhost:~/test/CGI.vvv', #localhost:3050\\n user='sysdba', password='masterkey'\\n #charset='UTF8' # specify a character set for the connection\\n )\\n cur = con.cursor()\\n statement = \\\"select * from FILES\\\"\\n cur.execute(statement)\\n # Retrieve all rows as a sequence and print that sequence:\\n print(cur.fetchall())\\n\\n # VVV export format: Volume,Path,Name,Size,Ext,Last modified,Description\\n\\n with open(outputfilepath, 'w') as csvfile:\\n writer = csv.writer(csvfile)\\n writer.writerow([ i[0] for i in cur.description ]) \\n writer.writerows(cur.fetchall())\",\n \"def exportToCsv(self, filepath):\\n table = list()\\n table.append(list(self.__header))\\n for a in self.__assays:\\n table.append(\\n [unicode(a.timestamp.isoformat()),\\n unicode(a.dab_cell_count),\\n unicode(a.hem_cell_count),\\n unicode(a.dab_dabhemfraction),\\n unicode(a.img_path)])\\n # File encoding will be same as it expected by Excel on machine where\\n # this file was created.\\n encoding = locale.getpreferredencoding()\\n with open(filepath, mode='wb') as f:\\n writer = UnicodeWriter(f, encoding=encoding, delimiter=';')\\n writer.writerows(table)\",\n \"def to_csv(self, filename: str):\\n\\n if filename and filename.endswith(\\\".csv\\\"):\\n filename = filename[:-4]\\n\\n if len(self.analysis) > 0:\\n export_to_csv(\\n [\\n \\\"ticker\\\",\\n \\\"date\\\",\\n \\\"category\\\",\\n \\\"analyst\\\",\\n \\\"rating\\\",\\n \\\"price_from\\\",\\n \\\"price_to\\\",\\n ],\\n self.analysis,\\n f\\\"{filename}-analysts.csv\\\",\\n )\\n\\n return export_to_csv(self.headers, self.data, f\\\"{filename}.csv\\\")\",\n \"def write_df_tocsv(df, file_name):\\n try:\\n df.to_csv(os.path.join(script_dir, file_name))\\n except:\\n pass\",\n \"def write_csv(self, outfile, collapse_orders=False, show_age=False):\\r\\n # Write header row\\r\\n outfile.write(self.get_csv_header(collapse_orders, show_age).encode())\\r\\n\\r\\n # Write content\\r\\n for x in self.records:\\r\\n x.write_csv(outfile, collapse_orders, show_age)\",\n \"def toCsv(self, csv_path):\\n ser = pd.Series(self)\\n ser.to_csv(csv_path)\",\n \"def _csv_export(self, exppath):\\n with open(exppath, 'w') as csvfile:\\n csvwriter = csv.writer(csvfile, delimiter=',', skipinitialspace=True)\\n csvwriter.writerow(['hexstr','dmc','name'])\\n for clr in self.lookup_table:\\n csvwriter.writerow([clr.hex.to_str(), clr.id, clr.name])\",\n \"def _export_data_to_csv(self, source, target):\\n self.log.info(f\\\"Dumping data into {target}\\\")\\n source.to_csv(target, index=False)\",\n \"def write_csv(self, file):\\n # Write header row\\n file.write('Timestamp,MessageType,Queue,Price,Volume,OrderID\\\\n')\\n # Write content\\n for x in self.records:\\n row = (str(x[0]) + ',' + x[1][\\\"MessageType\\\"] + ',' +\\n x[1][\\\"Queue\\\"] + ',' + str(x[1][\\\"Price\\\"]) + ',' +\\n str(x[1][\\\"Volume\\\"]) + ',' + str(x[1][\\\"OrderID\\\"]) + '\\\\n')\\n file.write(row)\",\n \"def export_csv(table_name, out_file):\\n con = sqlite3.connect(dbname)\\n import pandas as pd\\n\\n df = pd.read_sql_query(f\\\"SELECT * FROM {table_name}\\\", con)\\n df.to_csv(out_file, index=False)\",\n \"def to_csv(self, path):\\n results = self.all()\\n if self.stop_check is not None and self.stop_check():\\n return\\n results.to_csv(path)\",\n \"def give_data_to_csv(file_of_data, data):\\n with open(file_of_data, 'a') as f:\\n writer = csv.writer(f)\\n writer.writerow(data)\",\n \"def write_output(basis, filename):\\n\\n logging.info('Writing output to {}'.format(filename))\\n\\n basis.to_csv(filename)\",\n \"def to_csv_files(self, path):\\n self._to_dict_tree().to_csv_files(path)\",\n \"def info2csv(df, csv_path):\\n df.to_csv(csv_path, index=False)\",\n \"def write_csv(fn, toCSV):\\n keys = toCSV[0].keys()\\n with open(fn, 'w') as output_file:\\n dict_writer = csv.DictWriter(output_file, keys)\\n dict_writer.writeheader()\\n dict_writer.writerows(toCSV)\",\n \"def writeToCSV(self, filepath):\\r\\n\\t\\twith open(filepath, 'w') as outputFile:\\r\\n\\t\\t\\toutputFile.write(str(self))\",\n \"def createFileCSV(table, path=\\\"./prediction\\\"):\\t\\n\\tif len(table) < 1:\\n\\t\\traise NameError('Empty Table!')\\n\\telse:\\n\\t\\tfile = open(path + '.csv', 'w+')\\n\\n\\t\\tfile.write(table[0].toStringHeaders() + \\\"\\\\n\\\")\\n\\n\\t\\tfor row in table:\\n\\t\\t\\tfile.write(row.toStringCSV() + '\\\\n')\\n\\t\\tfile.close()\",\n \"def write_csv(data_frame, file_name):\\n data_frame.coalesce(1).write \\\\\\n .option('header', True).mode('overwrite') \\\\\\n .save(f'outputs/{file_name}', format('csv'))\",\n \"def save_csv_file():\\n global output_on_display, import_lst, column_names, data\\n if data_base == '':\\n mistake_load_table()\\n else:\\n column_names = data[0]\\n save_name = asksaveasfilename(title=\\\"Select file\\\", filetypes=((\\\"CSV\\\", \\\"*.csv\\\"), (\\\"all files\\\", \\\"*.*\\\")),\\n confirmoverwrite=True, defaultextension='.csv')\\n step = len(column_names)\\n data_csv = import_lst\\n if len(data_csv[0]) == step:\\n pass\\n else:\\n data_csv = import_lst[step::]\\n\\n with open(save_name, 'w+') as csv_file:\\n csv_writer = csv.writer(csv_file)\\n csv_writer.writerow(column_names)\\n csv_writer.writerows(data_csv)\",\n \"def write_csv(list_file, path):\\n\\n\\twith open(path, 'w') as f:\\n\\t\\twriter = csv.writer(f, delimiter=',')\\n\\t\\tfor i in list_file:\\n\\t\\t\\twriter.writerow(i)\",\n \"def export2csv(data, input_filename, output_filename):\\n assert input_filename\\n assert os.path.exists(output_filename) and os.path.isfile(output_filename)\\n str2write_xyz = input_filename + \\\",\\\"\\n for i in data:\\n for j in i:\\n str2write_xyz += str(j) + \\\",\\\"\\n filen = input_filename.split(\\\".\\\")[0]\\n str2write_xyz += filen[filen.find(os.sep) + 1:].replace(\\\"_\\\", \\\",\\\").replace(\\\"-\\\", \\\".\\\").replace(\\\"/\\\", \\\",\\\").replace(\\\"\\\\\\\\\\\", \\\",\\\") + \\\"\\\\n\\\"\\n with open(output_filename, \\\"a\\\") as filep:\\n filep.write(str2write_xyz)\",\n \"def write_csv(self):\\n self.tableView.df.to_csv('Data export.csv', index=False)\\n print('CSV file exported')\",\n \"def write_csv_file(filepath, fieldnames, rows):\\n headers = [{'label': field} for field in fieldnames]\\n with open(filepath, 'w') as f_buf:\\n outfile = CsvWriter()\\n outfile.set_headers(headers)\\n outfile._datas = rows\\n outfile.render(f_buf)\",\n \"def write_csv(file_name, data):\\n\\n with open(file_name, \\\"w\\\") as fp:\\n\\n writer = RiscvInstructionTraceCsv(fp)\\n writer.start_new_trace()\\n\\n for entry in data:\\n writer.write_trace_entry(entry)\",\n \"def export_csv(df, season_id, league_name, league_id):\\r\\n file_name = '{}_{}_{}.csv'.format(league_name,league_id,season_id)\\r\\n current_dir = os.path.dirname(__file__)\\r\\n path_name = os.path.join(current_dir, '../{}'.format(season_id))\\r\\n if not os.path.exists(path_name):\\r\\n os.mkdir(path_name)\\r\\n\\r\\n export_name = os.path.join(path_name, file_name)\\r\\n df.to_csv(export_name, index=False, encoding='utf-8')\",\n \"def to_csv(self, file_path: str) -> None:\\n with open(file_path, \\\"w\\\") as f:\\n writer = csv.writer(f)\\n writer.writerow([\\\"parameter\\\", \\\"value\\\"])\\n writer.writerow([\\\"render\\\", str(self.render)])\\n writer.writerow([\\\"sleep\\\", str(self.sleep)])\\n writer.writerow([\\\"log_frequency\\\", str(self.log_frequency)])\\n writer.writerow([\\\"video\\\", str(self.video)])\\n writer.writerow([\\\"video_fps\\\", str(self.video_fps)])\\n writer.writerow([\\\"video_dir\\\", str(self.video_dir)])\\n writer.writerow([\\\"num_episodes\\\", str(self.num_episodes)])\\n writer.writerow([\\\"gifs\\\", str(self.gifs)])\\n writer.writerow([\\\"gifdir\\\", str(self.gif_dir)])\\n writer.writerow([\\\"video_frequency\\\", str(self.video_frequency)])\",\n \"def save_csv(self, filename: str, type='n', **args):\\n if type == 'n':\\n df = self.export_nodes()\\n else:\\n df = self.export_edges()\\n df.to_csv(filename, index=False)\",\n \"def save_csv(outputfile):\\n with open(outputfile, 'w', newline='') as outfile:\\n writer = csv.writer(outfile)\\n writer.writerow(DATA_KEYS)\\n\\n # Add data to csv-file\\n for data in data_list:\\n writer.writerow(data)\",\n \"def write_csv(self, filelike):\\r\\n items = self.rows()\\r\\n writer = unicodecsv.writer(filelike, encoding=\\\"utf-8\\\")\\r\\n writer.writerow(self.header())\\r\\n for item in items:\\r\\n writer.writerow(item)\",\n \"def write_csv(self, file: str, table: str, libref: str =\\\"\\\", nosub: bool =False, dsopts: dict = None, opts: dict = None) -> 'The LOG showing the results of the step':\\n dsopts = dsopts if dsopts is not None else {}\\n opts = opts if opts is not None else {}\\n\\n code = \\\"filename x \\\\\\\"\\\"+file+\\\"\\\\\\\";\\\\n\\\"\\n code += \\\"options nosource;\\\\n\\\"\\n code += \\\"proc export data=\\\"\\n\\n if len(libref):\\n code += libref+\\\".\\\"\\n\\n code += \\\"'\\\"+table.strip()+\\\"'n \\\"+self._sb._dsopts(dsopts)+\\\" outfile=x dbms=csv replace; \\\"\\n code += self._sb._expopts(opts)+\\\" run\\\\n;\\\"\\n code += \\\"options source;\\\\n\\\"\\n\\n if nosub:\\n print(code)\\n else:\\n ll = self.submit(code, \\\"text\\\")\\n return ll['LOG']\",\n \"def write_csv(self, file, collapse=False):\\n # Write header row\\n file.write('Timestamp')\\n for x in self.measures:\\n file.write(',' + x)\\n file.write('\\\\n')\\n\\n if collapse:\\n last_ts = None\\n next_write = None\\n # Write content\\n for x in self.records:\\n if last_ts is None:\\n last_ts = x[0]\\n next_write = x\\n elif x[0] == last_ts:\\n next_write = x\\n else:\\n if next_write is not None:\\n self.__write_record(file, next_write)\\n last_ts = x[0]\\n next_write = x\\n if next_write is not None:\\n self.__write_record(file, next_write)\\n else:\\n # Write content\\n for x in self.records:\\n self.__write_record(file, x)\",\n \"def output_csv(df):\\n # remove existing plot\\n if os.path.exists(\\\"files/converted.csv\\\"):\\n os.remove(\\\"files/converted.csv\\\")\\n # save csv\\n df.to_csv('files/converted.csv')\",\n \"def to_csv_file(self, records):\\n self._file_manager.make_dir_when_no_dir(self._directory)\\n csv_file = os.path.join(self._directory, self._file_name + \\\".csv\\\")\\n record_lines = [rec.to_csv_string() + \\\"\\\\n\\\" for rec in records]\\n self._file_manager.write_lines(csv_file, record_lines)\",\n \"def csv(self, destination_path):\\n # todo - test for single and duplicate base cases\\n to_csv(self._axl_data, destination_path)\",\n \"def csv_file(data,output_dir,filename,order = [],head = True):\\n with open(output_dir + filename + '.csv', 'w') as f:\\n write = csv.writer(f)\\n write.writerows(manip.dic_to_list(data,order,head),)\\n return None\",\n \"def writecsvfile(filename, columnnames, data):\\n with open(filename, 'w', newline='') as csv_file:\\n writer = csv.writer(csv_file, delimiter=',')\\n writer.writerow(columnnames) # header row\\n for row in data:\\n writer.writerow(row[:])\",\n \"def write_to_csv(agents, filename):\\n log.info(\\\"Writing CSV file '%s'...\\\" % filename)\\n with open(filename, 'w', newline='') as csvfile:\\n writer = csv.DictWriter(csvfile, fieldnames=place_detail_keys)\\n writer.writeheader()\\n writer.writerows(agents)\",\n \"def write_csv(self, outputfile):\\n d = csv.writer(outputfile, quoting=csv.QUOTE_ALL)\\n for row in self.translations.iteritems():\\n d.writerow(row)\",\n \"def write_csv(self, filename: str, author_data: list):\\n split_file = filename.split('/')\\n new_filename = \\\"\\\"\\n for index, component in enumerate(split_file):\\n if index == len(split_file) - 1:\\n break\\n new_filename += component + \\\"/\\\"\\n new_filename += \\\"results_\\\" + split_file[-1]\\n with open(new_filename, 'w') as csvfile:\\n result_writer = csv.writer(csvfile)\\n result_writer.writerow(self._csv_headings)\\n for author in author_data:\\n result_writer.writerow(self.author_data_to_str(author))\\n csvfile.close()\",\n \"def prepare_out_csv(output_dir, filename):\\n out_columns_pi = ['fasta_file', 'acc.code',\\n 'organism', 'EC.code', 'species',\\n 'note', 'pi', 'modification', 'category']\\n string = ''\\n for i in out_columns_pi:\\n if i == out_columns_pi[-1]:\\n string += i\\n else:\\n string += i+','\\n string += '\\\\n'\\n with open(output_dir+filename, 'w') as f:\\n f.write(string)\",\n \"def write_csv(path: str, df_players: pd.DataFrame) -> None:\\n df_players.to_csv(path, index=False, header=True)\",\n \"def to_csv(self, path):\\n for table in ['datasets', 'dataruns', 'hyperpartitions', 'classifiers']:\\n df = pd.read_sql('SELECT * FROM %s' % table, self.session.bind)\\n df.to_csv(os.path.join(path, '%s.csv' % table), index=False)\",\n \"def write_csv(header_row, data_rows, filename, course_id):\\n shared.ensure_directory_exists(utils.ANSWERS_DISTRIBUTION_REPORTS_DIRECTORY,\\n course_id.org, course_id.course)\\n\\n\\n path = shared.get_safe_file_path(utils.ANSWERS_DISTRIBUTION_REPORTS_DIRECTORY,\\n course_id.org, course_id.course,\\n filename)\\n ## need to encode the unico path in order to open the file in prod env\\n path = path.encode('utf-8')\\n\\n with open(path, \\\"wb\\\") as ofile:\\n writer = csv.writer(ofile, quoting=csv.QUOTE_ALL)\\n writer.writerow(header_row)\\n for datarow in data_rows:\\n encoded_row = [cleanup_newlines(unicode(s).encode('utf-8'))\\n for s in datarow]\\n writer.writerow(encoded_row)\",\n \"def write_csv(filename, **values):\\n writeheader = not os.path.isfile(filename)\\n fieldnames = sorted(values.keys())\\n\\n with open(filename, 'a') as f:\\n writer = csv.DictWriter(f, fieldnames, dialect='excel-tab')\\n if writeheader:\\n writer.writeheader()\\n writer.writerow(values)\",\n \"def write_file(data,file_name):\\r\\n\\twith open(file_name,'wb') as new_csv_file:\\r\\n\\t\\twrtr = writer(new_csv_file)\\r\\n\\t\\tfor row in data:\\r\\n\\t\\t\\twrtr.writerow(row)\",\n \"def convert_to_csv(cls, input_file: str) -> None:\\r\\n parser = cls(file_path=input_file)\\r\\n\\r\\n output_dir = pathlib.Path(pathlib.Path(input_file).parent, \\\"results\\\")\\r\\n os.makedirs(output_dir, exist_ok=True)\\r\\n for statement_period, statement_activities in parser._statements.items():\\r\\n path = pathlib.Path(output_dir, statement_period)\\r\\n statement_activities.to_csv(path)\",\n \"def toCSV(self, outFileName):\\n with open(outFileName, 'wb') as outFile:\\n writer = csv.writer(outFile)\\n writer.writerow(self.getHeaderList())\\n for name, (results, params) in self.result.iteritems():\\n writer.writerow([name] + list(results) + list(params))\",\n \"def createcsv(fileName):\\n fileName = os.path.join(pathtofolder(), 'datas', fileName)\\n fileFormat = '.csv'\\n file = f'{fileName + fileFormat}'\\n\\n csvKeys = [\\\"product_page_url\\\", \\\"universal_product_code\\\", \\\"title\\\",\\n \\\"price_including_tax\\\", \\\"price_excluding_tax\\\", \\\"number_available\\\",\\n \\\"product_description\\\", \\\"category\\\", \\\"review_rating\\\", \\\"image_url\\\"]\\n\\n addon = excelExport('excel')\\n\\n with open(file, 'w', newline=\\\"\\\", encoding='utf-8') as csvFile:\\n csvFile.write(addon) # Define the separator as <\\\">.\\n resultWriter = csv.writer(csvFile, delimiter = '|', dialect = \\\"excel\\\")\\n resultWriter.writerow(csvKeys)\\n pass\",\n \"def write_to_csv(self, output_dir, delimiter, include_language, filename=None):\\n\\n if filename is not None:\\n self.filename = filename\\n\\n if len(self.values) == 0:\\n logger.info(\\\"Nothing to export.\\\")\\n return\\n\\n if not os.path.exists(output_dir):\\n os.makedirs(output_dir)\\n\\n file_path = os.path.join(output_dir, self.filename)\\n\\n # write search results to UTF8-encoded CSV file (see also http://stackoverflow.com/a/844443)\\n with codecs.open(file_path, 'w', encoding='utf8') as fp:\\n logger.info('Exporting search results to ' + file_path + '...')\\n writer = csv.writer(fp, delimiter=delimiter)\\n\\n column_names = SearchResult.get_column_names(include_language)\\n\\n # write header of CSV file\\n writer.writerow(column_names)\\n\\n count = 0\\n try:\\n for row in self.get_rows(include_language):\\n if len(row) == len(column_names):\\n writer.writerow(row)\\n count = count + 1\\n else:\\n raise IllegalArgumentError(\\n str(abs(len(column_names) - len(row))) + ' parameter(s) is/are missing for \\\"'\\n + str(row) + '\\\"')\\n\\n except UnicodeEncodeError:\\n logger.error('Encoding error while writing data for: ' + str(row))\\n\\n logger.info(str(count) + ' search results have been exported.')\",\n \"def writeCSV(self, outfilename, delimiter=';'):\\n\\n input = open(self.filename)\\n dialect = csv.Sniffer().sniff(input.read(1024))\\n input.seek(0)\\n reader = csv.reader(input, dialect)\\n out = open(outfilename, \\\"w\\\")\\n writer = csv.writer(out, delimiter=delimiter, quotechar='\\\"', quoting=csv.QUOTE_ALL)\\n\\n for row in reader:\\n writer.writerow(row)\\n\\n input.close()\\n out.close()\",\n \"def export(self, directory, fname=None):\\r\\n if not fname:\\r\\n try:\\r\\n fname = self.dt_0.strftime('%Y.%m.%d_%H.%M.%S') + '.txt'\\r\\n except AttributeError:\\r\\n fname = self.fname\\r\\n\\r\\n self.data.to_csv(Path(directory, fname), header=None, float_format='%.7g')\\r\\n logger.info(f'{self} export to folder {directory}.')\",\n \"def write_output_csv(filename, **kwargs):\\n import csv\\n import time\\n\\n intermediate = kwargs.pop(\\\"intermediate\\\", False)\\n\\n keys = sorted(kwargs.keys())\\n num_vars = len(keys)\\n\\n if intermediate:\\n full_filename = filename + \\\"_interm\\\"\\n else:\\n dot_index = filename.rfind('.')\\n if dot_index != -1:\\n full_filename = (filename[:dot_index]\\n + time.strftime(\\\"%Y-%m-%d-%H.%M.%S\\\")\\n + filename[dot_index:])\\n else:\\n full_filename = filename + time.strftime(\\\"%Y-%m-%d-%H.%M.%S\\\")\\n\\n # add current time to filename as an identifier\\n with open(full_filename, 'w', newline='') as csvfile:\\n\\n writer = csv.writer(csvfile)\\n\\n # write header\\n writer.writerow(keys)\\n\\n num_entries = len(kwargs[keys[0]])\\n for i in range(num_entries):\\n writer.writerow(kwargs[keys[j]][i] for j in range(num_vars))\",\n \"def write_csv(file_names: list, csv_file_path: str):\\n with open(csv_file_path, mode='w') as csv_file:\\n writer = csv.writer(csv_file)\\n writer.writerows(enumerate(file_names))\",\n \"def create_csv_file(self):\\r\\n # Create a new csv-file\\r\\n with open(self.fname, 'w') as f:\\r\\n writer = csv.writer(f, dialect='excel')\\r\\n writer.writerow(['set_time',\\r\\n 'read_time_P_ac',\\r\\n 'read_time_P_bat',\\r\\n 'soc',\\r\\n 'set_value',\\r\\n 'P_ac',\\r\\n 'P_bat'])\",\n \"def writeCSV(filename):\\n if not filename.endswith('.csv'):\\n filename += '.csv'\\n with open(filename, 'wb') as csvfile:\\n filewriter = csv.writer(csvfile, delimiter=',',\\n quotechar='|', quoting=csv.QUOTE_MINIMAL)\\n for x in range(numRows):\\n scores = quizScores()\\n types = getTypes(quizScores())\\n extra = []\\n filewriter.writerow([x] + types + scores + extra)\",\n \"def to_csv(self, path: pathlib.Path):\\n combined = self.combined_df\\n assert combined[CommonFields.LOCATION_ID].notna().all()\\n common_df.write_csv(\\n combined, path, structlog.get_logger(), [CommonFields.LOCATION_ID, CommonFields.DATE]\\n )\\n if self.provenance is not None:\\n provenance_path = str(path).replace(\\\".csv\\\", \\\"-provenance.csv\\\")\\n self.provenance.sort_index().to_csv(provenance_path)\",\n \"def dump_csv(f, rra, out):\\n w = writer(out)\\n for row in dump(f, rra):\\n w.writerow([s.strip() for s in row])\",\n \"def write_csv(path, lines, headers):\\n print \\\"Opening %s for score output\\\" % base_name(path)\\n\\n try:\\n f = open(path, 'wb')\\n writer = csv.writer(f)\\n writer.writerow(headers)\\n writer.writerows(lines)\\n except IOError:\\n print \\\"Cannot open %s\\\" % path\\n else:\\n print \\\"Scores successfully written to %s\\\" % path\\n f.close()\",\n \"def export_table(path, path_out):\\n table = rb.get_table(path)\\n table.to_csv(path_out, index=False)\\n return\",\n \"def file(self):\\n result = []\\n completePath = CompletePath(self.path, self.filename) \\n with open(completePath.path(), 'w', newline='') as csvfile:\\n fieldnames = ['Activity', 'Points']\\n writer = csv.DictWriter(csvfile, fieldnames = fieldnames)\\n writer.writeheader()\\n for i in range ( len( self.groupPriority.rows() ) ):\\n tmp = self.groupPriority.rows()[i]\\n self.log.info ( \\\"FinalCSV\\\", \\\"file\\\",\\\"data {0},{1}\\\".format( tmp.activity(), tmp.points() ) )\\n writer.writerow({'Activity': tmp.activity(), 'Points': tmp.points()})\\n self.log.info(\\\"FinalCSV\\\", \\\"file\\\", \\\"Elaborated file: {0}\\\".format ( completePath.path() ) )\",\n \"def _file_writer(file, extension, output_data, headers):\\n if extension == 'csv':\\n separator = ','\\n else:\\n separator = ' '\\n writer = csv.writer(file, delimiter=separator, quotechar='|')\\n if headers is True:\\n writer.writerow(['Login', 'P/N', 'Password', 'P/N'])\\n writer.writerows(output_data)\",\n \"def to_file(self, target_filename, **to_csv_kwargs):\\n # TODO: to_file saves will need trigger log file in future versions\\n self.df.to_csv(target_filename, index=False, **to_csv_kwargs)\",\n \"def to_csv(self, filename, delimiter=\\\",\\\", recommended_only=False, include_io=True):\\n df = self.to_df(recommended_only, include_io)\\n df.to_csv(filename, index=False, sep=delimiter)\",\n \"def outputapidata_csv(filename, data, headers=None):\\n with open(filename,'w',encoding='utf-8',newline = \\\"\\\", ) as f:\\n if headers:\\n writer = csv.DictWriter(f,fieldnames = headers)\\n writer.writeheader()\\n else:\\n writer = csv.DictWriter(f)\\n writer.writerows(out)\",\n \"def write_table_to_csv(table: List[List], filename: str):\\n with open(filename, 'w') as csvfile:\\n writer = csv.writer(csvfile, delimiter='\\\\t')\\n for row in table:\\n writer.writerow(row)\",\n \"def save_csv(df, fp):\\n if not os.path.isdir(os.path.split(fp)[0]):\\n os.makedirs(os.path.split(fp)[0])\\n\\n df.to_csv(fp, index=False)\",\n \"def exportCSV(self, fields=None, coding=None, delimiter='semicolon'):\\n\\n result = self.generateCSV(fields=fields, delimiter=delimiter)\\n\\n # encode the result\\n charset = self._site_encoding()\\n if coding:\\n result = result.decode(charset).encode(coding)\\n else:\\n coding = charset\\n\\n # set headers and return\\n setheader = self.request.RESPONSE.setHeader\\n setheader('Content-Length', len(result))\\n setheader('Content-Type',\\n 'text/x-comma-separated-values; charset=%s' % coding)\\n setheader('Content-Disposition',\\n 'attachment; filename=%s.csv' % self.context.getId())\\n return result\",\n \"def write_csv(self, directory = None):\\n if ((directory is None) and\\n (self._session.config.folder_basename is not None)):\\n directory = self._session.results._full_path\\n else:\\n return\\n \\n file = CSV_file(self, directory)\\n file.write()\\n return file\",\n \"def to_csv(self, filename='coins.csv', header=True):\\n if filename:\\n self.coins_history.to_csv(path_or_buf=filename, index=False, header=header)\",\n \"def write_data_to_csv(data, csv_file=\\\"\\\"):\\n with open(csv_file, \\\"w\\\", encoding='utf-8-sig', newline='') as csvfile:\\n if data:\\n writer = csv.writer(csvfile, dialect=\\\"excel\\\")\\n writer.writerows(data)\\n return csv_file\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7477921","0.74023473","0.72890913","0.7163044","0.7147001","0.7147001","0.7116825","0.7090414","0.70343846","0.7002203","0.6979948","0.695002","0.6947416","0.69094193","0.6906419","0.68957627","0.686277","0.68464404","0.6839928","0.6741557","0.67377245","0.6722057","0.6709376","0.66893274","0.6675873","0.6635221","0.66198117","0.66143894","0.6599823","0.65955347","0.65928584","0.65908945","0.6589834","0.6581155","0.6573714","0.65724874","0.6571121","0.6565729","0.6552608","0.65482575","0.6515353","0.65096897","0.6506697","0.6502048","0.65018696","0.6501579","0.6497683","0.6492334","0.6488493","0.64872086","0.64859664","0.64856493","0.6470195","0.6468018","0.64676076","0.6457958","0.6455051","0.6453729","0.64490724","0.6447092","0.6446349","0.64341253","0.64233625","0.6416361","0.6399761","0.6398013","0.63973415","0.6392537","0.6392399","0.6377791","0.6364521","0.63536775","0.6346468","0.634394","0.6340014","0.6332672","0.63305855","0.63275915","0.63177925","0.63104635","0.6309918","0.63083875","0.6301818","0.62955326","0.6289734","0.6260985","0.62520516","0.624875","0.6244487","0.62425184","0.6230875","0.6225744","0.62248707","0.6219745","0.621756","0.6217045","0.62019575","0.6199866","0.61966085","0.61837745","0.6182814"],"string":"[\n \"0.7477921\",\n \"0.74023473\",\n \"0.72890913\",\n \"0.7163044\",\n \"0.7147001\",\n \"0.7147001\",\n \"0.7116825\",\n \"0.7090414\",\n \"0.70343846\",\n \"0.7002203\",\n \"0.6979948\",\n \"0.695002\",\n \"0.6947416\",\n \"0.69094193\",\n \"0.6906419\",\n \"0.68957627\",\n \"0.686277\",\n \"0.68464404\",\n \"0.6839928\",\n \"0.6741557\",\n \"0.67377245\",\n \"0.6722057\",\n \"0.6709376\",\n \"0.66893274\",\n \"0.6675873\",\n \"0.6635221\",\n \"0.66198117\",\n \"0.66143894\",\n \"0.6599823\",\n \"0.65955347\",\n \"0.65928584\",\n \"0.65908945\",\n \"0.6589834\",\n \"0.6581155\",\n \"0.6573714\",\n \"0.65724874\",\n \"0.6571121\",\n \"0.6565729\",\n \"0.6552608\",\n \"0.65482575\",\n \"0.6515353\",\n \"0.65096897\",\n \"0.6506697\",\n \"0.6502048\",\n \"0.65018696\",\n \"0.6501579\",\n \"0.6497683\",\n \"0.6492334\",\n \"0.6488493\",\n \"0.64872086\",\n \"0.64859664\",\n \"0.64856493\",\n \"0.6470195\",\n \"0.6468018\",\n \"0.64676076\",\n \"0.6457958\",\n \"0.6455051\",\n \"0.6453729\",\n \"0.64490724\",\n \"0.6447092\",\n \"0.6446349\",\n \"0.64341253\",\n \"0.64233625\",\n \"0.6416361\",\n \"0.6399761\",\n \"0.6398013\",\n \"0.63973415\",\n \"0.6392537\",\n \"0.6392399\",\n \"0.6377791\",\n \"0.6364521\",\n \"0.63536775\",\n \"0.6346468\",\n \"0.634394\",\n \"0.6340014\",\n \"0.6332672\",\n \"0.63305855\",\n \"0.63275915\",\n \"0.63177925\",\n \"0.63104635\",\n \"0.6309918\",\n \"0.63083875\",\n \"0.6301818\",\n \"0.62955326\",\n \"0.6289734\",\n \"0.6260985\",\n \"0.62520516\",\n \"0.624875\",\n \"0.6244487\",\n \"0.62425184\",\n \"0.6230875\",\n \"0.6225744\",\n \"0.62248707\",\n \"0.6219745\",\n \"0.621756\",\n \"0.6217045\",\n \"0.62019575\",\n \"0.6199866\",\n \"0.61966085\",\n \"0.61837745\",\n \"0.6182814\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":9989,"cells":{"query":{"kind":"string","value":"Retrieve, update or delete a person instance."},"document":{"kind":"string","value":"def PrintVisitante(request, pk):\n try:\n visitante = Visitante.objects.get(pk=pk)\n except Visitante.DoesNotExist:\n return Response(status=status.HTTP_404_NOT_FOUND)\n\n if request.method == 'GET':\n serializer = VisitanteSerializer(visitante)\n return Response(serializer.data)\n\n elif request.method == 'PUT':\n serializer = VisitanteSerializer(visitante, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def person(self, person_id):\r\n return persons.Person(self, person_id)","def edit_person():\n # get person name from user\n responses = accept_inputs([\"Person's name\"])\n person_name = responses[\"Person's name\"]\n # check for existence\n results = query_with_results(\"select * from person where name = ?\", [person_name])\n if len(results) == 0:\n print(\"No person found with name '%s'.\" % person_name)\n return\n else:\n # get id of person\n id = query_with_results(\"select id from person where name = ?\", [person_name])[0][0]\n # the task exists, so ask the user for the new description\n responses = accept_inputs([\"New name\"])\n # update db\n query_no_results(\"update person set name = ? where id = ?\", [responses[\"New name\"], id])\n print(\"Person with old name '%s' changed to '%s'.\" % (person_name, responses[\"New name\"]))","def get(self, key):\n person = self._data.get(key)\n\n if not person:\n raise NotFoundError(\"{} could not be found\".format(key))\n\n return Person(key, person)","def fetch_by_id(self, person_id: int) -> PersonModel:\n person_db_model = PersonDBModel.query.get(person_id)\n if not person_db_model:\n raise PersonNotFound(person_id)\n person = PersonModel.from_db(person_db_model)\n self.logger.info(f'Successfully fetched Person {person.first_name} {person.last_name} by ID {person_id}')\n return person","def get(self,id):\r\n person = get_one_by_persons_id(id=id)\r\n if not person:\r\n api.abort(404)\r\n else:\r\n return person","def get(self,id):\r\n person = get_one(id=id)\r\n if not person:\r\n api.abort(404)\r\n else:\r\n return person","def update(lname, person):\n # Does the person exist in people?\n if lname in PEOPLE:\n PEOPLE[lname][\"fname\"] = person.get(\"fname\")\n PEOPLE[lname][\"timestamp\"] = get_timestamp()\n\n return PEOPLE[lname]\n\n # otherwise, nope, that's an error\n else:\n abort(\n 404, \"Person with last name {lname} not found\".format(lname=lname)\n )","def edit_person(self, treeview):\n model, iter_ = treeview.get_selection().get_selected()\n if iter_:\n handle = model.get_value(iter_, 0)\n try:\n person = self.dbstate.db.get_person_from_handle(handle)\n EditPerson(self.dbstate, self.uistate, [], person)\n except WindowActiveError:\n pass","def read_person(person_id):\n try:\n conn = sqlite3.connect(settings.database_name)\n conn.row_factory = sqlite3.Row\n c = conn.cursor()\n c.execute(\"PRAGMA foreign_keys = ON\")\n c.execute(\"SELECT * FROM person WHERE personid =?\", (person_id,))\n _person = None\n for row in c:\n _person = Person()\n _person.person_id = row[\"personid\"]\n _person.first_name = row[\"firstname\"]\n _person.last_name = row[\"lastname\"]\n _person.middle_initial = row[\"middleinitial\"]\n _person.nick_name = row[\"nickname\"]\n _person.date_of_birth = row[\"dateofbirth\"]\n _person.date_of_death = row[\"dateofdeath\"]\n conn.close()\n return _person\n except:\n return None","def get_new_entry(in_person):\n person = Person()\n person.name = get_name()\n person.phone = input(\"Enter Phone Number: \")\n return person","def get_person(self, id):\n try:\n person = Person.get(Person.id == id)\n data = model_to_dict(person)\n except DoesNotExist:\n response.status = 404\n data = \"Not found\"\n return dict(name='Person', data=data)","def person(self, person_id=None):\r\n if person_id is None:\r\n return pp.CurrentPerson(self)\r\n\r\n return pp.Person(self, person_id)","def rm_person():\n # get person name from user\n responses = accept_inputs([\"Person name\"])\n person_name = responses[\"Person name\"]\n # check for existence of person\n results = query_with_results(\"select id from person where name = ?\", [person_name])\n if len(results) == 0:\n print(\"No person found with name '%s' that we could remove.\" % person_name)\n return\n # the person exists, so remove it\n query_no_results(\"delete from person where name = ?\", [person_name])\n # remove all associations with tasks\n query_no_results(\"delete from task_person_pair where person = ?\", [results[0][0]])\n print(\"Person with name '%s' removed.\" % person_name)","def find(self, person):\n page = self.find_page(person)\n try:\n entity_id = self.get_entity_id(page.title)\n entity = self.get_entity(entity_id)\n person.dob = self.get_birthday(entity)\n person.occupation = self.get_occupation(entity)\n person.nationality = self.get_country_of_citizenship(entity)\n res_domicile = self.get_domicile(entity)\n if res_domicile:\n person.domicile = res_domicile\n elif person.nationality == self.get_birthcountry(entity):\n person.domicile = person.nationality # this is an assumption!\n birth_name = self.get_birth_name(entity)\n person.middle_name = self.get_middle_name(birth_name, person)\n if page:\n person.is_famous = 'True'\n else:\n person.is_famous = ''\n person.net_worth = self.get_networth(entity)\n person.description = page.summary\n person.set_raw()\n except:\n pass","def __ui_update_person(self):\n to_update_person_id = int(input(\"Introduce the ID of the person you want to update: \"))\n updated_person_name = input(\"Updated name: \").strip()\n updated_phone_number = input(\"Updated phone number: \").strip()\n self.__person_service.service_update_person(to_update_person_id, updated_person_name, updated_phone_number)\n print(\"Person successfully updated!\\n\")","def get_one(self, index, *args, **kw):\n person = M.People.query.get(index=index)\n log.debug('person {}'.format(person))\n if(person):\n kw['_id'] = person._id\n return super(PeopleAPIController, self).get_one(*args, **kw)","def remove_person(self, per: str):\n if per in self._people:\n self._people.remove(per)\n else:\n raise IDDoesNotExist","def get_person(self, id):\n PERSON = \"\"\"SELECT name FROM Person\n WHERE id = %s\"\"\"\n\n ret = None\n try:\n self.db_cursor.execute(\"\"\"SELECT name, id FROM Person WHERE id = %s\"\"\", (id,))\n self.db_cursor.execute(PERSON, (id,))\n self.db_connection.commit()\n p_attribs = self.db_cursor.fetchall()\n ret = Person()\n ret.name = p_attribs[0][0]\n ret.id = id\n\n except:\n logging.warning(\"DBAdapter: Error- cannot retrieve person: \" + str(id))\n return None\n\n return ret","def add_and_delete():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n\n logger.info('Add and display a Person called Fred; then delete him...')\n logger.info('Add Fred in one step')\n\n new_person = Person.create(\n person_name = 'Fred',\n lives_in_town = 'Seattle',\n nickname = 'Fearless')\n new_person.save()\n\n logger.info('Show Fred')\n aperson = Person.get(Person.person_name == 'Fred')\n\n logger.info(f'We just created {aperson.person_name}, who lives in {aperson.lives_in_town}')\n logger.info('but now we will delete him...')\n\n aperson.delete_instance()\n\n logger.info('Reading and print all Person records (but not Fred; he has been deleted)...')\n\n for person in Person:\n logger.info(f'{person.person_name} lives in {person.lives_in_town} and likes to be known as {person.nickname}')\n\n except Exception as e:\n logger.info(e)\n\n finally:\n database.close()","def getPerson(self, fingerprint):\n\t\tif fingerprint in self.people:\n\t\t\tp = self.people[fingerprint]\n\t\telse:\n\t\t\tp = Person.Person(fingerprint)\n\t\t\tself.people[fingerprint] = p\n\t\treturn p","def set_person(self, new_person, updating=False):\n PERSON_QUERY = \"\"\"UPDATE Person SET name = %s WHERE id = %s\"\"\" if updating \\\n else \"\"\"INSERT INTO Person (name, id) VALUES (%s, %s)\"\"\"\n\n self.db_cursor.execute(PERSON_QUERY, (new_person.name, new_person.id))\n self.db_connection.commit()","def edit_person(self, pk):","def PersonSave(self, PersonID=None, Title='', NameFirst='', NameMiddle='', NameLast='', DateBirth=None, AddrStr='', AddrZip='',\n\t\t\tAddrCitytownNr=None, Phone1Nr='', Cellphone1Nr='', Fax='', Email='', CivilStatus='', Sex='', EthnicOrig=None, Religion='',\n\t\t\tCitizenship='', SssNr = '', NatIdNr = '', Occupation = ''):\n\t\t# If PersonID is None, then we have a new entry\n\t\tif EthnicOrig in ['',None]:\n\t\t\tEthnicOrig = None\n\t\telse:\n\t\t\tEthnicOrig = int(EthnicOrig)\n\t\tif AddrCitytownNr not in [None, '']:\n\t\t\tAddrCitytownNr = int(AddrCitytownNr)\n\t\telse:\n\t\t\tAddrCitytownNr = None\n\t\tif DateBirth not in [None, '']:\n\t\t\t#log.debug(DateBirth)\n\t\t\tbday = time.strptime(str(DateBirth),DATE_FORMAT)\n\t\t\tbdate = datetime.datetime(bday.tm_year,bday.tm_mon,bday.tm_mday)\n\t\telse:\n\t\t\tbdate = None\n\t\tif PersonID not in [None, '']:\n\t\t\ttry:\n\t\t\t\tperson = model.Person.get(int(PersonID))\n\t\t\texcept SQLObjectNotFound:\n\t\t\t\tperson = none\n\t\t\t\tturbogears.flash(\"The person couldn't be found, so we're creating a new record\")\n\t\telse:\n\t\t\tperson = None\n\t\tif not person:\n\t\t\t# Create new person and customer record\n\t\t\tperson = model.Person(NameFirst=NameFirst,NameLast=NameLast,NameMiddle=NameMiddle,\\\n\t\t\t\tAddrStr=AddrStr,AddrCitytownNrID=AddrCitytownNr,Sex=Sex, Religion=Religion,\\\n\t\t\t\tDateBirth=bdate, EthnicOrig=EthnicOrig,Citizenship = Citizenship,SssNr = SssNr,\\\n\t\t\t\tNatIdNr = NatIdNr,Occupation = Occupation)\n\t\telse:\n\t\t\t# Update the person information\n\t\t\tperson.Citizenship = Citizenship\n\t\t\tperson.SssNr = SssNr\n\t\t\tperson.NatIdNr = NatIdNr\n\t\t\tperson.Occupation = Occupation\n\t\t\tperson.Title = Title\n\t\t\tperson.NameFirst = NameFirst\n\t\t\tperson.NameMiddle = NameMiddle\n\t\t\tperson.NameLast = NameLast\n\t\t\tperson.DateBirth = bdate\n\t\t\tperson.AddrStr = AddrStr\n\t\t\tperson.AddrZip = AddrZip\n\t\t\tperson.AddrCitytownNrID = AddrCitytownNr\n\t\t\tperson.Phone1Nr = Phone1Nr\n\t\t\tperson.Cellphone1Nr = Cellphone1Nr\n\t\t\tperson.Fax = Fax\n\t\t\tperson.Email = Email\n\t\t\tperson.CivilStatus = CivilStatus\n\t\t\tperson.Sex = Sex\n\t\t\tperson.EthnicOrigID = EthnicOrig\n\t\t# Update/add customer information\n\t\tif AddrCitytownNr:\n\t\t\tcitytown = model.AddressCityTown.get(AddrCitytownNr)\n\t\t\tAddressLabel = '%s\\n%s\\n%s, %s\\n%s\\n%s' % (AddrStr, citytown.Name, citytown.Block, citytown.District, citytown.State, citytown.ZipCode)\n\t\telse:\n\t\t\tAddressLabel = AddrStr\n\t\tPatientName = ('%s %s,%s,%s' % (Title, NameFirst, NameMiddle, NameLast)).replace(',,',',').replace(',', ' ').strip()\n\t\tif len(person.Customer)==0:\n\t\t\tcustomer = model.InvCustomer(Name=PatientName ,CityID=person.AddrCitytownNrID , AddressLabel=AddressLabel, CreditAmount=0.0, \\\n\t\t\t\t\t\t\tInventoryLocation=self.GetDefaultCustomerLocationID(), ExternalID=person.id)\n\t\telse:\n\t\t\tcustomer = person.Customer[0]\n\t\t\tcustomer.AddressLabel = AddressLabel\n\t\t\tcustomer.Name = PatientName\n\t\traise cherrypy.HTTPRedirect(\"?PersonID=%d\" % person.id)\n\t\treturn \"OK\"","def rm_person_from_task():\n # get person name from user\n responses = accept_inputs([\"Person name\", \"Task label\"])\n person_name = responses[\"Person name\"]\n task_label = responses[\"Task label\"]\n # check for existence of person\n person_results = query_with_results(\"select id from person where name = ?\", [person_name])\n if len(person_results) == 0:\n print(\"No person found with name '%s'.\" % person_name)\n return\n # check for the existence of task\n task_results = query_with_results(\"select * from task where label = ?\", [task_label])\n if len(task_results) == 0:\n print(\"No task found with label '%s'.\" % task_label)\n return\n # disassociate the person from the task\n query_no_results(\"delete from task_person_pair where person = ? and task = ?\", [person_results[0][0], task_label])\n print(\"Person '%s' removed from task with label '%s'.\" % (person_name, task_label))","def get_person_by_id(self, demanded_id):\n stored_person = self._registry[demanded_id]\n returned_person = Person(\n stored_person['status'],\n Address(\n stored_person['address']['street'],\n stored_person['address']['city']\n ),\n Name(\n stored_person['name']['firstname'],\n stored_person['name']['lastname']\n ),\n stored_person['version']\n )\n return returned_person","def read_one(lname):\n # Does the person exist in people?\n if lname in PEOPLE:\n person = PEOPLE.get(lname)\n\n # otherwise, nope, not found\n else:\n abort(\n 404, \"Person with last name {lname} not found\".format(lname=lname)\n )\n\n return person","def populate_person():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Person class')\n logger.info('Note how I use constants and a list of tuples as a simple '\n 'schema')\n logger.info('Normally you probably will have prompted for this from a '\n 'user')\n\n PERSON_NAME = 0\n LIVES_IN_TOWN = 1\n NICKNAME = 2\n\n people = [\n ('Andrew', 'Sumner', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Pam', 'Coventry', 'PJ'),\n ('Steven', 'Colchester', None),\n ]\n\n logger.info('Creating Person records: iterate through the list of tuples')\n logger.info('Prepare to explain any errors with exceptions')\n logger.info('and the transaction tells the database to fail on error')\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for person in people:\n with database.transaction():\n new_person = Person.create(\n person_name=person[PERSON_NAME],\n lives_in_town=person[LIVES_IN_TOWN],\n nickname=person[NICKNAME])\n new_person.save()\n logger.info('Database add successful')\n\n logger.info('Print the Person records we saved...')\n for saved_person in Person:\n logger.info(f'{saved_person.person_name} lives in '\n f'{saved_person.lives_in_town} '\n f'and likes to be known as {saved_person.nickname}')\n\n except Exception as e:\n logger.info(f'Error creating = {person[PERSON_NAME]}')\n logger.info(e)\n logger.info('See how the database protects our data')\n\n finally:\n logger.info('database closes')\n database.close()","def delete(lname):\n # Does the person to delete exist?\n if lname in PEOPLE:\n del PEOPLE[lname]\n return make_response(\n \"{lname} successfully deleted\".format(lname=lname), 200\n )\n\n # Otherwise, nope, person to delete not found\n else:\n abort(\n 404, \"Person with last name {lname} not found\".format(lname=lname)\n )","def person(request, pk):\r\n person = get_object_or_404(Person, pk=pk)\r\n return HttpResponse('Person: %s' % person)","def get_person(request, person_id):\n person = get_object_or_404(Person, pk=person_id)\n\n\n return render_to_response('people/person_detail.html', {\n 'person': person,\n })","def get_object(self):\n # return Person.objects.get(user=self.request.user)\n p,c = Person.objects.get_or_create(user=self.request.user)\n return p","def create( self, person ):\n Misc = MVC.loadHelper( 'Misc' )\n new_person = {}\n if 'wikipedia' not in person or person['wikipedia'] == '':\n return False\n qry = \"\"\"SELECT * FROM `%s`.`people` WHERE `wikipedia` = \"%s\";\"\"\" % ( self.db_name, person['wikipedia'] )\n exists = Mysql.ex( qry )\n if len( exists ) != 0:\n self.updateDiff( person, exists[0] )\n person_id = exists[0]['id']\n else:\n if 'name' in person:\n new_person['name'] = person['name']\n else:\n new_person['name'] = ''\n if 'slug' not in person:\n if 'name' not in person:\n new_person['name'] = ''\n else:\n new_person['slug'] = Misc.slug( person['name'] )\n if 'wikipedia' not in person:\n new_person['wikipedia'] = ''\n else:\n new_person['wikipedia'] = person['wikipedia']\n Mysql.insert( 'people', new_person )\n person_id = self.getByWiki( new_person['wikipedia'] )['id']\n if 'meta' in person:\n self.createMeta( person_id, person['meta'] )\n return person_id","def change_person(self):\n # TODO:\n # if render_person is none: throw error message to click first\n\n info = self.current_person()\n info[\"dob\"] = str(info[\"dob\"])\n # launch module\n self.EditPeople.edit_person(info)\n self.EditPeople.show()","def test_createperson(self):\n p = model.Person(firstname=\"Tobias\", lastname=\"Thelen\",\n email=\"tthelen@uos.de\", hobbies=[\"singen\",\"springen\",\"fröhlichsein\"])\n id = p.store()\n\n p2 = model.Person(id=id)\n self.assertEqual(p.id, p2.id)\n self.assertEqual(p.firstname, p2.firstname)\n self.assertEqual(p.lastname, p2.lastname)\n self.assertEqual(p.email, p2.email)\n self.assertEqual(p.hobbies, p2.hobbies)","def __ui_remove_person(self):\n remove_person_id = int(input(\"Introduce the ID of the person you want to remove: \"))\n self.__person_service.service_remove_person(remove_person_id)\n print(\"Person successfully removed from your agenda!\\n\")","def put(self, id):\n adm = Administration()\n print(api.payload)\n p = Person.from_dict(api.payload)\n if p is not None:\n p.set_id(id)\n adm.save_person(p)\n return p, 200\n\n else:\n return '', 500","def test_update_person(self):\n user = User.objects.create(username='test_user')\n user.set_password('test123')\n user.save()\n self.client.login(username='test_user', password='test123')\n\n data = {'first_name': 'Daenerys'}\n response = self.client.patch(self.url, data, format='json')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(Person.objects.count(), 1)\n self.assertEqual(Person.objects.first().first_name, 'Daenerys')","def change_person_to_db(self):\n self.EditPeople.update_sql(self.sql)\n info = self.EditPeople.updated_info()\n self.fullname.setText(info[\"fullname\"])\n # self.search_people_by_name(info['fullname'])","def test_findperson(self):\n p = model.Person(firstname=\"Tobias\", lastname=\"Thelen\",\n email=\"tthelen@uos.de\", hobbies=[\"singen\",\"springen\",\"fröhlichsein\"])\n p.store()\n p = model.Person(firstname=\"Tobias\", lastname=\"Müller\",\n email=\"tmueller@test.test\", hobbies=[\"rappen\", \"hüpfen\", \"lustigsein\"])\n p.store()\n\n persons = model.Person.find(firstname=\"Tobias\")\n self.assertEqual(len(persons), 2)","def update_person(self, person_to_update_id, updated_person):\n self.__load_persons_from_file_into_memory()\n super().update_person(person_to_update_id, updated_person)\n self.__save_persons_from_memory_to_file()","def insert_person():\r\n body = request.get_json()\r\n\r\n try:\r\n INSERT_PERSON_SCHEMA.validate(body)\r\n except SchemaError as err:\r\n raise ServiceBodyError(str(err))\r\n\r\n with sqlite_client:\r\n person = (body.get('name'), body.get('cpf'))\r\n message = add_person(sqlite_client, person)\r\n\r\n return jsonify({'id': message})","def api_can_update(self):\n person1 = User(name=\"test person1\",\n bio=\"test person1\",\n contact_info=\"test person\")\n person2 = User(name=\"test person2\",\n bio=\"test person2\",\n contact_info=\"test person\")\n person1.save()\n person2.save()\n # update_person = self.client.put(\n # reverse('details', kwargs={'pk': person1.id}),\n # person2, format='json'\n # )\n self.assertEqual(self.client.get('/api/guru'), 200)","def find_person_by_id(self, person_id):\r\n try:\r\n person_id = int(person_id)\r\n except ValueError:\r\n raise PersonIDException(\"Error! The person ID has to be a positive integer!\")\r\n if person_id <= 0:\r\n raise PersonIDException(\"Error! The person ID has to be a positive integer!\")\r\n\r\n return self.__person_repository.find_by_id(person_id)","def testIPerson(self):\n # The id is obtained from the person object directly, uniqueness is enforced\n id = self.person.id\n self.failUnlessEqual(id, 'abc123', \"Person object returned incorrect id.\")","def update_person(context, obj, params):\n\n obj.gender = params.get('gender')\n obj.first_name = params.get('first_name')\n obj.last_name = params.get('last_name')\n obj.title = params.get('title')\n if params.get('language'):\n obj.language = params.get('language')\n else:\n obj.language = Language.objects.filter(id=params.get('language_id')).first()\n obj.institution = params.get('institution')\n obj.department = params.get('department')\n obj.job_description = params.get('job_description')\n # count isMain (non-deleted) object, we must always have 1 main contact\n # count_main_contact = _count_main_contact(params)\n\n # for email in params.get('emails', []):\n # if not email.get('val'):\n # continue\n # if count_main_contact == 0:\n # email['isMain'] = True\n # count_main_contact = 1\n # if email.get('id'):\n # update_email(context, email)\n # else:\n # email['person'] = obj\n # create_email(context, email)\n # for phone in params.get('phones', []):\n # if not phone.get('val'):\n # continue\n # if count_main_contact == 0:\n # phone['isMain'] = True\n # count_main_contact = 1\n # if phone.get('id'):\n # update_phone(context, phone)\n # else:\n # phone['person'] = obj\n # create_phone(context, phone)\n # for address in params.get('addresses', []):\n # if count_main_contact == 0:\n # address['isMain'] = True\n # count_main_contact = 1\n # if address.get('id'):\n # update_address(context, address)\n # else:\n # address['person'] = obj\n # create_address(context, address)\n\n # if not (obj.emails.exists() or obj.phones.exists() or obj.addresses.exists()):\n # raise ValueError(\"At least one contact must be specified\")","def get_person(self):\n person_dict = {\n 'firstname': self.__firstname,\n 'lastname': self.__lastname,\n 'height': self.__height,\n 'weight': self.__weight,\n 'age': self.__age\n }\n return person_dict","def create_new_person(self, person: PersonModel) -> PersonModel:\n # First Create a User row\n try:\n user = UserModel(person.username, PersonDBModel.__tablename__)\n new_user = UserService().create_new_user(user)\n except UserCreationException as err:\n raise PersonCreationException(err)\n # Now Create a Person\n person_db_model = person.to_db()\n person_db_model.person_id = new_user.user_id\n self.db_session.add(person_db_model)\n self.db_session.commit()\n if not person_db_model:\n raise PersonCreationException()\n self.logger.info(f'Successfully Created a new Person object with ID {person_db_model.person_id}')\n return PersonModel.from_db(person_db_model)","def create_person(self):","def create(person):\n lname = person.get(\"lname\", None)\n fname = person.get(\"fname\", None)\n\n # Does the person exist already?\n if lname not in PEOPLE and lname is not None:\n PEOPLE[lname] = {\n \"lname\": lname,\n \"fname\": fname,\n \"timestamp\": get_timestamp(),\n }\n return PEOPLE[lname], 201\n\n # Otherwise, they exist, that's an error\n else:\n abort(\n 406,\n \"Person with last name {lname} already exists\".format(lname=lname),\n )","def get_person(self, id):\n if self.people is None:\n self.people = self.get_people()\n\n for person in self.people:\n if person['person']['id'] == id:\n return person['person']\n\n return None","def get_object(self,pk):\n # return Person.objects.get(user=self.request.user)\n p,c = Person.objects.get_or_create(user_id=pk)\n return p","def save_person(self, person_id, person_name, person_role):\n cursor = self.cur()\n cursor.execute('INSERT OR IGNORE INTO person (person_id, name, role) VALUES(?, ?, ?)',\n (person_id, person_name, person_role)\n )","def __write_person(self, handle, nbr):\n try:\n person = self.database.get_person_from_handle(handle)\n self.__write_row(nbr, handle, person)\n except:\n person = \"NOT FOUND\"\n self.__write_row(nbr, handle, person)","def test_updating_record_with_dictionary_args(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"Johnny\", last_name=\"John\", age=2\n )\n\n test_domain.repository_for(Person)._dao.update(person, {\"age\": 10})\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\n assert u_person is not None\n assert u_person.age == 10","def test_updating_record_with_kwargs(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"Johnny\", last_name=\"John\", age=2\n )\n\n test_domain.repository_for(Person)._dao.update(person, age=10)\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\n assert u_person is not None\n assert u_person.age == 10","def select_person():\r\n body = request.get_json()\r\n\r\n try:\r\n SELECT_PERSON_SCHEMA.validate(body)\r\n except SchemaError as err:\r\n raise ServiceBodyError(str(err))\r\n\r\n with sqlite_client:\r\n message = get_person(sqlite_client, body.get('id'))\r\n\r\n return jsonify({'name': message[0][1], 'cpf': message[0][2]})","def person_object_factory():\n person = {\n 'lastName': rl_fake().last_name(),\n 'gender': random.choice(('M', 'F'))\n }\n\n # Make the person's name match their gender.\n person['firstName'] = rl_fake().first_name_male() if person['gender'] == 'M' else rl_fake().first_name_female()\n\n # These are all optional in the DB. Over time, we'll try all possibilities.\n if flip():\n person['birthday'] = rl_fake().date_of_birth(minimum_age=18).strftime('%Y-%m-%d')\n if flip():\n person['phone'] = rl_fake().phone_number()\n if flip():\n person['email'] = rl_fake().email()\n return person","def add_person(self):\n data = dict(request.forms)\n pp = Person(**data)\n if pp.validate():\n pp.save()\n data = model_to_dict(pp)\n else:\n data = pp.validator().errors\n return dict(name='Person', data=data)","def create_person(person: Person = Body(...)):\n return person","def populate_persons():\n\n logging.basicConfig(level=logging.INFO)\n logger = logging.getLogger(__name__)\n\n database = SqliteDatabase('personjob.db')\n\n logger.info('Working with Person class')\n\n PERSON_NAME = 0\n LIVES_IN_TOWN = 1\n NICKNAME = 2\n\n people = [\n ('Andrew', 'Sultan', 'Andy'),\n ('Peter', 'Seattle', None),\n ('Susan', 'Boston', 'Beannie'),\n ('Pam', 'Coventry', 'PJ'),\n ('Steven', 'Stevens Pass', None),\n ('Ryan', 'New York', 'Private'),\n ('Pamela', 'Spokane', 'Patrol'),\n ('Monica', 'Portland', None),\n ]\n\n logger.info('Creating Person records: iterate through the list of tuples')\n\n try:\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON;')\n for person in people:\n with database.transaction():\n new_person = Person.create(\n person_name = person[PERSON_NAME],\n lives_in_town = person[LIVES_IN_TOWN],\n nickname = person[NICKNAME])\n new_person.save()\n logger.info('Database add successful')\n\n logger.info('Print the Person records we saved...')\n for saved_person in Person:\n logger.info(f'{saved_person.person_name} lives in {saved_person.lives_in_town} ' +\\\n f'and likes to be known as {saved_person.nickname}')\n\n except Exception as e:\n logger.info(f'Error creating = {person[PERSON_NAME]}')\n logger.info(e)\n\n finally:\n logger.info('database closes')\n database.close()","def add_person():\n # get values from user\n responses = accept_inputs([\"Name\"])\n # insert into db\n query_no_results(\"insert into person (name) values(?)\", [responses[\"Name\"]])\n print(\"New person created\")","def get_person(request):\n\n email = request.args.get(\"email\", None, str)\n # log_info(\"email is \" + email)\n\n if not email:\n log_info(\"get_person was called, but no email was provided in request\")\n return None\n\n if validators.email(email) and (email_requester := auth.check_teacher(request)):\n if email_requester and validators.email(email_requester):\n db = database.Database()\n student = db.get_student(email)\n return dict(student)\n\n elif validators.email(email) and (email_requester := auth.check_login(request)):\n if email_requester and validators.email(email_requester) and email == email_requester:\n db = database.Database()\n student = db.get_student(email)\n if 'notes' in student:\n del student['notes']\n\n return dict(student)\n\n log_info(\"No person with email \" + email + \" found in database\")\n return None","def add_person_to_task():\n # get values from user\n responses = accept_inputs([\"Person\", \"Task label\"])\n # get the person's ID\n id = query_with_results(\"select id from person where name = ?\", [responses[\"Person\"]])[0][0]\n # insert into db\n query_no_results(\"insert into task_person_pair (person, task) values(?, ?)\", [id, responses[\"Task label\"]])\n print(\"%s added to task %s\" % (responses[\"Person\"], responses[\"Task label\"]))","def insert_person(self, firstname, lastname, phone, age):\n cur = self.conn.cursor(pymysql.cursors.DictCursor)\n sql = 'INSERT INTO People (first_name, last_name, phone, age, time_added) VALUES (%s, %s, %s, %s, NOW())'\n result = cur.execute(sql, (firstname, lastname, phone, age))\n return result","def remove_person(self, document):\n del self.__people[document]","def testCreatePerson(self):\n\t\tpersonclassid = fwdb.fetchonevalue(con, \"SELECT Id FROM Class$ WHERE Name = 'CmPerson';\")\n\n\t\tnewObjId = -1\n\t\tcur = con.cursor()\n\t\tparams = cur.callproc(\n\t\t\t\"CreateObject$\",\n\t\t\t[personclassid, System.Data.SqlTypes.SqlInt32.Null, System.Data.SqlTypes.SqlGuid.Null])\n\t\tobjid = params[2]\n\t\tcur.close()\n\n\t\t#( We will be looping through the class tree found in ClassPar$, making sure\n\t\t#( the object is created in each of the tables that represent classes.\n\n\t\tcur = con.cursor()\n\t\tcur.execute(\n\t\t\t\"SELECT c.Name FROM ClassPar$ cp JOIN Class$ c ON c.Id = cp.Dst WHERE cp.Src = %s ORDER BY cp.Depth DESC;\" %\n\t\t\t(personclassid))\n\t\tclasstree = cur.fetchall()\n\t\tcur.close()\n\n\t\tfor classname in classtree:\n\t\t count = fwdb.fetchonevalue(con, \"SELECT COUNT(Id) FROM %s WHERE Id = %s;\" % (classname[0], objid))\n\t\t unitassert.assertEqual(count, 1, \"The object wasn't created in %s\" % (classname[0]), \"testCreatePerson\")\n\n\t\t#( Check that the update trigger updates CmObject.UpdDttm. A smalldatetime in SQL Sever\n\t\t#( has an accuracy of only one minute. Therefore, a comparison of UpdDttm will always fail.\n\t\t#( However, whenever anything is updated in CmObject, the UpdStmp field gets updated to\n\t\t#( a new value.\n\n\t\tupdstmp1 = fwdb.fetchonevalue(con,\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\" % (objid))\n\n\t\tcur = con.cursor()\n\t\tcur.execute(\"UPDATE CmPerson SET Gender = 1 WHERE Id = %s;\" % (objid))\n\t\tcur.close()\n\n\t\tupdstmp2 = fwdb.fetchonevalue(con,\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\" % (objid))\n\t\tunitassert.assertNotEqual(\n\t\t\tupdstmp1, updstmp2, \"CmObject.UpdStmp should have changed\", \"testInsertClass\")\n\n\t\t#( Check that the reference collection table insert trigger updates CmObject.UpdDttm.\n\t\t#( The trigger gets created by the Field$ insert trigger, but it is easier to test it\n\t\t#( here.\n\n\t\tupdstmp1 = fwdb.fetchonevalue(con,\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\" % (objid))\n\n\t\tcur = con.cursor()\n\t\tcur.execute(\"UPDATE CmPerson_Positions SET Src = Src WHERE Src = %s;\" % (objid))\n\t\tcur.close()\n\n\t\tupdstmp2 = fwdb.fetchonevalue(con,\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\" % (objid))\n\t\tunitassert.assertNotEqual(\n\t\t\tupdstmp1, updstmp2, \"CmObject.UpdStmp should have changed for CmPerson_Positions\", \"testInsertClass\")","def add_person():\n # Find the last used PK\n with sqlite3.connect('skeevy.db') as connection:\n cursor = connection.cursor()\n cursor.execute(\"SELECT id FROM person ORDER BY id DESC;\")\n for row in cursor.fetchone():\n last_pk = row\n\n # Auto-increment the primary key for the person table.\n last_pk = last_pk + 1\n\n # Prompt the user for the rest of their information.\n first_name = input(\"Enter your first name: \")\n middle_name = input(\"Enter your middle name: \")\n last_name = input(\"Enter your last name: \")\n suffix_name = input(\"Enter your suffix: \")\n e_mail = input(\"Enter your email: \")\n # Default status of the person is active (1).\n status = 1\n\n # Store the input in a variable.\n person_data = (last_pk, first_name, middle_name, last_name, suffix_name,\n e_mail, status)\n\n # Connect and insert the data into the person table.\n with sqlite3.connect('skeevy.db') as connection:\n cursor = connection.cursor()\n cursor.execute(\"INSERT INTO person VALUES(?, ?, ?, ?, ?, ?, ?);\",\n person_data)\n connection.commit()","def test_removeperson(self):\n p1, p2, p3 = self.create3persons()\n t = model.Team(name='Tigers', persons=[p1, p2, p3])\n id = t.store()\n t.remove_person(p2)\n t.store()\n\n t2 = model.Team(id=id)\n self.assertEqual(t2.persons, [p1.id, p3.id])\n\n with self.assertRaises(ValueError): # cannot be removed again\n t2.remove_person(p2)","def get_person(self, user_id):\n endpoint = '/user/{}'.format(user_id)\n return self.get_request(endpoint)","def remove_person(self, remove_person_id):\n self.__load_persons_from_file_into_memory()\n super().remove_person(remove_person_id)\n self.__save_persons_from_memory_to_file()","def delete_person(self, key):\n if key[:5] not in ['part_', 'init_']:\n raise Exception('model.delete_person() can only be performed on a' +\n 'participant or initiator record.')\n # TODO: Delete this notification after testing is complete\n if key[:5] == 'part_':\n message = ('Participant ' + self.get_participant(key)['email'] +\n ' deleted.')\n else:\n message = ('Initiator ' + self.get_initiator(key)['email'] +\n ' deleted.')\n print message\n # Delete the DB record\n self.client.delete(key)","def __ui_add_new_person(self):\n person_id = int(input(\"ID: \"))\n person_name = input(\"Name: \").strip()\n person_phone_number = input(\"Phone number: \").strip()\n self.__person_service.service_add_person(person_id, person_name, person_phone_number)\n print(\"Person successfully added to your agenda!\\n\")","def test_entity_retrieval_by_its_primary_key(\n self, test_domain, identifier, persisted_person\n ):\n person = test_domain.repository_for(Person)._dao.get(persisted_person.id)\n assert person is not None\n assert person.id == identifier\n assert person == persisted_person","def add_person(self, name, age):\n try:\n age = int(age)\n except Exception as e:\n _log.log(self.log, 'error', str(e).encode())\n return False\n\n person = _models.Person(name=name, age=age)\n\n # person key exists in cache, return false\n if _cache.person_exists(self.redis, self.people_key, person):\n return False\n\n # person key exists in db, add it to cache and return false\n if _orm.person_exists(self.session, _models.Person, _models.Person.name, name):\n try:\n _cache.add_people(self.redis, self.people_key, [person])\n except Exception as e:\n _log.log(self.log, 'warn', str(e).encode())\n \n return False\n\n # add person to db\n try:\n _orm.add_people(self.session, [person])\n _log.add_person(self.log, 'info', self.people_key, person, 'db')\n except Exception as e:\n _log.log(self.log, 'error', str(e).encode())\n return False\n\n # add person to cache\n try:\n _cache.add_people(self.redis, self.people_key, [person])\n _log.add_person(self.log, 'info', self.people_key, person, 'cache')\n except Exception as e:\n _log.log(self.log, 'warn', str(e).encode())\n\n return True","def set_person(self, person):\n if person.upper() == UNKNOWN.upper():\n self.person_name = UNKNOWN\n self.person_id = 'XX'\n else:\n self.person_name = person.lower()\n self.person_id = person.upper()[:2]","def test_delete_an_object_in_repository_by_id(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"John\", last_name=\"Doe\", age=22\n )\n\n persisted_person = test_domain.repository_for(Person)._dao.get(identifier)\n assert persisted_person is not None\n\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\n assert deleted_person is not None\n assert deleted_person.state_.is_destroyed is True\n\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(identifier)","def person(self, email=None, emailMD5=None, phone=None,\r\n twitter=None, facebookUsername=None):\r\n kwargs = list(\r\n filter(None, [email, emailMD5, phone, twitter, facebookUsername,]))\r\n\r\n if len(kwargs) != 1:\r\n raise TypeError(\r\n 'person() must be passed exactly one of '\r\n 'email, emailMD5, phone, twitter or facebookUsername')\r\n\r\n return resource.Person(self, email=email, emailMD5=emailMD5, phone=phone,\r\n twitter=twitter, facebookUsername=facebookUsername)","def test_deleting_a_persisted_entity(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"Jim\", last_name=\"Carrey\"\n )\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\n assert deleted_person is not None\n assert deleted_person.state_.is_destroyed is True\n\n with pytest.raises(ObjectNotFoundError):\n test_domain.repository_for(Person)._dao.get(identifier)","def replace_person(self,p):\n p.age = 15\n p.days = 0\n \n self.age_group[4].remove(p.identifier)\n self.age_group[0].add(p.identifier)\n \n if np.random.random() < self.sexual_activity_high:\n p.sexual_activity = 1\n self.high_sexual_activity.add(p.identifier)\n else:\n p.sexual_activity = 0\n \n p.cure(self)\n \n #remove all partnerships where p is involved in\n for i,ps in enumerate(self.partnerships):\n if p.identifier in [ps.persons[0].identifier,ps.persons[1].identifier]:\n ps.remove(self)\n self.partnerships[i] = None\n #if deleting steady partnership\n if ps.type == 1:\n self.number_of_steady -= 1\n self.partnerships = list(filter(None,self.partnerships))","def get_person_name(self, person_id):\n res = requests.get(url=\"https://api.ciscospark.com/v1/people/{}\".format(person_id),\n headers=self.headers)\n\n try:\n class person(object):\n firstName = res.json()['firstName']\n lastName = res.json()['lastName']\n\n return person\n except AttributeError as e:\n print(res.text)\n return None","def _set_person_id(cls, data):\n document_id = data.get(\"_id\")\n if document_id:\n data[\"person_id\"] = document_id\n return data","def upload_to_personfinder(person):\n action = \"write\"\n domain = \"rhok\"\n key = \"punsOMMYMAI27tkr\"\n\n url = build_personfinder_url(action, domain, key)\n logging.debug('URL: %s' % url)\n\n data = to_xml(persons=[person]).toxml().encode('utf8')\n logging.debug('data: %s' % data)\n req = urllib2.Request(\n url, data, { 'Content-Type': 'application/xml' })\n result = None\n result = urllib2.urlopen(req).read()\n logging.debug('return: %s' % result)\n\n return result","def test_updating_record_with_both_dictionary_args_and_kwargs(self, test_domain):\n identifier = uuid4()\n person = test_domain.repository_for(Person)._dao.create(\n id=identifier, first_name=\"Johnny\", last_name=\"John\", age=2\n )\n\n test_domain.repository_for(Person)._dao.update(\n person, {\"first_name\": \"Stephen\"}, age=10\n )\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\n assert u_person is not None\n assert u_person.age == 10\n assert u_person.first_name == \"Stephen\"","def create(self, person):\n current_id = self._generate_person_id()\n self._timeline.add_event({\n 'type': EventTimeLine.PERSON_CREATION,\n 'personId': current_id,\n 'status': person.status,\n 'address': person.address.to_dict(),\n 'name': person.name.to_dict()\n })\n return current_id","def update_people(pid):\n db_conn = get_connection()\n cur = db_conn.cursor()\n\n if request.method == 'GET':\n get_sql = \"\"\"SELECT \n id, firstname, lastname, address, country \n FROM peoples WHERE id = ?\"\"\"\n cur.execute(get_sql, (pid,))\n record = cur.fetchone() # fetches the top most row\n # Check if the given user id exists in our database\n if record:\n return render_template('update.jinja2', data=record)\n else:\n flash(\"Sorry! Couldn't get user with id {}\".format(pid))\n return redirect(url_for('list_people'))\n\n elif request.method == 'POST': # or use just else: because out of two accepted methods \n # if one is GET then the other accepted method is obiviously POST\n firstname = request.form['firstname']\n lastname = request.form['lastname']\n address = request.form['address']\n country = request.form['country']\n if firstname.strip():\n post_sql = \"\"\"UPDATE \n peoples set firstname=?, lastname=?, address=?, country=? \n where id=?\"\"\"\n cur.execute(post_sql, (\n firstname.strip(),\n lastname.strip(),\n address.strip(),\n country.strip(),\n pid\n ))\n db_conn.commit()\n flash(\"Record with id {} updated successfully!!!\".format(pid))\n return redirect(url_for('list_people'))","def fetch(self, person=None):\n if not person:\n # get the list.\n self.endpoint = 'people.json'\n else:\n self.endpoint = 'people/{0}.json'.format(person)\n\n request = self.get(self.construct_url())\n\n if request.status_code == 200:\n return json.loads(request.content)\n\n raise BasecampAPIError()","def find_person(self, searched_person_id):\n self.__load_persons_from_file_into_memory()\n return super().find_person(searched_person_id)","def do_update(self, arg):\n arg = arg.split()\n try:\n h = arg[0] + \".\" + arg[1]\n except:\n pass\n objects = storage.all()\n if len(arg) is 0:\n print(\"** class name missing **\")\n elif len(arg) == 1 and arg[0] in self.dict.keys():\n print(\"** instance id missing **\")\n elif arg[0] not in self.dict.keys():\n print(\"** class doesn't exist **\")\n elif h not in objects.keys():\n print(\"** no instance found **\")\n elif len(arg) <= 2:\n print(\"** attribute name missing **\")\n elif len(arg) <= 3:\n print(\"** value missing **\")\n else:\n setattr(objects[h], arg[2], arg[3])\n storage.save()","def put(self, patient_id):\n args = self.parser.parse_args()\n\n # Convert data_time and validate\n date_of_birth = utils.str2date(args.date_of_birth)\n if date_of_birth is None:\n abort()\n\n return_id = None\n with open_session() as session:\n record = Patient(args.first_name,\n args.last_name,\n args.gender,\n date_of_birth)\n\n session.add(record)\n session.flush()\n session.commit()\n\n return_id = record.id\n\n # Check for error handling\n if return_id is None:\n return gen_response(\"internal server error\")\n else:\n return gen_response({\"id\": return_id})","def add_person_to_db(self):\n fullname = self.AddPerson.add_person_to_db(self.sql)\n if fullname:\n self.fullname.setText(fullname)\n # likely same name as before so no triggered search\n self.search_people_by_name()","def PersonLookup(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def get_person(self, requestId):\n return self.get_json('/verification/%s/person' % str(requestId))","def save_person(self, new_person):\n self.__load_persons_from_file_into_memory()\n super().save_person(new_person)\n self.__save_persons_from_memory_to_file()","def parse_person(person):\n person_name = person['name']\n person_id = person['id']\n\n pages = get_pages_from_wiki_search(person_name)\n page = select_page_like_person(pages, person)\n\n wiki_person = {\n 'id': person_id, 'fullname': person_name,\n 'url': page.get('fullurl', None), \n 'declarator_profile': page.get('declarator_profile', None),\n 'words_intersection': page.get('words_intersection', None),\n }\n\n # if page with image\n if 'pageimage' in page:\n try:\n image = page['pageimage']\n url = page['original']['source']\n license = get_license_from_wiki(image, is_file=True)\n\n photo = {'title': image, 'url': url, 'license': license}\n wiki_person['photo'] = photo\n except KeyError as e:\n message = \"person_id: %i, Not found key: %s\" % (person_id, e)\n Querylog.error(message)\n return wiki_person","def get(self, id):\n adm = Administration()\n pers = adm.get_person_by_id(id)\n return pers","def show_person(uuid=None, fanchart=False):\n t0 = time.time()\n uuid = request.args.get(\"uuid\", uuid)\n fanchart_shown = request.args.get(\"fanchart\", fanchart)\n dbg = request.args.get(\"debug\", None)\n u_context = UserContext(user_session, current_user, request)\n\n with PersonReaderTx(\"read_tx\", u_context) as service:\n result = service.get_person_data(uuid)\n\n # result {'person':PersonBl, 'objs':{uniq_id:obj}, 'jscode':str, 'root':{root_type,root_user,batch_id}}\n if Status.has_failed(result):\n flash(f'{result.get(\"statustext\",\"error\")}', \"error\")\n person = result.get(\"person\")\n objs = result.get(\"objs\", [])\n print(f\"# Person with {len(objs)} objects\")\n jscode = result.get(\"jscode\", \"\")\n # Batch or Audit node data like {'root_type', 'root_user', 'id'}\n person.root = result.get(\"root\")\n\n stk_logger(u_context, f\"-> bp.scene.routes.show_person n={len(objs)}\")\n\n last_year_allowed = datetime.now().year - shareds.PRIVACY_LIMIT\n may_edit = current_user.has_role(\"audit\") # or current_user.has_role('admin')\n # may_edit = 0\n return render_template(\n \"/scene/person.html\",\n person=person,\n obj=objs,\n jscode=jscode,\n menuno=12,\n debug=dbg,\n last_year_allowed=last_year_allowed,\n elapsed=time.time() - t0,\n user_context=u_context,\n may_edit=may_edit,\n fanchart_shown=fanchart_shown,\n )","def test_05_get_person_by_name(self):\n p1 = Person.query.first()\n p1_data = p1.wrap()\n p1_f_name = p1_data[\"first_name\"]\n # find by first name only\n # get part of name and search\n q_string = \"?first_name={}\".format(p1_f_name[:3]) # TODO - verify the length\n rv = self.app.get('persons', query_string=q_string)\n data = json.loads(rv.data)\n self.assertEqual(data[\"count\"], 1)\n\n # find by first name and last name\n p1_l_name = p1_data[\"last_name\"]\n q_string = \"?first_name={}&last_name={}\".format(p1_f_name[:3], p1_l_name)\n rv = self.app.get('persons', query_string=q_string)\n data = json.loads(rv.data)\n self.assertEqual(data[\"count\"], 1)\n\n # find by first name and non-existing last name\n q_string = \"?first_name={}&last_name={}\".format(p1_f_name[:3], \"iAmNotThere\")\n rv = self.app.get('persons', query_string=q_string)\n data = json.loads(rv.data)\n self.assertEqual(data[\"count\"], 0)","def person_id(self, person_id):\n\n self._person_id = person_id","def get_instance(self, instance):\n return self._get(_instance.Instance, instance)","def create_person(conn, person, first_name, last_name):\n sql = ''' INSERT INTO person(firstname,lastname)\n VALUES(?,?) '''\n cur = conn.cursor() # cursor object\n cur.execute(sql, person)\n # print(str(cur.lastrowid))\n # return cur.lastrowid # returns the row id of the cursor object, the person id\n first_name.set('')\n last_name.set('')\n messagebox.showinfo('Success', 'Person Successfully Added to Database!')","def find_instance(self, name_or_id, ignore_missing=True):\n return self._find(\n _instance.Instance, name_or_id, ignore_missing=ignore_missing\n )"],"string":"[\n \"def person(self, person_id):\\r\\n return persons.Person(self, person_id)\",\n \"def edit_person():\\n # get person name from user\\n responses = accept_inputs([\\\"Person's name\\\"])\\n person_name = responses[\\\"Person's name\\\"]\\n # check for existence\\n results = query_with_results(\\\"select * from person where name = ?\\\", [person_name])\\n if len(results) == 0:\\n print(\\\"No person found with name '%s'.\\\" % person_name)\\n return\\n else:\\n # get id of person\\n id = query_with_results(\\\"select id from person where name = ?\\\", [person_name])[0][0]\\n # the task exists, so ask the user for the new description\\n responses = accept_inputs([\\\"New name\\\"])\\n # update db\\n query_no_results(\\\"update person set name = ? where id = ?\\\", [responses[\\\"New name\\\"], id])\\n print(\\\"Person with old name '%s' changed to '%s'.\\\" % (person_name, responses[\\\"New name\\\"]))\",\n \"def get(self, key):\\n person = self._data.get(key)\\n\\n if not person:\\n raise NotFoundError(\\\"{} could not be found\\\".format(key))\\n\\n return Person(key, person)\",\n \"def fetch_by_id(self, person_id: int) -> PersonModel:\\n person_db_model = PersonDBModel.query.get(person_id)\\n if not person_db_model:\\n raise PersonNotFound(person_id)\\n person = PersonModel.from_db(person_db_model)\\n self.logger.info(f'Successfully fetched Person {person.first_name} {person.last_name} by ID {person_id}')\\n return person\",\n \"def get(self,id):\\r\\n person = get_one_by_persons_id(id=id)\\r\\n if not person:\\r\\n api.abort(404)\\r\\n else:\\r\\n return person\",\n \"def get(self,id):\\r\\n person = get_one(id=id)\\r\\n if not person:\\r\\n api.abort(404)\\r\\n else:\\r\\n return person\",\n \"def update(lname, person):\\n # Does the person exist in people?\\n if lname in PEOPLE:\\n PEOPLE[lname][\\\"fname\\\"] = person.get(\\\"fname\\\")\\n PEOPLE[lname][\\\"timestamp\\\"] = get_timestamp()\\n\\n return PEOPLE[lname]\\n\\n # otherwise, nope, that's an error\\n else:\\n abort(\\n 404, \\\"Person with last name {lname} not found\\\".format(lname=lname)\\n )\",\n \"def edit_person(self, treeview):\\n model, iter_ = treeview.get_selection().get_selected()\\n if iter_:\\n handle = model.get_value(iter_, 0)\\n try:\\n person = self.dbstate.db.get_person_from_handle(handle)\\n EditPerson(self.dbstate, self.uistate, [], person)\\n except WindowActiveError:\\n pass\",\n \"def read_person(person_id):\\n try:\\n conn = sqlite3.connect(settings.database_name)\\n conn.row_factory = sqlite3.Row\\n c = conn.cursor()\\n c.execute(\\\"PRAGMA foreign_keys = ON\\\")\\n c.execute(\\\"SELECT * FROM person WHERE personid =?\\\", (person_id,))\\n _person = None\\n for row in c:\\n _person = Person()\\n _person.person_id = row[\\\"personid\\\"]\\n _person.first_name = row[\\\"firstname\\\"]\\n _person.last_name = row[\\\"lastname\\\"]\\n _person.middle_initial = row[\\\"middleinitial\\\"]\\n _person.nick_name = row[\\\"nickname\\\"]\\n _person.date_of_birth = row[\\\"dateofbirth\\\"]\\n _person.date_of_death = row[\\\"dateofdeath\\\"]\\n conn.close()\\n return _person\\n except:\\n return None\",\n \"def get_new_entry(in_person):\\n person = Person()\\n person.name = get_name()\\n person.phone = input(\\\"Enter Phone Number: \\\")\\n return person\",\n \"def get_person(self, id):\\n try:\\n person = Person.get(Person.id == id)\\n data = model_to_dict(person)\\n except DoesNotExist:\\n response.status = 404\\n data = \\\"Not found\\\"\\n return dict(name='Person', data=data)\",\n \"def person(self, person_id=None):\\r\\n if person_id is None:\\r\\n return pp.CurrentPerson(self)\\r\\n\\r\\n return pp.Person(self, person_id)\",\n \"def rm_person():\\n # get person name from user\\n responses = accept_inputs([\\\"Person name\\\"])\\n person_name = responses[\\\"Person name\\\"]\\n # check for existence of person\\n results = query_with_results(\\\"select id from person where name = ?\\\", [person_name])\\n if len(results) == 0:\\n print(\\\"No person found with name '%s' that we could remove.\\\" % person_name)\\n return\\n # the person exists, so remove it\\n query_no_results(\\\"delete from person where name = ?\\\", [person_name])\\n # remove all associations with tasks\\n query_no_results(\\\"delete from task_person_pair where person = ?\\\", [results[0][0]])\\n print(\\\"Person with name '%s' removed.\\\" % person_name)\",\n \"def find(self, person):\\n page = self.find_page(person)\\n try:\\n entity_id = self.get_entity_id(page.title)\\n entity = self.get_entity(entity_id)\\n person.dob = self.get_birthday(entity)\\n person.occupation = self.get_occupation(entity)\\n person.nationality = self.get_country_of_citizenship(entity)\\n res_domicile = self.get_domicile(entity)\\n if res_domicile:\\n person.domicile = res_domicile\\n elif person.nationality == self.get_birthcountry(entity):\\n person.domicile = person.nationality # this is an assumption!\\n birth_name = self.get_birth_name(entity)\\n person.middle_name = self.get_middle_name(birth_name, person)\\n if page:\\n person.is_famous = 'True'\\n else:\\n person.is_famous = ''\\n person.net_worth = self.get_networth(entity)\\n person.description = page.summary\\n person.set_raw()\\n except:\\n pass\",\n \"def __ui_update_person(self):\\n to_update_person_id = int(input(\\\"Introduce the ID of the person you want to update: \\\"))\\n updated_person_name = input(\\\"Updated name: \\\").strip()\\n updated_phone_number = input(\\\"Updated phone number: \\\").strip()\\n self.__person_service.service_update_person(to_update_person_id, updated_person_name, updated_phone_number)\\n print(\\\"Person successfully updated!\\\\n\\\")\",\n \"def get_one(self, index, *args, **kw):\\n person = M.People.query.get(index=index)\\n log.debug('person {}'.format(person))\\n if(person):\\n kw['_id'] = person._id\\n return super(PeopleAPIController, self).get_one(*args, **kw)\",\n \"def remove_person(self, per: str):\\n if per in self._people:\\n self._people.remove(per)\\n else:\\n raise IDDoesNotExist\",\n \"def get_person(self, id):\\n PERSON = \\\"\\\"\\\"SELECT name FROM Person\\n WHERE id = %s\\\"\\\"\\\"\\n\\n ret = None\\n try:\\n self.db_cursor.execute(\\\"\\\"\\\"SELECT name, id FROM Person WHERE id = %s\\\"\\\"\\\", (id,))\\n self.db_cursor.execute(PERSON, (id,))\\n self.db_connection.commit()\\n p_attribs = self.db_cursor.fetchall()\\n ret = Person()\\n ret.name = p_attribs[0][0]\\n ret.id = id\\n\\n except:\\n logging.warning(\\\"DBAdapter: Error- cannot retrieve person: \\\" + str(id))\\n return None\\n\\n return ret\",\n \"def add_and_delete():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n\\n logger.info('Add and display a Person called Fred; then delete him...')\\n logger.info('Add Fred in one step')\\n\\n new_person = Person.create(\\n person_name = 'Fred',\\n lives_in_town = 'Seattle',\\n nickname = 'Fearless')\\n new_person.save()\\n\\n logger.info('Show Fred')\\n aperson = Person.get(Person.person_name == 'Fred')\\n\\n logger.info(f'We just created {aperson.person_name}, who lives in {aperson.lives_in_town}')\\n logger.info('but now we will delete him...')\\n\\n aperson.delete_instance()\\n\\n logger.info('Reading and print all Person records (but not Fred; he has been deleted)...')\\n\\n for person in Person:\\n logger.info(f'{person.person_name} lives in {person.lives_in_town} and likes to be known as {person.nickname}')\\n\\n except Exception as e:\\n logger.info(e)\\n\\n finally:\\n database.close()\",\n \"def getPerson(self, fingerprint):\\n\\t\\tif fingerprint in self.people:\\n\\t\\t\\tp = self.people[fingerprint]\\n\\t\\telse:\\n\\t\\t\\tp = Person.Person(fingerprint)\\n\\t\\t\\tself.people[fingerprint] = p\\n\\t\\treturn p\",\n \"def set_person(self, new_person, updating=False):\\n PERSON_QUERY = \\\"\\\"\\\"UPDATE Person SET name = %s WHERE id = %s\\\"\\\"\\\" if updating \\\\\\n else \\\"\\\"\\\"INSERT INTO Person (name, id) VALUES (%s, %s)\\\"\\\"\\\"\\n\\n self.db_cursor.execute(PERSON_QUERY, (new_person.name, new_person.id))\\n self.db_connection.commit()\",\n \"def edit_person(self, pk):\",\n \"def PersonSave(self, PersonID=None, Title='', NameFirst='', NameMiddle='', NameLast='', DateBirth=None, AddrStr='', AddrZip='',\\n\\t\\t\\tAddrCitytownNr=None, Phone1Nr='', Cellphone1Nr='', Fax='', Email='', CivilStatus='', Sex='', EthnicOrig=None, Religion='',\\n\\t\\t\\tCitizenship='', SssNr = '', NatIdNr = '', Occupation = ''):\\n\\t\\t# If PersonID is None, then we have a new entry\\n\\t\\tif EthnicOrig in ['',None]:\\n\\t\\t\\tEthnicOrig = None\\n\\t\\telse:\\n\\t\\t\\tEthnicOrig = int(EthnicOrig)\\n\\t\\tif AddrCitytownNr not in [None, '']:\\n\\t\\t\\tAddrCitytownNr = int(AddrCitytownNr)\\n\\t\\telse:\\n\\t\\t\\tAddrCitytownNr = None\\n\\t\\tif DateBirth not in [None, '']:\\n\\t\\t\\t#log.debug(DateBirth)\\n\\t\\t\\tbday = time.strptime(str(DateBirth),DATE_FORMAT)\\n\\t\\t\\tbdate = datetime.datetime(bday.tm_year,bday.tm_mon,bday.tm_mday)\\n\\t\\telse:\\n\\t\\t\\tbdate = None\\n\\t\\tif PersonID not in [None, '']:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tperson = model.Person.get(int(PersonID))\\n\\t\\t\\texcept SQLObjectNotFound:\\n\\t\\t\\t\\tperson = none\\n\\t\\t\\t\\tturbogears.flash(\\\"The person couldn't be found, so we're creating a new record\\\")\\n\\t\\telse:\\n\\t\\t\\tperson = None\\n\\t\\tif not person:\\n\\t\\t\\t# Create new person and customer record\\n\\t\\t\\tperson = model.Person(NameFirst=NameFirst,NameLast=NameLast,NameMiddle=NameMiddle,\\\\\\n\\t\\t\\t\\tAddrStr=AddrStr,AddrCitytownNrID=AddrCitytownNr,Sex=Sex, Religion=Religion,\\\\\\n\\t\\t\\t\\tDateBirth=bdate, EthnicOrig=EthnicOrig,Citizenship = Citizenship,SssNr = SssNr,\\\\\\n\\t\\t\\t\\tNatIdNr = NatIdNr,Occupation = Occupation)\\n\\t\\telse:\\n\\t\\t\\t# Update the person information\\n\\t\\t\\tperson.Citizenship = Citizenship\\n\\t\\t\\tperson.SssNr = SssNr\\n\\t\\t\\tperson.NatIdNr = NatIdNr\\n\\t\\t\\tperson.Occupation = Occupation\\n\\t\\t\\tperson.Title = Title\\n\\t\\t\\tperson.NameFirst = NameFirst\\n\\t\\t\\tperson.NameMiddle = NameMiddle\\n\\t\\t\\tperson.NameLast = NameLast\\n\\t\\t\\tperson.DateBirth = bdate\\n\\t\\t\\tperson.AddrStr = AddrStr\\n\\t\\t\\tperson.AddrZip = AddrZip\\n\\t\\t\\tperson.AddrCitytownNrID = AddrCitytownNr\\n\\t\\t\\tperson.Phone1Nr = Phone1Nr\\n\\t\\t\\tperson.Cellphone1Nr = Cellphone1Nr\\n\\t\\t\\tperson.Fax = Fax\\n\\t\\t\\tperson.Email = Email\\n\\t\\t\\tperson.CivilStatus = CivilStatus\\n\\t\\t\\tperson.Sex = Sex\\n\\t\\t\\tperson.EthnicOrigID = EthnicOrig\\n\\t\\t# Update/add customer information\\n\\t\\tif AddrCitytownNr:\\n\\t\\t\\tcitytown = model.AddressCityTown.get(AddrCitytownNr)\\n\\t\\t\\tAddressLabel = '%s\\\\n%s\\\\n%s, %s\\\\n%s\\\\n%s' % (AddrStr, citytown.Name, citytown.Block, citytown.District, citytown.State, citytown.ZipCode)\\n\\t\\telse:\\n\\t\\t\\tAddressLabel = AddrStr\\n\\t\\tPatientName = ('%s %s,%s,%s' % (Title, NameFirst, NameMiddle, NameLast)).replace(',,',',').replace(',', ' ').strip()\\n\\t\\tif len(person.Customer)==0:\\n\\t\\t\\tcustomer = model.InvCustomer(Name=PatientName ,CityID=person.AddrCitytownNrID , AddressLabel=AddressLabel, CreditAmount=0.0, \\\\\\n\\t\\t\\t\\t\\t\\t\\tInventoryLocation=self.GetDefaultCustomerLocationID(), ExternalID=person.id)\\n\\t\\telse:\\n\\t\\t\\tcustomer = person.Customer[0]\\n\\t\\t\\tcustomer.AddressLabel = AddressLabel\\n\\t\\t\\tcustomer.Name = PatientName\\n\\t\\traise cherrypy.HTTPRedirect(\\\"?PersonID=%d\\\" % person.id)\\n\\t\\treturn \\\"OK\\\"\",\n \"def rm_person_from_task():\\n # get person name from user\\n responses = accept_inputs([\\\"Person name\\\", \\\"Task label\\\"])\\n person_name = responses[\\\"Person name\\\"]\\n task_label = responses[\\\"Task label\\\"]\\n # check for existence of person\\n person_results = query_with_results(\\\"select id from person where name = ?\\\", [person_name])\\n if len(person_results) == 0:\\n print(\\\"No person found with name '%s'.\\\" % person_name)\\n return\\n # check for the existence of task\\n task_results = query_with_results(\\\"select * from task where label = ?\\\", [task_label])\\n if len(task_results) == 0:\\n print(\\\"No task found with label '%s'.\\\" % task_label)\\n return\\n # disassociate the person from the task\\n query_no_results(\\\"delete from task_person_pair where person = ? and task = ?\\\", [person_results[0][0], task_label])\\n print(\\\"Person '%s' removed from task with label '%s'.\\\" % (person_name, task_label))\",\n \"def get_person_by_id(self, demanded_id):\\n stored_person = self._registry[demanded_id]\\n returned_person = Person(\\n stored_person['status'],\\n Address(\\n stored_person['address']['street'],\\n stored_person['address']['city']\\n ),\\n Name(\\n stored_person['name']['firstname'],\\n stored_person['name']['lastname']\\n ),\\n stored_person['version']\\n )\\n return returned_person\",\n \"def read_one(lname):\\n # Does the person exist in people?\\n if lname in PEOPLE:\\n person = PEOPLE.get(lname)\\n\\n # otherwise, nope, not found\\n else:\\n abort(\\n 404, \\\"Person with last name {lname} not found\\\".format(lname=lname)\\n )\\n\\n return person\",\n \"def populate_person():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Person class')\\n logger.info('Note how I use constants and a list of tuples as a simple '\\n 'schema')\\n logger.info('Normally you probably will have prompted for this from a '\\n 'user')\\n\\n PERSON_NAME = 0\\n LIVES_IN_TOWN = 1\\n NICKNAME = 2\\n\\n people = [\\n ('Andrew', 'Sumner', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Pam', 'Coventry', 'PJ'),\\n ('Steven', 'Colchester', None),\\n ]\\n\\n logger.info('Creating Person records: iterate through the list of tuples')\\n logger.info('Prepare to explain any errors with exceptions')\\n logger.info('and the transaction tells the database to fail on error')\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for person in people:\\n with database.transaction():\\n new_person = Person.create(\\n person_name=person[PERSON_NAME],\\n lives_in_town=person[LIVES_IN_TOWN],\\n nickname=person[NICKNAME])\\n new_person.save()\\n logger.info('Database add successful')\\n\\n logger.info('Print the Person records we saved...')\\n for saved_person in Person:\\n logger.info(f'{saved_person.person_name} lives in '\\n f'{saved_person.lives_in_town} '\\n f'and likes to be known as {saved_person.nickname}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {person[PERSON_NAME]}')\\n logger.info(e)\\n logger.info('See how the database protects our data')\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def delete(lname):\\n # Does the person to delete exist?\\n if lname in PEOPLE:\\n del PEOPLE[lname]\\n return make_response(\\n \\\"{lname} successfully deleted\\\".format(lname=lname), 200\\n )\\n\\n # Otherwise, nope, person to delete not found\\n else:\\n abort(\\n 404, \\\"Person with last name {lname} not found\\\".format(lname=lname)\\n )\",\n \"def person(request, pk):\\r\\n person = get_object_or_404(Person, pk=pk)\\r\\n return HttpResponse('Person: %s' % person)\",\n \"def get_person(request, person_id):\\n person = get_object_or_404(Person, pk=person_id)\\n\\n\\n return render_to_response('people/person_detail.html', {\\n 'person': person,\\n })\",\n \"def get_object(self):\\n # return Person.objects.get(user=self.request.user)\\n p,c = Person.objects.get_or_create(user=self.request.user)\\n return p\",\n \"def create( self, person ):\\n Misc = MVC.loadHelper( 'Misc' )\\n new_person = {}\\n if 'wikipedia' not in person or person['wikipedia'] == '':\\n return False\\n qry = \\\"\\\"\\\"SELECT * FROM `%s`.`people` WHERE `wikipedia` = \\\"%s\\\";\\\"\\\"\\\" % ( self.db_name, person['wikipedia'] )\\n exists = Mysql.ex( qry )\\n if len( exists ) != 0:\\n self.updateDiff( person, exists[0] )\\n person_id = exists[0]['id']\\n else:\\n if 'name' in person:\\n new_person['name'] = person['name']\\n else:\\n new_person['name'] = ''\\n if 'slug' not in person:\\n if 'name' not in person:\\n new_person['name'] = ''\\n else:\\n new_person['slug'] = Misc.slug( person['name'] )\\n if 'wikipedia' not in person:\\n new_person['wikipedia'] = ''\\n else:\\n new_person['wikipedia'] = person['wikipedia']\\n Mysql.insert( 'people', new_person )\\n person_id = self.getByWiki( new_person['wikipedia'] )['id']\\n if 'meta' in person:\\n self.createMeta( person_id, person['meta'] )\\n return person_id\",\n \"def change_person(self):\\n # TODO:\\n # if render_person is none: throw error message to click first\\n\\n info = self.current_person()\\n info[\\\"dob\\\"] = str(info[\\\"dob\\\"])\\n # launch module\\n self.EditPeople.edit_person(info)\\n self.EditPeople.show()\",\n \"def test_createperson(self):\\n p = model.Person(firstname=\\\"Tobias\\\", lastname=\\\"Thelen\\\",\\n email=\\\"tthelen@uos.de\\\", hobbies=[\\\"singen\\\",\\\"springen\\\",\\\"fröhlichsein\\\"])\\n id = p.store()\\n\\n p2 = model.Person(id=id)\\n self.assertEqual(p.id, p2.id)\\n self.assertEqual(p.firstname, p2.firstname)\\n self.assertEqual(p.lastname, p2.lastname)\\n self.assertEqual(p.email, p2.email)\\n self.assertEqual(p.hobbies, p2.hobbies)\",\n \"def __ui_remove_person(self):\\n remove_person_id = int(input(\\\"Introduce the ID of the person you want to remove: \\\"))\\n self.__person_service.service_remove_person(remove_person_id)\\n print(\\\"Person successfully removed from your agenda!\\\\n\\\")\",\n \"def put(self, id):\\n adm = Administration()\\n print(api.payload)\\n p = Person.from_dict(api.payload)\\n if p is not None:\\n p.set_id(id)\\n adm.save_person(p)\\n return p, 200\\n\\n else:\\n return '', 500\",\n \"def test_update_person(self):\\n user = User.objects.create(username='test_user')\\n user.set_password('test123')\\n user.save()\\n self.client.login(username='test_user', password='test123')\\n\\n data = {'first_name': 'Daenerys'}\\n response = self.client.patch(self.url, data, format='json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(Person.objects.count(), 1)\\n self.assertEqual(Person.objects.first().first_name, 'Daenerys')\",\n \"def change_person_to_db(self):\\n self.EditPeople.update_sql(self.sql)\\n info = self.EditPeople.updated_info()\\n self.fullname.setText(info[\\\"fullname\\\"])\\n # self.search_people_by_name(info['fullname'])\",\n \"def test_findperson(self):\\n p = model.Person(firstname=\\\"Tobias\\\", lastname=\\\"Thelen\\\",\\n email=\\\"tthelen@uos.de\\\", hobbies=[\\\"singen\\\",\\\"springen\\\",\\\"fröhlichsein\\\"])\\n p.store()\\n p = model.Person(firstname=\\\"Tobias\\\", lastname=\\\"Müller\\\",\\n email=\\\"tmueller@test.test\\\", hobbies=[\\\"rappen\\\", \\\"hüpfen\\\", \\\"lustigsein\\\"])\\n p.store()\\n\\n persons = model.Person.find(firstname=\\\"Tobias\\\")\\n self.assertEqual(len(persons), 2)\",\n \"def update_person(self, person_to_update_id, updated_person):\\n self.__load_persons_from_file_into_memory()\\n super().update_person(person_to_update_id, updated_person)\\n self.__save_persons_from_memory_to_file()\",\n \"def insert_person():\\r\\n body = request.get_json()\\r\\n\\r\\n try:\\r\\n INSERT_PERSON_SCHEMA.validate(body)\\r\\n except SchemaError as err:\\r\\n raise ServiceBodyError(str(err))\\r\\n\\r\\n with sqlite_client:\\r\\n person = (body.get('name'), body.get('cpf'))\\r\\n message = add_person(sqlite_client, person)\\r\\n\\r\\n return jsonify({'id': message})\",\n \"def api_can_update(self):\\n person1 = User(name=\\\"test person1\\\",\\n bio=\\\"test person1\\\",\\n contact_info=\\\"test person\\\")\\n person2 = User(name=\\\"test person2\\\",\\n bio=\\\"test person2\\\",\\n contact_info=\\\"test person\\\")\\n person1.save()\\n person2.save()\\n # update_person = self.client.put(\\n # reverse('details', kwargs={'pk': person1.id}),\\n # person2, format='json'\\n # )\\n self.assertEqual(self.client.get('/api/guru'), 200)\",\n \"def find_person_by_id(self, person_id):\\r\\n try:\\r\\n person_id = int(person_id)\\r\\n except ValueError:\\r\\n raise PersonIDException(\\\"Error! The person ID has to be a positive integer!\\\")\\r\\n if person_id <= 0:\\r\\n raise PersonIDException(\\\"Error! The person ID has to be a positive integer!\\\")\\r\\n\\r\\n return self.__person_repository.find_by_id(person_id)\",\n \"def testIPerson(self):\\n # The id is obtained from the person object directly, uniqueness is enforced\\n id = self.person.id\\n self.failUnlessEqual(id, 'abc123', \\\"Person object returned incorrect id.\\\")\",\n \"def update_person(context, obj, params):\\n\\n obj.gender = params.get('gender')\\n obj.first_name = params.get('first_name')\\n obj.last_name = params.get('last_name')\\n obj.title = params.get('title')\\n if params.get('language'):\\n obj.language = params.get('language')\\n else:\\n obj.language = Language.objects.filter(id=params.get('language_id')).first()\\n obj.institution = params.get('institution')\\n obj.department = params.get('department')\\n obj.job_description = params.get('job_description')\\n # count isMain (non-deleted) object, we must always have 1 main contact\\n # count_main_contact = _count_main_contact(params)\\n\\n # for email in params.get('emails', []):\\n # if not email.get('val'):\\n # continue\\n # if count_main_contact == 0:\\n # email['isMain'] = True\\n # count_main_contact = 1\\n # if email.get('id'):\\n # update_email(context, email)\\n # else:\\n # email['person'] = obj\\n # create_email(context, email)\\n # for phone in params.get('phones', []):\\n # if not phone.get('val'):\\n # continue\\n # if count_main_contact == 0:\\n # phone['isMain'] = True\\n # count_main_contact = 1\\n # if phone.get('id'):\\n # update_phone(context, phone)\\n # else:\\n # phone['person'] = obj\\n # create_phone(context, phone)\\n # for address in params.get('addresses', []):\\n # if count_main_contact == 0:\\n # address['isMain'] = True\\n # count_main_contact = 1\\n # if address.get('id'):\\n # update_address(context, address)\\n # else:\\n # address['person'] = obj\\n # create_address(context, address)\\n\\n # if not (obj.emails.exists() or obj.phones.exists() or obj.addresses.exists()):\\n # raise ValueError(\\\"At least one contact must be specified\\\")\",\n \"def get_person(self):\\n person_dict = {\\n 'firstname': self.__firstname,\\n 'lastname': self.__lastname,\\n 'height': self.__height,\\n 'weight': self.__weight,\\n 'age': self.__age\\n }\\n return person_dict\",\n \"def create_new_person(self, person: PersonModel) -> PersonModel:\\n # First Create a User row\\n try:\\n user = UserModel(person.username, PersonDBModel.__tablename__)\\n new_user = UserService().create_new_user(user)\\n except UserCreationException as err:\\n raise PersonCreationException(err)\\n # Now Create a Person\\n person_db_model = person.to_db()\\n person_db_model.person_id = new_user.user_id\\n self.db_session.add(person_db_model)\\n self.db_session.commit()\\n if not person_db_model:\\n raise PersonCreationException()\\n self.logger.info(f'Successfully Created a new Person object with ID {person_db_model.person_id}')\\n return PersonModel.from_db(person_db_model)\",\n \"def create_person(self):\",\n \"def create(person):\\n lname = person.get(\\\"lname\\\", None)\\n fname = person.get(\\\"fname\\\", None)\\n\\n # Does the person exist already?\\n if lname not in PEOPLE and lname is not None:\\n PEOPLE[lname] = {\\n \\\"lname\\\": lname,\\n \\\"fname\\\": fname,\\n \\\"timestamp\\\": get_timestamp(),\\n }\\n return PEOPLE[lname], 201\\n\\n # Otherwise, they exist, that's an error\\n else:\\n abort(\\n 406,\\n \\\"Person with last name {lname} already exists\\\".format(lname=lname),\\n )\",\n \"def get_person(self, id):\\n if self.people is None:\\n self.people = self.get_people()\\n\\n for person in self.people:\\n if person['person']['id'] == id:\\n return person['person']\\n\\n return None\",\n \"def get_object(self,pk):\\n # return Person.objects.get(user=self.request.user)\\n p,c = Person.objects.get_or_create(user_id=pk)\\n return p\",\n \"def save_person(self, person_id, person_name, person_role):\\n cursor = self.cur()\\n cursor.execute('INSERT OR IGNORE INTO person (person_id, name, role) VALUES(?, ?, ?)',\\n (person_id, person_name, person_role)\\n )\",\n \"def __write_person(self, handle, nbr):\\n try:\\n person = self.database.get_person_from_handle(handle)\\n self.__write_row(nbr, handle, person)\\n except:\\n person = \\\"NOT FOUND\\\"\\n self.__write_row(nbr, handle, person)\",\n \"def test_updating_record_with_dictionary_args(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"Johnny\\\", last_name=\\\"John\\\", age=2\\n )\\n\\n test_domain.repository_for(Person)._dao.update(person, {\\\"age\\\": 10})\\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\\n assert u_person is not None\\n assert u_person.age == 10\",\n \"def test_updating_record_with_kwargs(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"Johnny\\\", last_name=\\\"John\\\", age=2\\n )\\n\\n test_domain.repository_for(Person)._dao.update(person, age=10)\\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\\n assert u_person is not None\\n assert u_person.age == 10\",\n \"def select_person():\\r\\n body = request.get_json()\\r\\n\\r\\n try:\\r\\n SELECT_PERSON_SCHEMA.validate(body)\\r\\n except SchemaError as err:\\r\\n raise ServiceBodyError(str(err))\\r\\n\\r\\n with sqlite_client:\\r\\n message = get_person(sqlite_client, body.get('id'))\\r\\n\\r\\n return jsonify({'name': message[0][1], 'cpf': message[0][2]})\",\n \"def person_object_factory():\\n person = {\\n 'lastName': rl_fake().last_name(),\\n 'gender': random.choice(('M', 'F'))\\n }\\n\\n # Make the person's name match their gender.\\n person['firstName'] = rl_fake().first_name_male() if person['gender'] == 'M' else rl_fake().first_name_female()\\n\\n # These are all optional in the DB. Over time, we'll try all possibilities.\\n if flip():\\n person['birthday'] = rl_fake().date_of_birth(minimum_age=18).strftime('%Y-%m-%d')\\n if flip():\\n person['phone'] = rl_fake().phone_number()\\n if flip():\\n person['email'] = rl_fake().email()\\n return person\",\n \"def add_person(self):\\n data = dict(request.forms)\\n pp = Person(**data)\\n if pp.validate():\\n pp.save()\\n data = model_to_dict(pp)\\n else:\\n data = pp.validator().errors\\n return dict(name='Person', data=data)\",\n \"def create_person(person: Person = Body(...)):\\n return person\",\n \"def populate_persons():\\n\\n logging.basicConfig(level=logging.INFO)\\n logger = logging.getLogger(__name__)\\n\\n database = SqliteDatabase('personjob.db')\\n\\n logger.info('Working with Person class')\\n\\n PERSON_NAME = 0\\n LIVES_IN_TOWN = 1\\n NICKNAME = 2\\n\\n people = [\\n ('Andrew', 'Sultan', 'Andy'),\\n ('Peter', 'Seattle', None),\\n ('Susan', 'Boston', 'Beannie'),\\n ('Pam', 'Coventry', 'PJ'),\\n ('Steven', 'Stevens Pass', None),\\n ('Ryan', 'New York', 'Private'),\\n ('Pamela', 'Spokane', 'Patrol'),\\n ('Monica', 'Portland', None),\\n ]\\n\\n logger.info('Creating Person records: iterate through the list of tuples')\\n\\n try:\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON;')\\n for person in people:\\n with database.transaction():\\n new_person = Person.create(\\n person_name = person[PERSON_NAME],\\n lives_in_town = person[LIVES_IN_TOWN],\\n nickname = person[NICKNAME])\\n new_person.save()\\n logger.info('Database add successful')\\n\\n logger.info('Print the Person records we saved...')\\n for saved_person in Person:\\n logger.info(f'{saved_person.person_name} lives in {saved_person.lives_in_town} ' +\\\\\\n f'and likes to be known as {saved_person.nickname}')\\n\\n except Exception as e:\\n logger.info(f'Error creating = {person[PERSON_NAME]}')\\n logger.info(e)\\n\\n finally:\\n logger.info('database closes')\\n database.close()\",\n \"def add_person():\\n # get values from user\\n responses = accept_inputs([\\\"Name\\\"])\\n # insert into db\\n query_no_results(\\\"insert into person (name) values(?)\\\", [responses[\\\"Name\\\"]])\\n print(\\\"New person created\\\")\",\n \"def get_person(request):\\n\\n email = request.args.get(\\\"email\\\", None, str)\\n # log_info(\\\"email is \\\" + email)\\n\\n if not email:\\n log_info(\\\"get_person was called, but no email was provided in request\\\")\\n return None\\n\\n if validators.email(email) and (email_requester := auth.check_teacher(request)):\\n if email_requester and validators.email(email_requester):\\n db = database.Database()\\n student = db.get_student(email)\\n return dict(student)\\n\\n elif validators.email(email) and (email_requester := auth.check_login(request)):\\n if email_requester and validators.email(email_requester) and email == email_requester:\\n db = database.Database()\\n student = db.get_student(email)\\n if 'notes' in student:\\n del student['notes']\\n\\n return dict(student)\\n\\n log_info(\\\"No person with email \\\" + email + \\\" found in database\\\")\\n return None\",\n \"def add_person_to_task():\\n # get values from user\\n responses = accept_inputs([\\\"Person\\\", \\\"Task label\\\"])\\n # get the person's ID\\n id = query_with_results(\\\"select id from person where name = ?\\\", [responses[\\\"Person\\\"]])[0][0]\\n # insert into db\\n query_no_results(\\\"insert into task_person_pair (person, task) values(?, ?)\\\", [id, responses[\\\"Task label\\\"]])\\n print(\\\"%s added to task %s\\\" % (responses[\\\"Person\\\"], responses[\\\"Task label\\\"]))\",\n \"def insert_person(self, firstname, lastname, phone, age):\\n cur = self.conn.cursor(pymysql.cursors.DictCursor)\\n sql = 'INSERT INTO People (first_name, last_name, phone, age, time_added) VALUES (%s, %s, %s, %s, NOW())'\\n result = cur.execute(sql, (firstname, lastname, phone, age))\\n return result\",\n \"def remove_person(self, document):\\n del self.__people[document]\",\n \"def testCreatePerson(self):\\n\\t\\tpersonclassid = fwdb.fetchonevalue(con, \\\"SELECT Id FROM Class$ WHERE Name = 'CmPerson';\\\")\\n\\n\\t\\tnewObjId = -1\\n\\t\\tcur = con.cursor()\\n\\t\\tparams = cur.callproc(\\n\\t\\t\\t\\\"CreateObject$\\\",\\n\\t\\t\\t[personclassid, System.Data.SqlTypes.SqlInt32.Null, System.Data.SqlTypes.SqlGuid.Null])\\n\\t\\tobjid = params[2]\\n\\t\\tcur.close()\\n\\n\\t\\t#( We will be looping through the class tree found in ClassPar$, making sure\\n\\t\\t#( the object is created in each of the tables that represent classes.\\n\\n\\t\\tcur = con.cursor()\\n\\t\\tcur.execute(\\n\\t\\t\\t\\\"SELECT c.Name FROM ClassPar$ cp JOIN Class$ c ON c.Id = cp.Dst WHERE cp.Src = %s ORDER BY cp.Depth DESC;\\\" %\\n\\t\\t\\t(personclassid))\\n\\t\\tclasstree = cur.fetchall()\\n\\t\\tcur.close()\\n\\n\\t\\tfor classname in classtree:\\n\\t\\t count = fwdb.fetchonevalue(con, \\\"SELECT COUNT(Id) FROM %s WHERE Id = %s;\\\" % (classname[0], objid))\\n\\t\\t unitassert.assertEqual(count, 1, \\\"The object wasn't created in %s\\\" % (classname[0]), \\\"testCreatePerson\\\")\\n\\n\\t\\t#( Check that the update trigger updates CmObject.UpdDttm. A smalldatetime in SQL Sever\\n\\t\\t#( has an accuracy of only one minute. Therefore, a comparison of UpdDttm will always fail.\\n\\t\\t#( However, whenever anything is updated in CmObject, the UpdStmp field gets updated to\\n\\t\\t#( a new value.\\n\\n\\t\\tupdstmp1 = fwdb.fetchonevalue(con,\\\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\\\" % (objid))\\n\\n\\t\\tcur = con.cursor()\\n\\t\\tcur.execute(\\\"UPDATE CmPerson SET Gender = 1 WHERE Id = %s;\\\" % (objid))\\n\\t\\tcur.close()\\n\\n\\t\\tupdstmp2 = fwdb.fetchonevalue(con,\\\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\\\" % (objid))\\n\\t\\tunitassert.assertNotEqual(\\n\\t\\t\\tupdstmp1, updstmp2, \\\"CmObject.UpdStmp should have changed\\\", \\\"testInsertClass\\\")\\n\\n\\t\\t#( Check that the reference collection table insert trigger updates CmObject.UpdDttm.\\n\\t\\t#( The trigger gets created by the Field$ insert trigger, but it is easier to test it\\n\\t\\t#( here.\\n\\n\\t\\tupdstmp1 = fwdb.fetchonevalue(con,\\\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\\\" % (objid))\\n\\n\\t\\tcur = con.cursor()\\n\\t\\tcur.execute(\\\"UPDATE CmPerson_Positions SET Src = Src WHERE Src = %s;\\\" % (objid))\\n\\t\\tcur.close()\\n\\n\\t\\tupdstmp2 = fwdb.fetchonevalue(con,\\\"SELECT UpdStmp FROM CmObject WHERE Id = %s;\\\" % (objid))\\n\\t\\tunitassert.assertNotEqual(\\n\\t\\t\\tupdstmp1, updstmp2, \\\"CmObject.UpdStmp should have changed for CmPerson_Positions\\\", \\\"testInsertClass\\\")\",\n \"def add_person():\\n # Find the last used PK\\n with sqlite3.connect('skeevy.db') as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT id FROM person ORDER BY id DESC;\\\")\\n for row in cursor.fetchone():\\n last_pk = row\\n\\n # Auto-increment the primary key for the person table.\\n last_pk = last_pk + 1\\n\\n # Prompt the user for the rest of their information.\\n first_name = input(\\\"Enter your first name: \\\")\\n middle_name = input(\\\"Enter your middle name: \\\")\\n last_name = input(\\\"Enter your last name: \\\")\\n suffix_name = input(\\\"Enter your suffix: \\\")\\n e_mail = input(\\\"Enter your email: \\\")\\n # Default status of the person is active (1).\\n status = 1\\n\\n # Store the input in a variable.\\n person_data = (last_pk, first_name, middle_name, last_name, suffix_name,\\n e_mail, status)\\n\\n # Connect and insert the data into the person table.\\n with sqlite3.connect('skeevy.db') as connection:\\n cursor = connection.cursor()\\n cursor.execute(\\\"INSERT INTO person VALUES(?, ?, ?, ?, ?, ?, ?);\\\",\\n person_data)\\n connection.commit()\",\n \"def test_removeperson(self):\\n p1, p2, p3 = self.create3persons()\\n t = model.Team(name='Tigers', persons=[p1, p2, p3])\\n id = t.store()\\n t.remove_person(p2)\\n t.store()\\n\\n t2 = model.Team(id=id)\\n self.assertEqual(t2.persons, [p1.id, p3.id])\\n\\n with self.assertRaises(ValueError): # cannot be removed again\\n t2.remove_person(p2)\",\n \"def get_person(self, user_id):\\n endpoint = '/user/{}'.format(user_id)\\n return self.get_request(endpoint)\",\n \"def remove_person(self, remove_person_id):\\n self.__load_persons_from_file_into_memory()\\n super().remove_person(remove_person_id)\\n self.__save_persons_from_memory_to_file()\",\n \"def delete_person(self, key):\\n if key[:5] not in ['part_', 'init_']:\\n raise Exception('model.delete_person() can only be performed on a' +\\n 'participant or initiator record.')\\n # TODO: Delete this notification after testing is complete\\n if key[:5] == 'part_':\\n message = ('Participant ' + self.get_participant(key)['email'] +\\n ' deleted.')\\n else:\\n message = ('Initiator ' + self.get_initiator(key)['email'] +\\n ' deleted.')\\n print message\\n # Delete the DB record\\n self.client.delete(key)\",\n \"def __ui_add_new_person(self):\\n person_id = int(input(\\\"ID: \\\"))\\n person_name = input(\\\"Name: \\\").strip()\\n person_phone_number = input(\\\"Phone number: \\\").strip()\\n self.__person_service.service_add_person(person_id, person_name, person_phone_number)\\n print(\\\"Person successfully added to your agenda!\\\\n\\\")\",\n \"def test_entity_retrieval_by_its_primary_key(\\n self, test_domain, identifier, persisted_person\\n ):\\n person = test_domain.repository_for(Person)._dao.get(persisted_person.id)\\n assert person is not None\\n assert person.id == identifier\\n assert person == persisted_person\",\n \"def add_person(self, name, age):\\n try:\\n age = int(age)\\n except Exception as e:\\n _log.log(self.log, 'error', str(e).encode())\\n return False\\n\\n person = _models.Person(name=name, age=age)\\n\\n # person key exists in cache, return false\\n if _cache.person_exists(self.redis, self.people_key, person):\\n return False\\n\\n # person key exists in db, add it to cache and return false\\n if _orm.person_exists(self.session, _models.Person, _models.Person.name, name):\\n try:\\n _cache.add_people(self.redis, self.people_key, [person])\\n except Exception as e:\\n _log.log(self.log, 'warn', str(e).encode())\\n \\n return False\\n\\n # add person to db\\n try:\\n _orm.add_people(self.session, [person])\\n _log.add_person(self.log, 'info', self.people_key, person, 'db')\\n except Exception as e:\\n _log.log(self.log, 'error', str(e).encode())\\n return False\\n\\n # add person to cache\\n try:\\n _cache.add_people(self.redis, self.people_key, [person])\\n _log.add_person(self.log, 'info', self.people_key, person, 'cache')\\n except Exception as e:\\n _log.log(self.log, 'warn', str(e).encode())\\n\\n return True\",\n \"def set_person(self, person):\\n if person.upper() == UNKNOWN.upper():\\n self.person_name = UNKNOWN\\n self.person_id = 'XX'\\n else:\\n self.person_name = person.lower()\\n self.person_id = person.upper()[:2]\",\n \"def test_delete_an_object_in_repository_by_id(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"John\\\", last_name=\\\"Doe\\\", age=22\\n )\\n\\n persisted_person = test_domain.repository_for(Person)._dao.get(identifier)\\n assert persisted_person is not None\\n\\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\\n assert deleted_person is not None\\n assert deleted_person.state_.is_destroyed is True\\n\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(identifier)\",\n \"def person(self, email=None, emailMD5=None, phone=None,\\r\\n twitter=None, facebookUsername=None):\\r\\n kwargs = list(\\r\\n filter(None, [email, emailMD5, phone, twitter, facebookUsername,]))\\r\\n\\r\\n if len(kwargs) != 1:\\r\\n raise TypeError(\\r\\n 'person() must be passed exactly one of '\\r\\n 'email, emailMD5, phone, twitter or facebookUsername')\\r\\n\\r\\n return resource.Person(self, email=email, emailMD5=emailMD5, phone=phone,\\r\\n twitter=twitter, facebookUsername=facebookUsername)\",\n \"def test_deleting_a_persisted_entity(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"Jim\\\", last_name=\\\"Carrey\\\"\\n )\\n deleted_person = test_domain.repository_for(Person)._dao.delete(person)\\n assert deleted_person is not None\\n assert deleted_person.state_.is_destroyed is True\\n\\n with pytest.raises(ObjectNotFoundError):\\n test_domain.repository_for(Person)._dao.get(identifier)\",\n \"def replace_person(self,p):\\n p.age = 15\\n p.days = 0\\n \\n self.age_group[4].remove(p.identifier)\\n self.age_group[0].add(p.identifier)\\n \\n if np.random.random() < self.sexual_activity_high:\\n p.sexual_activity = 1\\n self.high_sexual_activity.add(p.identifier)\\n else:\\n p.sexual_activity = 0\\n \\n p.cure(self)\\n \\n #remove all partnerships where p is involved in\\n for i,ps in enumerate(self.partnerships):\\n if p.identifier in [ps.persons[0].identifier,ps.persons[1].identifier]:\\n ps.remove(self)\\n self.partnerships[i] = None\\n #if deleting steady partnership\\n if ps.type == 1:\\n self.number_of_steady -= 1\\n self.partnerships = list(filter(None,self.partnerships))\",\n \"def get_person_name(self, person_id):\\n res = requests.get(url=\\\"https://api.ciscospark.com/v1/people/{}\\\".format(person_id),\\n headers=self.headers)\\n\\n try:\\n class person(object):\\n firstName = res.json()['firstName']\\n lastName = res.json()['lastName']\\n\\n return person\\n except AttributeError as e:\\n print(res.text)\\n return None\",\n \"def _set_person_id(cls, data):\\n document_id = data.get(\\\"_id\\\")\\n if document_id:\\n data[\\\"person_id\\\"] = document_id\\n return data\",\n \"def upload_to_personfinder(person):\\n action = \\\"write\\\"\\n domain = \\\"rhok\\\"\\n key = \\\"punsOMMYMAI27tkr\\\"\\n\\n url = build_personfinder_url(action, domain, key)\\n logging.debug('URL: %s' % url)\\n\\n data = to_xml(persons=[person]).toxml().encode('utf8')\\n logging.debug('data: %s' % data)\\n req = urllib2.Request(\\n url, data, { 'Content-Type': 'application/xml' })\\n result = None\\n result = urllib2.urlopen(req).read()\\n logging.debug('return: %s' % result)\\n\\n return result\",\n \"def test_updating_record_with_both_dictionary_args_and_kwargs(self, test_domain):\\n identifier = uuid4()\\n person = test_domain.repository_for(Person)._dao.create(\\n id=identifier, first_name=\\\"Johnny\\\", last_name=\\\"John\\\", age=2\\n )\\n\\n test_domain.repository_for(Person)._dao.update(\\n person, {\\\"first_name\\\": \\\"Stephen\\\"}, age=10\\n )\\n u_person = test_domain.repository_for(Person)._dao.get(identifier)\\n assert u_person is not None\\n assert u_person.age == 10\\n assert u_person.first_name == \\\"Stephen\\\"\",\n \"def create(self, person):\\n current_id = self._generate_person_id()\\n self._timeline.add_event({\\n 'type': EventTimeLine.PERSON_CREATION,\\n 'personId': current_id,\\n 'status': person.status,\\n 'address': person.address.to_dict(),\\n 'name': person.name.to_dict()\\n })\\n return current_id\",\n \"def update_people(pid):\\n db_conn = get_connection()\\n cur = db_conn.cursor()\\n\\n if request.method == 'GET':\\n get_sql = \\\"\\\"\\\"SELECT \\n id, firstname, lastname, address, country \\n FROM peoples WHERE id = ?\\\"\\\"\\\"\\n cur.execute(get_sql, (pid,))\\n record = cur.fetchone() # fetches the top most row\\n # Check if the given user id exists in our database\\n if record:\\n return render_template('update.jinja2', data=record)\\n else:\\n flash(\\\"Sorry! Couldn't get user with id {}\\\".format(pid))\\n return redirect(url_for('list_people'))\\n\\n elif request.method == 'POST': # or use just else: because out of two accepted methods \\n # if one is GET then the other accepted method is obiviously POST\\n firstname = request.form['firstname']\\n lastname = request.form['lastname']\\n address = request.form['address']\\n country = request.form['country']\\n if firstname.strip():\\n post_sql = \\\"\\\"\\\"UPDATE \\n peoples set firstname=?, lastname=?, address=?, country=? \\n where id=?\\\"\\\"\\\"\\n cur.execute(post_sql, (\\n firstname.strip(),\\n lastname.strip(),\\n address.strip(),\\n country.strip(),\\n pid\\n ))\\n db_conn.commit()\\n flash(\\\"Record with id {} updated successfully!!!\\\".format(pid))\\n return redirect(url_for('list_people'))\",\n \"def fetch(self, person=None):\\n if not person:\\n # get the list.\\n self.endpoint = 'people.json'\\n else:\\n self.endpoint = 'people/{0}.json'.format(person)\\n\\n request = self.get(self.construct_url())\\n\\n if request.status_code == 200:\\n return json.loads(request.content)\\n\\n raise BasecampAPIError()\",\n \"def find_person(self, searched_person_id):\\n self.__load_persons_from_file_into_memory()\\n return super().find_person(searched_person_id)\",\n \"def do_update(self, arg):\\n arg = arg.split()\\n try:\\n h = arg[0] + \\\".\\\" + arg[1]\\n except:\\n pass\\n objects = storage.all()\\n if len(arg) is 0:\\n print(\\\"** class name missing **\\\")\\n elif len(arg) == 1 and arg[0] in self.dict.keys():\\n print(\\\"** instance id missing **\\\")\\n elif arg[0] not in self.dict.keys():\\n print(\\\"** class doesn't exist **\\\")\\n elif h not in objects.keys():\\n print(\\\"** no instance found **\\\")\\n elif len(arg) <= 2:\\n print(\\\"** attribute name missing **\\\")\\n elif len(arg) <= 3:\\n print(\\\"** value missing **\\\")\\n else:\\n setattr(objects[h], arg[2], arg[3])\\n storage.save()\",\n \"def put(self, patient_id):\\n args = self.parser.parse_args()\\n\\n # Convert data_time and validate\\n date_of_birth = utils.str2date(args.date_of_birth)\\n if date_of_birth is None:\\n abort()\\n\\n return_id = None\\n with open_session() as session:\\n record = Patient(args.first_name,\\n args.last_name,\\n args.gender,\\n date_of_birth)\\n\\n session.add(record)\\n session.flush()\\n session.commit()\\n\\n return_id = record.id\\n\\n # Check for error handling\\n if return_id is None:\\n return gen_response(\\\"internal server error\\\")\\n else:\\n return gen_response({\\\"id\\\": return_id})\",\n \"def add_person_to_db(self):\\n fullname = self.AddPerson.add_person_to_db(self.sql)\\n if fullname:\\n self.fullname.setText(fullname)\\n # likely same name as before so no triggered search\\n self.search_people_by_name()\",\n \"def PersonLookup(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def get_person(self, requestId):\\n return self.get_json('/verification/%s/person' % str(requestId))\",\n \"def save_person(self, new_person):\\n self.__load_persons_from_file_into_memory()\\n super().save_person(new_person)\\n self.__save_persons_from_memory_to_file()\",\n \"def parse_person(person):\\n person_name = person['name']\\n person_id = person['id']\\n\\n pages = get_pages_from_wiki_search(person_name)\\n page = select_page_like_person(pages, person)\\n\\n wiki_person = {\\n 'id': person_id, 'fullname': person_name,\\n 'url': page.get('fullurl', None), \\n 'declarator_profile': page.get('declarator_profile', None),\\n 'words_intersection': page.get('words_intersection', None),\\n }\\n\\n # if page with image\\n if 'pageimage' in page:\\n try:\\n image = page['pageimage']\\n url = page['original']['source']\\n license = get_license_from_wiki(image, is_file=True)\\n\\n photo = {'title': image, 'url': url, 'license': license}\\n wiki_person['photo'] = photo\\n except KeyError as e:\\n message = \\\"person_id: %i, Not found key: %s\\\" % (person_id, e)\\n Querylog.error(message)\\n return wiki_person\",\n \"def get(self, id):\\n adm = Administration()\\n pers = adm.get_person_by_id(id)\\n return pers\",\n \"def show_person(uuid=None, fanchart=False):\\n t0 = time.time()\\n uuid = request.args.get(\\\"uuid\\\", uuid)\\n fanchart_shown = request.args.get(\\\"fanchart\\\", fanchart)\\n dbg = request.args.get(\\\"debug\\\", None)\\n u_context = UserContext(user_session, current_user, request)\\n\\n with PersonReaderTx(\\\"read_tx\\\", u_context) as service:\\n result = service.get_person_data(uuid)\\n\\n # result {'person':PersonBl, 'objs':{uniq_id:obj}, 'jscode':str, 'root':{root_type,root_user,batch_id}}\\n if Status.has_failed(result):\\n flash(f'{result.get(\\\"statustext\\\",\\\"error\\\")}', \\\"error\\\")\\n person = result.get(\\\"person\\\")\\n objs = result.get(\\\"objs\\\", [])\\n print(f\\\"# Person with {len(objs)} objects\\\")\\n jscode = result.get(\\\"jscode\\\", \\\"\\\")\\n # Batch or Audit node data like {'root_type', 'root_user', 'id'}\\n person.root = result.get(\\\"root\\\")\\n\\n stk_logger(u_context, f\\\"-> bp.scene.routes.show_person n={len(objs)}\\\")\\n\\n last_year_allowed = datetime.now().year - shareds.PRIVACY_LIMIT\\n may_edit = current_user.has_role(\\\"audit\\\") # or current_user.has_role('admin')\\n # may_edit = 0\\n return render_template(\\n \\\"/scene/person.html\\\",\\n person=person,\\n obj=objs,\\n jscode=jscode,\\n menuno=12,\\n debug=dbg,\\n last_year_allowed=last_year_allowed,\\n elapsed=time.time() - t0,\\n user_context=u_context,\\n may_edit=may_edit,\\n fanchart_shown=fanchart_shown,\\n )\",\n \"def test_05_get_person_by_name(self):\\n p1 = Person.query.first()\\n p1_data = p1.wrap()\\n p1_f_name = p1_data[\\\"first_name\\\"]\\n # find by first name only\\n # get part of name and search\\n q_string = \\\"?first_name={}\\\".format(p1_f_name[:3]) # TODO - verify the length\\n rv = self.app.get('persons', query_string=q_string)\\n data = json.loads(rv.data)\\n self.assertEqual(data[\\\"count\\\"], 1)\\n\\n # find by first name and last name\\n p1_l_name = p1_data[\\\"last_name\\\"]\\n q_string = \\\"?first_name={}&last_name={}\\\".format(p1_f_name[:3], p1_l_name)\\n rv = self.app.get('persons', query_string=q_string)\\n data = json.loads(rv.data)\\n self.assertEqual(data[\\\"count\\\"], 1)\\n\\n # find by first name and non-existing last name\\n q_string = \\\"?first_name={}&last_name={}\\\".format(p1_f_name[:3], \\\"iAmNotThere\\\")\\n rv = self.app.get('persons', query_string=q_string)\\n data = json.loads(rv.data)\\n self.assertEqual(data[\\\"count\\\"], 0)\",\n \"def person_id(self, person_id):\\n\\n self._person_id = person_id\",\n \"def get_instance(self, instance):\\n return self._get(_instance.Instance, instance)\",\n \"def create_person(conn, person, first_name, last_name):\\n sql = ''' INSERT INTO person(firstname,lastname)\\n VALUES(?,?) '''\\n cur = conn.cursor() # cursor object\\n cur.execute(sql, person)\\n # print(str(cur.lastrowid))\\n # return cur.lastrowid # returns the row id of the cursor object, the person id\\n first_name.set('')\\n last_name.set('')\\n messagebox.showinfo('Success', 'Person Successfully Added to Database!')\",\n \"def find_instance(self, name_or_id, ignore_missing=True):\\n return self._find(\\n _instance.Instance, name_or_id, ignore_missing=ignore_missing\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6274317","0.6184409","0.6044379","0.60121447","0.60050964","0.5923112","0.59186417","0.5886551","0.58603877","0.5851857","0.58510053","0.58013856","0.5764969","0.57433015","0.5636165","0.5635824","0.5605609","0.5604004","0.55662686","0.5558843","0.5530182","0.5511921","0.54576296","0.5457219","0.5434714","0.5409145","0.5394645","0.53568256","0.53366166","0.5331182","0.5322537","0.5306216","0.5295327","0.52608335","0.52402437","0.5234113","0.52290076","0.52177477","0.52066684","0.51931965","0.5173823","0.51569515","0.5154822","0.5146532","0.51216966","0.5107751","0.51012254","0.50907236","0.5087151","0.50781524","0.50597596","0.50399613","0.50397366","0.50318664","0.50301254","0.50180846","0.49998993","0.49556828","0.49394396","0.49290898","0.4928306","0.4926045","0.49190086","0.48996237","0.48952731","0.48951703","0.48924792","0.48886055","0.488371","0.4880227","0.48739046","0.4836837","0.48352432","0.48342156","0.48325548","0.48232898","0.48156205","0.4810255","0.48075005","0.48030198","0.4797381","0.47873494","0.47840232","0.47830918","0.47711757","0.47616068","0.47553048","0.47501102","0.4742917","0.47261846","0.47164178","0.4710775","0.47037187","0.46794605","0.46678808","0.46611255","0.46559742","0.46540207","0.46482113","0.46396944","0.46375054"],"string":"[\n \"0.6274317\",\n \"0.6184409\",\n \"0.6044379\",\n \"0.60121447\",\n \"0.60050964\",\n \"0.5923112\",\n \"0.59186417\",\n \"0.5886551\",\n \"0.58603877\",\n \"0.5851857\",\n \"0.58510053\",\n \"0.58013856\",\n \"0.5764969\",\n \"0.57433015\",\n \"0.5636165\",\n \"0.5635824\",\n \"0.5605609\",\n \"0.5604004\",\n \"0.55662686\",\n \"0.5558843\",\n \"0.5530182\",\n \"0.5511921\",\n \"0.54576296\",\n \"0.5457219\",\n \"0.5434714\",\n \"0.5409145\",\n \"0.5394645\",\n \"0.53568256\",\n \"0.53366166\",\n \"0.5331182\",\n \"0.5322537\",\n \"0.5306216\",\n \"0.5295327\",\n \"0.52608335\",\n \"0.52402437\",\n \"0.5234113\",\n \"0.52290076\",\n \"0.52177477\",\n \"0.52066684\",\n \"0.51931965\",\n \"0.5173823\",\n \"0.51569515\",\n \"0.5154822\",\n \"0.5146532\",\n \"0.51216966\",\n \"0.5107751\",\n \"0.51012254\",\n \"0.50907236\",\n \"0.5087151\",\n \"0.50781524\",\n \"0.50597596\",\n \"0.50399613\",\n \"0.50397366\",\n \"0.50318664\",\n \"0.50301254\",\n \"0.50180846\",\n \"0.49998993\",\n \"0.49556828\",\n \"0.49394396\",\n \"0.49290898\",\n \"0.4928306\",\n \"0.4926045\",\n \"0.49190086\",\n \"0.48996237\",\n \"0.48952731\",\n \"0.48951703\",\n \"0.48924792\",\n \"0.48886055\",\n \"0.488371\",\n \"0.4880227\",\n \"0.48739046\",\n \"0.4836837\",\n \"0.48352432\",\n \"0.48342156\",\n \"0.48325548\",\n \"0.48232898\",\n \"0.48156205\",\n \"0.4810255\",\n \"0.48075005\",\n \"0.48030198\",\n \"0.4797381\",\n \"0.47873494\",\n \"0.47840232\",\n \"0.47830918\",\n \"0.47711757\",\n \"0.47616068\",\n \"0.47553048\",\n \"0.47501102\",\n \"0.4742917\",\n \"0.47261846\",\n \"0.47164178\",\n \"0.4710775\",\n \"0.47037187\",\n \"0.46794605\",\n \"0.46678808\",\n \"0.46611255\",\n \"0.46559742\",\n \"0.46540207\",\n \"0.46482113\",\n \"0.46396944\",\n \"0.46375054\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":9990,"cells":{"query":{"kind":"string","value":"Does a country like Mexico or United States get a code assigned?"},"document":{"kind":"string","value":"def test_country_code(self):\n\t\tcountry_name = 'United States'\n#\t\tpopulation = int(float(pop_dict['Value']))\n\t\tcode = get_country_code(country_name)\t\t\n\t\t#Assert methods verifies result received matches expected one\n\t\tself.assertEqual(code, 'usa')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def country(alpha_2_code: str) -> None:","def get_country_code(self):\n #method on the class InternationalMelonOrder\n\n return self.country_code\n # international has country code; domestic does not\n # WAI???","def get_country_code(country_name):\n for code, name in COUNTRIES.items():\n if name == country_name:\n return code\n elif country_name == 'Yemen, Rep.':\n return 'ye'\n elif country_name == 'Vietnam':\n return 'vn'\n elif country_name == 'Tanzania':\n return 'tz'\n elif country_name == 'Moldova':\n return 'md'\n elif country_name == 'Macao SAR, China':\n return 'mo'\n elif country_name == 'Macedonia, FYR':\n return 'mk'\n elif country_name == 'Libya':\n return 'ly'\n elif country_name == 'Lao PDR':\n return 'la'\n elif country_name == 'Korea, Dem. Rep.':\n return 'kp'\n elif country_name == 'Korea, Rep.':\n return 'kr'\n elif country_name == 'Gambia':\n return 'gm'\n elif country_name == 'Iran, Islamic Rep.':\n return 'ir'\n elif country_name == 'Hong Kong SAR':\n return 'hk'\n elif country_name == 'Congo, Dem. Rep.':\n return 'cd'\n elif country_name == 'Congo, Rep.':\n return 'cf'\n elif country_name == 'Macao SAR, China':\n return 'mo'\n elif country_name == 'Macedonia, FYR':\n return 'mk'\n elif country_name == 'Libya':\n return 'ly'\n elif country_name == 'Lao PDR':\n return 'la'\n elif country_name == 'Korea, Dem. Rep.':\n return 'kp'\n elif country_name == 'Korea, Rep.':\n return 'kr'\n elif country_name == 'Gambia':\n return 'gm'\n # If the country wasn't found, return None.\n return None","def USCode(self, short):\n states = {\n 'AK': 'Alaska',\n 'AL': 'Alabama',\n 'AR': 'Arkansas',\n 'AS': 'American Samoa',\n 'AZ': 'Arizona',\n 'CA': 'California',\n 'CO': 'Colorado',\n 'CT': 'Connecticut',\n 'DC': 'District of Columbia',\n 'DE': 'Delaware',\n 'FL': 'Florida',\n 'GA': 'Georgia',\n 'GU': 'Guam',\n 'HI': 'Hawaii',\n 'IA': 'Iowa',\n 'ID': 'Idaho',\n 'IL': 'Illinois',\n 'IN': 'Indiana',\n 'KS': 'Kansas',\n 'KY': 'Kentucky',\n 'LA': 'Louisiana',\n 'MA': 'Massachusetts',\n 'MD': 'Maryland',\n 'ME': 'Maine',\n 'MI': 'Michigan',\n 'MN': 'Minnesota',\n 'MO': 'Missouri',\n 'MP': 'Northern Mariana Islands',\n 'MS': 'Mississippi',\n 'MT': 'Montana',\n 'NA': 'National',\n 'NC': 'North Carolina',\n 'ND': 'North Dakota',\n 'NE': 'Nebraska',\n 'NH': 'New Hampshire',\n 'NJ': 'New Jersey',\n 'NM': 'New Mexico',\n 'NV': 'Nevada',\n 'NY': 'New York',\n 'OH': 'Ohio',\n 'OK': 'Oklahoma',\n 'OR': 'Oregon',\n 'PA': 'Pennsylvania',\n 'PR': 'Puerto Rico',\n 'RI': 'Rhode Island',\n 'SC': 'South Carolina',\n 'SD': 'South Dakota',\n 'TN': 'Tennessee',\n 'TX': 'Texas',\n 'UT': 'Utah',\n 'VA': 'Virginia',\n 'VI': 'Virgin Islands',\n 'VT': 'Vermont',\n 'WA': 'Washington',\n 'WI': 'Wisconsin',\n 'WV': 'West Virginia',\n 'WY': 'Wyoming'\n }\n return states.get(short)","def country_codes(country):\n countryObject = None\n try:\n countryObject = pycountry.countries.search_fuzzy(country)\n return countryObject[0].alpha_2\n except LookupError:\n pass\n try:\n splittedCountry = country.split(',')[0]\n countryObject = pycountry.countries.search_fuzzy(splittedCountry)\n return countryObject[0].alpha_2\n except LookupError:\n return 'No Code'","def country() -> str:","def get_country_code(country_name):\n # worldmap_chart = pygal.maps.world.World()\n # for code, name in worldmap_chart:\n\n for code, name in i18n.COUNTRIES:\n\n # for code, name in COUNTRIES.items():\n if name == country_name:\n print(code)\n return code\n # If the country wasn't found, return None.\n return None","def get_country_code(country_name) :\n for code, name in COUNTRIES.items() :\n if name==country_name :\n return code\n # if the country wasn't found, return None\n return None","def country_code(self) -> str | None:\n pass","def _is_valid_code(self, code):\r\n return code in COUNTRY_CODES","def mock_country_code_by_addr(self, ip_addr):\r\n ip_dict = {\r\n '1.0.0.0': 'CU',\r\n '2.0.0.0': 'IR',\r\n '3.0.0.0': 'SY',\r\n '4.0.0.0': 'SD',\r\n '5.0.0.0': 'AQ', # Antartica\r\n }\r\n return ip_dict.get(ip_addr, 'US')","def COUNTRY_CODE():\n COUNTRY_CODE = \"us/90210\"\n return COUNTRY_CODE","def get_country_code(country_name):\n for code, name in COUNTRIES.items():\n if name == country_name:\n return code\n # if string isn't found returns None\n else:\n continue","def test_country_name_in_countries(self):\n\t\tcountry_code = get_country_code('Andorra')\n\t\tself.assertEqual(country_code, 'ad')","def get_country_code(contry_name):\n for code, name in COUNTRIES.items():\n if name == contry_name:\n return code\n return None","def get_country_code(country_name):\n\n for code, name in COUNTRIES.items():\n if name == country_name:\n return code\n\n # If country was not found, return nothing\n return None","def country_identifier(name):\n if name.lower() in _country_dict.keys():\n return _country_dict[name.lower()]\n else:\n return name","def convert_country(country):\n if (country and 'China' in country) or \\\n country == 'Chin' or country == 'CHINA':\n country = 'China'\n elif country and 'Brazil' in country or \\\n country == 'Brasil' or \\\n country == 'ITA - Instituto Tecnologico de Aeronautica (':\n country = 'Brazil'\n elif country and 'Argentina' in country:\n country = 'Argentina'\n elif country == 'Czechia':\n country = 'Czech Republic'\n elif 'Norwegian' in country:\n country = 'Norway'\n elif country and 'United Kingdom' in country:\n country = 'United Kingdom'\n elif country and 'Hong Kong' in country:\n country = 'Hong Kong'\n elif country == 'Cameroun':\n country = 'Cameroon'\n elif (country and 'Chile' in country) or country == 'CHILE':\n country = 'Chile'\n elif (country and 'United States of America' in \\\n country) or country == 'United States' or country \\\n == 'USA' or 'Florida' in country or \\\n 'California' in country or\\\n country == 'National Reference Centre for' or \\\n country == 'United State of America' or \\\n country == 'U.S.A.' or \\\n country == 'Virginia':\n country = 'United States of America'\n elif country=='Republic of Panamá' or country=='Panamá' or 'Panama' in country:\n country = 'Panama'\n elif 'Canada' in country:\n country = 'Canada'\n elif 'Colombia' in country:\n country = 'Colombia'\n elif 'Spain' in country or country=='España':\n country = 'Spain'\n elif 'Iran' in country:\n country = 'Iran'\n elif 'Saudi Arabia' in country:\n country = 'Saudi Arabia'\n elif 'Italy' in country:\n country = 'Italy'\n elif 'Japan' in country:\n country = 'Japan'\n elif 'Germany' in country:\n country = 'Germany'\n elif 'Luxembourg' in country:\n country = 'Luxembourg'\n elif ('France' in country) or country == 'Marseille':\n country = 'France'\n elif country == 'ROC' or country == 'R. O. C':\n country = 'Taiwan'\n elif country == 'Brasil':\n country = 'Brazil'\n elif country == 'México' or 'Mexico' in country:\n country = 'Mexico'\n elif 'Slowakia' in country:\n country = 'Slowakia'\n elif country == 'Korea' or 'Republic of Korea' in country:\n country = 'South Korea'\n elif country == 'United Kindgom':\n country = 'United Kingdom'\n elif country and 'Netherlands' in country:\n country = 'Netherlands'\n elif country == 'Commonwealth of Australia' or 'Australia' in country:\n country = 'Australia'\n elif 'Singapore' in country:\n country = 'Singapore'\n elif country and (country[0].isdigit() or country[0] == '+'):\n country = 'N/A'\n return country","def country_code(self):\n return self.__country_code","def country_code(self):\n return self.__country_code","def get_country(ip):\r\n return geoip.country_code_by_addr(ip)","def test_country_name_not_in_countries(self):\n\t\tcountry_code = get_country_code('Venezuela, RB')\n\t\tself.assertEqual(country_code, 've')","def get_country(self, country):\n if country == \"United Kingdom\": return \"en\"\n if country == \"Portugal\": return \"pt\"\n\n result = self.session.get(\"https://en.ogame.gameforge.com\")\n soup = BeautifulSoup(result.content, \"html.parser\")\n\n code_list = soup.find(\"ul\", {\"id\": \"mmoList1\"})\n countries = {}\n for tag in code_list.find_all(\"li\"):\n link = tag.find(\"a\")[\"href\"]\n name = tag.string.strip() # name of the country\n code = link.split(\".\")[0].replace(\"//\", \"\")\n countries[name] = code # save to the dict\n\n # check if input was ok\n if not country in countries.keys():\n self.crash(\"Country\", country, \"was not found on the list.\")\n if len(countries[country]) != 2:\n self.crash(\"Can't fetch code for country\", country)\n\n return countries[country]","def get_country_code(country_name):\n # values = list(COUNTRIES.values())\n # keys = list(COUNTRIES.keys())\n #\n # try:\n # index = values.index(country_name)\n # except ValueError:\n # # Not found\n # return None\n #\n # return keys[index]\n\n for code, name in COUNTRIES.items():\n if name == country_name:\n return code\n\n return None","def get_continent(country_code):\r\n for c in continents:\r\n if c.contains(country_code):\r\n return c\r\n plog(\"INFO\", country_code + \" is not on any continent\")\r\n return None","def get_country_code(self):\n\n try:\n sub_div = next(sub_div for sub_div in pycountry.subdivisions if sub_div.name == self.location)\n country = next(country for country in pycountry.countries if country.alpha_2 == sub_div.country_code)\n return country.alpha_3\n except StopIteration as exc:\n print(\"Cannot find subdivision in\" + str(exc))\n return 'XXX'","def state(self):\n state_code = MISSING\n try:\n country_code = self.status.place[\"country_code\"]\n except TypeError:\n return MISSING\n place_type = self.status.place[\"place_type\"]\n if country_code == \"US\" and place_type == \"city\":\n full_name = self.status.place[\"full_name\"]\n state_code = full_name.split(\",\")[-1].strip().upper()\n state_code = state_code if state_code in valid_state_codes else MISSING\n else:\n pass\n return state_code","def resolveCountryCode(country_code):\n country_name = None\n if len(country_code) > 2:\n country_name = country_code\n country_code = next((cc for cc, country in countries.items() if country == country_code), None)\n if country_code not in countries:\n logger.error(\"Country code %s unknown. For a list of know codes execute:\")\n logger.error(sys.argv[0] + ' --list-countries \\tList all available countries that can be blocked.')\n sys.exit(255)\n if not country_name:\n country_name = countries[country_code]\n return [country_code, country_name]","def determine_us_state(area_code):\r\n if not isinstance(area_code, str):\r\n area_code = str(area_code)\r\n if area_code in area_code_mapping:\r\n return area_code_mapping[area_code][0]","def test_city_country(self):\n dublin_ireland = city_country('dublin', 'ireland')\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')","def test_city_country(self):\n\t\tformatted_address = city_country('santiago', 'chile')\n\t\tself.assertEqual(formatted_address, 'Santiago, Chile')","def flag(countrycode: str) -> str:\r\n\r\n code = [c for c in countrycode.lower() if c in ASCII_LOWER]\r\n if len(code) == 2:\r\n # Regional indicator symbols\r\n return flag_regional_indicator(code)\r\n if len(code) > 2 and len(code) < 7:\r\n # Tag sequence\r\n return flag_tag_sequence(code)\r\n found = ''.join(code)\r\n raise ValueError(\r\n 'invalid countrycode, found %d (%r) in %r.' %\r\n (len(found), found, countrycode))","def get_country_from_code(country_code):\n country = []\n countries = [(country.alpha_2, country.name) for country in pycountry.countries]\n for country_index in range(len(countries)):\n # index 0 is the country code selected from the form\n if(countries[country_index][0] == country_code):\n country.append(countries[country_index])\n return country[0][1]","def get_country_dcid(country_code: str) -> str:\n numeric_code = int(country_code, 10)\n if numeric_code in UN_COUNTRY_CODES:\n return UN_COUNTRY_CODES[numeric_code]\n return None","def country_letter_code(self):\n if \"countryLetterCode\" in self._prop_dict:\n return self._prop_dict[\"countryLetterCode\"]\n else:\n return None","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def get_country_code(self):\n\n return self.country_code","def country(name):\n return location_db().find(name=name)[\"country\"]","def country_code(self):\n return self._plrevgeoloc.countryCode","def test_city_country_population(self):\n\t\tformatted_address = city_country(\n\t\t\t'santiago', 'chile', '5000000')\n\t\tself.assertEqual(formatted_address, \n\t\t\t'Santiago, Chile - Population 5000000')","def city_country(city_name, country_name):\n city_country_combo = city_name + ', ' + country_name\n return city_country_combo.title()","def load_country_code_data():\n name_conversion = {\n 'East Timor': 'Timor-Leste',\n 'Republic of the Congo': 'Congo (Kinshasa)',\n 'Ivory Coast': 'Cote d\\'Ivoire',\n 'Macedonia': 'North Macedonia',\n 'Myanmar': 'Burma',\n 'Republic of Serbia': 'Serbia',\n 'Taiwan': 'Taiwan*',\n 'The Bahamas': 'Bahamas',\n 'United Republic of Tanzania': 'Tanzania',\n 'United States of America': 'US'\n }\n\n shapefile = os.path.join('data', 'ne_110m_admin_0_countries.shp')\n\n gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]\n gdf.columns = ['country', 'country_code', 'geometry']\n\n gdf.loc[gdf['country'].isin(name_conversion.keys()), 'country'] = gdf['country'].map(name_conversion)\n\n return gdf","def get_country_from_record(ip):\r\n record = geoip.record_by_addr(ip)\r\n if record != None:\r\n return record['country_code']","def test_get_country_by_geo_location(self):\n pass","def create_country_code_dict(df):\n\n code_dict = {}\n\n # check both country and counterpart country columns for unique country codes\n for col in ['Country', 'Counterpart Country']:\n for code in df[col + ' Code'].unique():\n code_dict[int(code)] = df.loc[df[col + ' Code'] == code][col + ' Name'].values[0]\n\n return code_dict","def country_codes():\n\n iso_sel = [\n Freedom_short.iso_code,\n Freedom_short.year,\n Freedom_short.country,\n Freedom_short.region,\n Freedom_short.hf_score,\n Freedom_short.hf_rank,\n Freedom_short.hf_quartile,\n ]\n\n # Use Pandas to perform the sql query\n #Grab 2017 Data Only for Dropdown\n codes_stmt = db.session.query(*iso_sel).filter(Freedom_short.year == 2017).order_by(Freedom_short.iso_code).statement\n codes_df = pd.read_sql_query(codes_stmt, db.session.bind)\n\n # Return a list of the column names (sample names)\n return jsonify(list(codes_df[\"iso_code\"]))","def checkPostalCode(self, code, country):\n if country == 'US':\n USZipCodeField().clean(code)","def test_valid_country():\n assert valid_country(\"Democratic Republic of Lungary\") is True\n assert valid_country(\"Kraznoviklandstan\") is True\n assert valid_country(\"kraznoviklandstan\") is True\n assert valid_country(\"KRAZNOVIKLANDSTAN\") is True\n\n assert valid_country(\"Democratic_Republic982759\") is False\n assert valid_country(\"Kraznoviklandsta\") is False\n assert valid_country(\"Principalities of Fryed\") is False\n assert valid_country(\"FRY\") is False","def country(self) -> str:\n return pulumi.get(self, \"country\")","def get_country_details(self,country):\n try:\n country_obj = pycountry.countries.get(name=country)\n if country_obj is None:\n c = pycountry.countries.search_fuzzy(country)\n country_obj = c[0]\n continent_code = pc.country_alpha2_to_continent_code(country_obj.alpha_2)\n continent = pc.convert_continent_code_to_continent_name(continent_code)\n return country_obj.alpha_3, continent\n except:\n if 'Congo' in country:\n country = 'Congo'\n elif country == 'Diamond Princess' or country == 'Laos' or country == 'MS Zaandam' or country == 'Holy See' or country == 'Timor-Leste':\n return country, country\n elif country == 'Korea, South' or country == 'South Korea':\n country = 'Korea, Republic of'\n elif country == 'Taiwan*':\n country = 'Taiwan'\n elif country == 'Burma':\n country = 'Myanmar'\n elif country == 'West Bank and Gaza':\n country = 'Gaza'\n else:\n return country, country\n country_obj = pycountry.countries.search_fuzzy(country)\n continent_code = pc.country_alpha2_to_continent_code(country_obj[0].alpha_2)\n continent = pc.convert_continent_code_to_continent_name(continent_code)\n return country_obj[0].alpha_3, continent","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_city_country(self):\n santiago_chile = city_country('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def get_country_info(country):\n return GoogleV3().geocode(country)","def test_single_word_swanage(self):\n result = location.lookup_location('Swanage GB')\n\n self.assertEqual(result['country'], 'GB')","def flag_regional_indicator(code: List[str]) -> str:\r\n\r\n return \"\".join([chr(ord(c.upper()) + OFFSET) for c in code])","def city_country(city, country):\n return city.title() + \", \" + country.title()","def _get_country_code(self, cr, uid, context=None):\n context = context or {}\n user_company = self.pool.get('res.users').browse(cr, uid, uid, context=context).company_id\n return user_company.partner_id and user_company.partner_id.country_id \\\n and user_company.partner_id.country_id.code or 'XX'","def test_city_country(self):\n santiago_chile = get_city_name('santiago', 'chile')\n self.assertEqual(santiago_chile, 'Santiago, Chile')","def test_get_country_states(self):\n pass","def country(self):\n # type: () -> string_types\n return self._country","def foreign(x): # electing to count 'Indian Territory' as domestic\n if x == 'United States' or x == 'Indian Territory':\n return 0\n else:\n return 1","def audience_simple(country):\r\n if country == 'US':\r\n return 'USA'\r\n elif country == 'IN':\r\n return 'India'\r\n else:\r\n return 'Other'","def city_country(city, country):\r\n\treturn(city.title() + ', ' + country.title())","def province():\r\n return _random.choice(\r\n [\r\n [\"Ontario\", \"ON\"],\r\n [\"Quebec\", \"QC\"],\r\n [\"Nova Scotia\", \"NS\"],\r\n [\"New Brunswick\", \"NB\"],\r\n [\"Manitoba\", \"MB\"],\r\n [\"British Columbia\", \"BC\"],\r\n [\"Prince Edward Island\", \"PE\"],\r\n [\"Saskatchewan\", \"SK\"],\r\n [\"Alberta\", \"AB\"],\r\n [\"Newfoundland and Labrador\", \"NL\"]\r\n ]\r\n )","def test_double_word_coombe_martin(self):\n result = location.lookup_location('Combe Martin GB')\n\n self.assertEqual(result['country'], 'GB')","def test_city_country(self):\n formatted_name = city_country('santiago', 'chile')\n self.assertEqual(formatted_name, 'Santiago, Chile')","def city_country(city, country):\n full_city = city + \", \" + country\n return full_city.title()","def country_flag(country):\n\tif not country:\n\t\treturn u''\n\tresult = Country.objects.filter(name__icontains=country)\n\tif result:\n\t\tc = result[0]\n\t\tiso = c.iso\n\t\tflag_location = iso_flag(iso)\n\t\treturn flag_location\n\treturn u''","def search_using_magento_code(cls, code):\n countries = cls.search([('code', '=', code)])\n\n if not countries:\n return cls.raise_user_error(\n \"country_not_found\", error_args=(code, )\n )\n\n return countries[0]","def _get_country_id(self, code2):\n if not hasattr(self, '_country_codes'):\n self._country_codes = {}\n\n if code2 not in self._country_codes.keys():\n self._country_codes[code2] = Country.objects.get(code2=code2).pk\n return self._country_codes[code2]","def country(self):\n return self.status.place['country']","def city_country(city, country):\n return(city.title() + \", \" + country.title())","def findCountryCode(self):\n RecordsWithCountry = []\n for state in pycountry.subdivisions:\n #print(state.name)\n for record in self.Records: \n if state.name == record.state:\n #print(state.country, record.state)\n r = RecordCountry(date=record.date,\n country=state.country.alpha_3,\n impressions=record.impressions,\n CTR=record.CTR)\n self.Records.remove(record)\n RecordsWithCountry.append(r)\n for record in self.Records: \n r = RecordCountry(date=record.date,\n country=\"XXX\",\n impressions=record.impressions,\n CTR=record.CTR)\n RecordsWithCountry.append(r)\n self.Records = RecordsWithCountry","def country(self) -> Optional[str]:\n return pulumi.get(self, \"country\")","def country(self) -> Optional[str]:\n return pulumi.get(self, \"country\")","def test_single_word_exeter(self):\n result = location.lookup_location('Exeter GB')\n\n self.assertEqual(result['country'], 'GB')","def country_hint(self, value):\n return None","def flag(countrycode: str) -> str:\r\n\r\n return flag(countrycode)","def get_city_country(city, country, population=''):\n if population:\n location = city + ' ' + country + ' ' + str(population)\n return location.title()\n\n else:\n location = city + ' ' + country\n return location.title()","def getUS() -> int:\n pass","def test_single_word_salisbury(self):\n result = location.lookup_location('Salisbury GB')\n\n self.assertEqual(result['country'], 'GB')","def city_country(city, country):\n city_and_country = city + ', ' + country\n return city_and_country.title()","def city_country(city, country):\n return(city + ', ' + country)","def test_city_country_population(self):\n \tdublin_ireland = city_country('dublin', 'ireland', population=500000)\n \tself.assertEqual(dublin_ireland, 'Dublin, Ireland - Population 500000')","def country(self):\n if self._country is not None:\n return self._country\n if not self.isValid():\n return None\n self._country = self.geocoder_results.country\n return self._country","def get_country(ip_addr):\r\n html = urllib2.urlopen('http://freegeoip.net/json/' + ip_addr).read()\\\r\n .decode('utf-8')\r\n responseJson = json.loads(html)\r\n return responseJson.get('country_code')","def test_city_country(self):\n formatted_city = get_full_city(\"santiago\", \"chile\")\n self.assertEqual(formatted_city, \"Santiago, Chile\")","def test_city_country_population(self):\n santiago_chile = get_city_name('santiago', 'chile','5000000')\n self.assertEqual(santiago_chile, 'Santiago, Chile - population 5000000')","def get_country(currency):\n us_currency = [x.lower() for x in ('US Dollar', 'USD', 'US Dollars')]\n ca_currency = [x.lower() for x in ('Canadian Dollar', 'CAD', 'Canadian Dollars')]\n currency = currency.lower()\n if currency in us_currency:\n return 'US'\n elif currency in ca_currency:\n return 'CA'\n else:\n return None","def get_ibge_code(city, state):\n df = pd.read_csv(IBGE_CODE_PATH)\n code = df[(df['state'] == state) & (df['city'] == city)]['cod_ibge'].values[0]\n\n return code","def city_country(city, country):\n c_c = '\"' + city + \", \" + country + '\"'\n return c_c.title()","def city_country(city_name, country_name):\n combi = f\"{city_name.title()}, {country_name.title()}\"\n return combi.title()","def timezone_offset_country():\r\n\r\n return _random.choice(\r\n [\r\n 'Eniwetoa',\r\n 'Hawaii',\r\n 'Alaska',\r\n 'Pacific',\r\n 'Mountain',\r\n 'Central',\r\n 'Eastern',\r\n 'Atlantic',\r\n 'Canada',\r\n 'Brazilia',\r\n 'Buenos Aries',\r\n 'Mid-Atlantic',\r\n 'Cape Verdes',\r\n 'Greenwich Mean Time',\r\n 'Dublin',\r\n 'Berlin',\r\n 'Rome',\r\n 'Israel',\r\n 'Cairo',\r\n 'Moscow',\r\n 'Kuwait',\r\n 'Abu Dhabi',\r\n 'Muscat',\r\n 'Islamabad',\r\n 'Karachi',\r\n 'Almaty',\r\n 'Dhaka',\r\n 'Bangkok, Jakarta',\r\n 'Hong Kong',\r\n 'Beijing',\r\n 'Tokyo',\r\n 'Osaka',\r\n 'Sydney',\r\n 'Melbourne',\r\n 'Guam',\r\n 'Magadan',\r\n 'Soloman Islands',\r\n 'Fiji',\r\n 'Wellington',\r\n 'Auckland',\r\n ]\r\n )"],"string":"[\n \"def country(alpha_2_code: str) -> None:\",\n \"def get_country_code(self):\\n #method on the class InternationalMelonOrder\\n\\n return self.country_code\\n # international has country code; domestic does not\\n # WAI???\",\n \"def get_country_code(country_name):\\n for code, name in COUNTRIES.items():\\n if name == country_name:\\n return code\\n elif country_name == 'Yemen, Rep.':\\n return 'ye'\\n elif country_name == 'Vietnam':\\n return 'vn'\\n elif country_name == 'Tanzania':\\n return 'tz'\\n elif country_name == 'Moldova':\\n return 'md'\\n elif country_name == 'Macao SAR, China':\\n return 'mo'\\n elif country_name == 'Macedonia, FYR':\\n return 'mk'\\n elif country_name == 'Libya':\\n return 'ly'\\n elif country_name == 'Lao PDR':\\n return 'la'\\n elif country_name == 'Korea, Dem. Rep.':\\n return 'kp'\\n elif country_name == 'Korea, Rep.':\\n return 'kr'\\n elif country_name == 'Gambia':\\n return 'gm'\\n elif country_name == 'Iran, Islamic Rep.':\\n return 'ir'\\n elif country_name == 'Hong Kong SAR':\\n return 'hk'\\n elif country_name == 'Congo, Dem. Rep.':\\n return 'cd'\\n elif country_name == 'Congo, Rep.':\\n return 'cf'\\n elif country_name == 'Macao SAR, China':\\n return 'mo'\\n elif country_name == 'Macedonia, FYR':\\n return 'mk'\\n elif country_name == 'Libya':\\n return 'ly'\\n elif country_name == 'Lao PDR':\\n return 'la'\\n elif country_name == 'Korea, Dem. Rep.':\\n return 'kp'\\n elif country_name == 'Korea, Rep.':\\n return 'kr'\\n elif country_name == 'Gambia':\\n return 'gm'\\n # If the country wasn't found, return None.\\n return None\",\n \"def USCode(self, short):\\n states = {\\n 'AK': 'Alaska',\\n 'AL': 'Alabama',\\n 'AR': 'Arkansas',\\n 'AS': 'American Samoa',\\n 'AZ': 'Arizona',\\n 'CA': 'California',\\n 'CO': 'Colorado',\\n 'CT': 'Connecticut',\\n 'DC': 'District of Columbia',\\n 'DE': 'Delaware',\\n 'FL': 'Florida',\\n 'GA': 'Georgia',\\n 'GU': 'Guam',\\n 'HI': 'Hawaii',\\n 'IA': 'Iowa',\\n 'ID': 'Idaho',\\n 'IL': 'Illinois',\\n 'IN': 'Indiana',\\n 'KS': 'Kansas',\\n 'KY': 'Kentucky',\\n 'LA': 'Louisiana',\\n 'MA': 'Massachusetts',\\n 'MD': 'Maryland',\\n 'ME': 'Maine',\\n 'MI': 'Michigan',\\n 'MN': 'Minnesota',\\n 'MO': 'Missouri',\\n 'MP': 'Northern Mariana Islands',\\n 'MS': 'Mississippi',\\n 'MT': 'Montana',\\n 'NA': 'National',\\n 'NC': 'North Carolina',\\n 'ND': 'North Dakota',\\n 'NE': 'Nebraska',\\n 'NH': 'New Hampshire',\\n 'NJ': 'New Jersey',\\n 'NM': 'New Mexico',\\n 'NV': 'Nevada',\\n 'NY': 'New York',\\n 'OH': 'Ohio',\\n 'OK': 'Oklahoma',\\n 'OR': 'Oregon',\\n 'PA': 'Pennsylvania',\\n 'PR': 'Puerto Rico',\\n 'RI': 'Rhode Island',\\n 'SC': 'South Carolina',\\n 'SD': 'South Dakota',\\n 'TN': 'Tennessee',\\n 'TX': 'Texas',\\n 'UT': 'Utah',\\n 'VA': 'Virginia',\\n 'VI': 'Virgin Islands',\\n 'VT': 'Vermont',\\n 'WA': 'Washington',\\n 'WI': 'Wisconsin',\\n 'WV': 'West Virginia',\\n 'WY': 'Wyoming'\\n }\\n return states.get(short)\",\n \"def country_codes(country):\\n countryObject = None\\n try:\\n countryObject = pycountry.countries.search_fuzzy(country)\\n return countryObject[0].alpha_2\\n except LookupError:\\n pass\\n try:\\n splittedCountry = country.split(',')[0]\\n countryObject = pycountry.countries.search_fuzzy(splittedCountry)\\n return countryObject[0].alpha_2\\n except LookupError:\\n return 'No Code'\",\n \"def country() -> str:\",\n \"def get_country_code(country_name):\\n # worldmap_chart = pygal.maps.world.World()\\n # for code, name in worldmap_chart:\\n\\n for code, name in i18n.COUNTRIES:\\n\\n # for code, name in COUNTRIES.items():\\n if name == country_name:\\n print(code)\\n return code\\n # If the country wasn't found, return None.\\n return None\",\n \"def get_country_code(country_name) :\\n for code, name in COUNTRIES.items() :\\n if name==country_name :\\n return code\\n # if the country wasn't found, return None\\n return None\",\n \"def country_code(self) -> str | None:\\n pass\",\n \"def _is_valid_code(self, code):\\r\\n return code in COUNTRY_CODES\",\n \"def mock_country_code_by_addr(self, ip_addr):\\r\\n ip_dict = {\\r\\n '1.0.0.0': 'CU',\\r\\n '2.0.0.0': 'IR',\\r\\n '3.0.0.0': 'SY',\\r\\n '4.0.0.0': 'SD',\\r\\n '5.0.0.0': 'AQ', # Antartica\\r\\n }\\r\\n return ip_dict.get(ip_addr, 'US')\",\n \"def COUNTRY_CODE():\\n COUNTRY_CODE = \\\"us/90210\\\"\\n return COUNTRY_CODE\",\n \"def get_country_code(country_name):\\n for code, name in COUNTRIES.items():\\n if name == country_name:\\n return code\\n # if string isn't found returns None\\n else:\\n continue\",\n \"def test_country_name_in_countries(self):\\n\\t\\tcountry_code = get_country_code('Andorra')\\n\\t\\tself.assertEqual(country_code, 'ad')\",\n \"def get_country_code(contry_name):\\n for code, name in COUNTRIES.items():\\n if name == contry_name:\\n return code\\n return None\",\n \"def get_country_code(country_name):\\n\\n for code, name in COUNTRIES.items():\\n if name == country_name:\\n return code\\n\\n # If country was not found, return nothing\\n return None\",\n \"def country_identifier(name):\\n if name.lower() in _country_dict.keys():\\n return _country_dict[name.lower()]\\n else:\\n return name\",\n \"def convert_country(country):\\n if (country and 'China' in country) or \\\\\\n country == 'Chin' or country == 'CHINA':\\n country = 'China'\\n elif country and 'Brazil' in country or \\\\\\n country == 'Brasil' or \\\\\\n country == 'ITA - Instituto Tecnologico de Aeronautica (':\\n country = 'Brazil'\\n elif country and 'Argentina' in country:\\n country = 'Argentina'\\n elif country == 'Czechia':\\n country = 'Czech Republic'\\n elif 'Norwegian' in country:\\n country = 'Norway'\\n elif country and 'United Kingdom' in country:\\n country = 'United Kingdom'\\n elif country and 'Hong Kong' in country:\\n country = 'Hong Kong'\\n elif country == 'Cameroun':\\n country = 'Cameroon'\\n elif (country and 'Chile' in country) or country == 'CHILE':\\n country = 'Chile'\\n elif (country and 'United States of America' in \\\\\\n country) or country == 'United States' or country \\\\\\n == 'USA' or 'Florida' in country or \\\\\\n 'California' in country or\\\\\\n country == 'National Reference Centre for' or \\\\\\n country == 'United State of America' or \\\\\\n country == 'U.S.A.' or \\\\\\n country == 'Virginia':\\n country = 'United States of America'\\n elif country=='Republic of Panamá' or country=='Panamá' or 'Panama' in country:\\n country = 'Panama'\\n elif 'Canada' in country:\\n country = 'Canada'\\n elif 'Colombia' in country:\\n country = 'Colombia'\\n elif 'Spain' in country or country=='España':\\n country = 'Spain'\\n elif 'Iran' in country:\\n country = 'Iran'\\n elif 'Saudi Arabia' in country:\\n country = 'Saudi Arabia'\\n elif 'Italy' in country:\\n country = 'Italy'\\n elif 'Japan' in country:\\n country = 'Japan'\\n elif 'Germany' in country:\\n country = 'Germany'\\n elif 'Luxembourg' in country:\\n country = 'Luxembourg'\\n elif ('France' in country) or country == 'Marseille':\\n country = 'France'\\n elif country == 'ROC' or country == 'R. O. C':\\n country = 'Taiwan'\\n elif country == 'Brasil':\\n country = 'Brazil'\\n elif country == 'México' or 'Mexico' in country:\\n country = 'Mexico'\\n elif 'Slowakia' in country:\\n country = 'Slowakia'\\n elif country == 'Korea' or 'Republic of Korea' in country:\\n country = 'South Korea'\\n elif country == 'United Kindgom':\\n country = 'United Kingdom'\\n elif country and 'Netherlands' in country:\\n country = 'Netherlands'\\n elif country == 'Commonwealth of Australia' or 'Australia' in country:\\n country = 'Australia'\\n elif 'Singapore' in country:\\n country = 'Singapore'\\n elif country and (country[0].isdigit() or country[0] == '+'):\\n country = 'N/A'\\n return country\",\n \"def country_code(self):\\n return self.__country_code\",\n \"def country_code(self):\\n return self.__country_code\",\n \"def get_country(ip):\\r\\n return geoip.country_code_by_addr(ip)\",\n \"def test_country_name_not_in_countries(self):\\n\\t\\tcountry_code = get_country_code('Venezuela, RB')\\n\\t\\tself.assertEqual(country_code, 've')\",\n \"def get_country(self, country):\\n if country == \\\"United Kingdom\\\": return \\\"en\\\"\\n if country == \\\"Portugal\\\": return \\\"pt\\\"\\n\\n result = self.session.get(\\\"https://en.ogame.gameforge.com\\\")\\n soup = BeautifulSoup(result.content, \\\"html.parser\\\")\\n\\n code_list = soup.find(\\\"ul\\\", {\\\"id\\\": \\\"mmoList1\\\"})\\n countries = {}\\n for tag in code_list.find_all(\\\"li\\\"):\\n link = tag.find(\\\"a\\\")[\\\"href\\\"]\\n name = tag.string.strip() # name of the country\\n code = link.split(\\\".\\\")[0].replace(\\\"//\\\", \\\"\\\")\\n countries[name] = code # save to the dict\\n\\n # check if input was ok\\n if not country in countries.keys():\\n self.crash(\\\"Country\\\", country, \\\"was not found on the list.\\\")\\n if len(countries[country]) != 2:\\n self.crash(\\\"Can't fetch code for country\\\", country)\\n\\n return countries[country]\",\n \"def get_country_code(country_name):\\n # values = list(COUNTRIES.values())\\n # keys = list(COUNTRIES.keys())\\n #\\n # try:\\n # index = values.index(country_name)\\n # except ValueError:\\n # # Not found\\n # return None\\n #\\n # return keys[index]\\n\\n for code, name in COUNTRIES.items():\\n if name == country_name:\\n return code\\n\\n return None\",\n \"def get_continent(country_code):\\r\\n for c in continents:\\r\\n if c.contains(country_code):\\r\\n return c\\r\\n plog(\\\"INFO\\\", country_code + \\\" is not on any continent\\\")\\r\\n return None\",\n \"def get_country_code(self):\\n\\n try:\\n sub_div = next(sub_div for sub_div in pycountry.subdivisions if sub_div.name == self.location)\\n country = next(country for country in pycountry.countries if country.alpha_2 == sub_div.country_code)\\n return country.alpha_3\\n except StopIteration as exc:\\n print(\\\"Cannot find subdivision in\\\" + str(exc))\\n return 'XXX'\",\n \"def state(self):\\n state_code = MISSING\\n try:\\n country_code = self.status.place[\\\"country_code\\\"]\\n except TypeError:\\n return MISSING\\n place_type = self.status.place[\\\"place_type\\\"]\\n if country_code == \\\"US\\\" and place_type == \\\"city\\\":\\n full_name = self.status.place[\\\"full_name\\\"]\\n state_code = full_name.split(\\\",\\\")[-1].strip().upper()\\n state_code = state_code if state_code in valid_state_codes else MISSING\\n else:\\n pass\\n return state_code\",\n \"def resolveCountryCode(country_code):\\n country_name = None\\n if len(country_code) > 2:\\n country_name = country_code\\n country_code = next((cc for cc, country in countries.items() if country == country_code), None)\\n if country_code not in countries:\\n logger.error(\\\"Country code %s unknown. For a list of know codes execute:\\\")\\n logger.error(sys.argv[0] + ' --list-countries \\\\tList all available countries that can be blocked.')\\n sys.exit(255)\\n if not country_name:\\n country_name = countries[country_code]\\n return [country_code, country_name]\",\n \"def determine_us_state(area_code):\\r\\n if not isinstance(area_code, str):\\r\\n area_code = str(area_code)\\r\\n if area_code in area_code_mapping:\\r\\n return area_code_mapping[area_code][0]\",\n \"def test_city_country(self):\\n dublin_ireland = city_country('dublin', 'ireland')\\n self.assertEqual(dublin_ireland, 'Dublin, Ireland')\",\n \"def test_city_country(self):\\n\\t\\tformatted_address = city_country('santiago', 'chile')\\n\\t\\tself.assertEqual(formatted_address, 'Santiago, Chile')\",\n \"def flag(countrycode: str) -> str:\\r\\n\\r\\n code = [c for c in countrycode.lower() if c in ASCII_LOWER]\\r\\n if len(code) == 2:\\r\\n # Regional indicator symbols\\r\\n return flag_regional_indicator(code)\\r\\n if len(code) > 2 and len(code) < 7:\\r\\n # Tag sequence\\r\\n return flag_tag_sequence(code)\\r\\n found = ''.join(code)\\r\\n raise ValueError(\\r\\n 'invalid countrycode, found %d (%r) in %r.' %\\r\\n (len(found), found, countrycode))\",\n \"def get_country_from_code(country_code):\\n country = []\\n countries = [(country.alpha_2, country.name) for country in pycountry.countries]\\n for country_index in range(len(countries)):\\n # index 0 is the country code selected from the form\\n if(countries[country_index][0] == country_code):\\n country.append(countries[country_index])\\n return country[0][1]\",\n \"def get_country_dcid(country_code: str) -> str:\\n numeric_code = int(country_code, 10)\\n if numeric_code in UN_COUNTRY_CODES:\\n return UN_COUNTRY_CODES[numeric_code]\\n return None\",\n \"def country_letter_code(self):\\n if \\\"countryLetterCode\\\" in self._prop_dict:\\n return self._prop_dict[\\\"countryLetterCode\\\"]\\n else:\\n return None\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def get_country_code(self):\\n\\n return self.country_code\",\n \"def country(name):\\n return location_db().find(name=name)[\\\"country\\\"]\",\n \"def country_code(self):\\n return self._plrevgeoloc.countryCode\",\n \"def test_city_country_population(self):\\n\\t\\tformatted_address = city_country(\\n\\t\\t\\t'santiago', 'chile', '5000000')\\n\\t\\tself.assertEqual(formatted_address, \\n\\t\\t\\t'Santiago, Chile - Population 5000000')\",\n \"def city_country(city_name, country_name):\\n city_country_combo = city_name + ', ' + country_name\\n return city_country_combo.title()\",\n \"def load_country_code_data():\\n name_conversion = {\\n 'East Timor': 'Timor-Leste',\\n 'Republic of the Congo': 'Congo (Kinshasa)',\\n 'Ivory Coast': 'Cote d\\\\'Ivoire',\\n 'Macedonia': 'North Macedonia',\\n 'Myanmar': 'Burma',\\n 'Republic of Serbia': 'Serbia',\\n 'Taiwan': 'Taiwan*',\\n 'The Bahamas': 'Bahamas',\\n 'United Republic of Tanzania': 'Tanzania',\\n 'United States of America': 'US'\\n }\\n\\n shapefile = os.path.join('data', 'ne_110m_admin_0_countries.shp')\\n\\n gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]\\n gdf.columns = ['country', 'country_code', 'geometry']\\n\\n gdf.loc[gdf['country'].isin(name_conversion.keys()), 'country'] = gdf['country'].map(name_conversion)\\n\\n return gdf\",\n \"def get_country_from_record(ip):\\r\\n record = geoip.record_by_addr(ip)\\r\\n if record != None:\\r\\n return record['country_code']\",\n \"def test_get_country_by_geo_location(self):\\n pass\",\n \"def create_country_code_dict(df):\\n\\n code_dict = {}\\n\\n # check both country and counterpart country columns for unique country codes\\n for col in ['Country', 'Counterpart Country']:\\n for code in df[col + ' Code'].unique():\\n code_dict[int(code)] = df.loc[df[col + ' Code'] == code][col + ' Name'].values[0]\\n\\n return code_dict\",\n \"def country_codes():\\n\\n iso_sel = [\\n Freedom_short.iso_code,\\n Freedom_short.year,\\n Freedom_short.country,\\n Freedom_short.region,\\n Freedom_short.hf_score,\\n Freedom_short.hf_rank,\\n Freedom_short.hf_quartile,\\n ]\\n\\n # Use Pandas to perform the sql query\\n #Grab 2017 Data Only for Dropdown\\n codes_stmt = db.session.query(*iso_sel).filter(Freedom_short.year == 2017).order_by(Freedom_short.iso_code).statement\\n codes_df = pd.read_sql_query(codes_stmt, db.session.bind)\\n\\n # Return a list of the column names (sample names)\\n return jsonify(list(codes_df[\\\"iso_code\\\"]))\",\n \"def checkPostalCode(self, code, country):\\n if country == 'US':\\n USZipCodeField().clean(code)\",\n \"def test_valid_country():\\n assert valid_country(\\\"Democratic Republic of Lungary\\\") is True\\n assert valid_country(\\\"Kraznoviklandstan\\\") is True\\n assert valid_country(\\\"kraznoviklandstan\\\") is True\\n assert valid_country(\\\"KRAZNOVIKLANDSTAN\\\") is True\\n\\n assert valid_country(\\\"Democratic_Republic982759\\\") is False\\n assert valid_country(\\\"Kraznoviklandsta\\\") is False\\n assert valid_country(\\\"Principalities of Fryed\\\") is False\\n assert valid_country(\\\"FRY\\\") is False\",\n \"def country(self) -> str:\\n return pulumi.get(self, \\\"country\\\")\",\n \"def get_country_details(self,country):\\n try:\\n country_obj = pycountry.countries.get(name=country)\\n if country_obj is None:\\n c = pycountry.countries.search_fuzzy(country)\\n country_obj = c[0]\\n continent_code = pc.country_alpha2_to_continent_code(country_obj.alpha_2)\\n continent = pc.convert_continent_code_to_continent_name(continent_code)\\n return country_obj.alpha_3, continent\\n except:\\n if 'Congo' in country:\\n country = 'Congo'\\n elif country == 'Diamond Princess' or country == 'Laos' or country == 'MS Zaandam' or country == 'Holy See' or country == 'Timor-Leste':\\n return country, country\\n elif country == 'Korea, South' or country == 'South Korea':\\n country = 'Korea, Republic of'\\n elif country == 'Taiwan*':\\n country = 'Taiwan'\\n elif country == 'Burma':\\n country = 'Myanmar'\\n elif country == 'West Bank and Gaza':\\n country = 'Gaza'\\n else:\\n return country, country\\n country_obj = pycountry.countries.search_fuzzy(country)\\n continent_code = pc.country_alpha2_to_continent_code(country_obj[0].alpha_2)\\n continent = pc.convert_continent_code_to_continent_name(continent_code)\\n return country_obj[0].alpha_3, continent\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_city_country(self):\\n santiago_chile = city_country('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def get_country_info(country):\\n return GoogleV3().geocode(country)\",\n \"def test_single_word_swanage(self):\\n result = location.lookup_location('Swanage GB')\\n\\n self.assertEqual(result['country'], 'GB')\",\n \"def flag_regional_indicator(code: List[str]) -> str:\\r\\n\\r\\n return \\\"\\\".join([chr(ord(c.upper()) + OFFSET) for c in code])\",\n \"def city_country(city, country):\\n return city.title() + \\\", \\\" + country.title()\",\n \"def _get_country_code(self, cr, uid, context=None):\\n context = context or {}\\n user_company = self.pool.get('res.users').browse(cr, uid, uid, context=context).company_id\\n return user_company.partner_id and user_company.partner_id.country_id \\\\\\n and user_company.partner_id.country_id.code or 'XX'\",\n \"def test_city_country(self):\\n santiago_chile = get_city_name('santiago', 'chile')\\n self.assertEqual(santiago_chile, 'Santiago, Chile')\",\n \"def test_get_country_states(self):\\n pass\",\n \"def country(self):\\n # type: () -> string_types\\n return self._country\",\n \"def foreign(x): # electing to count 'Indian Territory' as domestic\\n if x == 'United States' or x == 'Indian Territory':\\n return 0\\n else:\\n return 1\",\n \"def audience_simple(country):\\r\\n if country == 'US':\\r\\n return 'USA'\\r\\n elif country == 'IN':\\r\\n return 'India'\\r\\n else:\\r\\n return 'Other'\",\n \"def city_country(city, country):\\r\\n\\treturn(city.title() + ', ' + country.title())\",\n \"def province():\\r\\n return _random.choice(\\r\\n [\\r\\n [\\\"Ontario\\\", \\\"ON\\\"],\\r\\n [\\\"Quebec\\\", \\\"QC\\\"],\\r\\n [\\\"Nova Scotia\\\", \\\"NS\\\"],\\r\\n [\\\"New Brunswick\\\", \\\"NB\\\"],\\r\\n [\\\"Manitoba\\\", \\\"MB\\\"],\\r\\n [\\\"British Columbia\\\", \\\"BC\\\"],\\r\\n [\\\"Prince Edward Island\\\", \\\"PE\\\"],\\r\\n [\\\"Saskatchewan\\\", \\\"SK\\\"],\\r\\n [\\\"Alberta\\\", \\\"AB\\\"],\\r\\n [\\\"Newfoundland and Labrador\\\", \\\"NL\\\"]\\r\\n ]\\r\\n )\",\n \"def test_double_word_coombe_martin(self):\\n result = location.lookup_location('Combe Martin GB')\\n\\n self.assertEqual(result['country'], 'GB')\",\n \"def test_city_country(self):\\n formatted_name = city_country('santiago', 'chile')\\n self.assertEqual(formatted_name, 'Santiago, Chile')\",\n \"def city_country(city, country):\\n full_city = city + \\\", \\\" + country\\n return full_city.title()\",\n \"def country_flag(country):\\n\\tif not country:\\n\\t\\treturn u''\\n\\tresult = Country.objects.filter(name__icontains=country)\\n\\tif result:\\n\\t\\tc = result[0]\\n\\t\\tiso = c.iso\\n\\t\\tflag_location = iso_flag(iso)\\n\\t\\treturn flag_location\\n\\treturn u''\",\n \"def search_using_magento_code(cls, code):\\n countries = cls.search([('code', '=', code)])\\n\\n if not countries:\\n return cls.raise_user_error(\\n \\\"country_not_found\\\", error_args=(code, )\\n )\\n\\n return countries[0]\",\n \"def _get_country_id(self, code2):\\n if not hasattr(self, '_country_codes'):\\n self._country_codes = {}\\n\\n if code2 not in self._country_codes.keys():\\n self._country_codes[code2] = Country.objects.get(code2=code2).pk\\n return self._country_codes[code2]\",\n \"def country(self):\\n return self.status.place['country']\",\n \"def city_country(city, country):\\n return(city.title() + \\\", \\\" + country.title())\",\n \"def findCountryCode(self):\\n RecordsWithCountry = []\\n for state in pycountry.subdivisions:\\n #print(state.name)\\n for record in self.Records: \\n if state.name == record.state:\\n #print(state.country, record.state)\\n r = RecordCountry(date=record.date,\\n country=state.country.alpha_3,\\n impressions=record.impressions,\\n CTR=record.CTR)\\n self.Records.remove(record)\\n RecordsWithCountry.append(r)\\n for record in self.Records: \\n r = RecordCountry(date=record.date,\\n country=\\\"XXX\\\",\\n impressions=record.impressions,\\n CTR=record.CTR)\\n RecordsWithCountry.append(r)\\n self.Records = RecordsWithCountry\",\n \"def country(self) -> Optional[str]:\\n return pulumi.get(self, \\\"country\\\")\",\n \"def country(self) -> Optional[str]:\\n return pulumi.get(self, \\\"country\\\")\",\n \"def test_single_word_exeter(self):\\n result = location.lookup_location('Exeter GB')\\n\\n self.assertEqual(result['country'], 'GB')\",\n \"def country_hint(self, value):\\n return None\",\n \"def flag(countrycode: str) -> str:\\r\\n\\r\\n return flag(countrycode)\",\n \"def get_city_country(city, country, population=''):\\n if population:\\n location = city + ' ' + country + ' ' + str(population)\\n return location.title()\\n\\n else:\\n location = city + ' ' + country\\n return location.title()\",\n \"def getUS() -> int:\\n pass\",\n \"def test_single_word_salisbury(self):\\n result = location.lookup_location('Salisbury GB')\\n\\n self.assertEqual(result['country'], 'GB')\",\n \"def city_country(city, country):\\n city_and_country = city + ', ' + country\\n return city_and_country.title()\",\n \"def city_country(city, country):\\n return(city + ', ' + country)\",\n \"def test_city_country_population(self):\\n \\tdublin_ireland = city_country('dublin', 'ireland', population=500000)\\n \\tself.assertEqual(dublin_ireland, 'Dublin, Ireland - Population 500000')\",\n \"def country(self):\\n if self._country is not None:\\n return self._country\\n if not self.isValid():\\n return None\\n self._country = self.geocoder_results.country\\n return self._country\",\n \"def get_country(ip_addr):\\r\\n html = urllib2.urlopen('http://freegeoip.net/json/' + ip_addr).read()\\\\\\r\\n .decode('utf-8')\\r\\n responseJson = json.loads(html)\\r\\n return responseJson.get('country_code')\",\n \"def test_city_country(self):\\n formatted_city = get_full_city(\\\"santiago\\\", \\\"chile\\\")\\n self.assertEqual(formatted_city, \\\"Santiago, Chile\\\")\",\n \"def test_city_country_population(self):\\n santiago_chile = get_city_name('santiago', 'chile','5000000')\\n self.assertEqual(santiago_chile, 'Santiago, Chile - population 5000000')\",\n \"def get_country(currency):\\n us_currency = [x.lower() for x in ('US Dollar', 'USD', 'US Dollars')]\\n ca_currency = [x.lower() for x in ('Canadian Dollar', 'CAD', 'Canadian Dollars')]\\n currency = currency.lower()\\n if currency in us_currency:\\n return 'US'\\n elif currency in ca_currency:\\n return 'CA'\\n else:\\n return None\",\n \"def get_ibge_code(city, state):\\n df = pd.read_csv(IBGE_CODE_PATH)\\n code = df[(df['state'] == state) & (df['city'] == city)]['cod_ibge'].values[0]\\n\\n return code\",\n \"def city_country(city, country):\\n c_c = '\\\"' + city + \\\", \\\" + country + '\\\"'\\n return c_c.title()\",\n \"def city_country(city_name, country_name):\\n combi = f\\\"{city_name.title()}, {country_name.title()}\\\"\\n return combi.title()\",\n \"def timezone_offset_country():\\r\\n\\r\\n return _random.choice(\\r\\n [\\r\\n 'Eniwetoa',\\r\\n 'Hawaii',\\r\\n 'Alaska',\\r\\n 'Pacific',\\r\\n 'Mountain',\\r\\n 'Central',\\r\\n 'Eastern',\\r\\n 'Atlantic',\\r\\n 'Canada',\\r\\n 'Brazilia',\\r\\n 'Buenos Aries',\\r\\n 'Mid-Atlantic',\\r\\n 'Cape Verdes',\\r\\n 'Greenwich Mean Time',\\r\\n 'Dublin',\\r\\n 'Berlin',\\r\\n 'Rome',\\r\\n 'Israel',\\r\\n 'Cairo',\\r\\n 'Moscow',\\r\\n 'Kuwait',\\r\\n 'Abu Dhabi',\\r\\n 'Muscat',\\r\\n 'Islamabad',\\r\\n 'Karachi',\\r\\n 'Almaty',\\r\\n 'Dhaka',\\r\\n 'Bangkok, Jakarta',\\r\\n 'Hong Kong',\\r\\n 'Beijing',\\r\\n 'Tokyo',\\r\\n 'Osaka',\\r\\n 'Sydney',\\r\\n 'Melbourne',\\r\\n 'Guam',\\r\\n 'Magadan',\\r\\n 'Soloman Islands',\\r\\n 'Fiji',\\r\\n 'Wellington',\\r\\n 'Auckland',\\r\\n ]\\r\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.78264016","0.74500364","0.7422215","0.7363206","0.7362113","0.7261186","0.7252336","0.7212905","0.7122891","0.709406","0.7093551","0.7011302","0.6986172","0.69353557","0.69182456","0.68706924","0.6821515","0.6747886","0.6657928","0.6657928","0.6580908","0.65277565","0.6522259","0.65117985","0.6470256","0.6412281","0.6399336","0.63943017","0.6334446","0.6329194","0.63281584","0.6317035","0.6284034","0.6281811","0.62535614","0.62327266","0.62327266","0.62327266","0.62327266","0.62327266","0.62327266","0.62327266","0.62327266","0.6213675","0.6208997","0.61945325","0.6189992","0.61666167","0.61234987","0.6104066","0.6099949","0.60966295","0.6095781","0.6092238","0.6074024","0.60654944","0.60603386","0.60603386","0.60603386","0.6051428","0.6050381","0.6043717","0.60367787","0.60299283","0.6023238","0.60219145","0.601934","0.6017079","0.60116297","0.6002589","0.5993717","0.59901863","0.5977157","0.59729433","0.5964165","0.5963981","0.596331","0.59579736","0.59502906","0.5940016","0.5934712","0.5934712","0.5934255","0.59338534","0.5923045","0.5899671","0.587301","0.5872438","0.5865116","0.5860616","0.5859146","0.5852954","0.583661","0.5814726","0.57936484","0.5784853","0.5778458","0.5775242","0.5768699","0.57594025"],"string":"[\n \"0.78264016\",\n \"0.74500364\",\n \"0.7422215\",\n \"0.7363206\",\n \"0.7362113\",\n \"0.7261186\",\n \"0.7252336\",\n \"0.7212905\",\n \"0.7122891\",\n \"0.709406\",\n \"0.7093551\",\n \"0.7011302\",\n \"0.6986172\",\n \"0.69353557\",\n \"0.69182456\",\n \"0.68706924\",\n \"0.6821515\",\n \"0.6747886\",\n \"0.6657928\",\n \"0.6657928\",\n \"0.6580908\",\n \"0.65277565\",\n \"0.6522259\",\n \"0.65117985\",\n \"0.6470256\",\n \"0.6412281\",\n \"0.6399336\",\n \"0.63943017\",\n \"0.6334446\",\n \"0.6329194\",\n \"0.63281584\",\n \"0.6317035\",\n \"0.6284034\",\n \"0.6281811\",\n \"0.62535614\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.62327266\",\n \"0.6213675\",\n \"0.6208997\",\n \"0.61945325\",\n \"0.6189992\",\n \"0.61666167\",\n \"0.61234987\",\n \"0.6104066\",\n \"0.6099949\",\n \"0.60966295\",\n \"0.6095781\",\n \"0.6092238\",\n \"0.6074024\",\n \"0.60654944\",\n \"0.60603386\",\n \"0.60603386\",\n \"0.60603386\",\n \"0.6051428\",\n \"0.6050381\",\n \"0.6043717\",\n \"0.60367787\",\n \"0.60299283\",\n \"0.6023238\",\n \"0.60219145\",\n \"0.601934\",\n \"0.6017079\",\n \"0.60116297\",\n \"0.6002589\",\n \"0.5993717\",\n \"0.59901863\",\n \"0.5977157\",\n \"0.59729433\",\n \"0.5964165\",\n \"0.5963981\",\n \"0.596331\",\n \"0.59579736\",\n \"0.59502906\",\n \"0.5940016\",\n \"0.5934712\",\n \"0.5934712\",\n \"0.5934255\",\n \"0.59338534\",\n \"0.5923045\",\n \"0.5899671\",\n \"0.587301\",\n \"0.5872438\",\n \"0.5865116\",\n \"0.5860616\",\n \"0.5859146\",\n \"0.5852954\",\n \"0.583661\",\n \"0.5814726\",\n \"0.57936484\",\n \"0.5784853\",\n \"0.5778458\",\n \"0.5775242\",\n \"0.5768699\",\n \"0.57594025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73427856"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":9991,"cells":{"query":{"kind":"string","value":"Reads the csv file CSV file should contain ['Question', 'Answer'] columns Remove NaN values Throw error if format is bad or file does not exist"},"document":{"kind":"string","value":"def parse_csv_file(self, csv_file: str):\n try:\n df = pd.read_csv(csv_file)\n\n if not set(['Question', 'Answer']).issubset(df.columns):\n raise BadCSVFile(\n \"CSV file does not contain ['Question', 'Answer'] columns.\")\n\n df.dropna(inplace=True)\n\n except Exception as e:\n raise BadCSVFile(\n \"Error while reading the csv file. Please check the path of the file or the file might be curropted.\")\n\n return df"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def read_csv():","def read_csv_file(self):\n pass","def test_csv(self, input_file_path: str, answer_file_path: List[Dict]):\n with open(attach_path(answer_file_path), 'r') as answer_file:\n csv_file = open(attach_path(input_file_path))\n assert str(read_csv(csv_file)) == answer_file.read().strip()","def read_csv(self, csv_input):\n # https://stackoverflow.com/a/45063514\n dtypes = {\n 'lat': 'U',\n 'long': 'U'\n }\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''], dtype=dtypes)\n\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\n self.log.info('Data read from CSV %s' % csv_input)\n #print('Data read from CSV %s' % csv_input)","def import_data(csv_file):\n # skips bad lines\n data = pd.read_csv(csv_file, error_bad_lines=False)\n return data","def place_types_read_csv(self, csv_input):\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''])\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\n self.log.info('Data read from CSV %s' % csv_input)","def loadCSV(input_file):","def open_convert_and_clean_csv(csv_data_file):\n imported_data = tablib.Dataset().load(open(csv_data_file).read())\n dataset = []\n for row in imported_data:\n if float(row[1]) > 0 and float(row[2]) > 0:\n dataset.append((row[0], float(row[1]), float(row[2])))\n return dataset","def read_file(self, fn_input):\n if not os.path.exists(fn_input):\n raise Exception(\"ERROR: Input file does not exist: %s\" % fn_input)\n with open(fn_input, 'rt', newline='') as infile:\n reader = csv.reader(infile)\n self.data = []\n for row in reader:\n self.data.append([])\n for value in row:\n if value == 'nan':\n self.data[-1].append(None)\n else:\n try:\n self.data[-1].append(float(value))\n except:\n raise Exception(\"ERROR: unexpected text in input file: '%s'\" % str(value))","def parse(file_name):\n \n return pd.read_csv(file_name, na_values = '---')","def _read_csv(self):\n self.function_name = '_read_csv'\n with open(os.path.join(self.task.downloads, self.csv_name)) as csv_file:\n reader = csv.reader(csv_file, dialect='excel')\n for row in reader:\n self.input_data.append(row)","def read_specific_problem(filename):\r\n table = []\r\n with open(filename, newline='') as csvfile:\r\n reader = csv.reader(csvfile, skipinitialspace=True, delimiter=',')\r\n for row in reader:\r\n table.append(row)\r\n return table","def _read_csv(filename):\n loaded = pd.read_csv(filename, index_col=0)\n if len(loaded.columns) == 1:\n return pd.read_csv(filename, index_col=0, header=None)[1]\n else:\n return loaded","def load_and_clean(self,in_path):\n in_path = Path(in_path)\n try:\n df = pd.read_csv(in_path, index_col = 0, parse_dates = True, infer_datetime_format = True)\n except:\n print(\"Could not read csv file. Please check the path\")\n finally:\n #attempt to clean df\n df.dropna(inplace = True)\n df.drop_duplicates(inplace = True)\n df.sort_index()\n return df","def read_rf_csv():\n if os.path.exists(\"rf.csv\"):\n #print (\"--decision trees CSV imported\\n\")\n results = pd.read_csv(\"rf.csv\", index_col=0)\n else:\n print(\"log not found\")\n\n return results","def read_data_from_csv(filename: str) -> pd.DataFrame:\n try:\n data = pd.read_csv(filename)\n return data\n except(FileNotFoundError):\n print('Error: Could not read the data from csv.')\n return None","def import_csv_dataset():\n import_fields = pd.read_csv('redacted-2020-june-30-wprdc-.csv', header=None).to_numpy()[0, :]\n import_values = pd.read_csv('redacted-2020-june-30-wprdc-.csv').to_numpy()\n import_values = clean_values(import_values)\n return import_fields, import_values","def csv_loader(csv_file):\n df = pd.read_csv(csv_file, sep=';', parse_dates=['Data_Alteraçao'])\n pd.set_option('display.float_format', '{:.0f}'.format)\n\n df = df.fillna(0)\n df = df.drop(columns=['Cod. Pareamento', 'Cod. UF', 'Sigla UF', 'Cod. Subarea',\n 'Nome Subarea', 'Cod. Municipio', 'Nome Municipio', 'Codigo Agencia',\n 'Nome Agencia', 'Cod. Setor', 'Cod. Logradouro CNEFE',\n 'Tipo Logradouro CNEFE', 'Titulo Logradouro CNEFE',\n 'Nome Logradouro CNEFE', 'Nome Tratado CNEFE', 'Tipo Logradouro DNE',\n 'Titulo Logradouro DNE', 'Nome Logradouro DNE', 'Nome Tratado DNE',\n 'Logradouro Completo DNE', 'Distancia', 'Cod. Match', 'Motivo Match',\n 'CEPs Face', 'Localidade Face',\n 'Alterar Logradouro para DNE?', 'Observaçao', 'SIAPE Alteração',\n 'Nome Alteraçao', 'Data_Alteraçao', 'Status', 'Unnamed: 33'])\n\n # df.astype({'CEP Logradouro CNEFE': 'int32'}).dtypes\n\n df['CEP'] = df['CEP'].str.replace(' ', '', regex=False)\n\n ceps_dne = []\n for index, row in df.iterrows():\n if type(row.CEP) == str:\n for cep in row.CEP.split(','):\n # print(index, cep)\n ceps_dne.append(int(cep))\n\n ceps_cnefe = df['CEP Logradouro CNEFE'].astype(int).tolist()\n ceps = ceps_dne + ceps_cnefe\n ceps = list(set(ceps))\n return pd.Series(ceps)","def handle_csv(self):\n try:\n reader = csv.reader(open(self.options.datafile, 'r'))\n except IOError:\n errormsg(_('Cannot read \"{}\"'.format(self.options.datafile)))\n raise Exception(_('Cannot read \"{}\"'.format(self.options.datafile)))\n if self.options.var_type == 'name':\n try:\n self.header = reader.next()\n except StopIteration:\n errormsg(_('Data file \"{}\" contains no data'.format(\n self.options.datafile)))\n raise Exception(_('Data file \"{}\" contains no data'.format(\n self.options.datafile)))\n self.data = []\n for row in reader:\n self.data.append(row)","def read_test_rf_csv():\n if os.path.exists(\"test_rf.csv\"):\n #print (\"--testing CSV imported\\n\")\n results = pd.read_csv(\"test_rf.csv\", index_col=0)\n else:\n print(\"log not found\")\n\n return results","def _read_csv(input_file):\n with tf.gfile.Open(input_file, \"r\") as f:\n reader = csv.reader(f)\n lines = []\n for line in reader:\n lines.append(line)\n return lines[1:] # remove header","def _read_input_csv(in_file):\n with open(in_file, \"rU\") as in_handle:\n reader = csv.reader(in_handle)\n reader.next() # header\n for line in reader:\n if line: # empty lines\n (fc_id, lane, sample_id, genome, barcode) = line[:5]\n yield fc_id, lane, sample_id, genome, barcode","def test_misc_csv_read():\n r = csv_reader(\"../test/test.csv\")\n fields = r.hdr()\n data = str(fields)\n while True:\n row = r.row()\n if not row: break\n data += '\\n' + str(row)\n\n assert(data == \"\"\"\n['EVT_CODE*', 'EVT_DATE.DE', 'CODE', 'AGE', 'FRST', 'LST', 'SPEC', 'de.id']\n['tea', '2018/01/01', 'X', '35', 'PRE', 'WHO', 'BUG', '1']\n['coffee', '2018/05/05', 'X', '35', 'JAN,Z', 'WHO', 'FRG', '1']\n['water', '2018/01/01', 'Y', '35', 'TAN', 'POST', 'CAT', '2']\n \"\"\".strip())","def read_csv_from_file(self, file_name):\r\n try:\r\n self.csv_df = pd.read_csv(file_name)\r\n except FileNotFoundError:\r\n print('File does not exists!')","def parse_csv(csv_file):\n if os.path.isfile(csv_file) == True:\n num_lines = sum(1 for line in open(csv_file))\n if num_lines > 1:\n try:\n data = pd.read_csv(csv_file, index_col=False)\n data.insert(0, 'id', range(1, 1 + len(data)))\n return(data)\n except pd.parser.CParserError, err:\n message = \"Can't parse REDCap data. Check CSV file: \" + csv_file\n print(message)\n logging.critical(message)\n exit(3)\n else:\n message = \"CSV file does not contain data: \" + csv_file\n print(message)\n logging.warning(message)\n return(None)\n else:\n message = \"Can't read CSV file: \" + csv_file\n print(message)\n logging.critical(message)\n exit(4)","def check_valid_csvformat(self, csv_path):\n with open(self.csv_path, \"rb+\") as file_obj:\n reader = csv.reader(file_obj, delimiter=',') # CSV DictReader object\n self.check_valid_csv_header(reader.next())\n self.check_valid_csv_data(reader.next())","def read_data_from_csv(csv_file, header=None, **kwargs):\n if os.path.isabs(csv_file) == False:\n path_to_csv = os.path.join(csv_file)\n else:\n path_to_csv = csv_file\n row_list = []\n if \"field_sep\" not in kwargs.keys():\n field_sep = ','\n else:\n field_sep = kwargs.get(\"field_sep\")\n with open(path_to_csv, mode='r') as csv_file:\n csv_reader = csv.DictReader(csv_file, delimiter=field_sep, fieldnames=header)\n for record in csv_reader:\n if list(record.values())[0].startswith(\"#\") is not True:\n # IT'S A COMMENT IF IT STARTS WITH \"#\" \n # IF THIS IS YOUR HEADER ROW, SUPPLY A LIST OF COLUMN NAMES WHEN CALLING THE FUNCTION\n row_list.append(record)\n return row_list","def read_data_for_question_2():\n df_T_fare_info = read_csv_file_data(\"../Data/T_Fare_info_Q2.csv\")\n df_T_fare_info.set_index('FlightId', inplace=True)\n df_T_flight_info = read_csv_file_data(\"../Data/T_Flight_info_Q2.csv\")\n df_T_flight_info.set_index('FlightId', inplace=True)\n df_T_fare_info.dropna(inplace=True)\n df_T_flight_info.dropna(inplace=True)\n return df_T_fare_info, df_T_flight_info","def read_csv(file_path, has_header = True):\n with open(file_path) as f:\n if has_header: f.readline()\n data = []\n target =[]\n for line in f:\n line = line.strip().split(\",\")\n data.append([float(x) for x in line[:-1]])\n target.append([line[-1]])\n return data, target","def ReadData( fileName ):\n \n # define column names\n colNames = ['agency_cd', 'site_no', 'Date', 'Discharge', 'Quality']\n\n # open and read the file\n DataDF = pd.read_csv(fileName, header=1, names=colNames, \n delimiter=r\"\\s+\",parse_dates=[2], comment='#',\n na_values=['Eqp'])\n DataDF = DataDF.set_index('Date')\n \n # quantify the number of missing values\n MissingValues = DataDF[\"Discharge\"].isna().sum()\n \n ## Remove invalid streamflow data\n DataDF.Discharge[(DataDF['Discharge']<0)]=np.nan\n \n return( DataDF, MissingValues )","def read_csv_file_data(file_path):\n if os.path.exists(file_path):\n df = pandas.read_csv(file_path)\n else:\n raise ValueError('ERROR: file_path doesnt exist in read_csv_file_data()')\n return df","def read_from_csv(path):\n if not os.path.exists(path):\n return None\n if not path.endswith('.csv'):\n return None\n\n with open(path, 'r') as file:\n data = pd.read_csv(file, header=0)\n\n return data","def read_data_csv(path):\n df = pd.read_csv(path)\n df.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n return df","def read_csv(self, filepath, obs_vars = ['obs'], header = True):\n # determine if the type file is gzip\n filetype, encoding = mimetypes.guess_type(filepath)\n if encoding == 'gzip':\n self.data = pd.read_csv(filepath, compression='gzip')\n else:\n self.data = pd.read_csv(filepath)\n\n self.original_data = copy.deepcopy(self.data)\n if self.cutoff:\n self.data = self.data[:self.cutoff]\n \n self.data = self.data[obs_vars]\n self.N = self.data.shape[0]\n return True","def read_csv(self, csv_file):\n mylog.debug('Reading csv file %s for data' % csv_file)\n csv_data = pandas.read_csv(csv_file)\n mylog.debug('Read of csv file complete.')\n #mylog.debug('%s' % csv_data)\n #sometimes the csv has an empty dataframe #\n if csv_data.empty:\n mylog.debug('Data frame is empty; repopuating data')\n csv_info = []\n for item in csv_data:\n #add the data one cell at a time to the list #\n #for some reason, some csvs have the data #\n #with random decimal points #\n csv_info.append(item.split(\".\")[0])\n df = pandas.DataFrame(columns=csv_info)\n df.loc[0]=csv_info\n #write the data from the list back into the cells#\n #one at a time #\n for column in range(0, len(csv_info)): \n df.iloc[0,column] = csv_info[column]\n csv_data = df \n return csv_data","def read_csv(csv_file_path):\n res = [] #list\n # f = open(csv_file_path) #read file\n with open(csv_file_path,\"r\") as f:","def initial_csv_wrangling(csv_file):\n df = pd.read_csv(csv_file)\n df = df.fillna('')\n columns = list(df.columns)\n\n # check that \"url\" column exists (required)\n if 'url' not in columns:\n raise Exception('Input csv file requires a \"url\" column, which does not seem to exist. Exiting.')\n\n # check if \"pos_concepts\" column exists and parse accordingly (not required)\n if 'pos_concepts' in columns:\n print('Found \"pos_concepts\" column. Values will be split by pipe/vertical bar \"|\" into a python list.')\n df['pos_concepts'] = df['pos_concepts'].map(lambda x: list(set(x.split('|'))))\n\n # check if \"neg_concepts\" column exists and parse accordingly (not required)\n if \"neg_concepts\" in columns:\n print('Found \"neg_concepts\" column. Values will be split by pipe/vertical bar \"|\" into a python list.')\n df['neg_concepts'] = df['neg_concepts'].map(lambda x: list(set(x.split('|'))))\n\n # check if \"metadata\" column exists and load accordingly (not required)\n if \"metadata\" in columns:\n print('Found \"metadata\" column. Attempting to ingest.')\n try:\n df['metadata'] = df['metadata'].replace('','{}').map(json.loads)\n except:\n raise Exception('Value in \"metadata\" column does not seem to be a properly JSON formatted str.')\n\n return df","def read_csv():\n global csvdata\n global CONFIG\n if type(csvdata) == type(None):\n if not os.path.exists(CONFIG[\"csvfile\"]):\n csvdata = pandas.read_csv(CONFIG[\"csvrepo\"],\n na_values=[\"-999999\",\"NOT AVAILABLE\"])\n os.makedirs(CONFIG[\"cachedir\"],exist_ok=True)\n csvdata.to_csv(CONFIG[\"csvfile\"])\n else:\n csvdata = pandas.read_csv(CONFIG[\"csvfile\"])\n return csvdata","def load_csv():\n df = pd.read_csv(datafolder+filename, decimal=decimal).astype(\n {'min': 'float', 'max': 'float'})\n return df","def load_data_from_csv(csv_file):\n list=[]\n\n with open(csv_file) as csv_1:\n csv_out = csv.reader(csv_1) \n next(csv_out)\n for rows in csv_out: \n if len(rows) != 0:\n list.append([rows[0],int(rows[1]),int(rows[2])])\n \n return (list)","def readCsv(variables, path, pathCsv, estacion):\n # os.makedirs('../data/totalData/')\n dataVa = df.DataFrame()\n variables = variables\n mypath = path\n patron = re.compile(variables + '_'+estacion+'_\\d\\d\\d\\d-\\d\\d-\\d\\d' + '.*')\n for base, dirs, filess in os.walk(mypath, topdown=False):\n filess = sorted(filess)\n for value in filess:\n if patron.match(value) != None:\n tempData = df.read_csv(mypath + value)\n #tempData = completeMet(tempData)\n tempData = tempData.iloc[0:24, :]\n dataVa = concat([tempData, dataVa], axis=0)\n dataVa = dataVa.reset_index()\n dataVa = dataVa.drop(labels='index', axis=1)\n dataVa.to_csv(pathCsv + variables + '_'+ estacion +'_total.csv', encoding='utf-8', index=False)\n dataVa = df.DataFrame()","def readFile(self, fileName, questionIds, missing=0):\n numLines = self.getNumLines(fileName)\n\n try:\n reader = csv.reader(open(fileName, \"rU\"))\n except IOError:\n raise\n \n titles = next(reader)\n \n rowIndex = 0 \n numFields = len(questionIds)\n X = numpy.zeros((numLines-1, numFields))\n \n for row in reader:\n X[rowIndex, :] = self.csvRowToVector(row, questionIds, titles)\n rowIndex = rowIndex + 1 \n\n if missing == 0:\n logging.info(\"Keeping missing values as zero\")\n elif missing == 1:\n logging.info(\"Replacing missing values with mean.\")\n X = self.replaceMissingValues(X) \n elif missing == 2:\n logging.info(\"Replacing missing values with N(mu, sigma^2).\")\n X = self.replaceMissingValues2(X)\n elif missing == 3:\n logging.info(\"Replacing missing values with mode\")\n X = self.replaceMissingValuesMode(X)\n else:\n raise ValueError(\"Invalid missing value treatment: \" + missing)\n \n logging.info(\"Done - generated array with \" + str(X.shape[0]) + \" rows and \" + str(X.shape[1]) + \" columns\")\n return X, titles","def process_csv(filepath):\n suburb = get_suburb(filepath)\n read_file = pd.read_csv(filepath,\n infer_datetime_format=True,\n parse_dates=[\"SALE DATE\"],\n dayfirst=True)\n read_file[\"SUBURB\"] = suburb\n separate_date(read_file)\n return read_file","def _parse_csv(csv_file: str) -> pd.DataFrame:\n return pd.read_csv(csv_file, header=0)","def read_partslist_csv(csv: str)->pd.DataFrame:\n try:\n p_df = pd.read_csv(csv, sep='\\t', header=0, engine='python', na_values='', skipfooter=3,\n dtype={'BLItemNo': str, 'BLColorId': int, 'LDrawColorId': int, 'Qty': int})\n p_df = p_df.fillna({'BLColorId': '', 'Qty': 0})\n p_df = p_df.rename(mapper={'BLItemNo': 'ItemId', 'BLColorId': 'Color'}, axis=1)\n p_df = p_df.drop(columns=['ElementId', 'LdrawId', 'LDrawColorId'])\n return p_df\n except FileNotFoundError as e:\n print(e)\n return pd.DataFrame()","def read_csv_data(csv_path):\n\n return pd.read_csv(csv_path, sep=',', engine='python')","def data_from_csv(self, filepath):\n self.dataframe = pd.load_csv(filepath, separator='')","def _read_csv(filename):\n csv_file = open(filename, 'r')\n data = pandas.read_csv(csv_file, comment='!', quotechar=\"'\")\n csv_file.close()\n return data","def read_csv_ur10(self, csv_file):\r\n df = pd.read_csv(csv_file, sep=';', decimal=',', header=0)\r\n return df","def read_csv(path):\n rows = []\n with open(path) as csv_file:\n reader = csv.reader(csv_file)\n header = reader.next()\n if header[0].isdigit():\n print \"Warning: Discarding header that looks like numbers.\"\n for line in reader:\n rows.append(map(float, line))\n return np.array(rows)","def read_from_file_no_check(file_name: str) -> pd.DataFrame:\n return pd.read_csv(file_name)","def _read_csv(self, input_file, quotechar=None):\n with codecs.open(input_file, \"r\", encoding=\"UTF-8\") as f:\n reader = csv.reader(f, delimiter=\",\", quotechar=quotechar)\n examples = []\n seq_id = 0\n header = next(reader) # skip header\n for line in reader:\n example = InputExample(\n guid=seq_id, label=line[0], text_a=line[1])\n seq_id += 1\n examples.append(example)\n return examples","def read_csv(file_name, company=None):\n try:\n stock_data = pd.read_csv(file_name + '.csv', parse_dates=['date']).dropna()\n stock_data = stock_data.sort_values(by=['date'])\n except ValueError:\n stock_data = pd.read_csv(file_name + '.csv').dropna()\n\n if company is not None:\n stock_data = stock_data.loc[stock_data['company'] == company]\n\n return stock_data","def read_csv(input_file):\n csv.register_dialect('my_dialect',\n delimiter = ',',\n skipinitialspace=True)\n\n csv_list_of_rows = []\n with open(input_file, 'r', encoding='utf-8', errors='replace') as csv_file:\n reader = csv.reader(csv_file, dialect='my_dialect')\n for row in reader:\n item_count = 0\n for item in row:\n if ',' in item:\n row[item_count] = '\"' + item + '\"'\n item_count += 1\n csv_list_of_rows += [row]\n return csv_list_of_rows","def from_csv(self,path):\n self.csv_path = path\n\n try:\n fh = open(self.csv_path, \"r\")\n except IOError:\n print(\"Error: no such file or directory\") \n\n self.csv_dataframe = pd.DataFrame(pd.read_csv(self.csv_path, header=0, keep_default_na=False)).dropna(axis=0, how='any')\n test = pd.DataFrame(pd.read_csv(self.csv_path)).dropna(axis=0, how='any')\n types = [0 for i in range(len(test.dtypes))]\n a = fh.readline()\n a = a[:-1] # remove '\\n'\n x = a.split(',') # x stores the name of each column\n fh.close()\n\n #type transformation\n for i in range(len(test.dtypes)):\n if test.dtypes[i].name[0:3] == 'int' or test.dtypes[i].name[0:5] == 'float':\n if (x[i][0] == \"'\" or x[i][0] == '\"'):\n x[i] = x[i].replace('\\'', '').replace('\"', '')\n for j in test[x[i]]:\n if not (j == 0 or (j > 1000 and j < 2100)):\n types[i] = test.dtypes[i].name[0:5]\n break\n else:\n types[i] = 'year'\n elif test.dtypes[i].name[0:6] == 'object':\n if (x[i][0] == \"'\" or x[i][0] == '\"'):\n x[i] = x[i].replace('\\'', '').replace('\"', '')\n for j in test[x[i]]:\n if j != 0 and not(re.search(r'\\d+[/-]\\d+[/-]\\d+', j)):\n types[i] = 'varchar'\n break\n else:\n types[i] = 'date'\n \n name = path.rsplit('/', 1)[-1][:-4]\n self.table_info(name, x, types)\n self.import_method = methods_of_import[2] # = 'csv'\n\n self.show_csv_info()","def readcsv(data):\n data_lst = data.split('\\n')\n headers = data_lst[0].split(',')\n data = []\n for e in data_lst[1:]:\n data.append(e.split(','))\n # remove the space in the end of the file if exist\n if data[len(data)-1] == ['']:\n data.pop()\n return headers, data","def read_csv():\n csv_file = \"dow.csv\"\n\n # read the data from the csv file, parsing the Dates to make the x-axis, setting index_col to zero to remove it\n data_frame = pd.read_csv(csv_file, parse_dates=True, index_col=0)\n return data_frame","def read_data_csv_low(path):\n df = pd.read_csv(path, low_memory=False)\n df.drop([\"Unnamed: 0\"], axis=1, inplace=True)\n return df","def __read_csv(self) -> tuple:\n with open(self.csv_file) as f:\n reader = csv.reader(f)\n for row in reader:\n if row[0].isspace():\n raise StopIteration\n yield row","def load_data(csv_file):\n df = pd.read_csv(csv_file)\n col_index = list(df.columns.values)\n result_label = col_index[-1] # get label of the last column\n x = df.drop(columns=result_label, axis=1)\n y = df.iloc[:, -1]\n return x, y","def read_data(file_name):\n\n path= os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n path = op.join(path, 'Data')\n path_clean = op.join(path, 'Cleaned Data')\n path = op.join(path_clean, file_name)\n names = os.listdir(path_clean)\n if all(file_name != i for i in names):\n raise ValueError\n return pd.read_csv(path)","def test_read_in_file(self):\r\n filename = \"CrimeDataSmall.csv\"\r\n\r\n lst = cds.read_in_file(filename)\r\n\r\n self.assertIsInstance(lst, list, \"Returned datatype should be a list\")\r\n self.assertEqual(len(lst), 4, \"There should be 4 rows returned from CrimeDataSmall 1 header and 3 data rows\")\r\n self.assertEqual(len(lst[0]), 23, \"Each row should have 23 columns\")\r\n self.assertEqual(lst[0][1], \"Reported_Date\", \"Column 1 was incorrect header\")\r\n self.assertEqual(lst[0][7], \"Offense\", \"Column 7 was incorrect header\")\r\n self.assertEqual(lst[0][13], \"Zip Code\", \"Column 13 header was incorrect\")\r\n self.assertEqual(lst[1][1], \"03/19/2019\", \"Column 1 was incorrect in first data row\")\r\n self.assertEqual(lst[1][7], \"Vehicular – Non-Injury\", \"Column 7 was incorrect in first data row\")\r\n self.assertEqual(lst[1][13], \"64161\", \"Column 13 in first data row was incorrect\")\r\n self.assertEqual(lst[3][1], \"03/27/2019\", \"Column 1 was incorrect in 3rd data row\")\r\n self.assertEqual(lst[3][7], \"Embezzlement\", \"Column 7 was incorrect 3rd data row\")\r\n self.assertEqual(lst[3][13], \"64112\", \"Column 13 3rd data row was incorrect\")\r\n self.assertEqual(lst[3][11], \"4600, S WORNALL RD\", \"Column 11 3rd data row was incorrect. Use csv module to read \")","def readData(filename):\n #defining gobal variable (dataframe) to access it outside this function\n global dataframe\n #storing full CSV file into a dataframe(data structure)\n dataframe = pd.read_csv(filename)\n #type casting temperature column of dataframe to numeric data and ignoring '***' values\n dataframe['Temperature'] = pd.to_numeric(dataframe['Temperature'], errors='coerce')\n return dataframe","def read_data(path, filename, drop_col=\"index\", dt=\"float32\"):\n\tdata = pd.read_csv(path + filename, sep=\",\", dtype=dt)\n\tdata = data.drop(drop_col, axis=1)\n\treturn data.as_matrix()","def read_csv(csv_file):\r\n with open(csv_file, \"r\") as files:\r\n data = csv.reader(files)\r\n return list(data)","def get_data(self, csv_file):\n pass","def _loadCSVFile(self):\n self._df = pd.read_csv(\n self._pathfile, sep=CSV_SEPARATOR, index_col=CSV_INDEX_COL)","def get_df_from_csv(input_file_name):\n df = pd.read_csv(input_file_name)\n df = df.drop(['Unnamed: 0'], axis=1)\n return df","def csvread(infile):\n out_csv = []\n errors = []\n index = -1\n p = LineParser(csv=True)\n for line in infile:\n index += 1\n try:\n values = p.feed(line)\n except ListQuoteError as e:\n values = []\n e.line = line\n e.index = index\n errors.append(e)\n #\n out_csv.append(values)\n #\n if errors:\n e = CSVError(\"Parsing CSV failed. See 'errors' attribute.\")\n e.csv = out_csv\n e.errors = errors\n raise e\n return out_csv","def read_csv_file(csv_fname, ignore_first_row = True):\n \n X, y = [], []\n with open(csv_fname, 'r') as csv_file:\n csv_reader = csv.reader(csv_file)\n if ignore_first_row:\n next(csv_reader)\n for row in csv_reader:\n X.append(row[:-1])\n y.append(row[-1])\n return np.array(X), np.array(y)","def __load_csv(filename):\n fp = open(Parser.DATA_FOLDER_PATH + filename + '.csv', 'r')\n records = []\n for line in fp:\n items = line.strip().split(',')\n x, y, z = '0', '0', '0'\n if len(items) > 1:\n x = items[1]\n if len(items) > 2:\n y = items[2]\n if len(items) > 3:\n z = items[3]\n\n values = [x, y, z]\n records.append(values)\n\n # Discard some beginning data which may be noisy\n # del records[:int(len(records) / 30)]\n n = len(records)\n\n for i in range(n):\n rec = []\n # Consider X, Y, Z axes\n for k in range(3):\n # If can convert string to float\n try:\n val = float(records[i][k])\n except ValueError:\n val = 0\n rec.append(val)\n\n # Replace it\n records[i] = rec\n return records","def read_csv(\n filepath: str,\n **kwargs\n) -> Union[DenseFunctionalData, IrregularFunctionalData]:\n data = pd.read_csv(filepath, **kwargs)\n\n try:\n all_argvals = data.columns.astype(np.int64).to_numpy()\n except ValueError:\n all_argvals = np.arange(0, len(data.columns))\n\n if not data.isna().values.any():\n return _read_csv_dense(data, all_argvals)\n else:\n return _read_csv_irregular(data, all_argvals)","def read_csv(self, path):\n for file in os.listdir(path):\n if file[-4:] == \".csv\":\n name = file[:-4]\n table_index_header = cfg.get_list(\"table_index_header\", name)\n filename = os.path.join(path, file)\n self.input_data[name] = pd.read_csv(\n filename,\n index_col=list(range(int(table_index_header[0]))),\n header=list(range(int(table_index_header[1]))),\n squeeze=(\"series\" not in name),\n )\n self.check_input_data(warning=False)\n self.add_meta_data()\n return self","def test_spark_read_file_csv_delim(spark):\n path = os.getcwd() + '/data_processor/tests/test_data/test.csv'\n delim = ','\n read_df = IOHandler(spark).spark_read_file(path, delim)\n assert read_df.count() == 2","def read_csv(filename, cols=None, nrows=None):\n\n datecols = ['date_time', 'srch_ci', 'srch_co']\n dateparser = lambda x: pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S',\n errors='coerce')\n dtypes = {\n 'id': np.uint32,\n 'site_name': np.uint8,\n 'posa_continent': np.uint8,\n 'user_location_country': np.uint16,\n 'user_location_region': np.uint16,\n 'user_location_city': np.uint16,\n 'orig_destination_distance': np.float32,\n 'user_id': np.uint32,\n 'is_mobile': bool,\n 'is_package': bool,\n 'channel': np.uint8,\n 'srch_adults_cnt': np.uint8,\n 'srch_children_cnt': np.uint8,\n 'srch_rm_cnt': np.uint8,\n 'srch_destination_id': np.uint32,\n 'srch_destination_type_id': np.uint8,\n 'is_booking': bool,\n 'cnt': np.uint64,\n 'hotel_continent': np.uint8,\n 'hotel_country': np.uint16,\n 'hotel_market': np.uint16,\n 'hotel_cluster': np.uint8,\n }\n\n df = pd.read_csv(\n filename,\n nrows=nrows,\n usecols=cols,\n dtype=dtypes,\n parse_dates=[col for col in datecols if col in cols],\n date_parser=dateparser,\n )\n\n if 'date_time' in df.columns:\n df['month'] = df['date_time'].dt.month.astype(np.uint8)\n df['year'] = df['date_time'].dt.year.astype(np.uint16)\n\n return df","def process_file(filename):\n csv_paths = read_csv(filename)\n\n all_columns = {\n 'court', 'date', 'no', 'sys', 'reason', 'judgement', 'attachAsJudgement', 'attachments', 'type', 'historyHash', 'mainText', 'opinion', 'relatedIssues', 'party'\n }\n unused_columns = ['attachAsJudgement', 'attachments']\n\n data_len = 0\n for csv_name, csv_path in tqdm(csv_paths):\n print(csv_name)\n processed_name = 'no_text_data/' + csv_name\n df = dt.fread(csv_path)\n\n # remove unused columns\n if 'attachAsJudgement' in df.keys():\n del df[:, unused_columns]\n else:\n del df[:, 'attachments']\n\n # remove all text\n del df[:, ['opinion']]\n\n data_len += df.shape[0]\n df.to_csv(processed_name)\n print(\"Total data count: {}\".format(data_len))","def readExerciseCSV(file_info, verbose=False, sensor_id_category='id', data_types=None, quat_type=None, quat_order=\"WXYZ\"):\n\n tsID = file_info['tsID']\n filepath = file_info['filepath']\n\n if quat_order.upper() is not \"WXYZ\" and quat_type is not None:\n convert_quat = True\n quat_order = quat_order.upper()\n w_i = quat_order.index(\"W\")\n x_i = quat_order.index(\"X\")\n y_i = quat_order.index(\"Y\")\n z_i = quat_order.index(\"Z\")\n else:\n convert_quat = False\n\n required_keys = set(['sample_number', 'mode', 'exercise_name', 'timestamp'])\n if len(required_keys.intersection(file_info.keys())) == len(required_keys):\n exercise_name = file_info['exercise_name']\n mode = file_info['mode']\n sample_number = file_info['sample_number']\n timestamp = file_info['timestamp']\n else:\n print(\"Missing entries from required file_info argument.\")\n return None\n\n csvReader = None\n try:\n inputcsv = open(filepath, 'r')\n csvReader = csv.DictReader(inputcsv,\n skipinitialspace=True,\n delimiter=',',\n quotechar='|')\n\n except NameError as e:\n print(\"Error opening input file: {}\".format( e))\n return None\n\n sensors = {}\n\n for row in csvReader:\n sensor_id = row[sensor_id_category]\n if sensor_id not in sensors:\n sensors[sensor_id] = {}\n for category in row:\n if category == sensor_id_category:\n continue\n\n try:\n data = ast.literal_eval(row[category])\n except:\n print(\"readExerciseCSV: Failed to eval data {} in file {} on column {}\"\n .format(filepath, row[category], category))\n return None\n\n if convert_quat and category == quat_type:\n data = [ data[w_i], data[x_i], data[y_i], data[z_i] ]\n\n if category not in sensors[sensor_id]:\n sensors[sensor_id][category] = []\n\n sensors[sensor_id][category].append(data)\n\n inputcsv.close()\n return {\"tsID\": tsID,\n \"exercise_name\": exercise_name,\n \"mode\" : mode,\n \"sample_number\" : sample_number,\n \"timestamp\": timestamp,\n \"sensors\" : sensors}","def read_csv_file(file_name):\n table = []\n with open(file_name) as csvfile:\n csvreader = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\n for row in csvreader:\n table.append(row)\n return table","def csv_data_loader(path):\n\n logging.info(\"Loading file using SparkSession\")\n csvload = Spark.instance.spark() \\\n .read \\\n .format(\"csv\") \\\n .options(header=True) \\\n .options(mode=\"DROPMALFORMED\")\n\n return csvload.option(\"inferSchema\", \"true\").load(path)","def test_csvfile_single_row_of_data(fs: FakeFilesystem) -> None:\n contents = \"\"\"\"a\",\"b\"\n1,2\"\"\"\n fs.create_file(\"test.csv\", contents=contents)\n\n adapter = CSVFile(\"test.csv\")\n\n assert adapter.get_columns() == {\n \"a\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n \"b\": Float(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n }\n assert list(adapter.get_data({}, [])) == [{\"a\": 1.0, \"b\": 2.0, \"rowid\": 0}]","def __obtain_data_from_csv__(self, csvfile):\n data = csvfile.readlines()\n data = self.__parse_string_for_delimiter__(data)\n return data","def test_csv_reader_data_contents(process_data):\n data = process_data(file_name_or_type='clean_map.csv')\n\n # Check row types\n for row in data:\n assert(isinstance(row['Country'], str))\n assert(isinstance(row['City'], str))\n assert(isinstance(row['State_Or_Province'], str))\n assert(isinstance(row['Lat'], float))\n assert(isinstance(row['Long'], float))\n assert(isinstance(row['Altitude'], float))\n\n # Basic data checks\n assert len(data) == 180 # We have collected 180 rows\n assert data[0]['Country'] == 'Andorra'\n assert data[106]['Country'] == 'Japan'","def test_from_file_csv(self):\n with TemporaryDirectory() as tmp:\n fp, df_test = save_simple_dataframe(tmp, 'test.csv')\n df_read = BaseDataClass.from_file(fp).df\n self.assertEqual(\n pd.testing.assert_frame_equal(df_test, df_read),\n None,\n )","def load_csvFile(file_location, file_name,sep,encoding):\n try:\n fullpath=file_location+file_name\n df = pd.read_csv(fullpath, encoding=encoding,sep=sep)\n return df\n except IOError:\n print('Error loading the file: ' , file_name)\n sys.exit(1)","def read_data(self):\n fpath = './data/surveys.csv'\n self.data = pd.read_csv(fpath, header=0, low_memory=False)\n #print(self.data.head(n=5))\n print(self.data.shape)","def read_csv(csv_path):\n \n df = pd.read_csv(csv_path)\n\n return df","def read_csv(path):\n csv_data =[]\n \n with open(path, 'r') as csv_file:\n csv_read = csv.reader(csv_file, dialect='excel')\n for row in csv_read:\n csv_data.append(row)\n\n return(csv_data)","def load_from_csv(path, delimiter=','):\n return pd.read_csv(path,encoding = \"ISO-8859-1\",dtype=object)","def test_csvfile_get_data_impossible_filter(fs: FakeFilesystem) -> None:\n fs.create_file(\"test.csv\", contents=CONTENTS)\n\n adapter = CSVFile(\"test.csv\")\n assert list(adapter.get_data({\"index\": Impossible()}, [])) == []","def read_csv(path):\n return pd.read_csv(path)","def _read_csv(self, options, datas):\n csv_data = datas\n if not csv_data:\n return iter([])\n encoding = options.get('encoding')\n if not encoding:\n encoding = options['encoding'] = chardet.detect(csv_data)['encoding'].lower()\n # some versions of chardet (e.g. 2.3.0 but not 3.x) will return\n # utf-(16|32)(le|be), which for python means \"ignore / don't strip\n # BOM\". We don't want that, so rectify the encoding to non-marked\n # IFF the guessed encoding is LE/BE and csv_data starts with a BOM\n bom = BOM_MAP.get(encoding)\n if bom and csv_data.startswith(bom):\n encoding = options['encoding'] = encoding[:-2]\n if encoding != 'utf-8':\n csv_data = csv_data.decode(encoding).encode('utf-8')\n\n separator = options.get('separator')\n if not separator:\n # default for unspecified separator so user gets a message about\n # having to specify it\n separator = ','\n for candidate in (',', ';', '\\t', ' ', '|', unicodedata.lookup('unit separator')):\n # pass through the CSV and check if all rows are the same\n # length & at least 2-wide assume it's the correct one\n it = pycompat.csv_reader(io.BytesIO(csv_data), quotechar=options['quoting'], delimiter=candidate)\n w = None\n for row in it:\n width = len(row)\n if w is None:\n w = width\n if width == 1 or width != w:\n break # next candidate\n else: # nobreak\n separator = options['separator'] = candidate\n break\n csv_iterator = pycompat.csv_reader(\n io.BytesIO(csv_data),\n quotechar=options['quoting'],\n delimiter=separator)\n return (\n row for row in csv_iterator\n if any(x for x in row if x.strip())\n )","def import_from_csv(self) -> None:\n logging.info('import_from_csv')\n if self.target_table and str(self.target_table).lower() in [\"issue\", \"version\"]:\n if self.file_path and exists(self.file_path):\n # Read CSV file\n csv_data = pd.read_csv(self.file_path).to_dict('records')\n\n # Import Version\n if str(self.target_table).capitalize() == \"Version\":\n # Overwrite option\n if self.overwrite:\n self.session.query(Version).delete()\n click.echo('Overwrite Version table')\n\n for version in csv_data:\n if all(item in list(version.keys()) for item in ['tag', 'start_date', 'end_date']):\n newVersion=Version(\n project_id=version['project_id'],\n name=version[\"name\"], \n tag=version[\"tag\"], \n start_date=datetime.strptime(version[\"start_date\"], '%Y-%m-%d %H:%M:%S.%f'), \n end_date=datetime.strptime(version[\"end_date\"], '%Y-%m-%d %H:%M:%S.%f'), \n )\n \n try:\n self.session.add(newVersion)\n compute_version_metrics(self.session, self.configuration.current_branch, newVersion.project_id)\n click.echo('Importing ' + str(len(csv_data)) + ' version(s) on database')\n except Exception:\n logging.error(Exception)\n else:\n logging.error(\"CSV file no contain minimal mandatory fields\")\n sys.exit('CSV file no contain minimal mandatory fields')\n\n # Import Issue\n if str(self.target_table).capitalize() == \"Issue\":\n # Overwrite option\n if self.overwrite:\n self.session.query(Issue).delete()\n click.echo('Overwrite Issue table')\n\n for issue in csv_data:\n if all(item in list(issue.keys()) for item in ['number', 'created_at', 'updated_at']):\n newIssue=Issue(\n project_id=issue['project_id'],\n number=issue[\"number\"],\n title=issue[\"title\"],\n created_at=datetime.strptime(issue[\"created_at\"], '%Y-%m-%d %H:%M:%S.%f'),\n updated_at=datetime.strptime(issue[\"updated_at\"], '%Y-%m-%d %H:%M:%S.%f'))\n\n try:\n self.session.add(newIssue)\n click.echo('Importing ' + str(len(csv_data)) + ' issue(s) on database')\n except Exception:\n logging.error(Exception)\n else:\n logging.error(\"CSV file no contain minimal mandatory fields\")\n sys.exit('CSV file no contain minimal mandatory fields') \n\n self.session.commit()\n else:\n logging.error('File not found')\n sys.exit('File not found')\n else:\n logging.error('Target table not found')\n sys.exit('Target table not found')","def test_csvfile_different_types(fs: FakeFilesystem) -> None:\n contents = '''\"a\"\n1\n2.0\n\"test\"'''\n fs.create_file(\"test.csv\", contents=contents)\n\n adapter = CSVFile(\"test.csv\")\n\n assert adapter.get_columns() == {\n \"a\": String(\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\n order=Order.NONE,\n exact=True,\n ),\n }","def read_csv(config, input_file_path):\n header = read_csv_header(input_file_path)\n\n general = config['general']\n date_cols_types = ['date_cols',\n 'first_exp_date_cols',\n 'last_exp_date_cols',\n 'index_date_col',\n 'lookback_date_col']\n date_cols = utils.generate_list_columns(header, config, date_cols_types)\n # it turns out we should read the dates first in as strings\n date_cols_types = {date_col: str for date_col in date_cols}\n df = pd.read_csv(input_file_path, dtype=date_cols_types)\n # convert string dates to dates using the date format\n # Large dataset, conversion done in parallel\n if len(date_cols) > 50 or (df.shape[0] > 20000 and len(date_cols) > 1):\n print('parallel!')\n # we have to do this in parallel otherwise it takes forever\n df[date_cols] = parse_utils.apply_parallel(df[date_cols],\n parse_utils.parse_dates,\n format=general['date_format'])\n # Small dataset, faster to convert in non-parallel fashion\n elif len(date_cols) > 0:\n df[date_cols] = df[date_cols].apply(pd.to_datetime,\n format=general['date_format'])\n return df","def read_csv(path):\n column_headers = ['Country', 'Region', 'Pop. Density (per sq. mi.)', 'Infant mortality (per 1000 births)',\n 'GDP ($ per capita) dollars']\n df = pd.read_csv(path)\n return df[column_headers]","def read_csv_file(csv_file):\n return cudf.read_csv(csv_file, delimiter=' ',\n dtype=['int32', 'int32', 'float32'], header=None)","def read_csv_file(file_name):\n \n with open(file_name, newline='') as csv_file: # don't need to explicitly close the file now\n csv_table = []\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader:\n csv_table.append(row)\n return csv_table","def read_csv_file(input_csv_file_path):\n with open(input_csv_file_path, 'r', encoding='utf-8') as file_path:\n csv_reader = csv.reader(file_path)\n for row in itertools.islice(csv_reader, 1, None):\n yield (\n int(row[0]), row[2],\n datetime.datetime.strftime(\n datetime.datetime.strptime(row[-1], '%m/%d/%y'),\n '%Y-%m-%d'))","def readCSV(filename):\r\n data = list( csv.reader(open('HW_08_DBScan_Data_NOISY_v300.csv','r'),delimiter=','))\r\n for dIdx in range(len(data)):\r\n data[dIdx] = [float(data[dIdx][0]),float(data[dIdx][1]),float(data[dIdx][2])]\r\n #print(data[0])\r\n return data","def read_full_data(self):\n x=[]\n y=[]\n z=[]\n with open(self.file, 'r') as rf:\n reader = csv.reader(rf, delimiter=',')\n for row in reader:\n x.append(row[0])\n # Remove header from csv file, if it exists\n if x[0].split()[0] == '%':\n x.remove(row[0])\n else:\n y.append(row[1])\n z.append(row[2])\n return x,y,z"],"string":"[\n \"def read_csv():\",\n \"def read_csv_file(self):\\n pass\",\n \"def test_csv(self, input_file_path: str, answer_file_path: List[Dict]):\\n with open(attach_path(answer_file_path), 'r') as answer_file:\\n csv_file = open(attach_path(input_file_path))\\n assert str(read_csv(csv_file)) == answer_file.read().strip()\",\n \"def read_csv(self, csv_input):\\n # https://stackoverflow.com/a/45063514\\n dtypes = {\\n 'lat': 'U',\\n 'long': 'U'\\n }\\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''], dtype=dtypes)\\n\\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\\n self.log.info('Data read from CSV %s' % csv_input)\\n #print('Data read from CSV %s' % csv_input)\",\n \"def import_data(csv_file):\\n # skips bad lines\\n data = pd.read_csv(csv_file, error_bad_lines=False)\\n return data\",\n \"def place_types_read_csv(self, csv_input):\\n csv_data = pd.read_csv(csv_input, encoding='UTF-8', sep=',', na_values=[''])\\n self.table = csv_data.fillna('').applymap(lambda x: x.strip() if type(x) == str else x)\\n self.log.info('Data read from CSV %s' % csv_input)\",\n \"def loadCSV(input_file):\",\n \"def open_convert_and_clean_csv(csv_data_file):\\n imported_data = tablib.Dataset().load(open(csv_data_file).read())\\n dataset = []\\n for row in imported_data:\\n if float(row[1]) > 0 and float(row[2]) > 0:\\n dataset.append((row[0], float(row[1]), float(row[2])))\\n return dataset\",\n \"def read_file(self, fn_input):\\n if not os.path.exists(fn_input):\\n raise Exception(\\\"ERROR: Input file does not exist: %s\\\" % fn_input)\\n with open(fn_input, 'rt', newline='') as infile:\\n reader = csv.reader(infile)\\n self.data = []\\n for row in reader:\\n self.data.append([])\\n for value in row:\\n if value == 'nan':\\n self.data[-1].append(None)\\n else:\\n try:\\n self.data[-1].append(float(value))\\n except:\\n raise Exception(\\\"ERROR: unexpected text in input file: '%s'\\\" % str(value))\",\n \"def parse(file_name):\\n \\n return pd.read_csv(file_name, na_values = '---')\",\n \"def _read_csv(self):\\n self.function_name = '_read_csv'\\n with open(os.path.join(self.task.downloads, self.csv_name)) as csv_file:\\n reader = csv.reader(csv_file, dialect='excel')\\n for row in reader:\\n self.input_data.append(row)\",\n \"def read_specific_problem(filename):\\r\\n table = []\\r\\n with open(filename, newline='') as csvfile:\\r\\n reader = csv.reader(csvfile, skipinitialspace=True, delimiter=',')\\r\\n for row in reader:\\r\\n table.append(row)\\r\\n return table\",\n \"def _read_csv(filename):\\n loaded = pd.read_csv(filename, index_col=0)\\n if len(loaded.columns) == 1:\\n return pd.read_csv(filename, index_col=0, header=None)[1]\\n else:\\n return loaded\",\n \"def load_and_clean(self,in_path):\\n in_path = Path(in_path)\\n try:\\n df = pd.read_csv(in_path, index_col = 0, parse_dates = True, infer_datetime_format = True)\\n except:\\n print(\\\"Could not read csv file. Please check the path\\\")\\n finally:\\n #attempt to clean df\\n df.dropna(inplace = True)\\n df.drop_duplicates(inplace = True)\\n df.sort_index()\\n return df\",\n \"def read_rf_csv():\\n if os.path.exists(\\\"rf.csv\\\"):\\n #print (\\\"--decision trees CSV imported\\\\n\\\")\\n results = pd.read_csv(\\\"rf.csv\\\", index_col=0)\\n else:\\n print(\\\"log not found\\\")\\n\\n return results\",\n \"def read_data_from_csv(filename: str) -> pd.DataFrame:\\n try:\\n data = pd.read_csv(filename)\\n return data\\n except(FileNotFoundError):\\n print('Error: Could not read the data from csv.')\\n return None\",\n \"def import_csv_dataset():\\n import_fields = pd.read_csv('redacted-2020-june-30-wprdc-.csv', header=None).to_numpy()[0, :]\\n import_values = pd.read_csv('redacted-2020-june-30-wprdc-.csv').to_numpy()\\n import_values = clean_values(import_values)\\n return import_fields, import_values\",\n \"def csv_loader(csv_file):\\n df = pd.read_csv(csv_file, sep=';', parse_dates=['Data_Alteraçao'])\\n pd.set_option('display.float_format', '{:.0f}'.format)\\n\\n df = df.fillna(0)\\n df = df.drop(columns=['Cod. Pareamento', 'Cod. UF', 'Sigla UF', 'Cod. Subarea',\\n 'Nome Subarea', 'Cod. Municipio', 'Nome Municipio', 'Codigo Agencia',\\n 'Nome Agencia', 'Cod. Setor', 'Cod. Logradouro CNEFE',\\n 'Tipo Logradouro CNEFE', 'Titulo Logradouro CNEFE',\\n 'Nome Logradouro CNEFE', 'Nome Tratado CNEFE', 'Tipo Logradouro DNE',\\n 'Titulo Logradouro DNE', 'Nome Logradouro DNE', 'Nome Tratado DNE',\\n 'Logradouro Completo DNE', 'Distancia', 'Cod. Match', 'Motivo Match',\\n 'CEPs Face', 'Localidade Face',\\n 'Alterar Logradouro para DNE?', 'Observaçao', 'SIAPE Alteração',\\n 'Nome Alteraçao', 'Data_Alteraçao', 'Status', 'Unnamed: 33'])\\n\\n # df.astype({'CEP Logradouro CNEFE': 'int32'}).dtypes\\n\\n df['CEP'] = df['CEP'].str.replace(' ', '', regex=False)\\n\\n ceps_dne = []\\n for index, row in df.iterrows():\\n if type(row.CEP) == str:\\n for cep in row.CEP.split(','):\\n # print(index, cep)\\n ceps_dne.append(int(cep))\\n\\n ceps_cnefe = df['CEP Logradouro CNEFE'].astype(int).tolist()\\n ceps = ceps_dne + ceps_cnefe\\n ceps = list(set(ceps))\\n return pd.Series(ceps)\",\n \"def handle_csv(self):\\n try:\\n reader = csv.reader(open(self.options.datafile, 'r'))\\n except IOError:\\n errormsg(_('Cannot read \\\"{}\\\"'.format(self.options.datafile)))\\n raise Exception(_('Cannot read \\\"{}\\\"'.format(self.options.datafile)))\\n if self.options.var_type == 'name':\\n try:\\n self.header = reader.next()\\n except StopIteration:\\n errormsg(_('Data file \\\"{}\\\" contains no data'.format(\\n self.options.datafile)))\\n raise Exception(_('Data file \\\"{}\\\" contains no data'.format(\\n self.options.datafile)))\\n self.data = []\\n for row in reader:\\n self.data.append(row)\",\n \"def read_test_rf_csv():\\n if os.path.exists(\\\"test_rf.csv\\\"):\\n #print (\\\"--testing CSV imported\\\\n\\\")\\n results = pd.read_csv(\\\"test_rf.csv\\\", index_col=0)\\n else:\\n print(\\\"log not found\\\")\\n\\n return results\",\n \"def _read_csv(input_file):\\n with tf.gfile.Open(input_file, \\\"r\\\") as f:\\n reader = csv.reader(f)\\n lines = []\\n for line in reader:\\n lines.append(line)\\n return lines[1:] # remove header\",\n \"def _read_input_csv(in_file):\\n with open(in_file, \\\"rU\\\") as in_handle:\\n reader = csv.reader(in_handle)\\n reader.next() # header\\n for line in reader:\\n if line: # empty lines\\n (fc_id, lane, sample_id, genome, barcode) = line[:5]\\n yield fc_id, lane, sample_id, genome, barcode\",\n \"def test_misc_csv_read():\\n r = csv_reader(\\\"../test/test.csv\\\")\\n fields = r.hdr()\\n data = str(fields)\\n while True:\\n row = r.row()\\n if not row: break\\n data += '\\\\n' + str(row)\\n\\n assert(data == \\\"\\\"\\\"\\n['EVT_CODE*', 'EVT_DATE.DE', 'CODE', 'AGE', 'FRST', 'LST', 'SPEC', 'de.id']\\n['tea', '2018/01/01', 'X', '35', 'PRE', 'WHO', 'BUG', '1']\\n['coffee', '2018/05/05', 'X', '35', 'JAN,Z', 'WHO', 'FRG', '1']\\n['water', '2018/01/01', 'Y', '35', 'TAN', 'POST', 'CAT', '2']\\n \\\"\\\"\\\".strip())\",\n \"def read_csv_from_file(self, file_name):\\r\\n try:\\r\\n self.csv_df = pd.read_csv(file_name)\\r\\n except FileNotFoundError:\\r\\n print('File does not exists!')\",\n \"def parse_csv(csv_file):\\n if os.path.isfile(csv_file) == True:\\n num_lines = sum(1 for line in open(csv_file))\\n if num_lines > 1:\\n try:\\n data = pd.read_csv(csv_file, index_col=False)\\n data.insert(0, 'id', range(1, 1 + len(data)))\\n return(data)\\n except pd.parser.CParserError, err:\\n message = \\\"Can't parse REDCap data. Check CSV file: \\\" + csv_file\\n print(message)\\n logging.critical(message)\\n exit(3)\\n else:\\n message = \\\"CSV file does not contain data: \\\" + csv_file\\n print(message)\\n logging.warning(message)\\n return(None)\\n else:\\n message = \\\"Can't read CSV file: \\\" + csv_file\\n print(message)\\n logging.critical(message)\\n exit(4)\",\n \"def check_valid_csvformat(self, csv_path):\\n with open(self.csv_path, \\\"rb+\\\") as file_obj:\\n reader = csv.reader(file_obj, delimiter=',') # CSV DictReader object\\n self.check_valid_csv_header(reader.next())\\n self.check_valid_csv_data(reader.next())\",\n \"def read_data_from_csv(csv_file, header=None, **kwargs):\\n if os.path.isabs(csv_file) == False:\\n path_to_csv = os.path.join(csv_file)\\n else:\\n path_to_csv = csv_file\\n row_list = []\\n if \\\"field_sep\\\" not in kwargs.keys():\\n field_sep = ','\\n else:\\n field_sep = kwargs.get(\\\"field_sep\\\")\\n with open(path_to_csv, mode='r') as csv_file:\\n csv_reader = csv.DictReader(csv_file, delimiter=field_sep, fieldnames=header)\\n for record in csv_reader:\\n if list(record.values())[0].startswith(\\\"#\\\") is not True:\\n # IT'S A COMMENT IF IT STARTS WITH \\\"#\\\" \\n # IF THIS IS YOUR HEADER ROW, SUPPLY A LIST OF COLUMN NAMES WHEN CALLING THE FUNCTION\\n row_list.append(record)\\n return row_list\",\n \"def read_data_for_question_2():\\n df_T_fare_info = read_csv_file_data(\\\"../Data/T_Fare_info_Q2.csv\\\")\\n df_T_fare_info.set_index('FlightId', inplace=True)\\n df_T_flight_info = read_csv_file_data(\\\"../Data/T_Flight_info_Q2.csv\\\")\\n df_T_flight_info.set_index('FlightId', inplace=True)\\n df_T_fare_info.dropna(inplace=True)\\n df_T_flight_info.dropna(inplace=True)\\n return df_T_fare_info, df_T_flight_info\",\n \"def read_csv(file_path, has_header = True):\\n with open(file_path) as f:\\n if has_header: f.readline()\\n data = []\\n target =[]\\n for line in f:\\n line = line.strip().split(\\\",\\\")\\n data.append([float(x) for x in line[:-1]])\\n target.append([line[-1]])\\n return data, target\",\n \"def ReadData( fileName ):\\n \\n # define column names\\n colNames = ['agency_cd', 'site_no', 'Date', 'Discharge', 'Quality']\\n\\n # open and read the file\\n DataDF = pd.read_csv(fileName, header=1, names=colNames, \\n delimiter=r\\\"\\\\s+\\\",parse_dates=[2], comment='#',\\n na_values=['Eqp'])\\n DataDF = DataDF.set_index('Date')\\n \\n # quantify the number of missing values\\n MissingValues = DataDF[\\\"Discharge\\\"].isna().sum()\\n \\n ## Remove invalid streamflow data\\n DataDF.Discharge[(DataDF['Discharge']<0)]=np.nan\\n \\n return( DataDF, MissingValues )\",\n \"def read_csv_file_data(file_path):\\n if os.path.exists(file_path):\\n df = pandas.read_csv(file_path)\\n else:\\n raise ValueError('ERROR: file_path doesnt exist in read_csv_file_data()')\\n return df\",\n \"def read_from_csv(path):\\n if not os.path.exists(path):\\n return None\\n if not path.endswith('.csv'):\\n return None\\n\\n with open(path, 'r') as file:\\n data = pd.read_csv(file, header=0)\\n\\n return data\",\n \"def read_data_csv(path):\\n df = pd.read_csv(path)\\n df.drop([\\\"Unnamed: 0\\\"], axis=1, inplace=True)\\n return df\",\n \"def read_csv(self, filepath, obs_vars = ['obs'], header = True):\\n # determine if the type file is gzip\\n filetype, encoding = mimetypes.guess_type(filepath)\\n if encoding == 'gzip':\\n self.data = pd.read_csv(filepath, compression='gzip')\\n else:\\n self.data = pd.read_csv(filepath)\\n\\n self.original_data = copy.deepcopy(self.data)\\n if self.cutoff:\\n self.data = self.data[:self.cutoff]\\n \\n self.data = self.data[obs_vars]\\n self.N = self.data.shape[0]\\n return True\",\n \"def read_csv(self, csv_file):\\n mylog.debug('Reading csv file %s for data' % csv_file)\\n csv_data = pandas.read_csv(csv_file)\\n mylog.debug('Read of csv file complete.')\\n #mylog.debug('%s' % csv_data)\\n #sometimes the csv has an empty dataframe #\\n if csv_data.empty:\\n mylog.debug('Data frame is empty; repopuating data')\\n csv_info = []\\n for item in csv_data:\\n #add the data one cell at a time to the list #\\n #for some reason, some csvs have the data #\\n #with random decimal points #\\n csv_info.append(item.split(\\\".\\\")[0])\\n df = pandas.DataFrame(columns=csv_info)\\n df.loc[0]=csv_info\\n #write the data from the list back into the cells#\\n #one at a time #\\n for column in range(0, len(csv_info)): \\n df.iloc[0,column] = csv_info[column]\\n csv_data = df \\n return csv_data\",\n \"def read_csv(csv_file_path):\\n res = [] #list\\n # f = open(csv_file_path) #read file\\n with open(csv_file_path,\\\"r\\\") as f:\",\n \"def initial_csv_wrangling(csv_file):\\n df = pd.read_csv(csv_file)\\n df = df.fillna('')\\n columns = list(df.columns)\\n\\n # check that \\\"url\\\" column exists (required)\\n if 'url' not in columns:\\n raise Exception('Input csv file requires a \\\"url\\\" column, which does not seem to exist. Exiting.')\\n\\n # check if \\\"pos_concepts\\\" column exists and parse accordingly (not required)\\n if 'pos_concepts' in columns:\\n print('Found \\\"pos_concepts\\\" column. Values will be split by pipe/vertical bar \\\"|\\\" into a python list.')\\n df['pos_concepts'] = df['pos_concepts'].map(lambda x: list(set(x.split('|'))))\\n\\n # check if \\\"neg_concepts\\\" column exists and parse accordingly (not required)\\n if \\\"neg_concepts\\\" in columns:\\n print('Found \\\"neg_concepts\\\" column. Values will be split by pipe/vertical bar \\\"|\\\" into a python list.')\\n df['neg_concepts'] = df['neg_concepts'].map(lambda x: list(set(x.split('|'))))\\n\\n # check if \\\"metadata\\\" column exists and load accordingly (not required)\\n if \\\"metadata\\\" in columns:\\n print('Found \\\"metadata\\\" column. Attempting to ingest.')\\n try:\\n df['metadata'] = df['metadata'].replace('','{}').map(json.loads)\\n except:\\n raise Exception('Value in \\\"metadata\\\" column does not seem to be a properly JSON formatted str.')\\n\\n return df\",\n \"def read_csv():\\n global csvdata\\n global CONFIG\\n if type(csvdata) == type(None):\\n if not os.path.exists(CONFIG[\\\"csvfile\\\"]):\\n csvdata = pandas.read_csv(CONFIG[\\\"csvrepo\\\"],\\n na_values=[\\\"-999999\\\",\\\"NOT AVAILABLE\\\"])\\n os.makedirs(CONFIG[\\\"cachedir\\\"],exist_ok=True)\\n csvdata.to_csv(CONFIG[\\\"csvfile\\\"])\\n else:\\n csvdata = pandas.read_csv(CONFIG[\\\"csvfile\\\"])\\n return csvdata\",\n \"def load_csv():\\n df = pd.read_csv(datafolder+filename, decimal=decimal).astype(\\n {'min': 'float', 'max': 'float'})\\n return df\",\n \"def load_data_from_csv(csv_file):\\n list=[]\\n\\n with open(csv_file) as csv_1:\\n csv_out = csv.reader(csv_1) \\n next(csv_out)\\n for rows in csv_out: \\n if len(rows) != 0:\\n list.append([rows[0],int(rows[1]),int(rows[2])])\\n \\n return (list)\",\n \"def readCsv(variables, path, pathCsv, estacion):\\n # os.makedirs('../data/totalData/')\\n dataVa = df.DataFrame()\\n variables = variables\\n mypath = path\\n patron = re.compile(variables + '_'+estacion+'_\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d' + '.*')\\n for base, dirs, filess in os.walk(mypath, topdown=False):\\n filess = sorted(filess)\\n for value in filess:\\n if patron.match(value) != None:\\n tempData = df.read_csv(mypath + value)\\n #tempData = completeMet(tempData)\\n tempData = tempData.iloc[0:24, :]\\n dataVa = concat([tempData, dataVa], axis=0)\\n dataVa = dataVa.reset_index()\\n dataVa = dataVa.drop(labels='index', axis=1)\\n dataVa.to_csv(pathCsv + variables + '_'+ estacion +'_total.csv', encoding='utf-8', index=False)\\n dataVa = df.DataFrame()\",\n \"def readFile(self, fileName, questionIds, missing=0):\\n numLines = self.getNumLines(fileName)\\n\\n try:\\n reader = csv.reader(open(fileName, \\\"rU\\\"))\\n except IOError:\\n raise\\n \\n titles = next(reader)\\n \\n rowIndex = 0 \\n numFields = len(questionIds)\\n X = numpy.zeros((numLines-1, numFields))\\n \\n for row in reader:\\n X[rowIndex, :] = self.csvRowToVector(row, questionIds, titles)\\n rowIndex = rowIndex + 1 \\n\\n if missing == 0:\\n logging.info(\\\"Keeping missing values as zero\\\")\\n elif missing == 1:\\n logging.info(\\\"Replacing missing values with mean.\\\")\\n X = self.replaceMissingValues(X) \\n elif missing == 2:\\n logging.info(\\\"Replacing missing values with N(mu, sigma^2).\\\")\\n X = self.replaceMissingValues2(X)\\n elif missing == 3:\\n logging.info(\\\"Replacing missing values with mode\\\")\\n X = self.replaceMissingValuesMode(X)\\n else:\\n raise ValueError(\\\"Invalid missing value treatment: \\\" + missing)\\n \\n logging.info(\\\"Done - generated array with \\\" + str(X.shape[0]) + \\\" rows and \\\" + str(X.shape[1]) + \\\" columns\\\")\\n return X, titles\",\n \"def process_csv(filepath):\\n suburb = get_suburb(filepath)\\n read_file = pd.read_csv(filepath,\\n infer_datetime_format=True,\\n parse_dates=[\\\"SALE DATE\\\"],\\n dayfirst=True)\\n read_file[\\\"SUBURB\\\"] = suburb\\n separate_date(read_file)\\n return read_file\",\n \"def _parse_csv(csv_file: str) -> pd.DataFrame:\\n return pd.read_csv(csv_file, header=0)\",\n \"def read_partslist_csv(csv: str)->pd.DataFrame:\\n try:\\n p_df = pd.read_csv(csv, sep='\\\\t', header=0, engine='python', na_values='', skipfooter=3,\\n dtype={'BLItemNo': str, 'BLColorId': int, 'LDrawColorId': int, 'Qty': int})\\n p_df = p_df.fillna({'BLColorId': '', 'Qty': 0})\\n p_df = p_df.rename(mapper={'BLItemNo': 'ItemId', 'BLColorId': 'Color'}, axis=1)\\n p_df = p_df.drop(columns=['ElementId', 'LdrawId', 'LDrawColorId'])\\n return p_df\\n except FileNotFoundError as e:\\n print(e)\\n return pd.DataFrame()\",\n \"def read_csv_data(csv_path):\\n\\n return pd.read_csv(csv_path, sep=',', engine='python')\",\n \"def data_from_csv(self, filepath):\\n self.dataframe = pd.load_csv(filepath, separator='')\",\n \"def _read_csv(filename):\\n csv_file = open(filename, 'r')\\n data = pandas.read_csv(csv_file, comment='!', quotechar=\\\"'\\\")\\n csv_file.close()\\n return data\",\n \"def read_csv_ur10(self, csv_file):\\r\\n df = pd.read_csv(csv_file, sep=';', decimal=',', header=0)\\r\\n return df\",\n \"def read_csv(path):\\n rows = []\\n with open(path) as csv_file:\\n reader = csv.reader(csv_file)\\n header = reader.next()\\n if header[0].isdigit():\\n print \\\"Warning: Discarding header that looks like numbers.\\\"\\n for line in reader:\\n rows.append(map(float, line))\\n return np.array(rows)\",\n \"def read_from_file_no_check(file_name: str) -> pd.DataFrame:\\n return pd.read_csv(file_name)\",\n \"def _read_csv(self, input_file, quotechar=None):\\n with codecs.open(input_file, \\\"r\\\", encoding=\\\"UTF-8\\\") as f:\\n reader = csv.reader(f, delimiter=\\\",\\\", quotechar=quotechar)\\n examples = []\\n seq_id = 0\\n header = next(reader) # skip header\\n for line in reader:\\n example = InputExample(\\n guid=seq_id, label=line[0], text_a=line[1])\\n seq_id += 1\\n examples.append(example)\\n return examples\",\n \"def read_csv(file_name, company=None):\\n try:\\n stock_data = pd.read_csv(file_name + '.csv', parse_dates=['date']).dropna()\\n stock_data = stock_data.sort_values(by=['date'])\\n except ValueError:\\n stock_data = pd.read_csv(file_name + '.csv').dropna()\\n\\n if company is not None:\\n stock_data = stock_data.loc[stock_data['company'] == company]\\n\\n return stock_data\",\n \"def read_csv(input_file):\\n csv.register_dialect('my_dialect',\\n delimiter = ',',\\n skipinitialspace=True)\\n\\n csv_list_of_rows = []\\n with open(input_file, 'r', encoding='utf-8', errors='replace') as csv_file:\\n reader = csv.reader(csv_file, dialect='my_dialect')\\n for row in reader:\\n item_count = 0\\n for item in row:\\n if ',' in item:\\n row[item_count] = '\\\"' + item + '\\\"'\\n item_count += 1\\n csv_list_of_rows += [row]\\n return csv_list_of_rows\",\n \"def from_csv(self,path):\\n self.csv_path = path\\n\\n try:\\n fh = open(self.csv_path, \\\"r\\\")\\n except IOError:\\n print(\\\"Error: no such file or directory\\\") \\n\\n self.csv_dataframe = pd.DataFrame(pd.read_csv(self.csv_path, header=0, keep_default_na=False)).dropna(axis=0, how='any')\\n test = pd.DataFrame(pd.read_csv(self.csv_path)).dropna(axis=0, how='any')\\n types = [0 for i in range(len(test.dtypes))]\\n a = fh.readline()\\n a = a[:-1] # remove '\\\\n'\\n x = a.split(',') # x stores the name of each column\\n fh.close()\\n\\n #type transformation\\n for i in range(len(test.dtypes)):\\n if test.dtypes[i].name[0:3] == 'int' or test.dtypes[i].name[0:5] == 'float':\\n if (x[i][0] == \\\"'\\\" or x[i][0] == '\\\"'):\\n x[i] = x[i].replace('\\\\'', '').replace('\\\"', '')\\n for j in test[x[i]]:\\n if not (j == 0 or (j > 1000 and j < 2100)):\\n types[i] = test.dtypes[i].name[0:5]\\n break\\n else:\\n types[i] = 'year'\\n elif test.dtypes[i].name[0:6] == 'object':\\n if (x[i][0] == \\\"'\\\" or x[i][0] == '\\\"'):\\n x[i] = x[i].replace('\\\\'', '').replace('\\\"', '')\\n for j in test[x[i]]:\\n if j != 0 and not(re.search(r'\\\\d+[/-]\\\\d+[/-]\\\\d+', j)):\\n types[i] = 'varchar'\\n break\\n else:\\n types[i] = 'date'\\n \\n name = path.rsplit('/', 1)[-1][:-4]\\n self.table_info(name, x, types)\\n self.import_method = methods_of_import[2] # = 'csv'\\n\\n self.show_csv_info()\",\n \"def readcsv(data):\\n data_lst = data.split('\\\\n')\\n headers = data_lst[0].split(',')\\n data = []\\n for e in data_lst[1:]:\\n data.append(e.split(','))\\n # remove the space in the end of the file if exist\\n if data[len(data)-1] == ['']:\\n data.pop()\\n return headers, data\",\n \"def read_csv():\\n csv_file = \\\"dow.csv\\\"\\n\\n # read the data from the csv file, parsing the Dates to make the x-axis, setting index_col to zero to remove it\\n data_frame = pd.read_csv(csv_file, parse_dates=True, index_col=0)\\n return data_frame\",\n \"def read_data_csv_low(path):\\n df = pd.read_csv(path, low_memory=False)\\n df.drop([\\\"Unnamed: 0\\\"], axis=1, inplace=True)\\n return df\",\n \"def __read_csv(self) -> tuple:\\n with open(self.csv_file) as f:\\n reader = csv.reader(f)\\n for row in reader:\\n if row[0].isspace():\\n raise StopIteration\\n yield row\",\n \"def load_data(csv_file):\\n df = pd.read_csv(csv_file)\\n col_index = list(df.columns.values)\\n result_label = col_index[-1] # get label of the last column\\n x = df.drop(columns=result_label, axis=1)\\n y = df.iloc[:, -1]\\n return x, y\",\n \"def read_data(file_name):\\n\\n path= os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\\n path = op.join(path, 'Data')\\n path_clean = op.join(path, 'Cleaned Data')\\n path = op.join(path_clean, file_name)\\n names = os.listdir(path_clean)\\n if all(file_name != i for i in names):\\n raise ValueError\\n return pd.read_csv(path)\",\n \"def test_read_in_file(self):\\r\\n filename = \\\"CrimeDataSmall.csv\\\"\\r\\n\\r\\n lst = cds.read_in_file(filename)\\r\\n\\r\\n self.assertIsInstance(lst, list, \\\"Returned datatype should be a list\\\")\\r\\n self.assertEqual(len(lst), 4, \\\"There should be 4 rows returned from CrimeDataSmall 1 header and 3 data rows\\\")\\r\\n self.assertEqual(len(lst[0]), 23, \\\"Each row should have 23 columns\\\")\\r\\n self.assertEqual(lst[0][1], \\\"Reported_Date\\\", \\\"Column 1 was incorrect header\\\")\\r\\n self.assertEqual(lst[0][7], \\\"Offense\\\", \\\"Column 7 was incorrect header\\\")\\r\\n self.assertEqual(lst[0][13], \\\"Zip Code\\\", \\\"Column 13 header was incorrect\\\")\\r\\n self.assertEqual(lst[1][1], \\\"03/19/2019\\\", \\\"Column 1 was incorrect in first data row\\\")\\r\\n self.assertEqual(lst[1][7], \\\"Vehicular – Non-Injury\\\", \\\"Column 7 was incorrect in first data row\\\")\\r\\n self.assertEqual(lst[1][13], \\\"64161\\\", \\\"Column 13 in first data row was incorrect\\\")\\r\\n self.assertEqual(lst[3][1], \\\"03/27/2019\\\", \\\"Column 1 was incorrect in 3rd data row\\\")\\r\\n self.assertEqual(lst[3][7], \\\"Embezzlement\\\", \\\"Column 7 was incorrect 3rd data row\\\")\\r\\n self.assertEqual(lst[3][13], \\\"64112\\\", \\\"Column 13 3rd data row was incorrect\\\")\\r\\n self.assertEqual(lst[3][11], \\\"4600, S WORNALL RD\\\", \\\"Column 11 3rd data row was incorrect. Use csv module to read \\\")\",\n \"def readData(filename):\\n #defining gobal variable (dataframe) to access it outside this function\\n global dataframe\\n #storing full CSV file into a dataframe(data structure)\\n dataframe = pd.read_csv(filename)\\n #type casting temperature column of dataframe to numeric data and ignoring '***' values\\n dataframe['Temperature'] = pd.to_numeric(dataframe['Temperature'], errors='coerce')\\n return dataframe\",\n \"def read_data(path, filename, drop_col=\\\"index\\\", dt=\\\"float32\\\"):\\n\\tdata = pd.read_csv(path + filename, sep=\\\",\\\", dtype=dt)\\n\\tdata = data.drop(drop_col, axis=1)\\n\\treturn data.as_matrix()\",\n \"def read_csv(csv_file):\\r\\n with open(csv_file, \\\"r\\\") as files:\\r\\n data = csv.reader(files)\\r\\n return list(data)\",\n \"def get_data(self, csv_file):\\n pass\",\n \"def _loadCSVFile(self):\\n self._df = pd.read_csv(\\n self._pathfile, sep=CSV_SEPARATOR, index_col=CSV_INDEX_COL)\",\n \"def get_df_from_csv(input_file_name):\\n df = pd.read_csv(input_file_name)\\n df = df.drop(['Unnamed: 0'], axis=1)\\n return df\",\n \"def csvread(infile):\\n out_csv = []\\n errors = []\\n index = -1\\n p = LineParser(csv=True)\\n for line in infile:\\n index += 1\\n try:\\n values = p.feed(line)\\n except ListQuoteError as e:\\n values = []\\n e.line = line\\n e.index = index\\n errors.append(e)\\n #\\n out_csv.append(values)\\n #\\n if errors:\\n e = CSVError(\\\"Parsing CSV failed. See 'errors' attribute.\\\")\\n e.csv = out_csv\\n e.errors = errors\\n raise e\\n return out_csv\",\n \"def read_csv_file(csv_fname, ignore_first_row = True):\\n \\n X, y = [], []\\n with open(csv_fname, 'r') as csv_file:\\n csv_reader = csv.reader(csv_file)\\n if ignore_first_row:\\n next(csv_reader)\\n for row in csv_reader:\\n X.append(row[:-1])\\n y.append(row[-1])\\n return np.array(X), np.array(y)\",\n \"def __load_csv(filename):\\n fp = open(Parser.DATA_FOLDER_PATH + filename + '.csv', 'r')\\n records = []\\n for line in fp:\\n items = line.strip().split(',')\\n x, y, z = '0', '0', '0'\\n if len(items) > 1:\\n x = items[1]\\n if len(items) > 2:\\n y = items[2]\\n if len(items) > 3:\\n z = items[3]\\n\\n values = [x, y, z]\\n records.append(values)\\n\\n # Discard some beginning data which may be noisy\\n # del records[:int(len(records) / 30)]\\n n = len(records)\\n\\n for i in range(n):\\n rec = []\\n # Consider X, Y, Z axes\\n for k in range(3):\\n # If can convert string to float\\n try:\\n val = float(records[i][k])\\n except ValueError:\\n val = 0\\n rec.append(val)\\n\\n # Replace it\\n records[i] = rec\\n return records\",\n \"def read_csv(\\n filepath: str,\\n **kwargs\\n) -> Union[DenseFunctionalData, IrregularFunctionalData]:\\n data = pd.read_csv(filepath, **kwargs)\\n\\n try:\\n all_argvals = data.columns.astype(np.int64).to_numpy()\\n except ValueError:\\n all_argvals = np.arange(0, len(data.columns))\\n\\n if not data.isna().values.any():\\n return _read_csv_dense(data, all_argvals)\\n else:\\n return _read_csv_irregular(data, all_argvals)\",\n \"def read_csv(self, path):\\n for file in os.listdir(path):\\n if file[-4:] == \\\".csv\\\":\\n name = file[:-4]\\n table_index_header = cfg.get_list(\\\"table_index_header\\\", name)\\n filename = os.path.join(path, file)\\n self.input_data[name] = pd.read_csv(\\n filename,\\n index_col=list(range(int(table_index_header[0]))),\\n header=list(range(int(table_index_header[1]))),\\n squeeze=(\\\"series\\\" not in name),\\n )\\n self.check_input_data(warning=False)\\n self.add_meta_data()\\n return self\",\n \"def test_spark_read_file_csv_delim(spark):\\n path = os.getcwd() + '/data_processor/tests/test_data/test.csv'\\n delim = ','\\n read_df = IOHandler(spark).spark_read_file(path, delim)\\n assert read_df.count() == 2\",\n \"def read_csv(filename, cols=None, nrows=None):\\n\\n datecols = ['date_time', 'srch_ci', 'srch_co']\\n dateparser = lambda x: pd.to_datetime(x, format='%Y-%m-%d %H:%M:%S',\\n errors='coerce')\\n dtypes = {\\n 'id': np.uint32,\\n 'site_name': np.uint8,\\n 'posa_continent': np.uint8,\\n 'user_location_country': np.uint16,\\n 'user_location_region': np.uint16,\\n 'user_location_city': np.uint16,\\n 'orig_destination_distance': np.float32,\\n 'user_id': np.uint32,\\n 'is_mobile': bool,\\n 'is_package': bool,\\n 'channel': np.uint8,\\n 'srch_adults_cnt': np.uint8,\\n 'srch_children_cnt': np.uint8,\\n 'srch_rm_cnt': np.uint8,\\n 'srch_destination_id': np.uint32,\\n 'srch_destination_type_id': np.uint8,\\n 'is_booking': bool,\\n 'cnt': np.uint64,\\n 'hotel_continent': np.uint8,\\n 'hotel_country': np.uint16,\\n 'hotel_market': np.uint16,\\n 'hotel_cluster': np.uint8,\\n }\\n\\n df = pd.read_csv(\\n filename,\\n nrows=nrows,\\n usecols=cols,\\n dtype=dtypes,\\n parse_dates=[col for col in datecols if col in cols],\\n date_parser=dateparser,\\n )\\n\\n if 'date_time' in df.columns:\\n df['month'] = df['date_time'].dt.month.astype(np.uint8)\\n df['year'] = df['date_time'].dt.year.astype(np.uint16)\\n\\n return df\",\n \"def process_file(filename):\\n csv_paths = read_csv(filename)\\n\\n all_columns = {\\n 'court', 'date', 'no', 'sys', 'reason', 'judgement', 'attachAsJudgement', 'attachments', 'type', 'historyHash', 'mainText', 'opinion', 'relatedIssues', 'party'\\n }\\n unused_columns = ['attachAsJudgement', 'attachments']\\n\\n data_len = 0\\n for csv_name, csv_path in tqdm(csv_paths):\\n print(csv_name)\\n processed_name = 'no_text_data/' + csv_name\\n df = dt.fread(csv_path)\\n\\n # remove unused columns\\n if 'attachAsJudgement' in df.keys():\\n del df[:, unused_columns]\\n else:\\n del df[:, 'attachments']\\n\\n # remove all text\\n del df[:, ['opinion']]\\n\\n data_len += df.shape[0]\\n df.to_csv(processed_name)\\n print(\\\"Total data count: {}\\\".format(data_len))\",\n \"def readExerciseCSV(file_info, verbose=False, sensor_id_category='id', data_types=None, quat_type=None, quat_order=\\\"WXYZ\\\"):\\n\\n tsID = file_info['tsID']\\n filepath = file_info['filepath']\\n\\n if quat_order.upper() is not \\\"WXYZ\\\" and quat_type is not None:\\n convert_quat = True\\n quat_order = quat_order.upper()\\n w_i = quat_order.index(\\\"W\\\")\\n x_i = quat_order.index(\\\"X\\\")\\n y_i = quat_order.index(\\\"Y\\\")\\n z_i = quat_order.index(\\\"Z\\\")\\n else:\\n convert_quat = False\\n\\n required_keys = set(['sample_number', 'mode', 'exercise_name', 'timestamp'])\\n if len(required_keys.intersection(file_info.keys())) == len(required_keys):\\n exercise_name = file_info['exercise_name']\\n mode = file_info['mode']\\n sample_number = file_info['sample_number']\\n timestamp = file_info['timestamp']\\n else:\\n print(\\\"Missing entries from required file_info argument.\\\")\\n return None\\n\\n csvReader = None\\n try:\\n inputcsv = open(filepath, 'r')\\n csvReader = csv.DictReader(inputcsv,\\n skipinitialspace=True,\\n delimiter=',',\\n quotechar='|')\\n\\n except NameError as e:\\n print(\\\"Error opening input file: {}\\\".format( e))\\n return None\\n\\n sensors = {}\\n\\n for row in csvReader:\\n sensor_id = row[sensor_id_category]\\n if sensor_id not in sensors:\\n sensors[sensor_id] = {}\\n for category in row:\\n if category == sensor_id_category:\\n continue\\n\\n try:\\n data = ast.literal_eval(row[category])\\n except:\\n print(\\\"readExerciseCSV: Failed to eval data {} in file {} on column {}\\\"\\n .format(filepath, row[category], category))\\n return None\\n\\n if convert_quat and category == quat_type:\\n data = [ data[w_i], data[x_i], data[y_i], data[z_i] ]\\n\\n if category not in sensors[sensor_id]:\\n sensors[sensor_id][category] = []\\n\\n sensors[sensor_id][category].append(data)\\n\\n inputcsv.close()\\n return {\\\"tsID\\\": tsID,\\n \\\"exercise_name\\\": exercise_name,\\n \\\"mode\\\" : mode,\\n \\\"sample_number\\\" : sample_number,\\n \\\"timestamp\\\": timestamp,\\n \\\"sensors\\\" : sensors}\",\n \"def read_csv_file(file_name):\\n table = []\\n with open(file_name) as csvfile:\\n csvreader = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\\n for row in csvreader:\\n table.append(row)\\n return table\",\n \"def csv_data_loader(path):\\n\\n logging.info(\\\"Loading file using SparkSession\\\")\\n csvload = Spark.instance.spark() \\\\\\n .read \\\\\\n .format(\\\"csv\\\") \\\\\\n .options(header=True) \\\\\\n .options(mode=\\\"DROPMALFORMED\\\")\\n\\n return csvload.option(\\\"inferSchema\\\", \\\"true\\\").load(path)\",\n \"def test_csvfile_single_row_of_data(fs: FakeFilesystem) -> None:\\n contents = \\\"\\\"\\\"\\\"a\\\",\\\"b\\\"\\n1,2\\\"\\\"\\\"\\n fs.create_file(\\\"test.csv\\\", contents=contents)\\n\\n adapter = CSVFile(\\\"test.csv\\\")\\n\\n assert adapter.get_columns() == {\\n \\\"a\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n \\\"b\\\": Float(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n }\\n assert list(adapter.get_data({}, [])) == [{\\\"a\\\": 1.0, \\\"b\\\": 2.0, \\\"rowid\\\": 0}]\",\n \"def __obtain_data_from_csv__(self, csvfile):\\n data = csvfile.readlines()\\n data = self.__parse_string_for_delimiter__(data)\\n return data\",\n \"def test_csv_reader_data_contents(process_data):\\n data = process_data(file_name_or_type='clean_map.csv')\\n\\n # Check row types\\n for row in data:\\n assert(isinstance(row['Country'], str))\\n assert(isinstance(row['City'], str))\\n assert(isinstance(row['State_Or_Province'], str))\\n assert(isinstance(row['Lat'], float))\\n assert(isinstance(row['Long'], float))\\n assert(isinstance(row['Altitude'], float))\\n\\n # Basic data checks\\n assert len(data) == 180 # We have collected 180 rows\\n assert data[0]['Country'] == 'Andorra'\\n assert data[106]['Country'] == 'Japan'\",\n \"def test_from_file_csv(self):\\n with TemporaryDirectory() as tmp:\\n fp, df_test = save_simple_dataframe(tmp, 'test.csv')\\n df_read = BaseDataClass.from_file(fp).df\\n self.assertEqual(\\n pd.testing.assert_frame_equal(df_test, df_read),\\n None,\\n )\",\n \"def load_csvFile(file_location, file_name,sep,encoding):\\n try:\\n fullpath=file_location+file_name\\n df = pd.read_csv(fullpath, encoding=encoding,sep=sep)\\n return df\\n except IOError:\\n print('Error loading the file: ' , file_name)\\n sys.exit(1)\",\n \"def read_data(self):\\n fpath = './data/surveys.csv'\\n self.data = pd.read_csv(fpath, header=0, low_memory=False)\\n #print(self.data.head(n=5))\\n print(self.data.shape)\",\n \"def read_csv(csv_path):\\n \\n df = pd.read_csv(csv_path)\\n\\n return df\",\n \"def read_csv(path):\\n csv_data =[]\\n \\n with open(path, 'r') as csv_file:\\n csv_read = csv.reader(csv_file, dialect='excel')\\n for row in csv_read:\\n csv_data.append(row)\\n\\n return(csv_data)\",\n \"def load_from_csv(path, delimiter=','):\\n return pd.read_csv(path,encoding = \\\"ISO-8859-1\\\",dtype=object)\",\n \"def test_csvfile_get_data_impossible_filter(fs: FakeFilesystem) -> None:\\n fs.create_file(\\\"test.csv\\\", contents=CONTENTS)\\n\\n adapter = CSVFile(\\\"test.csv\\\")\\n assert list(adapter.get_data({\\\"index\\\": Impossible()}, [])) == []\",\n \"def read_csv(path):\\n return pd.read_csv(path)\",\n \"def _read_csv(self, options, datas):\\n csv_data = datas\\n if not csv_data:\\n return iter([])\\n encoding = options.get('encoding')\\n if not encoding:\\n encoding = options['encoding'] = chardet.detect(csv_data)['encoding'].lower()\\n # some versions of chardet (e.g. 2.3.0 but not 3.x) will return\\n # utf-(16|32)(le|be), which for python means \\\"ignore / don't strip\\n # BOM\\\". We don't want that, so rectify the encoding to non-marked\\n # IFF the guessed encoding is LE/BE and csv_data starts with a BOM\\n bom = BOM_MAP.get(encoding)\\n if bom and csv_data.startswith(bom):\\n encoding = options['encoding'] = encoding[:-2]\\n if encoding != 'utf-8':\\n csv_data = csv_data.decode(encoding).encode('utf-8')\\n\\n separator = options.get('separator')\\n if not separator:\\n # default for unspecified separator so user gets a message about\\n # having to specify it\\n separator = ','\\n for candidate in (',', ';', '\\\\t', ' ', '|', unicodedata.lookup('unit separator')):\\n # pass through the CSV and check if all rows are the same\\n # length & at least 2-wide assume it's the correct one\\n it = pycompat.csv_reader(io.BytesIO(csv_data), quotechar=options['quoting'], delimiter=candidate)\\n w = None\\n for row in it:\\n width = len(row)\\n if w is None:\\n w = width\\n if width == 1 or width != w:\\n break # next candidate\\n else: # nobreak\\n separator = options['separator'] = candidate\\n break\\n csv_iterator = pycompat.csv_reader(\\n io.BytesIO(csv_data),\\n quotechar=options['quoting'],\\n delimiter=separator)\\n return (\\n row for row in csv_iterator\\n if any(x for x in row if x.strip())\\n )\",\n \"def import_from_csv(self) -> None:\\n logging.info('import_from_csv')\\n if self.target_table and str(self.target_table).lower() in [\\\"issue\\\", \\\"version\\\"]:\\n if self.file_path and exists(self.file_path):\\n # Read CSV file\\n csv_data = pd.read_csv(self.file_path).to_dict('records')\\n\\n # Import Version\\n if str(self.target_table).capitalize() == \\\"Version\\\":\\n # Overwrite option\\n if self.overwrite:\\n self.session.query(Version).delete()\\n click.echo('Overwrite Version table')\\n\\n for version in csv_data:\\n if all(item in list(version.keys()) for item in ['tag', 'start_date', 'end_date']):\\n newVersion=Version(\\n project_id=version['project_id'],\\n name=version[\\\"name\\\"], \\n tag=version[\\\"tag\\\"], \\n start_date=datetime.strptime(version[\\\"start_date\\\"], '%Y-%m-%d %H:%M:%S.%f'), \\n end_date=datetime.strptime(version[\\\"end_date\\\"], '%Y-%m-%d %H:%M:%S.%f'), \\n )\\n \\n try:\\n self.session.add(newVersion)\\n compute_version_metrics(self.session, self.configuration.current_branch, newVersion.project_id)\\n click.echo('Importing ' + str(len(csv_data)) + ' version(s) on database')\\n except Exception:\\n logging.error(Exception)\\n else:\\n logging.error(\\\"CSV file no contain minimal mandatory fields\\\")\\n sys.exit('CSV file no contain minimal mandatory fields')\\n\\n # Import Issue\\n if str(self.target_table).capitalize() == \\\"Issue\\\":\\n # Overwrite option\\n if self.overwrite:\\n self.session.query(Issue).delete()\\n click.echo('Overwrite Issue table')\\n\\n for issue in csv_data:\\n if all(item in list(issue.keys()) for item in ['number', 'created_at', 'updated_at']):\\n newIssue=Issue(\\n project_id=issue['project_id'],\\n number=issue[\\\"number\\\"],\\n title=issue[\\\"title\\\"],\\n created_at=datetime.strptime(issue[\\\"created_at\\\"], '%Y-%m-%d %H:%M:%S.%f'),\\n updated_at=datetime.strptime(issue[\\\"updated_at\\\"], '%Y-%m-%d %H:%M:%S.%f'))\\n\\n try:\\n self.session.add(newIssue)\\n click.echo('Importing ' + str(len(csv_data)) + ' issue(s) on database')\\n except Exception:\\n logging.error(Exception)\\n else:\\n logging.error(\\\"CSV file no contain minimal mandatory fields\\\")\\n sys.exit('CSV file no contain minimal mandatory fields') \\n\\n self.session.commit()\\n else:\\n logging.error('File not found')\\n sys.exit('File not found')\\n else:\\n logging.error('Target table not found')\\n sys.exit('Target table not found')\",\n \"def test_csvfile_different_types(fs: FakeFilesystem) -> None:\\n contents = '''\\\"a\\\"\\n1\\n2.0\\n\\\"test\\\"'''\\n fs.create_file(\\\"test.csv\\\", contents=contents)\\n\\n adapter = CSVFile(\\\"test.csv\\\")\\n\\n assert adapter.get_columns() == {\\n \\\"a\\\": String(\\n filters=[Range, Equal, NotEqual, IsNull, IsNotNull],\\n order=Order.NONE,\\n exact=True,\\n ),\\n }\",\n \"def read_csv(config, input_file_path):\\n header = read_csv_header(input_file_path)\\n\\n general = config['general']\\n date_cols_types = ['date_cols',\\n 'first_exp_date_cols',\\n 'last_exp_date_cols',\\n 'index_date_col',\\n 'lookback_date_col']\\n date_cols = utils.generate_list_columns(header, config, date_cols_types)\\n # it turns out we should read the dates first in as strings\\n date_cols_types = {date_col: str for date_col in date_cols}\\n df = pd.read_csv(input_file_path, dtype=date_cols_types)\\n # convert string dates to dates using the date format\\n # Large dataset, conversion done in parallel\\n if len(date_cols) > 50 or (df.shape[0] > 20000 and len(date_cols) > 1):\\n print('parallel!')\\n # we have to do this in parallel otherwise it takes forever\\n df[date_cols] = parse_utils.apply_parallel(df[date_cols],\\n parse_utils.parse_dates,\\n format=general['date_format'])\\n # Small dataset, faster to convert in non-parallel fashion\\n elif len(date_cols) > 0:\\n df[date_cols] = df[date_cols].apply(pd.to_datetime,\\n format=general['date_format'])\\n return df\",\n \"def read_csv(path):\\n column_headers = ['Country', 'Region', 'Pop. Density (per sq. mi.)', 'Infant mortality (per 1000 births)',\\n 'GDP ($ per capita) dollars']\\n df = pd.read_csv(path)\\n return df[column_headers]\",\n \"def read_csv_file(csv_file):\\n return cudf.read_csv(csv_file, delimiter=' ',\\n dtype=['int32', 'int32', 'float32'], header=None)\",\n \"def read_csv_file(file_name):\\n \\n with open(file_name, newline='') as csv_file: # don't need to explicitly close the file now\\n csv_table = []\\n csv_reader = csv.reader(csv_file, delimiter=',')\\n for row in csv_reader:\\n csv_table.append(row)\\n return csv_table\",\n \"def read_csv_file(input_csv_file_path):\\n with open(input_csv_file_path, 'r', encoding='utf-8') as file_path:\\n csv_reader = csv.reader(file_path)\\n for row in itertools.islice(csv_reader, 1, None):\\n yield (\\n int(row[0]), row[2],\\n datetime.datetime.strftime(\\n datetime.datetime.strptime(row[-1], '%m/%d/%y'),\\n '%Y-%m-%d'))\",\n \"def readCSV(filename):\\r\\n data = list( csv.reader(open('HW_08_DBScan_Data_NOISY_v300.csv','r'),delimiter=','))\\r\\n for dIdx in range(len(data)):\\r\\n data[dIdx] = [float(data[dIdx][0]),float(data[dIdx][1]),float(data[dIdx][2])]\\r\\n #print(data[0])\\r\\n return data\",\n \"def read_full_data(self):\\n x=[]\\n y=[]\\n z=[]\\n with open(self.file, 'r') as rf:\\n reader = csv.reader(rf, delimiter=',')\\n for row in reader:\\n x.append(row[0])\\n # Remove header from csv file, if it exists\\n if x[0].split()[0] == '%':\\n x.remove(row[0])\\n else:\\n y.append(row[1])\\n z.append(row[2])\\n return x,y,z\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7320947","0.6934107","0.6727932","0.6726128","0.6720706","0.6720139","0.67085135","0.66700244","0.65878505","0.6561937","0.64199805","0.64198834","0.6413396","0.6406278","0.64025426","0.63658756","0.63533485","0.63505536","0.63503546","0.63334334","0.63267404","0.631348","0.6250852","0.6246433","0.6237337","0.62360805","0.62081647","0.6188999","0.61843365","0.61813664","0.61801046","0.6173528","0.61678964","0.61594003","0.6154207","0.6131353","0.6130938","0.61207646","0.61121136","0.6078378","0.60657936","0.6057842","0.6042885","0.60380113","0.6025305","0.602112","0.60165334","0.6015546","0.60149974","0.60142744","0.60110116","0.60041416","0.6001746","0.6000014","0.59967244","0.5991959","0.5982435","0.5978018","0.59535646","0.5953433","0.5948274","0.5939153","0.5935041","0.5929416","0.5923107","0.59010446","0.58997536","0.58979136","0.5891135","0.58875823","0.58782864","0.58735555","0.5863497","0.58612835","0.5861185","0.5860388","0.58584535","0.5855206","0.58478034","0.5829574","0.5816927","0.58120674","0.5811532","0.579698","0.5793567","0.5792295","0.57869124","0.57849586","0.5784356","0.5784286","0.5781253","0.5777831","0.5772065","0.57705534","0.57682735","0.57650745","0.57571894","0.57334733","0.5733209","0.57266814"],"string":"[\n \"0.7320947\",\n \"0.6934107\",\n \"0.6727932\",\n \"0.6726128\",\n \"0.6720706\",\n \"0.6720139\",\n \"0.67085135\",\n \"0.66700244\",\n \"0.65878505\",\n \"0.6561937\",\n \"0.64199805\",\n \"0.64198834\",\n \"0.6413396\",\n \"0.6406278\",\n \"0.64025426\",\n \"0.63658756\",\n \"0.63533485\",\n \"0.63505536\",\n \"0.63503546\",\n \"0.63334334\",\n \"0.63267404\",\n \"0.631348\",\n \"0.6250852\",\n \"0.6246433\",\n \"0.6237337\",\n \"0.62360805\",\n \"0.62081647\",\n \"0.6188999\",\n \"0.61843365\",\n \"0.61813664\",\n \"0.61801046\",\n \"0.6173528\",\n \"0.61678964\",\n \"0.61594003\",\n \"0.6154207\",\n \"0.6131353\",\n \"0.6130938\",\n \"0.61207646\",\n \"0.61121136\",\n \"0.6078378\",\n \"0.60657936\",\n \"0.6057842\",\n \"0.6042885\",\n \"0.60380113\",\n \"0.6025305\",\n \"0.602112\",\n \"0.60165334\",\n \"0.6015546\",\n \"0.60149974\",\n \"0.60142744\",\n \"0.60110116\",\n \"0.60041416\",\n \"0.6001746\",\n \"0.6000014\",\n \"0.59967244\",\n \"0.5991959\",\n \"0.5982435\",\n \"0.5978018\",\n \"0.59535646\",\n \"0.5953433\",\n \"0.5948274\",\n \"0.5939153\",\n \"0.5935041\",\n \"0.5929416\",\n \"0.5923107\",\n \"0.59010446\",\n \"0.58997536\",\n \"0.58979136\",\n \"0.5891135\",\n \"0.58875823\",\n \"0.58782864\",\n \"0.58735555\",\n \"0.5863497\",\n \"0.58612835\",\n \"0.5861185\",\n \"0.5860388\",\n \"0.58584535\",\n \"0.5855206\",\n \"0.58478034\",\n \"0.5829574\",\n \"0.5816927\",\n \"0.58120674\",\n \"0.5811532\",\n \"0.579698\",\n \"0.5793567\",\n \"0.5792295\",\n \"0.57869124\",\n \"0.57849586\",\n \"0.5784356\",\n \"0.5784286\",\n \"0.5781253\",\n \"0.5777831\",\n \"0.5772065\",\n \"0.57705534\",\n \"0.57682735\",\n \"0.57650745\",\n \"0.57571894\",\n \"0.57334733\",\n \"0.5733209\",\n \"0.57266814\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77133363"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":9992,"cells":{"query":{"kind":"string","value":"Create a mechanism to vectorize the data Create vector for our current dataset"},"document":{"kind":"string","value":"def build_model(self, documents):\n self.vectorizer = TfidfVectorizer(\n stop_words='english', lowercase=True).fit(documents)\n self.vectors = self.vectorizer.transform(documents)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def vectorize(self):\n return vectorize(self)","def _vectorize_data(self, docs: []):\n print('Vectorizing data...')\n tfidf = TfidfVectorizer()\n encoded_data = tfidf.fit_transform(docs)\n return encoded_data","def get_data_vector(self) -> DataVector:\n raise NotImplementedError(\"Method `get_data_vector` is not implemented!\")","def vectorized_loops(self, data):\n\n # TODO: finish this.\n return np.add(np.multiply(data,data), data)","def numpy_vector(self):\n pass","def data_vector(self) -> np.ndarray:\r\n return np.dot(\r\n self.linear_obj_list[0].mapping_matrix.T, self.w_tilde.dirty_image\r\n )","def vectorize_data(data, word_idx, sentence_size, memory_size):\n S = []\n Q = []\n A = []\n for story, query, answer in data:\n ss = []\n for i, sentence in enumerate(story, 1):\n ls = max(0, sentence_size - len(sentence))\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\n\n # take only the most recent sentences that fit in memory\n ss = ss[::-1][:memory_size][::-1]\n\n # Make the last word of each sentence the time 'word' which \n # corresponds to vector of lookup table\n for i in range(len(ss)):\n ss[i][-1] = len(word_idx) - memory_size - i + len(ss)\n\n # pad to memory_size\n lm = max(0, memory_size - len(ss))\n for _ in range(lm):\n ss.append([0] * sentence_size)\n\n lq = max(0, sentence_size - len(query))\n q = [word_idx[w] for w in query] + [0] * lq\n\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\n for a in answer:\n y[word_idx[a]] = 1\n\n S.append(ss)\n Q.append(q)\n A.append(y)\n return np.array(S), np.array(Q), np.array(A)","def CreateVector(self) -> BaseVector:","def non_vectorized_loops(self, data):\n\n non_vectorized = np.zeros(data.shape)\n for row in range(data.shape[0]):\n for col in range(data.shape[1]):\n non_vectorized[row][col] = (data[row][col] * data[row][col] +\n data[row][col])\n return non_vectorized","def create_vector(self):\n\n series = {'PLANNEDTIME_ARR': [int(self.planned_arrival)],\n 'rain': [float(self.rain)],\n 'temp': [float(self.temp)],\n 'distance_centre': [float(self.distance_centre)],\n 'day_into_year': [int(self.t_minus_1118)],\n 'STOPPOINTID_label': [int(self.stop_id)],\n 'DAYOFWEEK_label': [int(self.day_of_week)],\n 'MONTH_label': [int(self.month)],\n 'holiday_label': [int(self.holiday)],\n 'cluster_label': [int(self.cluster)]\n }\n\n self.vector = pd.DataFrame(series)","def AsVector(self) -> BaseVector:","def _vectorize(self, vectorizer = None):\n\n\t\tvectorizer = vectorizer if vectorizer else self.vectorizer;\n\n\t\tself.training_set_vector = vectorizer.fit_transform(self.training_set)\n\n\t\tself.testing_set_vector = vectorizer.transform(self.testing_set)","def calc_vector_from_ndarray(data):\n return data[1:] - data[0:-1]","def create_vectors(self):\n self.localStatistics = []\n self.lastStatistics = []\n self.globalV = []\n self.estimate = []\n self.delta = []\n self.drift = []\n self.slack = [] # only for coordBased model","def numpy_container_gen_data(no_substripes):\n q11vec = np.zeros(no_substripes, dtype=float)\n q12vec = np.zeros(no_substripes, dtype=float)\n q21vec = np.zeros(no_substripes, dtype=float)\n q22vec = np.zeros(no_substripes, dtype=float)\n x1vec = np.zeros(no_substripes, dtype=float)\n x2vec = np.zeros(no_substripes, dtype=float)\n y1vec = np.zeros(no_substripes, dtype=float)\n y2vec = np.zeros(no_substripes, dtype=float)\n xvec = np.zeros(no_substripes, dtype=float)\n yvec = np.zeros(no_substripes, dtype=float)\n\n for idx in range(no_substripes):\n q11vec[idx], q12vec[idx], q21vec[idx], q22vec[idx], x1vec[idx], \\\n x2vec[idx], y1vec[idx], y2vec[idx], xvec[idx], \\\n yvec[idx] = generate_data()\n\n return q11vec, q12vec, q21vec, q22vec, x1vec, x2vec, y1vec, y2vec, xvec, \\\n yvec","def create(cls, vals: npt.NDArray[np.float64]) -> DataVector:\n return vals.view(cls)","def vectorize_method(func):\n @wraps(func)\n def inner(obj, *iargs, **ikwargs):\n\n def functionalized_method(*args, **kwargs):\n return func(obj, *args, **kwargs)\n\n return np.vectorize(functionalized_method, otypes=[np.dtype('c16')])(*iargs, **ikwargs)\n #return np.vectorize(functionalized_method)(*iargs, **ikwargs)\n return inner","def vectorize(self, penalty: float):\n self._vectorized_data = self._property_data.vectorize(penalty)","def make_data(self): \n s = numpy.arange(0.0, 10.0, 0.01)\n s = numpy.reshape(s, (10,10,10))\n s = numpy.transpose(s)\n\n v = numpy.zeros(3000, 'd')\n v[1::3] = 1.0\n v = numpy.reshape(v, (10,10,10,3))\n return s, v","def return_vec(self) :\r\n y_vec = np.concatenate((self.x_vec,self.v_vec))\r\n return y_vec","def make_data_vectors(data):\n result_dict = {'adjusted_close': list(), \n 'raw_close': list(),\n 'high': list(),\n 'low': list(),\n 'open':list(),\n 'volume': list(),\n 'date': list()\n }\n for datum in data:\n ratio = get_adjusted_ratio(datum)\n \n result_dict['adjusted_close'].append(datum['Adj Clos']) if \\\n 'Adj Clos' in datum else result_dict['adjusted_close'].append(None)\n result_dict['raw_close'].append(datum['Close'] * ratio) if \\\n 'Close' in datum else result_dict['raw_close'].append(None)\n result_dict['high'].append(datum['High'] * ratio) if \\\n 'High' in datum else result_dict['high'].append(None)\n result_dict['low'].append(datum['Low'] * ratio) if 'Low' in datum \\\n else result_dict['low'].append(None)\n result_dict['open'].append(datum['Open'] * ratio) if 'Open' in datum \\\n else result_dict['open'].append(None)\n result_dict['volume'].append(datum['Volume']) if 'Volume' in datum \\\n else result_dict['volume'].append(None)\n result_dict['date'].append(datum['date']) if 'date' in datum \\\n else result_dict['date'].append(None)\n\n volume = np.array(result_dict['volume'])\n adj_close = np.array(result_dict['adjusted_close'])\n close = np.array(result_dict['raw_close'])\n high = np.array(result_dict['high'])\n low = np.array(result_dict['low'])\n open_p = np.array(result_dict['open'])\n date = np.array(result_dict['date'])\n\n return {'open':open_p, 'close':close, 'low':low, \n 'high':high, 'adj_close':adj_close, \n 'volume':volume, 'date':date}","def __call__(self):\n return self._vector","def AsVector(self) -> ngsolve.la.BaseVector:","def vectorize_data(self, idf=False):\r\n vectorizer = Vectorizer()\r\n return vectorizer.vectorize_data(self.tweets, idf)","def _vectorize_and_sparsify_data(self,\n vec: Vectorizer,\n ids: List[str],\n batch_size: int = 50) \\\n -> sp.sparse.csr.csr_matrix:\n\n X = []\n samples = self._generate_samples(ids, 'x')\n while True:\n X_list = list(take(batch_size, samples))\n if not X_list: break\n X_part = vec.transform(X_list)\n del X_list\n X.append(X_part)\n del X_part\n\n return sp.sparse.csr_matrix(np.vstack([x.todense() for x in X]))","def create_vectorized_features(data_dir, feature_version=2):\n extractor = PEFeatureExtractor(feature_version)\n\n print(\"Vectorizing training set\")\n X_path = os.path.join(data_dir, \"X_train.dat\")\n y_path = os.path.join(data_dir, \"y_train.dat\")\n raw_feature_paths = [os.path.join(data_dir, \"train_features_{}.jsonl\".format(i)) for i in range(6)]\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\n\n print(\"Vectorizing test set\")\n X_path = os.path.join(data_dir, \"X_test.dat\")\n y_path = os.path.join(data_dir, \"y_test.dat\")\n raw_feature_paths = [os.path.join(data_dir, \"test_features.jsonl\")]\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)","def vectorize_data(self, data, idf=False):\r\n\r\n # collect only the cleaned text of the tweet\r\n text = []\r\n for tweet in data:\r\n if not tweet.get_processed_text():\r\n tweet.set_processed_text(self.clean_tweet(tweet))\r\n text.append(tweet.get_processed_text())\r\n\r\n # vectorize tweets\r\n\r\n if idf:\r\n vectorizer = TfidfVectorizer(min_df=((len(data) // 1000) + 1), max_df=10000, ngram_range=(1, 3))\r\n else:\r\n vectorizer = CountVectorizer(min_df=((len(data) // 1000) + 1), max_df=10000, ngram_range=(1, 3))\r\n\r\n # vectorizer = TFVectorizing()\r\n vectors = vectorizer.fit_transform(text)\r\n return vectors","def generate_vector(self,dim=0,v=None):\n vec = dl.Vector()\n self.init_vector(vec,dim)\n if v is not None:\n vec[:]=v\n return vec","def get_vector_representation(self):\r\n vectorizer = CountVectorizer(lowercase=False,\r\n tokenizer=lambda x: x, # Tokenization should already be done by preprocessor\r\n stop_words=None,\r\n min_df=5,\r\n max_features=None, ## use all features\r\n ngram_range=(1, 1), ## This uses only unigram counts\r\n binary=False) ## This sets the beatures to be frequency counts\r\n pipeline = Pipeline([('vec', vectorizer), ('tfidf', TfidfTransformer())])\r\n\r\n X = pipeline.fit_transform(self.df['tokens'])\r\n Y = self.df['label'].values\r\n return ((X[:self.train_index], Y[:self.train_index]),\r\n (X[self.train_index:self.valid_index], Y[self.train_index:self.valid_index]),\r\n (X[self.valid_index:], Y[self.valid_index:]))","def vectorize_unpack(args):\n return vectorize(*args)","def vectorize_unpack(args):\n return vectorize(*args)","def vec(self):\n return np.matrix(self.val.ravel()).transpose()","def transform(self, data):\n unflattened = [unflatten_vec(d) for d in data]\n return numpy.array([self.signature(uf)\n for uf in unflattened])","def Generate_Custom(f, n, m):\n return np.fromfunction(np.vectorize(f, otypes=[float]), (n,m))","def vectorize(self,clean_path):\n \n #load pretrained embedding model (GloVe)\n glove = spacy.load('en_core_web_lg')\n #extract unique words (aka vocabulary)\n unique_words = set()\n for d in self.docs: \n txt = d.text\n doc = glove(txt)\n for word in doc: \n if word.has_vector:\n unique_words.add(word.text)\n #change set to list type\n unique_words = list(unique_words)\n #save vector representation\n word_vectors = np.array([glove(word).vector for word in unique_words if glove(word).has_vector])\n #index vectors by corresponding word \n corpus_vectors = pd.DataFrame(word_vectors, index=unique_words)\n with open(clean_path + 'corpus_vectors.pkl', 'wb') as f:\n pickle.dump(corpus_vectors,f)\n self.vectors = corpus_vectors\n print('Saved embedding vectors.')\n return","def result_to_vector(results):\n return [vectorized_result(x) for x in results]","def build(self, data: np.ndarray):\n ret = data.dot(self.eigenvectors)\n self.pca_predictor_vars = ret\n return ret","def vectorized_result(y):\r\n\r\n e = np.zeros((10,1))\r\n ## making an array of length 10 having zero in all the indices\r\n\r\n e[y] = 1.0\r\n ## at index y assigning the value 1.0\r\n\r\n return e","def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):\r\n \r\n data = super().vectorize(source_text, target_text, use_dataset_max_lengths)\r\n \r\n mltm_x_vector = self.mltm_vectorizer.vectorize(source_text.lower())\r\n mltm_x_vector = mltm_x_vector.astype(np.float32)\r\n \r\n data[\"x_source_mltm_vector\"] = mltm_x_vector\r\n return data","def fvector(data, method ):\n\n fv = 0\n if method['type'] == 'lbp':\n \n\n lbpkern = lbpsimple.generateKernel2()\n \n imlbp = lbpsimple.lbp2oneslice(data, lbpkern)\n\n fv,bins = lbpsimple.features(imlbp)\n\n #pdb.set_trace();\n elif method['type'] == 'hist':\n \n fv, bins = numpy.histogram( data,range(-200,2000,20))\n fv = fv[10:15]\n #fv, bins = numpy.histogram( data)\n pass\n\n else:\n raise Exception('Unknow method for feature vector: %s' %(method))\n\n return fv","def reflect(data, mapfunc = lambda x:x):\n data2 = np.zeros([tsize, npsi])\n # Copy the original data\n for i in np.arange(ntheta):\n data2[i,:] = data[i,:]\n # Now fill in the remainder\n for i in np.arange(ntheta, tsize):\n t0 = tsize - 1 - i\n data2[i,:] = mapfunc(data[t0,:])\n return data2","def to_vec(self):\n column_count = self.column_count\n vector = Matrix(size=(self.row_count * column_count, 1))\n for key, value in self.data.items():\n row, column = key[:2]\n subscript = (column * column_count + row, 0)\n vector.set(subscript, value)\n return vector","def vectorize(self, *args, **kwargs):\n kwargs['add_start'] = False\n kwargs['add_end'] = False\n return TorchRankerAgent.vectorize(self, *args, **kwargs)","def vectorize(self, phrase):\n return np.array([0])","def _asvector(self, arr):\n result = self._moveaxis(arr, [-2, -1], [0, 1])\n return self.domain.element(result)","def as_vector(self) -> np.ndarray:\n psi = self.A[0]\n for i in range(1, len(self.A)):\n psi = merge_mps_tensor_pair(psi, self.A[i])\n assert psi.ndim == 3\n assert psi.shape[1] == 1 and psi.shape[2] == 1\n return psi.reshape(-1)","def hash_vector(self,v):\r\n # you will need to use self.functions for this method\r\n x = np.array([f(v[0]) for f in self.functions])\r\n #print (x)\r\n return x\r\n raise NotImplementedError","def create_vectorizer(ds):\n vectorize_layer = TextVectorization(\n standardize=clean_text,\n split=\"whitespace\",\n max_tokens=MAX_WORDS - 1,\n output_mode=\"int\",\n output_sequence_length=MAX_LEN,\n )\n vectorize_layer.adapt(ds.map(lambda text, label: text))\n return vectorize_layer","def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\n terms = list(self.index.get_terms())\n terms.sort()\n vector = np.zeros(len(terms))\n\n for (term, weight) in query_vector:\n index = terms.index(term)\n vector[index] = weight\n\n return vector","def feed(self, vector):\n return vector","def augment_data(self,data):\n d = np.empty([data.shape[0]+2,data.shape[1]+2])\n d[1:-1,1:-1] = data\n d[0,1:-1] = data[0,:]\n d[1:-1,0] = data[:,0]\n d[-1,1:-1] = data[-1,:]\n d[1:-1,-1] = data[:,-1]\n d[0,0] = data[0,0]\n d[-1,-1] = data[-1,-1]\n d[0,-1] = data[0,-1]\n d[-1,0] = data[-1,0]\n return d","def _process(self, data: np.ndarray) -> np.ndarray:\n if not self.is_trained:\n raise DataProcessorError(\"SVD must be trained on data before it can be used.\")\n\n # IQ axis is reduced by projection\n if self._n_shots == 0:\n # level1 average mode\n dims = self._n_circs, self._n_slots\n else:\n # level1 single mode\n dims = self._n_circs, self._n_shots, self._n_slots\n\n projected_data = np.zeros(dims, dtype=object)\n\n for idx in range(self._n_slots):\n scale = self.parameters.scales[idx]\n axis = self.parameters.main_axes[idx]\n mean_i = self.parameters.i_means[idx]\n mean_q = self.parameters.q_means[idx]\n\n if self._n_shots != 0:\n # Single shot\n for circ_idx in range(self._n_circs):\n centered = [\n data[circ_idx, :, idx, 0] - mean_i,\n data[circ_idx, :, idx, 1] - mean_q,\n ]\n projected_data[circ_idx, :, idx] = axis @ np.array(centered) / scale\n else:\n # Averaged\n centered = [data[:, idx, 0] - mean_i, data[:, idx, 1] - mean_q]\n projected_data[:, idx] = axis @ np.array(centered) / scale\n\n return projected_data","def wrapDBVector(self,vec):\n return vec.todense()","def apply(self: T, target: types.Vector) -> types.Vector:","def _compute_new_data(self, desvars, responses, objectives, constraints, metadata):\n # type: (dict, dict, dict, dict, dict) -> np.ndarray\n raise NotImplementedError","def get_vector(self, name: str) -> ndarray:\n vector_path = self._get_path(name)\n vector_df = DataFrame(read_csv(vector_path, header=None))\n ixs = list(map(lambda i: i - 1, vector_df.iloc[:, 0]))\n vals = vector_df.iloc[:, 1]\n return util.create_array(ixs, vals, self.number_of_nodes)","def apply(self, v):\n u = np.zeros(self.Dimension, dtype=complex)\n for me in self.Elements:\n for index in range(v.Elements.size):\n if index == me.j:\n u[me.i] += me.val * v.Elements[index]\n u = Vector(u) \n return u","def flatten_to_vector(data):\n depth = len(np.array(data).shape)\n \n if depth == 2:\n return data\n else: \n vector_depth = 2\n dimension_reduction_depth = vector_depth + 1\n \n return flatten_to_depth(data, current_depth = len(np.array(data).shape), stop_depth = dimension_reduction_depth)","def vectorized(self):\n return False","def toVector(tupL,n,v):\n v = numpy.zeros(n)\n for (i,val) in tupL:\n #print i,v\n v[i]=val\n return v","def process(self, data):\n allocating = (self._output is None)\n ind = 0\n for i, (name, feature) in enumerate(self.features):\n if allocating:\n x = feature.compute(data)\n self.feature_indices[name] = (ind, ind+x.size)\n ind += x.size\n\n if self._output is None:\n self._output = x\n else:\n self._output = np.hstack([self._output, x])\n else:\n self._output[self.feature_indices[name][0]:\n self.feature_indices[name][1]] = \\\n feature.compute(data)\n\n return self._output","def to_countvectors(self):\n if hasattr(self, \"ifp\"):\n df = self.to_dataframe()\n return to_countvectors(df)\n raise AttributeError(\"Please use the `run` method before\")","def apply_new_basis(new_base, vector_array):\n return np.dot(new_base, vector_array).T","def convert2Vec(self, data, vec):\r\n\t\tif (type(vec) == c_int):\r\n\t\t\treturn c_int(data[0])\r\n\t\tif (type(vec) == c_float):\r\n\t\t\treturn c_float(data[0])\r\n\t\tfor i in range(0, len(vec)):\r\n\t\t\tvec[i] = data[i]\r\n\t\treturn vec","def tovector(self):\n return np.hstack(self.values())","def set_vector(self, i_vec, nda):\n if i_vec<0 or i_vec>len(self.cumulative_length.nda)-1:\n print('VectorOfVectors: Error: bad i_vec', i_vec)\n return \n if len(nda.shape) != 1:\n print('VectorOfVectors: Error: nda had bad shape', nda.shape)\n return\n start = 0 if i_vec == 0 else self.cumulative_length.nda[i_vec-1]\n end = start + len(nda)\n while end >= len(self.flattened_data.nda):\n self.flattened_data.nda.resize(2*len(self.flattened_data.nda))\n self.flattened_data.nda[start:end] = nda\n self.cumulative_length.nda[i_vec] = end","def randvec(self, X):\n raise NotImplementedError","def vectorize_data(data, word_idx, sentence_size, memory_size,answer_size):\n S = []\n Q = []\n AS = []\n L = []\n label_nums = set([len(dt[2]) for dt in data])\n assert(len(label_nums) == 1)\n label_num = list(label_nums)[0]\n for story, query, answer, label in data:\n lq = max(0, sentence_size - len(query))\n q = [word_idx[w] for w in query] + [0] * lq\n\n ss = []\n for i, sentence in enumerate(story, 1):\n ls = max(0, sentence_size - len(sentence))\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\n\n if len(ss) > memory_size:\n # Use Jaccard similarity to determine the most relevant sentences\n q_words = set(q)\n least_like_q = sorted(ss, cmp = lambda x, y: jaccard(set(x), q_words) < jaccard(set(y), q_words))[:len(ss)-memory_size]\n for sent in least_like_q:\n # Remove the first occurrence of sent. A list comprehension as in [sent for sent in ss if sent not in least_like_q]\n # should not be used, as it would remove multiple occurrences of the same sentence, some of which might actually make the cutoff.\n ss.remove(sent)\n else:\n # pad to memory_size\n lm = max(0, memory_size - len(ss))\n for _ in range(lm):\n ss.append([0] * sentence_size)\n\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\n\n sa = []\n for i, sentence in enumerate(answer, 1):\n ls = max(0, answer_size - len(sentence))\n sa.append([word_idx[w] for w in sentence] + [0] * ls)\n\n lb = np.zeros(label_num)\n lb[int(label)] = 1\n\n S.append(ss)\n Q.append(q)\n AS.append(sa)\n L.append(lb)\n Q = pad_sequences(Q, sentence_size)\n\n return np.array(S), np.array(Q), np.array(AS), np.array(L)","def to_vect(self):\n # Make sure mems dimension is last\n return np.reshape(self.transpose('validtime','y','x','mem').to_array().values, (self.nstate(), self.nmems()))","def get_vectors(self, corpus_size, vectors_size, vectors_type):\n vectors = np.zeros((corpus_size, vectors_size))\n for i in range(0, corpus_size):\n prefix = vectors_type + '_' + str(i)\n vectors[i] = self.model_dbow.docvecs[prefix]\n return vectors","def __init__(self, data):\n\n self.xs = np.array(sorted(data))\n self.N = float(len(self.xs))\n self.ys = np.arange(1, self.N + 1) / self.N","def apply(self, vec):\n raise NotImplementedError","def vec(x, y=0.0, z=0.0):\n return np.matrix((x, y, z)).T","def VectorField(\n adata: anndata.AnnData,\n basis: Union[None, str] = None,\n layer: Union[None, str] = None,\n dims: Union[int, list, None] = None,\n genes: Union[list, None] = None,\n normalize: bool = False,\n grid_velocity: bool = False,\n grid_num: int = 50,\n velocity_key: str = \"velocity_S\",\n method: str = \"SparseVFC\",\n min_vel_corr: float = 0.6,\n restart_num: int = 5,\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\n model_buffer_path: Union[str, None] = None,\n return_vf_object: bool = False,\n map_topography: bool = False,\n pot_curl_div: bool = False,\n cores: int = 1,\n result_key: Union[str, None] = None,\n copy: bool = False,\n **kwargs,\n) -> Union[anndata.AnnData, base_vectorfield]:\n logger = LoggerManager.gen_logger(\"dynamo-topography\")\n logger.info(\"vectorfield calculation begins...\", indent_level=1)\n logger.log_time()\n adata = copy_adata(adata) if copy else adata\n\n if basis is not None:\n logger.info(\n \"Retrieve X and V based on basis: %s. \\n \"\n \" Vector field will be learned in the %s space.\" % (basis.upper(), basis.upper())\n )\n X = adata.obsm[\"X_\" + basis].copy()\n V = adata.obsm[\"velocity_\" + basis].copy()\n\n if np.isscalar(dims):\n X, V = X[:, :dims], V[:, :dims]\n elif type(dims) is list:\n X, V = X[:, dims], V[:, dims]\n else:\n logger.info(\n \"Retrieve X and V based on `genes`, layer: %s. \\n \"\n \" Vector field will be learned in the gene expression space.\" % layer\n )\n valid_genes = (\n list(set(genes).intersection(adata.var.index))\n if genes is not None\n else adata.var_names[adata.var.use_for_transition]\n )\n if layer == \"X\":\n X = adata[:, valid_genes].X.copy()\n X = np.expm1(X)\n else:\n X = inverse_norm(adata, adata.layers[layer])\n\n V = adata[:, valid_genes].layers[velocity_key].copy()\n\n if sp.issparse(X):\n X, V = X.A, V.A\n\n Grid = None\n if X.shape[1] < 4 or grid_velocity:\n logger.info(\"Generating high dimensional grids and convert into a row matrix.\")\n # smart way for generating high dimensional grids and convert into a row matrix\n min_vec, max_vec = (\n X.min(0),\n X.max(0),\n )\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\n\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\n Grid = np.array([i.flatten() for i in Grid_list]).T\n\n if X is None:\n raise Exception(f\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\")\n elif V is None:\n raise Exception(\"V is None. Make sure you passed the correct V.\")\n\n logger.info(\"Learning vector field with method: %s.\" % (method.lower()))\n if method.lower() == \"sparsevfc\":\n vf_kwargs = {\n \"M\": None,\n \"a\": 5,\n \"beta\": None,\n \"ecr\": 1e-5,\n \"gamma\": 0.9,\n \"lambda_\": 3,\n \"minP\": 1e-5,\n \"MaxIter\": 30,\n \"theta\": 0.75,\n \"div_cur_free_kernels\": False,\n \"velocity_based_sampling\": True,\n \"sigma\": 0.8,\n \"eta\": 0.5,\n \"seed\": 0,\n }\n elif method.lower() == \"dynode\":\n try:\n from dynode.vectorfield import networkModels\n from dynode.vectorfield.samplers import VelocityDataSampler\n\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\n from dynode.vectorfield.losses_weighted import MSE\n from .scVectorField import dynode_vectorfield\n except ImportError:\n raise ImportError(\"You need to install the package `dynode`.\" \"install dynode via `pip install dynode`\")\n\n velocity_data_sampler = VelocityDataSampler(adata={\"X\": X, \"V\": V}, normalize_velocity=normalize)\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\n\n cwd, cwt = os.getcwd(), datetime.datetime.now()\n\n if model_buffer_path is None:\n model_buffer_path = cwd + \"/\" + basis + \"_\" + str(cwt.year) + \"_\" + str(cwt.month) + \"_\" + str(cwt.day)\n main_warning(\"the buffer path saving the dynode model is in %s\" % (model_buffer_path))\n\n vf_kwargs = {\n \"model\": networkModels,\n \"sirens\": False,\n \"enforce_positivity\": False,\n \"velocity_data_sampler\": velocity_data_sampler,\n \"time_course_data_sampler\": None,\n \"network_dim\": X.shape[1],\n \"velocity_loss_function\": MSE(), # CosineDistance(), # #MSE(), MAD()\n # BinomialChannel(p=0.1, alpha=1)\n \"time_course_loss_function\": None,\n \"velocity_x_initialize\": X,\n \"time_course_x0_initialize\": None,\n \"smoothing_factor\": None,\n \"stability_factor\": None,\n \"load_model_from_buffer\": False,\n \"buffer_path\": model_buffer_path,\n \"hidden_features\": 256,\n \"hidden_layers\": 3,\n \"first_omega_0\": 30.0,\n \"hidden_omega_0\": 30.0,\n }\n train_kwargs = {\n \"max_iter\": int(max_iter),\n \"velocity_batch_size\": 50,\n \"time_course_batch_size\": 100,\n \"autoencoder_batch_size\": 50,\n \"velocity_lr\": 1e-4,\n \"velocity_x_lr\": 0,\n \"time_course_lr\": 1e-4,\n \"time_course_x0_lr\": 1e4,\n \"autoencoder_lr\": 1e-4,\n \"velocity_sample_fraction\": 1,\n \"time_course_sample_fraction\": 1,\n \"iter_per_sample_update\": None,\n }\n else:\n raise ValueError(\"current only support two methods, SparseVFC and dynode\")\n\n vf_kwargs = update_dict(vf_kwargs, kwargs)\n\n if restart_num > 0:\n if len(restart_seed) != restart_num:\n main_warning(\n f\"the length of {restart_seed} is different from {restart_num}, \" f\"using `np.range(restart_num) * 100\"\n )\n restart_seed = np.arange(restart_num) * 100\n restart_counter, cur_vf_list, res_list = 0, [], []\n while True:\n if method.lower() == \"sparsevfc\":\n kwargs.update({\"seed\": restart_seed[restart_counter]})\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n cur_vf_dict = VecFld.train(**train_kwargs)\n\n # consider refactor with .simulation.evaluation.py\n reference, prediction = (\n cur_vf_dict[\"Y\"][cur_vf_dict[\"valid_ind\"]],\n cur_vf_dict[\"V\"][cur_vf_dict[\"valid_ind\"]],\n )\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\n\n cur_vf_list += [cur_vf_dict]\n res_list += [res]\n if res < min_vel_corr:\n restart_counter += 1\n main_info(\n f\"current cosine correlation between input velocities and learned velocities is less than \"\n f\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\",\n indent_level=2,\n )\n else:\n vf_dict = cur_vf_dict\n break\n\n if restart_counter > restart_num - 1:\n main_warning(\n f\"Cosine correlation between input velocities and learned velocities is less than\"\n f\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\"\n )\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\n\n break\n else:\n if method.lower() == \"sparsevfc\":\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\n elif method.lower() == \"dynode\":\n train_kwargs = update_dict(train_kwargs, kwargs)\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\n # {\"VecFld\": VecFld.train(**kwargs)}\n vf_dict = VecFld.train(**train_kwargs)\n\n if result_key is None:\n vf_key = \"VecFld\" if basis is None else \"VecFld_\" + basis\n else:\n vf_key = result_key if basis is None else result_key + \"_\" + basis\n\n vf_dict[\"method\"] = method\n if basis is not None:\n key = \"velocity_\" + basis + \"_\" + method\n X_copy_key = \"X_\" + basis + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"obsm\")\n logger.info_insert_adata(X_copy_key, adata_attr=\"obsm\")\n adata.obsm[key] = vf_dict[\"V\"]\n adata.obsm[X_copy_key] = vf_dict[\"X\"]\n\n vf_dict[\"dims\"] = dims\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n else:\n key = velocity_key + \"_\" + method\n\n logger.info_insert_adata(key, adata_attr=\"layers\")\n adata.layers[key] = sp.csr_matrix((adata.shape))\n adata.layers[key][:, valid_genes] = vf_dict[\"V\"]\n\n vf_dict[\"layer\"] = layer\n vf_dict[\"genes\"] = genes\n vf_dict[\"velocity_key\"] = velocity_key\n\n logger.info_insert_adata(vf_key, adata_attr=\"uns\")\n adata.uns[vf_key] = vf_dict\n\n if map_topography:\n tp_kwargs = {\"n\": 25}\n tp_kwargs = update_dict(tp_kwargs, kwargs)\n\n logger.info(\"Mapping topography...\")\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n\n adata = topography(\n adata,\n basis=basis,\n X=X,\n layer=layer,\n dims=None,\n VecFld=vf_dict,\n **tp_kwargs,\n )\n if pot_curl_div:\n logger.info(f\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\")\n\n ddhodge(adata, basis=basis, cores=cores)\n if X.shape[1] == 2:\n logger.info(\"Computing curl...\")\n curl(adata, basis=basis)\n\n logger.info(\"Computing divergence...\")\n divergence(adata, basis=basis)\n\n control_point, inlier_prob, valid_ids = (\n \"control_point_\" + basis if basis is not None else \"control_point\",\n \"inlier_prob_\" + basis if basis is not None else \"inlier_prob\",\n vf_dict[\"valid_ind\"],\n )\n if method.lower() == \"sparsevfc\":\n logger.info_insert_adata(control_point, adata_attr=\"obs\")\n logger.info_insert_adata(inlier_prob, adata_attr=\"obs\")\n\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\n adata.obs.loc[adata.obs_names[vf_dict[\"ctrl_idx\"]], control_point] = True\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\"P\"].flatten()\n\n # angles between observed velocity and that predicted by vector field across cells:\n cell_angels = np.zeros(adata.n_obs)\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\"V\"]):\n # fix the u, v norm == 0 in angle function\n cell_angels[i] = angle(u, v)\n\n if basis is not None:\n temp_key = \"obs_vf_angle_\" + basis\n\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n else:\n temp_key = \"obs_vf_angle\"\n logger.info_insert_adata(temp_key, adata_attr=\"obs\")\n adata.obs[temp_key] = cell_angels\n\n logger.finish_progress(\"VectorField\")\n if return_vf_object:\n return VecFld\n elif copy:\n return adata\n return None","def _generate_transform(self):\n A = np.zeros([self.m, self.n])\n idx = np.arange(self.m)\n for i in idx:\n A[i, max(i-self. k+1, 0):min(i+1, self.n)] = np.ones(\n min(i+1, self.n)-max(i-self. k+1, 0)) / self.k\n return A","def vectorize(features, vocab):\n vec = lil_matrix((1, len(vocab)))\n for feat in features:\n try:\n fidx = vocab[feat]\n vec[0, fidx] += 1.0\n except KeyError:\n pass\n return vec","def getVector(lstOfValues):\n return MatrixExtended([[v] for v in lstOfValues])","def newVectorArray(numpart:int, ndim:int = 0, data_type=float, data_order:str='F') -> NP.ndarray:\n if ndim <= 0:\n ndim = pympscore.GetSpaceDim()\n return NP.empty((numpart,ndim), dtype=data_type, order=data_order)","def _process(self, data: np.ndarray) -> np.ndarray:","def _process(self, data: np.ndarray) -> np.ndarray:","def vectorize(items, cls=None):\n if cls is None:\n cls = type(items[0])\n res = r.std.vector(cls)()\n for i in items:\n res.push_back(i)\n return res","def getParamsToVector(self):\n #vectorise and concat weights arrays\n weights = np.concatenate( ( self.w_ih.flatten(), self.w_ho.flatten() ) )\n # concat biases vectors\n biases = np.concatenate( ( self.b_h, self.b_o ) )\n # concat weights and biases into params\n params = np.concatenate( ( weights, biases ) )\n return params","def vectorize(data, word_dict):\n\n # Index words\n inputs_list = data['input']\n targets_list = data['target']\n\n inputs = []\n for k in range(len(inputs_list)):\n year,month,day = inputs_list[k][0].split('/')\n year, month, day = int(year),int(month),int(day)\n if k == 0:\n week_index = datetime.timedelta(days=0)\n time0 = datetime.date(year,month,day)\n else:\n time1 = datetime.date(year,month,day)\n week_index = time1-time0\n tp_list = [word_dict[w] for w in inputs_list[k][1]]\n inputs.append([week_index.days,tp_list])\n\n year, month, day = targets_list[0].split('/')\n year, month, day = int(year), int(month), int(day)\n time1 = datetime.date(year, month, day)\n week_index = time1 - time0\n targets = [week_index.days,word_dict[targets_list[1]]]\n return {'input':inputs,'target':targets,'<PAD>':word_dict['<PAD>']}","def get_vector(self): \n #print(self.state)\n '''\n print(\"\"\"\n Price {}\n Last Price {}\n Last Period Transaction {}\n Last Transaction {}\n Las Value {}\n Last day {}\n Last hour {}\n Last minute {}\n --------------\n Balance {}\n Bag {}\n \"\"\".format(\n self.state['price'],\n self.states[-1]['price'],\n self.states[-1]['transaction'],\n self.transactions[-1]['transaction'],\n self.value,\n self.state['day'],\n self.state['hour'],\n self.state['minute'], \n self.balance, \n self.bag, \n )) \n ''' \n self.state_vector = np.array([\n self.state['price'],\n self.states[-1]['price'],\n self.states[-1]['transaction'],\n self.transactions[-1]['transaction'],\n self.value,\n self.state['day'],\n self.state['hour'],\n self.state['minute'],\n ])\n\n return self.state_vector","def transform_data(features):\n\n def cart2pol(my_row):\n x = my_row[0]\n y = my_row[1]\n rho = np.sqrt(x**2 + y**2)\n phi = np.arctan2(y, x)\n return [rho, phi]\n\n #vfunc = np.vectorize(cart2pol)\n #transformed = vfunc(features)\n out = []\n #print(features, \"\\n\\n\")\n for row in features:\n out.append(cart2pol(row))\n out = np.array(out)\n #print(\"out is\\n\", out)\n \"\"\"\n x = out[:,0]\n y = out[:,1]\n \n plt.scatter(x,y)\n plt.show()\n \"\"\"\n return out","def evaluate_as_vector(self, chain_state): \n def vector_representation(n, ordering, it):\n return self.mapping.subspace(zip(ordering,it))\n return self._evaluate(vector_representation, chain_state)","def prepare(self):\n ls=len(self.v)\n self.S=numpy.zeros(ls)\n self.A=numpy.zeros((ls,ls))\n\n for k,v in self.e.items():\n b,e=k\n bi,ei=self.rv[b],self.rv[e]\n self.A[bi,bi]-=v\n self.A[bi,ei]+=v","def gen_vector(size):\n solution = []\n for i in range(size):\n rand_num = uniform(-size, size)\n solution.append(rand_num)\n return np.array(solution)","def custom_np_function(vector):\n summation = 0\n for item in vector:\n summation += item ** 2\n return summation","def generate_V(self):\n\n n_samples, n_dimensions, L = self.n_samples, self.n_dimensions, self.L\n\n V = np.zeros([L + 1, n_dimensions], dtype=int)\n V[1:, 0] = [1 << (self.scale - j) for j in range(1, L + 1)]\n\n for i in range(n_dimensions - 1):\n\n m = np.array(directions[i], dtype=int)\n s = len(m) - 1\n\n # The following code discards the first row of the ``m`` array\n # Because it has floating point errors, e.g. values of 2.24e-314\n if L <= s:\n V[1:, i + 1] = [m[j] << (self.scale - j) for j in range(1, L + 1)]\n else:\n V[1 : s + 1, i + 1] = [\n m[j] << (self.scale - j) for j in range(1, s + 1)\n ]\n for j in range(s + 1, L + 1):\n V[j, i + 1] = V[j - s, i + 1] ^ (V[j - s, i + 1] >> s)\n for k in range(1, s):\n V[j, i + 1] ^= ((m[0] >> (s - 1 - k)) & 1) * V[j - k][i + 1]\n\n return V","def vectorize(vector_space, sentence):\n vector = [0] * len(vector_space)\n for word in sentence[0].split():\n vector[vector_space[word]] = 1\n return vector","def initial_vector(self):\n\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])","def _process(self, data: np.ndarray) -> np.ndarray:\n return data[..., 0] * self.scale","def vecvel(x, SAMPLING, TYPE=2):\n\n dims = x.shape\n N = dims[0]\n v = np.zeros((N, dims[1]))\n # print(v.shape)\n\n if TYPE == 2:\n v[2:(N - 3)] = SAMPLING / 6 * (x[5:N].values + x[4:(N - 1)].values - x[2:(N - 3)].values - x[1:(\n N - 4)].values) # SAMPLING/6*(x[5:N,] + x[4:(N-1),] - x[2:(N-3),] - x[1:(N-4),])\n v[1] = SAMPLING / 2 * (x[3:4].values - x[1:2].values) # SAMPLING/2*(x[3,:] - x[1,:])\n v[(N - 1)] = SAMPLING / 2 * (\n x[N - 1:N].values - x[(N - 3):(N - 2)].values) # SAMPLING/2*(x[N,:] - x[(N-2),:])\n else:\n v[2:(N - 2)] = SAMPLING / 2 * (x[3:N - 1].values - x[1:(N - 3)].values) # SAMPLING/2*(x[3:N,:] - x[1:(N-2),:])\n\n return (v)","def _local_ts(self, *data):\n arr = self.function(*data)\n if self.var.func_input_dtype == 'numpy':\n arr = xr.DataArray(arr, coords=self.coords)\n arr.name = self.name\n return arr","def rescale_data(self):\n\n # Dividing every array of simulated data vectors by the mean of that array.\n '''# Didnt work\n for key in self.data.keys():\n self.data[key] /= np.mean(self.data[key])\n '''\n\n self.rescaled = True\n\n # Mean normalization\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] -= np.mean(self.data[key])\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\n \"\"\"\n\n # Median normalization\n \"\"\" didnt work, still dividing by large number \n for key in self.data.keys():\n self.data[key] -= np.median(self.data[key])\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\n \"\"\"\n\n # Divide by median\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] -= np.median(self.data[key])\n self.data[key] /= (np.median(self.data[key]))\n \"\"\"\n\n # Take logarithm of data\n \"\"\" didnt work\n for key in self.data.keys():\n self.data[key] = np.log10(self.data[key])\n \"\"\"\n\n # Scale by length of vector\n \"\"\"\n for key in self.data.keys():\n self.data[key] /= np.linalg.norm(self.Cl_noiseless)\n \"\"\"\n\n \n # Scale by negative of the natural logarithm \n for key in self.data.keys():\n self.data[key] = -1 * np.log(self.data[key]) \n \n \"\"\"\n # Scale by subtracting the mean and dividing by std\n std = np.nanstd(self.data['data'])\n mean = np.nanmean(self.data['data'])\n for key in self.data.keys():\n # self.data[key] -= np.log(self.Cl_noiseless) # -1* # scale this same way\n # self.data[key] -= self.Cl_noiseless # -1* # scale this same way\n self.data[key] -= mean \n self.data[key] /= std\n \"\"\"","def predict(self):\n for src_p, pair in enumerate(self.pairs):\n dst_p = pair[1].argmax()\n dst_ind = pair[0][dst_p]\n\n self.vector_field.append(np.hstack([self.frame_0[src_p], self.frame_1[dst_ind]]))\n\n self.vector_field = np.vstack(self.vector_field)\n\n return self.vector_field","def as_vector(self):\n return self.pdm.as_vector()","def _as_vector(self):\n return np.hstack([self.global_parameters, self.weights])","def create_feature_vector(self, files=[], name=\"\"):\n\n if( len(files)==0 ):\n return\n\n epsilon = 1e-8\n set = []\n\n #iterating all files obtaining the significant data to compute the feature vectors\n for file in files:\n\n #reading the csv files and keeping the first 3 columns (x,y,time)\n file_data = pd.read_csv(file)\n file_data = file_data.to_numpy()\n data = np.zeros((file_data.shape[0],7))\n data[:,0:3] = file_data[:,0:3]\n\n #computing the other interesting features\n angle = np.arctan(data[:,1]/(data[:,0]+epsilon))\n velocity = np.sqrt( np.square(data[:,1]) + np.square(data[:,0]) )\n log_curvature = np.log10( velocity/(angle+epsilon) )\n acceleration = np.sqrt( np.square(velocity) + np.square(velocity*angle) )\n\n #assigning the new computed features\n data[:,3] = angle\n data[:,4] = velocity\n data[:,5] = log_curvature\n data[:,6] = acceleration\n\n #normalizing the data\n data = self.normalization(data)\n set.append(data)\n\n return set","def XPLMSetDatavf(inDataRef, inValues, inoffset, inCount):"],"string":"[\n \"def vectorize(self):\\n return vectorize(self)\",\n \"def _vectorize_data(self, docs: []):\\n print('Vectorizing data...')\\n tfidf = TfidfVectorizer()\\n encoded_data = tfidf.fit_transform(docs)\\n return encoded_data\",\n \"def get_data_vector(self) -> DataVector:\\n raise NotImplementedError(\\\"Method `get_data_vector` is not implemented!\\\")\",\n \"def vectorized_loops(self, data):\\n\\n # TODO: finish this.\\n return np.add(np.multiply(data,data), data)\",\n \"def numpy_vector(self):\\n pass\",\n \"def data_vector(self) -> np.ndarray:\\r\\n return np.dot(\\r\\n self.linear_obj_list[0].mapping_matrix.T, self.w_tilde.dirty_image\\r\\n )\",\n \"def vectorize_data(data, word_idx, sentence_size, memory_size):\\n S = []\\n Q = []\\n A = []\\n for story, query, answer in data:\\n ss = []\\n for i, sentence in enumerate(story, 1):\\n ls = max(0, sentence_size - len(sentence))\\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\\n\\n # take only the most recent sentences that fit in memory\\n ss = ss[::-1][:memory_size][::-1]\\n\\n # Make the last word of each sentence the time 'word' which \\n # corresponds to vector of lookup table\\n for i in range(len(ss)):\\n ss[i][-1] = len(word_idx) - memory_size - i + len(ss)\\n\\n # pad to memory_size\\n lm = max(0, memory_size - len(ss))\\n for _ in range(lm):\\n ss.append([0] * sentence_size)\\n\\n lq = max(0, sentence_size - len(query))\\n q = [word_idx[w] for w in query] + [0] * lq\\n\\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\\n for a in answer:\\n y[word_idx[a]] = 1\\n\\n S.append(ss)\\n Q.append(q)\\n A.append(y)\\n return np.array(S), np.array(Q), np.array(A)\",\n \"def CreateVector(self) -> BaseVector:\",\n \"def non_vectorized_loops(self, data):\\n\\n non_vectorized = np.zeros(data.shape)\\n for row in range(data.shape[0]):\\n for col in range(data.shape[1]):\\n non_vectorized[row][col] = (data[row][col] * data[row][col] +\\n data[row][col])\\n return non_vectorized\",\n \"def create_vector(self):\\n\\n series = {'PLANNEDTIME_ARR': [int(self.planned_arrival)],\\n 'rain': [float(self.rain)],\\n 'temp': [float(self.temp)],\\n 'distance_centre': [float(self.distance_centre)],\\n 'day_into_year': [int(self.t_minus_1118)],\\n 'STOPPOINTID_label': [int(self.stop_id)],\\n 'DAYOFWEEK_label': [int(self.day_of_week)],\\n 'MONTH_label': [int(self.month)],\\n 'holiday_label': [int(self.holiday)],\\n 'cluster_label': [int(self.cluster)]\\n }\\n\\n self.vector = pd.DataFrame(series)\",\n \"def AsVector(self) -> BaseVector:\",\n \"def _vectorize(self, vectorizer = None):\\n\\n\\t\\tvectorizer = vectorizer if vectorizer else self.vectorizer;\\n\\n\\t\\tself.training_set_vector = vectorizer.fit_transform(self.training_set)\\n\\n\\t\\tself.testing_set_vector = vectorizer.transform(self.testing_set)\",\n \"def calc_vector_from_ndarray(data):\\n return data[1:] - data[0:-1]\",\n \"def create_vectors(self):\\n self.localStatistics = []\\n self.lastStatistics = []\\n self.globalV = []\\n self.estimate = []\\n self.delta = []\\n self.drift = []\\n self.slack = [] # only for coordBased model\",\n \"def numpy_container_gen_data(no_substripes):\\n q11vec = np.zeros(no_substripes, dtype=float)\\n q12vec = np.zeros(no_substripes, dtype=float)\\n q21vec = np.zeros(no_substripes, dtype=float)\\n q22vec = np.zeros(no_substripes, dtype=float)\\n x1vec = np.zeros(no_substripes, dtype=float)\\n x2vec = np.zeros(no_substripes, dtype=float)\\n y1vec = np.zeros(no_substripes, dtype=float)\\n y2vec = np.zeros(no_substripes, dtype=float)\\n xvec = np.zeros(no_substripes, dtype=float)\\n yvec = np.zeros(no_substripes, dtype=float)\\n\\n for idx in range(no_substripes):\\n q11vec[idx], q12vec[idx], q21vec[idx], q22vec[idx], x1vec[idx], \\\\\\n x2vec[idx], y1vec[idx], y2vec[idx], xvec[idx], \\\\\\n yvec[idx] = generate_data()\\n\\n return q11vec, q12vec, q21vec, q22vec, x1vec, x2vec, y1vec, y2vec, xvec, \\\\\\n yvec\",\n \"def create(cls, vals: npt.NDArray[np.float64]) -> DataVector:\\n return vals.view(cls)\",\n \"def vectorize_method(func):\\n @wraps(func)\\n def inner(obj, *iargs, **ikwargs):\\n\\n def functionalized_method(*args, **kwargs):\\n return func(obj, *args, **kwargs)\\n\\n return np.vectorize(functionalized_method, otypes=[np.dtype('c16')])(*iargs, **ikwargs)\\n #return np.vectorize(functionalized_method)(*iargs, **ikwargs)\\n return inner\",\n \"def vectorize(self, penalty: float):\\n self._vectorized_data = self._property_data.vectorize(penalty)\",\n \"def make_data(self): \\n s = numpy.arange(0.0, 10.0, 0.01)\\n s = numpy.reshape(s, (10,10,10))\\n s = numpy.transpose(s)\\n\\n v = numpy.zeros(3000, 'd')\\n v[1::3] = 1.0\\n v = numpy.reshape(v, (10,10,10,3))\\n return s, v\",\n \"def return_vec(self) :\\r\\n y_vec = np.concatenate((self.x_vec,self.v_vec))\\r\\n return y_vec\",\n \"def make_data_vectors(data):\\n result_dict = {'adjusted_close': list(), \\n 'raw_close': list(),\\n 'high': list(),\\n 'low': list(),\\n 'open':list(),\\n 'volume': list(),\\n 'date': list()\\n }\\n for datum in data:\\n ratio = get_adjusted_ratio(datum)\\n \\n result_dict['adjusted_close'].append(datum['Adj Clos']) if \\\\\\n 'Adj Clos' in datum else result_dict['adjusted_close'].append(None)\\n result_dict['raw_close'].append(datum['Close'] * ratio) if \\\\\\n 'Close' in datum else result_dict['raw_close'].append(None)\\n result_dict['high'].append(datum['High'] * ratio) if \\\\\\n 'High' in datum else result_dict['high'].append(None)\\n result_dict['low'].append(datum['Low'] * ratio) if 'Low' in datum \\\\\\n else result_dict['low'].append(None)\\n result_dict['open'].append(datum['Open'] * ratio) if 'Open' in datum \\\\\\n else result_dict['open'].append(None)\\n result_dict['volume'].append(datum['Volume']) if 'Volume' in datum \\\\\\n else result_dict['volume'].append(None)\\n result_dict['date'].append(datum['date']) if 'date' in datum \\\\\\n else result_dict['date'].append(None)\\n\\n volume = np.array(result_dict['volume'])\\n adj_close = np.array(result_dict['adjusted_close'])\\n close = np.array(result_dict['raw_close'])\\n high = np.array(result_dict['high'])\\n low = np.array(result_dict['low'])\\n open_p = np.array(result_dict['open'])\\n date = np.array(result_dict['date'])\\n\\n return {'open':open_p, 'close':close, 'low':low, \\n 'high':high, 'adj_close':adj_close, \\n 'volume':volume, 'date':date}\",\n \"def __call__(self):\\n return self._vector\",\n \"def AsVector(self) -> ngsolve.la.BaseVector:\",\n \"def vectorize_data(self, idf=False):\\r\\n vectorizer = Vectorizer()\\r\\n return vectorizer.vectorize_data(self.tweets, idf)\",\n \"def _vectorize_and_sparsify_data(self,\\n vec: Vectorizer,\\n ids: List[str],\\n batch_size: int = 50) \\\\\\n -> sp.sparse.csr.csr_matrix:\\n\\n X = []\\n samples = self._generate_samples(ids, 'x')\\n while True:\\n X_list = list(take(batch_size, samples))\\n if not X_list: break\\n X_part = vec.transform(X_list)\\n del X_list\\n X.append(X_part)\\n del X_part\\n\\n return sp.sparse.csr_matrix(np.vstack([x.todense() for x in X]))\",\n \"def create_vectorized_features(data_dir, feature_version=2):\\n extractor = PEFeatureExtractor(feature_version)\\n\\n print(\\\"Vectorizing training set\\\")\\n X_path = os.path.join(data_dir, \\\"X_train.dat\\\")\\n y_path = os.path.join(data_dir, \\\"y_train.dat\\\")\\n raw_feature_paths = [os.path.join(data_dir, \\\"train_features_{}.jsonl\\\".format(i)) for i in range(6)]\\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\\n\\n print(\\\"Vectorizing test set\\\")\\n X_path = os.path.join(data_dir, \\\"X_test.dat\\\")\\n y_path = os.path.join(data_dir, \\\"y_test.dat\\\")\\n raw_feature_paths = [os.path.join(data_dir, \\\"test_features.jsonl\\\")]\\n nrows = sum([1 for fp in raw_feature_paths for line in open(fp)])\\n vectorize_subset(X_path, y_path, raw_feature_paths, extractor, nrows)\",\n \"def vectorize_data(self, data, idf=False):\\r\\n\\r\\n # collect only the cleaned text of the tweet\\r\\n text = []\\r\\n for tweet in data:\\r\\n if not tweet.get_processed_text():\\r\\n tweet.set_processed_text(self.clean_tweet(tweet))\\r\\n text.append(tweet.get_processed_text())\\r\\n\\r\\n # vectorize tweets\\r\\n\\r\\n if idf:\\r\\n vectorizer = TfidfVectorizer(min_df=((len(data) // 1000) + 1), max_df=10000, ngram_range=(1, 3))\\r\\n else:\\r\\n vectorizer = CountVectorizer(min_df=((len(data) // 1000) + 1), max_df=10000, ngram_range=(1, 3))\\r\\n\\r\\n # vectorizer = TFVectorizing()\\r\\n vectors = vectorizer.fit_transform(text)\\r\\n return vectors\",\n \"def generate_vector(self,dim=0,v=None):\\n vec = dl.Vector()\\n self.init_vector(vec,dim)\\n if v is not None:\\n vec[:]=v\\n return vec\",\n \"def get_vector_representation(self):\\r\\n vectorizer = CountVectorizer(lowercase=False,\\r\\n tokenizer=lambda x: x, # Tokenization should already be done by preprocessor\\r\\n stop_words=None,\\r\\n min_df=5,\\r\\n max_features=None, ## use all features\\r\\n ngram_range=(1, 1), ## This uses only unigram counts\\r\\n binary=False) ## This sets the beatures to be frequency counts\\r\\n pipeline = Pipeline([('vec', vectorizer), ('tfidf', TfidfTransformer())])\\r\\n\\r\\n X = pipeline.fit_transform(self.df['tokens'])\\r\\n Y = self.df['label'].values\\r\\n return ((X[:self.train_index], Y[:self.train_index]),\\r\\n (X[self.train_index:self.valid_index], Y[self.train_index:self.valid_index]),\\r\\n (X[self.valid_index:], Y[self.valid_index:]))\",\n \"def vectorize_unpack(args):\\n return vectorize(*args)\",\n \"def vectorize_unpack(args):\\n return vectorize(*args)\",\n \"def vec(self):\\n return np.matrix(self.val.ravel()).transpose()\",\n \"def transform(self, data):\\n unflattened = [unflatten_vec(d) for d in data]\\n return numpy.array([self.signature(uf)\\n for uf in unflattened])\",\n \"def Generate_Custom(f, n, m):\\n return np.fromfunction(np.vectorize(f, otypes=[float]), (n,m))\",\n \"def vectorize(self,clean_path):\\n \\n #load pretrained embedding model (GloVe)\\n glove = spacy.load('en_core_web_lg')\\n #extract unique words (aka vocabulary)\\n unique_words = set()\\n for d in self.docs: \\n txt = d.text\\n doc = glove(txt)\\n for word in doc: \\n if word.has_vector:\\n unique_words.add(word.text)\\n #change set to list type\\n unique_words = list(unique_words)\\n #save vector representation\\n word_vectors = np.array([glove(word).vector for word in unique_words if glove(word).has_vector])\\n #index vectors by corresponding word \\n corpus_vectors = pd.DataFrame(word_vectors, index=unique_words)\\n with open(clean_path + 'corpus_vectors.pkl', 'wb') as f:\\n pickle.dump(corpus_vectors,f)\\n self.vectors = corpus_vectors\\n print('Saved embedding vectors.')\\n return\",\n \"def result_to_vector(results):\\n return [vectorized_result(x) for x in results]\",\n \"def build(self, data: np.ndarray):\\n ret = data.dot(self.eigenvectors)\\n self.pca_predictor_vars = ret\\n return ret\",\n \"def vectorized_result(y):\\r\\n\\r\\n e = np.zeros((10,1))\\r\\n ## making an array of length 10 having zero in all the indices\\r\\n\\r\\n e[y] = 1.0\\r\\n ## at index y assigning the value 1.0\\r\\n\\r\\n return e\",\n \"def vectorize(self, source_text, target_text, use_dataset_max_lengths=True):\\r\\n \\r\\n data = super().vectorize(source_text, target_text, use_dataset_max_lengths)\\r\\n \\r\\n mltm_x_vector = self.mltm_vectorizer.vectorize(source_text.lower())\\r\\n mltm_x_vector = mltm_x_vector.astype(np.float32)\\r\\n \\r\\n data[\\\"x_source_mltm_vector\\\"] = mltm_x_vector\\r\\n return data\",\n \"def fvector(data, method ):\\n\\n fv = 0\\n if method['type'] == 'lbp':\\n \\n\\n lbpkern = lbpsimple.generateKernel2()\\n \\n imlbp = lbpsimple.lbp2oneslice(data, lbpkern)\\n\\n fv,bins = lbpsimple.features(imlbp)\\n\\n #pdb.set_trace();\\n elif method['type'] == 'hist':\\n \\n fv, bins = numpy.histogram( data,range(-200,2000,20))\\n fv = fv[10:15]\\n #fv, bins = numpy.histogram( data)\\n pass\\n\\n else:\\n raise Exception('Unknow method for feature vector: %s' %(method))\\n\\n return fv\",\n \"def reflect(data, mapfunc = lambda x:x):\\n data2 = np.zeros([tsize, npsi])\\n # Copy the original data\\n for i in np.arange(ntheta):\\n data2[i,:] = data[i,:]\\n # Now fill in the remainder\\n for i in np.arange(ntheta, tsize):\\n t0 = tsize - 1 - i\\n data2[i,:] = mapfunc(data[t0,:])\\n return data2\",\n \"def to_vec(self):\\n column_count = self.column_count\\n vector = Matrix(size=(self.row_count * column_count, 1))\\n for key, value in self.data.items():\\n row, column = key[:2]\\n subscript = (column * column_count + row, 0)\\n vector.set(subscript, value)\\n return vector\",\n \"def vectorize(self, *args, **kwargs):\\n kwargs['add_start'] = False\\n kwargs['add_end'] = False\\n return TorchRankerAgent.vectorize(self, *args, **kwargs)\",\n \"def vectorize(self, phrase):\\n return np.array([0])\",\n \"def _asvector(self, arr):\\n result = self._moveaxis(arr, [-2, -1], [0, 1])\\n return self.domain.element(result)\",\n \"def as_vector(self) -> np.ndarray:\\n psi = self.A[0]\\n for i in range(1, len(self.A)):\\n psi = merge_mps_tensor_pair(psi, self.A[i])\\n assert psi.ndim == 3\\n assert psi.shape[1] == 1 and psi.shape[2] == 1\\n return psi.reshape(-1)\",\n \"def hash_vector(self,v):\\r\\n # you will need to use self.functions for this method\\r\\n x = np.array([f(v[0]) for f in self.functions])\\r\\n #print (x)\\r\\n return x\\r\\n raise NotImplementedError\",\n \"def create_vectorizer(ds):\\n vectorize_layer = TextVectorization(\\n standardize=clean_text,\\n split=\\\"whitespace\\\",\\n max_tokens=MAX_WORDS - 1,\\n output_mode=\\\"int\\\",\\n output_sequence_length=MAX_LEN,\\n )\\n vectorize_layer.adapt(ds.map(lambda text, label: text))\\n return vectorize_layer\",\n \"def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\\n terms = list(self.index.get_terms())\\n terms.sort()\\n vector = np.zeros(len(terms))\\n\\n for (term, weight) in query_vector:\\n index = terms.index(term)\\n vector[index] = weight\\n\\n return vector\",\n \"def feed(self, vector):\\n return vector\",\n \"def augment_data(self,data):\\n d = np.empty([data.shape[0]+2,data.shape[1]+2])\\n d[1:-1,1:-1] = data\\n d[0,1:-1] = data[0,:]\\n d[1:-1,0] = data[:,0]\\n d[-1,1:-1] = data[-1,:]\\n d[1:-1,-1] = data[:,-1]\\n d[0,0] = data[0,0]\\n d[-1,-1] = data[-1,-1]\\n d[0,-1] = data[0,-1]\\n d[-1,0] = data[-1,0]\\n return d\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n if not self.is_trained:\\n raise DataProcessorError(\\\"SVD must be trained on data before it can be used.\\\")\\n\\n # IQ axis is reduced by projection\\n if self._n_shots == 0:\\n # level1 average mode\\n dims = self._n_circs, self._n_slots\\n else:\\n # level1 single mode\\n dims = self._n_circs, self._n_shots, self._n_slots\\n\\n projected_data = np.zeros(dims, dtype=object)\\n\\n for idx in range(self._n_slots):\\n scale = self.parameters.scales[idx]\\n axis = self.parameters.main_axes[idx]\\n mean_i = self.parameters.i_means[idx]\\n mean_q = self.parameters.q_means[idx]\\n\\n if self._n_shots != 0:\\n # Single shot\\n for circ_idx in range(self._n_circs):\\n centered = [\\n data[circ_idx, :, idx, 0] - mean_i,\\n data[circ_idx, :, idx, 1] - mean_q,\\n ]\\n projected_data[circ_idx, :, idx] = axis @ np.array(centered) / scale\\n else:\\n # Averaged\\n centered = [data[:, idx, 0] - mean_i, data[:, idx, 1] - mean_q]\\n projected_data[:, idx] = axis @ np.array(centered) / scale\\n\\n return projected_data\",\n \"def wrapDBVector(self,vec):\\n return vec.todense()\",\n \"def apply(self: T, target: types.Vector) -> types.Vector:\",\n \"def _compute_new_data(self, desvars, responses, objectives, constraints, metadata):\\n # type: (dict, dict, dict, dict, dict) -> np.ndarray\\n raise NotImplementedError\",\n \"def get_vector(self, name: str) -> ndarray:\\n vector_path = self._get_path(name)\\n vector_df = DataFrame(read_csv(vector_path, header=None))\\n ixs = list(map(lambda i: i - 1, vector_df.iloc[:, 0]))\\n vals = vector_df.iloc[:, 1]\\n return util.create_array(ixs, vals, self.number_of_nodes)\",\n \"def apply(self, v):\\n u = np.zeros(self.Dimension, dtype=complex)\\n for me in self.Elements:\\n for index in range(v.Elements.size):\\n if index == me.j:\\n u[me.i] += me.val * v.Elements[index]\\n u = Vector(u) \\n return u\",\n \"def flatten_to_vector(data):\\n depth = len(np.array(data).shape)\\n \\n if depth == 2:\\n return data\\n else: \\n vector_depth = 2\\n dimension_reduction_depth = vector_depth + 1\\n \\n return flatten_to_depth(data, current_depth = len(np.array(data).shape), stop_depth = dimension_reduction_depth)\",\n \"def vectorized(self):\\n return False\",\n \"def toVector(tupL,n,v):\\n v = numpy.zeros(n)\\n for (i,val) in tupL:\\n #print i,v\\n v[i]=val\\n return v\",\n \"def process(self, data):\\n allocating = (self._output is None)\\n ind = 0\\n for i, (name, feature) in enumerate(self.features):\\n if allocating:\\n x = feature.compute(data)\\n self.feature_indices[name] = (ind, ind+x.size)\\n ind += x.size\\n\\n if self._output is None:\\n self._output = x\\n else:\\n self._output = np.hstack([self._output, x])\\n else:\\n self._output[self.feature_indices[name][0]:\\n self.feature_indices[name][1]] = \\\\\\n feature.compute(data)\\n\\n return self._output\",\n \"def to_countvectors(self):\\n if hasattr(self, \\\"ifp\\\"):\\n df = self.to_dataframe()\\n return to_countvectors(df)\\n raise AttributeError(\\\"Please use the `run` method before\\\")\",\n \"def apply_new_basis(new_base, vector_array):\\n return np.dot(new_base, vector_array).T\",\n \"def convert2Vec(self, data, vec):\\r\\n\\t\\tif (type(vec) == c_int):\\r\\n\\t\\t\\treturn c_int(data[0])\\r\\n\\t\\tif (type(vec) == c_float):\\r\\n\\t\\t\\treturn c_float(data[0])\\r\\n\\t\\tfor i in range(0, len(vec)):\\r\\n\\t\\t\\tvec[i] = data[i]\\r\\n\\t\\treturn vec\",\n \"def tovector(self):\\n return np.hstack(self.values())\",\n \"def set_vector(self, i_vec, nda):\\n if i_vec<0 or i_vec>len(self.cumulative_length.nda)-1:\\n print('VectorOfVectors: Error: bad i_vec', i_vec)\\n return \\n if len(nda.shape) != 1:\\n print('VectorOfVectors: Error: nda had bad shape', nda.shape)\\n return\\n start = 0 if i_vec == 0 else self.cumulative_length.nda[i_vec-1]\\n end = start + len(nda)\\n while end >= len(self.flattened_data.nda):\\n self.flattened_data.nda.resize(2*len(self.flattened_data.nda))\\n self.flattened_data.nda[start:end] = nda\\n self.cumulative_length.nda[i_vec] = end\",\n \"def randvec(self, X):\\n raise NotImplementedError\",\n \"def vectorize_data(data, word_idx, sentence_size, memory_size,answer_size):\\n S = []\\n Q = []\\n AS = []\\n L = []\\n label_nums = set([len(dt[2]) for dt in data])\\n assert(len(label_nums) == 1)\\n label_num = list(label_nums)[0]\\n for story, query, answer, label in data:\\n lq = max(0, sentence_size - len(query))\\n q = [word_idx[w] for w in query] + [0] * lq\\n\\n ss = []\\n for i, sentence in enumerate(story, 1):\\n ls = max(0, sentence_size - len(sentence))\\n ss.append([word_idx[w] for w in sentence] + [0] * ls)\\n\\n if len(ss) > memory_size:\\n # Use Jaccard similarity to determine the most relevant sentences\\n q_words = set(q)\\n least_like_q = sorted(ss, cmp = lambda x, y: jaccard(set(x), q_words) < jaccard(set(y), q_words))[:len(ss)-memory_size]\\n for sent in least_like_q:\\n # Remove the first occurrence of sent. A list comprehension as in [sent for sent in ss if sent not in least_like_q]\\n # should not be used, as it would remove multiple occurrences of the same sentence, some of which might actually make the cutoff.\\n ss.remove(sent)\\n else:\\n # pad to memory_size\\n lm = max(0, memory_size - len(ss))\\n for _ in range(lm):\\n ss.append([0] * sentence_size)\\n\\n y = np.zeros(len(word_idx) + 1) # 0 is reserved for nil word\\n\\n sa = []\\n for i, sentence in enumerate(answer, 1):\\n ls = max(0, answer_size - len(sentence))\\n sa.append([word_idx[w] for w in sentence] + [0] * ls)\\n\\n lb = np.zeros(label_num)\\n lb[int(label)] = 1\\n\\n S.append(ss)\\n Q.append(q)\\n AS.append(sa)\\n L.append(lb)\\n Q = pad_sequences(Q, sentence_size)\\n\\n return np.array(S), np.array(Q), np.array(AS), np.array(L)\",\n \"def to_vect(self):\\n # Make sure mems dimension is last\\n return np.reshape(self.transpose('validtime','y','x','mem').to_array().values, (self.nstate(), self.nmems()))\",\n \"def get_vectors(self, corpus_size, vectors_size, vectors_type):\\n vectors = np.zeros((corpus_size, vectors_size))\\n for i in range(0, corpus_size):\\n prefix = vectors_type + '_' + str(i)\\n vectors[i] = self.model_dbow.docvecs[prefix]\\n return vectors\",\n \"def __init__(self, data):\\n\\n self.xs = np.array(sorted(data))\\n self.N = float(len(self.xs))\\n self.ys = np.arange(1, self.N + 1) / self.N\",\n \"def apply(self, vec):\\n raise NotImplementedError\",\n \"def vec(x, y=0.0, z=0.0):\\n return np.matrix((x, y, z)).T\",\n \"def VectorField(\\n adata: anndata.AnnData,\\n basis: Union[None, str] = None,\\n layer: Union[None, str] = None,\\n dims: Union[int, list, None] = None,\\n genes: Union[list, None] = None,\\n normalize: bool = False,\\n grid_velocity: bool = False,\\n grid_num: int = 50,\\n velocity_key: str = \\\"velocity_S\\\",\\n method: str = \\\"SparseVFC\\\",\\n min_vel_corr: float = 0.6,\\n restart_num: int = 5,\\n restart_seed: Union[None, list] = [0, 100, 200, 300, 400],\\n model_buffer_path: Union[str, None] = None,\\n return_vf_object: bool = False,\\n map_topography: bool = False,\\n pot_curl_div: bool = False,\\n cores: int = 1,\\n result_key: Union[str, None] = None,\\n copy: bool = False,\\n **kwargs,\\n) -> Union[anndata.AnnData, base_vectorfield]:\\n logger = LoggerManager.gen_logger(\\\"dynamo-topography\\\")\\n logger.info(\\\"vectorfield calculation begins...\\\", indent_level=1)\\n logger.log_time()\\n adata = copy_adata(adata) if copy else adata\\n\\n if basis is not None:\\n logger.info(\\n \\\"Retrieve X and V based on basis: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the %s space.\\\" % (basis.upper(), basis.upper())\\n )\\n X = adata.obsm[\\\"X_\\\" + basis].copy()\\n V = adata.obsm[\\\"velocity_\\\" + basis].copy()\\n\\n if np.isscalar(dims):\\n X, V = X[:, :dims], V[:, :dims]\\n elif type(dims) is list:\\n X, V = X[:, dims], V[:, dims]\\n else:\\n logger.info(\\n \\\"Retrieve X and V based on `genes`, layer: %s. \\\\n \\\"\\n \\\" Vector field will be learned in the gene expression space.\\\" % layer\\n )\\n valid_genes = (\\n list(set(genes).intersection(adata.var.index))\\n if genes is not None\\n else adata.var_names[adata.var.use_for_transition]\\n )\\n if layer == \\\"X\\\":\\n X = adata[:, valid_genes].X.copy()\\n X = np.expm1(X)\\n else:\\n X = inverse_norm(adata, adata.layers[layer])\\n\\n V = adata[:, valid_genes].layers[velocity_key].copy()\\n\\n if sp.issparse(X):\\n X, V = X.A, V.A\\n\\n Grid = None\\n if X.shape[1] < 4 or grid_velocity:\\n logger.info(\\\"Generating high dimensional grids and convert into a row matrix.\\\")\\n # smart way for generating high dimensional grids and convert into a row matrix\\n min_vec, max_vec = (\\n X.min(0),\\n X.max(0),\\n )\\n min_vec = min_vec - 0.01 * np.abs(max_vec - min_vec)\\n max_vec = max_vec + 0.01 * np.abs(max_vec - min_vec)\\n\\n Grid_list = np.meshgrid(*[np.linspace(i, j, grid_num) for i, j in zip(min_vec, max_vec)])\\n Grid = np.array([i.flatten() for i in Grid_list]).T\\n\\n if X is None:\\n raise Exception(f\\\"X is None. Make sure you passed the correct X or {basis} dimension reduction method.\\\")\\n elif V is None:\\n raise Exception(\\\"V is None. Make sure you passed the correct V.\\\")\\n\\n logger.info(\\\"Learning vector field with method: %s.\\\" % (method.lower()))\\n if method.lower() == \\\"sparsevfc\\\":\\n vf_kwargs = {\\n \\\"M\\\": None,\\n \\\"a\\\": 5,\\n \\\"beta\\\": None,\\n \\\"ecr\\\": 1e-5,\\n \\\"gamma\\\": 0.9,\\n \\\"lambda_\\\": 3,\\n \\\"minP\\\": 1e-5,\\n \\\"MaxIter\\\": 30,\\n \\\"theta\\\": 0.75,\\n \\\"div_cur_free_kernels\\\": False,\\n \\\"velocity_based_sampling\\\": True,\\n \\\"sigma\\\": 0.8,\\n \\\"eta\\\": 0.5,\\n \\\"seed\\\": 0,\\n }\\n elif method.lower() == \\\"dynode\\\":\\n try:\\n from dynode.vectorfield import networkModels\\n from dynode.vectorfield.samplers import VelocityDataSampler\\n\\n # from dynode.vectorfield.losses_weighted import MAD, BinomialChannel, WassersteinDistance, CosineDistance\\n from dynode.vectorfield.losses_weighted import MSE\\n from .scVectorField import dynode_vectorfield\\n except ImportError:\\n raise ImportError(\\\"You need to install the package `dynode`.\\\" \\\"install dynode via `pip install dynode`\\\")\\n\\n velocity_data_sampler = VelocityDataSampler(adata={\\\"X\\\": X, \\\"V\\\": V}, normalize_velocity=normalize)\\n max_iter = 2 * 100000 * np.log(X.shape[0]) / (250 + np.log(X.shape[0]))\\n\\n cwd, cwt = os.getcwd(), datetime.datetime.now()\\n\\n if model_buffer_path is None:\\n model_buffer_path = cwd + \\\"/\\\" + basis + \\\"_\\\" + str(cwt.year) + \\\"_\\\" + str(cwt.month) + \\\"_\\\" + str(cwt.day)\\n main_warning(\\\"the buffer path saving the dynode model is in %s\\\" % (model_buffer_path))\\n\\n vf_kwargs = {\\n \\\"model\\\": networkModels,\\n \\\"sirens\\\": False,\\n \\\"enforce_positivity\\\": False,\\n \\\"velocity_data_sampler\\\": velocity_data_sampler,\\n \\\"time_course_data_sampler\\\": None,\\n \\\"network_dim\\\": X.shape[1],\\n \\\"velocity_loss_function\\\": MSE(), # CosineDistance(), # #MSE(), MAD()\\n # BinomialChannel(p=0.1, alpha=1)\\n \\\"time_course_loss_function\\\": None,\\n \\\"velocity_x_initialize\\\": X,\\n \\\"time_course_x0_initialize\\\": None,\\n \\\"smoothing_factor\\\": None,\\n \\\"stability_factor\\\": None,\\n \\\"load_model_from_buffer\\\": False,\\n \\\"buffer_path\\\": model_buffer_path,\\n \\\"hidden_features\\\": 256,\\n \\\"hidden_layers\\\": 3,\\n \\\"first_omega_0\\\": 30.0,\\n \\\"hidden_omega_0\\\": 30.0,\\n }\\n train_kwargs = {\\n \\\"max_iter\\\": int(max_iter),\\n \\\"velocity_batch_size\\\": 50,\\n \\\"time_course_batch_size\\\": 100,\\n \\\"autoencoder_batch_size\\\": 50,\\n \\\"velocity_lr\\\": 1e-4,\\n \\\"velocity_x_lr\\\": 0,\\n \\\"time_course_lr\\\": 1e-4,\\n \\\"time_course_x0_lr\\\": 1e4,\\n \\\"autoencoder_lr\\\": 1e-4,\\n \\\"velocity_sample_fraction\\\": 1,\\n \\\"time_course_sample_fraction\\\": 1,\\n \\\"iter_per_sample_update\\\": None,\\n }\\n else:\\n raise ValueError(\\\"current only support two methods, SparseVFC and dynode\\\")\\n\\n vf_kwargs = update_dict(vf_kwargs, kwargs)\\n\\n if restart_num > 0:\\n if len(restart_seed) != restart_num:\\n main_warning(\\n f\\\"the length of {restart_seed} is different from {restart_num}, \\\" f\\\"using `np.range(restart_num) * 100\\\"\\n )\\n restart_seed = np.arange(restart_num) * 100\\n restart_counter, cur_vf_list, res_list = 0, [], []\\n while True:\\n if method.lower() == \\\"sparsevfc\\\":\\n kwargs.update({\\\"seed\\\": restart_seed[restart_counter]})\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n cur_vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n cur_vf_dict = VecFld.train(**train_kwargs)\\n\\n # consider refactor with .simulation.evaluation.py\\n reference, prediction = (\\n cur_vf_dict[\\\"Y\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n cur_vf_dict[\\\"V\\\"][cur_vf_dict[\\\"valid_ind\\\"]],\\n )\\n true_normalized = reference / (np.linalg.norm(reference, axis=1).reshape(-1, 1) + 1e-20)\\n predict_normalized = prediction / (np.linalg.norm(prediction, axis=1).reshape(-1, 1) + 1e-20)\\n res = np.mean(true_normalized * predict_normalized) * prediction.shape[1]\\n\\n cur_vf_list += [cur_vf_dict]\\n res_list += [res]\\n if res < min_vel_corr:\\n restart_counter += 1\\n main_info(\\n f\\\"current cosine correlation between input velocities and learned velocities is less than \\\"\\n f\\\"{min_vel_corr}. Make a {restart_counter}-th vector field reconstruction trial.\\\",\\n indent_level=2,\\n )\\n else:\\n vf_dict = cur_vf_dict\\n break\\n\\n if restart_counter > restart_num - 1:\\n main_warning(\\n f\\\"Cosine correlation between input velocities and learned velocities is less than\\\"\\n f\\\" {min_vel_corr} after {restart_num} trials of vector field reconstruction.\\\"\\n )\\n vf_dict = cur_vf_list[np.argmax(np.array(res_list))]\\n\\n break\\n else:\\n if method.lower() == \\\"sparsevfc\\\":\\n VecFld = SvcVectorfield(X, V, Grid, **vf_kwargs)\\n vf_dict = VecFld.train(normalize=normalize, **kwargs)\\n elif method.lower() == \\\"dynode\\\":\\n train_kwargs = update_dict(train_kwargs, kwargs)\\n VecFld = dynode_vectorfield(X, V, Grid, **vf_kwargs)\\n # {\\\"VecFld\\\": VecFld.train(**kwargs)}\\n vf_dict = VecFld.train(**train_kwargs)\\n\\n if result_key is None:\\n vf_key = \\\"VecFld\\\" if basis is None else \\\"VecFld_\\\" + basis\\n else:\\n vf_key = result_key if basis is None else result_key + \\\"_\\\" + basis\\n\\n vf_dict[\\\"method\\\"] = method\\n if basis is not None:\\n key = \\\"velocity_\\\" + basis + \\\"_\\\" + method\\n X_copy_key = \\\"X_\\\" + basis + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"obsm\\\")\\n logger.info_insert_adata(X_copy_key, adata_attr=\\\"obsm\\\")\\n adata.obsm[key] = vf_dict[\\\"V\\\"]\\n adata.obsm[X_copy_key] = vf_dict[\\\"X\\\"]\\n\\n vf_dict[\\\"dims\\\"] = dims\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n else:\\n key = velocity_key + \\\"_\\\" + method\\n\\n logger.info_insert_adata(key, adata_attr=\\\"layers\\\")\\n adata.layers[key] = sp.csr_matrix((adata.shape))\\n adata.layers[key][:, valid_genes] = vf_dict[\\\"V\\\"]\\n\\n vf_dict[\\\"layer\\\"] = layer\\n vf_dict[\\\"genes\\\"] = genes\\n vf_dict[\\\"velocity_key\\\"] = velocity_key\\n\\n logger.info_insert_adata(vf_key, adata_attr=\\\"uns\\\")\\n adata.uns[vf_key] = vf_dict\\n\\n if map_topography:\\n tp_kwargs = {\\\"n\\\": 25}\\n tp_kwargs = update_dict(tp_kwargs, kwargs)\\n\\n logger.info(\\\"Mapping topography...\\\")\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n\\n adata = topography(\\n adata,\\n basis=basis,\\n X=X,\\n layer=layer,\\n dims=None,\\n VecFld=vf_dict,\\n **tp_kwargs,\\n )\\n if pot_curl_div:\\n logger.info(f\\\"Running ddhodge to estimate vector field based pseudotime in {basis} basis...\\\")\\n\\n ddhodge(adata, basis=basis, cores=cores)\\n if X.shape[1] == 2:\\n logger.info(\\\"Computing curl...\\\")\\n curl(adata, basis=basis)\\n\\n logger.info(\\\"Computing divergence...\\\")\\n divergence(adata, basis=basis)\\n\\n control_point, inlier_prob, valid_ids = (\\n \\\"control_point_\\\" + basis if basis is not None else \\\"control_point\\\",\\n \\\"inlier_prob_\\\" + basis if basis is not None else \\\"inlier_prob\\\",\\n vf_dict[\\\"valid_ind\\\"],\\n )\\n if method.lower() == \\\"sparsevfc\\\":\\n logger.info_insert_adata(control_point, adata_attr=\\\"obs\\\")\\n logger.info_insert_adata(inlier_prob, adata_attr=\\\"obs\\\")\\n\\n adata.obs[control_point], adata.obs[inlier_prob] = False, np.nan\\n adata.obs.loc[adata.obs_names[vf_dict[\\\"ctrl_idx\\\"]], control_point] = True\\n adata.obs.loc[adata.obs_names[valid_ids], inlier_prob] = vf_dict[\\\"P\\\"].flatten()\\n\\n # angles between observed velocity and that predicted by vector field across cells:\\n cell_angels = np.zeros(adata.n_obs)\\n for i, u, v in zip(valid_ids, V[valid_ids], vf_dict[\\\"V\\\"]):\\n # fix the u, v norm == 0 in angle function\\n cell_angels[i] = angle(u, v)\\n\\n if basis is not None:\\n temp_key = \\\"obs_vf_angle_\\\" + basis\\n\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n else:\\n temp_key = \\\"obs_vf_angle\\\"\\n logger.info_insert_adata(temp_key, adata_attr=\\\"obs\\\")\\n adata.obs[temp_key] = cell_angels\\n\\n logger.finish_progress(\\\"VectorField\\\")\\n if return_vf_object:\\n return VecFld\\n elif copy:\\n return adata\\n return None\",\n \"def _generate_transform(self):\\n A = np.zeros([self.m, self.n])\\n idx = np.arange(self.m)\\n for i in idx:\\n A[i, max(i-self. k+1, 0):min(i+1, self.n)] = np.ones(\\n min(i+1, self.n)-max(i-self. k+1, 0)) / self.k\\n return A\",\n \"def vectorize(features, vocab):\\n vec = lil_matrix((1, len(vocab)))\\n for feat in features:\\n try:\\n fidx = vocab[feat]\\n vec[0, fidx] += 1.0\\n except KeyError:\\n pass\\n return vec\",\n \"def getVector(lstOfValues):\\n return MatrixExtended([[v] for v in lstOfValues])\",\n \"def newVectorArray(numpart:int, ndim:int = 0, data_type=float, data_order:str='F') -> NP.ndarray:\\n if ndim <= 0:\\n ndim = pympscore.GetSpaceDim()\\n return NP.empty((numpart,ndim), dtype=data_type, order=data_order)\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\",\n \"def vectorize(items, cls=None):\\n if cls is None:\\n cls = type(items[0])\\n res = r.std.vector(cls)()\\n for i in items:\\n res.push_back(i)\\n return res\",\n \"def getParamsToVector(self):\\n #vectorise and concat weights arrays\\n weights = np.concatenate( ( self.w_ih.flatten(), self.w_ho.flatten() ) )\\n # concat biases vectors\\n biases = np.concatenate( ( self.b_h, self.b_o ) )\\n # concat weights and biases into params\\n params = np.concatenate( ( weights, biases ) )\\n return params\",\n \"def vectorize(data, word_dict):\\n\\n # Index words\\n inputs_list = data['input']\\n targets_list = data['target']\\n\\n inputs = []\\n for k in range(len(inputs_list)):\\n year,month,day = inputs_list[k][0].split('/')\\n year, month, day = int(year),int(month),int(day)\\n if k == 0:\\n week_index = datetime.timedelta(days=0)\\n time0 = datetime.date(year,month,day)\\n else:\\n time1 = datetime.date(year,month,day)\\n week_index = time1-time0\\n tp_list = [word_dict[w] for w in inputs_list[k][1]]\\n inputs.append([week_index.days,tp_list])\\n\\n year, month, day = targets_list[0].split('/')\\n year, month, day = int(year), int(month), int(day)\\n time1 = datetime.date(year, month, day)\\n week_index = time1 - time0\\n targets = [week_index.days,word_dict[targets_list[1]]]\\n return {'input':inputs,'target':targets,'<PAD>':word_dict['<PAD>']}\",\n \"def get_vector(self): \\n #print(self.state)\\n '''\\n print(\\\"\\\"\\\"\\n Price {}\\n Last Price {}\\n Last Period Transaction {}\\n Last Transaction {}\\n Las Value {}\\n Last day {}\\n Last hour {}\\n Last minute {}\\n --------------\\n Balance {}\\n Bag {}\\n \\\"\\\"\\\".format(\\n self.state['price'],\\n self.states[-1]['price'],\\n self.states[-1]['transaction'],\\n self.transactions[-1]['transaction'],\\n self.value,\\n self.state['day'],\\n self.state['hour'],\\n self.state['minute'], \\n self.balance, \\n self.bag, \\n )) \\n ''' \\n self.state_vector = np.array([\\n self.state['price'],\\n self.states[-1]['price'],\\n self.states[-1]['transaction'],\\n self.transactions[-1]['transaction'],\\n self.value,\\n self.state['day'],\\n self.state['hour'],\\n self.state['minute'],\\n ])\\n\\n return self.state_vector\",\n \"def transform_data(features):\\n\\n def cart2pol(my_row):\\n x = my_row[0]\\n y = my_row[1]\\n rho = np.sqrt(x**2 + y**2)\\n phi = np.arctan2(y, x)\\n return [rho, phi]\\n\\n #vfunc = np.vectorize(cart2pol)\\n #transformed = vfunc(features)\\n out = []\\n #print(features, \\\"\\\\n\\\\n\\\")\\n for row in features:\\n out.append(cart2pol(row))\\n out = np.array(out)\\n #print(\\\"out is\\\\n\\\", out)\\n \\\"\\\"\\\"\\n x = out[:,0]\\n y = out[:,1]\\n \\n plt.scatter(x,y)\\n plt.show()\\n \\\"\\\"\\\"\\n return out\",\n \"def evaluate_as_vector(self, chain_state): \\n def vector_representation(n, ordering, it):\\n return self.mapping.subspace(zip(ordering,it))\\n return self._evaluate(vector_representation, chain_state)\",\n \"def prepare(self):\\n ls=len(self.v)\\n self.S=numpy.zeros(ls)\\n self.A=numpy.zeros((ls,ls))\\n\\n for k,v in self.e.items():\\n b,e=k\\n bi,ei=self.rv[b],self.rv[e]\\n self.A[bi,bi]-=v\\n self.A[bi,ei]+=v\",\n \"def gen_vector(size):\\n solution = []\\n for i in range(size):\\n rand_num = uniform(-size, size)\\n solution.append(rand_num)\\n return np.array(solution)\",\n \"def custom_np_function(vector):\\n summation = 0\\n for item in vector:\\n summation += item ** 2\\n return summation\",\n \"def generate_V(self):\\n\\n n_samples, n_dimensions, L = self.n_samples, self.n_dimensions, self.L\\n\\n V = np.zeros([L + 1, n_dimensions], dtype=int)\\n V[1:, 0] = [1 << (self.scale - j) for j in range(1, L + 1)]\\n\\n for i in range(n_dimensions - 1):\\n\\n m = np.array(directions[i], dtype=int)\\n s = len(m) - 1\\n\\n # The following code discards the first row of the ``m`` array\\n # Because it has floating point errors, e.g. values of 2.24e-314\\n if L <= s:\\n V[1:, i + 1] = [m[j] << (self.scale - j) for j in range(1, L + 1)]\\n else:\\n V[1 : s + 1, i + 1] = [\\n m[j] << (self.scale - j) for j in range(1, s + 1)\\n ]\\n for j in range(s + 1, L + 1):\\n V[j, i + 1] = V[j - s, i + 1] ^ (V[j - s, i + 1] >> s)\\n for k in range(1, s):\\n V[j, i + 1] ^= ((m[0] >> (s - 1 - k)) & 1) * V[j - k][i + 1]\\n\\n return V\",\n \"def vectorize(vector_space, sentence):\\n vector = [0] * len(vector_space)\\n for word in sentence[0].split():\\n vector[vector_space[word]] = 1\\n return vector\",\n \"def initial_vector(self):\\n\\n return asarray([np.random.uniform(l, u) for l, u in self.bounds])\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n return data[..., 0] * self.scale\",\n \"def vecvel(x, SAMPLING, TYPE=2):\\n\\n dims = x.shape\\n N = dims[0]\\n v = np.zeros((N, dims[1]))\\n # print(v.shape)\\n\\n if TYPE == 2:\\n v[2:(N - 3)] = SAMPLING / 6 * (x[5:N].values + x[4:(N - 1)].values - x[2:(N - 3)].values - x[1:(\\n N - 4)].values) # SAMPLING/6*(x[5:N,] + x[4:(N-1),] - x[2:(N-3),] - x[1:(N-4),])\\n v[1] = SAMPLING / 2 * (x[3:4].values - x[1:2].values) # SAMPLING/2*(x[3,:] - x[1,:])\\n v[(N - 1)] = SAMPLING / 2 * (\\n x[N - 1:N].values - x[(N - 3):(N - 2)].values) # SAMPLING/2*(x[N,:] - x[(N-2),:])\\n else:\\n v[2:(N - 2)] = SAMPLING / 2 * (x[3:N - 1].values - x[1:(N - 3)].values) # SAMPLING/2*(x[3:N,:] - x[1:(N-2),:])\\n\\n return (v)\",\n \"def _local_ts(self, *data):\\n arr = self.function(*data)\\n if self.var.func_input_dtype == 'numpy':\\n arr = xr.DataArray(arr, coords=self.coords)\\n arr.name = self.name\\n return arr\",\n \"def rescale_data(self):\\n\\n # Dividing every array of simulated data vectors by the mean of that array.\\n '''# Didnt work\\n for key in self.data.keys():\\n self.data[key] /= np.mean(self.data[key])\\n '''\\n\\n self.rescaled = True\\n\\n # Mean normalization\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] -= np.mean(self.data[key])\\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Median normalization\\n \\\"\\\"\\\" didnt work, still dividing by large number \\n for key in self.data.keys():\\n self.data[key] -= np.median(self.data[key])\\n self.data[key] /= (np.max(self.data[key]) - np.min(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Divide by median\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] -= np.median(self.data[key])\\n self.data[key] /= (np.median(self.data[key]))\\n \\\"\\\"\\\"\\n\\n # Take logarithm of data\\n \\\"\\\"\\\" didnt work\\n for key in self.data.keys():\\n self.data[key] = np.log10(self.data[key])\\n \\\"\\\"\\\"\\n\\n # Scale by length of vector\\n \\\"\\\"\\\"\\n for key in self.data.keys():\\n self.data[key] /= np.linalg.norm(self.Cl_noiseless)\\n \\\"\\\"\\\"\\n\\n \\n # Scale by negative of the natural logarithm \\n for key in self.data.keys():\\n self.data[key] = -1 * np.log(self.data[key]) \\n \\n \\\"\\\"\\\"\\n # Scale by subtracting the mean and dividing by std\\n std = np.nanstd(self.data['data'])\\n mean = np.nanmean(self.data['data'])\\n for key in self.data.keys():\\n # self.data[key] -= np.log(self.Cl_noiseless) # -1* # scale this same way\\n # self.data[key] -= self.Cl_noiseless # -1* # scale this same way\\n self.data[key] -= mean \\n self.data[key] /= std\\n \\\"\\\"\\\"\",\n \"def predict(self):\\n for src_p, pair in enumerate(self.pairs):\\n dst_p = pair[1].argmax()\\n dst_ind = pair[0][dst_p]\\n\\n self.vector_field.append(np.hstack([self.frame_0[src_p], self.frame_1[dst_ind]]))\\n\\n self.vector_field = np.vstack(self.vector_field)\\n\\n return self.vector_field\",\n \"def as_vector(self):\\n return self.pdm.as_vector()\",\n \"def _as_vector(self):\\n return np.hstack([self.global_parameters, self.weights])\",\n \"def create_feature_vector(self, files=[], name=\\\"\\\"):\\n\\n if( len(files)==0 ):\\n return\\n\\n epsilon = 1e-8\\n set = []\\n\\n #iterating all files obtaining the significant data to compute the feature vectors\\n for file in files:\\n\\n #reading the csv files and keeping the first 3 columns (x,y,time)\\n file_data = pd.read_csv(file)\\n file_data = file_data.to_numpy()\\n data = np.zeros((file_data.shape[0],7))\\n data[:,0:3] = file_data[:,0:3]\\n\\n #computing the other interesting features\\n angle = np.arctan(data[:,1]/(data[:,0]+epsilon))\\n velocity = np.sqrt( np.square(data[:,1]) + np.square(data[:,0]) )\\n log_curvature = np.log10( velocity/(angle+epsilon) )\\n acceleration = np.sqrt( np.square(velocity) + np.square(velocity*angle) )\\n\\n #assigning the new computed features\\n data[:,3] = angle\\n data[:,4] = velocity\\n data[:,5] = log_curvature\\n data[:,6] = acceleration\\n\\n #normalizing the data\\n data = self.normalization(data)\\n set.append(data)\\n\\n return set\",\n \"def XPLMSetDatavf(inDataRef, inValues, inoffset, inCount):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7192277","0.66099465","0.6579369","0.65683573","0.6435415","0.6372212","0.63627356","0.636164","0.63602024","0.6243461","0.6200177","0.61812717","0.617205","0.6124774","0.60975635","0.60963786","0.6080632","0.6075216","0.6062782","0.6045074","0.60133845","0.5999284","0.5997596","0.59675825","0.59646636","0.591961","0.5910293","0.587802","0.5864247","0.5856837","0.5856837","0.5855001","0.58406603","0.5811325","0.58044505","0.5786299","0.5778878","0.5767882","0.5764094","0.57594246","0.5755664","0.57430786","0.57280594","0.572739","0.5724774","0.57234806","0.5683541","0.5680184","0.56715167","0.56691426","0.5646057","0.563464","0.5631152","0.5629668","0.56275","0.56204563","0.5617573","0.56150854","0.56058985","0.5601859","0.5589075","0.558854","0.5569834","0.55660343","0.5561654","0.55591464","0.55570424","0.5554814","0.55481696","0.5536904","0.55309564","0.5528972","0.5521573","0.5518367","0.55123186","0.5511639","0.55077404","0.5505417","0.5503282","0.5503282","0.549822","0.5497263","0.5495383","0.5484123","0.5480711","0.547385","0.5470622","0.54668075","0.5466495","0.54657406","0.5450851","0.5443188","0.54399514","0.5438897","0.54367846","0.54296565","0.54255164","0.5425031","0.542366","0.5421165","0.5419852"],"string":"[\n \"0.7192277\",\n \"0.66099465\",\n \"0.6579369\",\n \"0.65683573\",\n \"0.6435415\",\n \"0.6372212\",\n \"0.63627356\",\n \"0.636164\",\n \"0.63602024\",\n \"0.6243461\",\n \"0.6200177\",\n \"0.61812717\",\n \"0.617205\",\n \"0.6124774\",\n \"0.60975635\",\n \"0.60963786\",\n \"0.6080632\",\n \"0.6075216\",\n \"0.6062782\",\n \"0.6045074\",\n \"0.60133845\",\n \"0.5999284\",\n \"0.5997596\",\n \"0.59675825\",\n \"0.59646636\",\n \"0.591961\",\n \"0.5910293\",\n \"0.587802\",\n \"0.5864247\",\n \"0.5856837\",\n \"0.5856837\",\n \"0.5855001\",\n \"0.58406603\",\n \"0.5811325\",\n \"0.58044505\",\n \"0.5786299\",\n \"0.5778878\",\n \"0.5767882\",\n \"0.5764094\",\n \"0.57594246\",\n \"0.5755664\",\n \"0.57430786\",\n \"0.57280594\",\n \"0.572739\",\n \"0.5724774\",\n \"0.57234806\",\n \"0.5683541\",\n \"0.5680184\",\n \"0.56715167\",\n \"0.56691426\",\n \"0.5646057\",\n \"0.563464\",\n \"0.5631152\",\n \"0.5629668\",\n \"0.56275\",\n \"0.56204563\",\n \"0.5617573\",\n \"0.56150854\",\n \"0.56058985\",\n \"0.5601859\",\n \"0.5589075\",\n \"0.558854\",\n \"0.5569834\",\n \"0.55660343\",\n \"0.5561654\",\n \"0.55591464\",\n \"0.55570424\",\n \"0.5554814\",\n \"0.55481696\",\n \"0.5536904\",\n \"0.55309564\",\n \"0.5528972\",\n \"0.5521573\",\n \"0.5518367\",\n \"0.55123186\",\n \"0.5511639\",\n \"0.55077404\",\n \"0.5505417\",\n \"0.5503282\",\n \"0.5503282\",\n \"0.549822\",\n \"0.5497263\",\n \"0.5495383\",\n \"0.5484123\",\n \"0.5480711\",\n \"0.547385\",\n \"0.5470622\",\n \"0.54668075\",\n \"0.5466495\",\n \"0.54657406\",\n \"0.5450851\",\n \"0.5443188\",\n \"0.54399514\",\n \"0.5438897\",\n \"0.54367846\",\n \"0.54296565\",\n \"0.54255164\",\n \"0.5425031\",\n \"0.542366\",\n \"0.5421165\",\n \"0.5419852\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":9993,"cells":{"query":{"kind":"string","value":"Returns a vector for a given query"},"document":{"kind":"string","value":"def get_vector(self, query: list):\n if len(query) == 0:\n raise BadQueryParameter(\"Query (list) can not be empty.\")\n\n return self.vectorizer.transform(query)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\n terms = list(self.index.get_terms())\n terms.sort()\n vector = np.zeros(len(terms))\n\n for (term, weight) in query_vector:\n index = terms.index(term)\n vector[index] = weight\n\n return vector","def transform_query(vectorizer_model, query):\n x_request = vectorizer_model.transform(query)\n x0 = x_request.toarray()\n return x0","def _get_query_representation(self, query, index):\n term_frequencies = {term: query.count(term) for term in query}\n vec = np.zeros(shape=(index.num_terms,), dtype=np.float64)\n for i, term in enumerate(sorted(index.get_index_terms())):\n vec[i] = self._tfidf(\n term_frequencies.get(term, 0),\n index.get_document_frequency(term),\n index.get_document_count()\n )\n return vec","def calcQueryVector(self):\n query = input(\"Query: \");\n ana = StemmingAnalyzer() ### lowercases, stems, ignores stopwords\n tokens = [token.text for token in ana(query)]\n\n queryVector = {}\n for token in tokens:\n if token in self.invertedIndex.keys():\n if token in queryVector.keys():\n queryVector[token]+=1;\n else:\n queryVector[token] = 1;\n\n return self.normalizeQueryVector(queryVector);","def AsVector(self) -> ngsolve.la.BaseVector:","def result_to_vector(results):\n return [vectorized_result(x) for x in results]","def generate_query_vector(q, q_dict, inv_index):\n # Create the query vector\n query_vector = dict(Counter(q_dict[q]))\n\n # Add to this query vector, all the indexed terms\n for i_term in inv_index:\n if i_term not in query_vector:\n query_vector[i_term] = 0\n\n return query_vector","def buildQueryVector(self, termList):\n\t\tquery = self.createVector(\" \".join(termList))\n\t\treturn query","def vector(self):\n return self.q[1:4]","def query_to_word_vector(query_string, corpus):\n inv_index = vsm_retrieval.get_inverted_index(corpus)\n word_vec = np.zeros(len(inv_index))\n query_word_list = vsm_retrieval.convert_query(query_string)\n for count_vec, word in enumerate(inv_index):\n if word in query_word_list:\n word_vec[count_vec] = 1\n return word_vec","def get_vector(self, word):\n string = \"SELECT * FROM Vectors WHERE name=?\"\n params = (word,)\n self.cur.execute(string, params)\n raw_vector = self.cur.fetchone()\n if raw_vector is None:\n raise KeyError(\"Vector not found\")\n else:\n vector = pickle.loads(raw_vector[1])\n return vector","def find(self, query: str) -> TsInfoVector:\n info = parse_ts_query(ts_query=query)\n domain = self.create_netatmo_connection()[1]\n meas = domain.get_measurement(**info)\n\n # noinspection PyArgumentList\n tsi = TsInfo(\n name=meas.ts_id,\n point_fx=meas.data_type.point_interpretation,\n delta_t=np.nan,\n olson_tz_id=meas.station.place['timezone'],\n data_period=UtcPeriod(meas.module.last_setup, meas.module.last_seen),\n created=meas.module.last_setup,\n modified=meas.module.last_seen\n )\n\n # noinspection PyArgumentList\n tsiv = TsInfoVector()\n tsiv.append(tsi)\n return tsiv","def __call__(self):\n return self._vector","def AsVector(self) -> BaseVector:","def vector_q(q_1: Q) -> Q:\n\n end_q_type = f\"vector_q({q_1.q_type})\"\n\n v = Q(\n [0, q_1.x, q_1.y, q_1.z],\n q_type=end_q_type,\n representation=q_1.representation,\n )\n return v","def create_query_vector(ix, term_dict, bow):\n\n\tqfv = list()\n\tfor idx, tf in bow:\n\t\t# get term from dict index\n\t\tterm = ix[idx]\n\t\t# filter out terms not contained in self.term_dict\n\t\tif term not in term_dict:\n\t\t\tcontinue\n\t\t# append term w/ tf to tfv\n\t\tqfv.append((term, tf))\n\treturn scale_to_L1_norm(defaultdict(float, qfv))","def call_single_vec(self, input_value):\n _, eigVectors = self.getEigen(input_value)\n return eigVectors[:,:,-1]","def query(_from, _select, _geomselect=None, _where=None, _groupby=None, _limit=None):\n # INSTEAD MAKE INTO CLASS\n # WITH .fields attr\n # AND .__iter__()\n # AND .get_vectordata()\n # AND MAKE EACH YIELDED ROW A VECTOR FEATURE CLASS\n # THIS WAY ALLOWING CHAINED QUERIES\n\n # parse args\n iterables = _from\n columnfuncs = _select\n geomfunc = _geomselect\n condition = _where\n key = _groupby\n n = _limit\n \n # first yield header as list of column names\n colnames = [each[0] for each in columnfuncs]\n yield colnames\n\n # make an iterable that yields every combinaion of all input iterables' items\n if len(iterables) == 1:\n iterable = iterables[0]\n else:\n iterable = itertools.product(*iterables)\n\n # iterate and add\n if key:\n groups = groupby(iterable, key)\n\n # limit\n if n:\n groups = limit(groups, n)\n \n for items in groups:\n # filter\n if condition:\n items = where(items, condition)\n \n # aggregate\n # NOTE: columnfuncs and geomfunc must expect an iterable as input and return a single row,geom pair\n item = aggreg(items, columnfuncs, geomfunc)\n yield item\n \n else:\n # filter\n if condition:\n iterable = where(iterable, condition)\n\n # limit\n if n:\n iterable = limit(iterable, n)\n\n # select\n for item in select(iterable, columnfuncs, geomfunc):\n yield item","def generate_vector(self,dim=0,v=None):\n vec = dl.Vector()\n self.init_vector(vec,dim)\n if v is not None:\n vec[:]=v\n return vec","def query_and_bundle(session, fields, offset, limit, filter_):\n q = session.query(*fields) # raw query\n q = q.offset(offset) if filter_ is None else q.filter(filter_) # filter / offset\n ids, vectors = zip(*q.limit(limit)) # unravel results\n # bundle into arrays\n _ids = np.array(ids, dtype=STR_TYPE)\n _str_vectors = [json.loads(vector) for vector in vectors]\n _vectors = np.array(_str_vectors, dtype=FLOAT_TYPE)\n return _ids, _vectors","def get_vector(self,term):\n return self.dict.get(term)","def take_vec(self):\n vec = aux.vec(self.numbers)\n\n return vec","def evaluate_as_vector(self, chain_state): \n def vector_representation(n, ordering, it):\n return self.mapping.subspace(zip(ordering,it))\n return self._evaluate(vector_representation, chain_state)","def to_vec(self):\n column_count = self.column_count\n vector = Matrix(size=(self.row_count * column_count, 1))\n for key, value in self.data.items():\n row, column = key[:2]\n subscript = (column * column_count + row, 0)\n vector.set(subscript, value)\n return vector","def vector_from_matrix(v_as_matrix):\r\n return [row[0] for row in v_as_matrix]","def as_vector(self):\n return self.pdm.as_vector()","def vec(self):\n return np.matrix(self.val.ravel()).transpose()","def query(self, query):","def _get_query_vec(self, inputs):\n if self._flags.model_type == \"bilstm_net\":\n network = self.bilstm_net\n elif self._flags.model_type == \"bow_net\":\n network = self.bow_net\n elif self._flags.model_type == \"cnn_net\":\n network = self.cnn_net\n elif self._flags.model_type == \"lstm_net\":\n network = self.lstm_net\n elif self._flags.model_type == \"gru_net\":\n network = self.gru_net\n else:\n raise ValueError(\"Unknown network type!\")\n\n prefix_letter_pool = network(inputs[\"prefix_letter_id\"],\n \"wordid_embedding\",\n self._flags.vocab_size,\n self._flags.emb_dim,\n hid_dim=self.hid_dim,\n fc_dim=0,\n emb_lr=self._flags.emb_lr)\n if isinstance(prefix_letter_pool, list):\n #max-pooling\n prefix_pool = _parse_raw_att(prefix_letter_pool, self, 'prefix')\n else:\n prefix_pool = [prefix_letter_pool]\n\n if self._flags.prefix_word_id:\n prefix_word_pool = network(inputs[\"prefix_word_id\"],\n \"wordid_embedding\",\n self._flags.vocab_size,\n self._flags.emb_dim,\n hid_dim=self.hid_dim,\n fc_dim=0,\n emb_lr=self._flags.emb_lr)\n if isinstance(prefix_word_pool, list):\n #max-pooling\n prefix_word_raw, prefix_word_att = _parse_raw_att(prefix_word_pool, self, 'prefix')\n prefix_pool[0] = fluid.layers.concat([prefix_pool[0], prefix_word_raw], axis=1)\n prefix_pool[1] = fluid.layers.concat([prefix_pool[1], prefix_word_att], axis=1)\n else:\n prefix_pool[0] = fluid.layers.concat([prefix_pool[0], prefix_word_pool], axis=1)\n\n prefix_vec = mlp_pool(prefix_pool, self._flags.prefix_mlp.split(','), self.hid_dim)\n #vector layer\n #fluid.layers.Print(inputs[\"prefix_letter_id\"])\n #fluid.layers.Print(inputs[\"prefix_word_id\"])\n #fluid.layers.Print(prefix_vec)\n loc_vec = None\n if self._flags.use_geohash:\n loc_vec = fluid.layers.reshape(fluid.layers.cast(x=inputs['prefix_loc_geoid'],\n dtype=\"float32\"), [-1, 40])\n loc_vec = fluid.layers.fc(input=loc_vec, size=self.hid_dim, act=\"leaky_relu\", \n param_attr=fluid.ParamAttr(name='loc_fc_weight'),\n bias_attr=fluid.ParamAttr(name='loc_fc_bias'))\n\n # day_vec = fluid.layers.reshape(fluid.layers.cast(x=inputs['day_id'],\n # dtype=\"float32\"), [-1, 14])\n # day_vec = fluid.layers.fc(input=loc_vec, size=self.hid_dim, act=\"leaky_relu\", \n # param_attr=fluid.ParamAttr(name='day_weight'),\n # bias_attr=fluid.ParamAttr(name='day_bias'))\n \n context_pool = fluid.layers.concat([prefix_vec, loc_vec], axis=1) if loc_vec is not None else prefix_vec\n context_vec = fluid.layers.fc(input=context_pool, size=self._flags.fc_dim, act=self._flags.activate,\n param_attr=fluid.ParamAttr(name='context_fc_weight'),\n bias_attr=fluid.ParamAttr(name='context_fc_bias'))\n return context_vec, context_pool","def query_vectorize(self, query_words: List) -> np.ndarray:\n weights = np.zeros(shape=self.num_terms)\n terms = set(query_words)\n\n for term in terms:\n if term not in self.vector_mapping:\n continue\n else:\n index = self.vector_mapping[term]\n weights[index] = self.tf_idf_weight_query(term, query_words)\n\n return weights","def getVector(text):\n url = cfg.use_vectoriser\n res = requests.post(url, json={'text': text, 'access_key': cfg.vectoriser_access_key})\n res_dictionary = res.json()\n return res_dictionary['vectors']","def feed(self, vector):\n return vector","def projection_to_vector(projection):\n\tglobal __n_graphs\n\tglobal __graph_id_to_list_id\n\n\tvector = np.zeros(__n_graphs)\n\tfor p in projection:\n\t\tlist_id = __graph_id_to_list_id[p.id]\n\t\tvector[list_id] = 1\n\treturn vector","def getVector(self, p):\n vector = {}\n i = 0\n tr = ParseDumpWiki.normName(p)\n if(self.db.isInPage(tr)):\n for w, (idf, docs) in self.db.invertedIndex.items():\n if (p in docs):\n vector[i] = idf * docs[p]\n i += 1\n else:\n freqDist = self.db.transformDocument(wikipedia.page(p).content)\n indexesWords = list(self.db.invertedIndex.keys())\n commonWords = set(indexesWords).intersection(freqDist.keys())\n for w in commonWords:\n idf, docs = self.db.invertedIndex[w]\n vector[indexesWords.index(w)] = idf * freqDist[w]\n return vector","def __getitem__(self, *args):\n return _ida_hexrays.qvector_lvar_t___getitem__(self, *args)","def unit_vector(self,vector):\n\t\tunit_vector_query=0;\n\t\tfor word in vector:\n\t\t\tunit_vector_query += vector[word]*vector[word];\n\t\tunit_vector_query = math.sqrt(unit_vector_query);\n\t\treturn unit_vector_query","def __call__(self):\n return self._representation_vector","def CreateVector(self) -> BaseVector:","def getVector(lstOfValues):\n return MatrixExtended([[v] for v in lstOfValues])","def __getitem__(self, name):\n return np.array(self._vectors[name])","def get_data_vector(self) -> DataVector:\n raise NotImplementedError(\"Method `get_data_vector` is not implemented!\")","def get_vector(word, model):\n return model.wv[word]","def wrapDBVector(self,vec):\n return vec.todense()","def find_similar_with_index(self, vectors: dict, query: str, q_v: np.ndarray) -> List:\n\n terms = list(set([w.lower() for w in query.split(\" \")]))\n\n relevant_docs = list(set(\n [doc for t in terms for doc in self.inverted_index[t]]\n ))\n\n sim_results = []\n for d in relevant_docs:\n sim_results.append((self.cosine_similarity(vectors[d], q_v), d))\n\n sim_results = sorted(sim_results, key=lambda t: t[0], reverse=True)\n\n return [t[1] for t in sim_results]","def get_value(self, query):\n indices = self.get_dimension_indices(query)\n index = self.get_value_index(indices)\n value = self.get_value_by_index(index)\n return value","def vector(self):\n \n v_list = Householder.triangle_operation(self)[1]\n \n return(v_list)","def get_data(self, query):\n result = input(\"{}: \".format(query))\n return result","def retrieve(self, query_vectors, n_docs):\n\n # non-ddp initialization (init_retrieval() is called at ddp initialization, if no ddp, then it's never called,\n # so it has to be initalized separately.\n if not dist.is_initialized() and not self.retriever.is_initialized():\n logger.info(\"Initializing index at first query\")\n self.retriever.init_index()\n\n # single GPU training\n #if not dist.is_initialized():\n doc_ids, doc_vectors = self._main_retrieve(query_vectors)\n return doc_vectors, self.retriever.get_doc_dicts(doc_ids)\n\n # # distributed training\n # world_size = dist.get_world_size(group=self.process_group)\n\n # # gather logic\n # gather_list = None\n # if self._is_main():\n # gather_list = [torch.empty(query_vectors.shape, dtype=torch.float32) for _ in range(world_size)]\n # dist.gather(query_vectors, dst=0, gather_list=gather_list, group=self.process_group)\n\n # # scatter logic\n # n_queries = query_vectors.shape[0]\n # scatter_ids = []\n # scatter_vectors = []\n # if self._is_main():\n # assert len(gather_list) == world_size\n # ids, vectors = self._main_retrieve(torch.cat(gather_list))\n # scatter_ids = self._chunk_tensor(ids, n_queries)\n # scatter_vectors = self._chunk_tensor(vectors, n_queries)\n # doc_ids = self._scattered(scatter_ids, [n_queries, self.n_docs], target_type=torch.int64)\n # doc_vectors = self._scattered(scatter_vectors, [n_queries, self.n_docs, query_vectors.shape[1]])\n\n # return doc_vectors, self.retriever.get_doc_dicts(doc_ids)","def get_vector(self): \n #print(self.state)\n '''\n print(\"\"\"\n Price {}\n Last Price {}\n Last Period Transaction {}\n Last Transaction {}\n Las Value {}\n Last day {}\n Last hour {}\n Last minute {}\n --------------\n Balance {}\n Bag {}\n \"\"\".format(\n self.state['price'],\n self.states[-1]['price'],\n self.states[-1]['transaction'],\n self.transactions[-1]['transaction'],\n self.value,\n self.state['day'],\n self.state['hour'],\n self.state['minute'], \n self.balance, \n self.bag, \n )) \n ''' \n self.state_vector = np.array([\n self.state['price'],\n self.states[-1]['price'],\n self.states[-1]['transaction'],\n self.transactions[-1]['transaction'],\n self.value,\n self.state['day'],\n self.state['hour'],\n self.state['minute'],\n ])\n\n return self.state_vector","def query(universe: pd.DataFrame, query_string: str) -> List[Stock]:\n scope = list(map(\n lambda stock: Stock(name=stock[2], stock_id=stock[1], price=stock[3]),\n universe.query(query_string).to_numpy()\n ))\n print(\"debug!!!!!\")\n print([(stock.get_id(), stock.get_name(), stock.get_price()) for stock in scope])\n return scope","def query3() :","def vector(self, x):\n if isinstance(x, tuple):\n index = self.tuple_to_index[x]\n elif isinstance(x, str):\n index = self.string_to_index[x]\n else:\n index = x\n\n return self.vectors[index]","def get_vector(size):\n ret = lib.myarray_construct(size, size)\n return _asarray(ret.data, (ret.n_rows, ret.n_cols))","def return_vec(self) :\r\n y_vec = np.concatenate((self.x_vec,self.v_vec))\r\n return y_vec","def vector(self):\n return self.__vector","def data_from_ucr_query(self):\n raise NotImplementedError","def vec(x, y=0.0, z=0.0):\n return np.matrix((x, y, z)).T","def matrixToVector(self,mx):\n return FreeCAD.Base.Vector(mx[0]/1000,mx[1]/1000,mx[2]/1000)","def __getitem__(self, x):\n return self.query(x)","def CustomRetrievalQuery(self, query):\n\n\t\tpoints_dat = self.session.execute(query)\n\n\t\tpoints = []\n\n\t\tfor point_dat in points_dat:\n\t\t\tpoints.append(Point(\n\t\t\t\tDeviceID = point_dat.deviceid,\n\t\t\t\tLatitude = point_dat.latitude,\n\t\t\t\tLongitude = point_dat.longitude,\n\t\t\t\tDateTime = point_dat.datetime))\n\t\treturn points","def convert_query(self, query):\n rescaled = pd.read_json('[' + query + ']') / self.SCALE_FACTOR\n model_input = rescaled.values.reshape(1, -1)\n return model_input","def at(self, *args):\n return _ida_hexrays.qvector_lvar_t_at(self, *args)","def where(self, value, operator=\"\"):\n return f\"\"\"\nto_tsvector('english', json->>'{sqlq(self.name)}') @@ plainto_tsquery(${{arg}}::text)\"\"\"","def get_vector(self, token):\n try:\n idx = self.token_to_idx[token]\n except KeyError:\n print(\"Input token <{}> is not in the model. Will return None type vector\".format(token))\n return None\n return self.embeddings_mat[idx]","def vector(self):\n return self._representation_vector","def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:\n poly_box = ogr.Geometry(ogr.wkbLinearRing)\n poly_box.AddPoint(query.minx, query.maxy)\n poly_box.AddPoint(query.maxx, query.maxy)\n poly_box.AddPoint(query.maxx, query.miny)\n poly_box.AddPoint(query.minx, query.miny)\n poly_box.AddPoint(query.minx, query.maxy)\n # Create a Polygon object from the ring.\n poly = ogr.Geometry(ogr.wkbPolygon)\n poly.AddGeometry(poly_box)\n\n # # Create a vector datasource in memory:\n mem_driver = ogr.GetDriverByName('MEMORY')\n mem_ds = mem_driver.CreateDataSource('memdata')\n mem_layer = mem_ds.CreateLayer('0', self.vec_srs, geom_type=ogr.wkbPolygon)\n feature_def = mem_layer.GetLayerDefn()\n out_feature = ogr.Feature(feature_def)\n # Set new geometry from the Polygon object (bounding box):\n out_feature.SetGeometry(poly)\n # Add new feature to output Layer\n mem_layer.CreateFeature(out_feature)\n\n # Crate the output vector patch datasource:\n out_driver = ogr.GetDriverByName('MEMORY')\n out_mem_ds = out_driver.CreateDataSource('memdata')\n # Clip it with the bounding box:\n out_layer = out_mem_ds.CreateLayer('0', self.vec_srs, geom_type=ogr.wkbMultiPolygon)\n ogr.Layer.Clip(self.ds.GetLayer(), mem_layer, out_layer)\n\n # Check that there is no curve geometry in the output patch:\n self._check_curve(layer=out_layer)\n\n sample = {\"mask\": out_mem_ds, \"crs\": self.crs, \"bbox\": query}\n\n return sample","def _asvector(self, arr):\n result = self._moveaxis(arr, [-2, -1], [0, 1])\n return self.domain.element(result)","def get(self, request, search_string=None):\n query = SearchQuery(search_string)\n\n name_vector = SearchVector('name', weight='A')\n description_vector = SearchVector('description', weight='B')\n vectors = name_vector + description_vector\n qs = Product.objects\n qs = qs.annotate(search=vectors).filter(search=query)\n qs = qs.annotate(rank=SearchRank(vectors, query)).order_by('-rank')\n print(qs)\n return Response(ProductSerializer(qs, many=True).data)","def visit_query(self, query):\n return query","def __getitem__(self, key) -> np.ndarray:\n if isinstance(key, str):\n index = self.get_index(word=key)\n elif isinstance(key, int):\n index = key\n else:\n raise TypeError(\n 'Only support int and str type of input',\n )\n vector = self._get_vector(index)\n return vector","def update_query_vector(\n self,\n query_vector: List[Tuple[str, float]],\n relevant_doc_ids: set,\n non_relevant_doc_ids: set,\n ) -> List[Tuple[str, float]]:\n non_relevant_doc_ids = list(non_relevant_doc_ids)\n relevant_doc_ids = list(relevant_doc_ids)\n nr_len = len(non_relevant_doc_ids)\n r_len = len(relevant_doc_ids)\n\n # If no relevance information then do nothing\n if r_len == 0 and nr_len == 0:\n return query_vector\n\n # Get vector form of documents and queries\n query_vector = self._to_full_vector(query_vector)\n relevant_vectors = np.array(\n [self.index.get_docID_vector(doc_id) for doc_id in relevant_doc_ids]\n )\n non_relevant_vectors = np.array(\n [self.index.get_docID_vector(doc_id) for doc_id in non_relevant_doc_ids]\n )\n\n # Rocchio algorithm for update\n relevant_vector = relevant_vectors.sum(axis=0)\n non_relevant_vector = non_relevant_vectors.sum(axis=0)\n updated_query_vector = query_vector\n if r_len > 0:\n updated_query_vector = (\n query_vector + self.beta * (1 / r_len) * relevant_vector\n )\n if nr_len > 0:\n updated_query_vector = (\n updated_query_vector - self.gamma * (1 / nr_len) * non_relevant_vector\n )\n\n # Convert back to sparse vector\n terms = list(self.index.get_terms())\n terms.sort()\n updated_query_vector = [\n (term, weight)\n for (term, weight) in zip(terms, updated_query_vector)\n if weight > 0\n ]\n\n return updated_query_vector","def toVector(tupL,n,v):\n v = numpy.zeros(n)\n for (i,val) in tupL:\n #print i,v\n v[i]=val\n return v","def make_query(self):","def v(self) -> np.ndarray:\n return self.array[:, 1:] if self.scalar_vector else self.array[:, :3]","def get_vector(self, word):\n\n if word in self.glove.stoi:\n return self.glove.vectors[self.glove.stoi[word]]\n else:\n return None","def get_vertical_vector(q):\n P0, P1, P2, P3 = q\n P0_up = copy.deepcopy(P0)\n P0_up.depth = P0_up.depth - 1.0\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\n p1 = Vector.fromPoint(P0_up)\n v1 = (p1 - p0).norm()\n return v1","def make_single_vector_query(vector_clip):\n if not adaptive_clip_learning_rate:\n return tensorflow_privacy.GaussianAverageQuery(\n l2_norm_clip=vector_clip,\n sum_stddev=vector_clip * noise_multiplier * num_vectors**0.5,\n denominator=expected_total_weight)\n else:\n # Without geometric updating, the update is c = c - lr * loss, so for\n # multiple vectors we set the learning rate to be on the same scale as the\n # initial clip. That way big vectors get big updates, small vectors\n # small updates. With geometric updating, the update is\n # c = c * exp(-lr * loss) so the learning rate should be independent of\n # the initial clip.\n if geometric_clip_update:\n learning_rate = adaptive_clip_learning_rate\n else:\n learning_rate = adaptive_clip_learning_rate * vector_clip / clip\n return tensorflow_privacy.QuantileAdaptiveClipAverageQuery(\n initial_l2_norm_clip=vector_clip,\n noise_multiplier=noise_multiplier,\n target_unclipped_quantile=target_unclipped_quantile,\n learning_rate=learning_rate,\n clipped_count_stddev=clipped_count_stddev,\n expected_num_records=expected_num_clients,\n geometric_update=geometric_clip_update,\n denominator=expected_total_weight)","def make_vector(start, finish):\n init = complex(start[0], start[1])\n end = complex(finish[0], finish[1])\n\n vector = end - init\n return vector","def fromSequence(seq): # returns Vector\n return Vector(*seq)","def query(self):","def carto_query(query):\n params = {'q': query, 'api_key': apikey(serv=\"cdb\")}\n d = api_query(api_url=CDB_URL, params=params)['rows']\n logging.info(\"Returned %d rows\" % len(d))\n return d","def query2df(query):\n df = pd.DataFrame(data = list(itertools.product([0, 1], repeat=len(query.variables))), columns=query.variables)\n df['p'] = query.values.flatten()\n return df","def vector(x, y, z):\n return point_or_vector(x,y,z,0.0)","def generador_v(vector_n, constante):\n\n v = []\n\n for x in range(len(vector_n)):\n nv = vector_n[x] // constante # // = Division entera\n v.append(nv)\n\n # print(\"valores n: \", vector_n)\n # print(\"valores v: \", v)\n\n return v","def get_vector(self, name: str) -> ndarray:\n vector_path = self._get_path(name)\n vector_df = DataFrame(read_csv(vector_path, header=None))\n ixs = list(map(lambda i: i - 1, vector_df.iloc[:, 0]))\n vals = vector_df.iloc[:, 1]\n return util.create_array(ixs, vals, self.number_of_nodes)","def get_vectors(self, corpus_size, vectors_size, vectors_type):\n vectors = np.zeros((corpus_size, vectors_size))\n for i in range(0, corpus_size):\n prefix = vectors_type + '_' + str(i)\n vectors[i] = self.model_dbow.docvecs[prefix]\n return vectors","def get_data(cls, my_query_result):\n return my_query_result","def matrix_to_vector(matrix, system):\n\n outvec = Vector.zero\n vects = system.base_vectors()\n for i, x in enumerate(matrix):\n outvec += x * vects[i]\n return outvec","def vec(self) -> Vec:\n return Vec(self.x, self.y, self.z)","def parameters_to_vector(self) -> np.ndarray:\n return nn.utils.parameters_to_vector(self.parameters()).detach().cpu().numpy()","def execute_sparql(client: NeptuneClient, query: str) -> pd.DataFrame:\n data = client.read_sparql(query)\n df = None\n if \"results\" in data and \"bindings\" in data[\"results\"]:\n df = pd.DataFrame(data[\"results\"][\"bindings\"])\n df.applymap(lambda x: x[\"value\"])\n else:\n df = pd.DataFrame(data)\n\n return df","def getVectors(self,graph):\n return [Vector.createFromTwoTuples(graph[i],graph[i+1]) for i in range(len(graph)-1)]","def soql_query(self, query):\n self.builtin.log(\"Running SOQL Query: {}\".format(query))\n return self.cumulusci.sf.query_all(query)","def getResults(self, queryVec: dict):\n result = set()\n for word in queryVec.keys():\n if word in self.invertedIndex:\n result.update(self.invertedIndex[word].keys())\n #print (result)\n #print (self.corpusCount)\n return result","def get_vector(self, u_name):\n try:\n return pickle.loads(self.vectors_db.get(u_name))\n except TypeError as er:\n logging.debug(\"%s not exists\" % u_name)\n return","def __getitem__(self, *args):\n return _ida_hexrays.qvector_ccase_t___getitem__(self, *args)","def question_to_vec(question, embeddings, dim):\r\n\r\n words = question.split()\r\n\r\n counter = 0\r\n res = np.zeros(dim)\r\n for word in words:\r\n if word in embeddings:\r\n res += np.array(embeddings[word])\r\n counter += 1\r\n if counter!=0:\r\n return res/counter # mean of all word embeddings\r\n else:\r\n return res # vector of zeros\r","def query(self, var, evidence={}):\n q = self._enumeration_ask(var, evidence).reshape(-1, 1) #-1 means numpy will figure out the value. -1,1 gives array of arrays with 1 value [[1], [2], [3], ..] 1 equals column\n #reshape(-1,1) gir x kolonner med 1 elem i hver\n #reshape(1,-1) gir én rad med x elementer\n return Variable(var, self.bayesian_network.variables[var].no_states, q)","def __getitem__(self, arg0: ngsolve.ngstd.IntRange) -> BaseVector:","def get_doc_vector(doc_id, model_id):\n queue = get_vec_queue(app.config)\n data = queue.get_by_id((doc_id, model_id))\n if data is not None:\n return jsonify(doc=data.tolist())\n return jsonify(err=f\"{doc_id} not found\"), 404"],"string":"[\n \"def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\\n terms = list(self.index.get_terms())\\n terms.sort()\\n vector = np.zeros(len(terms))\\n\\n for (term, weight) in query_vector:\\n index = terms.index(term)\\n vector[index] = weight\\n\\n return vector\",\n \"def transform_query(vectorizer_model, query):\\n x_request = vectorizer_model.transform(query)\\n x0 = x_request.toarray()\\n return x0\",\n \"def _get_query_representation(self, query, index):\\n term_frequencies = {term: query.count(term) for term in query}\\n vec = np.zeros(shape=(index.num_terms,), dtype=np.float64)\\n for i, term in enumerate(sorted(index.get_index_terms())):\\n vec[i] = self._tfidf(\\n term_frequencies.get(term, 0),\\n index.get_document_frequency(term),\\n index.get_document_count()\\n )\\n return vec\",\n \"def calcQueryVector(self):\\n query = input(\\\"Query: \\\");\\n ana = StemmingAnalyzer() ### lowercases, stems, ignores stopwords\\n tokens = [token.text for token in ana(query)]\\n\\n queryVector = {}\\n for token in tokens:\\n if token in self.invertedIndex.keys():\\n if token in queryVector.keys():\\n queryVector[token]+=1;\\n else:\\n queryVector[token] = 1;\\n\\n return self.normalizeQueryVector(queryVector);\",\n \"def AsVector(self) -> ngsolve.la.BaseVector:\",\n \"def result_to_vector(results):\\n return [vectorized_result(x) for x in results]\",\n \"def generate_query_vector(q, q_dict, inv_index):\\n # Create the query vector\\n query_vector = dict(Counter(q_dict[q]))\\n\\n # Add to this query vector, all the indexed terms\\n for i_term in inv_index:\\n if i_term not in query_vector:\\n query_vector[i_term] = 0\\n\\n return query_vector\",\n \"def buildQueryVector(self, termList):\\n\\t\\tquery = self.createVector(\\\" \\\".join(termList))\\n\\t\\treturn query\",\n \"def vector(self):\\n return self.q[1:4]\",\n \"def query_to_word_vector(query_string, corpus):\\n inv_index = vsm_retrieval.get_inverted_index(corpus)\\n word_vec = np.zeros(len(inv_index))\\n query_word_list = vsm_retrieval.convert_query(query_string)\\n for count_vec, word in enumerate(inv_index):\\n if word in query_word_list:\\n word_vec[count_vec] = 1\\n return word_vec\",\n \"def get_vector(self, word):\\n string = \\\"SELECT * FROM Vectors WHERE name=?\\\"\\n params = (word,)\\n self.cur.execute(string, params)\\n raw_vector = self.cur.fetchone()\\n if raw_vector is None:\\n raise KeyError(\\\"Vector not found\\\")\\n else:\\n vector = pickle.loads(raw_vector[1])\\n return vector\",\n \"def find(self, query: str) -> TsInfoVector:\\n info = parse_ts_query(ts_query=query)\\n domain = self.create_netatmo_connection()[1]\\n meas = domain.get_measurement(**info)\\n\\n # noinspection PyArgumentList\\n tsi = TsInfo(\\n name=meas.ts_id,\\n point_fx=meas.data_type.point_interpretation,\\n delta_t=np.nan,\\n olson_tz_id=meas.station.place['timezone'],\\n data_period=UtcPeriod(meas.module.last_setup, meas.module.last_seen),\\n created=meas.module.last_setup,\\n modified=meas.module.last_seen\\n )\\n\\n # noinspection PyArgumentList\\n tsiv = TsInfoVector()\\n tsiv.append(tsi)\\n return tsiv\",\n \"def __call__(self):\\n return self._vector\",\n \"def AsVector(self) -> BaseVector:\",\n \"def vector_q(q_1: Q) -> Q:\\n\\n end_q_type = f\\\"vector_q({q_1.q_type})\\\"\\n\\n v = Q(\\n [0, q_1.x, q_1.y, q_1.z],\\n q_type=end_q_type,\\n representation=q_1.representation,\\n )\\n return v\",\n \"def create_query_vector(ix, term_dict, bow):\\n\\n\\tqfv = list()\\n\\tfor idx, tf in bow:\\n\\t\\t# get term from dict index\\n\\t\\tterm = ix[idx]\\n\\t\\t# filter out terms not contained in self.term_dict\\n\\t\\tif term not in term_dict:\\n\\t\\t\\tcontinue\\n\\t\\t# append term w/ tf to tfv\\n\\t\\tqfv.append((term, tf))\\n\\treturn scale_to_L1_norm(defaultdict(float, qfv))\",\n \"def call_single_vec(self, input_value):\\n _, eigVectors = self.getEigen(input_value)\\n return eigVectors[:,:,-1]\",\n \"def query(_from, _select, _geomselect=None, _where=None, _groupby=None, _limit=None):\\n # INSTEAD MAKE INTO CLASS\\n # WITH .fields attr\\n # AND .__iter__()\\n # AND .get_vectordata()\\n # AND MAKE EACH YIELDED ROW A VECTOR FEATURE CLASS\\n # THIS WAY ALLOWING CHAINED QUERIES\\n\\n # parse args\\n iterables = _from\\n columnfuncs = _select\\n geomfunc = _geomselect\\n condition = _where\\n key = _groupby\\n n = _limit\\n \\n # first yield header as list of column names\\n colnames = [each[0] for each in columnfuncs]\\n yield colnames\\n\\n # make an iterable that yields every combinaion of all input iterables' items\\n if len(iterables) == 1:\\n iterable = iterables[0]\\n else:\\n iterable = itertools.product(*iterables)\\n\\n # iterate and add\\n if key:\\n groups = groupby(iterable, key)\\n\\n # limit\\n if n:\\n groups = limit(groups, n)\\n \\n for items in groups:\\n # filter\\n if condition:\\n items = where(items, condition)\\n \\n # aggregate\\n # NOTE: columnfuncs and geomfunc must expect an iterable as input and return a single row,geom pair\\n item = aggreg(items, columnfuncs, geomfunc)\\n yield item\\n \\n else:\\n # filter\\n if condition:\\n iterable = where(iterable, condition)\\n\\n # limit\\n if n:\\n iterable = limit(iterable, n)\\n\\n # select\\n for item in select(iterable, columnfuncs, geomfunc):\\n yield item\",\n \"def generate_vector(self,dim=0,v=None):\\n vec = dl.Vector()\\n self.init_vector(vec,dim)\\n if v is not None:\\n vec[:]=v\\n return vec\",\n \"def query_and_bundle(session, fields, offset, limit, filter_):\\n q = session.query(*fields) # raw query\\n q = q.offset(offset) if filter_ is None else q.filter(filter_) # filter / offset\\n ids, vectors = zip(*q.limit(limit)) # unravel results\\n # bundle into arrays\\n _ids = np.array(ids, dtype=STR_TYPE)\\n _str_vectors = [json.loads(vector) for vector in vectors]\\n _vectors = np.array(_str_vectors, dtype=FLOAT_TYPE)\\n return _ids, _vectors\",\n \"def get_vector(self,term):\\n return self.dict.get(term)\",\n \"def take_vec(self):\\n vec = aux.vec(self.numbers)\\n\\n return vec\",\n \"def evaluate_as_vector(self, chain_state): \\n def vector_representation(n, ordering, it):\\n return self.mapping.subspace(zip(ordering,it))\\n return self._evaluate(vector_representation, chain_state)\",\n \"def to_vec(self):\\n column_count = self.column_count\\n vector = Matrix(size=(self.row_count * column_count, 1))\\n for key, value in self.data.items():\\n row, column = key[:2]\\n subscript = (column * column_count + row, 0)\\n vector.set(subscript, value)\\n return vector\",\n \"def vector_from_matrix(v_as_matrix):\\r\\n return [row[0] for row in v_as_matrix]\",\n \"def as_vector(self):\\n return self.pdm.as_vector()\",\n \"def vec(self):\\n return np.matrix(self.val.ravel()).transpose()\",\n \"def query(self, query):\",\n \"def _get_query_vec(self, inputs):\\n if self._flags.model_type == \\\"bilstm_net\\\":\\n network = self.bilstm_net\\n elif self._flags.model_type == \\\"bow_net\\\":\\n network = self.bow_net\\n elif self._flags.model_type == \\\"cnn_net\\\":\\n network = self.cnn_net\\n elif self._flags.model_type == \\\"lstm_net\\\":\\n network = self.lstm_net\\n elif self._flags.model_type == \\\"gru_net\\\":\\n network = self.gru_net\\n else:\\n raise ValueError(\\\"Unknown network type!\\\")\\n\\n prefix_letter_pool = network(inputs[\\\"prefix_letter_id\\\"],\\n \\\"wordid_embedding\\\",\\n self._flags.vocab_size,\\n self._flags.emb_dim,\\n hid_dim=self.hid_dim,\\n fc_dim=0,\\n emb_lr=self._flags.emb_lr)\\n if isinstance(prefix_letter_pool, list):\\n #max-pooling\\n prefix_pool = _parse_raw_att(prefix_letter_pool, self, 'prefix')\\n else:\\n prefix_pool = [prefix_letter_pool]\\n\\n if self._flags.prefix_word_id:\\n prefix_word_pool = network(inputs[\\\"prefix_word_id\\\"],\\n \\\"wordid_embedding\\\",\\n self._flags.vocab_size,\\n self._flags.emb_dim,\\n hid_dim=self.hid_dim,\\n fc_dim=0,\\n emb_lr=self._flags.emb_lr)\\n if isinstance(prefix_word_pool, list):\\n #max-pooling\\n prefix_word_raw, prefix_word_att = _parse_raw_att(prefix_word_pool, self, 'prefix')\\n prefix_pool[0] = fluid.layers.concat([prefix_pool[0], prefix_word_raw], axis=1)\\n prefix_pool[1] = fluid.layers.concat([prefix_pool[1], prefix_word_att], axis=1)\\n else:\\n prefix_pool[0] = fluid.layers.concat([prefix_pool[0], prefix_word_pool], axis=1)\\n\\n prefix_vec = mlp_pool(prefix_pool, self._flags.prefix_mlp.split(','), self.hid_dim)\\n #vector layer\\n #fluid.layers.Print(inputs[\\\"prefix_letter_id\\\"])\\n #fluid.layers.Print(inputs[\\\"prefix_word_id\\\"])\\n #fluid.layers.Print(prefix_vec)\\n loc_vec = None\\n if self._flags.use_geohash:\\n loc_vec = fluid.layers.reshape(fluid.layers.cast(x=inputs['prefix_loc_geoid'],\\n dtype=\\\"float32\\\"), [-1, 40])\\n loc_vec = fluid.layers.fc(input=loc_vec, size=self.hid_dim, act=\\\"leaky_relu\\\", \\n param_attr=fluid.ParamAttr(name='loc_fc_weight'),\\n bias_attr=fluid.ParamAttr(name='loc_fc_bias'))\\n\\n # day_vec = fluid.layers.reshape(fluid.layers.cast(x=inputs['day_id'],\\n # dtype=\\\"float32\\\"), [-1, 14])\\n # day_vec = fluid.layers.fc(input=loc_vec, size=self.hid_dim, act=\\\"leaky_relu\\\", \\n # param_attr=fluid.ParamAttr(name='day_weight'),\\n # bias_attr=fluid.ParamAttr(name='day_bias'))\\n \\n context_pool = fluid.layers.concat([prefix_vec, loc_vec], axis=1) if loc_vec is not None else prefix_vec\\n context_vec = fluid.layers.fc(input=context_pool, size=self._flags.fc_dim, act=self._flags.activate,\\n param_attr=fluid.ParamAttr(name='context_fc_weight'),\\n bias_attr=fluid.ParamAttr(name='context_fc_bias'))\\n return context_vec, context_pool\",\n \"def query_vectorize(self, query_words: List) -> np.ndarray:\\n weights = np.zeros(shape=self.num_terms)\\n terms = set(query_words)\\n\\n for term in terms:\\n if term not in self.vector_mapping:\\n continue\\n else:\\n index = self.vector_mapping[term]\\n weights[index] = self.tf_idf_weight_query(term, query_words)\\n\\n return weights\",\n \"def getVector(text):\\n url = cfg.use_vectoriser\\n res = requests.post(url, json={'text': text, 'access_key': cfg.vectoriser_access_key})\\n res_dictionary = res.json()\\n return res_dictionary['vectors']\",\n \"def feed(self, vector):\\n return vector\",\n \"def projection_to_vector(projection):\\n\\tglobal __n_graphs\\n\\tglobal __graph_id_to_list_id\\n\\n\\tvector = np.zeros(__n_graphs)\\n\\tfor p in projection:\\n\\t\\tlist_id = __graph_id_to_list_id[p.id]\\n\\t\\tvector[list_id] = 1\\n\\treturn vector\",\n \"def getVector(self, p):\\n vector = {}\\n i = 0\\n tr = ParseDumpWiki.normName(p)\\n if(self.db.isInPage(tr)):\\n for w, (idf, docs) in self.db.invertedIndex.items():\\n if (p in docs):\\n vector[i] = idf * docs[p]\\n i += 1\\n else:\\n freqDist = self.db.transformDocument(wikipedia.page(p).content)\\n indexesWords = list(self.db.invertedIndex.keys())\\n commonWords = set(indexesWords).intersection(freqDist.keys())\\n for w in commonWords:\\n idf, docs = self.db.invertedIndex[w]\\n vector[indexesWords.index(w)] = idf * freqDist[w]\\n return vector\",\n \"def __getitem__(self, *args):\\n return _ida_hexrays.qvector_lvar_t___getitem__(self, *args)\",\n \"def unit_vector(self,vector):\\n\\t\\tunit_vector_query=0;\\n\\t\\tfor word in vector:\\n\\t\\t\\tunit_vector_query += vector[word]*vector[word];\\n\\t\\tunit_vector_query = math.sqrt(unit_vector_query);\\n\\t\\treturn unit_vector_query\",\n \"def __call__(self):\\n return self._representation_vector\",\n \"def CreateVector(self) -> BaseVector:\",\n \"def getVector(lstOfValues):\\n return MatrixExtended([[v] for v in lstOfValues])\",\n \"def __getitem__(self, name):\\n return np.array(self._vectors[name])\",\n \"def get_data_vector(self) -> DataVector:\\n raise NotImplementedError(\\\"Method `get_data_vector` is not implemented!\\\")\",\n \"def get_vector(word, model):\\n return model.wv[word]\",\n \"def wrapDBVector(self,vec):\\n return vec.todense()\",\n \"def find_similar_with_index(self, vectors: dict, query: str, q_v: np.ndarray) -> List:\\n\\n terms = list(set([w.lower() for w in query.split(\\\" \\\")]))\\n\\n relevant_docs = list(set(\\n [doc for t in terms for doc in self.inverted_index[t]]\\n ))\\n\\n sim_results = []\\n for d in relevant_docs:\\n sim_results.append((self.cosine_similarity(vectors[d], q_v), d))\\n\\n sim_results = sorted(sim_results, key=lambda t: t[0], reverse=True)\\n\\n return [t[1] for t in sim_results]\",\n \"def get_value(self, query):\\n indices = self.get_dimension_indices(query)\\n index = self.get_value_index(indices)\\n value = self.get_value_by_index(index)\\n return value\",\n \"def vector(self):\\n \\n v_list = Householder.triangle_operation(self)[1]\\n \\n return(v_list)\",\n \"def get_data(self, query):\\n result = input(\\\"{}: \\\".format(query))\\n return result\",\n \"def retrieve(self, query_vectors, n_docs):\\n\\n # non-ddp initialization (init_retrieval() is called at ddp initialization, if no ddp, then it's never called,\\n # so it has to be initalized separately.\\n if not dist.is_initialized() and not self.retriever.is_initialized():\\n logger.info(\\\"Initializing index at first query\\\")\\n self.retriever.init_index()\\n\\n # single GPU training\\n #if not dist.is_initialized():\\n doc_ids, doc_vectors = self._main_retrieve(query_vectors)\\n return doc_vectors, self.retriever.get_doc_dicts(doc_ids)\\n\\n # # distributed training\\n # world_size = dist.get_world_size(group=self.process_group)\\n\\n # # gather logic\\n # gather_list = None\\n # if self._is_main():\\n # gather_list = [torch.empty(query_vectors.shape, dtype=torch.float32) for _ in range(world_size)]\\n # dist.gather(query_vectors, dst=0, gather_list=gather_list, group=self.process_group)\\n\\n # # scatter logic\\n # n_queries = query_vectors.shape[0]\\n # scatter_ids = []\\n # scatter_vectors = []\\n # if self._is_main():\\n # assert len(gather_list) == world_size\\n # ids, vectors = self._main_retrieve(torch.cat(gather_list))\\n # scatter_ids = self._chunk_tensor(ids, n_queries)\\n # scatter_vectors = self._chunk_tensor(vectors, n_queries)\\n # doc_ids = self._scattered(scatter_ids, [n_queries, self.n_docs], target_type=torch.int64)\\n # doc_vectors = self._scattered(scatter_vectors, [n_queries, self.n_docs, query_vectors.shape[1]])\\n\\n # return doc_vectors, self.retriever.get_doc_dicts(doc_ids)\",\n \"def get_vector(self): \\n #print(self.state)\\n '''\\n print(\\\"\\\"\\\"\\n Price {}\\n Last Price {}\\n Last Period Transaction {}\\n Last Transaction {}\\n Las Value {}\\n Last day {}\\n Last hour {}\\n Last minute {}\\n --------------\\n Balance {}\\n Bag {}\\n \\\"\\\"\\\".format(\\n self.state['price'],\\n self.states[-1]['price'],\\n self.states[-1]['transaction'],\\n self.transactions[-1]['transaction'],\\n self.value,\\n self.state['day'],\\n self.state['hour'],\\n self.state['minute'], \\n self.balance, \\n self.bag, \\n )) \\n ''' \\n self.state_vector = np.array([\\n self.state['price'],\\n self.states[-1]['price'],\\n self.states[-1]['transaction'],\\n self.transactions[-1]['transaction'],\\n self.value,\\n self.state['day'],\\n self.state['hour'],\\n self.state['minute'],\\n ])\\n\\n return self.state_vector\",\n \"def query(universe: pd.DataFrame, query_string: str) -> List[Stock]:\\n scope = list(map(\\n lambda stock: Stock(name=stock[2], stock_id=stock[1], price=stock[3]),\\n universe.query(query_string).to_numpy()\\n ))\\n print(\\\"debug!!!!!\\\")\\n print([(stock.get_id(), stock.get_name(), stock.get_price()) for stock in scope])\\n return scope\",\n \"def query3() :\",\n \"def vector(self, x):\\n if isinstance(x, tuple):\\n index = self.tuple_to_index[x]\\n elif isinstance(x, str):\\n index = self.string_to_index[x]\\n else:\\n index = x\\n\\n return self.vectors[index]\",\n \"def get_vector(size):\\n ret = lib.myarray_construct(size, size)\\n return _asarray(ret.data, (ret.n_rows, ret.n_cols))\",\n \"def return_vec(self) :\\r\\n y_vec = np.concatenate((self.x_vec,self.v_vec))\\r\\n return y_vec\",\n \"def vector(self):\\n return self.__vector\",\n \"def data_from_ucr_query(self):\\n raise NotImplementedError\",\n \"def vec(x, y=0.0, z=0.0):\\n return np.matrix((x, y, z)).T\",\n \"def matrixToVector(self,mx):\\n return FreeCAD.Base.Vector(mx[0]/1000,mx[1]/1000,mx[2]/1000)\",\n \"def __getitem__(self, x):\\n return self.query(x)\",\n \"def CustomRetrievalQuery(self, query):\\n\\n\\t\\tpoints_dat = self.session.execute(query)\\n\\n\\t\\tpoints = []\\n\\n\\t\\tfor point_dat in points_dat:\\n\\t\\t\\tpoints.append(Point(\\n\\t\\t\\t\\tDeviceID = point_dat.deviceid,\\n\\t\\t\\t\\tLatitude = point_dat.latitude,\\n\\t\\t\\t\\tLongitude = point_dat.longitude,\\n\\t\\t\\t\\tDateTime = point_dat.datetime))\\n\\t\\treturn points\",\n \"def convert_query(self, query):\\n rescaled = pd.read_json('[' + query + ']') / self.SCALE_FACTOR\\n model_input = rescaled.values.reshape(1, -1)\\n return model_input\",\n \"def at(self, *args):\\n return _ida_hexrays.qvector_lvar_t_at(self, *args)\",\n \"def where(self, value, operator=\\\"\\\"):\\n return f\\\"\\\"\\\"\\nto_tsvector('english', json->>'{sqlq(self.name)}') @@ plainto_tsquery(${{arg}}::text)\\\"\\\"\\\"\",\n \"def get_vector(self, token):\\n try:\\n idx = self.token_to_idx[token]\\n except KeyError:\\n print(\\\"Input token <{}> is not in the model. Will return None type vector\\\".format(token))\\n return None\\n return self.embeddings_mat[idx]\",\n \"def vector(self):\\n return self._representation_vector\",\n \"def __getitem__(self, query: BoundingBox) -> Dict[str, Any]:\\n poly_box = ogr.Geometry(ogr.wkbLinearRing)\\n poly_box.AddPoint(query.minx, query.maxy)\\n poly_box.AddPoint(query.maxx, query.maxy)\\n poly_box.AddPoint(query.maxx, query.miny)\\n poly_box.AddPoint(query.minx, query.miny)\\n poly_box.AddPoint(query.minx, query.maxy)\\n # Create a Polygon object from the ring.\\n poly = ogr.Geometry(ogr.wkbPolygon)\\n poly.AddGeometry(poly_box)\\n\\n # # Create a vector datasource in memory:\\n mem_driver = ogr.GetDriverByName('MEMORY')\\n mem_ds = mem_driver.CreateDataSource('memdata')\\n mem_layer = mem_ds.CreateLayer('0', self.vec_srs, geom_type=ogr.wkbPolygon)\\n feature_def = mem_layer.GetLayerDefn()\\n out_feature = ogr.Feature(feature_def)\\n # Set new geometry from the Polygon object (bounding box):\\n out_feature.SetGeometry(poly)\\n # Add new feature to output Layer\\n mem_layer.CreateFeature(out_feature)\\n\\n # Crate the output vector patch datasource:\\n out_driver = ogr.GetDriverByName('MEMORY')\\n out_mem_ds = out_driver.CreateDataSource('memdata')\\n # Clip it with the bounding box:\\n out_layer = out_mem_ds.CreateLayer('0', self.vec_srs, geom_type=ogr.wkbMultiPolygon)\\n ogr.Layer.Clip(self.ds.GetLayer(), mem_layer, out_layer)\\n\\n # Check that there is no curve geometry in the output patch:\\n self._check_curve(layer=out_layer)\\n\\n sample = {\\\"mask\\\": out_mem_ds, \\\"crs\\\": self.crs, \\\"bbox\\\": query}\\n\\n return sample\",\n \"def _asvector(self, arr):\\n result = self._moveaxis(arr, [-2, -1], [0, 1])\\n return self.domain.element(result)\",\n \"def get(self, request, search_string=None):\\n query = SearchQuery(search_string)\\n\\n name_vector = SearchVector('name', weight='A')\\n description_vector = SearchVector('description', weight='B')\\n vectors = name_vector + description_vector\\n qs = Product.objects\\n qs = qs.annotate(search=vectors).filter(search=query)\\n qs = qs.annotate(rank=SearchRank(vectors, query)).order_by('-rank')\\n print(qs)\\n return Response(ProductSerializer(qs, many=True).data)\",\n \"def visit_query(self, query):\\n return query\",\n \"def __getitem__(self, key) -> np.ndarray:\\n if isinstance(key, str):\\n index = self.get_index(word=key)\\n elif isinstance(key, int):\\n index = key\\n else:\\n raise TypeError(\\n 'Only support int and str type of input',\\n )\\n vector = self._get_vector(index)\\n return vector\",\n \"def update_query_vector(\\n self,\\n query_vector: List[Tuple[str, float]],\\n relevant_doc_ids: set,\\n non_relevant_doc_ids: set,\\n ) -> List[Tuple[str, float]]:\\n non_relevant_doc_ids = list(non_relevant_doc_ids)\\n relevant_doc_ids = list(relevant_doc_ids)\\n nr_len = len(non_relevant_doc_ids)\\n r_len = len(relevant_doc_ids)\\n\\n # If no relevance information then do nothing\\n if r_len == 0 and nr_len == 0:\\n return query_vector\\n\\n # Get vector form of documents and queries\\n query_vector = self._to_full_vector(query_vector)\\n relevant_vectors = np.array(\\n [self.index.get_docID_vector(doc_id) for doc_id in relevant_doc_ids]\\n )\\n non_relevant_vectors = np.array(\\n [self.index.get_docID_vector(doc_id) for doc_id in non_relevant_doc_ids]\\n )\\n\\n # Rocchio algorithm for update\\n relevant_vector = relevant_vectors.sum(axis=0)\\n non_relevant_vector = non_relevant_vectors.sum(axis=0)\\n updated_query_vector = query_vector\\n if r_len > 0:\\n updated_query_vector = (\\n query_vector + self.beta * (1 / r_len) * relevant_vector\\n )\\n if nr_len > 0:\\n updated_query_vector = (\\n updated_query_vector - self.gamma * (1 / nr_len) * non_relevant_vector\\n )\\n\\n # Convert back to sparse vector\\n terms = list(self.index.get_terms())\\n terms.sort()\\n updated_query_vector = [\\n (term, weight)\\n for (term, weight) in zip(terms, updated_query_vector)\\n if weight > 0\\n ]\\n\\n return updated_query_vector\",\n \"def toVector(tupL,n,v):\\n v = numpy.zeros(n)\\n for (i,val) in tupL:\\n #print i,v\\n v[i]=val\\n return v\",\n \"def make_query(self):\",\n \"def v(self) -> np.ndarray:\\n return self.array[:, 1:] if self.scalar_vector else self.array[:, :3]\",\n \"def get_vector(self, word):\\n\\n if word in self.glove.stoi:\\n return self.glove.vectors[self.glove.stoi[word]]\\n else:\\n return None\",\n \"def get_vertical_vector(q):\\n P0, P1, P2, P3 = q\\n P0_up = copy.deepcopy(P0)\\n P0_up.depth = P0_up.depth - 1.0\\n p0 = Vector.fromPoint(P0) # fromPoint converts to ECEF\\n p1 = Vector.fromPoint(P0_up)\\n v1 = (p1 - p0).norm()\\n return v1\",\n \"def make_single_vector_query(vector_clip):\\n if not adaptive_clip_learning_rate:\\n return tensorflow_privacy.GaussianAverageQuery(\\n l2_norm_clip=vector_clip,\\n sum_stddev=vector_clip * noise_multiplier * num_vectors**0.5,\\n denominator=expected_total_weight)\\n else:\\n # Without geometric updating, the update is c = c - lr * loss, so for\\n # multiple vectors we set the learning rate to be on the same scale as the\\n # initial clip. That way big vectors get big updates, small vectors\\n # small updates. With geometric updating, the update is\\n # c = c * exp(-lr * loss) so the learning rate should be independent of\\n # the initial clip.\\n if geometric_clip_update:\\n learning_rate = adaptive_clip_learning_rate\\n else:\\n learning_rate = adaptive_clip_learning_rate * vector_clip / clip\\n return tensorflow_privacy.QuantileAdaptiveClipAverageQuery(\\n initial_l2_norm_clip=vector_clip,\\n noise_multiplier=noise_multiplier,\\n target_unclipped_quantile=target_unclipped_quantile,\\n learning_rate=learning_rate,\\n clipped_count_stddev=clipped_count_stddev,\\n expected_num_records=expected_num_clients,\\n geometric_update=geometric_clip_update,\\n denominator=expected_total_weight)\",\n \"def make_vector(start, finish):\\n init = complex(start[0], start[1])\\n end = complex(finish[0], finish[1])\\n\\n vector = end - init\\n return vector\",\n \"def fromSequence(seq): # returns Vector\\n return Vector(*seq)\",\n \"def query(self):\",\n \"def carto_query(query):\\n params = {'q': query, 'api_key': apikey(serv=\\\"cdb\\\")}\\n d = api_query(api_url=CDB_URL, params=params)['rows']\\n logging.info(\\\"Returned %d rows\\\" % len(d))\\n return d\",\n \"def query2df(query):\\n df = pd.DataFrame(data = list(itertools.product([0, 1], repeat=len(query.variables))), columns=query.variables)\\n df['p'] = query.values.flatten()\\n return df\",\n \"def vector(x, y, z):\\n return point_or_vector(x,y,z,0.0)\",\n \"def generador_v(vector_n, constante):\\n\\n v = []\\n\\n for x in range(len(vector_n)):\\n nv = vector_n[x] // constante # // = Division entera\\n v.append(nv)\\n\\n # print(\\\"valores n: \\\", vector_n)\\n # print(\\\"valores v: \\\", v)\\n\\n return v\",\n \"def get_vector(self, name: str) -> ndarray:\\n vector_path = self._get_path(name)\\n vector_df = DataFrame(read_csv(vector_path, header=None))\\n ixs = list(map(lambda i: i - 1, vector_df.iloc[:, 0]))\\n vals = vector_df.iloc[:, 1]\\n return util.create_array(ixs, vals, self.number_of_nodes)\",\n \"def get_vectors(self, corpus_size, vectors_size, vectors_type):\\n vectors = np.zeros((corpus_size, vectors_size))\\n for i in range(0, corpus_size):\\n prefix = vectors_type + '_' + str(i)\\n vectors[i] = self.model_dbow.docvecs[prefix]\\n return vectors\",\n \"def get_data(cls, my_query_result):\\n return my_query_result\",\n \"def matrix_to_vector(matrix, system):\\n\\n outvec = Vector.zero\\n vects = system.base_vectors()\\n for i, x in enumerate(matrix):\\n outvec += x * vects[i]\\n return outvec\",\n \"def vec(self) -> Vec:\\n return Vec(self.x, self.y, self.z)\",\n \"def parameters_to_vector(self) -> np.ndarray:\\n return nn.utils.parameters_to_vector(self.parameters()).detach().cpu().numpy()\",\n \"def execute_sparql(client: NeptuneClient, query: str) -> pd.DataFrame:\\n data = client.read_sparql(query)\\n df = None\\n if \\\"results\\\" in data and \\\"bindings\\\" in data[\\\"results\\\"]:\\n df = pd.DataFrame(data[\\\"results\\\"][\\\"bindings\\\"])\\n df.applymap(lambda x: x[\\\"value\\\"])\\n else:\\n df = pd.DataFrame(data)\\n\\n return df\",\n \"def getVectors(self,graph):\\n return [Vector.createFromTwoTuples(graph[i],graph[i+1]) for i in range(len(graph)-1)]\",\n \"def soql_query(self, query):\\n self.builtin.log(\\\"Running SOQL Query: {}\\\".format(query))\\n return self.cumulusci.sf.query_all(query)\",\n \"def getResults(self, queryVec: dict):\\n result = set()\\n for word in queryVec.keys():\\n if word in self.invertedIndex:\\n result.update(self.invertedIndex[word].keys())\\n #print (result)\\n #print (self.corpusCount)\\n return result\",\n \"def get_vector(self, u_name):\\n try:\\n return pickle.loads(self.vectors_db.get(u_name))\\n except TypeError as er:\\n logging.debug(\\\"%s not exists\\\" % u_name)\\n return\",\n \"def __getitem__(self, *args):\\n return _ida_hexrays.qvector_ccase_t___getitem__(self, *args)\",\n \"def question_to_vec(question, embeddings, dim):\\r\\n\\r\\n words = question.split()\\r\\n\\r\\n counter = 0\\r\\n res = np.zeros(dim)\\r\\n for word in words:\\r\\n if word in embeddings:\\r\\n res += np.array(embeddings[word])\\r\\n counter += 1\\r\\n if counter!=0:\\r\\n return res/counter # mean of all word embeddings\\r\\n else:\\r\\n return res # vector of zeros\\r\",\n \"def query(self, var, evidence={}):\\n q = self._enumeration_ask(var, evidence).reshape(-1, 1) #-1 means numpy will figure out the value. -1,1 gives array of arrays with 1 value [[1], [2], [3], ..] 1 equals column\\n #reshape(-1,1) gir x kolonner med 1 elem i hver\\n #reshape(1,-1) gir én rad med x elementer\\n return Variable(var, self.bayesian_network.variables[var].no_states, q)\",\n \"def __getitem__(self, arg0: ngsolve.ngstd.IntRange) -> BaseVector:\",\n \"def get_doc_vector(doc_id, model_id):\\n queue = get_vec_queue(app.config)\\n data = queue.get_by_id((doc_id, model_id))\\n if data is not None:\\n return jsonify(doc=data.tolist())\\n return jsonify(err=f\\\"{doc_id} not found\\\"), 404\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.710231","0.6852143","0.6806123","0.6790017","0.65516216","0.6484472","0.6346682","0.62224436","0.62009585","0.61882883","0.6144658","0.6134512","0.61188674","0.6103813","0.6086374","0.6070219","0.60688305","0.5987167","0.59024227","0.5888981","0.5858321","0.5857002","0.58434856","0.5775602","0.57752436","0.57535666","0.57353854","0.57132566","0.57094085","0.5704476","0.57023203","0.5678361","0.56635714","0.56561077","0.5643078","0.56327593","0.563078","0.562452","0.5615026","0.5614331","0.56126416","0.5612179","0.5611415","0.5609985","0.5609917","0.56038004","0.5600976","0.55618167","0.55523676","0.55494565","0.554624","0.5544179","0.5542126","0.5537419","0.55196476","0.54996276","0.5492505","0.54871327","0.5477199","0.5467045","0.5456166","0.5446685","0.5438237","0.54358464","0.54296184","0.5425941","0.542501","0.5422589","0.54221314","0.541912","0.5417684","0.5402837","0.54023826","0.53958","0.53957903","0.53951025","0.5377901","0.5377006","0.53751403","0.53673095","0.5365664","0.535663","0.5353147","0.5339691","0.53369343","0.5336912","0.53359586","0.53309226","0.53206176","0.530793","0.53027153","0.53026813","0.5299876","0.5293892","0.5292561","0.5290936","0.52871364","0.5284292","0.5280891","0.5280526"],"string":"[\n \"0.710231\",\n \"0.6852143\",\n \"0.6806123\",\n \"0.6790017\",\n \"0.65516216\",\n \"0.6484472\",\n \"0.6346682\",\n \"0.62224436\",\n \"0.62009585\",\n \"0.61882883\",\n \"0.6144658\",\n \"0.6134512\",\n \"0.61188674\",\n \"0.6103813\",\n \"0.6086374\",\n \"0.6070219\",\n \"0.60688305\",\n \"0.5987167\",\n \"0.59024227\",\n \"0.5888981\",\n \"0.5858321\",\n \"0.5857002\",\n \"0.58434856\",\n \"0.5775602\",\n \"0.57752436\",\n \"0.57535666\",\n \"0.57353854\",\n \"0.57132566\",\n \"0.57094085\",\n \"0.5704476\",\n \"0.57023203\",\n \"0.5678361\",\n \"0.56635714\",\n \"0.56561077\",\n \"0.5643078\",\n \"0.56327593\",\n \"0.563078\",\n \"0.562452\",\n \"0.5615026\",\n \"0.5614331\",\n \"0.56126416\",\n \"0.5612179\",\n \"0.5611415\",\n \"0.5609985\",\n \"0.5609917\",\n \"0.56038004\",\n \"0.5600976\",\n \"0.55618167\",\n \"0.55523676\",\n \"0.55494565\",\n \"0.554624\",\n \"0.5544179\",\n \"0.5542126\",\n \"0.5537419\",\n \"0.55196476\",\n \"0.54996276\",\n \"0.5492505\",\n \"0.54871327\",\n \"0.5477199\",\n \"0.5467045\",\n \"0.5456166\",\n \"0.5446685\",\n \"0.5438237\",\n \"0.54358464\",\n \"0.54296184\",\n \"0.5425941\",\n \"0.542501\",\n \"0.5422589\",\n \"0.54221314\",\n \"0.541912\",\n \"0.5417684\",\n \"0.5402837\",\n \"0.54023826\",\n \"0.53958\",\n \"0.53957903\",\n \"0.53951025\",\n \"0.5377901\",\n \"0.5377006\",\n \"0.53751403\",\n \"0.53673095\",\n \"0.5365664\",\n \"0.535663\",\n \"0.5353147\",\n \"0.5339691\",\n \"0.53369343\",\n \"0.5336912\",\n \"0.53359586\",\n \"0.53309226\",\n \"0.53206176\",\n \"0.530793\",\n \"0.53027153\",\n \"0.53026813\",\n \"0.5299876\",\n \"0.5293892\",\n \"0.5292561\",\n \"0.5290936\",\n \"0.52871364\",\n \"0.5284292\",\n \"0.5280891\",\n \"0.5280526\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7983144"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":9994,"cells":{"query":{"kind":"string","value":"Returns cosine similarity between the input question and our dataset questions"},"document":{"kind":"string","value":"def get_cosine_similarity(self, query: list):\n question_vector = self.get_vector(query)\n\n return cosine_similarity(question_vector, self.vectors).flatten()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def cosine_similarity(self, source_doc, input_doc):\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\n return ((tfidf * tfidf.T).A)[0, 1]","def calculate_cosine_similarity(self):\n tfidf_matrix = self.calculate_tfidf()\n\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\n\n return cosine_similarity","def get_cosin_sim(question, contexts):\r\n cos_sim_for_question = []\r\n for context in contexts :\r\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\r\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\r\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\r\n cos_sim_for_question.append(cos_sim)\r\n return pd.Series(cos_sim_for_question)","def getGloveoCosineSimilarity(question1, question2):\n questions = [question1, question2]\n\n ## for the sentences we need to get the count vectors\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\n count_vectors = vec.fit_transform(questions)\n\n ## get the vocabulary of words from the questions\n vocab_index = vec.vocabulary_\n\n ## get the index of the words and embeddings\n index_word = {v:k for k, v in vocab_index.items()}\n\n ## get the question vectors\n question_vectors = np.zeros((count_vectors.shape[0], 300))\n\n ## iterate through count vectors for each word get the embeddings\n ## for each embedding, we will then average by the number of words\n ## this will be then used for cosine similarity\n for i in range(count_vectors.shape[0]):\n row = count_vectors[i, :].toarray()\n word_ids = np.where(row > 0)[1]\n word_counts = row[:, word_ids][0]\n numWords = np.sum(word_counts)\n\n ## if there are no words, continue\n if numWords == 0:\n continue\n\n ## initialize the word embeddings to 0\n word_embeddings = np.zeros((word_ids.shape[0], 300))\n\n ## update the word embeddings\n for j in range(word_ids.shape[0]):\n word_id = word_ids[j]\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\n\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])","def cosine_similarity(X):\n matrix = X.dot(X.transpose()).todense()\n mat_len = len(matrix)\n norms = [0] * mat_len\n for i in range(0, mat_len):\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\n norm_mat = np.matrix(norms)\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)","def get_cosine_sim(self):\r\n return CosineSimilarity().calculate_similarity(self.tweets)","def cosine_similarity(self, x, y):\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))","def compute_cosine_similarity(self):\n cos_matrix = []\n for i in range(len(self.train_vec)):\n val = self.vec1 * self.train_vec[i]\n cos_matrix.append(val[0])\n out = np.argmax(cos_matrix)\n print(self.train_output[out])","def get_cosine_similarity(doc1, doc2):\n count_vectorizer = CountVectorizer(stop_words='english')\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\n dtm = sparse_matrix.todense()\n df_dtm = pd.DataFrame(data=dtm, \n columns=count_vectorizer.get_feature_names(), \n index=['doc1', 'doc2'])\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\n similarity_score = round(similarity_matrix[0][1], 6)\n return similarity_score","def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\n # return cosine_similarity(v1, v2)[0][0]","def cosine_similarity(cls, vec_a, vec_b):\n return np.dot(vec_a, vec_b) / \\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))","def cosine_similarity(y_true, y_pred, axis=-1):\n y_true = nn.l2_normalize(y_true, axis=axis)\n y_pred = nn.l2_normalize(y_pred, axis=axis)\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)","def cosine_similarities(q, k = 10):\n\n query = []\n query.append(q)\n query = [word.lower() for word in query]\n table = str.maketrans('', '', string.punctuation)\n query = [word.translate(table) for word in query]\n query = [re.sub(r'\\d+', 'num', word) for word in query]\n query = [[word for word in data.split() if word not in stopwords] for data in query]\n query = [\" \".join([stemmer.stem(word) for word in data]) for data in query]\n queryVectorizer = vectorizer.transform(query).toarray()\n\n #cosine similarity formula below, not needed because importing cosine_similarity from sklearn\n #cx = lambda a,b : np.inner(a, b)/(np.linalg.norm(a)*np.linalg.norm(b)) \n \n cosine_similarities = cosine_similarity(queryVectorizer[:], tfidf)\n related_docs_indices = cosine_similarities.flatten().argsort()[-k][::-1]\n return (related_docs_indices)","def get_similarity(df):\n count = CountVectorizer()\n count_matrix = count.fit_transform(df[\"bag_of_words\"])\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\n return cosine_sim","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\n w12 = torch.sum(x1 * x2, dim)\n w1 = torch.norm(x1, 2, dim)\n w2 = torch.norm(x2, 2, dim)\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def cosine_similarity(a, b):\n cs = dot_product(a, b)/(norm(a) * norm(b))\n return cs","def cosine_similarity(x1, x2, dim=1, eps=1e-8):\r\n w12 = torch.sum(x1 * x2, dim)\r\n w1 = torch.norm(x1, 2, dim)\r\n w2 = torch.norm(x2, 2, dim)\r\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()","def cosine_similarity(v1, v2):\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))","def CosineSimilarity(test_vec, source_vecs):\n cos_dist = 0\n for source_vec in source_vecs:\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\n return cos_dist / len(source_vecs)","def compute_cosine(pred, obs):\n assert(pred.numel() == obs.numel()), \\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\n str(pred.size()), str(obs.size()))\n\n def normalise(x, dim=1):\n \"\"\" compute L2 norm and normalise x \"\"\"\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\n if dim>0:\n x /= norm.unsqueeze(dim)\n return x\n\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\n dim = len(pred.size()) - 1\n assert(dim>=0 and dim <=2), \\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\n\n p_norm = normalise(pred, dim=dim)\n v_norm = normalise(obs, dim=dim)\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )","def cosine_similarity(v1, v2):\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\n return sim","def get_simlilarity(self, text_to_query):\n\n texts = []\n\n for path in self.path_list:\n with open(path) as file:\n texts.append(file.read())\n texts.append(text_to_query)\n\n tfidf = TfidfVectorizer(tokenizer=clean_tonkenize)\n tfidf_matrix = tfidf.fit_transform(texts)\n\n return cosine_similarity(tfidf_matrix)","def cosine_sim(matrix):\n if type(matrix) is not csr_matrix:\n matrix = csr_matrix(matrix)\n\n return cosine_similarity(matrix, dense_output=False)","def cosine_similarity(vec1, vec2) -> float:\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))","def cosine_similarity(v1: Vector, v2: Vector) -> float:\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))","def cosine_similarity(vector_x, vector_y):\n if(len(vector_x)!=len(vector_y)):\n raise Exception('Vectors must be the same dimensions')\n \n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))","def cosine_similarity(a, b):\n if a.ndim != 1 or b.ndim != 1:\n raise InvalidShapeException(a,b)\n\n if len(a) != len(b):\n raise InvalidLengthException(a,b)\n \n mag_a = np.linalg.norm(a)\n mag_b = np.linalg.norm(b)\n\n return np.dot(a,b)/(mag_a*mag_b)","def cosineDistanceMatrix():\n\n\tmatrix = movieMatrix()\n\tsimilarity = np.dot(matrix, matrix.T)\n\tsquareMag = np.diag(similarity)\n\tinvSquareMag = 1/squareMag\n\tinvSquareMag[np.isinf(invSquareMag)]=0\n\tinvMag = np.sqrt(invSquareMag)\n\tcosine = similarity * invMag\n\tcosine = cosine.T * invMag\n\treturn cosine","def cosine_similarity(vec_x, vec_y):\n sim_prod = 0.0\n len_x = 0\n len_y = 0\n\n for ngram in vec_x:\n len_x += vec_x[ngram] ** 2\n\n for ngram in vec_y:\n len_y += vec_y[ngram] ** 2\n\n len_x = math.sqrt(len_x)\n len_y = math.sqrt(len_y)\n\n for ngram in vec_x:\n if ngram in vec_y:\n sim_prod += vec_x[ngram] * vec_y[ngram]\n\n return sim_prod / (len_x * len_y)","def cosine_similarity(v1, v2):\n # Cosine Sim:\n # Get the words that both have in common\n\n v1words = set(v1.keys())\n v2words = set(v2.keys())\n\n numerator_words = v1words.intersection(v2words)\n\n # Multiply and sum those counts\n numerator = 0.0\n for word in numerator_words:\n numerator += v1[word] * v2[word]\n\n\n # Divide by the sqrt of the product of the sum of the squares of the counts\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\n\n return numerator/denominator","def cosine(arr1, arr2):\n\n if arr1 is None or arr2 is None:\n return np.NaN\n if not isinstance(arr1, list):\n arr1 = [arr1]\n if any(pd.isnull(arr1)):\n return np.NaN\n if not isinstance(arr2, list):\n arr2 = [arr2]\n if any(pd.isnull(arr2)):\n return np.NaN\n # Create cosine measure object\n measure = sm.Cosine()\n # Call the function to compute the cosine measure.\n return measure.get_raw_score(arr1, arr2)","def test_cossim(self):\n metrics = SimilarityMetrics()\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test1,0.0)\n\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\n np.testing.assert_almost_equal(test2,-1.0)\n\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\n np.testing.assert_almost_equal(test3,1.0)","def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\n return cosine_similarity(word2vec.wv.vectors)","def cosine_sim(im, s):\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices ","def cosineSimilarity(index, nPages, query): \n scores = defaultdict(int)\n terms = query.split()\n qw = {t: tf_idf(1, nPages, len(index[t])) for t in terms if t in index}\n query_len = np.linalg.norm(list(qw.values()))\n for term in qw:\n query_weight = qw[term] / query_len\n for url, weight in index[term]:\n scores[url] += weight * query_weight\n return sorted(scores.items(), key=lambda x: x[1], reverse=True)","def cosine_similarity_tensorflow(tf_word_representation_A, tf_words_representation_B):\n a_normalized = tf.nn.l2_normalize(tf_word_representation_A, axis=-1)\n b_normalized = tf.nn.l2_normalize(tf_words_representation_B, axis=-1)\n similarity = tf.reduce_sum(\n tf.multiply(a_normalized, b_normalized), \n axis=-1\n )\n \n return similarity","def cosine(X,Y=None,dense_output=True):\n #Reemplace NaN with Zero\n X[np.isnan(X)] = 0\n if Y is not None:\n Y[np.isnan(Y)] = 0\n \n return cosine_similarity(X,Y,dense_output)","def cosine_similarity(document, cluster):\n num = dot(document.vector, cluster.centroid)\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\n\n return num / den","def test_cosine_similarity():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity.py_func(vector1, vector1)\n score12 = cosine_similarity.py_func(vector1, vector2)\n score22 = cosine_similarity.py_func(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def cossim(corpus):\n files = os.listdir()\n vectorizer = TfidfVectorizer()\n trsfm = vectorizer.fit_transform(corpus)\n columns = vectorizer.get_feature_names()\n df_tfidf = pd.DataFrame(trsfm.toarray(), columns = columns, index = corpus)\n out = cosine_similarity(trsfm)\n df_result = pd.DataFrame(out, columns = files, index = files)\n return df_result","def similarity(self, query, documents):\n\n bow_query = self.dictionary.doc2bow(query)\n bow_docs = [self.dictionary.doc2bow(document) for document in documents]\n\n index = SoftCosineSimilarity(bow_docs, self.matrix)\n similarities = index[bow_query]\n\n return similarities","def cosine_similarity(u, v):\n\n distance = 0.0\n\n ### START CODE HERE ###\n # Compute the dot product between u and v (≈1 line)\n dot = np.dot(u, v)\n # Compute the L2 norm of u (≈1 line)\n norm_u = np.sqrt(np.dot(u, u))\n\n # Compute the L2 norm of v (≈1 line)\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\n # Compute the cosine similarity defined by formula (1) (≈1 line)\n cosine_similarity = dot / (norm_u * norm_v)\n ### END CODE HERE ###\n\n return cosine_similarity","def cosine_value(self,doc_vector,query_vector):\n\t\tvalue=0;i=0;\n\t\tunit_vector_query=self.unit_vector(query_vector);\n\t\tunit_vector_doc=self.unit_vector(doc_vector);\n\t\titerate=0\n\t\tfor word in query_vector:\n\t\t\tif word in doc_vector:\n\t\t\t\tvalue+=query_vector[word]*doc_vector[word]\n\t\tif unit_vector_query != 0:\n\t\t\tvalue = value/(unit_vector_query*unit_vector_doc)\n\t\telse:\n\t\t\tvalue = 0\n\t\treturn value","def test_cosine_similarity_compiled():\n vector1 = np.array([1, 1, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score12 == 2 / np.sqrt(2 * 4), \"Expected different score.\"\n assert score11 == score22 == 1.0, \"Expected different score.\"","def compute_cosine_sim(vec1, vec2):\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\n np.sum(np.square(vec2.reshape(300, ))))\n\n similarity = numer / denom\n\n return similarity","def _do_action_calculate_similarity_cosine_express(self):\n self._run_express_job(\"com.directv.recommend.express.CosineCFTrainer\")\n self._scan_table(\"content/item_item_similarities\")","def pairwise_cosine_similarity(x, y):\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\n return torch.mm(x, torch.transpose(y, 1, 0))","def cosine_similarity(user, business):\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))","def cosine_similarity(a, b):\n\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\n cos_similarity = numerator/denominator\n return cos_similarity","def cos_sim(com_feat,ref_feat):\n # Fill this in\n a = numpy.squeeze(com_feat)\n b = numpy.squeeze(ref_feat)\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))","def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\n assert u.shape[0] == v.shape[0], \"Input vector must have same shape.\"\n uv = 0\n uu = 0\n vv = 0\n for i in range(u.shape[0]):\n uv += u[i] * v[i]\n uu += u[i] * u[i]\n vv += v[i] * v[i]\n cosine_score = 0\n if uu != 0 and vv != 0:\n cosine_score = uv / np.sqrt(uu * vv)\n return np.float64(cosine_score)","def getLexicalSimilarityScore(corpus:List[str]):\n\n # Generate the tf-idf vectors for the corpus\n tfidf = TfidfVectorizer()\n X = tfidf.fit_transform(corpus)\n\n # Compute cosine similarity score\n cosine_sim_score = cosine_similarity(X, X) # 2d matrix of scores, each vector against all other vector\n \n # return student's scores only\n return cosine_sim_score[0][1:]","def get_tfidf_similarity(questions, paragraphs):\n vectorizer = TfidfVectorizer(lowercase = False)\n all_text = questions + paragraphs\n vectorizer.fit(all_text)\n similarity = vectorizer.transform(questions) * vectorizer.transform(paragraphs).T\n return np.array(similarity.todense())","def calculate_cosine_dist(main_text, new_text):\n wordbag = set(\" \".join([main_text, new_text]).split(\" \"))\n dot_prod = 0\n main_text = main_text.split(\" \")\n new_text = new_text.split(\" \")\n\n for word in wordbag:\n if word in main_text and word in new_text:\n # only worth looking at if word is in both. Otherwise dot prod = 0\n count_A = sum(np.array(main_text) == word)\n count_B = sum(np.array(new_text) == word)\n dot_prod += count_A * count_B\n\n return float(dot_prod) / (len(main_text) * len(new_text))","def similarityQuestions(self, row):\n q1 = self.stemQuestion(row[3])\n q2 = self.stemQuestion(row[4])\n \n # Compute similarity of the two questions#\n sim = seqmatch(None, q1, q2).ratio()\n #sim = self.computeSimilarity(q1, q2)\n if sim > 0.6: #we guess they are duplicate questions\n if row[5] == \"1\": #true positive\n self.tp += 1\n else: #false positive\n self.fp += 1\n else: #we guess they are different questions\n if row[5] == \"0\": #true negative\n self.tn += 1\n else: #false negative\n self.fn += 1","def compute_cosine_similarity(base_vector, target_vector):\n\n np.seterr(all='print')\n cosine_similarity = 0\n\n try:\n base_vector = np.longdouble(base_vector)\n target_vector = np.longdouble(target_vector)\n vector_dot_products = np.dot(base_vector, target_vector)\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\n\n if vector_norms == 0.0:\n print 'Error in vec in compute_cosine_similarity'\n print target_vector\n\n except Exception, e:\n print(str(e))\n\n return cosine_similarity","def cosine_sim(a: np.ndarray, \n b: np.ndarray \n ) -> float:\n return (\n 1 + a.dot(b) / \n (np.linalg.norm(a)*np.linalg.norm(b))\n ) / 2","def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\n cos = nn.CosineSimilarity(0)\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\n sim: torch.Tensor = cos(d1, d2)\n return sim","def _cosine_matrix(self, x1, x2):\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\n x1 = K.expand_dims(x1, axis=2)\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\n x2 = K.expand_dims(x2, axis=1)\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\n cos_matrix = self._cosine_similarity(x1, x2)\n return cos_matrix","def cosine_scoring(query, doc_lengths, index):\n idf_dict_vector = compute_idf_vector(len(doc_lengths), index)\n doc_scores = {}\n\n for q in query:\n if q in idf_dict_vector:\n wt_q = idf_dict_vector[q] * query[q]\n else:\n wt_q = 0\n\n for tup in index[q][1:]:\n wf_q = idf_dict_vector[q] * tup[1]\n if tup[0] in doc_scores:\n doc_scores[tup[0]] += wt_q * wf_q\n else:\n doc_scores[tup[0]] = wt_q * wf_q\n\n for doc in doc_scores:\n doc_scores[doc] = doc_scores[doc] / doc_lengths[doc]\n\n return doc_scores","def cosine_similarity_pure(X, Y=None):\n X, Y = _check_pairwise_arrays(X, Y)\n X_normalized = normalize_pure(X, copy=True)\n if X is Y:\n Y_normalized = X_normalized\n else:\n Y_normalized = normalize_pure(Y, copy=True)\n K = dot_2d(X_normalized, Y_normalized)\n return K","def cosineSimilarity(dict1,dict2):\n product1 = 0.0\n product2 = 0.0\n for key in dict1.keys():\n product1 += (dict1[key] * dict1[key])\n for key in dict2.keys():\n product2 += (dict2[key] * dict2[key])\n product1 = math.sqrt(product1)\n product2 = math.sqrt(product2)\n fenmu = product1 * product2\n fenzi = 0.0\n for key in dict1.keys():\n if key in dict2:\n fenzi += (dict1[key] * dict2[key])\n cosSim = fenzi / fenmu\n return cosSim","def get_similarity(self, ):\r\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\r\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\r\n index=self.customer_vendor_matrix.index,\r\n columns=self.customer_vendor_matrix.index)\r\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\r\n # self.rating_matrix,)\r\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\r\n # index=self.customer_vendor_matrix.index,\r\n # columns=self.customer_vendor_matrix.index)\r\n return customer_cos_similarity,\r\n # return customer_pearson_similarity run too slowly\r","def tf_cosine_distance(self, a, b):\n normalize_a = tf.nn.l2_normalize(a, -1)\n normalize_b = tf.nn.l2_normalize(b, -1)\n cos_similarity = tf.reduce_sum(\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\n )\n return (1.0 - cos_similarity) / 2.0","def word_similarity(self):\n y_true = []\n y_pred = []\n for i in open(\"data/word_sim_dataset.txt\").read().split('\\n'):\n i = self.preprocessor(i)\n w1 = i.split()[-1]\n w2 = i.split()[-2] \n st = float(i.split()[-3]) / 4 #dataset has scale from 0 to 4\n \n try:\n w1 = self.embeddings_index[w1] \n w2 = self.embeddings_index[w2] \n w1 = w1 / np.linalg.norm(w1)\n w2 = w2 / np.linalg.norm(w2)\n y_pred.append(np.dot(w1,w2))\n y_true.append(st)\n except:\n pass\n if y_true == []:\n return 1.0\n return mean_squared_error(y_true, y_pred, squared=False)","def calculate_similarity(self, tweets):\r\n if (len(tweets) == 1):\r\n return 0\r\n vectors = self.vectorizer.vectorize_data(tweets, False)\r\n\r\n temp = cosine_similarity(vectors[0:-1], vectors)\r\n temp = [item for sublist in temp for item in sublist]\r\n sim = sum(temp) / len(temp)\r\n return sim","def predict_cosine_similarities(sess, word_A, words_B):\n\n word_A_id, _ = sentence_to_word_ids(word_A, word_to_index)\n words_B_ids, split_sentence = sentence_to_word_ids(words_B, word_to_index)\n\n evaluated_cos_similarities = sess.run(\n cosine_similarities, \n feed_dict={\n tf_word_A_id: word_A_id,\n tf_words_B_ids: words_B_ids\n }\n )\n return evaluated_cos_similarities, split_sentence","def cosine_sim(self, u_name, v_name):\n u_vector = self.get_vector(u_name)\n v_vector = self.get_vector(v_name)\n u_norm = self.vectors_norm_db.get(u_name)\n v_norm = self.vectors_norm_db.get(v_name)\n\n numerator = self.scalar_product(u_vector.items, v_vector.items)\n denominator = float(u_norm) * float(v_norm)\n \n try:\n # round the cosine similarity two digits after the decimal point \n cosine = round(numerator / denominator, 2)\n except ZeroDivisionError:\n logging.error(\"division by zero for %s and %s !\" \\\n % (u_name, v_name))\n cosine = 0\n \n logging.debug(\"%s %s = %s \" \\\n % (u_name, v_name, cosine))\n \n return cosine","def cosine_similarity_matrix(references: np.ndarray, queries: np.ndarray) -> np.ndarray:\n size1 = references.shape[0]\n size2 = queries.shape[0]\n scores = np.zeros((size1, size2))\n for i in range(size1):\n for j in range(size2):\n scores[i, j] = cosine_similarity(references[i, :], queries[j, :])\n return scores","def cosin_sim_pairs(a, b):\n wordsA = set(a.keys())\n wordsB = set(b.keys())\n inter = wordsA.intersection(wordsB)\n if(len(inter) == 0):\n return 0.0\n aa, bb, ab = 0, 0, 0\n for k in inter:\n aa += a[k] ** 2\n bb += b[k] ** 2\n ab += a[k] * b[k]\n for k in wordsA - inter:\n aa += a[k] ** 2\n for k in wordsB - inter:\n bb += b[k] ** 2\n return ab / float(math.sqrt(aa) * math.sqrt(bb))","def get_similarity_score(text_vectors, X, factor=None):\n similarity_scores = cosine_similarity(X, text_vectors, dense_output=True)\n return similarity_scores * factor","def getSemanticSimilarityScore(corpus:List[str]):\n\n model_ans = corpus[0]\n model_ans_embedding = model.encode(model_ans, convert_to_tensor=True)\n\n student_responses = corpus[1:]\n semantic_score = []\n\n for student_res in student_responses:\n student_res_embedding = model.encode(student_res, convert_to_tensor=True)\n\n #Compute semantic cosine-similarity\n cosine_score = util.pytorch_cos_sim(model_ans_embedding, student_res_embedding) # cosine_score is a 2D tensor object\n semantic_score.append(cosine_score.item())\n return semantic_score","def cosine_similarity(v1, v2):\n v1_len = 0\n v2_len = 0\n dot_product = 0\n\n for context_id, count in v1.items():\n v1_len += count ** 2\n if context_id in v2:\n dot_product += count*v2[context_id]\n for count in v2.values():\n v2_len += count ** 2\n\n v1_len = math.sqrt(v1_len)\n v2_len = math.sqrt(v2_len)\n return dot_product/(v1_len * v2_len)","def get_cosine_similarities(keywords: List[str],\n matrices: Matrices,\n word2onehot: Dict[str, int]\n ) -> None:\n for i in combinations(keywords, 2):\n print(i[0], i[1], cosine_sim(\n matrices.embedding[\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\n ]\n ))","def cosine_distances(X, Y):\n # should not need X_norm_squared because if you could precompute that as\n # well as Y, then you should just pre-compute the output and not even\n # call this function.\n if X is Y:\n X = Y = np.asanyarray(X)\n else:\n X = np.asanyarray(X)\n Y = np.asanyarray(Y)\n\n if X.shape[1] != Y.shape[1]:\n raise ValueError(\"Incompatible dimension for X and Y matrices\")\n\n return 1. - ssd.cdist(X, Y, 'cosine')","def cosine_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x_1` and rows of `x_2`\n # \"ij,ij->i\" := output[i] = sum_j x1[i, j] * x2[i, j]\n cos_thetas = tf.linalg.einsum(\"ij,ij->i\", x1, x2)\n cos_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n cos_distances = tf.maximum(cos_distances, 0.0)\n\n return cos_distances","def _cosine_distance(a, b, data_is_normalized=False):\n if not data_is_normalized:\n a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\n b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\n return 1. - np.dot(a, b.T)","def _cosine_distance(a, b, data_is_normalized=False):\n if not data_is_normalized:\n a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\n b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\n return 1. - np.dot(a, b.T)","def cosine_sim_cf(matrix):\n if type(matrix) is not lil_matrix:\n matrix = lil_matrix(matrix)\n\n n = matrix.shape[0]\n rows, cols, data = [], [], []\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\n\n for i in range(n):\n i_ratings, i_items = matrix.data[i], user_items[i]\n for j in range(i, n):\n j_ratings, j_items = matrix.data[j], user_items[j]\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\n i_item_ctd, j_item_ctd = 0, 0\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\n j_item_ctd += 1\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\n i_item_ctd += 1\n else:\n i_idx = i_items[i_item_ctd][1]\n j_idx = j_items[j_item_ctd][1]\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\n sum_denominator_i += i_ratings[i_idx] ** 2\n sum_denominator_j += j_ratings[j_idx] ** 2\n i_item_ctd += 1\n j_item_ctd += 1\n\n if sum_numerator == 0: continue\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\n rows.append(i), cols.append(j), data.append(s)\n if i != j: rows.append(j), cols.append(i), data.append(s)\n\n return csr_matrix((data, (rows, cols)))","def cos_sim(a, b):\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\n for i in movies: #replace movies\n numerator += (ratingai * ratingbi)\n sosA += ratingai * ratingai\n sosB += ratingbi * ratingbi\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\n return numerator / denom","def test_cosine_similarity_matrix():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def compute_cosine_sim(vec1, vec2):\r\n\r\n vec1 = np.array(vec1)\r\n vec2 = np.array(vec2)\r\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))","def content_similarity(self, movie1, movie2):\n v1, v2 = self.get_tfidf(movie1), self.get_tfidf(movie2)\n return self.cosine_similarity(v1, v2)","def _cosine_similarity_update(preds: Tensor, target: Tensor) ->Tuple[Tensor, Tensor]:\n _check_same_shape(preds, target)\n preds = preds.float()\n target = target.float()\n return preds, target","def DocSim(self,testDF,listCourse,inCourse):\r\n #Reference the VDF dictionary to get the doc embedding for the listCourse\r\n vectorA = self.VDF[\"Doc\"][listCourse]\r\n #Calculate the doc embedding for the input course\r\n vectorB = self._DocSim(testDF,inCourse)\r\n \r\n #Convert vectors to column vectors to be fed into the cosine_similarity function. \r\n A = np.expand_dims(vectorA,0)\r\n B = np.expand_dims(vectorB,0)\r\n #Calculate the cosine similarity between the two vectors.\r\n sim = cosine_similarity(A,B)\r\n return float(sim)","def cosine_distance(u, v):\n #print u,v\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))","def cosine_dist(d1, d2):\n suma=0\n for x in d1:\n if x in d2:\n suma+=(d1[x]*d2[x])\n sqrt1=0\n sqrt2=0\n for i in d1:\n sqrt1+=math.pow(d1[i],2)\n for i in d2:\n sqrt2+=math.pow(d2[i],2)\n return 1-suma/(math.sqrt(sqrt1)*math.sqrt(sqrt2))","def cosia(p, q):\n if not all(isinstance(x, Point) for x in (p, q)):\n raise TypeError(\"cosia() expects 2 Points\")\n return (p * q) / math.sqrt(p.normsq() * p.normsq())","def dist_cosine(src, dest, qgraml=None):\n\treturn 1 - sim_cosine(src, dest, qgraml)","def cosine_collection_distance(x1, x2):\n x1 = tf.cast(x1, dtype=tf.float32)\n x2 = tf.cast(x2, dtype=tf.float32)\n\n # dot product between rows of `x1` and columns of `x2` transpose\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\n pairwise_distances = 1 - cos_thetas\n\n # deal with numerical inaccuracies setting small negatives to zero\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\n\n return pairwise_distances","def cosine_sim(im, s):\n return im.mm(s.t())","def test_cosine_similarity_matrix_compiled():\n vectors1 = np.array([[1, 1, 0, 0],\n [1, 0, 1, 1]])\n vectors2 = np.array([[0, 1, 1, 0],\n [0, 0, 1, 1]])\n\n scores = cosine_similarity_matrix(vectors1, vectors2)\n expected_scores = np.array([[0.5, 0.],\n [0.40824829, 0.81649658]])\n assert scores == pytest.approx(expected_scores, 1e-7), \"Expected different scores.\"","def adjusted_cosine_sim(matrix):\n if type(matrix) is not csr_matrix:\n matrix = csr_matrix(matrix)\n\n matrix = _subtract_row_mean(matrix)\n return cosine_similarity(matrix, dense_output=False)","def cosine_sim(im, s,):\n return im.mm(s.t())","def similarity(self, word1: str, word2: str, metric='cosine') -> float:\n if 0 == self.word2idx.get(word1, 0) or 0 == self.word2idx.get(word2, 0):\n return 0.\n\n return self.similarity_vec(self[word1], self[word2], metric=metric)\n # vec1 = self.__getitem__(word1).reshape((1, -1))\n # vec2 = self.__getitem__(word2).reshape((1, -1))\n # return 1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1)","def cosine_sim_numpy(im, s):\n return im.dot(s.T)","def test_cosine_similarity_all_zeros_compiled():\n vector1 = np.array([0, 0, 0, 0])\n vector2 = np.array([1, 1, 1, 1])\n score11 = cosine_similarity(vector1, vector1)\n score12 = cosine_similarity(vector1, vector2)\n score22 = cosine_similarity(vector2, vector2)\n\n assert score11 == score12 == 0.0, \"Expected different score.\"\n assert score22 == 1.0, \"Expected different score.\"","def cosine(xs: Tensor, ys: Tensor, epsilon: float = 1e-8) -> Tensor:\n mat = xs @ ys.t()\n x_norm = xs.norm(2, dim=1) + epsilon\n y_norm = ys.norm(2, dim=1) + epsilon\n x_diag = (1 / x_norm).diag()\n y_diag = (1 / y_norm).diag()\n return x_diag @ mat @ y_diag","def sc(self) -> float:\n a = np.dot(self.true - np.mean(self.true), self.predicted - np.mean(self.predicted))\n b = np.linalg.norm(self.true - np.mean(self.true))\n c = np.linalg.norm(self.predicted - np.mean(self.predicted))\n e = b * c\n return float(np.arccos(a / e))","def cosine_sim(news_data_dict: dict, song_data_dict: dict):\n cosine_dict = defaultdict(list)\n\n for news_yr, news_txt in news_data_dict.items():\n news_txt_flat = []\n for nt in news_txt:\n news_txt_flat += nt\n news_tf = Counter(news_txt_flat)\n\n if news_yr <= 2011:\n song_txt_flat = []\n for i in range(5):\n song_yr = news_yr+i\n for st in song_data_dict[song_yr]:\n song_txt_flat += st\n song_tf = Counter(song_txt_flat)\n\n all_words = list(set(song_txt_flat + news_txt_flat))\n news_array = []\n song_array = []\n for aw in all_words:\n if aw in news_tf.keys():\n news_array.append(news_tf[aw])\n else:\n news_array.append(0)\n if aw in song_tf.keys():\n song_array.append(song_tf[aw])\n else:\n song_array.append(0)\n\n norm_news = norm(np.asarray(news_array))\n norm_song = norm(np.asarray(song_array))\n\n cosine = np.dot(news_array, song_array) / (norm_news * norm_song)\n cosine = round(cosine, 3)\n cosine_dict[news_yr].append(cosine)\n\n return cosine_dict"],"string":"[\n \"def cosine_similarity(self, source_doc, input_doc):\\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\\n return ((tfidf * tfidf.T).A)[0, 1]\",\n \"def calculate_cosine_similarity(self):\\n tfidf_matrix = self.calculate_tfidf()\\n\\n cosine_similarity = linear_kernel(tfidf_matrix, tfidf_matrix) # Cosine similarity matrix calculation\\n\\n return cosine_similarity\",\n \"def get_cosin_sim(question, contexts):\\r\\n cos_sim_for_question = []\\r\\n for context in contexts :\\r\\n cv = CountVectorizer(stop_words=MY_STOPWORDS, lowercase=False)\\r\\n matrix = cv.fit_transform(pd.DataFrame([question, context])[0]).toarray()\\r\\n cos_sim = dot(matrix[0], matrix[1])/(norm(matrix[0])*norm(matrix[1]))\\r\\n cos_sim_for_question.append(cos_sim)\\r\\n return pd.Series(cos_sim_for_question)\",\n \"def getGloveoCosineSimilarity(question1, question2):\\n questions = [question1, question2]\\n\\n ## for the sentences we need to get the count vectors\\n vec = CountVectorizer(max_features=5000, stop_words=None,binary=True)\\n count_vectors = vec.fit_transform(questions)\\n\\n ## get the vocabulary of words from the questions\\n vocab_index = vec.vocabulary_\\n\\n ## get the index of the words and embeddings\\n index_word = {v:k for k, v in vocab_index.items()}\\n\\n ## get the question vectors\\n question_vectors = np.zeros((count_vectors.shape[0], 300))\\n\\n ## iterate through count vectors for each word get the embeddings\\n ## for each embedding, we will then average by the number of words\\n ## this will be then used for cosine similarity\\n for i in range(count_vectors.shape[0]):\\n row = count_vectors[i, :].toarray()\\n word_ids = np.where(row > 0)[1]\\n word_counts = row[:, word_ids][0]\\n numWords = np.sum(word_counts)\\n\\n ## if there are no words, continue\\n if numWords == 0:\\n continue\\n\\n ## initialize the word embeddings to 0\\n word_embeddings = np.zeros((word_ids.shape[0], 300))\\n\\n ## update the word embeddings\\n for j in range(word_ids.shape[0]):\\n word_id = word_ids[j]\\n word_embeddings[j, :] = word_counts[j] * gloveDict[index_word[word_id]]\\n question_vectors[i, :] = np.sum(word_embeddings, axis=0) / numWords\\n\\n return(cosine_similarity(question_vectors[0], question_vectors[1])[0][0])\",\n \"def cosine_similarity(X):\\n matrix = X.dot(X.transpose()).todense()\\n mat_len = len(matrix)\\n norms = [0] * mat_len\\n for i in range(0, mat_len):\\n norms[i] = 1.0 / np.sqrt(matrix.item((i, i)))\\n norm_mat = np.matrix(norms)\\n return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)\",\n \"def get_cosine_sim(self):\\r\\n return CosineSimilarity().calculate_similarity(self.tweets)\",\n \"def cosine_similarity(self, x, y):\\n return np.dot(x, y) / (np.linalg.norm(x) * np.linalg.norm(y))\",\n \"def compute_cosine_similarity(self):\\n cos_matrix = []\\n for i in range(len(self.train_vec)):\\n val = self.vec1 * self.train_vec[i]\\n cos_matrix.append(val[0])\\n out = np.argmax(cos_matrix)\\n print(self.train_output[out])\",\n \"def get_cosine_similarity(doc1, doc2):\\n count_vectorizer = CountVectorizer(stop_words='english')\\n sparse_matrix = count_vectorizer.fit_transform(raw_documents=[doc1, doc2])\\n dtm = sparse_matrix.todense()\\n df_dtm = pd.DataFrame(data=dtm, \\n columns=count_vectorizer.get_feature_names(), \\n index=['doc1', 'doc2'])\\n similarity_matrix = cosine_similarity(df_dtm, df_dtm)\\n similarity_score = round(similarity_matrix[0][1], 6)\\n return similarity_score\",\n \"def cosine_similarity(self, v1: np.ndarray, v2: np.ndarray) -> float:\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\\n # return cosine_similarity(v1, v2)[0][0]\",\n \"def cosine_similarity(cls, vec_a, vec_b):\\n return np.dot(vec_a, vec_b) / \\\\\\n (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))\",\n \"def cosine_similarity(y_true, y_pred, axis=-1):\\n y_true = nn.l2_normalize(y_true, axis=axis)\\n y_pred = nn.l2_normalize(y_pred, axis=axis)\\n return -math_ops.reduce_sum(y_true * y_pred, axis=axis)\",\n \"def cosine_similarities(q, k = 10):\\n\\n query = []\\n query.append(q)\\n query = [word.lower() for word in query]\\n table = str.maketrans('', '', string.punctuation)\\n query = [word.translate(table) for word in query]\\n query = [re.sub(r'\\\\d+', 'num', word) for word in query]\\n query = [[word for word in data.split() if word not in stopwords] for data in query]\\n query = [\\\" \\\".join([stemmer.stem(word) for word in data]) for data in query]\\n queryVectorizer = vectorizer.transform(query).toarray()\\n\\n #cosine similarity formula below, not needed because importing cosine_similarity from sklearn\\n #cx = lambda a,b : np.inner(a, b)/(np.linalg.norm(a)*np.linalg.norm(b)) \\n \\n cosine_similarities = cosine_similarity(queryVectorizer[:], tfidf)\\n related_docs_indices = cosine_similarities.flatten().argsort()[-k][::-1]\\n return (related_docs_indices)\",\n \"def get_similarity(df):\\n count = CountVectorizer()\\n count_matrix = count.fit_transform(df[\\\"bag_of_words\\\"])\\n cosine_sim = cosine_similarity(count_matrix, count_matrix)\\n return cosine_sim\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\n w12 = torch.sum(x1 * x2, dim)\\n w1 = torch.norm(x1, 2, dim)\\n w2 = torch.norm(x2, 2, dim)\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def cosine_similarity(a, b):\\n cs = dot_product(a, b)/(norm(a) * norm(b))\\n return cs\",\n \"def cosine_similarity(x1, x2, dim=1, eps=1e-8):\\r\\n w12 = torch.sum(x1 * x2, dim)\\r\\n w1 = torch.norm(x1, 2, dim)\\r\\n w2 = torch.norm(x2, 2, dim)\\r\\n return (w12 / (w1 * w2).clamp(min=eps)).squeeze()\",\n \"def cosine_similarity(v1, v2):\\n return np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2))\",\n \"def CosineSimilarity(test_vec, source_vecs):\\n cos_dist = 0\\n for source_vec in source_vecs:\\n cos_dist += FacePredictor.findCosineDistance(test_vec, source_vec)\\n return cos_dist / len(source_vecs)\",\n \"def compute_cosine(pred, obs):\\n assert(pred.numel() == obs.numel()), \\\\\\n 'Size of observation and prediction tensors much match. Received: pred %s, obs %s.'%(\\n str(pred.size()), str(obs.size()))\\n\\n def normalise(x, dim=1):\\n \\\"\\\"\\\" compute L2 norm and normalise x \\\"\\\"\\\"\\n norm = torch.sqrt( torch.pow(x,2.).sum(dim) )\\n if dim>0:\\n x /= norm.unsqueeze(dim)\\n return x\\n\\n # if we have one-dimensional tensors, compute cosine similarity along first dimension (0).\\n # if we have two-dimensional tensors, compute cosine similarity along second dimension (1).\\n # if we have three-dimensional tensors, compute cosine similarity along third dimension (2).\\n # i.e. first dimension is considered the feature vector (will be reduced to a scalar, the cos.sim.)\\n dim = len(pred.size()) - 1\\n assert(dim>=0 and dim <=2), \\\\\\n 'This function only computes cosine similarity between 1D, 2D or 3D tensors! Received dim==%i'%(dim)\\n\\n p_norm = normalise(pred, dim=dim)\\n v_norm = normalise(obs, dim=dim)\\n return torch.nn.functional.cosine_similarity( p_norm, v_norm, dim=dim )\",\n \"def cosine_similarity(v1, v2):\\n sim = np.sum(v1*v2)/np.sqrt(np.sum(v1**2))/np.sqrt(np.sum(v2**2))\\n return sim\",\n \"def get_simlilarity(self, text_to_query):\\n\\n texts = []\\n\\n for path in self.path_list:\\n with open(path) as file:\\n texts.append(file.read())\\n texts.append(text_to_query)\\n\\n tfidf = TfidfVectorizer(tokenizer=clean_tonkenize)\\n tfidf_matrix = tfidf.fit_transform(texts)\\n\\n return cosine_similarity(tfidf_matrix)\",\n \"def cosine_sim(matrix):\\n if type(matrix) is not csr_matrix:\\n matrix = csr_matrix(matrix)\\n\\n return cosine_similarity(matrix, dense_output=False)\",\n \"def cosine_similarity(vec1, vec2) -> float:\\n return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))\",\n \"def cosine_similarity(v1: Vector, v2: Vector) -> float:\\n return dot_product(v1, v2) / (vector_len(v1) * vector_len(v2))\",\n \"def cosine_similarity(vector_x, vector_y):\\n if(len(vector_x)!=len(vector_y)):\\n raise Exception('Vectors must be the same dimensions')\\n \\n return 1-np.dot(vector_x,vector_y)/(np.linalg.norm(vector_x)*np.linalg.norm(vector_y))\",\n \"def cosine_similarity(a, b):\\n if a.ndim != 1 or b.ndim != 1:\\n raise InvalidShapeException(a,b)\\n\\n if len(a) != len(b):\\n raise InvalidLengthException(a,b)\\n \\n mag_a = np.linalg.norm(a)\\n mag_b = np.linalg.norm(b)\\n\\n return np.dot(a,b)/(mag_a*mag_b)\",\n \"def cosineDistanceMatrix():\\n\\n\\tmatrix = movieMatrix()\\n\\tsimilarity = np.dot(matrix, matrix.T)\\n\\tsquareMag = np.diag(similarity)\\n\\tinvSquareMag = 1/squareMag\\n\\tinvSquareMag[np.isinf(invSquareMag)]=0\\n\\tinvMag = np.sqrt(invSquareMag)\\n\\tcosine = similarity * invMag\\n\\tcosine = cosine.T * invMag\\n\\treturn cosine\",\n \"def cosine_similarity(vec_x, vec_y):\\n sim_prod = 0.0\\n len_x = 0\\n len_y = 0\\n\\n for ngram in vec_x:\\n len_x += vec_x[ngram] ** 2\\n\\n for ngram in vec_y:\\n len_y += vec_y[ngram] ** 2\\n\\n len_x = math.sqrt(len_x)\\n len_y = math.sqrt(len_y)\\n\\n for ngram in vec_x:\\n if ngram in vec_y:\\n sim_prod += vec_x[ngram] * vec_y[ngram]\\n\\n return sim_prod / (len_x * len_y)\",\n \"def cosine_similarity(v1, v2):\\n # Cosine Sim:\\n # Get the words that both have in common\\n\\n v1words = set(v1.keys())\\n v2words = set(v2.keys())\\n\\n numerator_words = v1words.intersection(v2words)\\n\\n # Multiply and sum those counts\\n numerator = 0.0\\n for word in numerator_words:\\n numerator += v1[word] * v2[word]\\n\\n\\n # Divide by the sqrt of the product of the sum of the squares of the counts\\n denominator = math.sqrt(math.magnitude(list(v1.values())) * math.magnitude(list(v2.values())))\\n\\n return numerator/denominator\",\n \"def cosine(arr1, arr2):\\n\\n if arr1 is None or arr2 is None:\\n return np.NaN\\n if not isinstance(arr1, list):\\n arr1 = [arr1]\\n if any(pd.isnull(arr1)):\\n return np.NaN\\n if not isinstance(arr2, list):\\n arr2 = [arr2]\\n if any(pd.isnull(arr2)):\\n return np.NaN\\n # Create cosine measure object\\n measure = sm.Cosine()\\n # Call the function to compute the cosine measure.\\n return measure.get_raw_score(arr1, arr2)\",\n \"def test_cossim(self):\\n metrics = SimilarityMetrics()\\n test1 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test1,0.0)\\n\\n test2 = metrics.cosine_similarity(np.asarray([1,-1]),np.asarray([-1,1]))\\n np.testing.assert_almost_equal(test2,-1.0)\\n\\n test3 = metrics.cosine_similarity(np.asarray([1,1]),np.asarray([1,1]))\\n np.testing.assert_almost_equal(test3,1.0)\",\n \"def get_cosine_similarity(word2vec: Word2Vec) -> np.ndarray:\\n return cosine_similarity(word2vec.wv.vectors)\",\n \"def cosine_sim(im, s):\\n return im.mm(s.t()) #image.mm(sentence.t()) & mm() Performs a matrix multiplication of the matrices \",\n \"def cosineSimilarity(index, nPages, query): \\n scores = defaultdict(int)\\n terms = query.split()\\n qw = {t: tf_idf(1, nPages, len(index[t])) for t in terms if t in index}\\n query_len = np.linalg.norm(list(qw.values()))\\n for term in qw:\\n query_weight = qw[term] / query_len\\n for url, weight in index[term]:\\n scores[url] += weight * query_weight\\n return sorted(scores.items(), key=lambda x: x[1], reverse=True)\",\n \"def cosine_similarity_tensorflow(tf_word_representation_A, tf_words_representation_B):\\n a_normalized = tf.nn.l2_normalize(tf_word_representation_A, axis=-1)\\n b_normalized = tf.nn.l2_normalize(tf_words_representation_B, axis=-1)\\n similarity = tf.reduce_sum(\\n tf.multiply(a_normalized, b_normalized), \\n axis=-1\\n )\\n \\n return similarity\",\n \"def cosine(X,Y=None,dense_output=True):\\n #Reemplace NaN with Zero\\n X[np.isnan(X)] = 0\\n if Y is not None:\\n Y[np.isnan(Y)] = 0\\n \\n return cosine_similarity(X,Y,dense_output)\",\n \"def cosine_similarity(document, cluster):\\n num = dot(document.vector, cluster.centroid)\\n den = linalg.norm(document.vector) * linalg.norm(cluster.centroid)\\n\\n return num / den\",\n \"def test_cosine_similarity():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity.py_func(vector1, vector1)\\n score12 = cosine_similarity.py_func(vector1, vector2)\\n score22 = cosine_similarity.py_func(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cossim(corpus):\\n files = os.listdir()\\n vectorizer = TfidfVectorizer()\\n trsfm = vectorizer.fit_transform(corpus)\\n columns = vectorizer.get_feature_names()\\n df_tfidf = pd.DataFrame(trsfm.toarray(), columns = columns, index = corpus)\\n out = cosine_similarity(trsfm)\\n df_result = pd.DataFrame(out, columns = files, index = files)\\n return df_result\",\n \"def similarity(self, query, documents):\\n\\n bow_query = self.dictionary.doc2bow(query)\\n bow_docs = [self.dictionary.doc2bow(document) for document in documents]\\n\\n index = SoftCosineSimilarity(bow_docs, self.matrix)\\n similarities = index[bow_query]\\n\\n return similarities\",\n \"def cosine_similarity(u, v):\\n\\n distance = 0.0\\n\\n ### START CODE HERE ###\\n # Compute the dot product between u and v (≈1 line)\\n dot = np.dot(u, v)\\n # Compute the L2 norm of u (≈1 line)\\n norm_u = np.sqrt(np.dot(u, u))\\n\\n # Compute the L2 norm of v (≈1 line)\\n norm_v = np.sqrt(np.dot(v, v)) ##np.linalg.norm(u)\\n # Compute the cosine similarity defined by formula (1) (≈1 line)\\n cosine_similarity = dot / (norm_u * norm_v)\\n ### END CODE HERE ###\\n\\n return cosine_similarity\",\n \"def cosine_value(self,doc_vector,query_vector):\\n\\t\\tvalue=0;i=0;\\n\\t\\tunit_vector_query=self.unit_vector(query_vector);\\n\\t\\tunit_vector_doc=self.unit_vector(doc_vector);\\n\\t\\titerate=0\\n\\t\\tfor word in query_vector:\\n\\t\\t\\tif word in doc_vector:\\n\\t\\t\\t\\tvalue+=query_vector[word]*doc_vector[word]\\n\\t\\tif unit_vector_query != 0:\\n\\t\\t\\tvalue = value/(unit_vector_query*unit_vector_doc)\\n\\t\\telse:\\n\\t\\t\\tvalue = 0\\n\\t\\treturn value\",\n \"def test_cosine_similarity_compiled():\\n vector1 = np.array([1, 1, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score12 == 2 / np.sqrt(2 * 4), \\\"Expected different score.\\\"\\n assert score11 == score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def compute_cosine_sim(vec1, vec2):\\n numer = np.dot(vec1.reshape((300,)), vec2.reshape((300,)))\\n denom = np.sqrt(np.sum(np.square(vec1.reshape(300, )))) * np.sqrt(\\n np.sum(np.square(vec2.reshape(300, ))))\\n\\n similarity = numer / denom\\n\\n return similarity\",\n \"def _do_action_calculate_similarity_cosine_express(self):\\n self._run_express_job(\\\"com.directv.recommend.express.CosineCFTrainer\\\")\\n self._scan_table(\\\"content/item_item_similarities\\\")\",\n \"def pairwise_cosine_similarity(x, y):\\n x = torch.div(x, torch.sqrt(torch.max(torch.sum(x ** 2), 1e-12)))\\n y = torch.div(y, torch.sqrt(torch.max(torch.sum(y ** 2), 1e-12)))\\n return torch.mm(x, torch.transpose(y, 1, 0))\",\n \"def cosine_similarity(user, business):\\n return len(user.intersection(business)) / (math.sqrt(len(user)) * math.sqrt(len(business)))\",\n \"def cosine_similarity(a, b):\\n\\n numerator = tf.reduce_sum(tf.multiply(a, b), axis=1)\\n denominator = tf.multiply(tf.norm(a, axis=1), tf.norm(b, axis=1))\\n cos_similarity = numerator/denominator\\n return cos_similarity\",\n \"def cos_sim(com_feat,ref_feat):\\n # Fill this in\\n a = numpy.squeeze(com_feat)\\n b = numpy.squeeze(ref_feat)\\n return numpy.dot(a, b) / (numpy.linalg.norm(a) * numpy.linalg.norm(b))\",\n \"def cosine_similarity(u: np.ndarray, v: np.ndarray) -> np.float64:\\n assert u.shape[0] == v.shape[0], \\\"Input vector must have same shape.\\\"\\n uv = 0\\n uu = 0\\n vv = 0\\n for i in range(u.shape[0]):\\n uv += u[i] * v[i]\\n uu += u[i] * u[i]\\n vv += v[i] * v[i]\\n cosine_score = 0\\n if uu != 0 and vv != 0:\\n cosine_score = uv / np.sqrt(uu * vv)\\n return np.float64(cosine_score)\",\n \"def getLexicalSimilarityScore(corpus:List[str]):\\n\\n # Generate the tf-idf vectors for the corpus\\n tfidf = TfidfVectorizer()\\n X = tfidf.fit_transform(corpus)\\n\\n # Compute cosine similarity score\\n cosine_sim_score = cosine_similarity(X, X) # 2d matrix of scores, each vector against all other vector\\n \\n # return student's scores only\\n return cosine_sim_score[0][1:]\",\n \"def get_tfidf_similarity(questions, paragraphs):\\n vectorizer = TfidfVectorizer(lowercase = False)\\n all_text = questions + paragraphs\\n vectorizer.fit(all_text)\\n similarity = vectorizer.transform(questions) * vectorizer.transform(paragraphs).T\\n return np.array(similarity.todense())\",\n \"def calculate_cosine_dist(main_text, new_text):\\n wordbag = set(\\\" \\\".join([main_text, new_text]).split(\\\" \\\"))\\n dot_prod = 0\\n main_text = main_text.split(\\\" \\\")\\n new_text = new_text.split(\\\" \\\")\\n\\n for word in wordbag:\\n if word in main_text and word in new_text:\\n # only worth looking at if word is in both. Otherwise dot prod = 0\\n count_A = sum(np.array(main_text) == word)\\n count_B = sum(np.array(new_text) == word)\\n dot_prod += count_A * count_B\\n\\n return float(dot_prod) / (len(main_text) * len(new_text))\",\n \"def similarityQuestions(self, row):\\n q1 = self.stemQuestion(row[3])\\n q2 = self.stemQuestion(row[4])\\n \\n # Compute similarity of the two questions#\\n sim = seqmatch(None, q1, q2).ratio()\\n #sim = self.computeSimilarity(q1, q2)\\n if sim > 0.6: #we guess they are duplicate questions\\n if row[5] == \\\"1\\\": #true positive\\n self.tp += 1\\n else: #false positive\\n self.fp += 1\\n else: #we guess they are different questions\\n if row[5] == \\\"0\\\": #true negative\\n self.tn += 1\\n else: #false negative\\n self.fn += 1\",\n \"def compute_cosine_similarity(base_vector, target_vector):\\n\\n np.seterr(all='print')\\n cosine_similarity = 0\\n\\n try:\\n base_vector = np.longdouble(base_vector)\\n target_vector = np.longdouble(target_vector)\\n vector_dot_products = np.dot(base_vector, target_vector)\\n vector_norms = np.linalg.norm(base_vector) * np.linalg.norm(target_vector)\\n cosine_similarity = np.divide(vector_dot_products, vector_norms)\\n\\n if vector_norms == 0.0:\\n print 'Error in vec in compute_cosine_similarity'\\n print target_vector\\n\\n except Exception, e:\\n print(str(e))\\n\\n return cosine_similarity\",\n \"def cosine_sim(a: np.ndarray, \\n b: np.ndarray \\n ) -> float:\\n return (\\n 1 + a.dot(b) / \\n (np.linalg.norm(a)*np.linalg.norm(b))\\n ) / 2\",\n \"def __modelSimilarity(self, mOrig: nn.Module, mDest: nn.Module) -> torch.Tensor:\\n cos = nn.CosineSimilarity(0)\\n d1 = nn.utils.parameters_to_vector(mOrig.parameters())\\n d2 = nn.utils.parameters_to_vector(mDest.parameters())\\n sim: torch.Tensor = cos(d1, d2)\\n return sim\",\n \"def _cosine_matrix(self, x1, x2):\\n # expand h1 shape to (batch_size, x1_timesteps, 1, embedding_size)\\n x1 = K.expand_dims(x1, axis=2)\\n # expand x2 shape to (batch_size, 1, x2_timesteps, embedding_size)\\n x2 = K.expand_dims(x2, axis=1)\\n # cosine matrix (batch_size, h1_timesteps, h2_timesteps)\\n cos_matrix = self._cosine_similarity(x1, x2)\\n return cos_matrix\",\n \"def cosine_scoring(query, doc_lengths, index):\\n idf_dict_vector = compute_idf_vector(len(doc_lengths), index)\\n doc_scores = {}\\n\\n for q in query:\\n if q in idf_dict_vector:\\n wt_q = idf_dict_vector[q] * query[q]\\n else:\\n wt_q = 0\\n\\n for tup in index[q][1:]:\\n wf_q = idf_dict_vector[q] * tup[1]\\n if tup[0] in doc_scores:\\n doc_scores[tup[0]] += wt_q * wf_q\\n else:\\n doc_scores[tup[0]] = wt_q * wf_q\\n\\n for doc in doc_scores:\\n doc_scores[doc] = doc_scores[doc] / doc_lengths[doc]\\n\\n return doc_scores\",\n \"def cosine_similarity_pure(X, Y=None):\\n X, Y = _check_pairwise_arrays(X, Y)\\n X_normalized = normalize_pure(X, copy=True)\\n if X is Y:\\n Y_normalized = X_normalized\\n else:\\n Y_normalized = normalize_pure(Y, copy=True)\\n K = dot_2d(X_normalized, Y_normalized)\\n return K\",\n \"def cosineSimilarity(dict1,dict2):\\n product1 = 0.0\\n product2 = 0.0\\n for key in dict1.keys():\\n product1 += (dict1[key] * dict1[key])\\n for key in dict2.keys():\\n product2 += (dict2[key] * dict2[key])\\n product1 = math.sqrt(product1)\\n product2 = math.sqrt(product2)\\n fenmu = product1 * product2\\n fenzi = 0.0\\n for key in dict1.keys():\\n if key in dict2:\\n fenzi += (dict1[key] * dict2[key])\\n cosSim = fenzi / fenmu\\n return cosSim\",\n \"def get_similarity(self, ):\\r\\n customer_cos_similarity = cosine_similarity(self.rating_matrix, self.rating_matrix)\\r\\n customer_cos_similarity = pd.DataFrame(customer_cos_similarity,\\r\\n index=self.customer_vendor_matrix.index,\\r\\n columns=self.customer_vendor_matrix.index)\\r\\n # customer_pearson_similarity = np.corrcoef(self.rating_matrix,\\r\\n # self.rating_matrix,)\\r\\n # customer_pearson_similarity = pd.DataFrame(customer_pearson_similarity,\\r\\n # index=self.customer_vendor_matrix.index,\\r\\n # columns=self.customer_vendor_matrix.index)\\r\\n return customer_cos_similarity,\\r\\n # return customer_pearson_similarity run too slowly\\r\",\n \"def tf_cosine_distance(self, a, b):\\n normalize_a = tf.nn.l2_normalize(a, -1)\\n normalize_b = tf.nn.l2_normalize(b, -1)\\n cos_similarity = tf.reduce_sum(\\n tf.multiply(normalize_a, normalize_b), axis=-1, keep_dims=True\\n )\\n return (1.0 - cos_similarity) / 2.0\",\n \"def word_similarity(self):\\n y_true = []\\n y_pred = []\\n for i in open(\\\"data/word_sim_dataset.txt\\\").read().split('\\\\n'):\\n i = self.preprocessor(i)\\n w1 = i.split()[-1]\\n w2 = i.split()[-2] \\n st = float(i.split()[-3]) / 4 #dataset has scale from 0 to 4\\n \\n try:\\n w1 = self.embeddings_index[w1] \\n w2 = self.embeddings_index[w2] \\n w1 = w1 / np.linalg.norm(w1)\\n w2 = w2 / np.linalg.norm(w2)\\n y_pred.append(np.dot(w1,w2))\\n y_true.append(st)\\n except:\\n pass\\n if y_true == []:\\n return 1.0\\n return mean_squared_error(y_true, y_pred, squared=False)\",\n \"def calculate_similarity(self, tweets):\\r\\n if (len(tweets) == 1):\\r\\n return 0\\r\\n vectors = self.vectorizer.vectorize_data(tweets, False)\\r\\n\\r\\n temp = cosine_similarity(vectors[0:-1], vectors)\\r\\n temp = [item for sublist in temp for item in sublist]\\r\\n sim = sum(temp) / len(temp)\\r\\n return sim\",\n \"def predict_cosine_similarities(sess, word_A, words_B):\\n\\n word_A_id, _ = sentence_to_word_ids(word_A, word_to_index)\\n words_B_ids, split_sentence = sentence_to_word_ids(words_B, word_to_index)\\n\\n evaluated_cos_similarities = sess.run(\\n cosine_similarities, \\n feed_dict={\\n tf_word_A_id: word_A_id,\\n tf_words_B_ids: words_B_ids\\n }\\n )\\n return evaluated_cos_similarities, split_sentence\",\n \"def cosine_sim(self, u_name, v_name):\\n u_vector = self.get_vector(u_name)\\n v_vector = self.get_vector(v_name)\\n u_norm = self.vectors_norm_db.get(u_name)\\n v_norm = self.vectors_norm_db.get(v_name)\\n\\n numerator = self.scalar_product(u_vector.items, v_vector.items)\\n denominator = float(u_norm) * float(v_norm)\\n \\n try:\\n # round the cosine similarity two digits after the decimal point \\n cosine = round(numerator / denominator, 2)\\n except ZeroDivisionError:\\n logging.error(\\\"division by zero for %s and %s !\\\" \\\\\\n % (u_name, v_name))\\n cosine = 0\\n \\n logging.debug(\\\"%s %s = %s \\\" \\\\\\n % (u_name, v_name, cosine))\\n \\n return cosine\",\n \"def cosine_similarity_matrix(references: np.ndarray, queries: np.ndarray) -> np.ndarray:\\n size1 = references.shape[0]\\n size2 = queries.shape[0]\\n scores = np.zeros((size1, size2))\\n for i in range(size1):\\n for j in range(size2):\\n scores[i, j] = cosine_similarity(references[i, :], queries[j, :])\\n return scores\",\n \"def cosin_sim_pairs(a, b):\\n wordsA = set(a.keys())\\n wordsB = set(b.keys())\\n inter = wordsA.intersection(wordsB)\\n if(len(inter) == 0):\\n return 0.0\\n aa, bb, ab = 0, 0, 0\\n for k in inter:\\n aa += a[k] ** 2\\n bb += b[k] ** 2\\n ab += a[k] * b[k]\\n for k in wordsA - inter:\\n aa += a[k] ** 2\\n for k in wordsB - inter:\\n bb += b[k] ** 2\\n return ab / float(math.sqrt(aa) * math.sqrt(bb))\",\n \"def get_similarity_score(text_vectors, X, factor=None):\\n similarity_scores = cosine_similarity(X, text_vectors, dense_output=True)\\n return similarity_scores * factor\",\n \"def getSemanticSimilarityScore(corpus:List[str]):\\n\\n model_ans = corpus[0]\\n model_ans_embedding = model.encode(model_ans, convert_to_tensor=True)\\n\\n student_responses = corpus[1:]\\n semantic_score = []\\n\\n for student_res in student_responses:\\n student_res_embedding = model.encode(student_res, convert_to_tensor=True)\\n\\n #Compute semantic cosine-similarity\\n cosine_score = util.pytorch_cos_sim(model_ans_embedding, student_res_embedding) # cosine_score is a 2D tensor object\\n semantic_score.append(cosine_score.item())\\n return semantic_score\",\n \"def cosine_similarity(v1, v2):\\n v1_len = 0\\n v2_len = 0\\n dot_product = 0\\n\\n for context_id, count in v1.items():\\n v1_len += count ** 2\\n if context_id in v2:\\n dot_product += count*v2[context_id]\\n for count in v2.values():\\n v2_len += count ** 2\\n\\n v1_len = math.sqrt(v1_len)\\n v2_len = math.sqrt(v2_len)\\n return dot_product/(v1_len * v2_len)\",\n \"def get_cosine_similarities(keywords: List[str],\\n matrices: Matrices,\\n word2onehot: Dict[str, int]\\n ) -> None:\\n for i in combinations(keywords, 2):\\n print(i[0], i[1], cosine_sim(\\n matrices.embedding[\\n word2onehot[i[0]]], matrices.embedding[word2onehot[i[1]]\\n ]\\n ))\",\n \"def cosine_distances(X, Y):\\n # should not need X_norm_squared because if you could precompute that as\\n # well as Y, then you should just pre-compute the output and not even\\n # call this function.\\n if X is Y:\\n X = Y = np.asanyarray(X)\\n else:\\n X = np.asanyarray(X)\\n Y = np.asanyarray(Y)\\n\\n if X.shape[1] != Y.shape[1]:\\n raise ValueError(\\\"Incompatible dimension for X and Y matrices\\\")\\n\\n return 1. - ssd.cdist(X, Y, 'cosine')\",\n \"def cosine_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x_1` and rows of `x_2`\\n # \\\"ij,ij->i\\\" := output[i] = sum_j x1[i, j] * x2[i, j]\\n cos_thetas = tf.linalg.einsum(\\\"ij,ij->i\\\", x1, x2)\\n cos_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n cos_distances = tf.maximum(cos_distances, 0.0)\\n\\n return cos_distances\",\n \"def _cosine_distance(a, b, data_is_normalized=False):\\n if not data_is_normalized:\\n a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\\n b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\\n return 1. - np.dot(a, b.T)\",\n \"def _cosine_distance(a, b, data_is_normalized=False):\\n if not data_is_normalized:\\n a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)\\n b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)\\n return 1. - np.dot(a, b.T)\",\n \"def cosine_sim_cf(matrix):\\n if type(matrix) is not lil_matrix:\\n matrix = lil_matrix(matrix)\\n\\n n = matrix.shape[0]\\n rows, cols, data = [], [], []\\n user_items = [sorted([(item, idx) for idx, item in enumerate(matrix.rows[i])]) for i in range(n)]\\n\\n for i in range(n):\\n i_ratings, i_items = matrix.data[i], user_items[i]\\n for j in range(i, n):\\n j_ratings, j_items = matrix.data[j], user_items[j]\\n sum_numerator, sum_denominator_i, sum_denominator_j = 0, 0, 0\\n i_item_ctd, j_item_ctd = 0, 0\\n while i_item_ctd < len(i_items) and j_item_ctd < len(j_items):\\n if i_items[i_item_ctd][0] > j_items[j_item_ctd][0]:\\n j_item_ctd += 1\\n elif i_items[i_item_ctd][0] < j_items[j_item_ctd][0]:\\n i_item_ctd += 1\\n else:\\n i_idx = i_items[i_item_ctd][1]\\n j_idx = j_items[j_item_ctd][1]\\n sum_numerator += i_ratings[i_idx] * j_ratings[j_idx]\\n sum_denominator_i += i_ratings[i_idx] ** 2\\n sum_denominator_j += j_ratings[j_idx] ** 2\\n i_item_ctd += 1\\n j_item_ctd += 1\\n\\n if sum_numerator == 0: continue\\n s = sum_numerator / (math.sqrt(sum_denominator_i) * math.sqrt(sum_denominator_j))\\n rows.append(i), cols.append(j), data.append(s)\\n if i != j: rows.append(j), cols.append(i), data.append(s)\\n\\n return csr_matrix((data, (rows, cols)))\",\n \"def cos_sim(a, b):\\n numerator, sosA, sosB = 0, 0, 0 #sum of squares for denom\\n for i in movies: #replace movies\\n numerator += (ratingai * ratingbi)\\n sosA += ratingai * ratingai\\n sosB += ratingbi * ratingbi\\n denom = sqrt(sosA * sosB) #make sure denom won't be 0!\\n return numerator / denom\",\n \"def test_cosine_similarity_matrix():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix.py_func(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def compute_cosine_sim(vec1, vec2):\\r\\n\\r\\n vec1 = np.array(vec1)\\r\\n vec2 = np.array(vec2)\\r\\n return np.dot(vec1, vec2)/(norm(vec1) * norm(vec2))\",\n \"def content_similarity(self, movie1, movie2):\\n v1, v2 = self.get_tfidf(movie1), self.get_tfidf(movie2)\\n return self.cosine_similarity(v1, v2)\",\n \"def _cosine_similarity_update(preds: Tensor, target: Tensor) ->Tuple[Tensor, Tensor]:\\n _check_same_shape(preds, target)\\n preds = preds.float()\\n target = target.float()\\n return preds, target\",\n \"def DocSim(self,testDF,listCourse,inCourse):\\r\\n #Reference the VDF dictionary to get the doc embedding for the listCourse\\r\\n vectorA = self.VDF[\\\"Doc\\\"][listCourse]\\r\\n #Calculate the doc embedding for the input course\\r\\n vectorB = self._DocSim(testDF,inCourse)\\r\\n \\r\\n #Convert vectors to column vectors to be fed into the cosine_similarity function. \\r\\n A = np.expand_dims(vectorA,0)\\r\\n B = np.expand_dims(vectorB,0)\\r\\n #Calculate the cosine similarity between the two vectors.\\r\\n sim = cosine_similarity(A,B)\\r\\n return float(sim)\",\n \"def cosine_distance(u, v):\\n #print u,v\\n return numpy.dot(u, v) / (math.sqrt(numpy.dot(u, u)) * math.sqrt(numpy.dot(v, v)))\",\n \"def cosine_dist(d1, d2):\\n suma=0\\n for x in d1:\\n if x in d2:\\n suma+=(d1[x]*d2[x])\\n sqrt1=0\\n sqrt2=0\\n for i in d1:\\n sqrt1+=math.pow(d1[i],2)\\n for i in d2:\\n sqrt2+=math.pow(d2[i],2)\\n return 1-suma/(math.sqrt(sqrt1)*math.sqrt(sqrt2))\",\n \"def cosia(p, q):\\n if not all(isinstance(x, Point) for x in (p, q)):\\n raise TypeError(\\\"cosia() expects 2 Points\\\")\\n return (p * q) / math.sqrt(p.normsq() * p.normsq())\",\n \"def dist_cosine(src, dest, qgraml=None):\\n\\treturn 1 - sim_cosine(src, dest, qgraml)\",\n \"def cosine_collection_distance(x1, x2):\\n x1 = tf.cast(x1, dtype=tf.float32)\\n x2 = tf.cast(x2, dtype=tf.float32)\\n\\n # dot product between rows of `x1` and columns of `x2` transpose\\n cos_thetas = tf.linalg.matmul(x1, x2, transpose_b=True)\\n pairwise_distances = 1 - cos_thetas\\n\\n # deal with numerical inaccuracies setting small negatives to zero\\n pairwise_distances = tf.maximum(pairwise_distances, 0.0)\\n\\n return pairwise_distances\",\n \"def cosine_sim(im, s):\\n return im.mm(s.t())\",\n \"def test_cosine_similarity_matrix_compiled():\\n vectors1 = np.array([[1, 1, 0, 0],\\n [1, 0, 1, 1]])\\n vectors2 = np.array([[0, 1, 1, 0],\\n [0, 0, 1, 1]])\\n\\n scores = cosine_similarity_matrix(vectors1, vectors2)\\n expected_scores = np.array([[0.5, 0.],\\n [0.40824829, 0.81649658]])\\n assert scores == pytest.approx(expected_scores, 1e-7), \\\"Expected different scores.\\\"\",\n \"def adjusted_cosine_sim(matrix):\\n if type(matrix) is not csr_matrix:\\n matrix = csr_matrix(matrix)\\n\\n matrix = _subtract_row_mean(matrix)\\n return cosine_similarity(matrix, dense_output=False)\",\n \"def cosine_sim(im, s,):\\n return im.mm(s.t())\",\n \"def similarity(self, word1: str, word2: str, metric='cosine') -> float:\\n if 0 == self.word2idx.get(word1, 0) or 0 == self.word2idx.get(word2, 0):\\n return 0.\\n\\n return self.similarity_vec(self[word1], self[word2], metric=metric)\\n # vec1 = self.__getitem__(word1).reshape((1, -1))\\n # vec2 = self.__getitem__(word2).reshape((1, -1))\\n # return 1 - distance.cdist(vec1, vec2, metric=metric).reshape(-1)\",\n \"def cosine_sim_numpy(im, s):\\n return im.dot(s.T)\",\n \"def test_cosine_similarity_all_zeros_compiled():\\n vector1 = np.array([0, 0, 0, 0])\\n vector2 = np.array([1, 1, 1, 1])\\n score11 = cosine_similarity(vector1, vector1)\\n score12 = cosine_similarity(vector1, vector2)\\n score22 = cosine_similarity(vector2, vector2)\\n\\n assert score11 == score12 == 0.0, \\\"Expected different score.\\\"\\n assert score22 == 1.0, \\\"Expected different score.\\\"\",\n \"def cosine(xs: Tensor, ys: Tensor, epsilon: float = 1e-8) -> Tensor:\\n mat = xs @ ys.t()\\n x_norm = xs.norm(2, dim=1) + epsilon\\n y_norm = ys.norm(2, dim=1) + epsilon\\n x_diag = (1 / x_norm).diag()\\n y_diag = (1 / y_norm).diag()\\n return x_diag @ mat @ y_diag\",\n \"def sc(self) -> float:\\n a = np.dot(self.true - np.mean(self.true), self.predicted - np.mean(self.predicted))\\n b = np.linalg.norm(self.true - np.mean(self.true))\\n c = np.linalg.norm(self.predicted - np.mean(self.predicted))\\n e = b * c\\n return float(np.arccos(a / e))\",\n \"def cosine_sim(news_data_dict: dict, song_data_dict: dict):\\n cosine_dict = defaultdict(list)\\n\\n for news_yr, news_txt in news_data_dict.items():\\n news_txt_flat = []\\n for nt in news_txt:\\n news_txt_flat += nt\\n news_tf = Counter(news_txt_flat)\\n\\n if news_yr <= 2011:\\n song_txt_flat = []\\n for i in range(5):\\n song_yr = news_yr+i\\n for st in song_data_dict[song_yr]:\\n song_txt_flat += st\\n song_tf = Counter(song_txt_flat)\\n\\n all_words = list(set(song_txt_flat + news_txt_flat))\\n news_array = []\\n song_array = []\\n for aw in all_words:\\n if aw in news_tf.keys():\\n news_array.append(news_tf[aw])\\n else:\\n news_array.append(0)\\n if aw in song_tf.keys():\\n song_array.append(song_tf[aw])\\n else:\\n song_array.append(0)\\n\\n norm_news = norm(np.asarray(news_array))\\n norm_song = norm(np.asarray(song_array))\\n\\n cosine = np.dot(news_array, song_array) / (norm_news * norm_song)\\n cosine = round(cosine, 3)\\n cosine_dict[news_yr].append(cosine)\\n\\n return cosine_dict\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77647394","0.77439165","0.7479039","0.7361782","0.72787845","0.7188363","0.7178258","0.7110796","0.7093376","0.7010269","0.6990073","0.6989606","0.6962196","0.6950967","0.6913966","0.6902417","0.68863827","0.6876185","0.68280274","0.67813444","0.6764831","0.6733065","0.67303395","0.6728337","0.671574","0.6715376","0.67021084","0.66984135","0.6681687","0.66805714","0.6591491","0.65638167","0.6555198","0.6548311","0.6538768","0.65386343","0.6532816","0.65044445","0.65029174","0.6502094","0.6499636","0.649853","0.64862597","0.6479651","0.6465686","0.6448526","0.64388824","0.6437905","0.6429644","0.6426476","0.6393708","0.63845307","0.63826215","0.6374942","0.6373403","0.63648874","0.6354272","0.63524383","0.6318742","0.6301161","0.6300805","0.62952596","0.62899786","0.62899023","0.62842","0.6262119","0.6259077","0.62480915","0.62405986","0.62397796","0.6213531","0.62109643","0.61950403","0.6192869","0.6166553","0.6142074","0.6138533","0.6138533","0.6133282","0.6100032","0.60901976","0.6084552","0.6071394","0.60597295","0.60476446","0.603827","0.6030966","0.60255194","0.6008154","0.60062903","0.6001346","0.5994463","0.5990115","0.5984138","0.5980134","0.59767634","0.59691674","0.5967277","0.5962343","0.5962008"],"string":"[\n \"0.77647394\",\n \"0.77439165\",\n \"0.7479039\",\n \"0.7361782\",\n \"0.72787845\",\n \"0.7188363\",\n \"0.7178258\",\n \"0.7110796\",\n \"0.7093376\",\n \"0.7010269\",\n \"0.6990073\",\n \"0.6989606\",\n \"0.6962196\",\n \"0.6950967\",\n \"0.6913966\",\n \"0.6902417\",\n \"0.68863827\",\n \"0.6876185\",\n \"0.68280274\",\n \"0.67813444\",\n \"0.6764831\",\n \"0.6733065\",\n \"0.67303395\",\n \"0.6728337\",\n \"0.671574\",\n \"0.6715376\",\n \"0.67021084\",\n \"0.66984135\",\n \"0.6681687\",\n \"0.66805714\",\n \"0.6591491\",\n \"0.65638167\",\n \"0.6555198\",\n \"0.6548311\",\n \"0.6538768\",\n \"0.65386343\",\n \"0.6532816\",\n \"0.65044445\",\n \"0.65029174\",\n \"0.6502094\",\n \"0.6499636\",\n \"0.649853\",\n \"0.64862597\",\n \"0.6479651\",\n \"0.6465686\",\n \"0.6448526\",\n \"0.64388824\",\n \"0.6437905\",\n \"0.6429644\",\n \"0.6426476\",\n \"0.6393708\",\n \"0.63845307\",\n \"0.63826215\",\n \"0.6374942\",\n \"0.6373403\",\n \"0.63648874\",\n \"0.6354272\",\n \"0.63524383\",\n \"0.6318742\",\n \"0.6301161\",\n \"0.6300805\",\n \"0.62952596\",\n \"0.62899786\",\n \"0.62899023\",\n \"0.62842\",\n \"0.6262119\",\n \"0.6259077\",\n \"0.62480915\",\n \"0.62405986\",\n \"0.62397796\",\n \"0.6213531\",\n \"0.62109643\",\n \"0.61950403\",\n \"0.6192869\",\n \"0.6166553\",\n \"0.6142074\",\n \"0.6138533\",\n \"0.6138533\",\n \"0.6133282\",\n \"0.6100032\",\n \"0.60901976\",\n \"0.6084552\",\n \"0.6071394\",\n \"0.60597295\",\n \"0.60476446\",\n \"0.603827\",\n \"0.6030966\",\n \"0.60255194\",\n \"0.6008154\",\n \"0.60062903\",\n \"0.6001346\",\n \"0.5994463\",\n \"0.5990115\",\n \"0.5984138\",\n \"0.5980134\",\n \"0.59767634\",\n \"0.59691674\",\n \"0.5967277\",\n \"0.5962343\",\n \"0.5962008\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7567193"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":9995,"cells":{"query":{"kind":"string","value":"API function that can be used by other codebase It takes query (string) and n top similar questions along with thier answers"},"document":{"kind":"string","value":"def perform_query(self, query: str, n: int = 5):\n query = [query]\n cosine_similarities = self.get_cosine_similarity(query)\n indices = cosine_similarities.argsort()[:-n-1:-1]\n return self.data[\n self.data.index.isin(indices)\n ].reset_index(drop=True).to_dict(orient='records')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def top_n_satisfy2(content, n):\n #print(n)\n sum_satisfy = 0.0\n query_num = 0.0\n for qid in content:\n label_sort = []\n score = []\n all_info = content[qid]\n num_label1 = 0\n for info in all_info:\n if info[0] > 0:\n num_label1 += 1\n label_sort.append([info[0], info[1]])\n label_sort.sort(key=take_second, reverse=True)\n satisfy = 0.0\n count = 0\n size = len(label_sort)\n for i in range(min(n, size)):\n cur_label = label_sort[i][0]\n if cur_label > 0:\n satisfy += 1\n cur_satisfy = satisfy / min(n, num_label1)\n sum_satisfy += cur_satisfy\n query_num += 1\n return sum_satisfy / query_num","def top_n_satisfy(content, n):\n sum_satisfy = 0.0\n query_num = 0.0\n for qid in content:\n label_sort = []\n score = []\n all_info = content[qid]\n for info in all_info:\n label_sort.append([info[0], info[1]])\n score.append(info[1])\n label_sort.sort(key=take_first, reverse=True)\n score.sort(reverse=True)\n satisfy = 0.0\n count = 0\n size = len(label_sort)\n for i in range(size):\n cur_label = label_sort[i][0]\n cur_score = label_sort[i][1]\n if cur_label < 1:\n break\n if i >= n:\n break\n index = score.index(cur_score)\n count += 1\n if index < n:\n satisfy += 1\n if count == 0:\n sum_satisfy += 0.0\n query_num += 1\n else:\n sum_satisfy += satisfy / float(count)\n query_num += 1\n return sum_satisfy / query_num","def top_sentences(query, sentences, idf, n):\n ll=[]\n for s in sentences:\n st=sentences[s]\n st=[word.lower() for word in st]\n found_word=0\n total_idf=0\n\n for word in query:\n if word in st:\n total_idf+=idf[word]\n found_word+=1 \n ll.append((total_idf,found_word/len(st),s))\n ll.sort(reverse=True)\n #print(ll)\n ans=[]\n for i in range(n):\n ans.append(ll[i][2])\n #print(\"answer is : \",*ans)\n return ans","def top_sentences(query, sentences, idfs, n):\n\n # claculate idfs of each sentence\n sent_score = dict()\n for sentence in sentences:\n sent_score[sentence] = 0\n for query_word in query:\n if query_word in sentences[sentence]:\n sent_score[sentence] += idfs[query_word]\n\n # create sorted list of sentences\n sorted_sentences = sorted(sent_score, key= lambda item: sent_score[item], reverse= True)\n\n # re-order sentences with the same rank of idfs according to query term density\n loop_sentences = sorted_sentences.copy()\n for sentence1 in loop_sentences:\n for sentence2 in loop_sentences:\n if sentence1 != sentence2:\n if sent_score[sentence1] == sent_score[sentence2]:\n qtd1 = query_term_density(sentence1, query, sentences)\n qtd2 = query_term_density(sentence2, query, sentences)\n index1 = sorted_sentences.index(sentence1)\n index2 = sorted_sentences.index(sentence2)\n if qtd1 > qtd2:\n if index1 > index2:\n sorted_sentences[index2], sorted_sentences[index1] = sorted_sentences[index1], sorted_sentences[index2]\n elif qtd1 < qtd2:\n if index1 < index2:\n sorted_sentences[index2], sorted_sentences[index1] = sorted_sentences[index1], sorted_sentences[index2]\n\n # get list contains top n sentences\n top_sentences = []\n for index in range(n):\n top_sentences.append(sorted_sentences[index]) \n\n return top_sentences","def top_sentences(query, sentences, idfs, n):\n rank = []\n\n for sentence in sentences:\n sentence_values = [sentence, 0, 0]\n\n for word in query:\n if word in sentences[sentence]:\n # Compute matching word measure. Sum of IDF values.\n sentence_values[1] += idfs[word]\n # Compute query term density. Proportion of words in a sentence that are in the query.\n sentence_values[2] += sentences[sentence].count(\n word) / len(sentences[sentence])\n\n rank.append(sentence_values)\n\n rank = sorted(rank, key=lambda x: (x[1], x[2]), reverse=True)[:n]\n \n return [sentence for sentence, mwm, qtd in rank]","def top_5_similar_2(list_string, my_nlp=nlp1, model_type=my_model, doc_topic=my_doc_topic):\n vec = my_nlp.transform(list_string)\n vtrans = model_type.transform(vec)\n array_5 = pairwise_distances(vtrans, doc_topic, metric='cosine').argsort()[0][0:5]\n # result_df = df_reviews[['game_link']].iloc[array_5]\n return df_reviews[['game']].iloc[array_5]\n # return(\"test\")\n return result_df","def top_python_questions(url=cached_so_url):\n response = requests.get(url)\n soup = BeautifulSoup(response.text, \"html.parser\")\n ge_1m = []\n lt_1m = []\n\n question_summary = soup.find_all(\"div\", class_=\"question-summary\")\n for question in question_summary:\n views = int(question.find(\"div\", class_=\"views\").get(\"title\").split(\" \")[0].replace(\",\", \"\"))\n _question = question.find(\"a\", class_=\"question-hyperlink\").get_text()\n votes = int(question.find(\"span\", class_=\"vote-count-post\").get_text())\n \n if views >= 1000000:\n ge_1m.append((_question, votes))\n else:\n lt_1m.append((_question, votes))\n\n return sorted(ge_1m, key=lambda x: x[1], reverse=True)","def top_sentences(query, sentences, idfs, n):\n scored_sentences = {}\n for word in query:\n # print(f\"Searching for {word}\")\n for k, v in sentences.items():\n\n # Ignore headings\n if k.strip(\"=\") != k:\n continue\n\n if word.lower() in v:\n \n try:\n check = scored_sentences[k]\n except:\n scored_sentences[k] = 0\n\n scored_sentences[k] += idfs[word]\n\n # print(scored_sentences)\n # exit()\n\n # print(f\"Scored Sentences:\\n\\t{scored_sentences}\")\n final_result = []\n while len(final_result) < n:\n top = \"\"\n g = 0.0\n s = False\n\n for k, v in scored_sentences.items():\n\n if float(v) >= float(g):\n\n # Query term density calculation\n if float(v) == float(g):\n\n old_s_set = set(top.split(\" \"))\n new_s_set = set(k.split(\" \"))\n q_set = set(query)\n\n # similarities between words in question and our query words\n inter_new = float(len(new_s_set & q_set) / len(k))\n inter_old = float(len(old_s_set & q_set) / len(top))\n\n if inter_new < inter_old:\n continue\n\n g = v\n top = k\n\n if top:\n final_result.append(top)\n del scored_sentences[top]\n else:\n final_result.append(\"Not enough context for additional results.\")\n return final_result\n \n return final_result","def top_sentences(query, sentences, idfs, n):\n ranking = {}\n qtd = {}\n\n for s in sentences:\n value = 0\n # Calculate qtm for each sentence\n for w in sentences[s]:\n if w in query:\n value += 1\n qtd[s] = value/len(sentences[s])\n # calculate sum of idfs for each sentence\n value = 0\n for word in query:\n if word in sentences[s]:\n value += idfs[word]\n ranking[s] = value\n # sort the ranking according to the values\n sortedRank = sorted(ranking.items(), key=lambda x: x[1], reverse=True)\n # if they have same idfs, sort according to qtd\n change = True\n while change:\n change = False\n for i, s in enumerate(sortedRank):\n if i == len(sortedRank)-1:\n break\n if s[1] == sortedRank[i+1][1]:\n if qtd[s[0]] < qtd[sortedRank[i+1][0]]:\n sortedRank[i], sortedRank[i+1] = sortedRank[i+1], sortedRank[i]\n change = True\n break\n finalRank = []\n for j,s in enumerate(sortedRank):\n if j == n:\n break\n finalRank.append(s[0])\n return finalRank","def test_get_top_n_words_same_frequency(self):\n expected = ['happy', 'man']\n actual = get_top_n_words({'happy': 2, 'man': 2}, 2)\n self.assertEqual(expected, actual)\n expected = ['happy']\n actual = get_top_n_words({'happy': 2, 'man': 2}, 1)\n self.assertEqual(expected, actual)","def test_get_top_n_words_more_number(self):\n expected = ['man', 'happy']\n actual = get_top_n_words({'happy': 2, 'man': 3}, 10)\n self.assertEqual(expected, actual)","def top_python_questions(url=cached_so_url):\n html = get_html(url)\n soup = BeautifulSoup(html, 'html.parser')\n questions = [question.text for question in\\\n soup.find_all('a', class_='question-hyperlink')][:50]\n votes = [vote.text for vote in\\\n soup.find_all('span', class_='vote-count-post')]\n views = [view.text.replace('\\r\\n', '').replace(' views', '').strip() for view in\\\n soup.find_all('div', class_='views')]\n \n top_questions = list(zip(questions, votes, views))\n top_questions = [(t[0], int(t[1])) for t in top_questions if 'm' in t[2]]\n return sorted(top_questions, key=lambda x: x[1], reverse=True)","def most_similar(self, queries: Union[str, List[str], Dict[str, str]], topn: int = 10,\n score_function: str = \"cos_sim\"):\n result = {}\n if self.corpus_embeddings and self.index is None:\n logger.warning(f\"No index found. Please add corpus and build index first, e.g. with `build_index()`.\"\n f\"Now returning slow search result.\")\n return super().most_similar(queries, topn, score_function=score_function)\n if not self.corpus_embeddings:\n logger.error(\"No corpus_embeddings found. Please add corpus first, e.g. with `add_corpus()`.\")\n return result\n if isinstance(queries, str) or not hasattr(queries, '__len__'):\n queries = [queries]\n if isinstance(queries, list):\n queries = {id: query for id, query in enumerate(queries)}\n result = {qid: {} for qid, query in queries.items()}\n queries_texts = list(queries.values())\n queries_embeddings = self._get_vector(queries_texts)\n # We use hnswlib knn_query method to find the top_k_hits\n corpus_ids, distances = self.index.knn_query(queries_embeddings, k=topn)\n # We extract corpus ids and scores for each query\n for i, (qid, query) in enumerate(queries.items()):\n hits = [{'corpus_id': id, 'score': 1 - distance} for id, distance in zip(corpus_ids[i], distances[i])]\n hits = sorted(hits, key=lambda x: x['score'], reverse=True)\n for hit in hits:\n result[qid][hit['corpus_id']] = hit['score']\n\n return result","def top_question_words(args, examples, word_dict):\n word_count = Counter()\n for ex in examples:\n for w in ex['question']:\n w = Dictionary.normalize(w)\n if args.uncased_question:\n w = w.lower()\n if w in word_dict:\n word_count.update([w])\n return word_count.most_common(args.tune_partial)","def search():\n\n question = request.get_json()\n question = question['questions']\n\n prediction = pipe.run(query=question[0], top_k_retriever=3, top_k_reader=3)\n answer = []\n \n for res in prediction['answers']:\n answer.append(res['answer'])\n\n result = {\"results\":[prediction]}\n return json.dumps(result)","def search(self, query: str, n=10):\n query_terms = self.tokenize(query)\n scores = self.calc_cosine_scores(query_terms)\n\n # return the top N document\n results = nlargest(n, scores.items(), key=lambda x: x[1])\n for i, (doc_id, score) in enumerate(results):\n print(i, self.documents[doc_id], score)","def google_qa(question, **kwargs):\n nlp = StanfordCoreNLP('stanford-corenlp-full-2018-10-05', lang='zh')\n answer_scores = defaultdict(int)\n answer_types = [('PERSON',), ('STATE_OR_PROVINCE', 'CITY'), ('DATE', 'TIME')]\n if question.startswith('谁') or question.endswith('谁'):\n answer_type = answer_types[0]\n max_ngram = 1\n elif '哪里' in question:\n answer_type = answer_types[1]\n max_ngram = 2\n else:\n answer_type = answer_types[2]\n max_ngram = 3\n query_list = rewritten_queries(question)\n for query in query_list:\n for summary in get_summaries(query.query, **kwargs):\n for sentence in sentences(summary, nlp):\n for ngram in candidate_answers(sentence, query.query, answer_type, max_ngram):\n answer_scores[ngram] += ngram_score(\n ngram, query.score)\n ngrams_with_scores = sorted(answer_scores.items(),\n key=lambda x: x[1],\n reverse=True)\n return [(\"\".join(ngram), score)\n for (ngram, score) in ngrams_with_scores]","def get_similarities(self, query, n=5):\n scores = self.get_scores(query)\n rank_n = np.argsort(scores)[::-1]\n if n > 0:\n rank_n = rank_n[:n]\n return [self.corpus[i] for i in rank_n]","def top_sentences(query, sentences, idfs, n):\n # identifies the sentences that are the best match for the query.\n top_sens = dict()\n for sentence, tokens in sentences.items():\n # add query rank to the idfs dictionary\n # top_sens is a dictionary of two columns, both initally empty\n query_tokens = len([word for word in tokens if word in query])\n value = query_tokens / (len(tokens))\n for word, idf_score in idfs.items():\n if word in query and word in tokens:\n # 'matching word measure'\n value += idf_score\n top_sens[sentence] = value\n # if a tie, prefer a higher 'query term density' -- /= : divide by and update value\n # defined as the proportion of words in the sentence that are also words in the query. For example, if a sentence has 10 words, 3 of which are in the query, then the sentence’s query term density is 0.3.\n # list of sentences to query ranked according to idf x[1] and if a tie, then density x[2] ; reverse=True: descending order\n # sentence list x[0] of length n ( [:n] )\n top_sens_rank = sorted(top_sens, key=top_sens.get, reverse=True)\n return top_sens_rank[0:n]","def test_get_top_n_words_ideal(self):\n expected = ['man']\n actual = get_top_n_words({'happy': 2, 'man': 3}, 1)\n self.assertEqual(expected, actual)","def top_sentences(query, sentences, idfs, n):\n sentence_scores = dict()\n\n for sentence, words in sentences.items():\n words_in_query = query.intersection(words)\n \n # idf value of sentence\n idf = 0\n for word in words_in_query:\n idf += idfs[word]\n \n # query term density of sentence\n num_words_in_query = sum(map(lambda x: x in words_in_query, words))\n query_term_density = num_words_in_query / len(words)\n\n # update sentence scores with idf and query term density values\n sentence_scores[sentence] = {'idf': idf, 'qtd': query_term_density}\n \n # rank sentences by idf then query term density\n ranked_sentences = sorted(sentence_scores.items(), key=lambda x: (x[1]['idf'], x[1]['qtd']), reverse=True)\n ranked_sentences = [x[0] for x in ranked_sentences]\n\n return ranked_sentences[:n]","def __ranking_function(self, doc, query_tokens):","def top_python_questions(url=cached_so_url):\n content = load_page(url)\n soup = BeautifulSoup(content)\n questions = [(question.select_one('a.question-hyperlink').string.strip(),\n int(question.select_one('span.vote-count-post').string.strip()))\n for question in soup.find_all(class_='question-summary')\n if question.select_one('div.views').string.strip().endswith('m views')]\n return sorted(questions, key=lambda x: -x[1])","def most_similar(self, queries: Union[str, List[str], Dict[str, str]], topn: int = 10,\n score_function: str = \"cos_sim\"):\n result = {}\n if self.corpus_embeddings and self.index is None:\n logger.warning(f\"No index found. Please add corpus and build index first, e.g. with `build_index()`.\"\n f\"Now returning slow search result.\")\n return super().most_similar(queries, topn, score_function=score_function)\n if not self.corpus_embeddings:\n logger.error(\"No corpus_embeddings found. Please add corpus first, e.g. with `add_corpus()`.\")\n return result\n if isinstance(queries, str) or not hasattr(queries, '__len__'):\n queries = [queries]\n if isinstance(queries, list):\n queries = {id: query for id, query in enumerate(queries)}\n result = {qid: {} for qid, query in queries.items()}\n queries_texts = list(queries.values())\n queries_embeddings = self._get_vector(queries_texts)\n # Annoy get_nns_by_vector can only search for one vector at a time\n for idx, (qid, query) in enumerate(queries.items()):\n corpus_ids, distances = self.index.get_nns_by_vector(queries_embeddings[idx], topn, include_distances=True)\n for corpus_id, distance in zip(corpus_ids, distances):\n score = 1 - (distance ** 2) / 2\n result[qid][corpus_id] = score\n\n return result","def google_qa_quick(questions, **kwargs):\n nlp = StanfordCoreNLP('stanford-corenlp-full-2018-10-05', lang='zh')\n all_summary = []\n print('start qa_quick')\n try:\n for index, question in enumerate(questions):\n print('Start dealing with question {}.'.format(index))\n all_summary.append(get_summaries(question, **kwargs))\n except:\n pass\n\n result = []\n answer_types = [('PERSON',), ('STATE_OR_PROVINCE', 'CITY'), ('DATE', 'TIME')]\n for question, summaries in zip(questions, all_summary):\n answer_scores = defaultdict(int)\n if question.startswith('谁') or question.endswith('谁'):\n answer_type = answer_types[0]\n max_ngram = 1\n elif '哪里' in question:\n answer_type = answer_types[1]\n max_ngram = 2\n else:\n answer_type = answer_types[2]\n max_ngram = 3\n for summary in summaries:\n for sentence in sentences(summary, nlp):\n for ngram in candidate_answers(sentence, question, answer_type, max_ngram):\n answer_scores[ngram] += ngram_score(\n ngram, 1)\n ngrams_with_scores = sorted(answer_scores.items(),\n key=lambda x: x[1],\n reverse=True)\n result.append([(\"\".join(ngram), score)\n for (ngram, score) in ngrams_with_scores])\n return result","def find_top_five(request_dict, slow_queries):\n\n if slow_queries:\n request_counter = Counter({k: v[1] // v[0] for k, v in request_dict.items()})\n sorted_pairs = [i[1] for i in request_counter.most_common(5)]\n else:\n request_counter = Counter({k: v[0] for k, v in request_dict.items()})\n sorted_pairs = [i[1] for i in request_counter.most_common(5)]\n\n return sorted_pairs","def get_query(question_number):\n query = {\n 1: \"select a.name as artist_name, Count(1) As Plays from songplays as sp inner join artists as a on sp.artist_id = a.artist_id Group By Name Order By Plays Desc Limit 10\"\n ,2: \"select s.title as song_title, Count(1) As Plays from songplays as sp inner join songs as s on sp.song_id = s.song_id Group By title Order By Plays Desc Limit 10\"\n ,3: \"select t.hour, Count(1) As Plays from songplays as sp inner join time as t on sp.start_time = t.start_time Group By t.hour Order By hour Desc\"\n }\n if question_number == 4:\n query = str(input(\"Please enter your query: \"))\n return query\n else:\n return query.get(question_number,\"Invalid question number!\")","def search(self, query):\n top_texts, top_ids, top_scores = self.retrieve_topn(query)\n return self.similarity.re_rank(query, top_texts, top_ids)","def top_n_scores(snack_data, percentage_data, n, snack_query, protein_query, carb_query, fat_query):\n\tstart_time = time.time()\n\n\t#Loop through the snacks in dictionary and compute the score for each one\n\tscores_list = []\n\t\n\tfor title, info in snack_data.items():\n\t\tscore = get_score(snack_data, percentage_data, title, snack_query, protein_query, carb_query, fat_query)\n\t\tscores_list.append((title, score))\n\n\tscores_list.sort(key=lambda tup: tup[1], reverse=True)\n\tprint(\"top_n_scores() time: --- %s seconds ---\" % (time.time() - start_time))\n\n\treturn scores_list[0:n]","def topMatches(prefs, person, n=5, similarity=sim_pearson):\n all_matches = [(similarity(prefs, person, other), other) \n for other in prefs.keys()\n if person != other]\n all_matches.sort()\n all_matches.reverse()\n return all_matches[0:n]","def search(self, query, n=500, filter_stopwords=False):\n\n def query_score(terms, title):\n \"\"\"Score the search query based on the title.\"\"\"\n\n def term_score(term, word):\n # print (term, word)\n if word.startswith(term):\n return float(len(term)) / len(word)\n else:\n return 0.0\n\n words = list(self._clean_words(title))\n return sum(term_score(t, w) for t, w in product(terms, words))\n\n terms = list(\n self._clean_words(query, filter_stopwords=filter_stopwords)\n )\n if not terms:\n raise gen.Return(final)\n term_groups = [terms]\n\n trie = self.get_ptrie()\n for term in terms:\n new_group = []\n for t in fuzzy_match(term, trie, 1):\n print \"T\", (term, t)\n new_group.append(t or term)\n if new_group not in term_groups:\n term_groups.append(new_group)\n #if t not in terms:\n # terms.append(t)\n #print \"TERMS\"\n #print terms\n\n def flatten(seq):\n nseq = []\n for item in seq:\n if isinstance(item, list):\n nseq.extend(flatten(item))\n else:\n nseq.append(item)\n return nseq\n\n final = {\n 'terms': flatten(term_groups),\n 'results': []\n }\n print term_groups\n all_results_sorted = []\n for terms in term_groups:\n with self._r.pipeline() as pipe:\n pipe.zinterstore('$tmp', terms, aggregate='max')\n pipe.zrevrange('$tmp', 0, n, True)\n # response = pipe.execute()\n response = yield gen.Task(pipe.execute)\n scored_ids = response[1]\n if not scored_ids:\n continue\n # raise gen.Return(final)\n titles = yield gen.Task(self._r.hmget, '$titles', [i[0] for i in scored_ids])\n results = imap(\n lambda x: x[0] + (titles[x[1]],),\n izip(scored_ids, titles)\n )\n # final['results'] = sorted(\n # results_sorted = sorted(\n # results,\n # key=lambda r: query_score(terms, r[2]) * r[1],\n # reverse=True\n # )\n all_results_sorted.extend(results)\n print \"all_results_sorted\"\n print all_results_sorted\n results_sorted = sorted(\n all_results_sorted,\n key=lambda r: r[1],\n reverse=True\n )\n\n final['results'] = results_sorted[:n]\n raise gen.Return(final)","def test_most_similar_topn(self):\n self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=5)), 5)\n self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=10)), 10)\n\n predicted = self.vectors.most_similar('dog.n.01', topn=None)\n self.assertEqual(len(predicted), len(self.vectors.vocab) - 1)\n self.assertEqual(predicted[-1][0], 'gallant_fox.n.01')","def top_matches(prefs, person, n=5, similarity=sim_pearson):\n scores = [(similarity(prefs, person, other), other)\n for other in prefs if other != person]\n\n scores.sort()\n scores.reverse()\n return scores[0:n]","def rank_qa(graph, question1, answers1, method='deg_weight', qa_dist_fn = average_qa_distance, brackets=False):\n\tquestion = copy.deepcopy(question1)\n\tanswers = copy.deepcopy(answers1)\n\t\n\tif qa_dist_fn == None:\n\t\tqa_dist_fn = average_qa_distance\n\n\t# Replace uri's by indices and remove unrepresented & duplicate entities\n\tquestion = [find_vertex_index(graph,q,brackets) for q in question]\n\tquestion = list(set([q for q in question if q is not None]))\n \n\tif not question:\n\t\traise QAException(\"None of the entities in the question can be found in the graph.\")\n\n\tfor i,ans in enumerate(answers):\n\t\tans = [find_vertex_index(graph,entity,brackets) for entity in ans]\n\t\tans = [entity for entity in ans if entity is not None]\n\t\tanswers[i]=ans\n \n\t# Merge answers in one list for faster shortest path finding\n\tanswer_lengths = map(len, answers)\n\tanswers = [entity for ans in answers for entity in ans]\n \n\t# Calculate distances\n\tpairwise_dist = np.array(graph.shortest_paths(answers, question, weights=method))\n\n\t# Divide pairwise_dist in answers and calculate the distance for each answer\n\tqa_dist = np.array([])\n\tfor k, v in enumerate(answer_lengths):\n\t\tstart = sum(answer_lengths[:k])\n\t\tstop = sum(answer_lengths[:k+1])\n\t\tans_dist = pairwise_dist[start:stop,:]\n\n\t\tif len(ans_dist) == 0:\n\t\t\t qa_dist = np.append(qa_dist, np.Inf)\n\t\telse:\n\t\t\tqa_dist = np.append(qa_dist, qa_dist_fn(ans_dist))\n \n\treturn np.argmin(qa_dist), qa_dist","def query(q, top_n=12):\n print('Query: ' + q + '; Top N: ' + str(top_n))\n\n driver = None\n bad_request = False\n urls = set()\n try:\n driver = Fetcher.get_selenium_driver()\n driver.get('https://api.duckduckgo.com/?q=' + q + '&kl=wt-wt')\n except:\n print('An error occurred while searching query: ' + q)\n bad_request = True\n finally:\n try:\n if not bad_request:\n results = driver.find_elements_by_class_name('result__a')\n result_size = len(results)\n print('Result Size: ' + str(result_size))\n while result_size > 0 and len(urls) < top_n:\n for element in results:\n new_url = element.get_attribute('href')\n # TODO: Filter URLs if required\n urls.add(new_url)\n if len(urls) == top_n:\n break\n\n # Infinite Scroll\n if len(urls) < top_n:\n driver.execute_script('window.scrollTo(0, document.body.scrollHeight);')\n results = driver.find_elements_by_class_name('result__a')\n results = results[result_size:]\n result_size = len(results)\n print('Moved to Next Page. Result Size: ' + str(result_size))\n except:\n print('An error occurred while searching query: ' + q + ' and fetching results')\n finally:\n if driver:\n Fetcher.close_selenium_driver(driver)\n print('Search Completed')\n return urls","def test_get_top_n_words_incorrect_numbers(self):\n expected = []\n actual = get_top_n_words({}, -1)\n self.assertEqual(expected, actual)\n actual = get_top_n_words({'happy': 2}, 0)\n self.assertEqual(expected, actual)","def get_top_answers(self, N):\n return sorted(\n self.answers.iteritems(), key=operator.itemgetter(1),\n reverse=True)[:N]","def get_ranking(self,\n query=\"\",\n number=10,\n use_SVD=True #if false: VSM based similarity used\n ):\n if self.is_valid:\n print(\"Ranking - Query: \", query)\n\n # Preprocess string: Convert into bag of words\n bow = fp.string_transformation(query)\n # bow = ['underground','underwater','wireless','communications']\n # Doc sci.electronics/52434\n print(\"Ranking - BoW: \", bow)\n\n # Convert into term vector\n term_vec = self.tfidf_handler.convertBoWToTermVector(bow)\n print(\"Ranking - Term Vector: Non-Zero positions:\", term_vec.nonzero())\n print(\"Ranking - Term Vector: Non-Zero values:\", term_vec[term_vec.nonzero()])\n print(\"Ranking - Term Vector: Size:\", term_vec.size)\n\n if(use_SVD):\n # Calc. LSI ranking (SVD)\n doc_ind, similarities = self.svd_handler.get_ranking(vec=term_vec, number=number)\n else:\n # Calc. VSM ranking\n train_dtm = self.tfidf_handler.getTDM().transpose().tocsr()\n norms = norm(train_dtm, axis=1) * np.linalg.norm(term_vec) # Normalize\n sims = np.array(train_dtm.dot(term_vec) / norms)\n\n # Derive top-n\n topn = np.argsort(sims)[::-1][:number]\n similarities = sims[topn]\n doc_ind = topn\n\n return self.tfidf_handler.getDocuments(doc_ind), similarities\n else:\n print(\"Ranking: Error - Instantiation of LSI failed\")\n return [\"\"], [0]","def gen_questions(self, number_of_questions):","def rank_retrieve(self, query):\n k = 10 # notation used in videos\n scores = [0.0 for xx in range(len(self.titles))]\n # ------------------------------------------------------------------\n # TODO: Implement cosine similarity between a document and a list of\n # query words.\n\n # Right now, this code simply gets the score by taking the Jaccard\n # similarity between the query and every document.\n self.d_length = defaultdict(float)\n words_in_query = set()\n \n for word in query:\n words_in_query.add(word)\n\n query_words = Counter(words_in_query) # eliminate 'set' object not subscriptable for q_word_weight calculations\n \n for word in query:\n q_word_weight = 1 + math.log10(query_words[word])\n posting_list = self.get_posting(word)\n for doc in posting_list:\n scores[doc] += self.tfidf[doc][word] * q_word_weight # calculate numerator for scores\n\n for word in self.vocab:\n for doc in range(len(self.docs)):\n self.d_length[doc] += self.tfidf[doc][word] ** 2 # get d^2 for each document\n\n for doc in range(len(self.docs)):\n scores[doc] /= math.sqrt(self.d_length[doc]) # divide by denominator (normalizer sqrt(d^2) for each doc)\n # ------------------------------------------------------------------\n\n ranking = [idx for idx, sim in sorted(enumerate(scores),\n key = lambda xx : xx[1], reverse = True)]\n results = []\n for i in range(k):\n results.append((ranking[i], scores[ranking[i]]))\n return results","def perform_query(tweets_dict, index, tf, idf, rt, likes, score, get_input=True, query=None):\n print(\"Insert your query:\\n\")\n if get_input:\n query = input()\n ranked_docs = search(query, index, idf, tf, rt, likes, score) \n return query, ranked_docs","def search(query_string, k, line_num_dict, N):\n stemmer = PorterStemmer()\n stopwords_set = set(stopwords.words(\"english\"))\n\n # normalize the query\n term_list = normalize(query_string, stemmer, stopwords_set)\n \n query_freq = {} # num of occurences of every unique term\n for term in term_list:\n if term in query_freq:\n query_freq[term] = query_freq[term] + 1\n elif len(term) > 0: # add only term of non-zero length\n query_freq[term] = 1\n\n # retrieve only necessary posting lists in the order they appear in the file\n lines_to_get = []\n for term in query_freq.keys():\n lines_to_get += [line_num_dict[term]]\n lines_to_get.sort()\n\n # if no word in the quey occurs in the data, posting list will be empty\n if len(lines_to_get) == 0:\n print(\"No results found\")\n sys.exit(0)\n\n posting_list = get_posting_list(lines_to_get)\n\n (weight_query, doc_dict) = calc_weights(query_freq, posting_list, N)\n \n top_k = get_top_k(weight_query, doc_dict, k)\n\n # result = doc_id + score + title + url\n title_file = \"TUAW-dataset/data/title.txt\"\n post_url_file = \"TUAW-dataset/data/post_url.txt\"\n\n # sort based on doc_id for efficient retrieval\n docs_to_get = []\n for doc_id in top_k.keys():\n docs_to_get += [doc_id]\n docs_to_get.sort()\n\n current_index = 0\n with open(title_file) as title_fd, open(post_url_file) as post_url_fd:\n lines = zip(title_fd, post_url_fd)\n for i, line in enumerate(lines):\n if i == docs_to_get[current_index]:\n title_string = \"Title = \" + line[0]\n post_url_string = \"URL = \" + line[1]\n top_k[i][1] = title_string + post_url_string\n current_index += 1\n if current_index == len(docs_to_get):\n break\n\n # sort top_k based on score\n result = OrderedDict( sorted(top_k.items(), key=lambda t: t[1][0], reverse=True) )\n\n # print output\n num_results = 1\n for doc_id, [score, details] in result.items():\n print(str(num_results) + \". Doc_ID = \" + str(doc_id) + \" ; Score = \" + str(result[doc_id][0]))\n print(result[doc_id][1])\n num_results += 1","def top_files(query, files, idfs, n):\n file_scores = dict()\n\n for file, words in files.items():\n total_tf_idf = 0\n for word in query:\n total_tf_idf += words.count(word) * idfs[word]\n file_scores[file] = total_tf_idf\n\n ranked_files = sorted(file_scores.items(), key=lambda x: x[1], reverse=True)\n ranked_files = [x[0] for x in ranked_files]\n\n return ranked_files[:n]","def top_n(userid):\n agg = [s[\"search_id\"] for s in db_searches.find()]\n table = pd.DataFrame()\n table[\"searches\"] = Counter(agg).keys()\n table[\"count\"] = Counter(agg).values()\n table = table.sort_values(\"count\", ascending=False)\n table = table[:10]\n search_ids = table[\"searches\"].values\n counts = table[\"count\"].values\n n = 0\n top_n = []\n while n < len(search_ids):\n top_n.append([str(db_search_terms.find_one({\"_id\": search_ids[n]}).get(\"value\")), str(counts[n])])\n n += 1\n jsonob = jsonify(top_n=top_n)\n return jsonob","def question_new_search():","def cy_process_recommendations(entities, scores, n=10):\n r = c_funcs.cy_aggregate_scores(entities, scores, n)\n heapq.heapify(r)\n return {'result': [{\"item\": k, \"score\": v} for k, v in heapq.nlargest(\n n, r, key= lambda x: x[1])]}","def get_similar_products(user_input_emb, ref_catalog, n = 5):\r\n sim_list = []\r\n for i in range(len(ref_catalog)):\r\n desc_id = ref_catalog.iloc[i]['id']\r\n emb = ref_catalog.iloc[i]['desc_embedding']\r\n cos_sim = compute_cosine_sim(emb,user_input_emb)\r\n sim_list.append((desc_id, cos_sim))\r\n top_n = sorted(sim_list, key= lambda tup: tup[1], reverse = True)[:n]\r\n return top_n","def top_sentences(query, sentences, idfs, n):\n tf_idfs = []\n for sentence, words in sentences.items():\n tf_idf = 0\n\n for word in query:\n if word not in idfs:\n continue\n idf = idfs[word]\n tf = (1 if word in words else 0)\n tf_idf += idf * tf\n t = (sentence, tf_idf)\n tf_idfs.append(t)\n\n sorted_list = sorted(tf_idfs, key=sorter)\n sorted_list.reverse()\n file_list = [item[0] for item in sorted_list]\n\n return file_list[:n]","def top_files(query, files, idfs, n):\n ranking = {}\n for f in files:\n currentSum = 0\n for word in query:\n currentSum += files[f].count(word) * idfs[word]\n ranking[f] = currentSum\n sortedRank = sorted(ranking.keys(), key=lambda x: ranking[x], reverse=True)\n return sortedRank[:n]","def get_top_nationalities(result, n=5):\n nat_freq=pd.DataFrame(result['country'].value_counts())\n ratios=nat_freq[:n]/nat_freq.sum()*100\n res='The most common visitors are from'\n for i in range(0,len(ratios)):\n if i!=len(ratios)-1:\n res=res+f' {ratios.index[i]} ({np.round(ratios.country[i],2)}%),'\n else:\n res=res+f' and {ratios.index[i]} ({np.round(ratios.country[i],2)}%).'\n return res","def get_recommendations(df,song_title, similarity_score, num_recommends = 5):\r\n indices = pd.Series(df.index, index = df['track_name']).drop_duplicates()\r\n idx = indices[song_title]\r\n sim_scores = list(enumerate(similarity_score[idx]))\r\n sim_scores = sorted(sim_scores, key = lambda x: x[1],reverse = True)\r\n top_scores = sim_scores[1:num_recommends+1]\r\n song_indices = [i[0] for i in top_scores]\r\n return df[\"track_name\"].iloc[song_indices]","def enseemble_results_extra(self, questions, topn):\n ok_vocab = self.get_vocabulary()\n new_vocab = [(w, self.model.wv.vocab[w]) for w in ok_vocab]\n new_vocab = {w.upper(): v for w, v in new_vocab}\n new_vocab = dict(new_vocab)\n\n results = []\n for line_no, line in enumerate(utils.smart_open(questions)):\n # TODO: use level3 BLAS (=evaluate multiple questions at once), for speed\n line = utils.to_unicode(line)\n if line.startswith(': '):\n continue\n else:\n\n try:\n a, b, c, expected = [word.upper() for word in line.split()]\n except ValueError:\n logger.info(\"skipping invalid line #%i in %s\", line_no, questions)\n continue\n if a not in new_vocab or b not in new_vocab or c not in new_vocab or expected not in new_vocab:\n \"\"\"if a not in new_vocab:\n print(\"Dont know: \" + a)\n if b not in new_vocab:\n print(\"Dont know: \" + b)\n if c not in new_vocab:\n print(\"Dont know: \" + c)\n if expected not in new_vocab:\n print(\"Dont know: \" + expected)\n \"\"\"\n logger.debug(\"skipping line #%i with OOV words: %s\", line_no, line.strip())\n results.append(None)\n continue\n\n original_vocab = self.get_vocabulary()\n self.set_vocabulary(new_vocab)\n ignore = {a, b, c} # input words to be ignored\n #print('topn')\n #print(topn)\n # find the most likely prediction, ignoring OOV words and input words\n sims = self.most_similar(positive_words=[b, c], negative_words=[a], topn = topn)\n # print(\"sims\")\n #print(sims)\n self.set_vocabulary(original_vocab)\n inner_results = []\n for predict in sims:\n predicted = predict[0]\n predicted = predicted.upper()\n predicted_tuple = (predicted, predict[1])\n #print(predicted_tuple)\n inner_results.append(predicted_tuple)\n #print(predicted)\n results.append(inner_results)\n #print(results)\n\n return results","def get_topN_docs(click_model, queryID):\n # for every queryID, find the first top 10 relevant docs\n unordered_docs = [] # list of [ind, rank] for all relevant docs\n for ind in click_model[queryID]:\n document = click_model[queryID][ind]\n rank = document['rank']\n # moving the docID into the doc-dictionary, so I can shuffle documents and easily keep this info\n # a little hackey, but can be changed if needed later down the pipeline\n document['docID'] = ind\n if rank is not None:\n unordered_docs.append([ind, rank])\n # sorts unordered_docs by doc rankings\n ordered_docs = (sorted(unordered_docs, key=lambda docs: docs[1]))\n ordered_docIDs = list(zip(*ordered_docs))[0]\n\n ranked_docs = []\n for docID in ordered_docIDs:\n ranked_docs.append(click_model[queryID][docID])\n return ranked_docs","def test_questions_sortby(self):\n QuestionFactory(title=u'tags tags tags')\n\n self.refresh()\n\n # Advanced search for questions with sortby set to 3 which is\n # '-replies' which is different between Sphinx and ES.\n response = self.client.get(reverse('search.advanced'), {\n 'q': 'tags', 'tags': 'desktop', 'w': '2', 'a': '1', 'sortby': '3',\n 'format': 'json'\n })\n\n eq_(200, response.status_code)\n\n content = json.loads(response.content)\n eq_(content['total'], 1)","def query1():\n\n print(\"1. What are the most popular three articles of all time? Which \" +\n \"articles have been accessed the most?\\n\")\n\n query = \"\"\"\n SELECT articles.title, subq.hits FROM articles\n LEFT JOIN\n (SELECT COUNT(log.path) AS hits, log.path FROM log\n WHERE log.path LIKE '/article/%'\n AND log.status = '200 OK' AND log.method = 'GET'\n GROUP BY log.path) AS subq\n ON subq.path LIKE '/article/'||articles.slug\n ORDER BY subq.hits DESC LIMIT 3;\n \"\"\"\n\n response = db_query(query)\n\n for i, j in enumerate(response):\n # Convert tuple to list to allow writing. Format \"hits\" with comma\n # seperator. Print output.\n j = list(j)\n j[1] = str(format(j[1], ',d'))\n print(\" Title: '{}' - {} views\".format(*j))","def search(self, query, maxhits=100):","def MostSimilar(caseAttrib, queryValue, weight):\n # build query string\n queryFnc = {\n \"script_score\": {\n \"query\": {\n \"match\": {\n caseAttrib: queryValue\n }\n },\n \"script\": {\n \"params\": {\n \"attrib\": caseAttrib,\n \"weight\": weight\n },\n \"source\": \"params.weight * _score\" # right side is a dummy to help explanation method\n },\n \"_name\": caseAttrib\n }\n }\n return queryFnc","def top_files(query, files, idfs, n):\n # calculate term-frequency of each words in query\n tf = dict()\n for query_word in query:\n tf[query_word] = dict()\n for file_name in files:\n tf[query_word][file_name] = files[file_name].count(query_word)\n\n # claculate tf-idfs of each document\n tf_idfs = dict()\n for file_name in files:\n tf_idfs[file_name] = 0\n for query_word in query:\n tf_idfs[file_name] += tf[query_word][file_name] * idfs[query_word]\n \n # create sorted list by tf_idfs\n sorted_tf_idfs = sorted(tf_idfs, key= lambda item: tf_idfs[item], reverse= True)\n\n # return list contains top n file names\n top_files_names = []\n for index in range(n):\n top_files_names.append(sorted_tf_idfs[index]) \n\n return top_files_names","def ask_user_for_relevance(query_results):\n for i, result in enumerate(query_results):\n hdr = 'Result #%d ' % (i+1)\n prompt_text = 'Is result #%d relevant? [y/n] ' % (i+1)\n print '\\n' + hdr + '-'*(70 - len(hdr))\n print result.to_formatted_string()\n print '-'*70\n while True:\n user_in = raw_input(prompt_text).strip().lower()\n if user_in == 'y' or user_in == 'n':\n break\n if user_in == 'y':\n result.is_relevant = True","def get_relevant_documents(self, n_top_hits: Optional[int] = 10) -> List[str]:\n search_string_clean = [self.clean_document(self.search_string)]\n q_vec = self.vectorizer.transform(search_string_clean).toarray().reshape(self.df_tdm.shape[0], )\n sim = {} # Calculate the similarity\n for i in range(n_top_hits):\n print(i)\n sim[i] = np.dot(self.df_tdm.loc[:, i].values, q_vec) / np.linalg.norm(\n self.df_tdm.loc[:, i]) * np.linalg.norm(q_vec)\n # Sort the values\n sim_sorted = sorted(sim.items(), key=lambda item: item[1],\n reverse=True) # Print the articles and their similarity values\n for k, v in sim_sorted:\n if v != 0.0:\n self.search_results.append(self.documents_names[k])\n # print(docs[k])\n return self.search_results","def enseemble_results(self, questions, topn):\n ok_vocab = self.get_vocabulary()\n new_vocab = [(w, self.model.wv.vocab[w]) for w in ok_vocab]\n new_vocab = {w.upper(): v for w, v in new_vocab}\n new_vocab = dict(new_vocab)\n\n results = []\n for line_no, line in enumerate(utils.smart_open(questions)):\n # TODO: use level3 BLAS (=evaluate multiple questions at once), for speed\n line = utils.to_unicode(line)\n if line.startswith(': '):\n continue\n else:\n\n try:\n a, b, c, expected = [word.upper() for word in line.split()]\n except ValueError:\n logger.info(\"skipping invalid line #%i in %s\", line_no, questions)\n continue\n if a not in new_vocab or b not in new_vocab or c not in new_vocab or expected not in new_vocab:\n \"\"\"if a not in new_vocab:\n print(\"Dont know: \" + a)\n if b not in new_vocab:\n print(\"Dont know: \" + b)\n if c not in new_vocab:\n print(\"Dont know: \" + c)\n if expected not in new_vocab:\n print(\"Dont know: \" + expected)\n \"\"\"\n logger.debug(\"skipping line #%i with OOV words: %s\", line_no, line.strip())\n results.append(None)\n continue\n\n original_vocab = self.get_vocabulary()\n self.set_vocabulary(new_vocab)\n ignore = {a, b, c} # input words to be ignored\n #print('topn')\n #print(topn)\n # find the most likely prediction, ignoring OOV words and input words\n sims = self.most_similar(positive_words=[b, c], negative_words=[a], topn = topn)\n # print(\"sims\")\n #print(sims)\n self.set_vocabulary(original_vocab)\n inner_results = []\n for predict in sims:\n predicted = predict[0]\n predicted = predicted.upper()\n\n inner_results.append(predicted)\n #print(predicted)\n results.append(inner_results)\n #print(results)\n\n return results","def process_query(query):\n tmp= auto_fill_id_map[query]\n index = character_to_index_map[tmp]\n row = scores[index]\n\n t = []\n for i, v in enumerate(row):\n if v < 0:\n # do not add it this score (either from same movie or same character)\n continue\n t.append((v, i))\n\n t = sorted(t)\n indexes = [x[1] for x in t]\n results = []\n for i in reversed(indexes):\n results.append(data[i])\n\n # return results in sorted order\n return results[:20]","def top_by_ratings(self, n, metric=average):\n return top_movies","def show_answers(queries, answers, aggregation_predictions_string):\n\n ans_list = []\n for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):\n print(query)\n print(answer,type(answer))\n print(predicted_agg)\n answer = [i.strip() for i in answer.split(',')]\n print(answer)\n if (len(answer) == 1):\n if (predicted_agg == 'COUNT'):\n answer = len([i for i in answer])\n\n if (len(answer) > 1):\n if (predicted_agg == 'SUM'):\n try:\n answer = sum([float(i) for i in answer])\n except ValueError:\n answer = predicted_agg\n elif (predicted_agg == 'COUNT'):\n answer = len([i for i in answer])\n elif (predicted_agg == 'AVERAGE'):\n answer = sum([float(i) for i in answer]) / len([i for i in answer])\n elif (predicted_agg == 'NONE'):\n answer = answer\n else:\n answer = 'None'\n # if predicted_agg == \"NONE\":\n # print(\"Predicted answer: \" + answer)\n # else:\n # print(\"Predicted answer: \" + predicted_agg + \" > \" + answer)\n\n ans_list.append(answer)\n\n return ans_list","def top_by_num_of_ratings(self, n):\n return top_movies","def get_q(data):\n des_n = re.findall(r'^([Ss]ize )\\d+( size)', data['item_description'])\n name_n = re.findall(r'\\d+', data['name'])\n # import ipdb; ipdb.set_trace()\n if len(des_n) == 1 and len(name_n) == 1:\n if int(des_n[0]) == int(name_n[0]):\n ret_n = int(des_n[0])\n else:\n ret_n = 1\n return ret_n\n\t# pass\n # return pd.concat(qnty_matches).reset_index().drop_duplicates(\n # subset='index', keep='last').set_index('index')","def top_files(query, files, idf, n):\n # file name vs total idf value\n tfidf={}\n for file in files:\n total_idf=0\n for word in query:\n if word in files[file]:\n total_idf+=idf[word]\n tfidf[file]=total_idf\n ll=[(tfidf[i],i) for i in tfidf]\n ll.sort(reverse=True)\n ans=[]\n for i in range(n):\n ans.append(ll[i][1])\n return ans","def rank_results(result_list, search_title, search_artist, uploader_list):\n #scores = []\n #search_artist = search_artist.replace(\"+\", \" \").lower()\n search_title = search_title.replace(\"+\", \" \")\n #search_terms = search_title.split() + search_artist.split()\n\n ## Give score to each result\n #for index, title in enumerate(result_list):\n # title = title.lower()\n # score = 0\n\n # # One point for each word in result title\n # for term in search_terms:\n # if term in title:\n # score += 1\n\n # # 2 points if whole title in result, 2 points for whole artist, 4 points for both\n # if search_title in title:\n # score += 2\n # if search_artist in title:\n # score += 2\n # if search_title in title and search_artist in title:\n # score += 4\n # if search_title == title and (uploader_list[index] == search_artist+\" - topic\" or uploader_list[index] == 'various artists - topic' or uploader_list[index] == search_artist or uploader_list[index] == search_artist+'\\\\xa0'):\n # score += 100\n # if 'karaoke' in title:\n # score-=1000\n\n # scores.append(score)\n\n # return scores.index(max(scores))\n for index, title in enumerate(result_list):\n title = title\n if search_title == title:\n return index\n\n return 0","def top_controversial(self, n):\n return top_movies","def picksomequestions():\n answers = dict()\n for question in nlist:\n answers[question[0]] = question[1]\n if len(answers.keys()) > 50:\n break\n\n return answers","def picksomequestions():\n answers = dict()\n for question in nlist:\n answers[question[0]] = question[1]\n if len(answers.keys()) > 50:\n break\n\n return answers","def picksomequestions():\n answers = dict()\n for question in nlist:\n answers[question[0]] = question[1]\n if len(answers.keys()) > 50:\n break\n\n return answers","def query(self, query, limit=10):\n try:\n rows, word_ids = self.get_match_rows(query)\n except OperationalError:\n print(\"No documents found for \\\"{}\\\"\".format(query))\n return\n\n scores = self.get_scored_list(rows, word_ids)\n\n ranked_scores = sorted([(score, url) for (url, score) in scores.items()], reverse=1)\n for (score, urlid) in ranked_scores[0: limit]:\n print(\"{}\\t{}\".format(score, self.get_url_name(urlid)))\n\n return word_ids, [r[1] for r in ranked_scores]","def query1(transaction):\n query = f'''\n match $person isa person;\n (follower: $follower, followee: $person) isa connection;\n get; group $person; count;\n '''\n print(f\"\\nQuery 1:\\n {query}\")\n iterator = transaction.query(query)\n # To obtain the result for the \"count\" query, we need to look up the grakn python-client\n # source code: https://github.com/graknlabs/client-python/tree/master/grakn/service/Session\n # The object hierarchy needs to be looked up from the source code\n result = []\n\n for item in list(iterator): # Consume ResponseIterator into a list\n\n # Convert AnswerGroup object --> Value and apply the number() method of this instance\n counts = item.answers()[0].number()\n\n # Apply the owner() method of AnswerGroup object to identify parent concepts\n # This returns an Attribute instance, on which we apply the value() method\n person = next(item.owner().attributes()).value()\n result.append({'personID': person, 'numFollowers': counts})\n\n sorted_results = sorted(result, key=lambda x: x['numFollowers'], reverse=True)\n print(f\"Top 3 most-followed persons:\\n{sorted_results[:3]}\")\n\n return sorted_results","def query_article():\r\n conn, cur = connect()\r\n query1 = (\"select * from article limit 3\")\r\n cur.execute(query1)\r\n res = cur.fetchall()\r\n conn.close()\r\n print (\"\\nThe most popular three articles of all time:\\n\")\r\n for i in range(0, len(res), 1):\r\n print (res[i][0] + \" --> \" + str(res[i][1]) + \" views\")","def test_predictTopSong():\n\n user_recommend = svd.predictTopSong(algo_svd, testset, targetSongidList)\n\n # check type\n assert isinstance(user_recommend, pd.DataFrame)\n\n # check shape\n assert user_recommend.shape == (10, 6)\n\n # check sorted\n assert user_recommend.loc[0]['score'] == max(user_recommend.score)","def get_top_k(weight_query, doc_dict, k):\n \n # find fraction of all inlinks to doc_id\n total_num_inlinks = 0\n frac_inlinks = {}\n with open(num_inlinks_file) as f:\n doc_ids_set = doc_dict.keys()\n for i, line in enumerate(f):\n total_num_inlinks += int(line.strip())\n if i in doc_ids_set:\n frac_inlinks[i] = int(line.strip())\n \n\n for doc_id, frac in frac_inlinks.items():\n frac_inlinks[doc_id] = frac / total_num_inlinks\n\n # calculate score\n # score = alpha * frac_inlinks + (1 - alpha) * cosine similarity\n alpha = 0.5\n score = {}\n for doc_id, weight_doc in doc_dict.items():\n cosine_score = 0\n for term, weight in weight_doc.items():\n cosine_score += weight_doc[term] * weight_query[term]\n score[doc_id] = alpha * frac_inlinks[doc_id] + (1 - alpha) * cosine_score\n \n # sort based on score, high to low\n sorted_score = OrderedDict( sorted(score.items(), key=lambda t: t[1], reverse=True) )\n \n # type(top_k) == {doc_id: [score, \"doc_text\"]}\n # note top_k is not sorted based on score!\n top_k = {}\n num_results = 0\n for doc_id, score in sorted_score.items():\n num_results += 1\n top_k[doc_id] = [score, \"\"]\n if num_results == k:\n break\n return top_k","def scanStackOverFlow(userQuery):\n\n\t# Search parameters\n\tkeywords = userQuery.split(\" \")\n\tprecisionScoreTreshold = 2\n\t\n\n\t# Url Generation\n\tprefix = \"http://stackoverflow.com/questions/\"\n\tfor number in range(1, 99999):\n\t\tgeneratedUrl = prefix + str(number) + \"/\"\n\n\t\t# Get title of the page\n\t\tpage = requests.get(generatedUrl)\n\t\ttree = html.fromstring(page.content)\n\t\ttitle = tree.xpath('//title/text()')\n\t\ttitleInArray = title[0].split('-')\n\n\t\t# Scan Subject\n\t\tif titleInArray[0] != \"Page Not Found \":\n\t\t\tmainSubject = titleInArray[0]\n\t\t\tprecision = titleInArray[1]\n\t\n\t\t\tpassFirstCollection = 0\n\t\t\tfirstSelectionScore = 0\n\t\t\tfirstSelectionCount = 0\n\t\t\tmainSubjectSize = 0\n\t\t\tprint title[0]\n\t\t\tfor mesh in mainSubject.split(\" \"):\n\t\t\t\tmainSubjectSize = mainSubjectSize + 1\n\t\t\t\tfor query in keywords:\n\t\t\t\t\tif mesh == query:\n\t\t\t\t\t\tfirstSelectionCount = firstSelectionCount + 1\t\n\t\t\t\t\t\tpassFirstCollection = 1\n\t\n\t\t\tfirstSelectionScore = (float(firstSelectionCount) / float(len(keywords)))*float(100)\n\t\t\t#print firstSelectionScore\n\n\t\t\tif passFirstCollection == 1:\n\t\t\t\tprecisionSize = 0\n\t\t\t\tprecisionScore = 0\n\t\t\t\tprecisionCount = 0\t\n\t\t\t\tfor subMesh in precision.split(\" \"):\n\t\t\t\t\tprecisionSize = precisionSize + 1\n\t\t\t\t\tfor query in keywords:\n\t\t\t\t\t\tif subMesh == query:\n\t\t\t\t\t\t\tprecisionCount = precisionCount + 1\n\t\t\t\t\t\t\t\n\t\t\t\tprecisionScore = (float(precisionCount) / float(len(keywords))) * float(100)\n\t\t\t\tprint \"=> Match <=\"\n\t\t\t\tprint precisionScore\t\t\t\t\n\n\n\treturn 0","def explore_most_upvoted_search(request):\n if request.method == \"POST\":\n if \"token\" in request.data and request.data[\"token\"] != \"\" and request.data[\"token\"] is not None:\n if Token.objects.filter(key=request.data[\"token\"]).exists():\n token = get_object_or_404(Token, key=request.data[\"token\"])\n start_offset = request.data[\"offset\"]\n end_offset = start_offset + PAGE_OFFSET\n comments = Comment.objects.filter(text__contains=request.data[\"keyword\"]). \\\n order_by(\"-rate\")[start_offset:end_offset]\n serializer = PostCommentSerializer(comments, context={'user_id': token.user_id}, many=True)\n return Response({\"success\": 65,\n \"comments\": serializer.data,\n \"offset\": end_offset})\n else:\n return Response({\"error\": 17})","def search(index_data, link_data, stop_word_list, search_string):\n\n topN = 5\n\n query_terms = sanitize(search_strings, stop_word_list)\n print(query_terms)\n\n # get all links which contain all the query terms\n links = get_links(query_terms)\n print(\"\\nURLs containing all of the query terms (%d):\" % len(links))\n for l in links:\n print(l)\n\n # rank the links using Vector model\n vector_ranked = rank_links(index_data, query_terms, links)\n #print(ranked_list)\n \n # build a graph of the links\n graph = build_graph(link_data, links)\n\n # rank the links using Vector model\n page_ranked = calculate_pagerank_with_teleport(graph, EPSILON, 10) \n \n # return the data\n return links, vector_ranked, page_ranked","def textrank(sentences, top_n, stopwords=None):\n S = build_similarity_matrix(sentences, stopwords) \n sentence_ranking = page_rank(S)\n \n # Sort the sentence ranks\n ranked_sentence_indexes = [item[0] for item in sorted(enumerate(sentence_ranking), key=lambda item: -item[1])]\n selected_sentences = sorted(ranked_sentence_indexes[:top_n])\n summary = itemgetter(*selected_sentences)(sentences)\n return summary","def parse_input():\n if len(sys.argv) == 1 or len(sys.argv) > 3:\n print(\"\"\"Usage: ./search.py \"query\" [k]\nReturns the top k results of the search. The second argument is optional, by default k = 10.\"\"\")\n sys.exit(1)\n elif len(sys.argv) == 2:\n query_string = sys.argv[1]\n k = 10\n else:\n query_string = sys.argv[1]\n k = int(sys.argv[2])\n if k < 1 or k > 100000:\n print(\"Error! k must be between 1 and 100000, setting k = 10\")\n k = 10\n return (query_string, k)","def test_sort_fewer_than_n(self):\n e1 = Experience(rid=1, uid=3, experience=100)\n e2 = Experience(rid=1, uid=1, experience=89)\n e3 = Experience(rid=1, uid=12, experience=1343)\n db.session.add(e1)\n db.session.add(e2)\n db.session.add(e3)\n db.session.commit()\n list = top_n_in_order(1,5)\n self.assertEqual([(12, 1343), (3, 100), (1, 89)], list)","def query2():\n\n print(\"2. Who are the most popular article authors of all time?\\n\")\n\n query = \"\"\"\n SELECT authors.name, subq_author.hits FROM authors\n LEFT JOIN\n (SELECT articles.author, CAST(SUM(subq_article.hits) AS INTEGER)\n AS hits FROM articles\n LEFT JOIN\n (SELECT COUNT(log.path) AS hits, log.path FROM log\n WHERE log.path LIKE '/article/%'\n AND log.status = '200 OK' AND log.method = 'GET'\n GROUP BY log.path) AS subq_article\n ON subq_article.path LIKE '/article/'||articles.slug\n GROUP BY articles.author) AS subq_author\n ON authors.id = subq_author.author\n ORDER BY subq_author.hits DESC;\n \"\"\"\n\n response = db_query(query)\n\n for i, j in enumerate(response):\n # Convert tuple to list to allow writing. Format \"hits\" with comma\n # seperator. Print output.\n j = list(j)\n j[1] = str(format(j[1], ',d'))\n print(\" Author: '{}' - {} views\".format(*j))","def test_query_q(client, example_records, h, prefix):\n # Test query (q=)\n res = client.get(f'{prefix}?q=title.en:text', headers=h)\n assert res.status_code == 200\n assert res.json[\"hits\"][\"total\"] == 1\n assert res.json['sortBy'] == 'bestmatch'\n\n # Test sort\n res = client.get(f'{prefix}?q=*&sort=bestmatch', headers=h)\n assert res.status_code == 200\n assert res.json['sortBy'] == 'bestmatch'\n\n # Test size\n res = client.get(f'{prefix}?size=1&page=1', headers=h)\n assert res.status_code == 200\n assert res.json['hits']['total'] == 2\n assert len(res.json['hits']['hits']) == 1\n assert 'next' in res.json['links']","def query4(transaction, **params):\n query = f'''\n match $person isa person,\n has age > {params['age_lower']}, has age < {params['age_upper']};\n $country isa country, has name $country-name;\n (contains-city: $country, in-country: $city) isa has-city;\n (contains-residence: $city, in-city: $person) isa has-residence;\n get; group $country-name; count;\n '''\n print(f\"\\nQuery 4:\\n {query}\")\n iterator = transaction.query(query)\n result = []\n\n for item in list(iterator): # Consume ResponseIterator into a list\n counts = item.answers()[0].number()\n country = item.owner().value()\n result.append({'country': country, 'personCounts': counts})\n\n sorted_results = sorted(result, key=lambda x: x['personCounts'], reverse=True)\n print(f\"3 Countries with the most people with age > {params['age_lower']} and < {params['age_upper']}: \\\n \\n{sorted_results[:3]}\")\n return sorted_results","def pretty_qa(question, num=10):\n print(\"\\nQ: \" + question)\n for (j, (answer, score)) in enumerate(google_qa(question)[:num]):\n print(\"%s. %s (%s)\" % (j + 1, answer, score))","def test_sort_more_than_n(self):\n e1 = Experience(rid=1, uid=3, experience=100)\n e2 = Experience(rid=1, uid=1, experience=89)\n e3 = Experience(rid=1, uid=12, experience=1343)\n e4 = Experience(rid=1, uid=22, experience=1839)\n e5 = Experience(rid=1, uid=2, experience=20)\n db.session.add(e1)\n db.session.add(e2)\n db.session.add(e3)\n db.session.add(e4)\n db.session.add(e5)\n db.session.commit()\n list = top_n_in_order(1, 3)\n self.assertEqual([(22, 1839), (12, 1343), (3, 100)], list)","def test_query_algorithms_1queryfrom200Algorithms(self):\n query_string = 'displayName102'\n upload_data_list = []\n create_test_algorithm_list(upload_data_list, 200)\n documents = []\n create_test_documents_list(upload_data_list, documents, 200)\n index = search.Index(name=search_algorithm._INDEX_STRING)\n index.put(documents)\n # end of data preparation\n number = query_string.split('displayName')[1]\n data = {}\n data['algorithmId'] = 'algorithmId' + number\n data['algorithmSummary'] = 'algorithmSummary' + number\n data['displayName'] = 'displayName' + number\n data['linkURL'] = 'linkURL' + number\n right_answer_list= [data]\n # end right answer list preparation\n result = search_algorithm.query_algorithms(index, query_string)\n self.assertEqual(1, len(result), msg='Wrong number of algorithms')\n self.assertItemsEqual(right_answer_list, result, msg='Discrepancy in returned algorithms')","def process_question(qu):\n\n ## global ranking\n rank_info = {}\n rank_info_k = [\"viewcount\",\"score\",\"favoritecount\"]\n for k in rank_info_k:\n rank_info[k] = int(qu[k])\n qu.pop(k,None)\n\n rank_info[\"creationdate\"] = qu[\"creationdate\"]\n\n if qu[\"acceptedanswer\"]:\n qu[\"acceptedanswer\"] = list(qu[\"acceptedanswer\"])\n else:\n qu[\"acceptedanswer\"] = []\n\n qu.pop('comments',None) # discard comments, maybe add back later\n qu[\"rank_info\"] = rank_info\n\n return qu","async def qa_infer(query: dict, response: Response) -> dict:\n logger.debug(\"QUESTION ANSWER - predicting query: \" + str(query[\"query\"]))\n results = {}\n\n try:\n query_text = query[\"query\"]\n query_context = query[\"search_context\"]\n start = time.perf_counter()\n answers = MODELS.qa_model.answer(query_text, query_context)\n end = time.perf_counter()\n logger.info(answers)\n logger.info(f\"time: {end - start:0.4f} seconds\")\n results[\"answers\"] = answers\n results[\"question\"] = query_text\n\n except Exception:\n logger.error(f\"Unable to get results from QA model for {query}\")\n response.status_code = status.HTTP_500_INTERNAL_SERVER_ERROR\n raise\n return results","def get_answers():\n count = 1\n for i in range(200): # TODO : Fetch number of all items first\n r = requests.get('http://api.stackexchange.com/2.2/answers?site=eosio&filter=!b1MMEb*6iF.PM5&pagesize=100&page={}'.format(count))\n data = json.loads(r.text)\n for item in data['items']:\n own = item['owner']['user_id']\n dsp = item['owner']['display_name']\n qn_id = item['question_id']\n try:\n owner = User.objects.get(username=own, se_display_name=dsp)\n question = Question.objects.get(se_question_id=qn_id)\n except Exception:\n owner = None\n question = None\n if owner and question:\n Answer.objects.create(owner=owner, question=question, body=item['body'],\n se_question_id=qn_id, is_accepted=item['is_accepted'],\n se_answer_id=item['answer_id'], score=item['score'])\n\n count += 1\n print(count)","def test_top_n_counts():\n ngrams = NgramFrequencies()\n new_dic = {\n \"a\": 1,\n \"b\": 2,\n \"c\": 3,\n \"d\": 4\n }\n top_list = ngrams.top_n_counts(new_dic)\n assert top_list == [(\"d\", 4), (\"c\", 3), (\"b\", 2), (\"a\", 1)]","def suggestions(prefix : str = typer.Argument(...), n : int = typer.Option(5, help=\"Number of suggestions to display\")): \n response_url = url + \"/suggestions/\" + prefix + \"?suggestion_nums=\" + str(n) \n response = requests.get(response_url) \n for i in range(len(response.json())):\n typer.echo(response.json()[i])","def get_similar_scores_to_query(query, sentences):\n\n # Stores a list of tuples of:\n # (normalized score, sentence from sentences)\n out = []\n\n # Get the min and max score in sentences\n min_score = float('inf')\n max_score = -float('inf')\n\n for s in sentences:\n score = get_similar_score(query, s)\n\n if score < min_score:\n min_score = score\n if score > max_score:\n max_score = score\n\n out.append((score, s))\n\n # Normalize all scores\n for i in range(len(out)):\n if max_score - min_score == 0.0:\n norm = 0.0\n else:\n norm = (out[i][0] - min_score) / (max_score - min_score)\n out[i] = (norm, out[i][1])\n\n out = sorted(out, key=lambda tup: tup[0], reverse=True)\n\n print \"Query: \", query\n print \"Min similarity: \", min_score\n print \"Max similarity: \", max_score, \"\\n\"\n for o in out:\n print o\n\n # Return list sorted by score in reverse order\n return out","def test_most_similar(self):\n expected = [\n 'canine.n.02',\n 'hunting_dog.n.01',\n 'carnivore.n.01',\n 'placental.n.01',\n 'mammal.n.01'\n ]\n predicted = [result[0] for result in self.vectors.most_similar('dog.n.01', topn=5)]\n self.assertEqual(expected, predicted)","def query_and_fetch(query, top_n=12):\n global url_details, url_text\n print('Query: ' + query + '; Top N: ' + str(top_n))\n url_details = []\n url_text = []\n driver = None\n bad_request = False\n try:\n driver = Fetcher.get_selenium_driver()\n driver.get('https://api.duckduckgo.com/?q=' + query + '&kl=wt-wt')\n except:\n print('An error occurred while searching query: ' + query)\n Fetcher.close_selenium_driver(driver)\n Fetcher.search_driver = None\n bad_request = True\n finally:\n try:\n if not bad_request:\n results = driver.find_elements_by_class_name('result__a')\n result_size = len(results)\n print('Result Size: ' + str(result_size))\n while result_size > 0 and len(url_details) < top_n:\n urls = []\n for element in results:\n new_url = element.get_attribute('href')\n # TODO: Filter URLs if required\n print(new_url)\n urls.append(new_url)\n\n fetched_result = Fetcher.fetch_multiple(urls, top_n)\n\n for fetched_data in fetched_result:\n if not fetched_data[1] or len(fetched_data[1].strip()) == 0:\n continue\n details = dict()\n details['url'] = fetched_data[0]\n details['html'] = fetched_data[1]\n details['title'] = fetched_data[2]\n details['label'] = predict(fetched_data[3])\n url_details.append(details)\n url_text.append(fetched_data[3])\n if len(url_details) == top_n:\n break\n\n # Infinite Scroll\n if len(url_details) < top_n:\n driver.execute_script('window.scrollTo(0, document.body.scrollHeight);')\n results = driver.find_elements_by_class_name('result__a')\n results = results[result_size:]\n result_size = len(results)\n print('Moved to Next Page. Result Size: ' + str(result_size))\n except:\n print('An error occurred while searching query: '+ query + ' and fetching results')\n #finally:\n # if driver is not None:\n # Fetcher.close_selenium_driver(driver)\n setattr(flask.current_app, 'url_text', url_text)\n print('Search Completed')\n return url_details","def get_query(self, q, request):\r\n \r\n return self.model.objects.filter(filter__icontains=q).order_by('filter')[:50]","def _get_nearest(index, query, n, includes, formats) -> Response:\n embedding = model.embed(query)\n knn_query = {\n \"field\": \"embedding\",\n \"query_vector\": embedding,\n \"k\": 1000,\n \"num_candidates\": 1000,\n }\n if formats:\n knn_query[\"filter\"] = {\"terms\": {\"format\": formats}}\n\n response = target_es.search(\n index=index,\n knn=knn_query,\n collapse={\"field\": \"id\"},\n _source=default_includes + includes,\n size=n,\n )\n return {\n \"results\": {\n hit[\"_id\"]: {**hit[\"_source\"], \"score\": hit[\"_score\"]}\n for hit in response[\"hits\"][\"hits\"]\n },\n \"total\": response[\"hits\"][\"total\"][\"value\"],\n \"took\": response[\"took\"],\n }","def most_similar(self, article: str, topn: int = 5):\n return [article[0] for article in self._model.similar_by_word(article, topn)]"],"string":"[\n \"def top_n_satisfy2(content, n):\\n #print(n)\\n sum_satisfy = 0.0\\n query_num = 0.0\\n for qid in content:\\n label_sort = []\\n score = []\\n all_info = content[qid]\\n num_label1 = 0\\n for info in all_info:\\n if info[0] > 0:\\n num_label1 += 1\\n label_sort.append([info[0], info[1]])\\n label_sort.sort(key=take_second, reverse=True)\\n satisfy = 0.0\\n count = 0\\n size = len(label_sort)\\n for i in range(min(n, size)):\\n cur_label = label_sort[i][0]\\n if cur_label > 0:\\n satisfy += 1\\n cur_satisfy = satisfy / min(n, num_label1)\\n sum_satisfy += cur_satisfy\\n query_num += 1\\n return sum_satisfy / query_num\",\n \"def top_n_satisfy(content, n):\\n sum_satisfy = 0.0\\n query_num = 0.0\\n for qid in content:\\n label_sort = []\\n score = []\\n all_info = content[qid]\\n for info in all_info:\\n label_sort.append([info[0], info[1]])\\n score.append(info[1])\\n label_sort.sort(key=take_first, reverse=True)\\n score.sort(reverse=True)\\n satisfy = 0.0\\n count = 0\\n size = len(label_sort)\\n for i in range(size):\\n cur_label = label_sort[i][0]\\n cur_score = label_sort[i][1]\\n if cur_label < 1:\\n break\\n if i >= n:\\n break\\n index = score.index(cur_score)\\n count += 1\\n if index < n:\\n satisfy += 1\\n if count == 0:\\n sum_satisfy += 0.0\\n query_num += 1\\n else:\\n sum_satisfy += satisfy / float(count)\\n query_num += 1\\n return sum_satisfy / query_num\",\n \"def top_sentences(query, sentences, idf, n):\\n ll=[]\\n for s in sentences:\\n st=sentences[s]\\n st=[word.lower() for word in st]\\n found_word=0\\n total_idf=0\\n\\n for word in query:\\n if word in st:\\n total_idf+=idf[word]\\n found_word+=1 \\n ll.append((total_idf,found_word/len(st),s))\\n ll.sort(reverse=True)\\n #print(ll)\\n ans=[]\\n for i in range(n):\\n ans.append(ll[i][2])\\n #print(\\\"answer is : \\\",*ans)\\n return ans\",\n \"def top_sentences(query, sentences, idfs, n):\\n\\n # claculate idfs of each sentence\\n sent_score = dict()\\n for sentence in sentences:\\n sent_score[sentence] = 0\\n for query_word in query:\\n if query_word in sentences[sentence]:\\n sent_score[sentence] += idfs[query_word]\\n\\n # create sorted list of sentences\\n sorted_sentences = sorted(sent_score, key= lambda item: sent_score[item], reverse= True)\\n\\n # re-order sentences with the same rank of idfs according to query term density\\n loop_sentences = sorted_sentences.copy()\\n for sentence1 in loop_sentences:\\n for sentence2 in loop_sentences:\\n if sentence1 != sentence2:\\n if sent_score[sentence1] == sent_score[sentence2]:\\n qtd1 = query_term_density(sentence1, query, sentences)\\n qtd2 = query_term_density(sentence2, query, sentences)\\n index1 = sorted_sentences.index(sentence1)\\n index2 = sorted_sentences.index(sentence2)\\n if qtd1 > qtd2:\\n if index1 > index2:\\n sorted_sentences[index2], sorted_sentences[index1] = sorted_sentences[index1], sorted_sentences[index2]\\n elif qtd1 < qtd2:\\n if index1 < index2:\\n sorted_sentences[index2], sorted_sentences[index1] = sorted_sentences[index1], sorted_sentences[index2]\\n\\n # get list contains top n sentences\\n top_sentences = []\\n for index in range(n):\\n top_sentences.append(sorted_sentences[index]) \\n\\n return top_sentences\",\n \"def top_sentences(query, sentences, idfs, n):\\n rank = []\\n\\n for sentence in sentences:\\n sentence_values = [sentence, 0, 0]\\n\\n for word in query:\\n if word in sentences[sentence]:\\n # Compute matching word measure. Sum of IDF values.\\n sentence_values[1] += idfs[word]\\n # Compute query term density. Proportion of words in a sentence that are in the query.\\n sentence_values[2] += sentences[sentence].count(\\n word) / len(sentences[sentence])\\n\\n rank.append(sentence_values)\\n\\n rank = sorted(rank, key=lambda x: (x[1], x[2]), reverse=True)[:n]\\n \\n return [sentence for sentence, mwm, qtd in rank]\",\n \"def top_5_similar_2(list_string, my_nlp=nlp1, model_type=my_model, doc_topic=my_doc_topic):\\n vec = my_nlp.transform(list_string)\\n vtrans = model_type.transform(vec)\\n array_5 = pairwise_distances(vtrans, doc_topic, metric='cosine').argsort()[0][0:5]\\n # result_df = df_reviews[['game_link']].iloc[array_5]\\n return df_reviews[['game']].iloc[array_5]\\n # return(\\\"test\\\")\\n return result_df\",\n \"def top_python_questions(url=cached_so_url):\\n response = requests.get(url)\\n soup = BeautifulSoup(response.text, \\\"html.parser\\\")\\n ge_1m = []\\n lt_1m = []\\n\\n question_summary = soup.find_all(\\\"div\\\", class_=\\\"question-summary\\\")\\n for question in question_summary:\\n views = int(question.find(\\\"div\\\", class_=\\\"views\\\").get(\\\"title\\\").split(\\\" \\\")[0].replace(\\\",\\\", \\\"\\\"))\\n _question = question.find(\\\"a\\\", class_=\\\"question-hyperlink\\\").get_text()\\n votes = int(question.find(\\\"span\\\", class_=\\\"vote-count-post\\\").get_text())\\n \\n if views >= 1000000:\\n ge_1m.append((_question, votes))\\n else:\\n lt_1m.append((_question, votes))\\n\\n return sorted(ge_1m, key=lambda x: x[1], reverse=True)\",\n \"def top_sentences(query, sentences, idfs, n):\\n scored_sentences = {}\\n for word in query:\\n # print(f\\\"Searching for {word}\\\")\\n for k, v in sentences.items():\\n\\n # Ignore headings\\n if k.strip(\\\"=\\\") != k:\\n continue\\n\\n if word.lower() in v:\\n \\n try:\\n check = scored_sentences[k]\\n except:\\n scored_sentences[k] = 0\\n\\n scored_sentences[k] += idfs[word]\\n\\n # print(scored_sentences)\\n # exit()\\n\\n # print(f\\\"Scored Sentences:\\\\n\\\\t{scored_sentences}\\\")\\n final_result = []\\n while len(final_result) < n:\\n top = \\\"\\\"\\n g = 0.0\\n s = False\\n\\n for k, v in scored_sentences.items():\\n\\n if float(v) >= float(g):\\n\\n # Query term density calculation\\n if float(v) == float(g):\\n\\n old_s_set = set(top.split(\\\" \\\"))\\n new_s_set = set(k.split(\\\" \\\"))\\n q_set = set(query)\\n\\n # similarities between words in question and our query words\\n inter_new = float(len(new_s_set & q_set) / len(k))\\n inter_old = float(len(old_s_set & q_set) / len(top))\\n\\n if inter_new < inter_old:\\n continue\\n\\n g = v\\n top = k\\n\\n if top:\\n final_result.append(top)\\n del scored_sentences[top]\\n else:\\n final_result.append(\\\"Not enough context for additional results.\\\")\\n return final_result\\n \\n return final_result\",\n \"def top_sentences(query, sentences, idfs, n):\\n ranking = {}\\n qtd = {}\\n\\n for s in sentences:\\n value = 0\\n # Calculate qtm for each sentence\\n for w in sentences[s]:\\n if w in query:\\n value += 1\\n qtd[s] = value/len(sentences[s])\\n # calculate sum of idfs for each sentence\\n value = 0\\n for word in query:\\n if word in sentences[s]:\\n value += idfs[word]\\n ranking[s] = value\\n # sort the ranking according to the values\\n sortedRank = sorted(ranking.items(), key=lambda x: x[1], reverse=True)\\n # if they have same idfs, sort according to qtd\\n change = True\\n while change:\\n change = False\\n for i, s in enumerate(sortedRank):\\n if i == len(sortedRank)-1:\\n break\\n if s[1] == sortedRank[i+1][1]:\\n if qtd[s[0]] < qtd[sortedRank[i+1][0]]:\\n sortedRank[i], sortedRank[i+1] = sortedRank[i+1], sortedRank[i]\\n change = True\\n break\\n finalRank = []\\n for j,s in enumerate(sortedRank):\\n if j == n:\\n break\\n finalRank.append(s[0])\\n return finalRank\",\n \"def test_get_top_n_words_same_frequency(self):\\n expected = ['happy', 'man']\\n actual = get_top_n_words({'happy': 2, 'man': 2}, 2)\\n self.assertEqual(expected, actual)\\n expected = ['happy']\\n actual = get_top_n_words({'happy': 2, 'man': 2}, 1)\\n self.assertEqual(expected, actual)\",\n \"def test_get_top_n_words_more_number(self):\\n expected = ['man', 'happy']\\n actual = get_top_n_words({'happy': 2, 'man': 3}, 10)\\n self.assertEqual(expected, actual)\",\n \"def top_python_questions(url=cached_so_url):\\n html = get_html(url)\\n soup = BeautifulSoup(html, 'html.parser')\\n questions = [question.text for question in\\\\\\n soup.find_all('a', class_='question-hyperlink')][:50]\\n votes = [vote.text for vote in\\\\\\n soup.find_all('span', class_='vote-count-post')]\\n views = [view.text.replace('\\\\r\\\\n', '').replace(' views', '').strip() for view in\\\\\\n soup.find_all('div', class_='views')]\\n \\n top_questions = list(zip(questions, votes, views))\\n top_questions = [(t[0], int(t[1])) for t in top_questions if 'm' in t[2]]\\n return sorted(top_questions, key=lambda x: x[1], reverse=True)\",\n \"def most_similar(self, queries: Union[str, List[str], Dict[str, str]], topn: int = 10,\\n score_function: str = \\\"cos_sim\\\"):\\n result = {}\\n if self.corpus_embeddings and self.index is None:\\n logger.warning(f\\\"No index found. Please add corpus and build index first, e.g. with `build_index()`.\\\"\\n f\\\"Now returning slow search result.\\\")\\n return super().most_similar(queries, topn, score_function=score_function)\\n if not self.corpus_embeddings:\\n logger.error(\\\"No corpus_embeddings found. Please add corpus first, e.g. with `add_corpus()`.\\\")\\n return result\\n if isinstance(queries, str) or not hasattr(queries, '__len__'):\\n queries = [queries]\\n if isinstance(queries, list):\\n queries = {id: query for id, query in enumerate(queries)}\\n result = {qid: {} for qid, query in queries.items()}\\n queries_texts = list(queries.values())\\n queries_embeddings = self._get_vector(queries_texts)\\n # We use hnswlib knn_query method to find the top_k_hits\\n corpus_ids, distances = self.index.knn_query(queries_embeddings, k=topn)\\n # We extract corpus ids and scores for each query\\n for i, (qid, query) in enumerate(queries.items()):\\n hits = [{'corpus_id': id, 'score': 1 - distance} for id, distance in zip(corpus_ids[i], distances[i])]\\n hits = sorted(hits, key=lambda x: x['score'], reverse=True)\\n for hit in hits:\\n result[qid][hit['corpus_id']] = hit['score']\\n\\n return result\",\n \"def top_question_words(args, examples, word_dict):\\n word_count = Counter()\\n for ex in examples:\\n for w in ex['question']:\\n w = Dictionary.normalize(w)\\n if args.uncased_question:\\n w = w.lower()\\n if w in word_dict:\\n word_count.update([w])\\n return word_count.most_common(args.tune_partial)\",\n \"def search():\\n\\n question = request.get_json()\\n question = question['questions']\\n\\n prediction = pipe.run(query=question[0], top_k_retriever=3, top_k_reader=3)\\n answer = []\\n \\n for res in prediction['answers']:\\n answer.append(res['answer'])\\n\\n result = {\\\"results\\\":[prediction]}\\n return json.dumps(result)\",\n \"def search(self, query: str, n=10):\\n query_terms = self.tokenize(query)\\n scores = self.calc_cosine_scores(query_terms)\\n\\n # return the top N document\\n results = nlargest(n, scores.items(), key=lambda x: x[1])\\n for i, (doc_id, score) in enumerate(results):\\n print(i, self.documents[doc_id], score)\",\n \"def google_qa(question, **kwargs):\\n nlp = StanfordCoreNLP('stanford-corenlp-full-2018-10-05', lang='zh')\\n answer_scores = defaultdict(int)\\n answer_types = [('PERSON',), ('STATE_OR_PROVINCE', 'CITY'), ('DATE', 'TIME')]\\n if question.startswith('谁') or question.endswith('谁'):\\n answer_type = answer_types[0]\\n max_ngram = 1\\n elif '哪里' in question:\\n answer_type = answer_types[1]\\n max_ngram = 2\\n else:\\n answer_type = answer_types[2]\\n max_ngram = 3\\n query_list = rewritten_queries(question)\\n for query in query_list:\\n for summary in get_summaries(query.query, **kwargs):\\n for sentence in sentences(summary, nlp):\\n for ngram in candidate_answers(sentence, query.query, answer_type, max_ngram):\\n answer_scores[ngram] += ngram_score(\\n ngram, query.score)\\n ngrams_with_scores = sorted(answer_scores.items(),\\n key=lambda x: x[1],\\n reverse=True)\\n return [(\\\"\\\".join(ngram), score)\\n for (ngram, score) in ngrams_with_scores]\",\n \"def get_similarities(self, query, n=5):\\n scores = self.get_scores(query)\\n rank_n = np.argsort(scores)[::-1]\\n if n > 0:\\n rank_n = rank_n[:n]\\n return [self.corpus[i] for i in rank_n]\",\n \"def top_sentences(query, sentences, idfs, n):\\n # identifies the sentences that are the best match for the query.\\n top_sens = dict()\\n for sentence, tokens in sentences.items():\\n # add query rank to the idfs dictionary\\n # top_sens is a dictionary of two columns, both initally empty\\n query_tokens = len([word for word in tokens if word in query])\\n value = query_tokens / (len(tokens))\\n for word, idf_score in idfs.items():\\n if word in query and word in tokens:\\n # 'matching word measure'\\n value += idf_score\\n top_sens[sentence] = value\\n # if a tie, prefer a higher 'query term density' -- /= : divide by and update value\\n # defined as the proportion of words in the sentence that are also words in the query. For example, if a sentence has 10 words, 3 of which are in the query, then the sentence’s query term density is 0.3.\\n # list of sentences to query ranked according to idf x[1] and if a tie, then density x[2] ; reverse=True: descending order\\n # sentence list x[0] of length n ( [:n] )\\n top_sens_rank = sorted(top_sens, key=top_sens.get, reverse=True)\\n return top_sens_rank[0:n]\",\n \"def test_get_top_n_words_ideal(self):\\n expected = ['man']\\n actual = get_top_n_words({'happy': 2, 'man': 3}, 1)\\n self.assertEqual(expected, actual)\",\n \"def top_sentences(query, sentences, idfs, n):\\n sentence_scores = dict()\\n\\n for sentence, words in sentences.items():\\n words_in_query = query.intersection(words)\\n \\n # idf value of sentence\\n idf = 0\\n for word in words_in_query:\\n idf += idfs[word]\\n \\n # query term density of sentence\\n num_words_in_query = sum(map(lambda x: x in words_in_query, words))\\n query_term_density = num_words_in_query / len(words)\\n\\n # update sentence scores with idf and query term density values\\n sentence_scores[sentence] = {'idf': idf, 'qtd': query_term_density}\\n \\n # rank sentences by idf then query term density\\n ranked_sentences = sorted(sentence_scores.items(), key=lambda x: (x[1]['idf'], x[1]['qtd']), reverse=True)\\n ranked_sentences = [x[0] for x in ranked_sentences]\\n\\n return ranked_sentences[:n]\",\n \"def __ranking_function(self, doc, query_tokens):\",\n \"def top_python_questions(url=cached_so_url):\\n content = load_page(url)\\n soup = BeautifulSoup(content)\\n questions = [(question.select_one('a.question-hyperlink').string.strip(),\\n int(question.select_one('span.vote-count-post').string.strip()))\\n for question in soup.find_all(class_='question-summary')\\n if question.select_one('div.views').string.strip().endswith('m views')]\\n return sorted(questions, key=lambda x: -x[1])\",\n \"def most_similar(self, queries: Union[str, List[str], Dict[str, str]], topn: int = 10,\\n score_function: str = \\\"cos_sim\\\"):\\n result = {}\\n if self.corpus_embeddings and self.index is None:\\n logger.warning(f\\\"No index found. Please add corpus and build index first, e.g. with `build_index()`.\\\"\\n f\\\"Now returning slow search result.\\\")\\n return super().most_similar(queries, topn, score_function=score_function)\\n if not self.corpus_embeddings:\\n logger.error(\\\"No corpus_embeddings found. Please add corpus first, e.g. with `add_corpus()`.\\\")\\n return result\\n if isinstance(queries, str) or not hasattr(queries, '__len__'):\\n queries = [queries]\\n if isinstance(queries, list):\\n queries = {id: query for id, query in enumerate(queries)}\\n result = {qid: {} for qid, query in queries.items()}\\n queries_texts = list(queries.values())\\n queries_embeddings = self._get_vector(queries_texts)\\n # Annoy get_nns_by_vector can only search for one vector at a time\\n for idx, (qid, query) in enumerate(queries.items()):\\n corpus_ids, distances = self.index.get_nns_by_vector(queries_embeddings[idx], topn, include_distances=True)\\n for corpus_id, distance in zip(corpus_ids, distances):\\n score = 1 - (distance ** 2) / 2\\n result[qid][corpus_id] = score\\n\\n return result\",\n \"def google_qa_quick(questions, **kwargs):\\n nlp = StanfordCoreNLP('stanford-corenlp-full-2018-10-05', lang='zh')\\n all_summary = []\\n print('start qa_quick')\\n try:\\n for index, question in enumerate(questions):\\n print('Start dealing with question {}.'.format(index))\\n all_summary.append(get_summaries(question, **kwargs))\\n except:\\n pass\\n\\n result = []\\n answer_types = [('PERSON',), ('STATE_OR_PROVINCE', 'CITY'), ('DATE', 'TIME')]\\n for question, summaries in zip(questions, all_summary):\\n answer_scores = defaultdict(int)\\n if question.startswith('谁') or question.endswith('谁'):\\n answer_type = answer_types[0]\\n max_ngram = 1\\n elif '哪里' in question:\\n answer_type = answer_types[1]\\n max_ngram = 2\\n else:\\n answer_type = answer_types[2]\\n max_ngram = 3\\n for summary in summaries:\\n for sentence in sentences(summary, nlp):\\n for ngram in candidate_answers(sentence, question, answer_type, max_ngram):\\n answer_scores[ngram] += ngram_score(\\n ngram, 1)\\n ngrams_with_scores = sorted(answer_scores.items(),\\n key=lambda x: x[1],\\n reverse=True)\\n result.append([(\\\"\\\".join(ngram), score)\\n for (ngram, score) in ngrams_with_scores])\\n return result\",\n \"def find_top_five(request_dict, slow_queries):\\n\\n if slow_queries:\\n request_counter = Counter({k: v[1] // v[0] for k, v in request_dict.items()})\\n sorted_pairs = [i[1] for i in request_counter.most_common(5)]\\n else:\\n request_counter = Counter({k: v[0] for k, v in request_dict.items()})\\n sorted_pairs = [i[1] for i in request_counter.most_common(5)]\\n\\n return sorted_pairs\",\n \"def get_query(question_number):\\n query = {\\n 1: \\\"select a.name as artist_name, Count(1) As Plays from songplays as sp inner join artists as a on sp.artist_id = a.artist_id Group By Name Order By Plays Desc Limit 10\\\"\\n ,2: \\\"select s.title as song_title, Count(1) As Plays from songplays as sp inner join songs as s on sp.song_id = s.song_id Group By title Order By Plays Desc Limit 10\\\"\\n ,3: \\\"select t.hour, Count(1) As Plays from songplays as sp inner join time as t on sp.start_time = t.start_time Group By t.hour Order By hour Desc\\\"\\n }\\n if question_number == 4:\\n query = str(input(\\\"Please enter your query: \\\"))\\n return query\\n else:\\n return query.get(question_number,\\\"Invalid question number!\\\")\",\n \"def search(self, query):\\n top_texts, top_ids, top_scores = self.retrieve_topn(query)\\n return self.similarity.re_rank(query, top_texts, top_ids)\",\n \"def top_n_scores(snack_data, percentage_data, n, snack_query, protein_query, carb_query, fat_query):\\n\\tstart_time = time.time()\\n\\n\\t#Loop through the snacks in dictionary and compute the score for each one\\n\\tscores_list = []\\n\\t\\n\\tfor title, info in snack_data.items():\\n\\t\\tscore = get_score(snack_data, percentage_data, title, snack_query, protein_query, carb_query, fat_query)\\n\\t\\tscores_list.append((title, score))\\n\\n\\tscores_list.sort(key=lambda tup: tup[1], reverse=True)\\n\\tprint(\\\"top_n_scores() time: --- %s seconds ---\\\" % (time.time() - start_time))\\n\\n\\treturn scores_list[0:n]\",\n \"def topMatches(prefs, person, n=5, similarity=sim_pearson):\\n all_matches = [(similarity(prefs, person, other), other) \\n for other in prefs.keys()\\n if person != other]\\n all_matches.sort()\\n all_matches.reverse()\\n return all_matches[0:n]\",\n \"def search(self, query, n=500, filter_stopwords=False):\\n\\n def query_score(terms, title):\\n \\\"\\\"\\\"Score the search query based on the title.\\\"\\\"\\\"\\n\\n def term_score(term, word):\\n # print (term, word)\\n if word.startswith(term):\\n return float(len(term)) / len(word)\\n else:\\n return 0.0\\n\\n words = list(self._clean_words(title))\\n return sum(term_score(t, w) for t, w in product(terms, words))\\n\\n terms = list(\\n self._clean_words(query, filter_stopwords=filter_stopwords)\\n )\\n if not terms:\\n raise gen.Return(final)\\n term_groups = [terms]\\n\\n trie = self.get_ptrie()\\n for term in terms:\\n new_group = []\\n for t in fuzzy_match(term, trie, 1):\\n print \\\"T\\\", (term, t)\\n new_group.append(t or term)\\n if new_group not in term_groups:\\n term_groups.append(new_group)\\n #if t not in terms:\\n # terms.append(t)\\n #print \\\"TERMS\\\"\\n #print terms\\n\\n def flatten(seq):\\n nseq = []\\n for item in seq:\\n if isinstance(item, list):\\n nseq.extend(flatten(item))\\n else:\\n nseq.append(item)\\n return nseq\\n\\n final = {\\n 'terms': flatten(term_groups),\\n 'results': []\\n }\\n print term_groups\\n all_results_sorted = []\\n for terms in term_groups:\\n with self._r.pipeline() as pipe:\\n pipe.zinterstore('$tmp', terms, aggregate='max')\\n pipe.zrevrange('$tmp', 0, n, True)\\n # response = pipe.execute()\\n response = yield gen.Task(pipe.execute)\\n scored_ids = response[1]\\n if not scored_ids:\\n continue\\n # raise gen.Return(final)\\n titles = yield gen.Task(self._r.hmget, '$titles', [i[0] for i in scored_ids])\\n results = imap(\\n lambda x: x[0] + (titles[x[1]],),\\n izip(scored_ids, titles)\\n )\\n # final['results'] = sorted(\\n # results_sorted = sorted(\\n # results,\\n # key=lambda r: query_score(terms, r[2]) * r[1],\\n # reverse=True\\n # )\\n all_results_sorted.extend(results)\\n print \\\"all_results_sorted\\\"\\n print all_results_sorted\\n results_sorted = sorted(\\n all_results_sorted,\\n key=lambda r: r[1],\\n reverse=True\\n )\\n\\n final['results'] = results_sorted[:n]\\n raise gen.Return(final)\",\n \"def test_most_similar_topn(self):\\n self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=5)), 5)\\n self.assertEqual(len(self.vectors.most_similar('dog.n.01', topn=10)), 10)\\n\\n predicted = self.vectors.most_similar('dog.n.01', topn=None)\\n self.assertEqual(len(predicted), len(self.vectors.vocab) - 1)\\n self.assertEqual(predicted[-1][0], 'gallant_fox.n.01')\",\n \"def top_matches(prefs, person, n=5, similarity=sim_pearson):\\n scores = [(similarity(prefs, person, other), other)\\n for other in prefs if other != person]\\n\\n scores.sort()\\n scores.reverse()\\n return scores[0:n]\",\n \"def rank_qa(graph, question1, answers1, method='deg_weight', qa_dist_fn = average_qa_distance, brackets=False):\\n\\tquestion = copy.deepcopy(question1)\\n\\tanswers = copy.deepcopy(answers1)\\n\\t\\n\\tif qa_dist_fn == None:\\n\\t\\tqa_dist_fn = average_qa_distance\\n\\n\\t# Replace uri's by indices and remove unrepresented & duplicate entities\\n\\tquestion = [find_vertex_index(graph,q,brackets) for q in question]\\n\\tquestion = list(set([q for q in question if q is not None]))\\n \\n\\tif not question:\\n\\t\\traise QAException(\\\"None of the entities in the question can be found in the graph.\\\")\\n\\n\\tfor i,ans in enumerate(answers):\\n\\t\\tans = [find_vertex_index(graph,entity,brackets) for entity in ans]\\n\\t\\tans = [entity for entity in ans if entity is not None]\\n\\t\\tanswers[i]=ans\\n \\n\\t# Merge answers in one list for faster shortest path finding\\n\\tanswer_lengths = map(len, answers)\\n\\tanswers = [entity for ans in answers for entity in ans]\\n \\n\\t# Calculate distances\\n\\tpairwise_dist = np.array(graph.shortest_paths(answers, question, weights=method))\\n\\n\\t# Divide pairwise_dist in answers and calculate the distance for each answer\\n\\tqa_dist = np.array([])\\n\\tfor k, v in enumerate(answer_lengths):\\n\\t\\tstart = sum(answer_lengths[:k])\\n\\t\\tstop = sum(answer_lengths[:k+1])\\n\\t\\tans_dist = pairwise_dist[start:stop,:]\\n\\n\\t\\tif len(ans_dist) == 0:\\n\\t\\t\\t qa_dist = np.append(qa_dist, np.Inf)\\n\\t\\telse:\\n\\t\\t\\tqa_dist = np.append(qa_dist, qa_dist_fn(ans_dist))\\n \\n\\treturn np.argmin(qa_dist), qa_dist\",\n \"def query(q, top_n=12):\\n print('Query: ' + q + '; Top N: ' + str(top_n))\\n\\n driver = None\\n bad_request = False\\n urls = set()\\n try:\\n driver = Fetcher.get_selenium_driver()\\n driver.get('https://api.duckduckgo.com/?q=' + q + '&kl=wt-wt')\\n except:\\n print('An error occurred while searching query: ' + q)\\n bad_request = True\\n finally:\\n try:\\n if not bad_request:\\n results = driver.find_elements_by_class_name('result__a')\\n result_size = len(results)\\n print('Result Size: ' + str(result_size))\\n while result_size > 0 and len(urls) < top_n:\\n for element in results:\\n new_url = element.get_attribute('href')\\n # TODO: Filter URLs if required\\n urls.add(new_url)\\n if len(urls) == top_n:\\n break\\n\\n # Infinite Scroll\\n if len(urls) < top_n:\\n driver.execute_script('window.scrollTo(0, document.body.scrollHeight);')\\n results = driver.find_elements_by_class_name('result__a')\\n results = results[result_size:]\\n result_size = len(results)\\n print('Moved to Next Page. Result Size: ' + str(result_size))\\n except:\\n print('An error occurred while searching query: ' + q + ' and fetching results')\\n finally:\\n if driver:\\n Fetcher.close_selenium_driver(driver)\\n print('Search Completed')\\n return urls\",\n \"def test_get_top_n_words_incorrect_numbers(self):\\n expected = []\\n actual = get_top_n_words({}, -1)\\n self.assertEqual(expected, actual)\\n actual = get_top_n_words({'happy': 2}, 0)\\n self.assertEqual(expected, actual)\",\n \"def get_top_answers(self, N):\\n return sorted(\\n self.answers.iteritems(), key=operator.itemgetter(1),\\n reverse=True)[:N]\",\n \"def get_ranking(self,\\n query=\\\"\\\",\\n number=10,\\n use_SVD=True #if false: VSM based similarity used\\n ):\\n if self.is_valid:\\n print(\\\"Ranking - Query: \\\", query)\\n\\n # Preprocess string: Convert into bag of words\\n bow = fp.string_transformation(query)\\n # bow = ['underground','underwater','wireless','communications']\\n # Doc sci.electronics/52434\\n print(\\\"Ranking - BoW: \\\", bow)\\n\\n # Convert into term vector\\n term_vec = self.tfidf_handler.convertBoWToTermVector(bow)\\n print(\\\"Ranking - Term Vector: Non-Zero positions:\\\", term_vec.nonzero())\\n print(\\\"Ranking - Term Vector: Non-Zero values:\\\", term_vec[term_vec.nonzero()])\\n print(\\\"Ranking - Term Vector: Size:\\\", term_vec.size)\\n\\n if(use_SVD):\\n # Calc. LSI ranking (SVD)\\n doc_ind, similarities = self.svd_handler.get_ranking(vec=term_vec, number=number)\\n else:\\n # Calc. VSM ranking\\n train_dtm = self.tfidf_handler.getTDM().transpose().tocsr()\\n norms = norm(train_dtm, axis=1) * np.linalg.norm(term_vec) # Normalize\\n sims = np.array(train_dtm.dot(term_vec) / norms)\\n\\n # Derive top-n\\n topn = np.argsort(sims)[::-1][:number]\\n similarities = sims[topn]\\n doc_ind = topn\\n\\n return self.tfidf_handler.getDocuments(doc_ind), similarities\\n else:\\n print(\\\"Ranking: Error - Instantiation of LSI failed\\\")\\n return [\\\"\\\"], [0]\",\n \"def gen_questions(self, number_of_questions):\",\n \"def rank_retrieve(self, query):\\n k = 10 # notation used in videos\\n scores = [0.0 for xx in range(len(self.titles))]\\n # ------------------------------------------------------------------\\n # TODO: Implement cosine similarity between a document and a list of\\n # query words.\\n\\n # Right now, this code simply gets the score by taking the Jaccard\\n # similarity between the query and every document.\\n self.d_length = defaultdict(float)\\n words_in_query = set()\\n \\n for word in query:\\n words_in_query.add(word)\\n\\n query_words = Counter(words_in_query) # eliminate 'set' object not subscriptable for q_word_weight calculations\\n \\n for word in query:\\n q_word_weight = 1 + math.log10(query_words[word])\\n posting_list = self.get_posting(word)\\n for doc in posting_list:\\n scores[doc] += self.tfidf[doc][word] * q_word_weight # calculate numerator for scores\\n\\n for word in self.vocab:\\n for doc in range(len(self.docs)):\\n self.d_length[doc] += self.tfidf[doc][word] ** 2 # get d^2 for each document\\n\\n for doc in range(len(self.docs)):\\n scores[doc] /= math.sqrt(self.d_length[doc]) # divide by denominator (normalizer sqrt(d^2) for each doc)\\n # ------------------------------------------------------------------\\n\\n ranking = [idx for idx, sim in sorted(enumerate(scores),\\n key = lambda xx : xx[1], reverse = True)]\\n results = []\\n for i in range(k):\\n results.append((ranking[i], scores[ranking[i]]))\\n return results\",\n \"def perform_query(tweets_dict, index, tf, idf, rt, likes, score, get_input=True, query=None):\\n print(\\\"Insert your query:\\\\n\\\")\\n if get_input:\\n query = input()\\n ranked_docs = search(query, index, idf, tf, rt, likes, score) \\n return query, ranked_docs\",\n \"def search(query_string, k, line_num_dict, N):\\n stemmer = PorterStemmer()\\n stopwords_set = set(stopwords.words(\\\"english\\\"))\\n\\n # normalize the query\\n term_list = normalize(query_string, stemmer, stopwords_set)\\n \\n query_freq = {} # num of occurences of every unique term\\n for term in term_list:\\n if term in query_freq:\\n query_freq[term] = query_freq[term] + 1\\n elif len(term) > 0: # add only term of non-zero length\\n query_freq[term] = 1\\n\\n # retrieve only necessary posting lists in the order they appear in the file\\n lines_to_get = []\\n for term in query_freq.keys():\\n lines_to_get += [line_num_dict[term]]\\n lines_to_get.sort()\\n\\n # if no word in the quey occurs in the data, posting list will be empty\\n if len(lines_to_get) == 0:\\n print(\\\"No results found\\\")\\n sys.exit(0)\\n\\n posting_list = get_posting_list(lines_to_get)\\n\\n (weight_query, doc_dict) = calc_weights(query_freq, posting_list, N)\\n \\n top_k = get_top_k(weight_query, doc_dict, k)\\n\\n # result = doc_id + score + title + url\\n title_file = \\\"TUAW-dataset/data/title.txt\\\"\\n post_url_file = \\\"TUAW-dataset/data/post_url.txt\\\"\\n\\n # sort based on doc_id for efficient retrieval\\n docs_to_get = []\\n for doc_id in top_k.keys():\\n docs_to_get += [doc_id]\\n docs_to_get.sort()\\n\\n current_index = 0\\n with open(title_file) as title_fd, open(post_url_file) as post_url_fd:\\n lines = zip(title_fd, post_url_fd)\\n for i, line in enumerate(lines):\\n if i == docs_to_get[current_index]:\\n title_string = \\\"Title = \\\" + line[0]\\n post_url_string = \\\"URL = \\\" + line[1]\\n top_k[i][1] = title_string + post_url_string\\n current_index += 1\\n if current_index == len(docs_to_get):\\n break\\n\\n # sort top_k based on score\\n result = OrderedDict( sorted(top_k.items(), key=lambda t: t[1][0], reverse=True) )\\n\\n # print output\\n num_results = 1\\n for doc_id, [score, details] in result.items():\\n print(str(num_results) + \\\". Doc_ID = \\\" + str(doc_id) + \\\" ; Score = \\\" + str(result[doc_id][0]))\\n print(result[doc_id][1])\\n num_results += 1\",\n \"def top_files(query, files, idfs, n):\\n file_scores = dict()\\n\\n for file, words in files.items():\\n total_tf_idf = 0\\n for word in query:\\n total_tf_idf += words.count(word) * idfs[word]\\n file_scores[file] = total_tf_idf\\n\\n ranked_files = sorted(file_scores.items(), key=lambda x: x[1], reverse=True)\\n ranked_files = [x[0] for x in ranked_files]\\n\\n return ranked_files[:n]\",\n \"def top_n(userid):\\n agg = [s[\\\"search_id\\\"] for s in db_searches.find()]\\n table = pd.DataFrame()\\n table[\\\"searches\\\"] = Counter(agg).keys()\\n table[\\\"count\\\"] = Counter(agg).values()\\n table = table.sort_values(\\\"count\\\", ascending=False)\\n table = table[:10]\\n search_ids = table[\\\"searches\\\"].values\\n counts = table[\\\"count\\\"].values\\n n = 0\\n top_n = []\\n while n < len(search_ids):\\n top_n.append([str(db_search_terms.find_one({\\\"_id\\\": search_ids[n]}).get(\\\"value\\\")), str(counts[n])])\\n n += 1\\n jsonob = jsonify(top_n=top_n)\\n return jsonob\",\n \"def question_new_search():\",\n \"def cy_process_recommendations(entities, scores, n=10):\\n r = c_funcs.cy_aggregate_scores(entities, scores, n)\\n heapq.heapify(r)\\n return {'result': [{\\\"item\\\": k, \\\"score\\\": v} for k, v in heapq.nlargest(\\n n, r, key= lambda x: x[1])]}\",\n \"def get_similar_products(user_input_emb, ref_catalog, n = 5):\\r\\n sim_list = []\\r\\n for i in range(len(ref_catalog)):\\r\\n desc_id = ref_catalog.iloc[i]['id']\\r\\n emb = ref_catalog.iloc[i]['desc_embedding']\\r\\n cos_sim = compute_cosine_sim(emb,user_input_emb)\\r\\n sim_list.append((desc_id, cos_sim))\\r\\n top_n = sorted(sim_list, key= lambda tup: tup[1], reverse = True)[:n]\\r\\n return top_n\",\n \"def top_sentences(query, sentences, idfs, n):\\n tf_idfs = []\\n for sentence, words in sentences.items():\\n tf_idf = 0\\n\\n for word in query:\\n if word not in idfs:\\n continue\\n idf = idfs[word]\\n tf = (1 if word in words else 0)\\n tf_idf += idf * tf\\n t = (sentence, tf_idf)\\n tf_idfs.append(t)\\n\\n sorted_list = sorted(tf_idfs, key=sorter)\\n sorted_list.reverse()\\n file_list = [item[0] for item in sorted_list]\\n\\n return file_list[:n]\",\n \"def top_files(query, files, idfs, n):\\n ranking = {}\\n for f in files:\\n currentSum = 0\\n for word in query:\\n currentSum += files[f].count(word) * idfs[word]\\n ranking[f] = currentSum\\n sortedRank = sorted(ranking.keys(), key=lambda x: ranking[x], reverse=True)\\n return sortedRank[:n]\",\n \"def get_top_nationalities(result, n=5):\\n nat_freq=pd.DataFrame(result['country'].value_counts())\\n ratios=nat_freq[:n]/nat_freq.sum()*100\\n res='The most common visitors are from'\\n for i in range(0,len(ratios)):\\n if i!=len(ratios)-1:\\n res=res+f' {ratios.index[i]} ({np.round(ratios.country[i],2)}%),'\\n else:\\n res=res+f' and {ratios.index[i]} ({np.round(ratios.country[i],2)}%).'\\n return res\",\n \"def get_recommendations(df,song_title, similarity_score, num_recommends = 5):\\r\\n indices = pd.Series(df.index, index = df['track_name']).drop_duplicates()\\r\\n idx = indices[song_title]\\r\\n sim_scores = list(enumerate(similarity_score[idx]))\\r\\n sim_scores = sorted(sim_scores, key = lambda x: x[1],reverse = True)\\r\\n top_scores = sim_scores[1:num_recommends+1]\\r\\n song_indices = [i[0] for i in top_scores]\\r\\n return df[\\\"track_name\\\"].iloc[song_indices]\",\n \"def enseemble_results_extra(self, questions, topn):\\n ok_vocab = self.get_vocabulary()\\n new_vocab = [(w, self.model.wv.vocab[w]) for w in ok_vocab]\\n new_vocab = {w.upper(): v for w, v in new_vocab}\\n new_vocab = dict(new_vocab)\\n\\n results = []\\n for line_no, line in enumerate(utils.smart_open(questions)):\\n # TODO: use level3 BLAS (=evaluate multiple questions at once), for speed\\n line = utils.to_unicode(line)\\n if line.startswith(': '):\\n continue\\n else:\\n\\n try:\\n a, b, c, expected = [word.upper() for word in line.split()]\\n except ValueError:\\n logger.info(\\\"skipping invalid line #%i in %s\\\", line_no, questions)\\n continue\\n if a not in new_vocab or b not in new_vocab or c not in new_vocab or expected not in new_vocab:\\n \\\"\\\"\\\"if a not in new_vocab:\\n print(\\\"Dont know: \\\" + a)\\n if b not in new_vocab:\\n print(\\\"Dont know: \\\" + b)\\n if c not in new_vocab:\\n print(\\\"Dont know: \\\" + c)\\n if expected not in new_vocab:\\n print(\\\"Dont know: \\\" + expected)\\n \\\"\\\"\\\"\\n logger.debug(\\\"skipping line #%i with OOV words: %s\\\", line_no, line.strip())\\n results.append(None)\\n continue\\n\\n original_vocab = self.get_vocabulary()\\n self.set_vocabulary(new_vocab)\\n ignore = {a, b, c} # input words to be ignored\\n #print('topn')\\n #print(topn)\\n # find the most likely prediction, ignoring OOV words and input words\\n sims = self.most_similar(positive_words=[b, c], negative_words=[a], topn = topn)\\n # print(\\\"sims\\\")\\n #print(sims)\\n self.set_vocabulary(original_vocab)\\n inner_results = []\\n for predict in sims:\\n predicted = predict[0]\\n predicted = predicted.upper()\\n predicted_tuple = (predicted, predict[1])\\n #print(predicted_tuple)\\n inner_results.append(predicted_tuple)\\n #print(predicted)\\n results.append(inner_results)\\n #print(results)\\n\\n return results\",\n \"def get_topN_docs(click_model, queryID):\\n # for every queryID, find the first top 10 relevant docs\\n unordered_docs = [] # list of [ind, rank] for all relevant docs\\n for ind in click_model[queryID]:\\n document = click_model[queryID][ind]\\n rank = document['rank']\\n # moving the docID into the doc-dictionary, so I can shuffle documents and easily keep this info\\n # a little hackey, but can be changed if needed later down the pipeline\\n document['docID'] = ind\\n if rank is not None:\\n unordered_docs.append([ind, rank])\\n # sorts unordered_docs by doc rankings\\n ordered_docs = (sorted(unordered_docs, key=lambda docs: docs[1]))\\n ordered_docIDs = list(zip(*ordered_docs))[0]\\n\\n ranked_docs = []\\n for docID in ordered_docIDs:\\n ranked_docs.append(click_model[queryID][docID])\\n return ranked_docs\",\n \"def test_questions_sortby(self):\\n QuestionFactory(title=u'tags tags tags')\\n\\n self.refresh()\\n\\n # Advanced search for questions with sortby set to 3 which is\\n # '-replies' which is different between Sphinx and ES.\\n response = self.client.get(reverse('search.advanced'), {\\n 'q': 'tags', 'tags': 'desktop', 'w': '2', 'a': '1', 'sortby': '3',\\n 'format': 'json'\\n })\\n\\n eq_(200, response.status_code)\\n\\n content = json.loads(response.content)\\n eq_(content['total'], 1)\",\n \"def query1():\\n\\n print(\\\"1. What are the most popular three articles of all time? Which \\\" +\\n \\\"articles have been accessed the most?\\\\n\\\")\\n\\n query = \\\"\\\"\\\"\\n SELECT articles.title, subq.hits FROM articles\\n LEFT JOIN\\n (SELECT COUNT(log.path) AS hits, log.path FROM log\\n WHERE log.path LIKE '/article/%'\\n AND log.status = '200 OK' AND log.method = 'GET'\\n GROUP BY log.path) AS subq\\n ON subq.path LIKE '/article/'||articles.slug\\n ORDER BY subq.hits DESC LIMIT 3;\\n \\\"\\\"\\\"\\n\\n response = db_query(query)\\n\\n for i, j in enumerate(response):\\n # Convert tuple to list to allow writing. Format \\\"hits\\\" with comma\\n # seperator. Print output.\\n j = list(j)\\n j[1] = str(format(j[1], ',d'))\\n print(\\\" Title: '{}' - {} views\\\".format(*j))\",\n \"def search(self, query, maxhits=100):\",\n \"def MostSimilar(caseAttrib, queryValue, weight):\\n # build query string\\n queryFnc = {\\n \\\"script_score\\\": {\\n \\\"query\\\": {\\n \\\"match\\\": {\\n caseAttrib: queryValue\\n }\\n },\\n \\\"script\\\": {\\n \\\"params\\\": {\\n \\\"attrib\\\": caseAttrib,\\n \\\"weight\\\": weight\\n },\\n \\\"source\\\": \\\"params.weight * _score\\\" # right side is a dummy to help explanation method\\n },\\n \\\"_name\\\": caseAttrib\\n }\\n }\\n return queryFnc\",\n \"def top_files(query, files, idfs, n):\\n # calculate term-frequency of each words in query\\n tf = dict()\\n for query_word in query:\\n tf[query_word] = dict()\\n for file_name in files:\\n tf[query_word][file_name] = files[file_name].count(query_word)\\n\\n # claculate tf-idfs of each document\\n tf_idfs = dict()\\n for file_name in files:\\n tf_idfs[file_name] = 0\\n for query_word in query:\\n tf_idfs[file_name] += tf[query_word][file_name] * idfs[query_word]\\n \\n # create sorted list by tf_idfs\\n sorted_tf_idfs = sorted(tf_idfs, key= lambda item: tf_idfs[item], reverse= True)\\n\\n # return list contains top n file names\\n top_files_names = []\\n for index in range(n):\\n top_files_names.append(sorted_tf_idfs[index]) \\n\\n return top_files_names\",\n \"def ask_user_for_relevance(query_results):\\n for i, result in enumerate(query_results):\\n hdr = 'Result #%d ' % (i+1)\\n prompt_text = 'Is result #%d relevant? [y/n] ' % (i+1)\\n print '\\\\n' + hdr + '-'*(70 - len(hdr))\\n print result.to_formatted_string()\\n print '-'*70\\n while True:\\n user_in = raw_input(prompt_text).strip().lower()\\n if user_in == 'y' or user_in == 'n':\\n break\\n if user_in == 'y':\\n result.is_relevant = True\",\n \"def get_relevant_documents(self, n_top_hits: Optional[int] = 10) -> List[str]:\\n search_string_clean = [self.clean_document(self.search_string)]\\n q_vec = self.vectorizer.transform(search_string_clean).toarray().reshape(self.df_tdm.shape[0], )\\n sim = {} # Calculate the similarity\\n for i in range(n_top_hits):\\n print(i)\\n sim[i] = np.dot(self.df_tdm.loc[:, i].values, q_vec) / np.linalg.norm(\\n self.df_tdm.loc[:, i]) * np.linalg.norm(q_vec)\\n # Sort the values\\n sim_sorted = sorted(sim.items(), key=lambda item: item[1],\\n reverse=True) # Print the articles and their similarity values\\n for k, v in sim_sorted:\\n if v != 0.0:\\n self.search_results.append(self.documents_names[k])\\n # print(docs[k])\\n return self.search_results\",\n \"def enseemble_results(self, questions, topn):\\n ok_vocab = self.get_vocabulary()\\n new_vocab = [(w, self.model.wv.vocab[w]) for w in ok_vocab]\\n new_vocab = {w.upper(): v for w, v in new_vocab}\\n new_vocab = dict(new_vocab)\\n\\n results = []\\n for line_no, line in enumerate(utils.smart_open(questions)):\\n # TODO: use level3 BLAS (=evaluate multiple questions at once), for speed\\n line = utils.to_unicode(line)\\n if line.startswith(': '):\\n continue\\n else:\\n\\n try:\\n a, b, c, expected = [word.upper() for word in line.split()]\\n except ValueError:\\n logger.info(\\\"skipping invalid line #%i in %s\\\", line_no, questions)\\n continue\\n if a not in new_vocab or b not in new_vocab or c not in new_vocab or expected not in new_vocab:\\n \\\"\\\"\\\"if a not in new_vocab:\\n print(\\\"Dont know: \\\" + a)\\n if b not in new_vocab:\\n print(\\\"Dont know: \\\" + b)\\n if c not in new_vocab:\\n print(\\\"Dont know: \\\" + c)\\n if expected not in new_vocab:\\n print(\\\"Dont know: \\\" + expected)\\n \\\"\\\"\\\"\\n logger.debug(\\\"skipping line #%i with OOV words: %s\\\", line_no, line.strip())\\n results.append(None)\\n continue\\n\\n original_vocab = self.get_vocabulary()\\n self.set_vocabulary(new_vocab)\\n ignore = {a, b, c} # input words to be ignored\\n #print('topn')\\n #print(topn)\\n # find the most likely prediction, ignoring OOV words and input words\\n sims = self.most_similar(positive_words=[b, c], negative_words=[a], topn = topn)\\n # print(\\\"sims\\\")\\n #print(sims)\\n self.set_vocabulary(original_vocab)\\n inner_results = []\\n for predict in sims:\\n predicted = predict[0]\\n predicted = predicted.upper()\\n\\n inner_results.append(predicted)\\n #print(predicted)\\n results.append(inner_results)\\n #print(results)\\n\\n return results\",\n \"def process_query(query):\\n tmp= auto_fill_id_map[query]\\n index = character_to_index_map[tmp]\\n row = scores[index]\\n\\n t = []\\n for i, v in enumerate(row):\\n if v < 0:\\n # do not add it this score (either from same movie or same character)\\n continue\\n t.append((v, i))\\n\\n t = sorted(t)\\n indexes = [x[1] for x in t]\\n results = []\\n for i in reversed(indexes):\\n results.append(data[i])\\n\\n # return results in sorted order\\n return results[:20]\",\n \"def top_by_ratings(self, n, metric=average):\\n return top_movies\",\n \"def show_answers(queries, answers, aggregation_predictions_string):\\n\\n ans_list = []\\n for query, answer, predicted_agg in zip(queries, answers, aggregation_predictions_string):\\n print(query)\\n print(answer,type(answer))\\n print(predicted_agg)\\n answer = [i.strip() for i in answer.split(',')]\\n print(answer)\\n if (len(answer) == 1):\\n if (predicted_agg == 'COUNT'):\\n answer = len([i for i in answer])\\n\\n if (len(answer) > 1):\\n if (predicted_agg == 'SUM'):\\n try:\\n answer = sum([float(i) for i in answer])\\n except ValueError:\\n answer = predicted_agg\\n elif (predicted_agg == 'COUNT'):\\n answer = len([i for i in answer])\\n elif (predicted_agg == 'AVERAGE'):\\n answer = sum([float(i) for i in answer]) / len([i for i in answer])\\n elif (predicted_agg == 'NONE'):\\n answer = answer\\n else:\\n answer = 'None'\\n # if predicted_agg == \\\"NONE\\\":\\n # print(\\\"Predicted answer: \\\" + answer)\\n # else:\\n # print(\\\"Predicted answer: \\\" + predicted_agg + \\\" > \\\" + answer)\\n\\n ans_list.append(answer)\\n\\n return ans_list\",\n \"def top_by_num_of_ratings(self, n):\\n return top_movies\",\n \"def get_q(data):\\n des_n = re.findall(r'^([Ss]ize )\\\\d+( size)', data['item_description'])\\n name_n = re.findall(r'\\\\d+', data['name'])\\n # import ipdb; ipdb.set_trace()\\n if len(des_n) == 1 and len(name_n) == 1:\\n if int(des_n[0]) == int(name_n[0]):\\n ret_n = int(des_n[0])\\n else:\\n ret_n = 1\\n return ret_n\\n\\t# pass\\n # return pd.concat(qnty_matches).reset_index().drop_duplicates(\\n # subset='index', keep='last').set_index('index')\",\n \"def top_files(query, files, idf, n):\\n # file name vs total idf value\\n tfidf={}\\n for file in files:\\n total_idf=0\\n for word in query:\\n if word in files[file]:\\n total_idf+=idf[word]\\n tfidf[file]=total_idf\\n ll=[(tfidf[i],i) for i in tfidf]\\n ll.sort(reverse=True)\\n ans=[]\\n for i in range(n):\\n ans.append(ll[i][1])\\n return ans\",\n \"def rank_results(result_list, search_title, search_artist, uploader_list):\\n #scores = []\\n #search_artist = search_artist.replace(\\\"+\\\", \\\" \\\").lower()\\n search_title = search_title.replace(\\\"+\\\", \\\" \\\")\\n #search_terms = search_title.split() + search_artist.split()\\n\\n ## Give score to each result\\n #for index, title in enumerate(result_list):\\n # title = title.lower()\\n # score = 0\\n\\n # # One point for each word in result title\\n # for term in search_terms:\\n # if term in title:\\n # score += 1\\n\\n # # 2 points if whole title in result, 2 points for whole artist, 4 points for both\\n # if search_title in title:\\n # score += 2\\n # if search_artist in title:\\n # score += 2\\n # if search_title in title and search_artist in title:\\n # score += 4\\n # if search_title == title and (uploader_list[index] == search_artist+\\\" - topic\\\" or uploader_list[index] == 'various artists - topic' or uploader_list[index] == search_artist or uploader_list[index] == search_artist+'\\\\\\\\xa0'):\\n # score += 100\\n # if 'karaoke' in title:\\n # score-=1000\\n\\n # scores.append(score)\\n\\n # return scores.index(max(scores))\\n for index, title in enumerate(result_list):\\n title = title\\n if search_title == title:\\n return index\\n\\n return 0\",\n \"def top_controversial(self, n):\\n return top_movies\",\n \"def picksomequestions():\\n answers = dict()\\n for question in nlist:\\n answers[question[0]] = question[1]\\n if len(answers.keys()) > 50:\\n break\\n\\n return answers\",\n \"def picksomequestions():\\n answers = dict()\\n for question in nlist:\\n answers[question[0]] = question[1]\\n if len(answers.keys()) > 50:\\n break\\n\\n return answers\",\n \"def picksomequestions():\\n answers = dict()\\n for question in nlist:\\n answers[question[0]] = question[1]\\n if len(answers.keys()) > 50:\\n break\\n\\n return answers\",\n \"def query(self, query, limit=10):\\n try:\\n rows, word_ids = self.get_match_rows(query)\\n except OperationalError:\\n print(\\\"No documents found for \\\\\\\"{}\\\\\\\"\\\".format(query))\\n return\\n\\n scores = self.get_scored_list(rows, word_ids)\\n\\n ranked_scores = sorted([(score, url) for (url, score) in scores.items()], reverse=1)\\n for (score, urlid) in ranked_scores[0: limit]:\\n print(\\\"{}\\\\t{}\\\".format(score, self.get_url_name(urlid)))\\n\\n return word_ids, [r[1] for r in ranked_scores]\",\n \"def query1(transaction):\\n query = f'''\\n match $person isa person;\\n (follower: $follower, followee: $person) isa connection;\\n get; group $person; count;\\n '''\\n print(f\\\"\\\\nQuery 1:\\\\n {query}\\\")\\n iterator = transaction.query(query)\\n # To obtain the result for the \\\"count\\\" query, we need to look up the grakn python-client\\n # source code: https://github.com/graknlabs/client-python/tree/master/grakn/service/Session\\n # The object hierarchy needs to be looked up from the source code\\n result = []\\n\\n for item in list(iterator): # Consume ResponseIterator into a list\\n\\n # Convert AnswerGroup object --> Value and apply the number() method of this instance\\n counts = item.answers()[0].number()\\n\\n # Apply the owner() method of AnswerGroup object to identify parent concepts\\n # This returns an Attribute instance, on which we apply the value() method\\n person = next(item.owner().attributes()).value()\\n result.append({'personID': person, 'numFollowers': counts})\\n\\n sorted_results = sorted(result, key=lambda x: x['numFollowers'], reverse=True)\\n print(f\\\"Top 3 most-followed persons:\\\\n{sorted_results[:3]}\\\")\\n\\n return sorted_results\",\n \"def query_article():\\r\\n conn, cur = connect()\\r\\n query1 = (\\\"select * from article limit 3\\\")\\r\\n cur.execute(query1)\\r\\n res = cur.fetchall()\\r\\n conn.close()\\r\\n print (\\\"\\\\nThe most popular three articles of all time:\\\\n\\\")\\r\\n for i in range(0, len(res), 1):\\r\\n print (res[i][0] + \\\" --> \\\" + str(res[i][1]) + \\\" views\\\")\",\n \"def test_predictTopSong():\\n\\n user_recommend = svd.predictTopSong(algo_svd, testset, targetSongidList)\\n\\n # check type\\n assert isinstance(user_recommend, pd.DataFrame)\\n\\n # check shape\\n assert user_recommend.shape == (10, 6)\\n\\n # check sorted\\n assert user_recommend.loc[0]['score'] == max(user_recommend.score)\",\n \"def get_top_k(weight_query, doc_dict, k):\\n \\n # find fraction of all inlinks to doc_id\\n total_num_inlinks = 0\\n frac_inlinks = {}\\n with open(num_inlinks_file) as f:\\n doc_ids_set = doc_dict.keys()\\n for i, line in enumerate(f):\\n total_num_inlinks += int(line.strip())\\n if i in doc_ids_set:\\n frac_inlinks[i] = int(line.strip())\\n \\n\\n for doc_id, frac in frac_inlinks.items():\\n frac_inlinks[doc_id] = frac / total_num_inlinks\\n\\n # calculate score\\n # score = alpha * frac_inlinks + (1 - alpha) * cosine similarity\\n alpha = 0.5\\n score = {}\\n for doc_id, weight_doc in doc_dict.items():\\n cosine_score = 0\\n for term, weight in weight_doc.items():\\n cosine_score += weight_doc[term] * weight_query[term]\\n score[doc_id] = alpha * frac_inlinks[doc_id] + (1 - alpha) * cosine_score\\n \\n # sort based on score, high to low\\n sorted_score = OrderedDict( sorted(score.items(), key=lambda t: t[1], reverse=True) )\\n \\n # type(top_k) == {doc_id: [score, \\\"doc_text\\\"]}\\n # note top_k is not sorted based on score!\\n top_k = {}\\n num_results = 0\\n for doc_id, score in sorted_score.items():\\n num_results += 1\\n top_k[doc_id] = [score, \\\"\\\"]\\n if num_results == k:\\n break\\n return top_k\",\n \"def scanStackOverFlow(userQuery):\\n\\n\\t# Search parameters\\n\\tkeywords = userQuery.split(\\\" \\\")\\n\\tprecisionScoreTreshold = 2\\n\\t\\n\\n\\t# Url Generation\\n\\tprefix = \\\"http://stackoverflow.com/questions/\\\"\\n\\tfor number in range(1, 99999):\\n\\t\\tgeneratedUrl = prefix + str(number) + \\\"/\\\"\\n\\n\\t\\t# Get title of the page\\n\\t\\tpage = requests.get(generatedUrl)\\n\\t\\ttree = html.fromstring(page.content)\\n\\t\\ttitle = tree.xpath('//title/text()')\\n\\t\\ttitleInArray = title[0].split('-')\\n\\n\\t\\t# Scan Subject\\n\\t\\tif titleInArray[0] != \\\"Page Not Found \\\":\\n\\t\\t\\tmainSubject = titleInArray[0]\\n\\t\\t\\tprecision = titleInArray[1]\\n\\t\\n\\t\\t\\tpassFirstCollection = 0\\n\\t\\t\\tfirstSelectionScore = 0\\n\\t\\t\\tfirstSelectionCount = 0\\n\\t\\t\\tmainSubjectSize = 0\\n\\t\\t\\tprint title[0]\\n\\t\\t\\tfor mesh in mainSubject.split(\\\" \\\"):\\n\\t\\t\\t\\tmainSubjectSize = mainSubjectSize + 1\\n\\t\\t\\t\\tfor query in keywords:\\n\\t\\t\\t\\t\\tif mesh == query:\\n\\t\\t\\t\\t\\t\\tfirstSelectionCount = firstSelectionCount + 1\\t\\n\\t\\t\\t\\t\\t\\tpassFirstCollection = 1\\n\\t\\n\\t\\t\\tfirstSelectionScore = (float(firstSelectionCount) / float(len(keywords)))*float(100)\\n\\t\\t\\t#print firstSelectionScore\\n\\n\\t\\t\\tif passFirstCollection == 1:\\n\\t\\t\\t\\tprecisionSize = 0\\n\\t\\t\\t\\tprecisionScore = 0\\n\\t\\t\\t\\tprecisionCount = 0\\t\\n\\t\\t\\t\\tfor subMesh in precision.split(\\\" \\\"):\\n\\t\\t\\t\\t\\tprecisionSize = precisionSize + 1\\n\\t\\t\\t\\t\\tfor query in keywords:\\n\\t\\t\\t\\t\\t\\tif subMesh == query:\\n\\t\\t\\t\\t\\t\\t\\tprecisionCount = precisionCount + 1\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tprecisionScore = (float(precisionCount) / float(len(keywords))) * float(100)\\n\\t\\t\\t\\tprint \\\"=> Match <=\\\"\\n\\t\\t\\t\\tprint precisionScore\\t\\t\\t\\t\\n\\n\\n\\treturn 0\",\n \"def explore_most_upvoted_search(request):\\n if request.method == \\\"POST\\\":\\n if \\\"token\\\" in request.data and request.data[\\\"token\\\"] != \\\"\\\" and request.data[\\\"token\\\"] is not None:\\n if Token.objects.filter(key=request.data[\\\"token\\\"]).exists():\\n token = get_object_or_404(Token, key=request.data[\\\"token\\\"])\\n start_offset = request.data[\\\"offset\\\"]\\n end_offset = start_offset + PAGE_OFFSET\\n comments = Comment.objects.filter(text__contains=request.data[\\\"keyword\\\"]). \\\\\\n order_by(\\\"-rate\\\")[start_offset:end_offset]\\n serializer = PostCommentSerializer(comments, context={'user_id': token.user_id}, many=True)\\n return Response({\\\"success\\\": 65,\\n \\\"comments\\\": serializer.data,\\n \\\"offset\\\": end_offset})\\n else:\\n return Response({\\\"error\\\": 17})\",\n \"def search(index_data, link_data, stop_word_list, search_string):\\n\\n topN = 5\\n\\n query_terms = sanitize(search_strings, stop_word_list)\\n print(query_terms)\\n\\n # get all links which contain all the query terms\\n links = get_links(query_terms)\\n print(\\\"\\\\nURLs containing all of the query terms (%d):\\\" % len(links))\\n for l in links:\\n print(l)\\n\\n # rank the links using Vector model\\n vector_ranked = rank_links(index_data, query_terms, links)\\n #print(ranked_list)\\n \\n # build a graph of the links\\n graph = build_graph(link_data, links)\\n\\n # rank the links using Vector model\\n page_ranked = calculate_pagerank_with_teleport(graph, EPSILON, 10) \\n \\n # return the data\\n return links, vector_ranked, page_ranked\",\n \"def textrank(sentences, top_n, stopwords=None):\\n S = build_similarity_matrix(sentences, stopwords) \\n sentence_ranking = page_rank(S)\\n \\n # Sort the sentence ranks\\n ranked_sentence_indexes = [item[0] for item in sorted(enumerate(sentence_ranking), key=lambda item: -item[1])]\\n selected_sentences = sorted(ranked_sentence_indexes[:top_n])\\n summary = itemgetter(*selected_sentences)(sentences)\\n return summary\",\n \"def parse_input():\\n if len(sys.argv) == 1 or len(sys.argv) > 3:\\n print(\\\"\\\"\\\"Usage: ./search.py \\\"query\\\" [k]\\nReturns the top k results of the search. The second argument is optional, by default k = 10.\\\"\\\"\\\")\\n sys.exit(1)\\n elif len(sys.argv) == 2:\\n query_string = sys.argv[1]\\n k = 10\\n else:\\n query_string = sys.argv[1]\\n k = int(sys.argv[2])\\n if k < 1 or k > 100000:\\n print(\\\"Error! k must be between 1 and 100000, setting k = 10\\\")\\n k = 10\\n return (query_string, k)\",\n \"def test_sort_fewer_than_n(self):\\n e1 = Experience(rid=1, uid=3, experience=100)\\n e2 = Experience(rid=1, uid=1, experience=89)\\n e3 = Experience(rid=1, uid=12, experience=1343)\\n db.session.add(e1)\\n db.session.add(e2)\\n db.session.add(e3)\\n db.session.commit()\\n list = top_n_in_order(1,5)\\n self.assertEqual([(12, 1343), (3, 100), (1, 89)], list)\",\n \"def query2():\\n\\n print(\\\"2. Who are the most popular article authors of all time?\\\\n\\\")\\n\\n query = \\\"\\\"\\\"\\n SELECT authors.name, subq_author.hits FROM authors\\n LEFT JOIN\\n (SELECT articles.author, CAST(SUM(subq_article.hits) AS INTEGER)\\n AS hits FROM articles\\n LEFT JOIN\\n (SELECT COUNT(log.path) AS hits, log.path FROM log\\n WHERE log.path LIKE '/article/%'\\n AND log.status = '200 OK' AND log.method = 'GET'\\n GROUP BY log.path) AS subq_article\\n ON subq_article.path LIKE '/article/'||articles.slug\\n GROUP BY articles.author) AS subq_author\\n ON authors.id = subq_author.author\\n ORDER BY subq_author.hits DESC;\\n \\\"\\\"\\\"\\n\\n response = db_query(query)\\n\\n for i, j in enumerate(response):\\n # Convert tuple to list to allow writing. Format \\\"hits\\\" with comma\\n # seperator. Print output.\\n j = list(j)\\n j[1] = str(format(j[1], ',d'))\\n print(\\\" Author: '{}' - {} views\\\".format(*j))\",\n \"def test_query_q(client, example_records, h, prefix):\\n # Test query (q=)\\n res = client.get(f'{prefix}?q=title.en:text', headers=h)\\n assert res.status_code == 200\\n assert res.json[\\\"hits\\\"][\\\"total\\\"] == 1\\n assert res.json['sortBy'] == 'bestmatch'\\n\\n # Test sort\\n res = client.get(f'{prefix}?q=*&sort=bestmatch', headers=h)\\n assert res.status_code == 200\\n assert res.json['sortBy'] == 'bestmatch'\\n\\n # Test size\\n res = client.get(f'{prefix}?size=1&page=1', headers=h)\\n assert res.status_code == 200\\n assert res.json['hits']['total'] == 2\\n assert len(res.json['hits']['hits']) == 1\\n assert 'next' in res.json['links']\",\n \"def query4(transaction, **params):\\n query = f'''\\n match $person isa person,\\n has age > {params['age_lower']}, has age < {params['age_upper']};\\n $country isa country, has name $country-name;\\n (contains-city: $country, in-country: $city) isa has-city;\\n (contains-residence: $city, in-city: $person) isa has-residence;\\n get; group $country-name; count;\\n '''\\n print(f\\\"\\\\nQuery 4:\\\\n {query}\\\")\\n iterator = transaction.query(query)\\n result = []\\n\\n for item in list(iterator): # Consume ResponseIterator into a list\\n counts = item.answers()[0].number()\\n country = item.owner().value()\\n result.append({'country': country, 'personCounts': counts})\\n\\n sorted_results = sorted(result, key=lambda x: x['personCounts'], reverse=True)\\n print(f\\\"3 Countries with the most people with age > {params['age_lower']} and < {params['age_upper']}: \\\\\\n \\\\n{sorted_results[:3]}\\\")\\n return sorted_results\",\n \"def pretty_qa(question, num=10):\\n print(\\\"\\\\nQ: \\\" + question)\\n for (j, (answer, score)) in enumerate(google_qa(question)[:num]):\\n print(\\\"%s. %s (%s)\\\" % (j + 1, answer, score))\",\n \"def test_sort_more_than_n(self):\\n e1 = Experience(rid=1, uid=3, experience=100)\\n e2 = Experience(rid=1, uid=1, experience=89)\\n e3 = Experience(rid=1, uid=12, experience=1343)\\n e4 = Experience(rid=1, uid=22, experience=1839)\\n e5 = Experience(rid=1, uid=2, experience=20)\\n db.session.add(e1)\\n db.session.add(e2)\\n db.session.add(e3)\\n db.session.add(e4)\\n db.session.add(e5)\\n db.session.commit()\\n list = top_n_in_order(1, 3)\\n self.assertEqual([(22, 1839), (12, 1343), (3, 100)], list)\",\n \"def test_query_algorithms_1queryfrom200Algorithms(self):\\n query_string = 'displayName102'\\n upload_data_list = []\\n create_test_algorithm_list(upload_data_list, 200)\\n documents = []\\n create_test_documents_list(upload_data_list, documents, 200)\\n index = search.Index(name=search_algorithm._INDEX_STRING)\\n index.put(documents)\\n # end of data preparation\\n number = query_string.split('displayName')[1]\\n data = {}\\n data['algorithmId'] = 'algorithmId' + number\\n data['algorithmSummary'] = 'algorithmSummary' + number\\n data['displayName'] = 'displayName' + number\\n data['linkURL'] = 'linkURL' + number\\n right_answer_list= [data]\\n # end right answer list preparation\\n result = search_algorithm.query_algorithms(index, query_string)\\n self.assertEqual(1, len(result), msg='Wrong number of algorithms')\\n self.assertItemsEqual(right_answer_list, result, msg='Discrepancy in returned algorithms')\",\n \"def process_question(qu):\\n\\n ## global ranking\\n rank_info = {}\\n rank_info_k = [\\\"viewcount\\\",\\\"score\\\",\\\"favoritecount\\\"]\\n for k in rank_info_k:\\n rank_info[k] = int(qu[k])\\n qu.pop(k,None)\\n\\n rank_info[\\\"creationdate\\\"] = qu[\\\"creationdate\\\"]\\n\\n if qu[\\\"acceptedanswer\\\"]:\\n qu[\\\"acceptedanswer\\\"] = list(qu[\\\"acceptedanswer\\\"])\\n else:\\n qu[\\\"acceptedanswer\\\"] = []\\n\\n qu.pop('comments',None) # discard comments, maybe add back later\\n qu[\\\"rank_info\\\"] = rank_info\\n\\n return qu\",\n \"async def qa_infer(query: dict, response: Response) -> dict:\\n logger.debug(\\\"QUESTION ANSWER - predicting query: \\\" + str(query[\\\"query\\\"]))\\n results = {}\\n\\n try:\\n query_text = query[\\\"query\\\"]\\n query_context = query[\\\"search_context\\\"]\\n start = time.perf_counter()\\n answers = MODELS.qa_model.answer(query_text, query_context)\\n end = time.perf_counter()\\n logger.info(answers)\\n logger.info(f\\\"time: {end - start:0.4f} seconds\\\")\\n results[\\\"answers\\\"] = answers\\n results[\\\"question\\\"] = query_text\\n\\n except Exception:\\n logger.error(f\\\"Unable to get results from QA model for {query}\\\")\\n response.status_code = status.HTTP_500_INTERNAL_SERVER_ERROR\\n raise\\n return results\",\n \"def get_answers():\\n count = 1\\n for i in range(200): # TODO : Fetch number of all items first\\n r = requests.get('http://api.stackexchange.com/2.2/answers?site=eosio&filter=!b1MMEb*6iF.PM5&pagesize=100&page={}'.format(count))\\n data = json.loads(r.text)\\n for item in data['items']:\\n own = item['owner']['user_id']\\n dsp = item['owner']['display_name']\\n qn_id = item['question_id']\\n try:\\n owner = User.objects.get(username=own, se_display_name=dsp)\\n question = Question.objects.get(se_question_id=qn_id)\\n except Exception:\\n owner = None\\n question = None\\n if owner and question:\\n Answer.objects.create(owner=owner, question=question, body=item['body'],\\n se_question_id=qn_id, is_accepted=item['is_accepted'],\\n se_answer_id=item['answer_id'], score=item['score'])\\n\\n count += 1\\n print(count)\",\n \"def test_top_n_counts():\\n ngrams = NgramFrequencies()\\n new_dic = {\\n \\\"a\\\": 1,\\n \\\"b\\\": 2,\\n \\\"c\\\": 3,\\n \\\"d\\\": 4\\n }\\n top_list = ngrams.top_n_counts(new_dic)\\n assert top_list == [(\\\"d\\\", 4), (\\\"c\\\", 3), (\\\"b\\\", 2), (\\\"a\\\", 1)]\",\n \"def suggestions(prefix : str = typer.Argument(...), n : int = typer.Option(5, help=\\\"Number of suggestions to display\\\")): \\n response_url = url + \\\"/suggestions/\\\" + prefix + \\\"?suggestion_nums=\\\" + str(n) \\n response = requests.get(response_url) \\n for i in range(len(response.json())):\\n typer.echo(response.json()[i])\",\n \"def get_similar_scores_to_query(query, sentences):\\n\\n # Stores a list of tuples of:\\n # (normalized score, sentence from sentences)\\n out = []\\n\\n # Get the min and max score in sentences\\n min_score = float('inf')\\n max_score = -float('inf')\\n\\n for s in sentences:\\n score = get_similar_score(query, s)\\n\\n if score < min_score:\\n min_score = score\\n if score > max_score:\\n max_score = score\\n\\n out.append((score, s))\\n\\n # Normalize all scores\\n for i in range(len(out)):\\n if max_score - min_score == 0.0:\\n norm = 0.0\\n else:\\n norm = (out[i][0] - min_score) / (max_score - min_score)\\n out[i] = (norm, out[i][1])\\n\\n out = sorted(out, key=lambda tup: tup[0], reverse=True)\\n\\n print \\\"Query: \\\", query\\n print \\\"Min similarity: \\\", min_score\\n print \\\"Max similarity: \\\", max_score, \\\"\\\\n\\\"\\n for o in out:\\n print o\\n\\n # Return list sorted by score in reverse order\\n return out\",\n \"def test_most_similar(self):\\n expected = [\\n 'canine.n.02',\\n 'hunting_dog.n.01',\\n 'carnivore.n.01',\\n 'placental.n.01',\\n 'mammal.n.01'\\n ]\\n predicted = [result[0] for result in self.vectors.most_similar('dog.n.01', topn=5)]\\n self.assertEqual(expected, predicted)\",\n \"def query_and_fetch(query, top_n=12):\\n global url_details, url_text\\n print('Query: ' + query + '; Top N: ' + str(top_n))\\n url_details = []\\n url_text = []\\n driver = None\\n bad_request = False\\n try:\\n driver = Fetcher.get_selenium_driver()\\n driver.get('https://api.duckduckgo.com/?q=' + query + '&kl=wt-wt')\\n except:\\n print('An error occurred while searching query: ' + query)\\n Fetcher.close_selenium_driver(driver)\\n Fetcher.search_driver = None\\n bad_request = True\\n finally:\\n try:\\n if not bad_request:\\n results = driver.find_elements_by_class_name('result__a')\\n result_size = len(results)\\n print('Result Size: ' + str(result_size))\\n while result_size > 0 and len(url_details) < top_n:\\n urls = []\\n for element in results:\\n new_url = element.get_attribute('href')\\n # TODO: Filter URLs if required\\n print(new_url)\\n urls.append(new_url)\\n\\n fetched_result = Fetcher.fetch_multiple(urls, top_n)\\n\\n for fetched_data in fetched_result:\\n if not fetched_data[1] or len(fetched_data[1].strip()) == 0:\\n continue\\n details = dict()\\n details['url'] = fetched_data[0]\\n details['html'] = fetched_data[1]\\n details['title'] = fetched_data[2]\\n details['label'] = predict(fetched_data[3])\\n url_details.append(details)\\n url_text.append(fetched_data[3])\\n if len(url_details) == top_n:\\n break\\n\\n # Infinite Scroll\\n if len(url_details) < top_n:\\n driver.execute_script('window.scrollTo(0, document.body.scrollHeight);')\\n results = driver.find_elements_by_class_name('result__a')\\n results = results[result_size:]\\n result_size = len(results)\\n print('Moved to Next Page. Result Size: ' + str(result_size))\\n except:\\n print('An error occurred while searching query: '+ query + ' and fetching results')\\n #finally:\\n # if driver is not None:\\n # Fetcher.close_selenium_driver(driver)\\n setattr(flask.current_app, 'url_text', url_text)\\n print('Search Completed')\\n return url_details\",\n \"def get_query(self, q, request):\\r\\n \\r\\n return self.model.objects.filter(filter__icontains=q).order_by('filter')[:50]\",\n \"def _get_nearest(index, query, n, includes, formats) -> Response:\\n embedding = model.embed(query)\\n knn_query = {\\n \\\"field\\\": \\\"embedding\\\",\\n \\\"query_vector\\\": embedding,\\n \\\"k\\\": 1000,\\n \\\"num_candidates\\\": 1000,\\n }\\n if formats:\\n knn_query[\\\"filter\\\"] = {\\\"terms\\\": {\\\"format\\\": formats}}\\n\\n response = target_es.search(\\n index=index,\\n knn=knn_query,\\n collapse={\\\"field\\\": \\\"id\\\"},\\n _source=default_includes + includes,\\n size=n,\\n )\\n return {\\n \\\"results\\\": {\\n hit[\\\"_id\\\"]: {**hit[\\\"_source\\\"], \\\"score\\\": hit[\\\"_score\\\"]}\\n for hit in response[\\\"hits\\\"][\\\"hits\\\"]\\n },\\n \\\"total\\\": response[\\\"hits\\\"][\\\"total\\\"][\\\"value\\\"],\\n \\\"took\\\": response[\\\"took\\\"],\\n }\",\n \"def most_similar(self, article: str, topn: int = 5):\\n return [article[0] for article in self._model.similar_by_word(article, topn)]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6818519","0.6672054","0.66686153","0.6436746","0.6367996","0.63375914","0.63115406","0.6299758","0.6281875","0.6159052","0.61510074","0.61417896","0.6108233","0.61047775","0.6080384","0.60795337","0.60682553","0.60677445","0.60321194","0.60313493","0.60202634","0.5943792","0.59282786","0.5883638","0.58640796","0.58459437","0.58222127","0.5821715","0.58208704","0.5819854","0.58131915","0.58003765","0.5789463","0.57717574","0.5769974","0.5768074","0.57628274","0.5747921","0.5744626","0.5728144","0.5718997","0.5704514","0.56969684","0.5694169","0.5690981","0.5686057","0.56854177","0.56714","0.5656389","0.5647784","0.56376094","0.5631387","0.56034094","0.5603231","0.5562889","0.55566","0.55554795","0.55544275","0.55458975","0.55384076","0.5528554","0.550417","0.5492828","0.5475524","0.54708","0.54642636","0.544654","0.5434264","0.5413897","0.5409339","0.5409339","0.5409339","0.5397313","0.5386827","0.5386442","0.5382618","0.5382408","0.53807557","0.5377938","0.5360049","0.5359527","0.5358896","0.53565484","0.5351425","0.53506243","0.534295","0.5340915","0.53312486","0.5329707","0.5329024","0.53283936","0.53237087","0.53064364","0.52990496","0.5298626","0.5293841","0.52828914","0.5282359","0.5279228","0.52711827"],"string":"[\n \"0.6818519\",\n \"0.6672054\",\n \"0.66686153\",\n \"0.6436746\",\n \"0.6367996\",\n \"0.63375914\",\n \"0.63115406\",\n \"0.6299758\",\n \"0.6281875\",\n \"0.6159052\",\n \"0.61510074\",\n \"0.61417896\",\n \"0.6108233\",\n \"0.61047775\",\n \"0.6080384\",\n \"0.60795337\",\n \"0.60682553\",\n \"0.60677445\",\n \"0.60321194\",\n \"0.60313493\",\n \"0.60202634\",\n \"0.5943792\",\n \"0.59282786\",\n \"0.5883638\",\n \"0.58640796\",\n \"0.58459437\",\n \"0.58222127\",\n \"0.5821715\",\n \"0.58208704\",\n \"0.5819854\",\n \"0.58131915\",\n \"0.58003765\",\n \"0.5789463\",\n \"0.57717574\",\n \"0.5769974\",\n \"0.5768074\",\n \"0.57628274\",\n \"0.5747921\",\n \"0.5744626\",\n \"0.5728144\",\n \"0.5718997\",\n \"0.5704514\",\n \"0.56969684\",\n \"0.5694169\",\n \"0.5690981\",\n \"0.5686057\",\n \"0.56854177\",\n \"0.56714\",\n \"0.5656389\",\n \"0.5647784\",\n \"0.56376094\",\n \"0.5631387\",\n \"0.56034094\",\n \"0.5603231\",\n \"0.5562889\",\n \"0.55566\",\n \"0.55554795\",\n \"0.55544275\",\n \"0.55458975\",\n \"0.55384076\",\n \"0.5528554\",\n \"0.550417\",\n \"0.5492828\",\n \"0.5475524\",\n \"0.54708\",\n \"0.54642636\",\n \"0.544654\",\n \"0.5434264\",\n \"0.5413897\",\n \"0.5409339\",\n \"0.5409339\",\n \"0.5409339\",\n \"0.5397313\",\n \"0.5386827\",\n \"0.5386442\",\n \"0.5382618\",\n \"0.5382408\",\n \"0.53807557\",\n \"0.5377938\",\n \"0.5360049\",\n \"0.5359527\",\n \"0.5358896\",\n \"0.53565484\",\n \"0.5351425\",\n \"0.53506243\",\n \"0.534295\",\n \"0.5340915\",\n \"0.53312486\",\n \"0.5329707\",\n \"0.5329024\",\n \"0.53283936\",\n \"0.53237087\",\n \"0.53064364\",\n \"0.52990496\",\n \"0.5298626\",\n \"0.5293841\",\n \"0.52828914\",\n \"0.5282359\",\n \"0.5279228\",\n \"0.52711827\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54849684"},"document_rank":{"kind":"string","value":"63"}}},{"rowIdx":9996,"cells":{"query":{"kind":"string","value":"Get most likely categorical variables (onehot)"},"document":{"kind":"string","value":"def get_most_likely(self):\n c_cat = self.theta.argmax(axis=1)\n T = np.zeros((self.d, self.max))\n for i, c in enumerate(c_cat):\n T[i, c] = 1\n return T"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def unhot(one_hot_labels):\n return np.argmax(one_hot_labels, axis=1)","def infer_categorical_variables_in_place(df: pd.DataFrame):\n # infer which variables are categorical\n MAX_UNIQUE_VALUES = 10\n for column in df.columns:\n if df[column].nunique() <= MAX_UNIQUE_VALUES:\n df[column] = df[column].astype('category')","def from_one_hot_to_category_indices(data: ndarray):\n return np.argmax(data, axis=-1)","def one_hot(y_):\n y_ = y_.reshape(len(y_))\n n_values = int(np.max(y_)) + 1\n n_values = 6\n return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS","def one_hot(y, num_classes):\n return np.eye(num_classes)[y]","def one_hot_categorical_mode(logits):\n dist = tfd.Categorical(logits)\n return tf.cast(tf.one_hot(dist.mode(), dist.event_size), tf.float32)","def onehot_argmax(logits):\n return T.extra_ops.to_one_hot(T.argmax(logits,-1),logits.shape[-1])","def one_hot_decode(one_hot):\n if type(one_hot) is not np.ndarray or len(one_hot.shape) != 2:\n return None\n elif not np.where((one_hot == 0) | (one_hot == 1), True, False).all():\n return None\n elif np.sum(one_hot) != len(one_hot[0]):\n return None\n else:\n return np.argmax(one_hot, axis=0)","def from_onehot(onehot, dim=-1, dtype=None):\n indexes = torch.argmax(onehot, dim=dim)\n if dtype is not None:\n indexes = indexes.type(dtype)\n return indexes","def one_hot(x, dtype=np.float32):\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)","def one_hot(labels, classes=None):\n\n one_hot_ = K.utils.to_categorical(labels, classes)\n return(one_hot_)","def get_categorical(X):\n return list(X.columns[X.dtypes == \"object\"])","def decode_one_hot(x):\n s = []\n for onehot in x:\n one_index = np.argmax(onehot)\n c = indices_char[one_index]\n s.append(c) \n return ''.join(s)","def one_hot(input_df, columns):\n df = input_df.copy()\n\n for col in columns:\n dummies = pd.get_dummies(df[col].str.lower())\n dummies.drop(dummies.columns[-1], axis=1, inplace=True)\n df = df.drop(col, axis=1).merge(dummies, left_index=True, right_index=True)\n \n return df","def _onehot(y, n_classes=False):\n if not n_classes:\n \"\"\"Create one-hot encoded labels.\"\"\"\n n_classes = len(set(y))\n out = np.zeros((len(y), n_classes))\n for i, ii in enumerate(y):\n out[i][ii] += 1\n y_onehot = out.astype(int)\n return y_onehot","def one_hot(labels, classes=None):\n return K.utils.to_categorical(labels, classes)","def _one_hot(self, key):\n train_df = self.train.copy(deep=False)\n one_hot = pd.get_dummies(train_df[key],drop_first=True)\n one_hot.columns=[key+'_'+col for col in one_hot.columns.values]\n train_df = pd.concat([train_df,one_hot],axis=1,sort=True)\n train_dfg = train_df.groupby('fullVisitorId')\n return train_dfg[one_hot.columns.values].sum()","def get_type_by_onehot_vector(onehot_vector):\n return one_hot_encoder.inverse_transform([onehot_vector])[0][0]","def one_hot(df, cols):\n for col in cols:\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\n df = pd.concat([df, dummies], axis=1)\n df = df.drop(col, axis=1)\n return df","def make_onehot(x,num_labels=7):\n enc = OneHotEncoder(n_values=num_labels)\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()","def categorical(df):\r\n categorical_var=df.select_dtypes(include ='object').columns.tolist()\r\n\r\n return categorical_var","def to_categorical(x, n_col=None):\n if not n_col:\n n_col = np.amax(x) + 1\n one_hot = np.zeros((x.shape[0], n_col))\n one_hot[np.arange(x.shape[0]), x] = 1\n return one_hot","def find_categorical(self, df):\n# print(type(df),df.ndim)\n categorical = [key for key in df.keys() if df.dtypes[key] == np.dtype('O')]\n numeric = [key for key in df.keys() if df.dtypes[key] != np.dtype('O')]\n # correct naive expectations\n actual_categoric = ['MSSubClass']\n numeric = list(set(numeric) - set(actual_categoric))\n categorical = list(set(categorical).union(set(actual_categoric)))\n return categorical","def find_categorical(self, df):\n# print(type(df),df.ndim)\n categorical = [key for key in df.keys() if df.dtypes[key] == np.dtype('O')]\n numeric = [key for key in df.keys() if df.dtypes[key] != np.dtype('O')]\n # correct naive expectations\n actual_categoric = ['MSSubClass']\n numeric = list(set(numeric) - set(actual_categoric))\n categorical = list(set(categorical).union(set(actual_categoric)))\n return categorical","def accuracy_onehot(labels, preds):\n\tassert labels.shape[0]==preds.shape[0]\n\treturn np.sum(np.argmax(preds, axis=1) == np.argmax(labels, axis=1))/float(labels.shape[0])","def dummify_all_categorical(df):\n\n df = pd.get_dummies(df)\n df = dummify(df, \"detailed industry recode\")\n df = dummify(df, \"detailed occupation recode\") ## add some variables that are encoded as int64 but that are in fact categorical\n return df","def _get_one_hot(targets, num_classes):\n ret = np.zeros((num_classes, targets.shape[0]))\n ret[targets, np.arange(targets.size)] = 1\n return ret","def one_hot(seq):\n prot_seq = seq\n BASES = 'ARNDCQEGHILKMFPSTWYV'\n bases = np.array([base for base in BASES])\n feat = np.concatenate(\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\n in prot_seq])\n return feat","def max_arg_one_hot(arr: np.ndarray) -> np.ndarray:\n # using argmin because inverted indices in binary_to_one_hot\n return binary_to_one_hot(np.argmin(arr, axis=1))","def one_hot(x, num_classes, dtype=jnp.float32):\n return jax.nn.one_hot(x, num_classes).astype(dtype)","def one_hot_encode(df, ohe_cols):\n return pd.get_dummies(df, columns=ohe_cols)","def one_hot(df):\r\n # One-hot encode into \r\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\r\n for each in cols:\r\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\r\n df = pd.concat([df, dummies], axis=1)\r\n df = df.drop(cols,axis=1)\r\n return df","def one_hot(class_ids, num_classes):\n oh = np.zeros((len(class_ids), num_classes), dtype=np.float32)\n oh[np.arange(len(class_ids)), class_ids] = 1\n\n assert (oh.argmax(axis=1) == class_ids).all()\n assert (oh.sum(axis=1) == 1).all()\n\n return oh","def indices_to_one_hot(data, nb_classes):\n\ttargets = np.array(data).reshape(-1)\n\treturn np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def indices_to_one_hot(data, nb_classes):\n targets = np.array(data).reshape(-1)\n return np.eye(nb_classes)[targets]","def to_onehot(x, num_classes):\n return np.eye(num_classes, dtype='float32')[x]","def dense_to_one_hot(labels_dense, num_classes):\r\n print ('in onehot', labels_dense, num_classes)\r\n num_labels = labels_dense.shape[0]\r\n index_offset = numpy.arange(num_labels) * num_classes\r\n labels_one_hot = numpy.zeros((num_labels, num_classes))\r\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\r\n return labels_one_hot","def classify_columns(df_preds, verbose=0):\r\n train = copy.deepcopy(df_preds)\r\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\r\n max_nlp_char_size = 30\r\n max_cols_to_print = 30\r\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\r\n print('Classifying variables in data set...')\r\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\r\n cat_limit = 35\r\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\r\n def add(a,b):\r\n return a+b\r\n sum_all_cols = dict()\r\n orig_cols_total = train.shape[1]\r\n #Types of columns\r\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\r\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\r\n train = train[left_subtract(list(train),cols_delete)]\r\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\r\n columns={0:'type_of_column'})\r\n sum_all_cols['cols_delete'] = cols_delete\r\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\r\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\r\n sum_all_cols['string_bool_vars'] = string_bool_vars\r\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16','int32','int64',\r\n 'float16','float32','float64'] and len(\r\n train[x['index']].value_counts()) == 2 else 0, axis=1)\r\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\r\n sum_all_cols['num_bool_vars'] = num_bool_vars\r\n ###### This is where we take all Object vars and split them into diff kinds ###\r\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\r\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\r\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\r\n var_df['nlp_strings'] = 0\r\n var_df['discrete_strings'] = 0\r\n var_df['cat'] = 0\r\n var_df['id_col'] = 0\r\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\r\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\r\n for col in discrete_or_nlp_vars:\r\n #### first fill empty or missing vals since it will blowup ###\r\n train[col] = train[col].fillna(' ')\r\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\r\n ) >= max_nlp_char_size and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\r\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\r\n ) == len(train) and col not in string_bool_vars:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\r\n sum_all_cols['nlp_vars'] = nlp_vars\r\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\r\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\r\n ###### This happens only if a string column happens to be an ID column #######\r\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\r\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\r\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\r\n axis=1)\r\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\r\n sum_all_cols['factor_vars'] = factor_vars\r\n ########################################################################\r\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\r\n np.uint16, np.uint32, np.uint64,\r\n 'int8','int16',\r\n 'int32','int64'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ######### This is where we figure out whether a numeric col is date or id variable ###\r\n var_df['int'] = 0\r\n var_df['date_time'] = 0\r\n ### if a particular column is date-time type, now set it as a date time variable ##\r\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\r\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\r\n axis=1)\r\n ### this is where we save them as date time variables ###\r\n if len(var_df.loc[date_or_id==1]) != 0:\r\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\r\n if len(train[col].value_counts()) == len(train):\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n var_df.loc[var_df['index']==col,'id_col'] = 1\r\n else:\r\n if train[col].min() < 1900 or train[col].max() > 2050:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n try:\r\n pd.to_datetime(train[col],infer_datetime_format=True)\r\n var_df.loc[var_df['index']==col,'date_time'] = 1\r\n except:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'int'] = 1\r\n else:\r\n pass\r\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\r\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\r\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\r\n sum_all_cols['int_vars'] = int_vars\r\n copy_date_vars = copy.deepcopy(date_vars)\r\n for date_var in copy_date_vars:\r\n #### This test is to make sure sure date vars are actually date vars\r\n try:\r\n pd.to_datetime(train[date_var],infer_datetime_format=True)\r\n except:\r\n ##### if not a date var, then just add it to delete it from processing\r\n cols_delete.append(date_var)\r\n date_vars.remove(date_var)\r\n sum_all_cols['date_vars'] = date_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n sum_all_cols['cols_delete'] = cols_delete\r\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\r\n var_df['numeric'] = 0\r\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\r\n 'float32','float64'] else 0,\r\n axis=1)\r\n if len(var_df.loc[float_or_cat == 1]) > 0:\r\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\r\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\r\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\r\n var_df.loc[var_df['index']==col,'cat'] = 1\r\n else:\r\n if col not in num_bool_vars:\r\n var_df.loc[var_df['index']==col,'numeric'] = 1\r\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\r\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\r\n ######## V E R Y I M P O R T A N T ###################################################\r\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\r\n cat_vars_copy = copy.deepcopy(cat_vars)\r\n for cat in cat_vars_copy:\r\n if df_preds[cat].dtype==float:\r\n continuous_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'numeric'] = 1\r\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\r\n id_vars.append(cat)\r\n cat_vars.remove(cat)\r\n var_df.loc[var_df['index']==cat,'cat'] = 0\r\n var_df.loc[var_df['index']==cat,'id_col'] = 1\r\n sum_all_cols['cat_vars'] = cat_vars\r\n sum_all_cols['continuous_vars'] = continuous_vars\r\n sum_all_cols['id_vars'] = id_vars\r\n ###### This is where you consoldate the numbers ###########\r\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\r\n for col, sumval in var_dict_sum.items():\r\n if sumval == 0:\r\n print('%s of type=%s is not classified' %(col,train[col].dtype))\r\n elif sumval > 1:\r\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\r\n else:\r\n pass\r\n ############### This is where you print all the types of variables ##############\r\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\r\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\r\n if verbose == 1:\r\n print(\" Number of Numeric Columns = \", len(continuous_vars))\r\n print(\" Number of Integer-Categorical Columns = \", len(int_vars))\r\n print(\" Number of String-Categorical Columns = \", len(cat_vars))\r\n print(\" Number of Factor-Categorical Columns = \", len(factor_vars))\r\n print(\" Number of String-Boolean Columns = \", len(string_bool_vars))\r\n print(\" Number of Numeric-Boolean Columns = \", len(num_bool_vars))\r\n print(\" Number of Discrete String Columns = \", len(discrete_string_vars))\r\n print(\" Number of NLP String Columns = \", len(nlp_vars))\r\n print(\" Number of Date Time Columns = \", len(date_vars))\r\n print(\" Number of ID Columns = \", len(id_vars))\r\n print(\" Number of Columns to Delete = \", len(cols_delete))\r\n if verbose == 2:\r\n marthas_columns(df_preds,verbose=1)\r\n print(\" Numeric Columns: %s\" %continuous_vars[:max_cols_to_print])\r\n print(\" Integer-Categorical Columns: %s\" %int_vars[:max_cols_to_print])\r\n print(\" String-Categorical Columns: %s\" %cat_vars[:max_cols_to_print])\r\n print(\" Factor-Categorical Columns: %s\" %factor_vars[:max_cols_to_print])\r\n print(\" String-Boolean Columns: %s\" %string_bool_vars[:max_cols_to_print])\r\n print(\" Numeric-Boolean Columns: %s\" %num_bool_vars[:max_cols_to_print])\r\n print(\" Discrete String Columns: %s\" %discrete_string_vars[:max_cols_to_print])\r\n print(\" NLP text Columns: %s\" %nlp_vars[:max_cols_to_print])\r\n print(\" Date Time Columns: %s\" %date_vars[:max_cols_to_print])\r\n print(\" ID Columns: %s\" %id_vars[:max_cols_to_print])\r\n print(\" Columns that will not be considered in modeling: %s\" %cols_delete[:max_cols_to_print])\r\n ##### now collect all the column types and column names into a single dictionary to return!\r\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\r\n if len_sum_all_cols == orig_cols_total:\r\n print(' %d Predictors classified...' %orig_cols_total)\r\n #print(' This does not include the Target column(s)')\r\n else:\r\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\r\n len_sum_all_cols, orig_cols_total))\r\n ls = sum_all_cols.values()\r\n flat_list = [item for sublist in ls for item in sublist]\r\n if len(left_subtract(list(train),flat_list)) == 0:\r\n print(' Missing columns = None')\r\n else:\r\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\r\n return sum_all_cols","def get_categorical_features(self):\n return self.categorical_features","def _get_one_hot_array(num_left_cards, max_num_cards):\n one_hot = np.zeros(max_num_cards)\n if num_left_cards > 0:\n one_hot[num_left_cards - 1] = 1\n\n return one_hot","def one_hot_encode(self, meta_field):\n one_hot = pd.get_dummies(self.sample_meta[meta_field]).values\n return one_hot","def accuracy_onehot(y_pred, y_label):\n return (y_pred.argmax(dim=1) == y_label).float().mean().item()","def process_categorical_data(data_df):\n return pd.get_dummies(data_df, columns=Columns.categorical)","def to_categorical(target: np.ndarray, n_classes: int = None) -> np.ndarray:\n\tn_classes = n_classes if n_classes is not None else np.max(target) + 1\n\tbatch_size = target.shape[0]\n\tone_hot = np.zeros((batch_size, n_classes))\n\tone_hot[np.arange(batch_size), target] = 1\n\treturn one_hot","def test_one_hot_output_soft_maximum(self):\n vocab_data = [\"earth\", \"wind\", \"and\", \"fire\"]\n input_array = np.array([\"earth\", \"wind\", \"and\", \"fire\", \"michigan\", \"\"])\n expected_output = [\n [0, 1, 0, 0, 0],\n [0, 0, 1, 0, 0],\n [0, 0, 0, 1, 0],\n [0, 0, 0, 0, 1],\n [1, 0, 0, 0, 0],\n [0, 0, 0, 0, 0],\n ]\n\n input_data = keras.Input(shape=(1,), dtype=tf.string)\n layer = index_lookup.IndexLookup(\n max_tokens=None,\n num_oov_indices=1,\n mask_token=\"\",\n oov_token=\"[OOV]\",\n output_mode=index_lookup.ONE_HOT,\n vocabulary_dtype=tf.string,\n )\n layer.set_vocabulary(vocab_data)\n binary_data = layer(input_data)\n model = keras.Model(inputs=input_data, outputs=binary_data)\n output_dataset = model.predict(input_array)\n self.assertAllEqual(expected_output, output_dataset)","def test_one_hot_output_hard_maximum(self):\n vocab_data = [\"earth\", \"wind\", \"and\", \"fire\"]\n input_array = np.array([\"earth\", \"wind\", \"and\", \"fire\", \"michigan\", \"\"])\n expected_output = [\n [0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0],\n [0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0],\n [1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0],\n ]\n\n input_data = keras.Input(shape=(1,), dtype=tf.string)\n layer = index_lookup.IndexLookup(\n max_tokens=6,\n num_oov_indices=1,\n mask_token=\"\",\n oov_token=\"[OOV]\",\n output_mode=index_lookup.ONE_HOT,\n pad_to_max_tokens=True,\n vocabulary_dtype=tf.string,\n )\n layer.set_vocabulary(vocab_data)\n binary_data = layer(input_data)\n model = keras.Model(inputs=input_data, outputs=binary_data)\n output_dataset = model.predict(input_array)\n self.assertAllEqual(expected_output, output_dataset)","def one_hot_encode(df, col):\n return pd.get_dummies(df, columns=[col], drop_first=True)","def categorical(df):\n\n # variables which need to be transformed to categorical\n categorical = [\"prop_country_id\", \"visitor_location_country_id\"]\n\n for var in categorical:\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\n del df[var]\n\n return df","def one_hot_encoder(df, nan_as_category=True):\n original_columns = list(df.columns)\n categorical_columns = [col for col in df.columns if\n df[col].dtype == 'object']\n df = pd.get_dummies(df, columns=categorical_columns,\n dummy_na=nan_as_category)\n new_columns = [c for c in df.columns if c not in original_columns]\n return df, new_columns","def dense_to_one_hot(labels, n_classes=2):\n labels = np.array(labels)\n n_labels = labels.shape[0]\n index_offset = np.arange(n_labels) * n_classes\n labels_one_hot = np.zeros((n_labels, n_classes), dtype=np.float32)\n labels_one_hot.flat[index_offset + labels.ravel()] = 1\n return labels_one_hot","def one_hot_encoding(data):\r\n encoder = LabelEncoder()\r\n y = encoder.fit_transform(data)\r\n return(y)","def one_hot(labels):\n one_hot_labels = np.zeros(labels.shape + (n_actions,))\n for c in range(n_actions):\n one_hot_labels[labels == c, c] = 1.0\n return one_hot_labels","def mode(y):\n array = pd.DataFrame(np.array(y))\n mode = array.mode(axis=0).loc[0, :]\n count = array.apply(lambda x: x.value_counts().max())\n return mode.values, count.values","def one_hot_encoding(data):\n\n data_encoded = pd.get_dummies(data)\n\n return data_encoded","def ensemble(all_predict, size):\n mode_pred = np.zeros(shape=(size,1))\n for i in range(np.shape(all_predict)[1]):\n pred= mode(all_predict[:,i])\n # break ties randomly\n if pred[1] == 1:\n pred_val = random.randrange(2)\n else:\n pred_val = pred[0]\n mode_pred[i,0] = pred_val\n # return most common prediction\n return mode_pred","def to_onehot(labels: torch.Tensor, num_classes: int) -> torch.Tensor:\n if len(labels.size()) == 1:\n return F.one_hot(labels, num_classes).float()\n return labels","def indices_to_one_hot(cls_indeces, nb_classes):\n targets = np.array(cls_indeces).reshape(-1)\n return np.eye(nb_classes)[targets]","def one_hot_encoding(y):\n\n y_oh = np.zeros((y.shape[0], y.max() - y.min() + 1))\n\n # currently only works in min is actually 0\n for j in range(0, y_oh.shape[1]):\n y_oh[np.where(y == j), j] = 1\n\n return y_oh","def one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def dense_to_one_hot(labels_dense, num_classes=10):\n num_labels = labels_dense.shape[0]\n #print('check num_labels',num_labels)\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def to_one_hot_encoding(target_data):\n target_data = target_data.squeeze()\n n_class = len(np.unique(target_data))\n res = np.eye(n_class)[target_data.astype(int)]\n return res","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def get_one_hot_vector(number):\n from sklearn.preprocessing import LabelBinarizer\n encoder = LabelBinarizer()\n seed_vector = np.arange(10)\n encoder.fit(seed_vector)\n labels = encoder.transform(seed_vector)\n return labels.astype(np.float32)","def categorical_accuracy(preds, y):\n max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability 여기서 argmax를 찾는다\n correct = max_preds.squeeze(1).eq(y) # 같은것만 찾는 코드\n return correct.sum() / torch.FloatTensor([y.shape[0]])","def one_hot(Y, n_c):\n Y_onehot = np.zeros((Y.size, n_c))\n Y_onehot[np.arange(Y.size), Y] = 1\n return Y_onehot.T","def one_hot_enc(df, ohe_cols):\n for col in ohe_cols:\n df_ohe = pd.get_dummies(df[col], preffix=col)\n df = pd.concat([df, df_ohe], axis=1)\n df.drop(col, axis=1, inplace=True)\n return df","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def categorical_accuracy(preds, y):\n top_pred = preds.argmax(1, keepdim = True)\n correct = top_pred.eq(y.view_as(top_pred)).sum()\n acc = correct.float() / y.shape[0]\n return acc","def onehot_encode_labels(y):\n\treturn OneHotEncoder(categories=\"auto\", sparse=False).fit_transform(y.reshape(y.shape[0],1))","def conv_y_to_onehot_mat(labels):\n one_idx = np.array(labels)\n nkind = len(np.unique(one_idx))\n nlabels = len(one_idx)\n\n ret = np.zeros((nkind, nlabels))\n ret[one_idx, np.arange(nlabels)] = 1\n return ret","def categorical_accuracy(preds, y):\n max_preds = preds.argmax(\n dim=1, keepdim=True\n ) # get the index of the max probability\n correct = max_preds.squeeze(1).eq(y)\n return correct.sum() / torch.FloatTensor([y.shape[0]]).to(device)","def _mode(x, def_fill=ImputerMixin._def_fill):\n idx = x.as_data_frame(use_pandas=True)[x.columns[0]].value_counts().index\n\n # if the most common is null, then return the next most common.\n # if there is no next common (i.e., 100% null) then we return the def_fill\n return idx[0] if not pd.isnull(idx[0]) else idx[1] if idx.shape[0] > 1 else def_fill","def _maybe_one_hot(self, obs):\n if self.toOneHot:\n obs = np.reshape(obs, (1, -1))\n ints = obs.dot(self.multiplication_factor)\n x = np.zeros([obs.shape[0], self.one_hot_len])\n for i, j in enumerate(ints):\n x[i, j] = 1\n return x\n else:\n return obs","def dummify_features(df):\n colnames = df.columns\n le_dict = {}\n for col in colnames:\n le_dict[col] = preprocessing.LabelEncoder()\n le_dict[col].fit(df[col])\n df.loc[:, col] = le_dict[col].transform(df[col])\n\n enc = preprocessing.OneHotEncoder()\n enc.fit(df)\n X = enc.transform(df)\n\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\n # for cv in colnames:\n # for modality in le_dict[cv].classes_:\n # dummy_colnames.append(cv + '_' + modality)\n\n return X, dummy_colnames, enc","def to_categorical(y, num_classes):\n arr = np.eye(num_classes)[y]\n tensor = torch.LongTensor(arr)\n return autograd.Variable(tensor)","def one_hot_encode(x, n_classes):\n return np.eye(n_classes)[x]","def one_hot_encoding(self):\n \n try: \n\n # Encode dependent variable\n le = LabelEncoder()\n le.fit(self.data[\"consumption\"])\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\"consumption\"]))\n\n # Encode independent variable\n categorical_features = Config.FEATURE_DEFINITION[\"category_cols\"]\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\n oe = OrdinalEncoder()\n oe.fit(categorical_df)\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\n df_catindependent_enc.columns = categorical_df.columns\n\n except KeyError: \n\n st.write(\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\")\n st.write(self.data.columns != \"consumption\")\n df_dependent_enc = []\n df_catindependent_enc = []\n\n else:\n \n return df_dependent_enc, df_catindependent_enc","def dense_to_one_hot(labels_dense, num_classes):\r\n num_labels = labels_dense.shape[0]\r\n index_offset = numpy.arange(num_labels) * num_classes\r\n labels_one_hot = numpy.zeros((num_labels, num_classes))\r\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\r\n return labels_one_hot","def onehot(t, num_classes):\n assert isinstance(t, torch.LongTensor)\n return torch.zeros(t.size()[0], num_classes).scatter_(1, t.view(-1, 1), 1)","def one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def make_one_hot(y):\n one_hot = np.zeros((len(y), 10))\n for i in range(len(y)):\n one_hot[i, y[i]] = 1.\n return one_hot.transpose(1,0)","def char_one_hot(self, logit):\n prediction = tf.argmax(logit, axis=1)\n return slim.one_hot_encoding(prediction, self._params.num_char_classes)","def one_hot_converter(column):\n # encode class values as integers\n encoder = LabelEncoder()\n encoder.fit(column) \n encoded_ = encoder.transform(column)\n # convert integers to dummy variables, i.e., one-hot encoded\n encoded_column = to_categorical(encoded_)\n \n return encoded_column","def to_categorical(y, num_columns):\n y_cat = np.zeros((y.shape[0], num_columns))\n y_cat[range(y.shape[0]), y] = 1.0\n return y_cat","def dense_to_one_hot(labels_dense, num_classes):\n\tnum_labels = labels_dense.shape[0]\n\tindex_offset = numpy.arange(num_labels) * num_classes\n\tlabels_one_hot = numpy.zeros((num_labels, num_classes))\n\tlabels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n\treturn labels_one_hot","def to_categorical(y, num_classes):\n\treturn np.eye(num_classes, dtype='uint8')[y]","def _onehot_labels(self,\n labels,\n n_classes,\n axis=-1):\n onehot_labels = tf.one_hot(\n tf.squeeze(labels),\n depth=n_classes,\n axis=axis)\n return onehot_labels","def _one_hot(x, k, dtype=np.float32):\n return np.array(x[:, None] == np.arange(k), dtype)","def find_cats(column):\r\n return pd.Categorical(column).categories","def pre_process_data(df):\n\n # one-hot encode categorical values\n df = pd.get_dummies(df)\n\n return df","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = numpy.arange(num_labels) * num_classes\n labels_one_hot = numpy.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\n return x[self.categorical_features]","def one_hot(self, y):\n\n one_hot = np.zeros((self.n_samples, self.n_classes))\n\n # using np.array to select elements of another np.array\n\n # first diemention index\n # np.arange(self.n_samples) : (1, n_samples)\n # row vectors (0,1,2.....n_samples-1) \n\n # second dimention index\n # \n\n\n one_hot[np.arange(self.n_samples), y.T] = 1\n return one_hot","def to_categorical(y, num_classes):\n return np.eye(num_classes, dtype=\"uint8\")[y]","def dense_to_one_hot(labels_dense, num_classes):\n\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels)*num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot","def dense_to_one_hot(labels_dense, num_classes):\n num_labels = labels_dense.shape[0]\n index_offset = np.arange(num_labels) * num_classes\n labels_one_hot = np.zeros((num_labels, num_classes))\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n return labels_one_hot"],"string":"[\n \"def unhot(one_hot_labels):\\n return np.argmax(one_hot_labels, axis=1)\",\n \"def infer_categorical_variables_in_place(df: pd.DataFrame):\\n # infer which variables are categorical\\n MAX_UNIQUE_VALUES = 10\\n for column in df.columns:\\n if df[column].nunique() <= MAX_UNIQUE_VALUES:\\n df[column] = df[column].astype('category')\",\n \"def from_one_hot_to_category_indices(data: ndarray):\\n return np.argmax(data, axis=-1)\",\n \"def one_hot(y_):\\n y_ = y_.reshape(len(y_))\\n n_values = int(np.max(y_)) + 1\\n n_values = 6\\n return np.eye(n_values)[np.array(y_, dtype=np.int32)] # Returns FLOATS\",\n \"def one_hot(y, num_classes):\\n return np.eye(num_classes)[y]\",\n \"def one_hot_categorical_mode(logits):\\n dist = tfd.Categorical(logits)\\n return tf.cast(tf.one_hot(dist.mode(), dist.event_size), tf.float32)\",\n \"def onehot_argmax(logits):\\n return T.extra_ops.to_one_hot(T.argmax(logits,-1),logits.shape[-1])\",\n \"def one_hot_decode(one_hot):\\n if type(one_hot) is not np.ndarray or len(one_hot.shape) != 2:\\n return None\\n elif not np.where((one_hot == 0) | (one_hot == 1), True, False).all():\\n return None\\n elif np.sum(one_hot) != len(one_hot[0]):\\n return None\\n else:\\n return np.argmax(one_hot, axis=0)\",\n \"def from_onehot(onehot, dim=-1, dtype=None):\\n indexes = torch.argmax(onehot, dim=dim)\\n if dtype is not None:\\n indexes = indexes.type(dtype)\\n return indexes\",\n \"def one_hot(x, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(x.max()+1), dtype)\",\n \"def one_hot(labels, classes=None):\\n\\n one_hot_ = K.utils.to_categorical(labels, classes)\\n return(one_hot_)\",\n \"def get_categorical(X):\\n return list(X.columns[X.dtypes == \\\"object\\\"])\",\n \"def decode_one_hot(x):\\n s = []\\n for onehot in x:\\n one_index = np.argmax(onehot)\\n c = indices_char[one_index]\\n s.append(c) \\n return ''.join(s)\",\n \"def one_hot(input_df, columns):\\n df = input_df.copy()\\n\\n for col in columns:\\n dummies = pd.get_dummies(df[col].str.lower())\\n dummies.drop(dummies.columns[-1], axis=1, inplace=True)\\n df = df.drop(col, axis=1).merge(dummies, left_index=True, right_index=True)\\n \\n return df\",\n \"def _onehot(y, n_classes=False):\\n if not n_classes:\\n \\\"\\\"\\\"Create one-hot encoded labels.\\\"\\\"\\\"\\n n_classes = len(set(y))\\n out = np.zeros((len(y), n_classes))\\n for i, ii in enumerate(y):\\n out[i][ii] += 1\\n y_onehot = out.astype(int)\\n return y_onehot\",\n \"def one_hot(labels, classes=None):\\n return K.utils.to_categorical(labels, classes)\",\n \"def _one_hot(self, key):\\n train_df = self.train.copy(deep=False)\\n one_hot = pd.get_dummies(train_df[key],drop_first=True)\\n one_hot.columns=[key+'_'+col for col in one_hot.columns.values]\\n train_df = pd.concat([train_df,one_hot],axis=1,sort=True)\\n train_dfg = train_df.groupby('fullVisitorId')\\n return train_dfg[one_hot.columns.values].sum()\",\n \"def get_type_by_onehot_vector(onehot_vector):\\n return one_hot_encoder.inverse_transform([onehot_vector])[0][0]\",\n \"def one_hot(df, cols):\\n for col in cols:\\n dummies = pd.get_dummies(df[col], prefix=col, drop_first=False)\\n df = pd.concat([df, dummies], axis=1)\\n df = df.drop(col, axis=1)\\n return df\",\n \"def make_onehot(x,num_labels=7):\\n enc = OneHotEncoder(n_values=num_labels)\\n return enc.fit_transform(np.array(x).reshape(-1, 1)).toarray()\",\n \"def categorical(df):\\r\\n categorical_var=df.select_dtypes(include ='object').columns.tolist()\\r\\n\\r\\n return categorical_var\",\n \"def to_categorical(x, n_col=None):\\n if not n_col:\\n n_col = np.amax(x) + 1\\n one_hot = np.zeros((x.shape[0], n_col))\\n one_hot[np.arange(x.shape[0]), x] = 1\\n return one_hot\",\n \"def find_categorical(self, df):\\n# print(type(df),df.ndim)\\n categorical = [key for key in df.keys() if df.dtypes[key] == np.dtype('O')]\\n numeric = [key for key in df.keys() if df.dtypes[key] != np.dtype('O')]\\n # correct naive expectations\\n actual_categoric = ['MSSubClass']\\n numeric = list(set(numeric) - set(actual_categoric))\\n categorical = list(set(categorical).union(set(actual_categoric)))\\n return categorical\",\n \"def find_categorical(self, df):\\n# print(type(df),df.ndim)\\n categorical = [key for key in df.keys() if df.dtypes[key] == np.dtype('O')]\\n numeric = [key for key in df.keys() if df.dtypes[key] != np.dtype('O')]\\n # correct naive expectations\\n actual_categoric = ['MSSubClass']\\n numeric = list(set(numeric) - set(actual_categoric))\\n categorical = list(set(categorical).union(set(actual_categoric)))\\n return categorical\",\n \"def accuracy_onehot(labels, preds):\\n\\tassert labels.shape[0]==preds.shape[0]\\n\\treturn np.sum(np.argmax(preds, axis=1) == np.argmax(labels, axis=1))/float(labels.shape[0])\",\n \"def dummify_all_categorical(df):\\n\\n df = pd.get_dummies(df)\\n df = dummify(df, \\\"detailed industry recode\\\")\\n df = dummify(df, \\\"detailed occupation recode\\\") ## add some variables that are encoded as int64 but that are in fact categorical\\n return df\",\n \"def _get_one_hot(targets, num_classes):\\n ret = np.zeros((num_classes, targets.shape[0]))\\n ret[targets, np.arange(targets.size)] = 1\\n return ret\",\n \"def one_hot(seq):\\n prot_seq = seq\\n BASES = 'ARNDCQEGHILKMFPSTWYV'\\n bases = np.array([base for base in BASES])\\n feat = np.concatenate(\\n [[(bases == base.upper()).astype(int)] if str(base).upper() in BASES else np.array([[-1] * len(BASES)]) for base\\n in prot_seq])\\n return feat\",\n \"def max_arg_one_hot(arr: np.ndarray) -> np.ndarray:\\n # using argmin because inverted indices in binary_to_one_hot\\n return binary_to_one_hot(np.argmin(arr, axis=1))\",\n \"def one_hot(x, num_classes, dtype=jnp.float32):\\n return jax.nn.one_hot(x, num_classes).astype(dtype)\",\n \"def one_hot_encode(df, ohe_cols):\\n return pd.get_dummies(df, columns=ohe_cols)\",\n \"def one_hot(df):\\r\\n # One-hot encode into \\r\\n cols = ['job', 'marital', 'education', 'month', 'day_of_week', 'poutcome']\\r\\n for each in cols:\\r\\n dummies = pd.get_dummies(df[each], prefix=each, drop_first=False)\\r\\n df = pd.concat([df, dummies], axis=1)\\r\\n df = df.drop(cols,axis=1)\\r\\n return df\",\n \"def one_hot(class_ids, num_classes):\\n oh = np.zeros((len(class_ids), num_classes), dtype=np.float32)\\n oh[np.arange(len(class_ids)), class_ids] = 1\\n\\n assert (oh.argmax(axis=1) == class_ids).all()\\n assert (oh.sum(axis=1) == 1).all()\\n\\n return oh\",\n \"def indices_to_one_hot(data, nb_classes):\\n\\ttargets = np.array(data).reshape(-1)\\n\\treturn np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def indices_to_one_hot(data, nb_classes):\\n targets = np.array(data).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def to_onehot(x, num_classes):\\n return np.eye(num_classes, dtype='float32')[x]\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\r\\n print ('in onehot', labels_dense, num_classes)\\r\\n num_labels = labels_dense.shape[0]\\r\\n index_offset = numpy.arange(num_labels) * num_classes\\r\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\r\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\r\\n return labels_one_hot\",\n \"def classify_columns(df_preds, verbose=0):\\r\\n train = copy.deepcopy(df_preds)\\r\\n #### If there are 30 chars are more in a discrete_string_var, it is then considered an NLP variable\\r\\n max_nlp_char_size = 30\\r\\n max_cols_to_print = 30\\r\\n print('############## C L A S S I F Y I N G V A R I A B L E S ####################')\\r\\n print('Classifying variables in data set...')\\r\\n #### Cat_Limit defines the max number of categories a column can have to be called a categorical colum\\r\\n cat_limit = 35\\r\\n float_limit = 15 #### Make this limit low so that float variables below this limit become cat vars ###\\r\\n def add(a,b):\\r\\n return a+b\\r\\n sum_all_cols = dict()\\r\\n orig_cols_total = train.shape[1]\\r\\n #Types of columns\\r\\n cols_delete = [col for col in list(train) if (len(train[col].value_counts()) == 1\\r\\n ) | (train[col].isnull().sum()/len(train) >= 0.90)]\\r\\n train = train[left_subtract(list(train),cols_delete)]\\r\\n var_df = pd.Series(dict(train.dtypes)).reset_index(drop=False).rename(\\r\\n columns={0:'type_of_column'})\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n var_df['bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['bool','object']\\r\\n and len(train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n string_bool_vars = list(var_df[(var_df['bool'] ==1)]['index'])\\r\\n sum_all_cols['string_bool_vars'] = string_bool_vars\\r\\n var_df['num_bool'] = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16','int32','int64',\\r\\n 'float16','float32','float64'] and len(\\r\\n train[x['index']].value_counts()) == 2 else 0, axis=1)\\r\\n num_bool_vars = list(var_df[(var_df['num_bool'] ==1)]['index'])\\r\\n sum_all_cols['num_bool_vars'] = num_bool_vars\\r\\n ###### This is where we take all Object vars and split them into diff kinds ###\\r\\n discrete_or_nlp = var_df.apply(lambda x: 1 if x['type_of_column'] in ['object'] and x[\\r\\n 'index'] not in string_bool_vars+cols_delete else 0,axis=1)\\r\\n ######### This is where we figure out whether a string var is nlp or discrete_string var ###\\r\\n var_df['nlp_strings'] = 0\\r\\n var_df['discrete_strings'] = 0\\r\\n var_df['cat'] = 0\\r\\n var_df['id_col'] = 0\\r\\n discrete_or_nlp_vars = var_df.loc[discrete_or_nlp==1]['index'].values.tolist()\\r\\n if len(var_df.loc[discrete_or_nlp==1]) != 0:\\r\\n for col in discrete_or_nlp_vars:\\r\\n #### first fill empty or missing vals since it will blowup ###\\r\\n train[col] = train[col].fillna(' ')\\r\\n if train[col].map(lambda x: len(x) if type(x)==str else 0).mean(\\r\\n ) >= max_nlp_char_size and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'nlp_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) <= int(0.9*len(train)) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'discrete_strings'] = 1\\r\\n elif len(train[col].value_counts()) > cat_limit and len(train[col].value_counts()\\r\\n ) == len(train) and col not in string_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n nlp_vars = list(var_df[(var_df['nlp_strings'] ==1)]['index'])\\r\\n sum_all_cols['nlp_vars'] = nlp_vars\\r\\n discrete_string_vars = list(var_df[(var_df['discrete_strings'] ==1) ]['index'])\\r\\n sum_all_cols['discrete_string_vars'] = discrete_string_vars\\r\\n ###### This happens only if a string column happens to be an ID column #######\\r\\n #### DO NOT Add this to ID_VARS yet. It will be done later.. Dont change it easily...\\r\\n #### Category DTYPE vars are very special = they can be left as is and not disturbed in Python. ###\\r\\n var_df['dcat'] = var_df.apply(lambda x: 1 if str(x['type_of_column'])=='category' else 0,\\r\\n axis=1)\\r\\n factor_vars = list(var_df[(var_df['dcat'] ==1)]['index'])\\r\\n sum_all_cols['factor_vars'] = factor_vars\\r\\n ########################################################################\\r\\n date_or_id = var_df.apply(lambda x: 1 if x['type_of_column'] in [np.uint8,\\r\\n np.uint16, np.uint32, np.uint64,\\r\\n 'int8','int16',\\r\\n 'int32','int64'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ######### This is where we figure out whether a numeric col is date or id variable ###\\r\\n var_df['int'] = 0\\r\\n var_df['date_time'] = 0\\r\\n ### if a particular column is date-time type, now set it as a date time variable ##\\r\\n var_df['date_time'] = var_df.apply(lambda x: 1 if x['type_of_column'] in ['<M8[ns]','datetime64[ns]'] and x[\\r\\n 'index'] not in string_bool_vars+num_bool_vars+discrete_string_vars+nlp_vars else 0,\\r\\n axis=1)\\r\\n ### this is where we save them as date time variables ###\\r\\n if len(var_df.loc[date_or_id==1]) != 0:\\r\\n for col in var_df.loc[date_or_id==1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) == len(train):\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n var_df.loc[var_df['index']==col,'id_col'] = 1\\r\\n else:\\r\\n if train[col].min() < 1900 or train[col].max() > 2050:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n try:\\r\\n pd.to_datetime(train[col],infer_datetime_format=True)\\r\\n var_df.loc[var_df['index']==col,'date_time'] = 1\\r\\n except:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'int'] = 1\\r\\n else:\\r\\n pass\\r\\n int_vars = list(var_df[(var_df['int'] ==1)]['index'])\\r\\n date_vars = list(var_df[(var_df['date_time'] == 1)]['index'])\\r\\n id_vars = list(var_df[(var_df['id_col'] == 1)]['index'])\\r\\n sum_all_cols['int_vars'] = int_vars\\r\\n copy_date_vars = copy.deepcopy(date_vars)\\r\\n for date_var in copy_date_vars:\\r\\n #### This test is to make sure sure date vars are actually date vars\\r\\n try:\\r\\n pd.to_datetime(train[date_var],infer_datetime_format=True)\\r\\n except:\\r\\n ##### if not a date var, then just add it to delete it from processing\\r\\n cols_delete.append(date_var)\\r\\n date_vars.remove(date_var)\\r\\n sum_all_cols['date_vars'] = date_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n sum_all_cols['cols_delete'] = cols_delete\\r\\n ## This is an EXTREMELY complicated logic for cat vars. Don't change it unless you test it many times!\\r\\n var_df['numeric'] = 0\\r\\n float_or_cat = var_df.apply(lambda x: 1 if x['type_of_column'] in ['float16',\\r\\n 'float32','float64'] else 0,\\r\\n axis=1)\\r\\n if len(var_df.loc[float_or_cat == 1]) > 0:\\r\\n for col in var_df.loc[float_or_cat == 1]['index'].values.tolist():\\r\\n if len(train[col].value_counts()) > 2 and len(train[col].value_counts()\\r\\n ) <= float_limit and len(train[col].value_counts()) <= len(train):\\r\\n var_df.loc[var_df['index']==col,'cat'] = 1\\r\\n else:\\r\\n if col not in num_bool_vars:\\r\\n var_df.loc[var_df['index']==col,'numeric'] = 1\\r\\n cat_vars = list(var_df[(var_df['cat'] ==1)]['index'])\\r\\n continuous_vars = list(var_df[(var_df['numeric'] ==1)]['index'])\\r\\n ######## V E R Y I M P O R T A N T ###################################################\\r\\n ##### There are a couple of extra tests you need to do to remove abberations in cat_vars ###\\r\\n cat_vars_copy = copy.deepcopy(cat_vars)\\r\\n for cat in cat_vars_copy:\\r\\n if df_preds[cat].dtype==float:\\r\\n continuous_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'numeric'] = 1\\r\\n elif len(df_preds[cat].value_counts()) == df_preds.shape[0]:\\r\\n id_vars.append(cat)\\r\\n cat_vars.remove(cat)\\r\\n var_df.loc[var_df['index']==cat,'cat'] = 0\\r\\n var_df.loc[var_df['index']==cat,'id_col'] = 1\\r\\n sum_all_cols['cat_vars'] = cat_vars\\r\\n sum_all_cols['continuous_vars'] = continuous_vars\\r\\n sum_all_cols['id_vars'] = id_vars\\r\\n ###### This is where you consoldate the numbers ###########\\r\\n var_dict_sum = dict(zip(var_df.values[:,0], var_df.values[:,2:].sum(1)))\\r\\n for col, sumval in var_dict_sum.items():\\r\\n if sumval == 0:\\r\\n print('%s of type=%s is not classified' %(col,train[col].dtype))\\r\\n elif sumval > 1:\\r\\n print('%s of type=%s is classified into more then one type' %(col,train[col].dtype))\\r\\n else:\\r\\n pass\\r\\n ############### This is where you print all the types of variables ##############\\r\\n ####### Returns 8 vars in the following order: continuous_vars,int_vars,cat_vars,\\r\\n ### string_bool_vars,discrete_string_vars,nlp_vars,date_or_id_vars,cols_delete\\r\\n if verbose == 1:\\r\\n print(\\\" Number of Numeric Columns = \\\", len(continuous_vars))\\r\\n print(\\\" Number of Integer-Categorical Columns = \\\", len(int_vars))\\r\\n print(\\\" Number of String-Categorical Columns = \\\", len(cat_vars))\\r\\n print(\\\" Number of Factor-Categorical Columns = \\\", len(factor_vars))\\r\\n print(\\\" Number of String-Boolean Columns = \\\", len(string_bool_vars))\\r\\n print(\\\" Number of Numeric-Boolean Columns = \\\", len(num_bool_vars))\\r\\n print(\\\" Number of Discrete String Columns = \\\", len(discrete_string_vars))\\r\\n print(\\\" Number of NLP String Columns = \\\", len(nlp_vars))\\r\\n print(\\\" Number of Date Time Columns = \\\", len(date_vars))\\r\\n print(\\\" Number of ID Columns = \\\", len(id_vars))\\r\\n print(\\\" Number of Columns to Delete = \\\", len(cols_delete))\\r\\n if verbose == 2:\\r\\n marthas_columns(df_preds,verbose=1)\\r\\n print(\\\" Numeric Columns: %s\\\" %continuous_vars[:max_cols_to_print])\\r\\n print(\\\" Integer-Categorical Columns: %s\\\" %int_vars[:max_cols_to_print])\\r\\n print(\\\" String-Categorical Columns: %s\\\" %cat_vars[:max_cols_to_print])\\r\\n print(\\\" Factor-Categorical Columns: %s\\\" %factor_vars[:max_cols_to_print])\\r\\n print(\\\" String-Boolean Columns: %s\\\" %string_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Numeric-Boolean Columns: %s\\\" %num_bool_vars[:max_cols_to_print])\\r\\n print(\\\" Discrete String Columns: %s\\\" %discrete_string_vars[:max_cols_to_print])\\r\\n print(\\\" NLP text Columns: %s\\\" %nlp_vars[:max_cols_to_print])\\r\\n print(\\\" Date Time Columns: %s\\\" %date_vars[:max_cols_to_print])\\r\\n print(\\\" ID Columns: %s\\\" %id_vars[:max_cols_to_print])\\r\\n print(\\\" Columns that will not be considered in modeling: %s\\\" %cols_delete[:max_cols_to_print])\\r\\n ##### now collect all the column types and column names into a single dictionary to return!\\r\\n len_sum_all_cols = reduce(add,[len(v) for v in sum_all_cols.values()])\\r\\n if len_sum_all_cols == orig_cols_total:\\r\\n print(' %d Predictors classified...' %orig_cols_total)\\r\\n #print(' This does not include the Target column(s)')\\r\\n else:\\r\\n print('No of columns classified %d does not match %d total cols. Continuing...' %(\\r\\n len_sum_all_cols, orig_cols_total))\\r\\n ls = sum_all_cols.values()\\r\\n flat_list = [item for sublist in ls for item in sublist]\\r\\n if len(left_subtract(list(train),flat_list)) == 0:\\r\\n print(' Missing columns = None')\\r\\n else:\\r\\n print(' Missing columns = %s' %left_subtract(list(train),flat_list))\\r\\n return sum_all_cols\",\n \"def get_categorical_features(self):\\n return self.categorical_features\",\n \"def _get_one_hot_array(num_left_cards, max_num_cards):\\n one_hot = np.zeros(max_num_cards)\\n if num_left_cards > 0:\\n one_hot[num_left_cards - 1] = 1\\n\\n return one_hot\",\n \"def one_hot_encode(self, meta_field):\\n one_hot = pd.get_dummies(self.sample_meta[meta_field]).values\\n return one_hot\",\n \"def accuracy_onehot(y_pred, y_label):\\n return (y_pred.argmax(dim=1) == y_label).float().mean().item()\",\n \"def process_categorical_data(data_df):\\n return pd.get_dummies(data_df, columns=Columns.categorical)\",\n \"def to_categorical(target: np.ndarray, n_classes: int = None) -> np.ndarray:\\n\\tn_classes = n_classes if n_classes is not None else np.max(target) + 1\\n\\tbatch_size = target.shape[0]\\n\\tone_hot = np.zeros((batch_size, n_classes))\\n\\tone_hot[np.arange(batch_size), target] = 1\\n\\treturn one_hot\",\n \"def test_one_hot_output_soft_maximum(self):\\n vocab_data = [\\\"earth\\\", \\\"wind\\\", \\\"and\\\", \\\"fire\\\"]\\n input_array = np.array([\\\"earth\\\", \\\"wind\\\", \\\"and\\\", \\\"fire\\\", \\\"michigan\\\", \\\"\\\"])\\n expected_output = [\\n [0, 1, 0, 0, 0],\\n [0, 0, 1, 0, 0],\\n [0, 0, 0, 1, 0],\\n [0, 0, 0, 0, 1],\\n [1, 0, 0, 0, 0],\\n [0, 0, 0, 0, 0],\\n ]\\n\\n input_data = keras.Input(shape=(1,), dtype=tf.string)\\n layer = index_lookup.IndexLookup(\\n max_tokens=None,\\n num_oov_indices=1,\\n mask_token=\\\"\\\",\\n oov_token=\\\"[OOV]\\\",\\n output_mode=index_lookup.ONE_HOT,\\n vocabulary_dtype=tf.string,\\n )\\n layer.set_vocabulary(vocab_data)\\n binary_data = layer(input_data)\\n model = keras.Model(inputs=input_data, outputs=binary_data)\\n output_dataset = model.predict(input_array)\\n self.assertAllEqual(expected_output, output_dataset)\",\n \"def test_one_hot_output_hard_maximum(self):\\n vocab_data = [\\\"earth\\\", \\\"wind\\\", \\\"and\\\", \\\"fire\\\"]\\n input_array = np.array([\\\"earth\\\", \\\"wind\\\", \\\"and\\\", \\\"fire\\\", \\\"michigan\\\", \\\"\\\"])\\n expected_output = [\\n [0, 1, 0, 0, 0, 0],\\n [0, 0, 1, 0, 0, 0],\\n [0, 0, 0, 1, 0, 0],\\n [0, 0, 0, 0, 1, 0],\\n [1, 0, 0, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0],\\n ]\\n\\n input_data = keras.Input(shape=(1,), dtype=tf.string)\\n layer = index_lookup.IndexLookup(\\n max_tokens=6,\\n num_oov_indices=1,\\n mask_token=\\\"\\\",\\n oov_token=\\\"[OOV]\\\",\\n output_mode=index_lookup.ONE_HOT,\\n pad_to_max_tokens=True,\\n vocabulary_dtype=tf.string,\\n )\\n layer.set_vocabulary(vocab_data)\\n binary_data = layer(input_data)\\n model = keras.Model(inputs=input_data, outputs=binary_data)\\n output_dataset = model.predict(input_array)\\n self.assertAllEqual(expected_output, output_dataset)\",\n \"def one_hot_encode(df, col):\\n return pd.get_dummies(df, columns=[col], drop_first=True)\",\n \"def categorical(df):\\n\\n # variables which need to be transformed to categorical\\n categorical = [\\\"prop_country_id\\\", \\\"visitor_location_country_id\\\"]\\n\\n for var in categorical:\\n df = pd.concat([df, pd.get_dummies(df[var], prefix=var)], axis=1)\\n del df[var]\\n\\n return df\",\n \"def one_hot_encoder(df, nan_as_category=True):\\n original_columns = list(df.columns)\\n categorical_columns = [col for col in df.columns if\\n df[col].dtype == 'object']\\n df = pd.get_dummies(df, columns=categorical_columns,\\n dummy_na=nan_as_category)\\n new_columns = [c for c in df.columns if c not in original_columns]\\n return df, new_columns\",\n \"def dense_to_one_hot(labels, n_classes=2):\\n labels = np.array(labels)\\n n_labels = labels.shape[0]\\n index_offset = np.arange(n_labels) * n_classes\\n labels_one_hot = np.zeros((n_labels, n_classes), dtype=np.float32)\\n labels_one_hot.flat[index_offset + labels.ravel()] = 1\\n return labels_one_hot\",\n \"def one_hot_encoding(data):\\r\\n encoder = LabelEncoder()\\r\\n y = encoder.fit_transform(data)\\r\\n return(y)\",\n \"def one_hot(labels):\\n one_hot_labels = np.zeros(labels.shape + (n_actions,))\\n for c in range(n_actions):\\n one_hot_labels[labels == c, c] = 1.0\\n return one_hot_labels\",\n \"def mode(y):\\n array = pd.DataFrame(np.array(y))\\n mode = array.mode(axis=0).loc[0, :]\\n count = array.apply(lambda x: x.value_counts().max())\\n return mode.values, count.values\",\n \"def one_hot_encoding(data):\\n\\n data_encoded = pd.get_dummies(data)\\n\\n return data_encoded\",\n \"def ensemble(all_predict, size):\\n mode_pred = np.zeros(shape=(size,1))\\n for i in range(np.shape(all_predict)[1]):\\n pred= mode(all_predict[:,i])\\n # break ties randomly\\n if pred[1] == 1:\\n pred_val = random.randrange(2)\\n else:\\n pred_val = pred[0]\\n mode_pred[i,0] = pred_val\\n # return most common prediction\\n return mode_pred\",\n \"def to_onehot(labels: torch.Tensor, num_classes: int) -> torch.Tensor:\\n if len(labels.size()) == 1:\\n return F.one_hot(labels, num_classes).float()\\n return labels\",\n \"def indices_to_one_hot(cls_indeces, nb_classes):\\n targets = np.array(cls_indeces).reshape(-1)\\n return np.eye(nb_classes)[targets]\",\n \"def one_hot_encoding(y):\\n\\n y_oh = np.zeros((y.shape[0], y.max() - y.min() + 1))\\n\\n # currently only works in min is actually 0\\n for j in range(0, y_oh.shape[1]):\\n y_oh[np.where(y == j), j] = 1\\n\\n return y_oh\",\n \"def one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def dense_to_one_hot(labels_dense, num_classes=10):\\n num_labels = labels_dense.shape[0]\\n #print('check num_labels',num_labels)\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def to_one_hot_encoding(target_data):\\n target_data = target_data.squeeze()\\n n_class = len(np.unique(target_data))\\n res = np.eye(n_class)[target_data.astype(int)]\\n return res\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def get_one_hot_vector(number):\\n from sklearn.preprocessing import LabelBinarizer\\n encoder = LabelBinarizer()\\n seed_vector = np.arange(10)\\n encoder.fit(seed_vector)\\n labels = encoder.transform(seed_vector)\\n return labels.astype(np.float32)\",\n \"def categorical_accuracy(preds, y):\\n max_preds = preds.argmax(dim = 1, keepdim = True) # get the index of the max probability 여기서 argmax를 찾는다\\n correct = max_preds.squeeze(1).eq(y) # 같은것만 찾는 코드\\n return correct.sum() / torch.FloatTensor([y.shape[0]])\",\n \"def one_hot(Y, n_c):\\n Y_onehot = np.zeros((Y.size, n_c))\\n Y_onehot[np.arange(Y.size), Y] = 1\\n return Y_onehot.T\",\n \"def one_hot_enc(df, ohe_cols):\\n for col in ohe_cols:\\n df_ohe = pd.get_dummies(df[col], preffix=col)\\n df = pd.concat([df, df_ohe], axis=1)\\n df.drop(col, axis=1, inplace=True)\\n return df\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def categorical_accuracy(preds, y):\\n top_pred = preds.argmax(1, keepdim = True)\\n correct = top_pred.eq(y.view_as(top_pred)).sum()\\n acc = correct.float() / y.shape[0]\\n return acc\",\n \"def onehot_encode_labels(y):\\n\\treturn OneHotEncoder(categories=\\\"auto\\\", sparse=False).fit_transform(y.reshape(y.shape[0],1))\",\n \"def conv_y_to_onehot_mat(labels):\\n one_idx = np.array(labels)\\n nkind = len(np.unique(one_idx))\\n nlabels = len(one_idx)\\n\\n ret = np.zeros((nkind, nlabels))\\n ret[one_idx, np.arange(nlabels)] = 1\\n return ret\",\n \"def categorical_accuracy(preds, y):\\n max_preds = preds.argmax(\\n dim=1, keepdim=True\\n ) # get the index of the max probability\\n correct = max_preds.squeeze(1).eq(y)\\n return correct.sum() / torch.FloatTensor([y.shape[0]]).to(device)\",\n \"def _mode(x, def_fill=ImputerMixin._def_fill):\\n idx = x.as_data_frame(use_pandas=True)[x.columns[0]].value_counts().index\\n\\n # if the most common is null, then return the next most common.\\n # if there is no next common (i.e., 100% null) then we return the def_fill\\n return idx[0] if not pd.isnull(idx[0]) else idx[1] if idx.shape[0] > 1 else def_fill\",\n \"def _maybe_one_hot(self, obs):\\n if self.toOneHot:\\n obs = np.reshape(obs, (1, -1))\\n ints = obs.dot(self.multiplication_factor)\\n x = np.zeros([obs.shape[0], self.one_hot_len])\\n for i, j in enumerate(ints):\\n x[i, j] = 1\\n return x\\n else:\\n return obs\",\n \"def dummify_features(df):\\n colnames = df.columns\\n le_dict = {}\\n for col in colnames:\\n le_dict[col] = preprocessing.LabelEncoder()\\n le_dict[col].fit(df[col])\\n df.loc[:, col] = le_dict[col].transform(df[col])\\n\\n enc = preprocessing.OneHotEncoder()\\n enc.fit(df)\\n X = enc.transform(df)\\n\\n dummy_colnames = [cv + '_' + str(modality) for cv in colnames for modality in le_dict[cv].classes_]\\n # for cv in colnames:\\n # for modality in le_dict[cv].classes_:\\n # dummy_colnames.append(cv + '_' + modality)\\n\\n return X, dummy_colnames, enc\",\n \"def to_categorical(y, num_classes):\\n arr = np.eye(num_classes)[y]\\n tensor = torch.LongTensor(arr)\\n return autograd.Variable(tensor)\",\n \"def one_hot_encode(x, n_classes):\\n return np.eye(n_classes)[x]\",\n \"def one_hot_encoding(self):\\n \\n try: \\n\\n # Encode dependent variable\\n le = LabelEncoder()\\n le.fit(self.data[\\\"consumption\\\"])\\n df_dependent_enc = pd.DataFrame(le.transform(self.data[\\\"consumption\\\"]))\\n\\n # Encode independent variable\\n categorical_features = Config.FEATURE_DEFINITION[\\\"category_cols\\\"]\\n categorical_df = self.data.loc[:, self.data.columns.isin(categorical_features)]\\n oe = OrdinalEncoder()\\n oe.fit(categorical_df)\\n df_catindependent_enc = pd.DataFrame(oe.transform(categorical_df))\\n df_catindependent_enc.columns = categorical_df.columns\\n\\n except KeyError: \\n\\n st.write(\\\"Cannot perform one-hot encoding for numerical variables. Please check if variables are properly defined.\\\")\\n st.write(self.data.columns != \\\"consumption\\\")\\n df_dependent_enc = []\\n df_catindependent_enc = []\\n\\n else:\\n \\n return df_dependent_enc, df_catindependent_enc\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\r\\n num_labels = labels_dense.shape[0]\\r\\n index_offset = numpy.arange(num_labels) * num_classes\\r\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\r\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\r\\n return labels_one_hot\",\n \"def onehot(t, num_classes):\\n assert isinstance(t, torch.LongTensor)\\n return torch.zeros(t.size()[0], num_classes).scatter_(1, t.view(-1, 1), 1)\",\n \"def one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def make_one_hot(y):\\n one_hot = np.zeros((len(y), 10))\\n for i in range(len(y)):\\n one_hot[i, y[i]] = 1.\\n return one_hot.transpose(1,0)\",\n \"def char_one_hot(self, logit):\\n prediction = tf.argmax(logit, axis=1)\\n return slim.one_hot_encoding(prediction, self._params.num_char_classes)\",\n \"def one_hot_converter(column):\\n # encode class values as integers\\n encoder = LabelEncoder()\\n encoder.fit(column) \\n encoded_ = encoder.transform(column)\\n # convert integers to dummy variables, i.e., one-hot encoded\\n encoded_column = to_categorical(encoded_)\\n \\n return encoded_column\",\n \"def to_categorical(y, num_columns):\\n y_cat = np.zeros((y.shape[0], num_columns))\\n y_cat[range(y.shape[0]), y] = 1.0\\n return y_cat\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n\\tnum_labels = labels_dense.shape[0]\\n\\tindex_offset = numpy.arange(num_labels) * num_classes\\n\\tlabels_one_hot = numpy.zeros((num_labels, num_classes))\\n\\tlabels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n\\treturn labels_one_hot\",\n \"def to_categorical(y, num_classes):\\n\\treturn np.eye(num_classes, dtype='uint8')[y]\",\n \"def _onehot_labels(self,\\n labels,\\n n_classes,\\n axis=-1):\\n onehot_labels = tf.one_hot(\\n tf.squeeze(labels),\\n depth=n_classes,\\n axis=axis)\\n return onehot_labels\",\n \"def _one_hot(x, k, dtype=np.float32):\\n return np.array(x[:, None] == np.arange(k), dtype)\",\n \"def find_cats(column):\\r\\n return pd.Categorical(column).categories\",\n \"def pre_process_data(df):\\n\\n # one-hot encode categorical values\\n df = pd.get_dummies(df)\\n\\n return df\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = numpy.arange(num_labels) * num_classes\\n labels_one_hot = numpy.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def get_categorical_features(self, x: pd.DataFrame) -> pd.DataFrame:\\n return x[self.categorical_features]\",\n \"def one_hot(self, y):\\n\\n one_hot = np.zeros((self.n_samples, self.n_classes))\\n\\n # using np.array to select elements of another np.array\\n\\n # first diemention index\\n # np.arange(self.n_samples) : (1, n_samples)\\n # row vectors (0,1,2.....n_samples-1) \\n\\n # second dimention index\\n # \\n\\n\\n one_hot[np.arange(self.n_samples), y.T] = 1\\n return one_hot\",\n \"def to_categorical(y, num_classes):\\n return np.eye(num_classes, dtype=\\\"uint8\\\")[y]\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels)*num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\",\n \"def dense_to_one_hot(labels_dense, num_classes):\\n num_labels = labels_dense.shape[0]\\n index_offset = np.arange(num_labels) * num_classes\\n labels_one_hot = np.zeros((num_labels, num_classes))\\n labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\\n return labels_one_hot\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7186164","0.71264297","0.700594","0.6770333","0.66180325","0.6554686","0.65422523","0.6527132","0.6494277","0.6475236","0.64435905","0.64244854","0.64193946","0.64063036","0.6401098","0.63921547","0.63824326","0.63524544","0.63380814","0.6334604","0.6326123","0.63078684","0.6278045","0.6278045","0.62766755","0.6258983","0.6233294","0.62122726","0.62077695","0.62021357","0.61953115","0.618979","0.6168215","0.61177385","0.61144096","0.61144096","0.61144096","0.61144096","0.61142576","0.61112547","0.6109427","0.61038816","0.60920584","0.6084475","0.60690916","0.6066348","0.60603166","0.6058435","0.60525566","0.6046934","0.6046227","0.6025507","0.60159194","0.60019857","0.59911114","0.59768146","0.5956802","0.5955023","0.5944491","0.59305626","0.59303606","0.592948","0.5927545","0.5925727","0.5918222","0.5918222","0.5918222","0.5916507","0.59157","0.59143037","0.5913957","0.5912737","0.5912723","0.5911245","0.5909358","0.5908868","0.5901648","0.5893622","0.58935237","0.58899707","0.58898276","0.5889268","0.58872473","0.58739847","0.58709437","0.586825","0.58677053","0.58633375","0.58620393","0.5848712","0.58461803","0.58408016","0.5836674","0.5836196","0.5829766","0.58295614","0.58275986","0.5824419","0.58240557","0.58237636","0.5821441"],"string":"[\n \"0.7186164\",\n \"0.71264297\",\n \"0.700594\",\n \"0.6770333\",\n \"0.66180325\",\n \"0.6554686\",\n \"0.65422523\",\n \"0.6527132\",\n \"0.6494277\",\n \"0.6475236\",\n \"0.64435905\",\n \"0.64244854\",\n \"0.64193946\",\n \"0.64063036\",\n \"0.6401098\",\n \"0.63921547\",\n \"0.63824326\",\n \"0.63524544\",\n \"0.63380814\",\n \"0.6334604\",\n \"0.6326123\",\n \"0.63078684\",\n \"0.6278045\",\n \"0.6278045\",\n \"0.62766755\",\n \"0.6258983\",\n \"0.6233294\",\n \"0.62122726\",\n \"0.62077695\",\n \"0.62021357\",\n \"0.61953115\",\n \"0.618979\",\n \"0.6168215\",\n \"0.61177385\",\n \"0.61144096\",\n \"0.61144096\",\n \"0.61144096\",\n \"0.61144096\",\n \"0.61142576\",\n \"0.61112547\",\n \"0.6109427\",\n \"0.61038816\",\n \"0.60920584\",\n \"0.6084475\",\n \"0.60690916\",\n \"0.6066348\",\n \"0.60603166\",\n \"0.6058435\",\n \"0.60525566\",\n \"0.6046934\",\n \"0.6046227\",\n \"0.6025507\",\n \"0.60159194\",\n \"0.60019857\",\n \"0.59911114\",\n \"0.59768146\",\n \"0.5956802\",\n \"0.5955023\",\n \"0.5944491\",\n \"0.59305626\",\n \"0.59303606\",\n \"0.592948\",\n \"0.5927545\",\n \"0.5925727\",\n \"0.5918222\",\n \"0.5918222\",\n \"0.5918222\",\n \"0.5916507\",\n \"0.59157\",\n \"0.59143037\",\n \"0.5913957\",\n \"0.5912737\",\n \"0.5912723\",\n \"0.5911245\",\n \"0.5909358\",\n \"0.5908868\",\n \"0.5901648\",\n \"0.5893622\",\n \"0.58935237\",\n \"0.58899707\",\n \"0.58898276\",\n \"0.5889268\",\n \"0.58872473\",\n \"0.58739847\",\n \"0.58709437\",\n \"0.586825\",\n \"0.58677053\",\n \"0.58633375\",\n \"0.58620393\",\n \"0.5848712\",\n \"0.58461803\",\n \"0.58408016\",\n \"0.5836674\",\n \"0.5836196\",\n \"0.5829766\",\n \"0.58295614\",\n \"0.58275986\",\n \"0.5824419\",\n \"0.58240557\",\n \"0.58237636\",\n \"0.5821441\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":9997,"cells":{"query":{"kind":"string","value":"Ranking Based Utility Transformation. w(f(x)) / lambda = 1/mu if rank(x) <= mu 0 if mu < rank(x) < lambda mu 1/mu if lambda mu <= rank(x) where rank(x) is the number of at least equally good points, including it self. The number of good and bad points, mu, is ceil(lambda/4). That is, mu = 1 if lambda = 2 mu = 1 if lambda = 4 mu = 2 if lambda = 6, etc. If there exist tie points, the utility values are equally distributed for these points."},"document":{"kind":"string","value":"def ranking_based_utility_transformation(losses, lam, rho=0.25, negative=True):\n eps = 1e-14\n idx = np.argsort(losses)\n mu = int(np.ceil(lam * rho))\n _w = np.zeros(lam)\n _w[:mu] = 1 / mu\n _w[lam - mu:] = -1 / mu if negative else 0\n w = np.zeros(lam)\n istart = 0\n for i in range(losses.shape[0] - 1):\n if losses[idx[i + 1]] - losses[idx[i]] < eps * losses[idx[i]]:\n pass\n elif istart < i:\n w[istart:i + 1] = np.mean(_w[istart:i + 1])\n istart = i + 1\n else:\n w[i] = _w[i]\n istart = i + 1\n w[istart:] = np.mean(_w[istart:])\n return w, idx"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_good(self, m, n, rank, mu=2, ka=2):\n sr = random.random()\n s = []\n s.append(sr)\n for r in range(rank-1):\n newele = s[-1] * (1 + ka * random.random() / (rank-1))\n s.append(newele)\n s.reverse()\n \n # best_u = None\n # best_mu0 = 0\n # while best_mu0 == 0:\n # for _ in range(10):\n # A = np.random.rand(m,m)\n # A = scipy.linalg.orth(A)\n # u = A[:, :rank]\n # mu0 = self.compute_mu(u, m, rank)\n # print(\"mu0 : \", mu0)\n # if mu0 <= mu and mu0 >= best_mu0:\n # best_mu0 = mu0\n # best_u = u\n # print(\"mu0 for u:\", best_mu0)\n # # print(u.T @ u)\n \n # best_v = None\n # best_mu0 = 0\n # while best_mu0 == 0:\n # for _ in range(10):\n # B = np.random.rand(n,n)\n # B = scipy.linalg.orth(B)\n # v = B[:, :rank]\n # mu0 = self.compute_mu(v, n, rank)\n # print(\"mu0 : \", mu0)\n # if mu0 <= mu and mu0 >= best_mu0:\n # best_mu0 = mu0\n # best_v = v\n # print(\"mu0 for v:\", best_mu0)\n # u = best_u\n # v = best_v\n\n for _ in range(100):\n A = np.random.rand(m,m)\n A = scipy.linalg.orth(A)\n u = A[:, :rank]\n mu0 = self.compute_mu(u, m, rank)\n print(\"mu0 : \", mu0)\n if mu0 <= mu:\n break\n print(\"mu0 for u:\", mu0) \n\n for _ in range(10):\n B = np.random.rand(n,n)\n B = scipy.linalg.orth(B)\n v = B[:, :rank]\n mu0 = self.compute_mu(v, n, rank)\n print(\"mu0 : \", mu0)\n if mu0 <= mu:\n break\n print(\"mu0 for both:\", mu0)\n\n matrix = np.dot(u*s, v.T)\n \n kappa = s[0] / s[-1]\n print(\"kappa=\", kappa)\n \n ss = np.copy(s)\n for k in range(rank):\n ss[k] = s[k] / s[0]\n \n max_entry = np.max(np.abs(np.outer(u[:,:rank], v.T[:rank,:])))\n mu1 = max_entry * math.sqrt(m * n / rank)\n print(\"mu1=\", mu1)\n \n return matrix","def zipf(ranks, item):\n if item in ranks:\n return 0.1 / ranks[item]\n return -1","def pred(self,u,i):\r\n if self.user_based:\r\n bias=self.mu[u]\r\n else:\r\n bias=self.mu[i]\r\n pred=self.X[i,:].dot(self.W[:,u])+bias\r\n if pred<0:\r\n return 0\r\n if pred>5:\r\n return 5\r\n return pred","def fn(u):\n if u == n-1: return 1 # boundary condition \n ans = 0\n for v, _ in graph[u]: \n if dist[u] > dist[v]: ans += fn(v)\n return ans","def up_next_weight(data, weight_o):\n \n del0 = 4e-3 # minimum coordinate\n delta = 5e-3 # spacing of grid\n M = 10 # number of samples\n S = 5 # number of steps in the random walk\n\n N = data.shape[1]\n\n # Computing Universal Portfolio.\n r = np.ones(N) / N # Start each one at the uniform point\n b = np.ones(r.shape[0])\n \n allM = np.zeros((N, M)) # Take the average of m samples\n for m in range(M):\n b = r.copy()\n for i in range(S):\n bnew = b.copy()\n j = np.random.randint(N - 1)\n a = np.random.choice([-1, 1])\n bnew[j] = b[j] + (a * delta)\n bnew[N - 1] = b[N - 1] - (a * delta)\n if bnew[j] >= del0 and bnew[N - 1] >= del0:\n muliplier_x = min(1, np.exp((b[N - 1] - (2 * del0)) / (N * delta)))\n x = np.prod(data @ b) * muliplier_x\n muliplier_y = min(1, np.exp((bnew[N - 1] - (2 * del0)) / (N * delta)))\n y = np.prod(data @ bnew) * muliplier_y\n pr = min(y / x, 1) # or pr = min(x / y, 1)\n if np.random.rand() < pr:\n b = bnew.copy()\n allM[:, m] = b\n\n weight = np.mean(allM, 1) # Taking the average of m samples.\n return weight / sum(weight)","def score_items(X, U, mu,\n scoremethod='lowhigh',\n missingmethod='none',\n feature_weights=[]):\n\n # Use U to model and then reconstruct the data in X.\n # 1. Project all data in X into space defined by U,\n # then reconstruct it.\n if missingmethod.lower() != 'ignore':\n # All missing values should have been replaced with 0,\n # or non-existent.\n # 1a. Subtract the mean and project onto U\n proj = np.dot(U.T, (X - mu))\n # 1b. Reconstruct by projecting back up and adding mean\n reproj = np.dot(U, proj) + mu\n # 1c. Compute the residual\n #print('X:', X.T)\n #print('reproj:', reproj.T)\n err = X - reproj\n #print('err:', err.T)\n #input()\n \n else:\n # Missing method must be 'ignore' (Brand 2002)\n (err, reproj) = compute_error_with_missing(X, U, mu)\n\n # 2. Compute reconstruction error\n if scoremethod == 'low': # Blank out all errors > 0\n err[err>0] = 0\n elif scoremethod == 'high': # Blank out all errors < 0\n err[err<0] = 0\n else: # default, count everything\n pass\n \n # Weight features if requested\n if len(feature_weights) > 0:\n for i in range(len(feature_weights)):\n err[i,:] = err[i,:] * feature_weights[i]\n\n if missingmethod.lower() == 'ignore':\n # Only tally error for observed features.\n # This means that items with missing values are not penalized\n # for those features, which is probably the best we can do.\n scores = np.nansum(np.array(np.power(err, 2)), axis=0)\n else:\n scores = np.sum(np.array(np.power(err, 2)), axis=0)\n\n #print('scores:', scores)\n #print('reproj:', reproj)\n #input()\n return (scores, reproj)","def map_at_k(\n rating_true,\n rating_pred,\n col_user=DEFAULT_USER_COL,\n col_item=DEFAULT_ITEM_COL,\n col_rating=DEFAULT_RATING_COL,\n col_prediction=DEFAULT_PREDICTION_COL,\n relevancy_method=\"top_k\",\n k=DEFAULT_K,\n threshold=DEFAULT_THRESHOLD,\n):\n df_hit, df_hit_count, n_users = merge_ranking_true_pred(\n rating_true=rating_true,\n rating_pred=rating_pred,\n col_user=col_user,\n col_item=col_item,\n col_rating=col_rating,\n col_prediction=col_prediction,\n relevancy_method=relevancy_method,\n k=k,\n threshold=threshold,\n )\n\n if df_hit.shape[0] == 0:\n return 0.0\n\n # calculate reciprocal rank of items for each user and sum them up\n df_hit_sorted = df_hit.sort_values([col_user, \"rank\"])\n df_hit_sorted[\"rr\"] = (df_hit.groupby(col_user).cumcount() + 1) / df_hit[\"rank\"]\n df_hit_sorted = df_hit_sorted.groupby(col_user).agg({\"rr\": \"sum\"}).reset_index()\n\n df_merge = pd.merge(df_hit_sorted, df_hit_count, on=col_user)\n return (df_merge[\"rr\"] / df_merge[\"actual\"]).sum() / n_users","def styblinskitankfcn(x: np.ndarray) -> np.ndarray:\n n = x.shape[1]\n scores = np.zeros((x.shape[0], 1))\n for i in range(n):\n scores += x[:, i] ** 4 - 16 * x[:, i] ** 2 + 5 * x[:, i]\n scores *= 0.5\n return scores","def GoodmanKruskalLambdaR_calc(TP, FP, FN, TN):\n try:\n n = TP + FP + FN + TN\n part1 = 0.5 * (max(TP + FP, FN + TN) + max(TP + FN, FP + TN))\n return (TP + TN - part1) / (n - part1)\n except Exception:\n return \"None\"","def question_18():\n rbf = RadialBiasFunction()\n wins = 0.0\n for i in range(100):\n rbf.fit(1.5, 9)\n rbf_error = rbf.error()\n if rbf_error == 0:\n wins += 1\n rbf.resample()\n return wins / 100","def nac_w_optimal_r(fan_in, fan_out):\n fan = max(fan_in + fan_out, 5)\n r = scipy.optimize.bisect(lambda r: fan * nac_w_variance(r) - 2, 0, 10)\n return r","def custom_score(game, player):\n \n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n \n # First obtain the improved score\n player_legal_moves = game.get_legal_moves(player)\n opponent = game.get_opponent(player)\n opponent_legal_moves = game.get_legal_moves(opponent)\n improved = len(player_legal_moves) - len(opponent_legal_moves)\n if improved != 0:\n return float(improved)\n \n # Second get differences from center\n center_coordinates = center_y, center_x = get_center_coordinates(game)\n player_coordinates = game.get_player_location(player)\n opponent_coordinates = game.get_player_location(opponent)\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\n center_dist_diff = player_center_dist - opponent_center_dist\n \n # Third obtain next_moves\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \n improved_next = len(player_next_moves) - len(opponent_next_moves)\n \n # Put player and opponent feature differences in a tuple/vector surrogoate\n feature_diff_vector = (improved, center_dist_diff, improved_next)\n \n # Provide a weighting vector for the features of each player-participant\n weight_vector = (1.5,0.1,1.0)\n # Calculate the return value = weighted difference of players' features\n weighted_difference_dot_product = sum(p*q for p,q, \\\n in zip(feature_diff_vector, weight_vector))\n \n return float(weighted_difference_dot_product)","def recip_rank(recs, truth):\n good = recs['item'].isin(truth.index)\n npz, = np.nonzero(good)\n if len(npz):\n return 1.0 / (npz[0] + 1.0)\n else:\n return 0.0","def user_interaction_score(uv, recommended_News, ranked=True):\n\n iv = recommended_News[\"topical_vector\"]\n\n product = simple_doct_product(uv, iv)\n\n epsilon = 10e-5\n\n if (product + epsilon) > 1.0:\n vui = 0.99\n else:\n vui = beta_distribution(product)\n\n # Awared preference\n ita = beta_distribution(0.98)\n pui = vui * ita\n\n return pui","def prioritized_sweeping_policy_evaluation(self,pi, U1, k=maxint , epsilon=0.001):\n R, gamma ,expect_vec_u = self.R, self.gamma , self.expected_vec_utility\n h = []\n\n for s in range(self.nstates):\n heappush( h , (-self.rmax-random.uniform(0,1),s) )\n\n for i in count(0):\n U = U1.copy()\n\n (delta,s) = heappop(heap)\n\n U1[s] = R(s) + gamma * expect_vec_u(s,pi[s],U)\n\n delta = l1distance(U1[s],U[s])\n\n if i > k or delta < epsilon * (1 - gamma) / gamma:\n return U","def _wilcoxon(_sample_a, _sample_b):\n res = stats.ranksums(_sample_a, _sample_b)\n print('Wilcoxon rank-sum\\nstatistic: {}\\np-value: {}'.format(res[0], res[1]))\n print('-' * 10)","def custom_score_3(game, player):\n \n \n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n \n player_legal_moves = game.get_legal_moves(player)\n opponent = game.get_opponent(player)\n opponent_legal_moves = game.get_legal_moves(opponent)\n player_legal_move_count, opponent_legal_move_count = \\\n len(player_legal_moves), len(opponent_legal_moves)\n move_count_difference = player_legal_move_count - opponent_legal_move_count\n # Find coordinates of center box\n h, w = get_center_coordinates(game)\n # Retrieve player's coordinates\n y, x = game.get_player_location(player)\n # Obtain coordinate further, closest to origin\n furthest_coord, closest_coord = max(h - y, w -x), min(h - y, w - x)\n # Return weighted, vector-valued length from origin / sum of weights\n weighted_distance_from_center = \\\n math.sqrt((closest_coord**2 + 2*(furthest_coord**2)))/3\n feature_vector = (move_count_difference, weighted_distance_from_center)\n \n weight_vector = (1.0,0.1)\n \n weighted_difference_dot_product = sum(p*q for p,q, \\\n in zip(weight_vector, feature_vector)) \n \n return float(weighted_difference_dot_product)","def rankw(G, i, damp=0.85, visited=None):\n _, _, Lcin, Lwin, _, _ = G[i]\n\n sumr = 0\n for j, w in zip(Lcin, Lwin):\n _, Jscore, _, _, Jcout, Jwout = G[j]\n sumr += Jscore * w / sum(Jwout)\n return (1 - damp) + damp * sumr","def utility(self, util_f, priors=None):\n if priors is None: priors = self.group_size_dist()\n posteriors = self.dist() * priors[:,None,None] # p x m x r\n prob_R = posteriors.sum(axis=0) # m x r\n posteriors /= prob_R # p x m x r\n return (util_f(posteriors) * prob_R).sum(axis=1)","def inverse_rank_norm(values):\n #values = pd.Series([5, 7, 2, 1, 1])\n quantiles = (values.rank()-0.5)/(len(values))\n return ss.norm.ppf(quantiles)","def lowest_rank_approx(A,e):","def _weight(self, user_a, user_i):\n a_votes = self._votes[user_a]\n i_votes = self._votes[user_i]\n\n a_mean = self._averages[user_a]\n i_mean = self._averages[user_i]\n\n item_ndx = np.logical_and(np.isfinite(a_votes),\n np.isfinite(i_votes))\n\n a_deviations = a_mean - a_votes[item_ndx]\n i_deviations = i_mean - i_votes[item_ndx]\n\n numerator = np.sum(a_deviations * i_deviations)\n denominator = (np.sum(np.power(a_deviations, 2)) *\n np.sum(np.power(i_deviations, 2)))\n\n if denominator < _EPSILON:\n return 0\n return numerator / np.sqrt(denominator)","def calculate_WR():\n C = df['rating_avg'].mean()\n m = df['rating_count'].quantile(0.9)\n v = df['rating_count']\n R = df['rating_avg']\n return (v/(v+m) * R) + (m/(m+v) * C)","def calculate_weighted_results():\n pass","def griewankfcn(x: np.ndarray) -> np.ndarray:\n n = x.shape[1]\n sumcomp = np.zeros((x.shape[0], 1))\n prodcomp = np.ones((x.shape[0], 1))\n\n for i in range(n):\n sumcomp += x[:, i : i + 1] ** 2\n prodcomp *= np.cos(x[:, i : i + 1] / np.sqrt(i + 1))\n\n scores = (sumcomp / 4000) - prodcomp + 1\n return scores","def estimate(self, u, j):\n if not (self.trainset.knows_user(u) and self.trainset.knows_item(j)):\n raise PredictionImpossible('User and/or item is unknown.')\n\n u_ratings = self.trainset.ur[u]\n\n if self.weighting == 'linear':\n weight = sum(self.freqs[i, j, self.to_index(r)] for i, r in u_ratings)\n score = sum(self.sums[i, j, self.to_index(r)] for i, r in u_ratings)\n return score / weight\n\n # self.weighting == 'log' or None\n weights = [self.freqs[i, j, self.to_index(r)] for i, r in u_ratings]\n reciprocals = [1 / w if w else 0 for w in weights]\n scores = [self.sums[i, j, self.to_index(r)] for i, r in u_ratings]\n scores = [s * w for s, w in zip(scores, reciprocals)]\n\n if self.weighting is None:\n return np.mean(scores)\n # self.weighting == 'log'\n logs = [np.log(w + 1) if w >= 1 else 0 for w in weights]\n return np.dot(scores, logs) / np.sum(logs)","def evaluate_ranks(articles, rank_tuples):\n rank_tp=defaultdict(int)\n rank_fn=defaultdict(int)\n rank_fp=defaultdict(int)\n \n for article in articles:\n for mention in article.entity_mentions:\n form=mention.mention\n meaning=mention.gold_link\n sys_meaning=mention.sys_link\n t_gold=(form, meaning)\n t_sys=(form, sys_meaning)\n for rank, r_tuples in rank_tuples.items():\n if t_gold in r_tuples and t_sys in r_tuples:\n rank_tp[rank]+=1\n break\n elif t_gold in r_tuples:\n rank_fn[rank]+=1\n elif t_sys in r_tuples:\n rank_fp[rank]+=1\n print('tp', rank_tp)\n print('fp', rank_fp)\n print('fn', rank_fn)\n \n rank_prec={}\n rank_recall={}\n rank_f1={}\n \n for rank in range(1,13):\n if rank_tp[rank]+rank_fp[rank]>0:\n rank_prec[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fp[rank])\n else:\n rank_prec[rank]=0.0\n if rank_tp[rank]+rank_fn[rank]>0:\n rank_recall[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fn[rank])\n else:\n rank_recall[rank]=0.0\n if rank_prec[rank]+rank_recall[rank]>0:\n rank_f1[rank]=2*rank_prec[rank]*rank_recall[rank]/(rank_prec[rank]+rank_recall[rank])\n else:\n rank_f1[rank]=0.0\n print('precision', rank_prec)\n print()\n print('recall', rank_recall)\n print()\n print('f1', rank_f1)\n print()\n return rank_prec, rank_recall, rank_f1","def _estimate_stats_with_rel_info(self):\r\n nonrel_list = []\r\n rel_list = []\r\n for ele in self._ranking_list:\r\n score = ele[0]\r\n rel = ele[1]\r\n if rel:\r\n rel_list.append(score)\r\n else:\r\n nonrel_list.append(score)\r\n m1,v1 = self._compute_stat_from_list(rel_list) \r\n m0,v0 = self._compute_stat_from_list(nonrel_list)\r\n estimated_lambda = len(rel_list)*1.0/len(self._ranking_list)\r\n\r\n self._m1 = m1\r\n self._v1 = v1\r\n self._m0 = m0\r\n self._v0 = v0\r\n self._lambda = estimated_lambda\r\n\r\n if self._debug :\r\n print \"m1: %f, v1: %f, m0: %f, v0: %f\" %(self._m1,self._v1,self._m0,self._v0)\r\n print \"lambda: %f\" %(estimated_lambda)","def custom_score_6(game, player):\n \"\"\"custom_score_6 heuristic function aims towards weighted chances heuristics\n \"\"\"\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\n return float(length_my_player_moves*length_my_player_moves - 1.5*length_opp_payer_moves*length_opp_payer_moves)","def _discount_for_relative_rank_diff():\n # The LambdaLoss is not well defined when topn is active and topn <\n # list_size. The following implementation is based on Equation 18 proposed\n # in https://research.google/pubs/pub47258/. Please refer to\n # `DCGLambdaWeightV2` for a better implemention to handle topn.\n pair_valid_rank = _apply_pairwise_op(tf.logical_or,\n tf.less_equal(ranks, topn))\n rank_diff = tf.cast(\n tf.abs(_apply_pairwise_op(tf.subtract, ranks)), dtype=tf.float32)\n pair_discount = tf.where(\n tf.logical_and(tf.greater(rank_diff, 0), pair_valid_rank),\n tf.abs(\n self._rank_discount_fn(rank_diff) -\n self._rank_discount_fn(rank_diff + 1)), tf.zeros_like(rank_diff))\n return pair_discount","def rank_training(self, fleet):\n if (len(fleet.ships) == 0):\n return get_rank(0)\n ins = fleet.get_ship_instances()\n sum_lvl = sum(x.level for x in ins)\n avg_lvl = sum_lvl // len(fleet.ships)\n # auto S if fleet > 50% avg lvl of difficulty\n if (avg_lvl >= self.avg_lvl * 1.5):\n return get_rank(1)\n if (avg_lvl >= self.avg_lvl): # must succeed if fleet > avg lvl\n avg_weight = SUCCESS_THRESHOLD + (1 - SUCCESS_THRESHOLD) / 2\n wgt = random.gauss(avg_weight, (1 - SUCCESS_THRESHOLD) / 2) \\\n + inv_lerp(avg_lvl, self.avg_lvl, self.avg_lvl * 1.5) \\\n * (1 - SUCCESS_THRESHOLD) / 2\n wgt = max(SUCCESS_THRESHOLD, min(1.0, wgt))\n return get_rank(wgt)\n wgt = inv_lerp(avg_lvl, 0, self.avg_lvl) * (SUCCESS_THRESHOLD * 0.67)\n return get_rank(abs(random.gauss(0, SUCCESS_THRESHOLD * 0.33)) + wgt)","def wilcoxon_w(pairs: List[Tuple[Union[float, int], int]]) -> float:\n i = 0\n w = 0.0\n while i < len(pairs):\n absi = pairs[i][0]\n sum_sgn = pairs[i][1]\n j = i + 1\n while j < len(pairs) and pairs[j][0] == absi:\n sum_sgn += pairs[j][1]\n j += 1\n r = 1 + 0.5 * (i + j - 1) # smoothed rank\n w += r * sum_sgn\n i = j\n return w","def round_stochastic(w):\n w_ = np.floor(w)\n ps = w - w_ \n return w_ + np.random.binomial(1, p=ps)","def transfo_quantile(xx):\n\n # nn = np.zeros(len(xx))\n # oo = np.argsort(xx)\n # nn[oo] = np.arange(len(xx)) / len(xx) + 1 / (2 * len(xx))\n # return nn\n\n return rankdata(xx) / len(xx) - 1 / (2 * len(xx))","def wicked_utility_func(x):\n mean_global_opt = [0.2, 0.8]\n s_1 = 0.05\n s_2 = 0.05\n r_12 = -0.9\n cov_global_opt = [[s_1**2, r_12*s_1*s_2], \n [r_12*s_1*s_2, s_2**2]]\n rv_global = multivariate_normal(mean_global_opt, cov_global_opt)\n C_global = 1/rv_global.pdf(mean_global_opt)\n\n mean_local_opt = [0.7, 0.3]\n s_1 = 0.3\n s_2 = 0.2\n r_12 = 0.0\n cov_local_opt = [[s_1**2, r_12*s_1*s_2], \n [r_12*s_1*s_2, s_2**2]]\n rv_local = multivariate_normal(mean_local_opt, cov_local_opt)\n C_local = 0.8 * 1/rv_local.pdf(mean_local_opt)\n\n return C_global*rv_global.pdf(x[0:2]) + C_local*rv_local.pdf(x[0:2])","def f(ns, k):\n ns = list(enumerate(sorted(ns)))\n n = len(ns)\n min_u = unfair(ns[:k])\n nns = ns[k:]\n def g(mu, n):\n (i, n) = n\n unfairness = n - ns[i - k + 1][1]\n # print(i, n, unfairness)\n if unfairness < mu:\n return unfairness\n return mu\n return reduce(g, nns, min_u)","def mu(self, x):\n x = utils.to_float_if_int(x)\n utils.verify_is_numeric(x)\n\n return (\n 0\n if (x < self.__l or x > self.__r)\n else (x - self.__l) / (self.__n - self.__l)\n if (self.__l <= x <= self.__n)\n else (self.__r - x) / (self.__r - self.__n)\n )","def amannwhitneyu(x,y):\r\n n1 = len(x)\r\n n2 = len(y)\r\n ranked = rankdata(N.concatenate((x,y)))\r\n rankx = ranked[0:n1] # get the x-ranks\r\n ranky = ranked[n1:] # the rest are y-ranks\r\n u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx) # calc U for x\r\n u2 = n1*n2 - u1 # remainder is U for y\r\n bigu = max(u1,u2)\r\n smallu = min(u1,u2)\r\n T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores\r\n if T == 0:\r\n raise ValueError, 'All numbers are identical in amannwhitneyu'\r\n sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)\r\n z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc\r\n return smallu, 1.0 - azprob(z)","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def RScore(x,p,d):\n \n if x <= d[p][0.20]:\n return 5\n elif x <= d[p][0.4]:\n return 4\n elif x <= d[p][0.6]: \n return 3\n elif x <= d[p][0.8]:\n return 2\n else:\n return 1","def calculate_cornish_fisher_percentile(alpha, mu, sigma, skew, kurt):\n\n z = stats.norm.ppf(alpha)\n he2 = np.polynomial.hermite_e.hermeval(z, [0.0, 0.0, 1.0])\n he3 = np.polynomial.hermite_e.hermeval(z, [0.0, 0.0, 0.0, 1.0])\n he13 = np.polynomial.hermite_e.hermeval(z, [0.0, -1.0, 0.0, -2.0])\n\n w = (z +\n he2 * skew / 6 +\n he3 * kurt / 24 +\n he13 * (skew ** 2) / 36)\n\n return mu + sigma * w","def atiecorrect(rankvals):\r\n sorted,posn = ashellsort(N.array(rankvals))\r\n n = len(sorted)\r\n T = 0.0\r\n i = 0\r\n while (i<n-1):\r\n if sorted[i] == sorted[i+1]:\r\n nties = 1\r\n while (i<n-1) and (sorted[i] == sorted[i+1]):\r\n nties = nties +1\r\n i = i +1\r\n T = T + nties**3 - nties\r\n i = i+1\r\n T = T / float(n**3-n)\r\n return 1.0 - T","def evaluate_fitness(self, pos):\n _, index = self.tree.query(pos)\n return 1 - (self.fitness_function[index // self.resolution][index % self.resolution] - self.min) / (self.max - self.min)","def test_ww_softrank(self):\n\t\t#\n\t\tdetails= self.watcher.analyze(layers=[self.fc3_layer], randomize=True, mp_fit=True)\n\t\tprint(details[['ww_softrank','mp_softrank', 'lambda_max', 'rand_bulk_max', 'max_rand_eval']])\n\t\tactual = details.ww_softrank[0]\n\t\texpected = details.mp_softrank[0]\n\t\tself.assertAlmostEqual(actual,expected, places=1)\n\t\t\n\t\tmax_rand_eval = details.max_rand_eval[0]\n\t\tmax_eval = details.lambda_max[0]\n\t\texpected = max_rand_eval/max_eval\n\t\tself.assertAlmostEqual(actual,expected, places=2)","def ks_test_uniform(v, alpha):\n v = np.asarray(v).flatten()\n n = v.shape[0]\n\n # эмпирическая функция распределения\n v_var = np.sort(v)\n\n # теоритическая\n v_emp = np.arange(0, n)/n\n diff = np.abs(v_var - v_emp)\n d = np.max(diff)\n\n lmbd = d * np.sqrt(n) + 1/(6*np.sqrt(n))\n val = stats.kstwobign.ppf(alpha, n)\n\n print('lambda estimated: {}, table: {}'.format(lmbd, val))\n if lmbd < val:\n print('не можем опровергнуть')\n else:\n print('отвергаем')","def tsp(self, u):\n fitness = 0\n for i in range(1, len(u), 1):\n fitness = fitness + round(sqrt((u[i][0] - u[(i - 1)][0]) ** 2 + (u[i][1] - u[(i - 1)][1]) ** 2))\n\n fitness = fitness + round(sqrt((u[0][0] - u[(-1)][0]) ** 2 + (u[0][1] - u[(-1)][1]) ** 2))\n return fitness","def lmannwhitneyu(x,y):\r\n n1 = len(x)\r\n n2 = len(y)\r\n ranked = rankdata(x+y)\r\n rankx = ranked[0:n1] # get the x-ranks\r\n ranky = ranked[n1:] # the rest are y-ranks\r\n u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx) # calc U for x\r\n u2 = n1*n2 - u1 # remainder is U for y\r\n bigu = max(u1,u2)\r\n smallu = min(u1,u2)\r\n T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores\r\n if T == 0:\r\n raise ValueError, 'All numbers are identical in lmannwhitneyu'\r\n sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)\r\n z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc\r\n return smallu, 1.0 - zprob(z)","def value(a, y, weights, lambda_):\n\t\treturn 0.5* (np.linalg.norm(a-y)**2) / (a.shape[0] * a.shape[1])\n\t\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) ","def premium(self):\n premium = 0\n for weight, mean, std in zip(self.weights, self.means, self.stds):\n shift = (self.data['riskfree'] - mean) * self.data['maturity']\n moneyness = np.array(self.data['moneyness']) + shift\n premium += weight * blackscholes_norm(moneyness,\n self.data['maturity'],\n std, self.data['call'])\n return premium","def rank_transform(X):\n return np.apply_along_axis(scipy.stats.rankdata, 0, X)","def percenter(rank, max_rank):\n\treturn 100 * (rank/(max_rank or 1))","def GoodmanKruskalLambda_calc(TP, FP, FN, TN):\n try:\n n = TP + FP + FN + TN\n part1 = max(TP, FP) + max(FN, TN) + max(TP, FN) + max(FP, TN)\n part2 = max(TP + FP, FN + TN) + max(TP + FN, FP + TN)\n return (0.5 * (part1 - part2)) / (n - 0.5 * part2)\n except Exception:\n return \"None\"","def custom_score(game, player):\n # return penalize_corners_heuristic(game, player)\n # return favor_run_away_heuristic(game, player)\n return look_ahead_heuristic(game, player)","def computeRmse(model, data, n , sc):\n truth = data.map( lambda x: ((x[0], x[1]), x[2]) )\n truth.cache()\n ##print 'test zhou 0.....', truth.count() , '............', truth.take(10)\n\n predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))\n predictions.cache()\n # here let's rescale predicted ratings to 0-10 scale\n maxPrediction = predictions.map(lambda x: x[2]).max()\n minPrediction = predictions.map(lambda x: x[2]).min()\n maxRate = RatingScale\n minRate = RatingScaleMin\n ##print 'test zhou 1......', predictions.count(), '............', predictions.take(10)\n\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate )).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\n\n\n #predictedRating = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate ) )\n predictedRating = predictions.map(lambda x: ((x[0], x[1]), x[2] ) )\n predictedRating.cache()\n ##predictedRating.checkpoint()\n ##print 'test zhou 2.......', predictedRating.count(), '............', predictedRating.take(10)\n\n\n \n\n\n predictionsAndRatings = predictedRating.join(truth).values()\n #predictionsAndRatings = sc.union(predictedRating, truth)\n predictionsAndRatings.cache()\n #print 'test zhou 3........', predictionsAndRatings.count(), '............', predictionsAndRatings.take(10)\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), x[2])).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\n \n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))\n #return 1.0","def cal_expected_map(self, ranking_list, total_rel=0):\r\n s = 0.0\r\n pr = 0\r\n pn = 0\r\n for ele in reversed(ranking_list):\r\n rel_doc_cnt = ele[0]\r\n this_doc_cnt = ele[1]\r\n nonrel_doc_cnt = this_doc_cnt - rel_doc_cnt\r\n s += self.A(pr, pn, rel_doc_cnt, nonrel_doc_cnt)\r\n pr += rel_doc_cnt\r\n pn += nonrel_doc_cnt\r\n total_rel += rel_doc_cnt\r\n #print s/total_rel\r\n if total_rel == 0:\r\n return 0\r\n return s/total_rel","def custom_score_7(game, player):\n \"\"\"custom_score_7 heuristic function also aims towards weighted chances heuristics\n \"\"\"\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\n return float(1.5*length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)","def custom_score_2(game, player):\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n own_moves = len(game.get_legal_moves(player))\n\n if game.move_count < 15:\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\n return float(own_moves - opp_moves)\n\n delta = 0\n\n moves = game.get_legal_moves()\n initial_moves_count = len(moves)\n indexes = np.random.permutation(initial_moves_count)\n\n for i in range(0, min(4, initial_moves_count)):\n first_level = True\n simulation = game.copy()\n\n while True:\n moves = simulation.get_legal_moves()\n moves_count = len(moves)\n if moves_count == 0:\n if simulation.is_winner(player):\n delta = delta + 1\n else:\n delta = delta - 1\n break\n if first_level:\n selected_move = indexes[i]\n first_level = False\n else:\n selected_move = random.randint(0, moves_count - 1)\n\n simulation.apply_move(moves[selected_move])\n\n return float(own_moves + delta) #float(own_moves - opp_moves + 5 * delta)\n\n #return float(own_moves - opp_moves + free_area_score(game, player) - free_area_score(game, game.get_opponent(player)))","def custom_scoring(y_te, y_pred):\n #weights computed with training data set\n w = np.array([0.02409584, 0.00787456, 0.03685528, 0.01760536, 0.04589969, 0.8483942 , 0.01724058, 0.00203449]);\n \n ## F1 SCORES\n #evaluate F1 score, precision and recall for each label, \n #along with custom proportionally weighted F1 score\n #and built in weighted and macro F1 scores\n F1_tab, Ptab, Rtab, pf1 = F1_score(y_te, y_pred, w)\n f = F1Score(8, threshold = 0.5, average = 'weighted')\n f.update_state(y_te, y_pred)\n wf1 = f.result().numpy() #weighted f1 score\n f.reset_states()\n f = F1Score(8, threshold = 0.5, average = 'macro')\n f.update_state(y_te, y_pred)\n mf1 = f.result().numpy() #macro f1 score\n f.reset_states()\n\n ##EDIT DISTANCE\n #edit_dist_av = LevDistMultilabels(y_true, y_pred)\n\n ##ACCURACY\n #evaluate accuracy per label\n acc_tab = Acc(y_te, y_pred)\n\n return wf1, mf1, pf1, F1_tab, Ptab, Rtab, acc_tab","def process_weights(w, df, s, n):\n T, N = w.shape\n weight_mat = pd.DataFrame(index=df.index, columns=df.columns)\n idx = weight_mat.index\n\n def process(series):\n # True if less than 50% of obs. are constant\n return series.value_counts(dropna=False).max() < 0.5 * s\n\n previousValidWeights = None\n for t in range(n, T, s):\n t_s = min(T, t + s)\n weight_vect = np.zeros([N, 1])\n # check if the past data are constant\n # if asset i is constant set w_i = 0\n sub_X = df.iloc[t:t_s, :].copy()\n assets = sub_X.apply(process).values\n weight_vect[assets, 0] = w[t][assets]\n # Verify if sum(w) = 0\n if weight_vect.min()<0:\n weight_vect=weight_vect - weight_vect.min()\n\n if weight_vect.sum() == 0.:\n if previousValidWeights is None:\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, N]) / N\n else :\n weight_mat.iloc[t: t_s] = previousValidWeights\n elif weight_vect.sum() != 1.:\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, 1]) @ weight_vect.T / weight_vect.sum()\n previousValidWeights = np.ones([t_s - t, 1]) @ weight_vect.T / weight_vect.sum()\n else:\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, 1]) @ weight_vect.T\n previousValidWeights = np.ones([t_s - t, 1]) @ weight_vect.T\n\n if abs(weight_mat.iloc[t:t_s].sum().sum()) <= 1e-6:\n print('null prediction, investigate')\n\n return weight_mat","def predictRating(toPredict, candidateList):\n\n ratingRelevantCandidates = []\n\n #Remove candidates with no rating specified\n for candidate in candidateList:\n currentCandidate = candidate[1]\n\n if float(currentCandidate['vote_avg']) > 0:\n ratingRelevantCandidates.append((float(currentCandidate['vote_avg']), candidate))\n\n #print(\"ratings::::::::\",currentCandidate['vote_avg'])\n\n #Remove outlier candidates based on rating\n ratingMean = np.mean([x[0] for x in ratingRelevantCandidates])\n print(\"ratingMean\", ratingMean)\n ratingSD = np.std([x[0] for x in ratingRelevantCandidates])\n print(\"ratingSD\", ratingSD)\n\n finalRatings = [x for x in ratingRelevantCandidates if (float(x[0]) < ratingMean + ratingSD)]#1.5 *\n finalRatings = [x for x in finalRatings if (float(x[0]) > ratingMean - ratingSD)]#.75 *\n\n finalRatingCandidatesWithWeight = []\n\n #Weight each candidate based on vote count, direct and actor popularity and matching score from part 1\n for candidate in finalRatings:\n directorPoints = compareDirectorPoints(toPredict['director'], candidate[1][1]['director'])\n actorPoints = compareActorPoints(toPredict['cast'], candidate[1][1]['cast'])\n voteCountPoints = int(candidate[1][1]['vote_count'])\n matchPoints = candidate[1][0] / np.max([float(x[1][0]) for x in finalRatings]) * 100\n candidateWeight = PREDICTION_MATCHPOINTS_WEIGHT * matchPoints \\\n + PREDICTION_ACTOR_WEIGHT * actorPoints \\\n + PREDICTION_DIRECTOR_WEIGHT * directorPoints \\\n + PREDICTION_VOTECOUNT_WEIGHT * voteCountPoints\n\n finalRatingCandidatesWithWeight.append((candidateWeight, candidate[0]))\n\n #Calculate the prediction\n sumRatingCandidateWeights = np.sum([float(x[0]) for x in finalRatingCandidatesWithWeight])\n sumRatingTimesCandidateWeight = np.sum([float(x[0]) * float(x[1]) for x in finalRatingCandidatesWithWeight])\n\n ratingPrediction = float(sumRatingTimesCandidateWeight / sumRatingCandidateWeights)\n\n return ratingPrediction","def question5():\n \n null_dist = {38: 1, 39: 1, 40: 6, 41: 8, 42: 17, 43: 31, 44: 41, 45: 47, 46: 65, 47: 76,\n 48: 66, 49: 63, 50: 62, 51: 69, 52: 69, 53: 57, 54: 49, 55: 37, 56: 26, 57: 30,\n 58: 33, 59: 23, 60: 25, 61: 20, 62: 5, 63: 11, 64: 11, 65: 7, 66: 5, 67: 4,\n 68: 5, 69: 4, 70: 5, 71: 3, 72: 3, 73: 4, 74: 1, 75: 2, 80: 3, 81: 1, 82: 1,\n 85: 1, 87: 1, 92: 1}\n \n # find mean\n mean = 0\n for key in null_dist.keys():\n mean += key / float(1000) * null_dist[key]\n \n print mean\n \n # find standard deviation\n variance = 0\n for key in null_dist.keys():\n variance += (key - mean) ** 2 / float(1000) * null_dist[key]\n stdev = variance ** 0.5\n \n print stdev\n \n # find z-score\n score = 875\n z_score = (score - mean) / stdev\n \n print z_score","def pair_weights(self, labels, ranks):\n with tf.compat.v1.name_scope(name='precision_lambda_weight'):\n _check_tensor_shapes([labels, ranks])\n valid_pair, labels = _get_valid_pairs_and_clean_labels(labels)\n binary_labels = tf.cast(self._positive_fn(labels), dtype=tf.float32)\n label_diff = tf.abs(_apply_pairwise_op(tf.subtract, binary_labels))\n label_diff *= tf.cast(valid_pair, dtype=tf.float32)\n # i <= topn and j > topn or i > topn and j <= topn, i.e., xor(i <= topn, j\n # <= topn).\n rank_mask = _apply_pairwise_op(tf.math.logical_xor,\n tf.less_equal(ranks, self._topn))\n return label_diff * tf.cast(rank_mask, dtype=tf.float32)","def weighted_percentiles(data, wt, percentiles): \r\n assert np.greater_equal(percentiles, 0.0).all(), \"Percentiles less than zero\" \r\n assert np.less_equal(percentiles, 1.0).all(), \"Percentiles greater than one\" \r\n data = np.asarray(data) \r\n assert len(data.shape) == 1 \r\n if wt is None: \r\n wt = np.ones(data.shape, np.float) \r\n else: \r\n wt = np.asarray(wt, np.float) \r\n assert wt.shape == data.shape \r\n assert np.greater_equal(wt, 0.0).all(), \"Not all weights are non-negative.\" \r\n assert len(wt.shape) == 1 \r\n n = data.shape[0] \r\n assert n > 0 \r\n i = np.argsort(data) \r\n sd = np.take(data, i, axis=0) \r\n sw = np.take(wt, i, axis=0) \r\n aw = np.add.accumulate(sw) \r\n if not aw[-1] > 0: \r\n raise ValueError(\"Nonpositive weight sum\")\r\n w = (aw-0.5*sw)/aw[-1] \r\n spots = np.searchsorted(w, percentiles) \r\n o = [] \r\n for (s, p) in zip(spots, percentiles): \r\n if s == 0: \r\n o.append(sd[0]) \r\n elif s == n: \r\n o.append(sd[n-1]) \r\n else: \r\n f1 = (w[s] - p)/(w[s] - w[s-1]) \r\n f2 = (p - w[s-1])/(w[s] - w[s-1]) \r\n assert f1>=0 and f2>=0 and f1<=1 and f2<=1 \r\n assert abs(f1+f2-1.0) < 1e-6 \r\n o.append(sd[s-1]*f1 + sd[s]*f2) \r\n return o","def fitness(individual, divider, target_sum, target_multiply):\n\n sum_val = reduce(operator.add, individual[:divider], 0)\n multiply_val = reduce(operator.mul, individual[divider:], 1)\n \n sum_error = abs(target_sum - sum_val)\n sum_error = sum_error / target_sum\n\n multiply_error = abs(target_multiply - multiply_val)\n multiply_error = multiply_error / target_multiply\n\n #print(multiply_error, sum_error)\n #print(sum_error, multiply_error)\n return (multiply_error + sum_error)/2 * 100","def weighted_npv(y_true, y_pred):\n\n _, _, fnw, tnw = get_weighted_confusion_matrix(y_true, y_pred)\n \n return tnw / (tnw + fnw)","def apply_squashing_func(mu_, pi_, logp_pi):\n # Squash the output\n deterministic_policy = tf.nn.tanh(mu_)\n policy = tf.nn.tanh(pi_)\n\n # Squash correction (from original implementation)\n logp_pi -= tf.reduce_sum(tf.math.log(1 - policy ** 2 + EPS), axis=1)\n\n return deterministic_policy, policy, logp_pi","def update_item_feature(\n train, item_features, user_features, lambda_item,\n nz_item_userindices, I):\n for n, item_n in enumerate(nz_item_userindices):\n nnz_users_per_item = len(item_n[1]) # Number of users who rated item n\n if (nnz_users_per_item == 0): nnz_users_per_item = 1\n # Least squares solution\n A_n = np.dot(user_features[:,item_n[1]], user_features[:,item_n[1]].T) + lambda_item * nnz_users_per_item * I\n V_n = np.dot(user_features[:,item_n[1]], train.T[item_n[1],item_n[0]].todense())\n #if (n%3 == 0): print(\"item_n: {}\".format(item_n[0]), np.linalg.det(A_n))\n if (np.linalg.det(A_n) != 0): item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\n else: \n A_n[0,0] += 1; A_n[1,1] += 1; A_n[2,2] += 1; A_n[3,3] += 1; A_n[4,4] += 1; A_n[5,5] += 1 # if matrix A_n is singular, slightly modify several values\n item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)","def griewank(x):\n nopt = np.size(x)\n # if (nopt == 2) | (nopt == 10):\n xx = x\n if nopt == 2:\n d = 200.0\n else:\n d = 4000.0\n\n u1 = 0.0\n u2 = 1.0\n for j in range(nopt):\n u1 = u1 + xx[j]**2 / d\n u2 = u2 * np.cos(xx[j] / np.sqrt(float(j + 1)))\n\n f = u1 - u2 + 1\n return f","def item_rank(self, u, a, b, alphas, theta, transition_prob):\n no_of_states = len(alphas[u][0])\n\n # calculate distribution over the states for the user at time t+1\n p_t_plus_1 = []\n for k in range(no_of_states):\n total = 0\n for l in range(no_of_states):\n total += alphas[u][-1][k] * transition_prob[l][k]\n p_t_plus_1.append(total)\n# # print('\\np_t_plus_u1')\n # pprint(p_t_plus_1)\n\n item_rank = defaultdict(float)\n for i in range(len(theta)): # for each item\n item_rank[i] = -sum(p_t_plus_1[k] * (1 + b[k] * theta[i][k])**(-a[k]) for k in range(no_of_states))\n\n# # print(item_rank)\n items = sorted(item_rank, key=item_rank.__getitem__, reverse=True)\n\n return items","def ap(self, result, next_item):\n if next_item in result.index:\n rank = result.index.get_loc(next_item) + 1\n return 1.0 / rank\n else:\n return 0","def custom_score_5(game, player):\n \"\"\"custom_score_5 heuristic function defines chances heuristics\n \"\"\"\n if game.is_loser(player):\n return float(\"-inf\")\n\n if game.is_winner(player):\n return float(\"inf\")\n\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\n return float(length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)","def make_mean_strategy(min_points, num_rolls=5):\r\n def trololo(score, opp_score):\r\n bacon = opp_score // 10 + 1\r\n new_score = score + bacon\r\n new_sum = new_score + opp_score\r\n if bacon >= min_points and (new_sum % 10 == 7 or new_sum % 7 == 0):\r\n return 0\r\n else: return num_rolls\r\n return trololo","def update_measure(self):\r\n lam = len(self.fit)\r\n idx = np.argsort(self.fit + self.fitre)\r\n ranks = np.argsort(idx).reshape((2, lam))\r\n rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1])\r\n\r\n # compute rank change limits using both ranks[0] and ranks[1]\r\n r = np.arange(1, 2 * lam) # 2 * lam - 2 elements\r\n limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0,i] + 1 - (ranks[0,i] > ranks[1,i]))),\r\n self.theta*50) +\r\n Mh.prctile(np.abs(r - (ranks[1,i] + 1 - (ranks[1,i] > ranks[0,i]))),\r\n self.theta*50))\r\n for i in self.idx]\r\n # compute measurement\r\n # max: 1 rankchange in 2*lambda is always fine\r\n s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1) # lives roughly in 0..2*lambda\r\n self.noiseS += self.cum * (np.mean(s) - self.noiseS)\r\n return self.noiseS, s","def _getWeightedIndex(self, validQvals):\r\n validQvals = self._makePositive(validQvals)\r\n sumValue = sum(validQvals)\r\n arrow = random.random() * sumValue\r\n runningSum = 0\r\n for index, val in enumerate(validQvals):\r\n runningSum += val\r\n if runningSum >= arrow:\r\n return index\r\n return 0","def rank():\n return 0","def calculate_power_ranking(wins, losses, is_winning_streak,\n streak_length, recent_wins, recent_losses):\n pred1 = 0\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\n pred3 = recent_wins - recent_losses\n streak_factor = 1 if is_winning_streak else -1\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\n print pred1, pred2, pred3, pred4\n return pred1 + pred2 + pred3 + pred4","def relative_partition_function(self):\n return self.overall_norm**2","def lorentzian(mu, wid, x): \n return np.ones(len(x) ) / (1 + ( (x - mu) / (0.5 * wid) )**2)","def NormU(x):\n return sum(0.5*x**2)","def sampleWeight(self):\r\n x=random.random()\r\n i = 0\r\n n = len(self.weights)-1\r\n cummulativeWeight = 0\r\n #Distribute the exploration weight evenly among all the actions that have been\r\n #taken up to this point in time by any of the users\r\n if len(self.sampledActions) == 0:\r\n explorationWeight = 0\r\n else:\r\n explorationWeight = self.explorationFund / len(self.sampledActions)\r\n #Compute the normalization factor. If no action has been sampled by this user yet,\r\n #then each action k has weight eta*pi_k, where pi_k is the weight of k in the\r\n #prior distribution. Then, the normalization factor is the sum(eta*pi_k) for all k,\r\n #which is equal to eta*sum(pi_k), which is just eta, since the sum of the previous\r\n #weights has to add up to 1.\r\n #If one or more actions have been already sampled, the normalization factor is the\r\n #sum of 1) the weights already in self.weights, 2) the exploration fund, and 3) the\r\n #weights of the actions that are not yet in self.weights. Each one of these actions\r\n #has weight eta*pi_k (because it hasn't been sampled yet), so the total weight of the\r\n #mass of actions not yet in self.weights is eta*(1-sum(pi_l)), where the sum is over all\r\n #the weights already in self.weights\r\n if n < 0:\r\n normalizationFactor = self.priorBelief\r\n else:\r\n normalizationFactor = sum(self.weights) + self.explorationFund + \\\r\n self.priorBelief*(1-self.priorTopicDistr.cummulative[n])\r\n #Keep getting the next weight until the combined mass of the weights is less than the\r\n #random number x\r\n while True:\r\n w = self.__getitem__(i)\r\n if i in self.sampledActions:\r\n w += explorationWeight\r\n cummulativeWeight += w\r\n if x <= cummulativeWeight/normalizationFactor:\r\n if i not in self.sampledActions:\r\n self.sampledActions.append(i)\r\n return w\r\n i += 1","def _wk_test(self, alternative='two-sided', alpha=0.01):\n\n q0 = self.get_group_data(0, self.df_test_resampled, ['Q'])\n q1 = self.get_group_data(1, self.df_test_resampled, ['Q'])\n\n u_wk, p_wk = mannwhitneyu(q0, q1, alternative=alternative)\n stats_wk = ranksums(q0, q1)[0]\n\n if p_wk <= alpha:\n h = 1\n else:\n h = 0\n\n stats_wk = {'zval': stats_wk, 'pval': p_wk}\n\n return h, stats_wk","def weight_function(*, loudness, peak_num, means, means_trend, peaks_trend):\n return 0.333*(peak_num + means - loudness) + means_trend + peaks_trend","def expected_improvement(f_min, mu, sigma):\n # log-scaling might not be the best idea here, especially\n # if people use negative values to maximize output\n # v = (np.log(f_min) - mu) / sigma\n v = (f_min - mu) / sigma\n return (f_min * norm.cdf(v)\n - (np.exp(0.5 * sigma ** 2 + mu)\n * norm.cdf(v - sigma)))","def user_interaction(uv, recommended_News, ranked=True):\n\n iv = recommended_News[\"topical_vector\"]\n\n product = simple_doct_product(uv, iv)\n\n epsilon = 10e-5\n\n if (product + epsilon) > 1.0:\n vui = 0.99\n else:\n vui = beta_distribution(product)\n\n # Awared preference\n ita = beta_distribution(0.98)\n pui = vui * ita\n\n rand_num = np.random.random()\n\n if rand_num < pui:\n return True\n else:\n return False","def get_snr_weight(otf, snr):\n # filter\n weight = snr * np.abs(otf) ** 2\n\n # deal with any diverging points by averaging nearest pixels\n if np.any(np.isinf(weight)):\n xx, yy = np.meshgrid(range(weight.shape[1]), range(weight.shape[0]))\n xinds = xx[np.isinf(weight)]\n yinds = yy[np.isinf(weight)]\n\n # todo: not handling case where these points might be outside ROI\n for xi, yi in zip(xinds, yinds):\n weight[yi, xi] = np.mean([weight[yi + 1, xi], weight[yi - 1, xi], weight[yi, xi+1], weight[yi, xi-1]])\n\n return weight","def apply_squashing_func(mu, x, logp_pi):\n # Squash the output\n deterministic_policy = tf.tanh(mu)\n policy = tf.tanh(x)\n # OpenAI Variation:\n # To avoid evil machine precision error, strictly clip 1-pi**2 to [0,1] range.\n # logp_pi -= tf.reduce_sum(tf.log(clip_but_pass_gradient(1 - policy ** 2, lower=0, upper=1) + EPS), axis=1)\n # Squash correction (from original implementation)\n logp_pi -= tf.reduce_sum(tf.log(1 - policy ** 2 + EPS), axis=1) # - tanh'(z) ?\n\n return deterministic_policy, policy, logp_pi","def f(x):\n return delta_eps_mu(x, mu) - delta","def k(rating):\n if rating < 100: return 40\n if rating < 200: return 20\n return 10","def mrr(self):\n _test = self.drop_bad_ratings()\n merged = pd.merge(left=_test, right=self.predict, on=['user', 'item'], how='right')[\n ['user', 'item', 'rating_x', 'rating_y']]\n nott = np.vectorize(lambda x: not x)\n mrrs = []\n for user in merged.user.unique():\n frame = merged[merged.user == user].sort_values(by='rating_y', ascending=False)\n true_ratings = frame.rating_x.values\n positions = np.where(nott(np.isnan(true_ratings)))[0]\n if len(positions) > 0:\n mrrs.append(1 / (positions[0] + 1))\n else:\n mrrs.append(0)\n\n return sum(mrrs) / len(mrrs)","def f_U_function(W, gamma):\n if W > 0:\n if gamma == 1:\n utility = math.log(W)\n elif gamma >= 0:\n # Minus 1 or not.\n utility = math.pow(W, 1 - gamma) / (1 - gamma)\n else:\n print('The risk aversion parameter should be non-negative numbers.')\n else:\n print('The wealth should be non-negative. Now {}.'.format(W))\n utility = 0\n return utility","def Probability(rating1, rating2):\n return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400))","def frank_wolfe(initial_point, iterations, kernel, kernel_mean_map, test_points, steps='line-search'):\n line_search = False\n if steps == 'line-search':\n line_search = True\n rho_arr = np.empty(iterations)\n rho_arr[0] = frank_wolfe_step_line_search(initial_point, np.zeros((0)), np.zeros((0,2)), kernel, kernel_mean_map)\n elif type(steps) is str:\n rho_arr = frank_wolfe_steps(steps, iterations)\n elif type(steps) in [list, np.ndarray]:\n rho_arr = np.asarray(steps)\n else:\n raise Exception(\"Don't understand rho_method={}\".format(steps))\n\n assert len(rho_arr) == iterations\n ret = np.empty((iterations, initial_point.shape[1]))\n ret[0, :] = initial_point\n for i in xrange(1, iterations):\n # todo: optimal weights\n weights = frank_wolfe_weights(rho_arr, i)\n scores = frank_wolfe_scores(weights, ret[:i, :], test_points, kernel, kernel_mean_map)\n best_score_ix = np.argmin(scores)\n new_pt = test_points[best_score_ix, :]\n ret[i, :] = new_pt\n \n if line_search:\n rho_arr[i] = frank_wolfe_step_line_search(new_pt, weights, ret[:i, :], kernel, kernel_mean_map)\n final_weights = frank_wolfe_weights(rho_arr, iterations)\n return ret, final_weights","def get_tuned_f1(results_df):\n df = results_df\n scores = []\n f1s = []\n lambdas = np.arange(.5, 5, .05)\n\n def add_weighted(df, lam):\n \"\"\" Calculates different weighted PMI values after already having mut_inf scores \"\"\"\n df['mut_inf_weighted'] = df.mut_inf + (lam - 1) * (df.head_conditional + df.tail_conditional) / 2.\n\n for lam in lambdas:\n ss = StandardScaler()\n add_weighted(df, lam=lam)\n model = GaussianMixture(2, n_init=1)\n dat = ss.fit_transform(df[['mut_inf_weighted']])\n pred = model.fit_predict(dat)\n score = model.aic(dat)\n f1 = f1_score((model.means_.argmax() == df.label), pred)\n scores.append(score)\n f1s.append(f1)\n\n scores = np.array(scores)\n f1s = np.array(f1s)\n lam = lambdas[scores.argmax()]\n\n optimal_lambda = lambdas[scores.argmax()]\n optimal_f1 = f1s[scores.argmax()]\n return optimal_f1, optimal_lambda","def workout_rank(a, rank):\r\n # Check if workout score is empty\r\n if pd.isnull(a):\r\n return np.nan\r\n else:\r\n return int(rank)","def modified_z_score(x: np.ndarray) -> np.ndarray:\n return 0.6745 * (x - np.median(x)) / median_absolute_deviation(x)","def ltiecorrect(rankvals):\r\n sorted,posn = shellsort(rankvals)\r\n n = len(sorted)\r\n T = 0.0\r\n i = 0\r\n while (i<n-1):\r\n if sorted[i] == sorted[i+1]:\r\n nties = 1\r\n while (i<n-1) and (sorted[i] == sorted[i+1]):\r\n nties = nties +1\r\n i = i +1\r\n T = T + nties**3 - nties\r\n i = i+1\r\n T = T / float(n**3-n)\r\n return 1.0 - T","def bukinn6fcn(x: np.ndarray) -> np.ndarray:\n\n n = x.shape[1]\n assert n == 2, \"The Bukin N. 6 functions is only defined on a 2D space.\"\n\n X = x[:, 0]\n X2 = X**2\n Y = x[:, 1]\n\n scores = 100 * np.sqrt(np.abs(Y - 0.01 * X2)) + 0.01 * np.abs(X + 10)\n return scores","def penalty(self):\n assert len(self.weights) == len(self.means), \"Dimensions!\"\n out = np.exp(self.data['riskfree'] * self.data['maturity'])\n for weight, mean in zip(self.weights, self.means):\n out -= weight * np.exp(mean * self.data['maturity'])\n return (out**2).mean()**.5","def value(a, y, weights, lambda_):\n\t\treturn np.sum(np.nan_to_num(-y*np.log(a + 1e-15)-(1-y)*np.log(1-a + 1e-15))) / (a.shape[0] * a.shape[1])\n\t\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) ","def test_k_rank_approximate(corpus):\n return"],"string":"[\n \"def generate_good(self, m, n, rank, mu=2, ka=2):\\n sr = random.random()\\n s = []\\n s.append(sr)\\n for r in range(rank-1):\\n newele = s[-1] * (1 + ka * random.random() / (rank-1))\\n s.append(newele)\\n s.reverse()\\n \\n # best_u = None\\n # best_mu0 = 0\\n # while best_mu0 == 0:\\n # for _ in range(10):\\n # A = np.random.rand(m,m)\\n # A = scipy.linalg.orth(A)\\n # u = A[:, :rank]\\n # mu0 = self.compute_mu(u, m, rank)\\n # print(\\\"mu0 : \\\", mu0)\\n # if mu0 <= mu and mu0 >= best_mu0:\\n # best_mu0 = mu0\\n # best_u = u\\n # print(\\\"mu0 for u:\\\", best_mu0)\\n # # print(u.T @ u)\\n \\n # best_v = None\\n # best_mu0 = 0\\n # while best_mu0 == 0:\\n # for _ in range(10):\\n # B = np.random.rand(n,n)\\n # B = scipy.linalg.orth(B)\\n # v = B[:, :rank]\\n # mu0 = self.compute_mu(v, n, rank)\\n # print(\\\"mu0 : \\\", mu0)\\n # if mu0 <= mu and mu0 >= best_mu0:\\n # best_mu0 = mu0\\n # best_v = v\\n # print(\\\"mu0 for v:\\\", best_mu0)\\n # u = best_u\\n # v = best_v\\n\\n for _ in range(100):\\n A = np.random.rand(m,m)\\n A = scipy.linalg.orth(A)\\n u = A[:, :rank]\\n mu0 = self.compute_mu(u, m, rank)\\n print(\\\"mu0 : \\\", mu0)\\n if mu0 <= mu:\\n break\\n print(\\\"mu0 for u:\\\", mu0) \\n\\n for _ in range(10):\\n B = np.random.rand(n,n)\\n B = scipy.linalg.orth(B)\\n v = B[:, :rank]\\n mu0 = self.compute_mu(v, n, rank)\\n print(\\\"mu0 : \\\", mu0)\\n if mu0 <= mu:\\n break\\n print(\\\"mu0 for both:\\\", mu0)\\n\\n matrix = np.dot(u*s, v.T)\\n \\n kappa = s[0] / s[-1]\\n print(\\\"kappa=\\\", kappa)\\n \\n ss = np.copy(s)\\n for k in range(rank):\\n ss[k] = s[k] / s[0]\\n \\n max_entry = np.max(np.abs(np.outer(u[:,:rank], v.T[:rank,:])))\\n mu1 = max_entry * math.sqrt(m * n / rank)\\n print(\\\"mu1=\\\", mu1)\\n \\n return matrix\",\n \"def zipf(ranks, item):\\n if item in ranks:\\n return 0.1 / ranks[item]\\n return -1\",\n \"def pred(self,u,i):\\r\\n if self.user_based:\\r\\n bias=self.mu[u]\\r\\n else:\\r\\n bias=self.mu[i]\\r\\n pred=self.X[i,:].dot(self.W[:,u])+bias\\r\\n if pred<0:\\r\\n return 0\\r\\n if pred>5:\\r\\n return 5\\r\\n return pred\",\n \"def fn(u):\\n if u == n-1: return 1 # boundary condition \\n ans = 0\\n for v, _ in graph[u]: \\n if dist[u] > dist[v]: ans += fn(v)\\n return ans\",\n \"def up_next_weight(data, weight_o):\\n \\n del0 = 4e-3 # minimum coordinate\\n delta = 5e-3 # spacing of grid\\n M = 10 # number of samples\\n S = 5 # number of steps in the random walk\\n\\n N = data.shape[1]\\n\\n # Computing Universal Portfolio.\\n r = np.ones(N) / N # Start each one at the uniform point\\n b = np.ones(r.shape[0])\\n \\n allM = np.zeros((N, M)) # Take the average of m samples\\n for m in range(M):\\n b = r.copy()\\n for i in range(S):\\n bnew = b.copy()\\n j = np.random.randint(N - 1)\\n a = np.random.choice([-1, 1])\\n bnew[j] = b[j] + (a * delta)\\n bnew[N - 1] = b[N - 1] - (a * delta)\\n if bnew[j] >= del0 and bnew[N - 1] >= del0:\\n muliplier_x = min(1, np.exp((b[N - 1] - (2 * del0)) / (N * delta)))\\n x = np.prod(data @ b) * muliplier_x\\n muliplier_y = min(1, np.exp((bnew[N - 1] - (2 * del0)) / (N * delta)))\\n y = np.prod(data @ bnew) * muliplier_y\\n pr = min(y / x, 1) # or pr = min(x / y, 1)\\n if np.random.rand() < pr:\\n b = bnew.copy()\\n allM[:, m] = b\\n\\n weight = np.mean(allM, 1) # Taking the average of m samples.\\n return weight / sum(weight)\",\n \"def score_items(X, U, mu,\\n scoremethod='lowhigh',\\n missingmethod='none',\\n feature_weights=[]):\\n\\n # Use U to model and then reconstruct the data in X.\\n # 1. Project all data in X into space defined by U,\\n # then reconstruct it.\\n if missingmethod.lower() != 'ignore':\\n # All missing values should have been replaced with 0,\\n # or non-existent.\\n # 1a. Subtract the mean and project onto U\\n proj = np.dot(U.T, (X - mu))\\n # 1b. Reconstruct by projecting back up and adding mean\\n reproj = np.dot(U, proj) + mu\\n # 1c. Compute the residual\\n #print('X:', X.T)\\n #print('reproj:', reproj.T)\\n err = X - reproj\\n #print('err:', err.T)\\n #input()\\n \\n else:\\n # Missing method must be 'ignore' (Brand 2002)\\n (err, reproj) = compute_error_with_missing(X, U, mu)\\n\\n # 2. Compute reconstruction error\\n if scoremethod == 'low': # Blank out all errors > 0\\n err[err>0] = 0\\n elif scoremethod == 'high': # Blank out all errors < 0\\n err[err<0] = 0\\n else: # default, count everything\\n pass\\n \\n # Weight features if requested\\n if len(feature_weights) > 0:\\n for i in range(len(feature_weights)):\\n err[i,:] = err[i,:] * feature_weights[i]\\n\\n if missingmethod.lower() == 'ignore':\\n # Only tally error for observed features.\\n # This means that items with missing values are not penalized\\n # for those features, which is probably the best we can do.\\n scores = np.nansum(np.array(np.power(err, 2)), axis=0)\\n else:\\n scores = np.sum(np.array(np.power(err, 2)), axis=0)\\n\\n #print('scores:', scores)\\n #print('reproj:', reproj)\\n #input()\\n return (scores, reproj)\",\n \"def map_at_k(\\n rating_true,\\n rating_pred,\\n col_user=DEFAULT_USER_COL,\\n col_item=DEFAULT_ITEM_COL,\\n col_rating=DEFAULT_RATING_COL,\\n col_prediction=DEFAULT_PREDICTION_COL,\\n relevancy_method=\\\"top_k\\\",\\n k=DEFAULT_K,\\n threshold=DEFAULT_THRESHOLD,\\n):\\n df_hit, df_hit_count, n_users = merge_ranking_true_pred(\\n rating_true=rating_true,\\n rating_pred=rating_pred,\\n col_user=col_user,\\n col_item=col_item,\\n col_rating=col_rating,\\n col_prediction=col_prediction,\\n relevancy_method=relevancy_method,\\n k=k,\\n threshold=threshold,\\n )\\n\\n if df_hit.shape[0] == 0:\\n return 0.0\\n\\n # calculate reciprocal rank of items for each user and sum them up\\n df_hit_sorted = df_hit.sort_values([col_user, \\\"rank\\\"])\\n df_hit_sorted[\\\"rr\\\"] = (df_hit.groupby(col_user).cumcount() + 1) / df_hit[\\\"rank\\\"]\\n df_hit_sorted = df_hit_sorted.groupby(col_user).agg({\\\"rr\\\": \\\"sum\\\"}).reset_index()\\n\\n df_merge = pd.merge(df_hit_sorted, df_hit_count, on=col_user)\\n return (df_merge[\\\"rr\\\"] / df_merge[\\\"actual\\\"]).sum() / n_users\",\n \"def styblinskitankfcn(x: np.ndarray) -> np.ndarray:\\n n = x.shape[1]\\n scores = np.zeros((x.shape[0], 1))\\n for i in range(n):\\n scores += x[:, i] ** 4 - 16 * x[:, i] ** 2 + 5 * x[:, i]\\n scores *= 0.5\\n return scores\",\n \"def GoodmanKruskalLambdaR_calc(TP, FP, FN, TN):\\n try:\\n n = TP + FP + FN + TN\\n part1 = 0.5 * (max(TP + FP, FN + TN) + max(TP + FN, FP + TN))\\n return (TP + TN - part1) / (n - part1)\\n except Exception:\\n return \\\"None\\\"\",\n \"def question_18():\\n rbf = RadialBiasFunction()\\n wins = 0.0\\n for i in range(100):\\n rbf.fit(1.5, 9)\\n rbf_error = rbf.error()\\n if rbf_error == 0:\\n wins += 1\\n rbf.resample()\\n return wins / 100\",\n \"def nac_w_optimal_r(fan_in, fan_out):\\n fan = max(fan_in + fan_out, 5)\\n r = scipy.optimize.bisect(lambda r: fan * nac_w_variance(r) - 2, 0, 10)\\n return r\",\n \"def custom_score(game, player):\\n \\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n # First obtain the improved score\\n player_legal_moves = game.get_legal_moves(player)\\n opponent = game.get_opponent(player)\\n opponent_legal_moves = game.get_legal_moves(opponent)\\n improved = len(player_legal_moves) - len(opponent_legal_moves)\\n if improved != 0:\\n return float(improved)\\n \\n # Second get differences from center\\n center_coordinates = center_y, center_x = get_center_coordinates(game)\\n player_coordinates = game.get_player_location(player)\\n opponent_coordinates = game.get_player_location(opponent)\\n player_center_dist = get_distances_from_center(center_coordinates, player_coordinates)\\n opponent_center_dist = get_distances_from_center(center_coordinates, opponent_coordinates)\\n center_dist_diff = player_center_dist - opponent_center_dist\\n \\n # Third obtain next_moves\\n player_next_moves = [get_next_moves(game, move, list(move)) for move in player_legal_moves]\\n opponent_next_moves = [get_next_moves(game, move, list(move)) for move in opponent_legal_moves] \\n improved_next = len(player_next_moves) - len(opponent_next_moves)\\n \\n # Put player and opponent feature differences in a tuple/vector surrogoate\\n feature_diff_vector = (improved, center_dist_diff, improved_next)\\n \\n # Provide a weighting vector for the features of each player-participant\\n weight_vector = (1.5,0.1,1.0)\\n # Calculate the return value = weighted difference of players' features\\n weighted_difference_dot_product = sum(p*q for p,q, \\\\\\n in zip(feature_diff_vector, weight_vector))\\n \\n return float(weighted_difference_dot_product)\",\n \"def recip_rank(recs, truth):\\n good = recs['item'].isin(truth.index)\\n npz, = np.nonzero(good)\\n if len(npz):\\n return 1.0 / (npz[0] + 1.0)\\n else:\\n return 0.0\",\n \"def user_interaction_score(uv, recommended_News, ranked=True):\\n\\n iv = recommended_News[\\\"topical_vector\\\"]\\n\\n product = simple_doct_product(uv, iv)\\n\\n epsilon = 10e-5\\n\\n if (product + epsilon) > 1.0:\\n vui = 0.99\\n else:\\n vui = beta_distribution(product)\\n\\n # Awared preference\\n ita = beta_distribution(0.98)\\n pui = vui * ita\\n\\n return pui\",\n \"def prioritized_sweeping_policy_evaluation(self,pi, U1, k=maxint , epsilon=0.001):\\n R, gamma ,expect_vec_u = self.R, self.gamma , self.expected_vec_utility\\n h = []\\n\\n for s in range(self.nstates):\\n heappush( h , (-self.rmax-random.uniform(0,1),s) )\\n\\n for i in count(0):\\n U = U1.copy()\\n\\n (delta,s) = heappop(heap)\\n\\n U1[s] = R(s) + gamma * expect_vec_u(s,pi[s],U)\\n\\n delta = l1distance(U1[s],U[s])\\n\\n if i > k or delta < epsilon * (1 - gamma) / gamma:\\n return U\",\n \"def _wilcoxon(_sample_a, _sample_b):\\n res = stats.ranksums(_sample_a, _sample_b)\\n print('Wilcoxon rank-sum\\\\nstatistic: {}\\\\np-value: {}'.format(res[0], res[1]))\\n print('-' * 10)\",\n \"def custom_score_3(game, player):\\n \\n \\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n \\n player_legal_moves = game.get_legal_moves(player)\\n opponent = game.get_opponent(player)\\n opponent_legal_moves = game.get_legal_moves(opponent)\\n player_legal_move_count, opponent_legal_move_count = \\\\\\n len(player_legal_moves), len(opponent_legal_moves)\\n move_count_difference = player_legal_move_count - opponent_legal_move_count\\n # Find coordinates of center box\\n h, w = get_center_coordinates(game)\\n # Retrieve player's coordinates\\n y, x = game.get_player_location(player)\\n # Obtain coordinate further, closest to origin\\n furthest_coord, closest_coord = max(h - y, w -x), min(h - y, w - x)\\n # Return weighted, vector-valued length from origin / sum of weights\\n weighted_distance_from_center = \\\\\\n math.sqrt((closest_coord**2 + 2*(furthest_coord**2)))/3\\n feature_vector = (move_count_difference, weighted_distance_from_center)\\n \\n weight_vector = (1.0,0.1)\\n \\n weighted_difference_dot_product = sum(p*q for p,q, \\\\\\n in zip(weight_vector, feature_vector)) \\n \\n return float(weighted_difference_dot_product)\",\n \"def rankw(G, i, damp=0.85, visited=None):\\n _, _, Lcin, Lwin, _, _ = G[i]\\n\\n sumr = 0\\n for j, w in zip(Lcin, Lwin):\\n _, Jscore, _, _, Jcout, Jwout = G[j]\\n sumr += Jscore * w / sum(Jwout)\\n return (1 - damp) + damp * sumr\",\n \"def utility(self, util_f, priors=None):\\n if priors is None: priors = self.group_size_dist()\\n posteriors = self.dist() * priors[:,None,None] # p x m x r\\n prob_R = posteriors.sum(axis=0) # m x r\\n posteriors /= prob_R # p x m x r\\n return (util_f(posteriors) * prob_R).sum(axis=1)\",\n \"def inverse_rank_norm(values):\\n #values = pd.Series([5, 7, 2, 1, 1])\\n quantiles = (values.rank()-0.5)/(len(values))\\n return ss.norm.ppf(quantiles)\",\n \"def lowest_rank_approx(A,e):\",\n \"def _weight(self, user_a, user_i):\\n a_votes = self._votes[user_a]\\n i_votes = self._votes[user_i]\\n\\n a_mean = self._averages[user_a]\\n i_mean = self._averages[user_i]\\n\\n item_ndx = np.logical_and(np.isfinite(a_votes),\\n np.isfinite(i_votes))\\n\\n a_deviations = a_mean - a_votes[item_ndx]\\n i_deviations = i_mean - i_votes[item_ndx]\\n\\n numerator = np.sum(a_deviations * i_deviations)\\n denominator = (np.sum(np.power(a_deviations, 2)) *\\n np.sum(np.power(i_deviations, 2)))\\n\\n if denominator < _EPSILON:\\n return 0\\n return numerator / np.sqrt(denominator)\",\n \"def calculate_WR():\\n C = df['rating_avg'].mean()\\n m = df['rating_count'].quantile(0.9)\\n v = df['rating_count']\\n R = df['rating_avg']\\n return (v/(v+m) * R) + (m/(m+v) * C)\",\n \"def calculate_weighted_results():\\n pass\",\n \"def griewankfcn(x: np.ndarray) -> np.ndarray:\\n n = x.shape[1]\\n sumcomp = np.zeros((x.shape[0], 1))\\n prodcomp = np.ones((x.shape[0], 1))\\n\\n for i in range(n):\\n sumcomp += x[:, i : i + 1] ** 2\\n prodcomp *= np.cos(x[:, i : i + 1] / np.sqrt(i + 1))\\n\\n scores = (sumcomp / 4000) - prodcomp + 1\\n return scores\",\n \"def estimate(self, u, j):\\n if not (self.trainset.knows_user(u) and self.trainset.knows_item(j)):\\n raise PredictionImpossible('User and/or item is unknown.')\\n\\n u_ratings = self.trainset.ur[u]\\n\\n if self.weighting == 'linear':\\n weight = sum(self.freqs[i, j, self.to_index(r)] for i, r in u_ratings)\\n score = sum(self.sums[i, j, self.to_index(r)] for i, r in u_ratings)\\n return score / weight\\n\\n # self.weighting == 'log' or None\\n weights = [self.freqs[i, j, self.to_index(r)] for i, r in u_ratings]\\n reciprocals = [1 / w if w else 0 for w in weights]\\n scores = [self.sums[i, j, self.to_index(r)] for i, r in u_ratings]\\n scores = [s * w for s, w in zip(scores, reciprocals)]\\n\\n if self.weighting is None:\\n return np.mean(scores)\\n # self.weighting == 'log'\\n logs = [np.log(w + 1) if w >= 1 else 0 for w in weights]\\n return np.dot(scores, logs) / np.sum(logs)\",\n \"def evaluate_ranks(articles, rank_tuples):\\n rank_tp=defaultdict(int)\\n rank_fn=defaultdict(int)\\n rank_fp=defaultdict(int)\\n \\n for article in articles:\\n for mention in article.entity_mentions:\\n form=mention.mention\\n meaning=mention.gold_link\\n sys_meaning=mention.sys_link\\n t_gold=(form, meaning)\\n t_sys=(form, sys_meaning)\\n for rank, r_tuples in rank_tuples.items():\\n if t_gold in r_tuples and t_sys in r_tuples:\\n rank_tp[rank]+=1\\n break\\n elif t_gold in r_tuples:\\n rank_fn[rank]+=1\\n elif t_sys in r_tuples:\\n rank_fp[rank]+=1\\n print('tp', rank_tp)\\n print('fp', rank_fp)\\n print('fn', rank_fn)\\n \\n rank_prec={}\\n rank_recall={}\\n rank_f1={}\\n \\n for rank in range(1,13):\\n if rank_tp[rank]+rank_fp[rank]>0:\\n rank_prec[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fp[rank])\\n else:\\n rank_prec[rank]=0.0\\n if rank_tp[rank]+rank_fn[rank]>0:\\n rank_recall[rank]=rank_tp[rank]/(rank_tp[rank]+rank_fn[rank])\\n else:\\n rank_recall[rank]=0.0\\n if rank_prec[rank]+rank_recall[rank]>0:\\n rank_f1[rank]=2*rank_prec[rank]*rank_recall[rank]/(rank_prec[rank]+rank_recall[rank])\\n else:\\n rank_f1[rank]=0.0\\n print('precision', rank_prec)\\n print()\\n print('recall', rank_recall)\\n print()\\n print('f1', rank_f1)\\n print()\\n return rank_prec, rank_recall, rank_f1\",\n \"def _estimate_stats_with_rel_info(self):\\r\\n nonrel_list = []\\r\\n rel_list = []\\r\\n for ele in self._ranking_list:\\r\\n score = ele[0]\\r\\n rel = ele[1]\\r\\n if rel:\\r\\n rel_list.append(score)\\r\\n else:\\r\\n nonrel_list.append(score)\\r\\n m1,v1 = self._compute_stat_from_list(rel_list) \\r\\n m0,v0 = self._compute_stat_from_list(nonrel_list)\\r\\n estimated_lambda = len(rel_list)*1.0/len(self._ranking_list)\\r\\n\\r\\n self._m1 = m1\\r\\n self._v1 = v1\\r\\n self._m0 = m0\\r\\n self._v0 = v0\\r\\n self._lambda = estimated_lambda\\r\\n\\r\\n if self._debug :\\r\\n print \\\"m1: %f, v1: %f, m0: %f, v0: %f\\\" %(self._m1,self._v1,self._m0,self._v0)\\r\\n print \\\"lambda: %f\\\" %(estimated_lambda)\",\n \"def custom_score_6(game, player):\\n \\\"\\\"\\\"custom_score_6 heuristic function aims towards weighted chances heuristics\\n \\\"\\\"\\\"\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\\n return float(length_my_player_moves*length_my_player_moves - 1.5*length_opp_payer_moves*length_opp_payer_moves)\",\n \"def _discount_for_relative_rank_diff():\\n # The LambdaLoss is not well defined when topn is active and topn <\\n # list_size. The following implementation is based on Equation 18 proposed\\n # in https://research.google/pubs/pub47258/. Please refer to\\n # `DCGLambdaWeightV2` for a better implemention to handle topn.\\n pair_valid_rank = _apply_pairwise_op(tf.logical_or,\\n tf.less_equal(ranks, topn))\\n rank_diff = tf.cast(\\n tf.abs(_apply_pairwise_op(tf.subtract, ranks)), dtype=tf.float32)\\n pair_discount = tf.where(\\n tf.logical_and(tf.greater(rank_diff, 0), pair_valid_rank),\\n tf.abs(\\n self._rank_discount_fn(rank_diff) -\\n self._rank_discount_fn(rank_diff + 1)), tf.zeros_like(rank_diff))\\n return pair_discount\",\n \"def rank_training(self, fleet):\\n if (len(fleet.ships) == 0):\\n return get_rank(0)\\n ins = fleet.get_ship_instances()\\n sum_lvl = sum(x.level for x in ins)\\n avg_lvl = sum_lvl // len(fleet.ships)\\n # auto S if fleet > 50% avg lvl of difficulty\\n if (avg_lvl >= self.avg_lvl * 1.5):\\n return get_rank(1)\\n if (avg_lvl >= self.avg_lvl): # must succeed if fleet > avg lvl\\n avg_weight = SUCCESS_THRESHOLD + (1 - SUCCESS_THRESHOLD) / 2\\n wgt = random.gauss(avg_weight, (1 - SUCCESS_THRESHOLD) / 2) \\\\\\n + inv_lerp(avg_lvl, self.avg_lvl, self.avg_lvl * 1.5) \\\\\\n * (1 - SUCCESS_THRESHOLD) / 2\\n wgt = max(SUCCESS_THRESHOLD, min(1.0, wgt))\\n return get_rank(wgt)\\n wgt = inv_lerp(avg_lvl, 0, self.avg_lvl) * (SUCCESS_THRESHOLD * 0.67)\\n return get_rank(abs(random.gauss(0, SUCCESS_THRESHOLD * 0.33)) + wgt)\",\n \"def wilcoxon_w(pairs: List[Tuple[Union[float, int], int]]) -> float:\\n i = 0\\n w = 0.0\\n while i < len(pairs):\\n absi = pairs[i][0]\\n sum_sgn = pairs[i][1]\\n j = i + 1\\n while j < len(pairs) and pairs[j][0] == absi:\\n sum_sgn += pairs[j][1]\\n j += 1\\n r = 1 + 0.5 * (i + j - 1) # smoothed rank\\n w += r * sum_sgn\\n i = j\\n return w\",\n \"def round_stochastic(w):\\n w_ = np.floor(w)\\n ps = w - w_ \\n return w_ + np.random.binomial(1, p=ps)\",\n \"def transfo_quantile(xx):\\n\\n # nn = np.zeros(len(xx))\\n # oo = np.argsort(xx)\\n # nn[oo] = np.arange(len(xx)) / len(xx) + 1 / (2 * len(xx))\\n # return nn\\n\\n return rankdata(xx) / len(xx) - 1 / (2 * len(xx))\",\n \"def wicked_utility_func(x):\\n mean_global_opt = [0.2, 0.8]\\n s_1 = 0.05\\n s_2 = 0.05\\n r_12 = -0.9\\n cov_global_opt = [[s_1**2, r_12*s_1*s_2], \\n [r_12*s_1*s_2, s_2**2]]\\n rv_global = multivariate_normal(mean_global_opt, cov_global_opt)\\n C_global = 1/rv_global.pdf(mean_global_opt)\\n\\n mean_local_opt = [0.7, 0.3]\\n s_1 = 0.3\\n s_2 = 0.2\\n r_12 = 0.0\\n cov_local_opt = [[s_1**2, r_12*s_1*s_2], \\n [r_12*s_1*s_2, s_2**2]]\\n rv_local = multivariate_normal(mean_local_opt, cov_local_opt)\\n C_local = 0.8 * 1/rv_local.pdf(mean_local_opt)\\n\\n return C_global*rv_global.pdf(x[0:2]) + C_local*rv_local.pdf(x[0:2])\",\n \"def f(ns, k):\\n ns = list(enumerate(sorted(ns)))\\n n = len(ns)\\n min_u = unfair(ns[:k])\\n nns = ns[k:]\\n def g(mu, n):\\n (i, n) = n\\n unfairness = n - ns[i - k + 1][1]\\n # print(i, n, unfairness)\\n if unfairness < mu:\\n return unfairness\\n return mu\\n return reduce(g, nns, min_u)\",\n \"def mu(self, x):\\n x = utils.to_float_if_int(x)\\n utils.verify_is_numeric(x)\\n\\n return (\\n 0\\n if (x < self.__l or x > self.__r)\\n else (x - self.__l) / (self.__n - self.__l)\\n if (self.__l <= x <= self.__n)\\n else (self.__r - x) / (self.__r - self.__n)\\n )\",\n \"def amannwhitneyu(x,y):\\r\\n n1 = len(x)\\r\\n n2 = len(y)\\r\\n ranked = rankdata(N.concatenate((x,y)))\\r\\n rankx = ranked[0:n1] # get the x-ranks\\r\\n ranky = ranked[n1:] # the rest are y-ranks\\r\\n u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx) # calc U for x\\r\\n u2 = n1*n2 - u1 # remainder is U for y\\r\\n bigu = max(u1,u2)\\r\\n smallu = min(u1,u2)\\r\\n T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores\\r\\n if T == 0:\\r\\n raise ValueError, 'All numbers are identical in amannwhitneyu'\\r\\n sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)\\r\\n z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc\\r\\n return smallu, 1.0 - azprob(z)\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def RScore(x,p,d):\\n \\n if x <= d[p][0.20]:\\n return 5\\n elif x <= d[p][0.4]:\\n return 4\\n elif x <= d[p][0.6]: \\n return 3\\n elif x <= d[p][0.8]:\\n return 2\\n else:\\n return 1\",\n \"def calculate_cornish_fisher_percentile(alpha, mu, sigma, skew, kurt):\\n\\n z = stats.norm.ppf(alpha)\\n he2 = np.polynomial.hermite_e.hermeval(z, [0.0, 0.0, 1.0])\\n he3 = np.polynomial.hermite_e.hermeval(z, [0.0, 0.0, 0.0, 1.0])\\n he13 = np.polynomial.hermite_e.hermeval(z, [0.0, -1.0, 0.0, -2.0])\\n\\n w = (z +\\n he2 * skew / 6 +\\n he3 * kurt / 24 +\\n he13 * (skew ** 2) / 36)\\n\\n return mu + sigma * w\",\n \"def atiecorrect(rankvals):\\r\\n sorted,posn = ashellsort(N.array(rankvals))\\r\\n n = len(sorted)\\r\\n T = 0.0\\r\\n i = 0\\r\\n while (i<n-1):\\r\\n if sorted[i] == sorted[i+1]:\\r\\n nties = 1\\r\\n while (i<n-1) and (sorted[i] == sorted[i+1]):\\r\\n nties = nties +1\\r\\n i = i +1\\r\\n T = T + nties**3 - nties\\r\\n i = i+1\\r\\n T = T / float(n**3-n)\\r\\n return 1.0 - T\",\n \"def evaluate_fitness(self, pos):\\n _, index = self.tree.query(pos)\\n return 1 - (self.fitness_function[index // self.resolution][index % self.resolution] - self.min) / (self.max - self.min)\",\n \"def test_ww_softrank(self):\\n\\t\\t#\\n\\t\\tdetails= self.watcher.analyze(layers=[self.fc3_layer], randomize=True, mp_fit=True)\\n\\t\\tprint(details[['ww_softrank','mp_softrank', 'lambda_max', 'rand_bulk_max', 'max_rand_eval']])\\n\\t\\tactual = details.ww_softrank[0]\\n\\t\\texpected = details.mp_softrank[0]\\n\\t\\tself.assertAlmostEqual(actual,expected, places=1)\\n\\t\\t\\n\\t\\tmax_rand_eval = details.max_rand_eval[0]\\n\\t\\tmax_eval = details.lambda_max[0]\\n\\t\\texpected = max_rand_eval/max_eval\\n\\t\\tself.assertAlmostEqual(actual,expected, places=2)\",\n \"def ks_test_uniform(v, alpha):\\n v = np.asarray(v).flatten()\\n n = v.shape[0]\\n\\n # эмпирическая функция распределения\\n v_var = np.sort(v)\\n\\n # теоритическая\\n v_emp = np.arange(0, n)/n\\n diff = np.abs(v_var - v_emp)\\n d = np.max(diff)\\n\\n lmbd = d * np.sqrt(n) + 1/(6*np.sqrt(n))\\n val = stats.kstwobign.ppf(alpha, n)\\n\\n print('lambda estimated: {}, table: {}'.format(lmbd, val))\\n if lmbd < val:\\n print('не можем опровергнуть')\\n else:\\n print('отвергаем')\",\n \"def tsp(self, u):\\n fitness = 0\\n for i in range(1, len(u), 1):\\n fitness = fitness + round(sqrt((u[i][0] - u[(i - 1)][0]) ** 2 + (u[i][1] - u[(i - 1)][1]) ** 2))\\n\\n fitness = fitness + round(sqrt((u[0][0] - u[(-1)][0]) ** 2 + (u[0][1] - u[(-1)][1]) ** 2))\\n return fitness\",\n \"def lmannwhitneyu(x,y):\\r\\n n1 = len(x)\\r\\n n2 = len(y)\\r\\n ranked = rankdata(x+y)\\r\\n rankx = ranked[0:n1] # get the x-ranks\\r\\n ranky = ranked[n1:] # the rest are y-ranks\\r\\n u1 = n1*n2 + (n1*(n1+1))/2.0 - sum(rankx) # calc U for x\\r\\n u2 = n1*n2 - u1 # remainder is U for y\\r\\n bigu = max(u1,u2)\\r\\n smallu = min(u1,u2)\\r\\n T = math.sqrt(tiecorrect(ranked)) # correction factor for tied scores\\r\\n if T == 0:\\r\\n raise ValueError, 'All numbers are identical in lmannwhitneyu'\\r\\n sd = math.sqrt(T*n1*n2*(n1+n2+1)/12.0)\\r\\n z = abs((bigu-n1*n2/2.0) / sd) # normal approximation for prob calc\\r\\n return smallu, 1.0 - zprob(z)\",\n \"def value(a, y, weights, lambda_):\\n\\t\\treturn 0.5* (np.linalg.norm(a-y)**2) / (a.shape[0] * a.shape[1])\\n\\t\\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) \",\n \"def premium(self):\\n premium = 0\\n for weight, mean, std in zip(self.weights, self.means, self.stds):\\n shift = (self.data['riskfree'] - mean) * self.data['maturity']\\n moneyness = np.array(self.data['moneyness']) + shift\\n premium += weight * blackscholes_norm(moneyness,\\n self.data['maturity'],\\n std, self.data['call'])\\n return premium\",\n \"def rank_transform(X):\\n return np.apply_along_axis(scipy.stats.rankdata, 0, X)\",\n \"def percenter(rank, max_rank):\\n\\treturn 100 * (rank/(max_rank or 1))\",\n \"def GoodmanKruskalLambda_calc(TP, FP, FN, TN):\\n try:\\n n = TP + FP + FN + TN\\n part1 = max(TP, FP) + max(FN, TN) + max(TP, FN) + max(FP, TN)\\n part2 = max(TP + FP, FN + TN) + max(TP + FN, FP + TN)\\n return (0.5 * (part1 - part2)) / (n - 0.5 * part2)\\n except Exception:\\n return \\\"None\\\"\",\n \"def custom_score(game, player):\\n # return penalize_corners_heuristic(game, player)\\n # return favor_run_away_heuristic(game, player)\\n return look_ahead_heuristic(game, player)\",\n \"def computeRmse(model, data, n , sc):\\n truth = data.map( lambda x: ((x[0], x[1]), x[2]) )\\n truth.cache()\\n ##print 'test zhou 0.....', truth.count() , '............', truth.take(10)\\n\\n predictions = model.predictAll(data.map(lambda x: (x[0], x[1])))\\n predictions.cache()\\n # here let's rescale predicted ratings to 0-10 scale\\n maxPrediction = predictions.map(lambda x: x[2]).max()\\n minPrediction = predictions.map(lambda x: x[2]).min()\\n maxRate = RatingScale\\n minRate = RatingScaleMin\\n ##print 'test zhou 1......', predictions.count(), '............', predictions.take(10)\\n\\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate )).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\\n\\n\\n #predictedRating = predictions.map(lambda x: ((x[0], x[1]), (x[2]-minPrediction)/(maxPrediction-minPrediction)*(maxRate-minRate)+minRate ) )\\n predictedRating = predictions.map(lambda x: ((x[0], x[1]), x[2] ) )\\n predictedRating.cache()\\n ##predictedRating.checkpoint()\\n ##print 'test zhou 2.......', predictedRating.count(), '............', predictedRating.take(10)\\n\\n\\n \\n\\n\\n predictionsAndRatings = predictedRating.join(truth).values()\\n #predictionsAndRatings = sc.union(predictedRating, truth)\\n predictionsAndRatings.cache()\\n #print 'test zhou 3........', predictionsAndRatings.count(), '............', predictionsAndRatings.take(10)\\n #predictionsAndRatings = predictions.map(lambda x: ((x[0], x[1]), x[2])).join(data.map(lambda x: ((x[0], x[1]), x[2]))).values()\\n \\n return sqrt(predictionsAndRatings.map(lambda x: (x[0] - x[1]) ** 2).reduce(add) / float(n))\\n #return 1.0\",\n \"def cal_expected_map(self, ranking_list, total_rel=0):\\r\\n s = 0.0\\r\\n pr = 0\\r\\n pn = 0\\r\\n for ele in reversed(ranking_list):\\r\\n rel_doc_cnt = ele[0]\\r\\n this_doc_cnt = ele[1]\\r\\n nonrel_doc_cnt = this_doc_cnt - rel_doc_cnt\\r\\n s += self.A(pr, pn, rel_doc_cnt, nonrel_doc_cnt)\\r\\n pr += rel_doc_cnt\\r\\n pn += nonrel_doc_cnt\\r\\n total_rel += rel_doc_cnt\\r\\n #print s/total_rel\\r\\n if total_rel == 0:\\r\\n return 0\\r\\n return s/total_rel\",\n \"def custom_score_7(game, player):\\n \\\"\\\"\\\"custom_score_7 heuristic function also aims towards weighted chances heuristics\\n \\\"\\\"\\\"\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\\n return float(1.5*length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)\",\n \"def custom_score_2(game, player):\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n own_moves = len(game.get_legal_moves(player))\\n\\n if game.move_count < 15:\\n opp_moves = len(game.get_legal_moves(game.get_opponent(player)))\\n return float(own_moves - opp_moves)\\n\\n delta = 0\\n\\n moves = game.get_legal_moves()\\n initial_moves_count = len(moves)\\n indexes = np.random.permutation(initial_moves_count)\\n\\n for i in range(0, min(4, initial_moves_count)):\\n first_level = True\\n simulation = game.copy()\\n\\n while True:\\n moves = simulation.get_legal_moves()\\n moves_count = len(moves)\\n if moves_count == 0:\\n if simulation.is_winner(player):\\n delta = delta + 1\\n else:\\n delta = delta - 1\\n break\\n if first_level:\\n selected_move = indexes[i]\\n first_level = False\\n else:\\n selected_move = random.randint(0, moves_count - 1)\\n\\n simulation.apply_move(moves[selected_move])\\n\\n return float(own_moves + delta) #float(own_moves - opp_moves + 5 * delta)\\n\\n #return float(own_moves - opp_moves + free_area_score(game, player) - free_area_score(game, game.get_opponent(player)))\",\n \"def custom_scoring(y_te, y_pred):\\n #weights computed with training data set\\n w = np.array([0.02409584, 0.00787456, 0.03685528, 0.01760536, 0.04589969, 0.8483942 , 0.01724058, 0.00203449]);\\n \\n ## F1 SCORES\\n #evaluate F1 score, precision and recall for each label, \\n #along with custom proportionally weighted F1 score\\n #and built in weighted and macro F1 scores\\n F1_tab, Ptab, Rtab, pf1 = F1_score(y_te, y_pred, w)\\n f = F1Score(8, threshold = 0.5, average = 'weighted')\\n f.update_state(y_te, y_pred)\\n wf1 = f.result().numpy() #weighted f1 score\\n f.reset_states()\\n f = F1Score(8, threshold = 0.5, average = 'macro')\\n f.update_state(y_te, y_pred)\\n mf1 = f.result().numpy() #macro f1 score\\n f.reset_states()\\n\\n ##EDIT DISTANCE\\n #edit_dist_av = LevDistMultilabels(y_true, y_pred)\\n\\n ##ACCURACY\\n #evaluate accuracy per label\\n acc_tab = Acc(y_te, y_pred)\\n\\n return wf1, mf1, pf1, F1_tab, Ptab, Rtab, acc_tab\",\n \"def process_weights(w, df, s, n):\\n T, N = w.shape\\n weight_mat = pd.DataFrame(index=df.index, columns=df.columns)\\n idx = weight_mat.index\\n\\n def process(series):\\n # True if less than 50% of obs. are constant\\n return series.value_counts(dropna=False).max() < 0.5 * s\\n\\n previousValidWeights = None\\n for t in range(n, T, s):\\n t_s = min(T, t + s)\\n weight_vect = np.zeros([N, 1])\\n # check if the past data are constant\\n # if asset i is constant set w_i = 0\\n sub_X = df.iloc[t:t_s, :].copy()\\n assets = sub_X.apply(process).values\\n weight_vect[assets, 0] = w[t][assets]\\n # Verify if sum(w) = 0\\n if weight_vect.min()<0:\\n weight_vect=weight_vect - weight_vect.min()\\n\\n if weight_vect.sum() == 0.:\\n if previousValidWeights is None:\\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, N]) / N\\n else :\\n weight_mat.iloc[t: t_s] = previousValidWeights\\n elif weight_vect.sum() != 1.:\\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, 1]) @ weight_vect.T / weight_vect.sum()\\n previousValidWeights = np.ones([t_s - t, 1]) @ weight_vect.T / weight_vect.sum()\\n else:\\n weight_mat.iloc[t: t_s] = np.ones([t_s - t, 1]) @ weight_vect.T\\n previousValidWeights = np.ones([t_s - t, 1]) @ weight_vect.T\\n\\n if abs(weight_mat.iloc[t:t_s].sum().sum()) <= 1e-6:\\n print('null prediction, investigate')\\n\\n return weight_mat\",\n \"def predictRating(toPredict, candidateList):\\n\\n ratingRelevantCandidates = []\\n\\n #Remove candidates with no rating specified\\n for candidate in candidateList:\\n currentCandidate = candidate[1]\\n\\n if float(currentCandidate['vote_avg']) > 0:\\n ratingRelevantCandidates.append((float(currentCandidate['vote_avg']), candidate))\\n\\n #print(\\\"ratings::::::::\\\",currentCandidate['vote_avg'])\\n\\n #Remove outlier candidates based on rating\\n ratingMean = np.mean([x[0] for x in ratingRelevantCandidates])\\n print(\\\"ratingMean\\\", ratingMean)\\n ratingSD = np.std([x[0] for x in ratingRelevantCandidates])\\n print(\\\"ratingSD\\\", ratingSD)\\n\\n finalRatings = [x for x in ratingRelevantCandidates if (float(x[0]) < ratingMean + ratingSD)]#1.5 *\\n finalRatings = [x for x in finalRatings if (float(x[0]) > ratingMean - ratingSD)]#.75 *\\n\\n finalRatingCandidatesWithWeight = []\\n\\n #Weight each candidate based on vote count, direct and actor popularity and matching score from part 1\\n for candidate in finalRatings:\\n directorPoints = compareDirectorPoints(toPredict['director'], candidate[1][1]['director'])\\n actorPoints = compareActorPoints(toPredict['cast'], candidate[1][1]['cast'])\\n voteCountPoints = int(candidate[1][1]['vote_count'])\\n matchPoints = candidate[1][0] / np.max([float(x[1][0]) for x in finalRatings]) * 100\\n candidateWeight = PREDICTION_MATCHPOINTS_WEIGHT * matchPoints \\\\\\n + PREDICTION_ACTOR_WEIGHT * actorPoints \\\\\\n + PREDICTION_DIRECTOR_WEIGHT * directorPoints \\\\\\n + PREDICTION_VOTECOUNT_WEIGHT * voteCountPoints\\n\\n finalRatingCandidatesWithWeight.append((candidateWeight, candidate[0]))\\n\\n #Calculate the prediction\\n sumRatingCandidateWeights = np.sum([float(x[0]) for x in finalRatingCandidatesWithWeight])\\n sumRatingTimesCandidateWeight = np.sum([float(x[0]) * float(x[1]) for x in finalRatingCandidatesWithWeight])\\n\\n ratingPrediction = float(sumRatingTimesCandidateWeight / sumRatingCandidateWeights)\\n\\n return ratingPrediction\",\n \"def question5():\\n \\n null_dist = {38: 1, 39: 1, 40: 6, 41: 8, 42: 17, 43: 31, 44: 41, 45: 47, 46: 65, 47: 76,\\n 48: 66, 49: 63, 50: 62, 51: 69, 52: 69, 53: 57, 54: 49, 55: 37, 56: 26, 57: 30,\\n 58: 33, 59: 23, 60: 25, 61: 20, 62: 5, 63: 11, 64: 11, 65: 7, 66: 5, 67: 4,\\n 68: 5, 69: 4, 70: 5, 71: 3, 72: 3, 73: 4, 74: 1, 75: 2, 80: 3, 81: 1, 82: 1,\\n 85: 1, 87: 1, 92: 1}\\n \\n # find mean\\n mean = 0\\n for key in null_dist.keys():\\n mean += key / float(1000) * null_dist[key]\\n \\n print mean\\n \\n # find standard deviation\\n variance = 0\\n for key in null_dist.keys():\\n variance += (key - mean) ** 2 / float(1000) * null_dist[key]\\n stdev = variance ** 0.5\\n \\n print stdev\\n \\n # find z-score\\n score = 875\\n z_score = (score - mean) / stdev\\n \\n print z_score\",\n \"def pair_weights(self, labels, ranks):\\n with tf.compat.v1.name_scope(name='precision_lambda_weight'):\\n _check_tensor_shapes([labels, ranks])\\n valid_pair, labels = _get_valid_pairs_and_clean_labels(labels)\\n binary_labels = tf.cast(self._positive_fn(labels), dtype=tf.float32)\\n label_diff = tf.abs(_apply_pairwise_op(tf.subtract, binary_labels))\\n label_diff *= tf.cast(valid_pair, dtype=tf.float32)\\n # i <= topn and j > topn or i > topn and j <= topn, i.e., xor(i <= topn, j\\n # <= topn).\\n rank_mask = _apply_pairwise_op(tf.math.logical_xor,\\n tf.less_equal(ranks, self._topn))\\n return label_diff * tf.cast(rank_mask, dtype=tf.float32)\",\n \"def weighted_percentiles(data, wt, percentiles): \\r\\n assert np.greater_equal(percentiles, 0.0).all(), \\\"Percentiles less than zero\\\" \\r\\n assert np.less_equal(percentiles, 1.0).all(), \\\"Percentiles greater than one\\\" \\r\\n data = np.asarray(data) \\r\\n assert len(data.shape) == 1 \\r\\n if wt is None: \\r\\n wt = np.ones(data.shape, np.float) \\r\\n else: \\r\\n wt = np.asarray(wt, np.float) \\r\\n assert wt.shape == data.shape \\r\\n assert np.greater_equal(wt, 0.0).all(), \\\"Not all weights are non-negative.\\\" \\r\\n assert len(wt.shape) == 1 \\r\\n n = data.shape[0] \\r\\n assert n > 0 \\r\\n i = np.argsort(data) \\r\\n sd = np.take(data, i, axis=0) \\r\\n sw = np.take(wt, i, axis=0) \\r\\n aw = np.add.accumulate(sw) \\r\\n if not aw[-1] > 0: \\r\\n raise ValueError(\\\"Nonpositive weight sum\\\")\\r\\n w = (aw-0.5*sw)/aw[-1] \\r\\n spots = np.searchsorted(w, percentiles) \\r\\n o = [] \\r\\n for (s, p) in zip(spots, percentiles): \\r\\n if s == 0: \\r\\n o.append(sd[0]) \\r\\n elif s == n: \\r\\n o.append(sd[n-1]) \\r\\n else: \\r\\n f1 = (w[s] - p)/(w[s] - w[s-1]) \\r\\n f2 = (p - w[s-1])/(w[s] - w[s-1]) \\r\\n assert f1>=0 and f2>=0 and f1<=1 and f2<=1 \\r\\n assert abs(f1+f2-1.0) < 1e-6 \\r\\n o.append(sd[s-1]*f1 + sd[s]*f2) \\r\\n return o\",\n \"def fitness(individual, divider, target_sum, target_multiply):\\n\\n sum_val = reduce(operator.add, individual[:divider], 0)\\n multiply_val = reduce(operator.mul, individual[divider:], 1)\\n \\n sum_error = abs(target_sum - sum_val)\\n sum_error = sum_error / target_sum\\n\\n multiply_error = abs(target_multiply - multiply_val)\\n multiply_error = multiply_error / target_multiply\\n\\n #print(multiply_error, sum_error)\\n #print(sum_error, multiply_error)\\n return (multiply_error + sum_error)/2 * 100\",\n \"def weighted_npv(y_true, y_pred):\\n\\n _, _, fnw, tnw = get_weighted_confusion_matrix(y_true, y_pred)\\n \\n return tnw / (tnw + fnw)\",\n \"def apply_squashing_func(mu_, pi_, logp_pi):\\n # Squash the output\\n deterministic_policy = tf.nn.tanh(mu_)\\n policy = tf.nn.tanh(pi_)\\n\\n # Squash correction (from original implementation)\\n logp_pi -= tf.reduce_sum(tf.math.log(1 - policy ** 2 + EPS), axis=1)\\n\\n return deterministic_policy, policy, logp_pi\",\n \"def update_item_feature(\\n train, item_features, user_features, lambda_item,\\n nz_item_userindices, I):\\n for n, item_n in enumerate(nz_item_userindices):\\n nnz_users_per_item = len(item_n[1]) # Number of users who rated item n\\n if (nnz_users_per_item == 0): nnz_users_per_item = 1\\n # Least squares solution\\n A_n = np.dot(user_features[:,item_n[1]], user_features[:,item_n[1]].T) + lambda_item * nnz_users_per_item * I\\n V_n = np.dot(user_features[:,item_n[1]], train.T[item_n[1],item_n[0]].todense())\\n #if (n%3 == 0): print(\\\"item_n: {}\\\".format(item_n[0]), np.linalg.det(A_n))\\n if (np.linalg.det(A_n) != 0): item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\\n else: \\n A_n[0,0] += 1; A_n[1,1] += 1; A_n[2,2] += 1; A_n[3,3] += 1; A_n[4,4] += 1; A_n[5,5] += 1 # if matrix A_n is singular, slightly modify several values\\n item_features.T[:,item_n[0]] = np.linalg.solve(A_n,V_n)\",\n \"def griewank(x):\\n nopt = np.size(x)\\n # if (nopt == 2) | (nopt == 10):\\n xx = x\\n if nopt == 2:\\n d = 200.0\\n else:\\n d = 4000.0\\n\\n u1 = 0.0\\n u2 = 1.0\\n for j in range(nopt):\\n u1 = u1 + xx[j]**2 / d\\n u2 = u2 * np.cos(xx[j] / np.sqrt(float(j + 1)))\\n\\n f = u1 - u2 + 1\\n return f\",\n \"def item_rank(self, u, a, b, alphas, theta, transition_prob):\\n no_of_states = len(alphas[u][0])\\n\\n # calculate distribution over the states for the user at time t+1\\n p_t_plus_1 = []\\n for k in range(no_of_states):\\n total = 0\\n for l in range(no_of_states):\\n total += alphas[u][-1][k] * transition_prob[l][k]\\n p_t_plus_1.append(total)\\n# # print('\\\\np_t_plus_u1')\\n # pprint(p_t_plus_1)\\n\\n item_rank = defaultdict(float)\\n for i in range(len(theta)): # for each item\\n item_rank[i] = -sum(p_t_plus_1[k] * (1 + b[k] * theta[i][k])**(-a[k]) for k in range(no_of_states))\\n\\n# # print(item_rank)\\n items = sorted(item_rank, key=item_rank.__getitem__, reverse=True)\\n\\n return items\",\n \"def ap(self, result, next_item):\\n if next_item in result.index:\\n rank = result.index.get_loc(next_item) + 1\\n return 1.0 / rank\\n else:\\n return 0\",\n \"def custom_score_5(game, player):\\n \\\"\\\"\\\"custom_score_5 heuristic function defines chances heuristics\\n \\\"\\\"\\\"\\n if game.is_loser(player):\\n return float(\\\"-inf\\\")\\n\\n if game.is_winner(player):\\n return float(\\\"inf\\\")\\n\\n length_my_player_moves = len(game.get_legal_moves(player)) #Calculate length of available moves for myPlayer\\n length_opp_payer_moves = len(game.get_legal_moves(game.get_opponent(player)))#Calculate length of available moves for the oppositePlayer\\n return float(length_my_player_moves*length_my_player_moves - length_opp_payer_moves*length_opp_payer_moves)\",\n \"def make_mean_strategy(min_points, num_rolls=5):\\r\\n def trololo(score, opp_score):\\r\\n bacon = opp_score // 10 + 1\\r\\n new_score = score + bacon\\r\\n new_sum = new_score + opp_score\\r\\n if bacon >= min_points and (new_sum % 10 == 7 or new_sum % 7 == 0):\\r\\n return 0\\r\\n else: return num_rolls\\r\\n return trololo\",\n \"def update_measure(self):\\r\\n lam = len(self.fit)\\r\\n idx = np.argsort(self.fit + self.fitre)\\r\\n ranks = np.argsort(idx).reshape((2, lam))\\r\\n rankDelta = ranks[0] - ranks[1] - np.sign(ranks[0] - ranks[1])\\r\\n\\r\\n # compute rank change limits using both ranks[0] and ranks[1]\\r\\n r = np.arange(1, 2 * lam) # 2 * lam - 2 elements\\r\\n limits = [0.5 * (Mh.prctile(np.abs(r - (ranks[0,i] + 1 - (ranks[0,i] > ranks[1,i]))),\\r\\n self.theta*50) +\\r\\n Mh.prctile(np.abs(r - (ranks[1,i] + 1 - (ranks[1,i] > ranks[0,i]))),\\r\\n self.theta*50))\\r\\n for i in self.idx]\\r\\n # compute measurement\\r\\n # max: 1 rankchange in 2*lambda is always fine\\r\\n s = np.abs(rankDelta[self.idx]) - Mh.amax(limits, 1) # lives roughly in 0..2*lambda\\r\\n self.noiseS += self.cum * (np.mean(s) - self.noiseS)\\r\\n return self.noiseS, s\",\n \"def _getWeightedIndex(self, validQvals):\\r\\n validQvals = self._makePositive(validQvals)\\r\\n sumValue = sum(validQvals)\\r\\n arrow = random.random() * sumValue\\r\\n runningSum = 0\\r\\n for index, val in enumerate(validQvals):\\r\\n runningSum += val\\r\\n if runningSum >= arrow:\\r\\n return index\\r\\n return 0\",\n \"def rank():\\n return 0\",\n \"def calculate_power_ranking(wins, losses, is_winning_streak,\\n streak_length, recent_wins, recent_losses):\\n pred1 = 0\\n pred2 = round( ( ((wins*1.0)/(wins+losses)) - 0.500 ) * 16 * 9 * 10/9 )\\n pred3 = recent_wins - recent_losses\\n streak_factor = 1 if is_winning_streak else -1\\n pred4 = streak_factor * round( ( streak_length - 1 ) / 2.0 )\\n print pred1, pred2, pred3, pred4\\n return pred1 + pred2 + pred3 + pred4\",\n \"def relative_partition_function(self):\\n return self.overall_norm**2\",\n \"def lorentzian(mu, wid, x): \\n return np.ones(len(x) ) / (1 + ( (x - mu) / (0.5 * wid) )**2)\",\n \"def NormU(x):\\n return sum(0.5*x**2)\",\n \"def sampleWeight(self):\\r\\n x=random.random()\\r\\n i = 0\\r\\n n = len(self.weights)-1\\r\\n cummulativeWeight = 0\\r\\n #Distribute the exploration weight evenly among all the actions that have been\\r\\n #taken up to this point in time by any of the users\\r\\n if len(self.sampledActions) == 0:\\r\\n explorationWeight = 0\\r\\n else:\\r\\n explorationWeight = self.explorationFund / len(self.sampledActions)\\r\\n #Compute the normalization factor. If no action has been sampled by this user yet,\\r\\n #then each action k has weight eta*pi_k, where pi_k is the weight of k in the\\r\\n #prior distribution. Then, the normalization factor is the sum(eta*pi_k) for all k,\\r\\n #which is equal to eta*sum(pi_k), which is just eta, since the sum of the previous\\r\\n #weights has to add up to 1.\\r\\n #If one or more actions have been already sampled, the normalization factor is the\\r\\n #sum of 1) the weights already in self.weights, 2) the exploration fund, and 3) the\\r\\n #weights of the actions that are not yet in self.weights. Each one of these actions\\r\\n #has weight eta*pi_k (because it hasn't been sampled yet), so the total weight of the\\r\\n #mass of actions not yet in self.weights is eta*(1-sum(pi_l)), where the sum is over all\\r\\n #the weights already in self.weights\\r\\n if n < 0:\\r\\n normalizationFactor = self.priorBelief\\r\\n else:\\r\\n normalizationFactor = sum(self.weights) + self.explorationFund + \\\\\\r\\n self.priorBelief*(1-self.priorTopicDistr.cummulative[n])\\r\\n #Keep getting the next weight until the combined mass of the weights is less than the\\r\\n #random number x\\r\\n while True:\\r\\n w = self.__getitem__(i)\\r\\n if i in self.sampledActions:\\r\\n w += explorationWeight\\r\\n cummulativeWeight += w\\r\\n if x <= cummulativeWeight/normalizationFactor:\\r\\n if i not in self.sampledActions:\\r\\n self.sampledActions.append(i)\\r\\n return w\\r\\n i += 1\",\n \"def _wk_test(self, alternative='two-sided', alpha=0.01):\\n\\n q0 = self.get_group_data(0, self.df_test_resampled, ['Q'])\\n q1 = self.get_group_data(1, self.df_test_resampled, ['Q'])\\n\\n u_wk, p_wk = mannwhitneyu(q0, q1, alternative=alternative)\\n stats_wk = ranksums(q0, q1)[0]\\n\\n if p_wk <= alpha:\\n h = 1\\n else:\\n h = 0\\n\\n stats_wk = {'zval': stats_wk, 'pval': p_wk}\\n\\n return h, stats_wk\",\n \"def weight_function(*, loudness, peak_num, means, means_trend, peaks_trend):\\n return 0.333*(peak_num + means - loudness) + means_trend + peaks_trend\",\n \"def expected_improvement(f_min, mu, sigma):\\n # log-scaling might not be the best idea here, especially\\n # if people use negative values to maximize output\\n # v = (np.log(f_min) - mu) / sigma\\n v = (f_min - mu) / sigma\\n return (f_min * norm.cdf(v)\\n - (np.exp(0.5 * sigma ** 2 + mu)\\n * norm.cdf(v - sigma)))\",\n \"def user_interaction(uv, recommended_News, ranked=True):\\n\\n iv = recommended_News[\\\"topical_vector\\\"]\\n\\n product = simple_doct_product(uv, iv)\\n\\n epsilon = 10e-5\\n\\n if (product + epsilon) > 1.0:\\n vui = 0.99\\n else:\\n vui = beta_distribution(product)\\n\\n # Awared preference\\n ita = beta_distribution(0.98)\\n pui = vui * ita\\n\\n rand_num = np.random.random()\\n\\n if rand_num < pui:\\n return True\\n else:\\n return False\",\n \"def get_snr_weight(otf, snr):\\n # filter\\n weight = snr * np.abs(otf) ** 2\\n\\n # deal with any diverging points by averaging nearest pixels\\n if np.any(np.isinf(weight)):\\n xx, yy = np.meshgrid(range(weight.shape[1]), range(weight.shape[0]))\\n xinds = xx[np.isinf(weight)]\\n yinds = yy[np.isinf(weight)]\\n\\n # todo: not handling case where these points might be outside ROI\\n for xi, yi in zip(xinds, yinds):\\n weight[yi, xi] = np.mean([weight[yi + 1, xi], weight[yi - 1, xi], weight[yi, xi+1], weight[yi, xi-1]])\\n\\n return weight\",\n \"def apply_squashing_func(mu, x, logp_pi):\\n # Squash the output\\n deterministic_policy = tf.tanh(mu)\\n policy = tf.tanh(x)\\n # OpenAI Variation:\\n # To avoid evil machine precision error, strictly clip 1-pi**2 to [0,1] range.\\n # logp_pi -= tf.reduce_sum(tf.log(clip_but_pass_gradient(1 - policy ** 2, lower=0, upper=1) + EPS), axis=1)\\n # Squash correction (from original implementation)\\n logp_pi -= tf.reduce_sum(tf.log(1 - policy ** 2 + EPS), axis=1) # - tanh'(z) ?\\n\\n return deterministic_policy, policy, logp_pi\",\n \"def f(x):\\n return delta_eps_mu(x, mu) - delta\",\n \"def k(rating):\\n if rating < 100: return 40\\n if rating < 200: return 20\\n return 10\",\n \"def mrr(self):\\n _test = self.drop_bad_ratings()\\n merged = pd.merge(left=_test, right=self.predict, on=['user', 'item'], how='right')[\\n ['user', 'item', 'rating_x', 'rating_y']]\\n nott = np.vectorize(lambda x: not x)\\n mrrs = []\\n for user in merged.user.unique():\\n frame = merged[merged.user == user].sort_values(by='rating_y', ascending=False)\\n true_ratings = frame.rating_x.values\\n positions = np.where(nott(np.isnan(true_ratings)))[0]\\n if len(positions) > 0:\\n mrrs.append(1 / (positions[0] + 1))\\n else:\\n mrrs.append(0)\\n\\n return sum(mrrs) / len(mrrs)\",\n \"def f_U_function(W, gamma):\\n if W > 0:\\n if gamma == 1:\\n utility = math.log(W)\\n elif gamma >= 0:\\n # Minus 1 or not.\\n utility = math.pow(W, 1 - gamma) / (1 - gamma)\\n else:\\n print('The risk aversion parameter should be non-negative numbers.')\\n else:\\n print('The wealth should be non-negative. Now {}.'.format(W))\\n utility = 0\\n return utility\",\n \"def Probability(rating1, rating2):\\n return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400))\",\n \"def frank_wolfe(initial_point, iterations, kernel, kernel_mean_map, test_points, steps='line-search'):\\n line_search = False\\n if steps == 'line-search':\\n line_search = True\\n rho_arr = np.empty(iterations)\\n rho_arr[0] = frank_wolfe_step_line_search(initial_point, np.zeros((0)), np.zeros((0,2)), kernel, kernel_mean_map)\\n elif type(steps) is str:\\n rho_arr = frank_wolfe_steps(steps, iterations)\\n elif type(steps) in [list, np.ndarray]:\\n rho_arr = np.asarray(steps)\\n else:\\n raise Exception(\\\"Don't understand rho_method={}\\\".format(steps))\\n\\n assert len(rho_arr) == iterations\\n ret = np.empty((iterations, initial_point.shape[1]))\\n ret[0, :] = initial_point\\n for i in xrange(1, iterations):\\n # todo: optimal weights\\n weights = frank_wolfe_weights(rho_arr, i)\\n scores = frank_wolfe_scores(weights, ret[:i, :], test_points, kernel, kernel_mean_map)\\n best_score_ix = np.argmin(scores)\\n new_pt = test_points[best_score_ix, :]\\n ret[i, :] = new_pt\\n \\n if line_search:\\n rho_arr[i] = frank_wolfe_step_line_search(new_pt, weights, ret[:i, :], kernel, kernel_mean_map)\\n final_weights = frank_wolfe_weights(rho_arr, iterations)\\n return ret, final_weights\",\n \"def get_tuned_f1(results_df):\\n df = results_df\\n scores = []\\n f1s = []\\n lambdas = np.arange(.5, 5, .05)\\n\\n def add_weighted(df, lam):\\n \\\"\\\"\\\" Calculates different weighted PMI values after already having mut_inf scores \\\"\\\"\\\"\\n df['mut_inf_weighted'] = df.mut_inf + (lam - 1) * (df.head_conditional + df.tail_conditional) / 2.\\n\\n for lam in lambdas:\\n ss = StandardScaler()\\n add_weighted(df, lam=lam)\\n model = GaussianMixture(2, n_init=1)\\n dat = ss.fit_transform(df[['mut_inf_weighted']])\\n pred = model.fit_predict(dat)\\n score = model.aic(dat)\\n f1 = f1_score((model.means_.argmax() == df.label), pred)\\n scores.append(score)\\n f1s.append(f1)\\n\\n scores = np.array(scores)\\n f1s = np.array(f1s)\\n lam = lambdas[scores.argmax()]\\n\\n optimal_lambda = lambdas[scores.argmax()]\\n optimal_f1 = f1s[scores.argmax()]\\n return optimal_f1, optimal_lambda\",\n \"def workout_rank(a, rank):\\r\\n # Check if workout score is empty\\r\\n if pd.isnull(a):\\r\\n return np.nan\\r\\n else:\\r\\n return int(rank)\",\n \"def modified_z_score(x: np.ndarray) -> np.ndarray:\\n return 0.6745 * (x - np.median(x)) / median_absolute_deviation(x)\",\n \"def ltiecorrect(rankvals):\\r\\n sorted,posn = shellsort(rankvals)\\r\\n n = len(sorted)\\r\\n T = 0.0\\r\\n i = 0\\r\\n while (i<n-1):\\r\\n if sorted[i] == sorted[i+1]:\\r\\n nties = 1\\r\\n while (i<n-1) and (sorted[i] == sorted[i+1]):\\r\\n nties = nties +1\\r\\n i = i +1\\r\\n T = T + nties**3 - nties\\r\\n i = i+1\\r\\n T = T / float(n**3-n)\\r\\n return 1.0 - T\",\n \"def bukinn6fcn(x: np.ndarray) -> np.ndarray:\\n\\n n = x.shape[1]\\n assert n == 2, \\\"The Bukin N. 6 functions is only defined on a 2D space.\\\"\\n\\n X = x[:, 0]\\n X2 = X**2\\n Y = x[:, 1]\\n\\n scores = 100 * np.sqrt(np.abs(Y - 0.01 * X2)) + 0.01 * np.abs(X + 10)\\n return scores\",\n \"def penalty(self):\\n assert len(self.weights) == len(self.means), \\\"Dimensions!\\\"\\n out = np.exp(self.data['riskfree'] * self.data['maturity'])\\n for weight, mean in zip(self.weights, self.means):\\n out -= weight * np.exp(mean * self.data['maturity'])\\n return (out**2).mean()**.5\",\n \"def value(a, y, weights, lambda_):\\n\\t\\treturn np.sum(np.nan_to_num(-y*np.log(a + 1e-15)-(1-y)*np.log(1-a + 1e-15))) / (a.shape[0] * a.shape[1])\\n\\t\\t# return unregularize + (0.5*lambda_*np.sum(np.square(weights[-1])) / (a.shape[0] * a.shape[1])) \",\n \"def test_k_rank_approximate(corpus):\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60227036","0.59967417","0.5658467","0.5579704","0.55585","0.55581784","0.55484015","0.5513262","0.5466028","0.54607856","0.54588825","0.5439738","0.5396137","0.5386086","0.5380749","0.53742325","0.5363156","0.53529656","0.5346669","0.5311985","0.5311851","0.53034216","0.5298622","0.52984565","0.5284843","0.52811974","0.5270631","0.5267215","0.52643925","0.5258468","0.52459437","0.52442086","0.52365464","0.52312577","0.5210307","0.51914656","0.51847976","0.51712847","0.5167823","0.5167823","0.5159732","0.5157717","0.51562744","0.5147125","0.51259077","0.51254755","0.5125206","0.51217365","0.5117017","0.510222","0.5098637","0.5095484","0.50919616","0.5086357","0.5077935","0.50766844","0.50734776","0.50732446","0.5072347","0.5062083","0.50551355","0.5050802","0.50465596","0.5034513","0.5032379","0.50317353","0.50188136","0.5015851","0.50149065","0.5009748","0.50035685","0.49964586","0.4995039","0.49942538","0.4990702","0.49905747","0.49840036","0.49830604","0.4980585","0.49797177","0.49706697","0.4967482","0.49663782","0.49637353","0.49508435","0.494775","0.49476472","0.49406347","0.4940592","0.49367687","0.4933174","0.49259323","0.4925774","0.4924823","0.49239984","0.49222875","0.49205616","0.49192095","0.49190837","0.49178138"],"string":"[\n \"0.60227036\",\n \"0.59967417\",\n \"0.5658467\",\n \"0.5579704\",\n \"0.55585\",\n \"0.55581784\",\n \"0.55484015\",\n \"0.5513262\",\n \"0.5466028\",\n \"0.54607856\",\n \"0.54588825\",\n \"0.5439738\",\n \"0.5396137\",\n \"0.5386086\",\n \"0.5380749\",\n \"0.53742325\",\n \"0.5363156\",\n \"0.53529656\",\n \"0.5346669\",\n \"0.5311985\",\n \"0.5311851\",\n \"0.53034216\",\n \"0.5298622\",\n \"0.52984565\",\n \"0.5284843\",\n \"0.52811974\",\n \"0.5270631\",\n \"0.5267215\",\n \"0.52643925\",\n \"0.5258468\",\n \"0.52459437\",\n \"0.52442086\",\n \"0.52365464\",\n \"0.52312577\",\n \"0.5210307\",\n \"0.51914656\",\n \"0.51847976\",\n \"0.51712847\",\n \"0.5167823\",\n \"0.5167823\",\n \"0.5159732\",\n \"0.5157717\",\n \"0.51562744\",\n \"0.5147125\",\n \"0.51259077\",\n \"0.51254755\",\n \"0.5125206\",\n \"0.51217365\",\n \"0.5117017\",\n \"0.510222\",\n \"0.5098637\",\n \"0.5095484\",\n \"0.50919616\",\n \"0.5086357\",\n \"0.5077935\",\n \"0.50766844\",\n \"0.50734776\",\n \"0.50732446\",\n \"0.5072347\",\n \"0.5062083\",\n \"0.50551355\",\n \"0.5050802\",\n \"0.50465596\",\n \"0.5034513\",\n \"0.5032379\",\n \"0.50317353\",\n \"0.50188136\",\n \"0.5015851\",\n \"0.50149065\",\n \"0.5009748\",\n \"0.50035685\",\n \"0.49964586\",\n \"0.4995039\",\n \"0.49942538\",\n \"0.4990702\",\n \"0.49905747\",\n \"0.49840036\",\n \"0.49830604\",\n \"0.4980585\",\n \"0.49797177\",\n \"0.49706697\",\n \"0.4967482\",\n \"0.49663782\",\n \"0.49637353\",\n \"0.49508435\",\n \"0.494775\",\n \"0.49476472\",\n \"0.49406347\",\n \"0.4940592\",\n \"0.49367687\",\n \"0.4933174\",\n \"0.49259323\",\n \"0.4925774\",\n \"0.4924823\",\n \"0.49239984\",\n \"0.49222875\",\n \"0.49205616\",\n \"0.49192095\",\n \"0.49190837\",\n \"0.49178138\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.51569587"},"document_rank":{"kind":"string","value":"42"}}},{"rowIdx":9998,"cells":{"query":{"kind":"string","value":"Groups data in SimulationReport's by the value of alpha or gamma2"},"document":{"kind":"string","value":"def group_data(simulation_reports: List[SimulationReport]) -> Dict[float, SimulationTable]:\n heat_maps: OrderedDict[float, SimulationTable] = OrderedDict()\n for report in simulation_reports:\n if report.param not in heat_maps:\n param_name = \"alpha\" if report.growth_type == GrowthType.Polynomial else \"gamma2\"\n simulation_table = heat_maps.setdefault(\n report.param,\n SimulationTable(report.growth_type, param_name, report.param, OrderedDict()),\n )\n else:\n simulation_table = heat_maps[report.param]\n errors_by_prefix = simulation_table.errors.setdefault(report.prefix_length, [])\n errors_by_prefix.append((report.b0, report.error))\n\n return heat_maps"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf,\n alpha=3, thresh=0):\n if utool.DEBUG2:\n from ibeis.model.hots.smk import smk_debug\n smk_debug.rrr()\n smk_debug.check_wx2(wx2_rvecs=wx2_rvecs, wx2_aids=wx2_aids)\n wx_sublist = pdh.ensure_values(pdh.ensure_index(wx2_rvecs))\n if utool.VERBOSE:\n print('[smk_index] Compute Gamma alpha=%r, thresh=%r: ' % (alpha, thresh))\n mark1, end1_ = utool.log_progress(\n '[smk_index] Gamma group (by word): ', len(wx_sublist),\n flushfreq=100, writefreq=50, with_totaltime=True)\n # Get list of aids and rvecs w.r.t. words\n aids_list = pdh.ensure_values_subset(wx2_aids, wx_sublist)\n rvecs_list1 = pdh.ensure_values_subset(wx2_rvecs, wx_sublist)\n # Group by daids first and then by word index\n daid2_wx2_drvecs = utool.ddict(lambda: utool.ddict(list))\n for wx, aids, rvecs in zip(wx_sublist, aids_list, rvecs_list1):\n group_aids, groupxs = clustertool.group_indicies(aids)\n rvecs_group = clustertool.apply_grouping(rvecs, groupxs) # 2.9 ms\n for aid, rvecs_ in zip(group_aids, rvecs_group):\n daid2_wx2_drvecs[aid][wx] = rvecs_\n\n if utool.VERBOSE:\n end1_()\n\n # For every daid, compute its gamma using pregrouped rvecs\n # Summation over words for each aid\n if utool.VERBOSE:\n mark2, end2_ = utool.log_progress(\n '[smk_index] Gamma Sum (over daid): ', len(daid2_wx2_drvecs),\n flushfreq=100, writefreq=25, with_totaltime=True)\n # Get lists w.r.t daids\n aid_list = list(daid2_wx2_drvecs.keys())\n # list of mappings from words to rvecs foreach daid\n # [wx2_aidrvecs_1, ..., wx2_aidrvecs_nDaids,]\n _wx2_aidrvecs_list = list(daid2_wx2_drvecs.values())\n _aidwxs_iter = (list(wx2_aidrvecs.keys()) for wx2_aidrvecs in _wx2_aidrvecs_list)\n aidrvecs_list = [list(wx2_aidrvecs.values()) for wx2_aidrvecs in _wx2_aidrvecs_list]\n aididf_list = [[wx2_idf[wx] for wx in aidwxs] for aidwxs in _aidwxs_iter]\n\n #gamma_list = []\n if utool.DEBUG2:\n try:\n for count, (idf_list, rvecs_list) in enumerate(zip(aididf_list, aidrvecs_list)):\n assert len(idf_list) == len(rvecs_list), 'one list for each word'\n #gamma = smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)\n except Exception as ex:\n utool.printex(ex)\n utool.embed()\n raise\n gamma_list = [smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)\n for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)]\n\n if WITH_PANDAS:\n daid2_gamma = pdh.IntSeries(gamma_list, index=aid_list, name='gamma')\n else:\n daid2_gamma = dict(zip(aid_list, gamma_list))\n if utool.VERBOSE:\n end2_()\n\n return daid2_gamma","def metrics_group():","def test_add_alpha_diversity_values_to_mapping_file(self):\r\n\r\n # regular case no special cases for avg method\r\n expected_mapping_file_data = MAPPING_FILE_DATA_WITH_ALPHA_A\r\n expected_mapping_file_headers = ['SampleID', 'BarcodeSequence',\r\n 'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description',\r\n 'chao1_alpha', 'chao1_normalized_alpha', 'chao1_alpha_label',\r\n 'PD_whole_tree_alpha', 'PD_whole_tree_normalized_alpha',\r\n 'PD_whole_tree_alpha_label']\r\n\r\n out_mapping_file_data, out_mapping_file_headers =\\\r\n add_alpha_diversity_values_to_mapping_file(self.metrics,\r\n self.sample_ids, self.alpha_diversity_data,\r\n self.mapping_file_headers, self.mapping_file_data, 4, 'equal')\r\n\r\n self.assertEquals(out_mapping_file_data, expected_mapping_file_data)\r\n self.assertEquals(\r\n out_mapping_file_headers,\r\n expected_mapping_file_headers)\r\n\r\n # regular case no special cases for quantile method\r\n expected_mapping_file_data = MAPPING_FILE_DATA_WITH_ALPHA_B\r\n out_mapping_file_data, out_mapping_file_headers =\\\r\n add_alpha_diversity_values_to_mapping_file(self.metrics,\r\n self.sample_ids, self.alpha_diversity_data,\r\n self.mapping_file_headers, self.mapping_file_data, 4, 'quantile')\r\n\r\n self.assertEquals(out_mapping_file_data, expected_mapping_file_data)\r\n self.assertEquals(\r\n out_mapping_file_headers,\r\n expected_mapping_file_headers)","def gamma_h_subgroups(self):\n from .all import GammaH\n N = self.level()\n R = IntegerModRing(N)\n return [GammaH(N, H) for H in R.multiplicative_subgroups()]","def data_grouping(self):\n group_container, film_container, plank_container = [[] for a in range(self.tot_conditions)], \\\n [[] for a in range(self.tot_conditions)], \\\n [[] for a in range(self.tot_conditions)]\n\n for i in self.data_labels:\n group = int(i[:-1])\n group_container[group - 1].append(i)\n film_container[group - 1].append(self.film_count[self.data_labels.index(i)])\n plank_container[group - 1].append(self.plank_count[self.data_labels.index(i)])\n\n return group_container, film_container, plank_container","def test_group(self):\n obs_group, obs_nogroup = group(self.seqstruct, 0.75)\n exp_group = {'cluster_337': ['cluster_343', 'cluster_345',\n 'cluster_339'],\n 'cluster_347': ['cluster_338'],\n 'cluster_344': ['cluster_340']}\n exp_nogroup = [self.seqstruct[6], self.seqstruct[8]]\n\n self.assertEqual(obs_group, exp_group)\n self.assertEqual(obs_nogroup, exp_nogroup)","def data_group():\n ...","def test_group_by_hardware_info(self):\n self._test_group_by('Hardware Info', [1, 1, 2, 1, 1])","def data_for_grouping() -> NoReturn:\n raise NotImplementedError","def data_for_grouping() -> NoReturn:\n raise NotImplementedError","def group_data_by_gs(data_table):\n gene_data = collections.defaultdict(lambda: collections.defaultdict(list))\n for _idx, row in data_table.iterrows():\n samp = row['sample']\n gene = row['gene']\n gene_data[gene][samp].append({\n 'muttype': row['type'].strip(),\n 'normalized': row['Normalized'], # NMAF in the manuscript\n 'consequence': row['MissenseConsequence'].strip(),\n })\n return gene_data","def divisor_subgroups(self):\n return [Gamma0_constructor(M) for M in self.level().divisors()]","def get_filter_stats(data: AnnData) -> Tuple[pd.DataFrame, pd.DataFrame]:\n\n # cell stats\n gb1 = data.obs.groupby(\"Channel\")\n df_before = gb1.median()\n df_before = df_before.assign(total=gb1.size())\n df_before.rename(\n columns={\n \"n_genes\": \"median_n_genes_before\",\n \"n_counts\": \"median_n_umis_before\",\n \"percent_mito\": \"median_percent_mito_before\",\n },\n inplace=True,\n )\n\n data = data[data.obs[\"passed_qc\"]] # focusing only on filtered cells\n\n gb2 = data.obs.groupby(\"Channel\")\n df_after = gb2.median()\n df_after = df_after.assign(kept=gb2.size())\n df_after.rename(\n columns={\n \"n_genes\": \"median_n_genes\",\n \"n_counts\": \"median_n_umis\",\n \"percent_mito\": \"median_percent_mito\",\n },\n inplace=True,\n )\n df_cells = pd.concat((df_before, df_after), axis=1, sort=False)\n df_cells.fillna(0, inplace=True)\n df_cells[\"kept\"] = df_cells[\"kept\"].astype(int)\n df_cells[\"filt\"] = df_cells[\"total\"] - df_cells[\"kept\"]\n df_cells = df_cells[\n [\n \"kept\",\n \"median_n_genes\",\n \"median_n_umis\",\n \"median_percent_mito\",\n \"filt\",\n \"total\",\n \"median_n_genes_before\",\n \"median_n_umis_before\",\n \"median_percent_mito_before\",\n ]\n ]\n df_cells.sort_values(\"kept\", inplace=True)\n\n # gene stats\n idx = data.var[\"robust\"] == False\n df_genes = pd.DataFrame(\n {\n \"n_cells\": data.var.loc[idx, \"n_cells\"],\n \"percent_cells\": data.var.loc[idx, \"percent_cells\"],\n }\n )\n df_genes.index.name = \"gene\"\n df_genes.sort_values(\"n_cells\", ascending=False, inplace=True)\n\n return df_cells, df_genes","def group_mae(outputs: torch.Tensor, targets: torch.Tensor) -> List[Tuple[int, int, int, float, str]]:\n # groups = [\n # (-1, 1800, \"0-0.5h\"),\n # (1800, 3600, \"0.5-1h\"),\n # (3600, 7200, \"1-2h\"),\n # (7200, 10800, \"2-3h\"),\n # (10800, 14400, \"3-4h\"),\n # (14400, 18000, \"4-5h\"),\n # (18000, 21600, \"5-6h\"),\n # (21600, 25200, \"6-7h\"),\n # (25200, 28800, \"7-8h\"),\n # (28800, 32400, \"8-9h\"),\n # (32400, 36000, \"9-10h\"),\n # (36000, 39600, \"10-11h\"),\n # (39600, 43200, \"11-12\"),\n # (43200, 86400, \"12h - 1 day\"),\n # (86400, 172800, \"1 day - 2 days\"),\n # (172800, 259200, \"2 days - 3 days\"),\n # (259200, 345600, \"3 days - 4 days\"),\n # (345600, 432000, \"4 days - 5 days\"),\n # (432000, 518400, \"5 days - 6 days\"),\n # (518400, 604800, \"6 days - 1 week\"),\n # (604800, 155520000, \"1 week - 1 month\"),\n # (155520000, int(data_ranges[\"label\"][\"max\"]), \"> 1 month\")\n # ]\n groups = [\n (-1, 1800, \"0-0.5h\"),\n (1800, 3600, \"0.5-1h\"),\n (3600, 7200, \"1-2h\"),\n (7200, 10800, \"2-3h\"),\n (10800, 14400, \"3-4h\"),\n (14400, 21600, \"4-6h\"),\n (21600, 28800, \"6-8h\"),\n (28800, 36000, \"8-10h\"),\n (36000, 43200, \"10-12h\"),\n (43200, 50400, \"12-16h\"),\n (50400, 64800, \"16-20h\"),\n (64800, 86400, \"20-24h\"),\n (86400, 172800, \"1-2d\"),\n (172800, 259200, \"2-3d\"),\n (259200, 345600, \"3-4d\"),\n (345600, 432000, \"4-5d\"),\n (432000, 518400, \"5-6d\"),\n (518400, 604800, \"6-7d\"),\n (604800, 1209600, \"1-2w\"),\n (1209600, 2419200, \"2-4w\"),\n (2419200, int(data_ranges[\"label\"][\"max\"]), \"> 4w\")\n ]\n\n def scale(seconds: int) -> float:\n # half_range = (data_ranges[\"label\"][\"max\"] - data_ranges[\"label\"][\"min\"]) / 2\n # result = seconds / half_range\n # return -1 + result if seconds < half_range else result\n label_range = data_ranges[\"label\"][\"max\"]\n return seconds / label_range\n\n def process_group(x: torch.Tensor, y: torch.Tensor, group: Tuple[int, int, str]) -> Tuple[int, int, int, float,\n str]:\n criterion = nn.L1Loss(reduction=\"mean\")\n mask = (y > scale(group[0])) & (y <= scale(group[1]))\n # mask = (y > group[0]) & (y <= group[1])\n x = x[mask]\n y = y[mask]\n mae = 0.\n num_data = x.shape[0]\n if num_data > 0:\n loss = criterion(x, y)\n mae = loss.item()\n return group[0], group[1], num_data, mae, group[2]\n\n mae_groups = [process_group(outputs, targets, group) for group in groups]\n return mae_groups","def test_grouped(self):\n gfile = grades.writers.GradesFile(self.fname)\n gfile.table.compute_grouped_mean('Group')\n gfile.table_format = 'org'\n self.check_output(self.output_str2, gfile)","def get_report(dataset):\n\n dataset = dataset.round(2)\n print('Overall results (mean): ')\n display(dataset[['classifier', 'preprocessor', 'f1', 'precision', 'recall']].groupby(['preprocessor', 'classifier'])\n .mean().round(2))\n print('Overall results (max): ')\n display(dataset[['classifier', 'preprocessor', 'f1', 'precision', 'recall']].groupby(['preprocessor', 'classifier'])\n .max().round(2))\n print('Grouped by Preprocessor (mean):')\n display(dataset[['preprocessor', 'f1', 'precision', 'recall']].groupby('preprocessor').mean().round(2))\n print('Grouped by Classifier (mean):')\n display(dataset[['classifier', 'f1', 'precision', 'recall']].groupby('classifier').mean().round(2))\n\n preprocessors = dataset['preprocessor'].unique()\n metrics = ['f1', 'precision', 'recall']\n\n # For each metric, display top 10 rounds.\n for m in metrics:\n print(f'Top 10 by {m}:')\n display(dataset.sort_values(m, ascending=False).head(10).round(2))\n\n for p in preprocessors:\n for m in metrics:\n d = dataset[dataset['preprocessor'] == p]\n for c in dataset['classifier'].unique():\n plt.plot(d[d['classifier'] == c]['prior'].unique(), d[d['classifier'] == c].groupby('prior').mean()[m],\n label=str(c))\n plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n plt.title(m + ' - ' + str(p))\n plt.show()","def test_color_groups(self):\r\n data_colors = color_dict_to_objects(self.data_color_hsv)\r\n\r\n exp = None\r\n obs = color_groups(self.groups, data_colors, self.data_color_order)\r\n\r\n self.assertEqual(obs, exp)","def perSaccAcrossGroups(addExp2 = True, trim = True, spacing = .5, \\\n\t\taddSimulation = True, exclOverlap = True, exclY = True):\n\t\n\tfig = plt.figure(figsize = (5,10))\n\ttitle = \"Average towards-handle landings across groups - exclOverlap = %s - exclY = %s\" % (exclOverlap, exclY)\n\tplt.suptitle(title)\n\t\n\t# The first experiment has to be treated differently, because\n\t# it contains two dependent variables: absolute and corrected\n\tdm1 = getDM.getDM(exp = \"004A\", excludeErrors = True, driftCorr = True)\n\tlLandingsAbs = []\n\tlLandingsCorr = []\n\t\n\tfor sacc in [\"1\", \"2\", \"3\"]:\n\t\t\n\t\t# This is the same for corrected landing positions (the saccade\n\t\t# doesn't change; only the reference point does)\n\t\t_dm = dm1.select(\"endX%sNorm != ''\" % sacc)\n\t\t_dm = _dm.select(\"endX%sNorm > -.5\" % sacc)\n\t\t_dm = _dm.select(\"endX%sNorm < .5\" % sacc)\n\t\t\n\t\tif exclY:\n\t\t\t_dm = _dm.select(\"endY%sNorm != ''\" % sacc)\n\t\t\t_dm = _dm.select(\"endY%sNorm > -.5\" % sacc)\n\t\t\t_dm = _dm.select(\"endY%sNorm < .5\" % sacc)\n\t\t\n\t\t\n\t\tif trim:\n\t\t\t_dmAbs = _dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sNormToHandle\" % sacc)\n\t\t\t_dmCorr = _dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sCorrNormToHandle\" % sacc)\n\t\t\n\t\t# Determine avg landing position:\n\t\tavgAbs = _dmAbs[\"endX%sNormToHandle\" % sacc].mean()\n\t\tavgCorr = _dmCorr[\"endX%sCorrNormToHandle\" % sacc].mean()\n\t\t\n\t\t# TODO: determine error bars:\n\n\t\tlLandingsAbs.append(avgAbs)\n\t\tlLandingsCorr.append(avgCorr)\n\t\n\tplt.plot(lLandingsAbs, color = \"#f57900\", linewidth = 2, marker = \"o\")\n\tplt.plot(lLandingsCorr, color = \"#73d216\", linewidth = 2, marker = \"o\")\n\t\n\t# The other 2 experiments can be treated equally:\n\tcolList = [\"#ef2929\", \"#3465a4\"]\n\n\tfor exp in [\"004B\", \"004C\"]:\n\t\t\n\t\tcontinue\n\t\t\n\t\tif not addExp2:\n\t\t\tif exp == \"004B\":\n\t\t\t\tcontinue\n\t\tif not addSimulation:\n\t\t\tif exp == \"004C\":\n\t\t\t\tcontinue\n\n\t\tdm = getDM.getDM(exp = exp, excludeErrors = True, driftCorr = True)\n\t\t\n\t\tif exp == \"004C\":\n\t\t\tprint 'xxx'\n\t\t\tif exclOverlap:\n\t\t\t\tdm = dm.select(\"gap == 'zero'\")\n\n\t\tlLandingsAbs = []\n\t\t\n\t\tfor sacc in [\"1\", \"2\", \"3\", '4']:\n\t\t\t\n\t\t\t# TODO: how to filter only on-object saccades exp 2??\n\t\t\t_dm = dm.select(\"endX%sNorm != ''\" % sacc)\n\t\t\t_dm = _dm.select(\"endX%sNorm > -.5\" % sacc)\n\t\t\t_dm = _dm.select(\"endX%sNorm < .5\" % sacc)\n\t\t\t\n\t\t\tif exclY:\n\t\t\t\n\t\t\t\t_dm = _dm.select(\"endY%sNorm != ''\" % sacc)\n\t\t\t\t_dm = _dm.select(\"endY%sNorm > -.5\" % sacc)\n\t\t\t\t_dm = _dm.select(\"endY%sNorm < .5\" % sacc)\n\n\t\t\tif trim:\n\t\t\t\t_dm = _dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sNormToHandle\" % sacc)\n\t\t\t\n\t\t\t# Determine avg landing position:\n\t\t\tavgAbs = _dm[\"endX%sNormToHandle\" % sacc].mean()\n\t\t\tlLandingsAbs.append(avgAbs)\n\t\t\t\n\t\t\t\n\t\tcol = colList.pop()\n\t\tplt.plot(lLandingsAbs, color = col, linewidth = 2, marker = \"o\")\n\t\n\t# Modify plot:\n\tplt.legend([\"Exp1 abs\", \"Exp1 corr\", \"Exp2\", \"Sim\"])\n\t\t\n\tplt.axhline(0, color = \"#888a85\", linestyle = \"--\", linewidth = 2)\n\txLabels = [\"sacc 1\", \"sacc 2\", \"sacc 3\"]\n\txTicks = range(0,3)\n\tplt.xticks(xTicks, xLabels, rotation = .5)\n\tplt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)\n\t\n\tplt.ylim([-.5, .5])\n\t\n\tplt.savefig(\"%s.png\" % title)","def group_data(self):\n groups = []\n bug_map = self.bug_map()\n union_map = self.union_map(list(bug_map.keys()))\n # test_id grouping\n for union_id in set(union_map.values()):\n group = []\n for k, v in union_map.items():\n if v == union_id:\n group.extend(bug_map[k])\n if len(group) > 1:\n groups.append(group)\n return groups","def group_data(data):\n\n data_grouped = dict()\n\n for data_pt in data:\n resonance_id = data_pt.par['resonance_id']\n\n assignment = parse_assignment(resonance_id)\n index = int(assignment[0][0])\n\n data_grouped.setdefault((index, resonance_id), []).append(data_pt)\n\n return data_grouped","def profile_group(func, args, kwargs, func_result):\n (collection, key, condition, initial, reduce) = args[:5]\n report_kvs = _profile_query(collection)\n\n if key:\n report_kvs['Group_Key'] = _to_json(key)\n\n if condition:\n report_kvs['Group_Condition'] = _to_json(condition)\n\n if initial:\n report_kvs['Group_Initial'] = _to_json(initial)\n\n if reduce:\n report_kvs['Group_Reduce'] = reduce\n\n return report_kvs","def compare_alpha_diversities(rarefaction_lines, mapping_lines, category,\r\n depth=None, test_type='nonparametric', num_permutations=999):\r\n if test_type == 'nonparametric' and num_permutations < 1:\r\n raise ValueError(\"Invalid number of permutations: %d. Must be greater \"\r\n \"than zero.\" % num_permutations)\r\n\r\n rarefaction_data = parse_rarefaction(rarefaction_lines)\r\n mapping_data = parse_mapping_file_to_dict(mapping_lines)[0]\r\n # samid_pairs, treatment_pairs are in the same order\r\n samid_pairs, treatment_pairs = sampleId_pairs(mapping_data,\r\n rarefaction_data, category)\r\n\r\n ps_avg_div = get_per_sample_average_diversities(rarefaction_data, depth)\r\n\r\n ttest_results, ad_avgs = {}, {}\r\n for sid_pair, treatment_pair in zip(samid_pairs, treatment_pairs):\r\n # if there is only 1 sample for each treatment in a comparison, and mc\r\n # using mc method, will error (e.g. mc_t_two_sample([1],[1]).\r\n if len(sid_pair[0]) == 1 and len(sid_pair[1]) == 1:\r\n ttest_results[treatment_pair] = (None, None)\r\n # add alpha diversity averages and standard deviations. since their\r\n # is only a single sample if we are in this part of the loop, we can\r\n # just record the sample value as the avg and 0 as the std.\r\n ad_avgs[treatment_pair[0]] = (sid_pair[0][0], 0.)\r\n ad_avgs[treatment_pair[1]] = (sid_pair[1][0], 0.)\r\n else:\r\n i = array([ps_avg_div[x] for x in sid_pair[0]])\r\n j = array([ps_avg_div[x] for x in sid_pair[1]])\r\n # add alpha diversity averages and standard deviations.\r\n ad_avgs[treatment_pair[0]] = (i.mean(), i.std())\r\n ad_avgs[treatment_pair[1]] = (j.mean(), j.std())\r\n # conduct tests\r\n if isnan(np_min(i)) or isnan(np_min(j)):\r\n ttest_results[treatment_pair] = (None, None)\r\n continue\r\n if test_type == 'parametric':\r\n obs_t, p_val = t_two_sample(i, j)\r\n elif test_type == 'nonparametric':\r\n obs_t, _, _, p_val = mc_t_two_sample(i, j,\r\n permutations=num_permutations)\r\n if p_val is not None:\r\n p_val = float(format_p_value_for_num_iters(p_val,\r\n num_iters=num_permutations))\r\n elif p_val is None: # None will error in format_p_val\r\n obs_t, p_val = None, None\r\n else:\r\n raise ValueError(\"Invalid test type '%s'.\" % test_type)\r\n ttest_results[treatment_pair] = (obs_t, p_val)\r\n\r\n return ttest_results, ad_avgs","def formatdata(data,Params):\n\tmndata = dict()\n\talltrials = np.array([])\n\tfor k in range(len(Params[\"conditions\"])):\n\t\tconditionmean = data[0,k].mean(axis = 0)\n\t\tmndata.update({Params[\"conditions\"][k]: {'data' : data[0,k].mean(axis = 0), 'cmax' : conditionmean.max(), 'cmin' : conditionmean.min()}})\n\treturn mndata","def modelOnBetaGrid(sample,bins,N,l,u):\r\n\r\n betaGrid=np.linspace(l,u,N)\r\n traces=[]\r\n WAIC=dict()\r\n index=0\r\n\r\n for beta in betaGrid:\r\n trace=intensityLogGauss(sample,bins,beta)\r\n traces.append(trace['intensity'])\r\n WAIC[index]=trace\r\n index+=1\r\n\r\n df=pm.compare(WAIC,ic='WAIC')\r\n\r\n return betaGrid,df,traces","def test_compare_alpha_diversities(self):\r\n # test 'Dose' at 480 inputs\r\n category = 'Dose'\r\n depth = 480\r\n test_type = 'parametric'\r\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\r\n self.rarefaction_file,\r\n self.mapping_file, category=category, depth=depth,\r\n test_type=test_type)\r\n\r\n # hardcoded order of the terms in the keys otherwise would comps fail\r\n exp_tcomps = \\\r\n {('Control', '2xDose'): (1.1746048668554037, 0.44899351189030801),\r\n ('1xDose', '2xDose'): (1.7650193854830403, 0.17574514418562981),\r\n ('Control', '1xDose'): (0.43618805086434992, 0.7052689260099092)}\r\n\r\n # test each key in expected results -- this won't catch if\r\n # obs_tcomps has extra entries, but test that via the next call\r\n for k in exp_tcomps:\r\n assert_almost_equal(exp_tcomps[k], obs_tcomps[k])\r\n self.assertEqual(set(exp_tcomps.keys()), set(obs_tcomps.keys()))\r\n\r\n # test that returned alpha diversity averages are correct\r\n # dose\r\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\r\n # Control = ['Sam5']\r\n exp_ad_avgs = {'1xDose': (3.2511951575216664, 0.18664627928763661),\r\n '2xDose': (2.7539647172550001, 0.30099438035250015),\r\n 'Control': (3.3663303519925001, 0.0)}\r\n for k in exp_ad_avgs:\r\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\r\n\r\n # test 'Dose' at 480 inputs with nonparametric test\r\n seed(0) # set the seed to reproduce random MC pvals\r\n category = 'Dose'\r\n depth = 480\r\n test_type = 'nonparametric'\r\n num_permutations = 100\r\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\r\n self.rarefaction_file,\r\n self.mapping_file, category=category, depth=depth,\r\n test_type=test_type, num_permutations=num_permutations)\r\n exp_tcomps = {('1xDose', '2xDose'): (1.7650193854830403, 0.13),\r\n ('Control', '1xDose'): (0.43618805086434992, 0.83), ('Control',\r\n '2xDose'): (1.1746048668554037, 0.62)}\r\n # test each key in expected results -- this won't catch if\r\n # obs_tcomps has extra entries, but test that via the next call\r\n for k in exp_tcomps:\r\n assert_almost_equal(exp_tcomps[k], obs_tcomps[k])\r\n self.assertEqual(set(exp_tcomps.keys()), set(obs_tcomps.keys()))\r\n\r\n # test that returned alpha diversity averages are correct\r\n # dose\r\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\r\n # Control = ['Sam5']\r\n exp_ad_avgs = {'Control': (\r\n 3.3663303519925001,\r\n 0.0),\r\n '1xDose': (3.2511951575216664,\r\n 0.18664627928763661),\r\n '2xDose': (2.7539647172550001,\r\n 0.30099438035250015)}\r\n\r\n for k in exp_ad_avgs:\r\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\r\n\r\n # test it works with NA values\r\n # test 'Dose' at 500 inputs with paramteric test\r\n category = 'Dose'\r\n depth = 500\r\n test_type = 'parametric'\r\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\r\n self.rarefaction_file,\r\n self.mapping_file, category=category, depth=depth,\r\n test_type=test_type)\r\n exp_tcomps = \\\r\n {('Control', '2xDose'): (-0.63668873339963239, 0.63906168713487699),\r\n ('1xDose', '2xDose'): (None, None),\r\n ('Control', '1xDose'): (None, None)}\r\n for obs, exp in izip(obs_tcomps, exp_tcomps):\r\n self.assertEqual(obs, exp)\r\n \r\n # test that it works with nonparametric test - this was erroring.\r\n seed(0)\r\n test_type = 'nonparametric'\r\n exp_tcomps = \\\r\n {('Control', '2xDose'): (-0.63668873339963239, 0.672),\r\n ('1xDose', '2xDose'): (None, None),\r\n ('Control', '1xDose'): (None, None)}\r\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\r\n self.rarefaction_file,\r\n self.mapping_file, category=category, depth=depth,\r\n test_type=test_type)\r\n for obs, exp in izip(obs_tcomps, exp_tcomps):\r\n self.assertEqual(obs, exp)\r\n\r\n # test that returned alpha diversity averages are correct\r\n # dose\r\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\r\n # Control = ['Sam5']\r\n # will fail on nan comparison so avoid this\r\n exp_ad_avgs = {'1xDose': (nan, nan),\r\n '2xDose': (3.1955144893699998, 0.84206819489000018),\r\n 'Control': (2.2669008538500002, 0.0)}\r\n for k in exp_ad_avgs:\r\n if k != '1xDose':\r\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\r\n if k == '1xDose':\r\n self.assertTrue(all(map(isnan, obs_ad_avgs[k])))\r\n\r\n # test that it works when no depth is passed\r\n category = 'Dose'\r\n depth = None # should return depth = 910\r\n test_type = 'parametric'\r\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\r\n self.rarefaction_file,\r\n self.mapping_file, category=category, depth=depth,\r\n test_type=test_type)\r\n\r\n # hardcoded order of the terms in the keys otherwise would comps fail\r\n exp_tcomps = \\\r\n {('Control', '2xDose'): (3.3159701868634883, 0.1864642327553255),\r\n ('1xDose', '2xDose'): (-0.48227871733885291, 0.66260803238173183),\r\n ('Control', '1xDose'): (0.83283756452373126, 0.49255115337550748)}\r\n for obs, exp in izip(obs_tcomps, exp_tcomps):\r\n self.assertEqual(obs, exp)\r\n\r\n # test that returned alpha diversity averages are correct\r\n # dose\r\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\r\n # Control = ['Sam5']\r\n exp_ad_avgs = {'1xDose': (2.6763340901916668, 0.36025734786901326),\r\n '2xDose': (2.8358041871949999, 0.04611264137749993),\r\n 'Control': (3.1006488615725001, 0.0)}\r\n for k in exp_ad_avgs:\r\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])","def index():\n import numpy as np\n import random\n\n total_gender = {}\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\n\n groups = db(db.groups).select()\n freq_groups = {}\n grp_gender = {}\n for g in groups:\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\n grp_gender[g.code] = {}\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\n\n experiments = db(db.experiments).select()\n freq_experiments = {}\n exp_gender = {}\n for e in experiments:\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\n exp_gender[e.code] = {}\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\n\n grp_exp = {}\n for e in experiments:\n grp_exp[e.code] = {}\n for g in groups:\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\n\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)","def _compute_group_stats():\n group_stats = []\n \n wmt16_group = Group.objects.filter(name='WMT16')\n wmt16_users = _get_active_users_for_group(wmt16_group)\n \n # Aggregate information about participating groups.\n groups = set()\n for user in wmt16_users:\n for group in _identify_groups_for_user(user):\n groups.add(group)\n \n # TODO: move this to property of evaluation group or add dedicated data model.\n # GOAL: should be configurable from within the Django admin backend.\n #\n # MINIMAL: move to local_settings.py?\n #\n # The following dictionary defines the number of HITs each group should\n # have completed during the WMT16 evaluation campaign.\n \n for group in groups:\n _name = group.name\n \n _group_stats = HIT.compute_status_for_group(group)\n _total = _group_stats[0]\n \n if _total > 0 and not _name in GROUP_HIT_REQUIREMENTS.keys():\n _required = 0\n elif _name in GROUP_HIT_REQUIREMENTS.keys():\n _required = GROUP_HIT_REQUIREMENTS[_name]\n _delta = _total - _required\n _data = (_total, _required, _delta)\n \n if _data[0] > 0:\n group_stats.append((_name, _data))\n \n # Sort by number of remaining HITs.\n group_stats.sort(key=lambda x: x[1][2])\n \n # Add totals at the bottom.\n global_total = sum([x[1][0] for x in group_stats])\n global_required = sum([x[1][1] for x in group_stats])\n global_delta = global_total - global_required\n global_data = (global_total, global_required, global_delta)\n group_stats.append((\"Totals\", global_data))\n \n return group_stats","def Group(self) -> _n_5_t_0:","def Group(self) -> _n_5_t_0:","def aga_expression_entropies(adata):\n from scipy.stats import entropy\n groups_order, groups_masks = utils.select_groups(adata, smp='aga_groups')\n entropies = []\n for mask in groups_masks:\n X_mask = adata.X[mask]\n x_median = np.median(X_mask, axis=0)\n x_probs = (x_median - np.min(x_median)) / (np.max(x_median) - np.min(x_median))\n entropies.append(entropy(x_probs))\n return entropies","def _composition_handler(self):\n return {\n group : StockAnalyzer(data) \\\n for group, data in self.data.groupby(self.group_by)\n }","def test_group_by_roles_and_hardware_info(self):\n self._test_group_by('Roles and hardware info', [1, 2, 1, 1, 1])","def gdcs_reporter(metadata, analysistype, reportpath):\n # Initialise list to store all the GDCS genes, and genera in the analysis\n gdcs = list()\n genera = list()\n for sample in metadata:\n sample[analysistype].faidict = dict()\n if sample.general.bestassemblyfile != 'NA':\n if os.path.isdir(sample[analysistype].targetpath):\n # Update the fai dict with all the genes in the analysis, rather than just those with baited hits\n Reports.gdcs_fai(sample=sample,\n analysistype=analysistype)\n sample[analysistype].createreport = True\n # Determine which genera are present in the analysis\n if sample.general.closestrefseqgenus not in genera:\n genera.append(sample.general.closestrefseqgenus)\n try:\n # Add all the GDCS genes to the list\n for gene in sorted(sample[analysistype].faidict):\n if gene not in gdcs:\n gdcs.append(gene)\n except AttributeError:\n sample[analysistype].createreport = False\n else:\n sample[analysistype].createreport = False\n else:\n sample[analysistype].createreport = False\n sample.general.incomplete = True\n header = 'Strain,Genus,Matches,Pass/Fail,{},\\n'.format(','.join(sorted(gdcs)))\n data = str()\n with open(os.path.join(reportpath, '{}.csv'.format(analysistype)), 'w') as report:\n # Sort the samples in the report based on the closest refseq genus e.g. all samples with the same genus\n # will be grouped together in the report\n for genus in genera:\n for sample in metadata:\n if sample.general.closestrefseqgenus == genus:\n if sample[analysistype].createreport:\n sample[analysistype].totaldepth = list()\n # Add the sample to the report if it matches the current genus\n data += '{},{},'.format(sample.name, genus)\n # Initialise a variable to store the number of GDCS genes were matched\n count = 0\n # As I want the count to be in the report before all the gene results, this string will\n # store the specific sample information, and will be added to data once count is known\n specific = str()\n for gene in sorted(gdcs):\n # As there are different genes present in the GDCS databases for each organism of\n # interest, genes that did not match because they're absent in the specific database are\n # indicated using an X\n if gene not in [result for result in sample[analysistype].faidict]:\n specific += 'X,'\n else:\n try:\n specific += '{p_id},'.format(p_id=sample[analysistype].blastresults[gene])\n # Report the necessary information for each gene result\n count += 1\n # If the gene was missing from the results attribute, add a - to the cell\n except (KeyError, AttributeError):\n specific += '-,'\n # Determine whether the sample pass the necessary quality criteria:\n # Pass, all GDCS, mean coverage greater than 20X coverage;\n # ?: Indeterminate value;\n # -: Fail value\n # Allow one missing GDCS to still be considered a pass\n if count >= len(sample[analysistype].faidict) - 1:\n quality = '+'\n else:\n quality = '-'\n # Add the count, mean depth with standard deviation, the pass/fail determination,\n # and the total number of GDCS genes as well as the results\n data += '{hits}/{total},{fail},{gdcs}\\n'\\\n .format(hits=str(count),\n total=len(sample[analysistype].faidict),\n fail=quality,\n gdcs=specific)\n # Any samples with a best assembly of 'NA' are considered incomplete.\n else:\n data += '{},{},,,-\\n'.format(sample.name, sample.general.closestrefseqgenus)\n elif sample.general.closestrefseqgenus == 'NA':\n data += '{}\\n'.format(sample.name)\n # Write the header and data to file\n report.write(header)\n report.write(data)\n # Return the updated metadata object\n return metadata","def data_averaging_and_cleaning(self):\n groups, film, plank = self.data_grouping()\n\n for i in groups:\n self.organized_names.append(input('Enter label name for condition ' + str(i)))\n\n self.organized_film.append(sum(film[groups.index(i)]) / len(film[groups.index(i)]))\n try:\n self.organized_plank.append(sum(film[groups.index(i)]) / (sum(film[groups.index(i)]) +\n sum(plank[groups.index(i)])))\n except ZeroDivisionError:\n self.organized_plank.append(sum(film[groups.index(i)]) / 1)","def add_alpha_diversity_values_to_mapping_file(metrics, alpha_sample_ids,\r\n alpha_data, mapping_file_headers,\r\n mapping_file_data, bins, method='equal',\r\n missing_value_name='N/A'):\r\n norm = lambda x, x_min, x_max: (x - x_min) / (x_max - x_min)\r\n\r\n # data will be modified and returned so get your own copy\r\n new_mapping_file_data = deepcopy(mapping_file_data)\r\n new_mapping_file_headers = deepcopy(mapping_file_headers)\r\n\r\n # regular levels assigned based on equally spaced bins\r\n overall_probs = [i / bins for i in range(1, bins)]\r\n\r\n # if we are using the average method the levels are equal to the probs list\r\n if method == 'equal':\r\n levels = overall_probs\r\n\r\n for index, metric in enumerate(metrics):\r\n # get the alpha diversity value for the metric being evaluated\r\n data = [[row[index]] for row in alpha_data]\r\n metric_max = max(data)[0]\r\n metric_min = min(data)[0]\r\n\r\n # add headers for each metric\r\n new_mapping_file_headers.append('{0}_alpha'.format(metric))\r\n new_mapping_file_headers.append('{0}_normalized_alpha'.format(metric))\r\n new_mapping_file_headers.append('{0}_alpha_label'.format(metric))\r\n\r\n # when using the quantile method the levels change depending on the\r\n # metric being used; hence the calculation and normalization of the\r\n # data\r\n if method == 'quantile':\r\n levels = quantile([norm(element[0], metric_min, metric_max)\r\n for element in data], overall_probs)\r\n\r\n # get the normalized value of diversity and the tag for each value\r\n for value in data:\r\n norm_value = norm(value[0], metric_min, metric_max)\r\n value.append(norm_value)\r\n value.append(_get_level(norm_value, levels, 'bin'))\r\n\r\n # iterate using the mapping file instead of using the alpha diversity\r\n # data because more often that you will like you will have more samples\r\n # in the mapping file than in your downstream analysis data\r\n for row in new_mapping_file_data:\r\n try:\r\n data_index = alpha_sample_ids.index(row[0])\r\n\r\n # data fields should be strings\r\n row.extend(map(str, data[data_index]))\r\n except ValueError:\r\n row.extend([missing_value_name, missing_value_name,\r\n missing_value_name])\r\n\r\n return new_mapping_file_data, new_mapping_file_headers","def groups(self):\n\n\t\tprint \"completed minimization\"\n\t\tcopy(self.rootdir+'counterions-minimized.gro',self.rootdir+'system.gro')\n\t\tcopy(self.rootdir+'counterions.top',self.rootdir+'system.top')\n\t\tif self.simscale == 'aamd': grouptype = 'standard'\n\t\tif self.simscale == 'cgmd': grouptype = 'cgmd_water'\n\t\tself.grouping(grouptype=grouptype)","def group(simulations, groupby, sort=True, only_started=False, reverse=False):\n\n from collections import OrderedDict\n from pencilnew.math import natural_sort\n\n sim_dict_grouped = {}\n\n if type(simulations) == type(['list']):\n sim_list = simulations\n #elif type(simulations) == Simulations:\n# sim_list = simulations.sims\n else:\n print('!! ERROR: Dont know how to interprated simulations argument..')\n return False\n\n # sort out simulations that has not started\n if only_started == True: sim_list = [s for s in sim_list if s.started()]\n\n # special cases:\n if groupby in ['Lx', 'Ly', 'Lz']:\n if groupby[-1] == 'x': ii = 0\n elif groupby[-1] == 'y': ii = 1\n elif groupby[-1] == 'z': ii = 2\n for sim in sim_list:\n q = str(sim.param['lxyz'][ii])\n if (not q in sim_dict_grouped.keys()):\n sim_dict_grouped[q] = [sim]\n else:\n sim_dict_grouped[q].append(sim)\n \n elif groupby in ['nx', 'ny', 'nz']:\n for sim in sim_list:\n q = str(getattr(sim.dim, groupby))\n if (not q in sim_dict_grouped.keys()):\n sim_dict_grouped[q] = [sim]\n else:\n sim_dict_grouped[q].append(sim)\n\n # case the groupby-keyword can be found via __simulation__.get_value\n elif sim_list[0].get_value(groupby) != None:\n for sim in sim_list:\n q = str(sim.get_value(groupby))\n if (not q in sim_dict_grouped.keys()):\n sim_dict_grouped[q] = [sim]\n else:\n sim_dict_grouped[q].append(sim)\n\n else:\n print('!! ERROR: Coudnt group simulations, no fitting groupby-keyword has been found to match \"'+groupby+'\"!')\n return False\n\n if sort:\n sim_dict_grouped_n_sorted = OrderedDict()\n keys = sim_dict_grouped.keys()\n for key in natural_sort(keys, reverse=reverse):\n sim_dict_grouped_n_sorted[key] = sim_dict_grouped[key]\n\n return sim_dict_grouped_n_sorted\n\n return sim_dict_grouped","def get_stats(self, gene, groups=[\"SP\", \"SL06\", \"SL12\", \"SL24\",\"SL48\", \"SL96\",\"FP06\", \"FP12\", \"FP24\",\"FP48\", \"FP96\", \"FL\"]):\n\n try:\n pos = self.gene_header.index(gene)\n except:\n return [],[]\n\n limits = [0] + self.reorder_matrix(groups=groups)\n #print limits\n intervals = zip(limits[:-1],limits[1:])\n print \"Debugging: ave and stdev:-\"\n #print intervals\n #print self.data_matrix\n #print self.data_matrix[:,pos]\n #print [ (self.data_matrix[ i:j,pos ]) for i,j in intervals]\n ave = [ numpy.average(self.data_matrix[ i:j,pos ]) for i,j in intervals]\n print ave\n stdev = [ numpy.std(self.data_matrix[ i:j,pos ]) for i,j in intervals]\n print stdev\n return ave, stdev","def _process_data(self, data: Element) -> List[TestGroupReport]:\n # NOTE: XML output contains skipped testcases which are ignored.\n result = []\n\n for suite in data.getchildren():\n suite_name = suite.attrib[\"name\"]\n suite_report = TestGroupReport(\n name=suite_name,\n category=ReportCategories.TESTSUITE,\n )\n suite_has_run = False\n\n for testcase in suite.getchildren():\n\n testcase_name = testcase.attrib[\"name\"]\n testcase_report = TestCaseReport(name=testcase_name)\n\n if not testcase.getchildren():\n assertion_obj = RawAssertion(\n description=\"Passed\",\n content=\"Testcase {} passed\".format(testcase_name),\n passed=True,\n )\n testcase_report.append(registry.serialize(assertion_obj))\n else:\n for entry in testcase.getchildren():\n assertion_obj = RawAssertion(\n description=entry.tag,\n content=entry.text,\n passed=entry.tag != \"failure\",\n )\n testcase_report.append(\n registry.serialize(assertion_obj)\n )\n\n testcase_report.runtime_status = RuntimeStatus.FINISHED\n\n if testcase.attrib[\"status\"] != \"notrun\":\n suite_report.append(testcase_report)\n suite_has_run = True\n\n if suite_has_run:\n result.append(suite_report)\n\n return result","def GroupLassoADMM(As, bs, lam, groups, rho, alpha, maxiter=1000, abstol=1e-4, reltol=1e-2):\n \n n,D = As[0].shape\n m = len(As)\n \n Atbs = [A.T.dot(b) for (A,b) in zip(As,bs)]\n \n Ls = [Factor(A,rho) for A in As]\n Us = [L.T for L in Ls]\n \n x = np.zeros((m*D,1))\n z = 1e-5*np.random.randn(m*D,1)\n u = 1e-5*np.random.randn(m*D,1)\n \n # Indices of x for each timestep. x[Ts[t]] is the coefficient vector for time t\n Ts = [j*D + np.arange(D) for j in range(m)]\n \n history = {}\n history['objval'] = []\n history['gl_objval'] = []\n history['r_norm'] = []\n history['s_norm'] = []\n history['eps_pri'] = []\n history['eps_dual'] = []\n \n for k in range(maxiter):\n \n # x update\n for j in range(m):\n q = Atbs[j] + rho*(z[Ts[j]]-u[Ts[j]])\n\n if n >= D:\n x[Ts[j]] = Solve(Us[j],Solve(Ls[j],q))\n else:\n x[Ts[j]] = q/rho-As[j].T.dot(Solve(Us[j],Solve(Ls[j],As[j].dot(q))))/rho**2\n \n # z update\n zold = np.copy(z)\n x_hat = alpha*x+(1-alpha)*zold\n for g in groups:\n z[g] = Shrinkage(x_hat[g]+u[g], lam/rho)\n \n u = u+(x_hat-z)\n \n # record history\n history['objval'].append(ObjectiveADMM(As,bs,Ts,lam,groups,x,z))\n history['gl_objval'].append(ObjectiveGLASSO_block(As,bs,Ts,lam,groups,x))\n history['r_norm'].append(Norm(x-z))\n history['s_norm'].append(Norm(rho*(z-zold)))\n history['eps_pri'].append(np.sqrt(m)*abstol+reltol*np.max([Norm(x),Norm(z)]))\n history['eps_dual'].append(np.sqrt(m)*abstol+reltol*Norm(rho*u))\n \n # check for termination\n if (history['r_norm'][-1] < history['eps_pri'][-1]) and \\\n (history['s_norm'][-1] < history['eps_dual'][-1]):\n break\n \n # Return unbiased sparse predictor\n z = z.reshape(D,m, order = 'F')\n nz_coords = np.where(np.sum(abs(z), axis = 1) != 0)[0]\n if len(nz_coords) != 0: \n for j in range(m):\n z[nz_coords,j] = np.linalg.lstsq(As[j][:, nz_coords], bs[j])[0][:,0]\n \n return z, history","def test_properties_stats_group_by_group_by_and_sub_group_by_get(self):\n pass","def test_get_group_colors(self):\r\n\r\n data_colors = color_dict_to_objects(self.data_color_hsv)\r\n exp = (self.colors, data_colors, self.data_color_order)\r\n obs = get_group_colors(self.groups, self.colors, data_colors,\r\n self.data_color_order)\r\n\r\n self.assertEqual(obs, exp)","def __init__(self, \\\n model=\"IFBA\", \\\n enrichment=4.0, \\\n burnup=0.0 ):\n\n # Set characteristics used to define the group constants\n # within the data module.\n self.model = model\n self.enrichment = enrichment\n self.burnup = burnup \n \n # Get the group data object.\n IFBA = 0\n WABA = 0\n GAD = 0\n if (model == \"IFBA\"):\n IFBA = 1\n if (model == \"WABA\"):\n WABA = 1 \n if (model == \"GAD\"):\n GAD = 1 \n data = get_2g_parms(burnup, enrichment, IFBA, WABA, GAD)\n\n # Set the group data.\n self.D1 = data.DIFF1\n self.D2 = data.DIFF2\n self.A1 = data.ABS1\n self.A2 = data.ABS2\n self.F1 = data.NUFISS1\n self.F2 = data.NUFISS2\n self.S12 = data.REMOV1 # Note, this is strange but is\n self.R1 = data.REMOV1 + data.ABS1 # the way CASMO defines them.\n self.KINF = data.K_INF_NO_XE\n self.M2 = data.M2_XE\n\n # Peaking factor.\n self.peak = 0.0","def fairness_ranking(data, ranked_list, group_file, gamma=0.5, stop_prob=0.7, verbose=False):\n author_exposure = {}\n author_relevance = {}\n\n #calculate the exposure and relevance for each author\n for query in ranked_list:\n exposure = 1.0\n for doc in query[1]:\n relevance = doc[0]\n\n authors_id = data_combining.get_data(doc[1], data = data)['authors']['corpus_author_id']\n\n for a in authors_id:\n if int(a) not in author_exposure:\n author_exposure[int(a)] = exposure\n else:\n author_exposure[int(a)] += exposure\n\n if int(a) not in author_relevance:\n author_relevance[int(a)] = stop_prob * relevance\n else:\n author_relevance[int(a)] += stop_prob * relevance\n\n exposure *= gamma\n exposure *= (1 - stop_prob * relevance)\n\n #calculate group relevance and exposure\n group_exposure_sum = {}\n group_relevance_sum = {}\n group = pd.read_csv(group_file)\n for index, row in group.iterrows():\n curr_author_exposure = author_exposure[row['author_id']] if row['author_id'] in author_exposure else 0\n curr_author_relevance = author_relevance[row['author_id']] if row['author_id'] in author_relevance else 0\n\n\n if row['gid'] not in group_exposure_sum:\n group_exposure_sum[row['gid']] = curr_author_exposure\n else:\n group_exposure_sum[row['gid']] += curr_author_exposure\n\n if row['gid'] not in group_relevance_sum:\n group_relevance_sum[row['gid']] = curr_author_relevance\n else:\n group_relevance_sum[row['gid']] += curr_author_relevance\n\n total_group_relevance = sum(group_relevance_sum.values())\n total_group_exposure = sum(group_exposure_sum.values())\n\n squared_sum = 0\n\n group_exposure_and_relevance = {}\n\n for group, relevance in group_relevance_sum.items():\n group_relevance = relevance / total_group_relevance\n group_exposure = group_exposure_sum[group] / total_group_exposure\n\n group_exposure_and_relevance[group] = (group_relevance, group_exposure)\n if verbose:\n print('{}: rel {} | exp {}'.format(group, group_relevance, group_exposure))\n\n squared_sum += (group_exposure - group_relevance) ** 2\n\n if verbose:\n print('fair exposure: {}'.format(math.sqrt(squared_sum)))\n\n return (math.sqrt(squared_sum), group_exposure_and_relevance)","def generate_reports(self):\n print(\"Generating reports.\")\n # Input user memberships and utilities\n input_mean_util = dict()\n for g in range(self.num_groups):\n input_mean_util[g] = self.group_distributions.get_group_distribution(g)[0]\n input_mean_util[g] = input_mean_util[g]-input_mean_util[g][self.num_items-1]\n input_mean_util = pd.DataFrame.from_dict(data=input_mean_util, orient='index')\n input_mean_util.columns = [\"mean_input_util_item{0:03d}\".format(i) for i in range(self.num_items)]\n input_mean_util.reset_index(inplace=True, drop=True)\n\n input_memb_prob = self.group_distributions.get_group_probabilities()\n input_memb_prob = pd.DataFrame(data=input_memb_prob, columns=['input_memb_prob'])\n input_memb_prob.reset_index(inplace=True, drop=True)\n\n # Drawn user membership probabilities\n drawn_memb_prob = self.true_user_utilities.groupby(['user_group'])[['user_group']].count() / self.num_users\n drawn_memb_prob.rename(columns={'user_group': 'sim_memb_prob'}, inplace=True)\n drawn_memb_prob.reset_index(inplace=True, drop=True)\n\n # Drawn user utilities (mean)\n drawn_mean_util = self.true_user_utilities.groupby(['user_group']).mean()\n drawn_mean_util.drop(labels=['user_id'], axis=1, inplace=True)\n drawn_mean_util.columns = [\"mean_sim_util_item{0:03d}\".format(i) for i in range(self.num_items)]\n drawn_mean_util.reset_index(inplace=True, drop=True)\n\n user_summary = pd.concat(objs=[input_memb_prob, drawn_memb_prob, input_mean_util, drawn_mean_util], axis=1)\n user_summary.to_csv(os.path.join(self.output_dir, 'drawn_users_summary.csv'), index=False)\n\n # Checking design balance per user\n balance_per_user = self.data_set.groupby(['user_id']).sum()/(self.num_trips*self.shelf_size/self.num_items)\n balance_per_user.drop(labels=['trip', 'choice'], axis=1, inplace=True)\n balance_per_user.reset_index(inplace=True)\n balance_per_user.to_csv(os.path.join(self.output_dir, 'item_balance_per_user.csv'), index=False)\n\n print(\"Done with generating reports.\")","def GEEmacaGCMs(ptsFile,metric,timeStep,startYear,endYear,scenarios,buf,poly,models,\n username,folderOut, scalePix = 4000):\n \n # load required libraries\n import ee\n \n # Initialize the Earth Engine object, using the authentication credentials.\n ee.Initialize()\n\n ID_field = \"geeID\"\n\n #load pts or poly file\n pts1 = ee.FeatureCollection('users/' + username + '/' + str(ptsFile))\n\n time_d = {}\n time_d['month'] = 'projm'\n time_d['year'] = 'projy'\n \n for met in metric:\n\n for scenario in scenarios:\n\n for model in models:\n\n MACA = (ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY')\n .select(met)\n .filterMetadata('model', 'equals', model)\n .filterMetadata('scenario', 'equals', scenario))\n\n metL = [met]\n \n years = list(range(startYear, endYear + 1))\n yearsEE = ee.List(years)\n \n if all([(timeStep == 'year'),any([(met == 'tasmin'),(met == 'tasmax'),\n (met == 'huss'),(met == 'rsds'),\n (met == 'was')])]):\n\n def map_m(i):\n i = ee.Number(i).int()\n image2 = (MACA\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .first())\n filtered = (MACA\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .mean()\n .copyProperties(image2,['system:time_start','system:time_end']))\n return filtered\n\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\n\n elif (timeStep == 'month'):\n \n img_col = MACA.filter(ee.Filter.calendarRange(startYear, endYear, 'year'))\n\n elif all([(timeStep == 'year'),(met == 'pr')]):\n\n def map_m(i):\n i = ee.Number(i).int()\n image2 = (MACA\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .first())\n filtered = (MACA\n .filter(ee.Filter.calendarRange(i, i, 'year'))\n .sum()\n .copyProperties(image2,['system:time_start','system:time_end']))\n return filtered\n\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\n\n #else:\n #print(\"incorrect time step specified\")\n \n if buf > 0:\n bufL = [buf]\n def bufferPoly(feature):\n return feature.buffer(bufL[0])\n\n ptsB = pts1.map(bufferPoly)\n def table_m(image):\n table = (image\n .select(metL[0])\n .reduceRegions(collection = ptsB.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_ptsB',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n \n #print ('buffered pts by:' + str(buf) + ' for MACA: ' + met + ' ' + scenario + ' ' + model)\n\n elif poly > 0:\n \n def table_m(image):\n table = (image\n .select(metL[0])\n .reduceRegions(collection = pts1.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_poly1',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n \n #print ('spatial mean in poly: no buffer for MACA: ' + met + ' ' + scenario + ' ' + model)\n\n else:\n def table_m(image):\n table = (image\n .select(metL[0])\n .reduceRegions(collection = pts1.select([ID_field]),\n reducer = ee.Reducer.mean(),\n scale = scalePix))\n \n def table_add_date(f):\n return f.set('startDate', ee.Date(image.get('system:time_start')))\n\n return table.map(table_add_date)\n\n triplets = img_col.map(table_m).flatten()\n\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\n .filter(ee.Filter.neq('mean', None))\n .select(['.*'],None,False),\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_pts1',\n folder = folderOut,\n fileFormat = 'CSV')\n task_tc.start()\n \n #print('value at point: no buffer for MACA: ' + met + ' ' + scenario + ' ' + model)","def _group_energy_terms(ener_xvg):\n with open(ener_xvg) as f:\n all_lines = f.readlines()\n energy_types = [line.split('\"')[1] for line in all_lines if line[:3] == '@ s']\n energy_values = [float(x) * units.kilojoule_per_mole for x in all_lines[-1].split()[1:]]\n e_out = OrderedDict(zip(energy_types, energy_values))\n return e_out, ener_xvg","def get_plotting_data(each_misfit_windows_collection, iterations_list, snr_threshold, event_depth_dict):\n result = {}\n phases_zr = [\"P\", \"pP\", \"sP\", \"PP\", \"S\", \"sS\", \"SS\"]\n phases_t = [\"ScS\", \"S\", \"sS\", \"SS\"]\n conditions = {\n \"P\": {\n \"exclude_p\": False,\n \"exclude_s\": True\n },\n \"pP\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"sP\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"PP\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"S\": {\n \"exclude_p\": False,\n \"exclude_s\": False\n },\n \"sS\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"SS\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"ScS\": {\n \"exclude_p\": True,\n \"exclude_s\": True\n },\n \"surface_z\": {\n \"exclude_p\": False,\n \"exclude_s\": False\n },\n \"surface_r\": {\n \"exclude_p\": False,\n \"exclude_s\": False\n },\n \"surface_t\": {\n \"exclude_p\": False,\n \"exclude_s\": False\n },\n }\n # we can exrtact the information from the misfit_windows in the order of the pdf output.\n # order will be z,r,t[,surface_z,surface_r,surface_t]\n rep_net_sta = sorted(event_depth_dict.keys())[0]\n event_depth_this_event = event_depth_dict[rep_net_sta]\n if (event_depth_this_event > SURFACE_THRESHOLD):\n category_list = [\"z\", \"r\", \"t\"]\n category_phases = [phases_zr, phases_zr, phases_t]\n else:\n category_list = [\"z\", \"r\", \"t\", \"surface_z\", \"surface_r\", \"surface_t\"]\n category_phases = [phases_zr, phases_zr, phases_t,\n [\"surface_z\"], [\"surface_r\"], [\"surface_t\"]]\n for each_iteration in iterations_list:\n result[each_iteration] = {}\n for each_category, each_category_phases in zip(category_list, category_phases):\n result[each_iteration][each_category] = []\n for each_category_phase in each_category_phases:\n phase_condition = conditions[each_category_phase]\n cc = get_windows_cc(\n each_misfit_windows_collection[each_iteration], phase_condition[\n \"exclude_p\"], phase_condition[\"exclude_s\"],\n each_category, snr_threshold, each_category_phase)\n cc = cc[cc >= 0]\n deltat = get_windows_deltat(\n each_misfit_windows_collection[each_iteration], phase_condition[\n \"exclude_p\"], phase_condition[\"exclude_s\"],\n each_category, snr_threshold, each_category_phase)\n deltat = deltat[np.abs(deltat) <= 10]\n similarity = get_windows_similarity(\n each_misfit_windows_collection[each_iteration], phase_condition[\n \"exclude_p\"], phase_condition[\"exclude_s\"],\n each_category, snr_threshold, each_category_phase)\n similarity = similarity[similarity >= 0]\n result[each_iteration][each_category].append(\n {\"net_sta\": get_windows_net_sta(\n each_misfit_windows_collection[each_iteration], phase_condition[\n \"exclude_p\"], phase_condition[\"exclude_s\"],\n each_category, snr_threshold, each_category_phase),\n \"cc\": cc,\n \"deltat\": deltat,\n \"similarity\": similarity,\n }\n )\n # result:dict->each_iteration:dict->each_category:list as the dict showed before, we should return the category_phases\n # we should combine the surface wave phases to one page\n if (len(category_phases) == 6):\n for each_iteration in iterations_list:\n category_phases = [phases_zr, phases_zr, phases_t,\n [\"surface_z\", \"surface_r\", \"surface_t\"]]\n category_list = [\"z\", \"r\", \"t\", \"surface\"]\n result[each_iteration][\"surface\"] = []\n result[each_iteration][\"surface\"].append(\n result[each_iteration][\"surface_z\"][0])\n result[each_iteration][\"surface\"].append(\n result[each_iteration][\"surface_r\"][0])\n result[each_iteration][\"surface\"].append(\n result[each_iteration][\"surface_t\"][0])\n del result[each_iteration][\"surface_z\"]\n del result[each_iteration][\"surface_r\"]\n del result[each_iteration][\"surface_t\"]\n\n return result, category_phases, category_list","def create_report(result, *, decimal_places=3):\n\n # TODO add effect sizes to multiple comparisons.\n def single_population_string(population, with_stats=False, pop_pval=None, with_rank=True):\n if pop_pval is not None:\n return \"%s (p=%.*f)\" % (population, decimal_places, pop_pval)\n if with_stats:\n halfwidth = (result.rankdf.at[population, 'ci_upper'] - result.rankdf.at[population, 'ci_lower']) / 2\n mystats = []\n if (result.force_mode is not None and result.force_mode=='parametric') or \\\n (result.force_mode is None and result.all_normal):\n mystats.append(\"M=%.*f+-%.*f\" % (decimal_places, result.rankdf.at[population, 'mean'],\n decimal_places, halfwidth))\n mystats.append(\"SD=%.*f\" % (decimal_places, result.rankdf.at[population, 'std']))\n else:\n mystats.append(\"MD=%.*f+-%.*f\" % (decimal_places, result.rankdf.at[population, 'median'],\n decimal_places, halfwidth))\n mystats.append(\"MAD=%.*f\" % (decimal_places, result.rankdf.at[population, 'mad']))\n if with_rank:\n mystats.append(\"MR=%.*f\" % (decimal_places, result.rankdf.at[population, 'meanrank']))\n return \"%s (%s)\" % (population, \", \".join(mystats))\n else:\n return str(population)\n\n def create_population_string(populations, with_stats=False, pop_pvals=None, with_rank=False):\n if isinstance(populations, str):\n populations = [populations]\n population_strings = []\n for index, population in enumerate(populations):\n if pop_pvals is not None:\n cur_pval = pop_pvals[index]\n else:\n cur_pval = None\n population_strings.append(single_population_string(population, with_stats, cur_pval, with_rank))\n if len(populations) == 1:\n popstr = population_strings[0]\n elif len(populations) == 2:\n popstr = \" and \".join(population_strings)\n else:\n popstr = \", \".join(population_strings[:-1]) + \", and \" + population_strings[-1]\n return popstr\n\n if not isinstance(result, RankResult):\n raise TypeError(\"result must be of type RankResult and should be the outcome of calling the autorank function.\")\n\n print(\"The statistical analysis was conducted for %i populations with %i paired samples.\" % (len(result.rankdf),\n result.num_samples))\n print(\"The family-wise significance level of the tests is alpha=%.*f.\" % (decimal_places, result.alpha))\n\n if result.all_normal:\n not_normal = []\n min_pvalue = min(result.pvals_shapiro)\n print(\"We failed to reject the null hypothesis that the population is normal for all populations \"\n \"(minimal observed p-value=%.*f). Therefore, we assume that all populations are \"\n \"normal.\" % (decimal_places, min_pvalue))\n else:\n not_normal = []\n pvals = []\n normal = []\n for i, pval in enumerate(result.pvals_shapiro):\n if pval < result.alpha_normality:\n not_normal.append(result.rankdf.index[i])\n pvals.append(pval)\n else:\n normal.append(result.rankdf.index[i])\n if len(not_normal) == 1:\n population_term = 'population'\n else:\n population_term = 'populations'\n print(\"We rejected the null hypothesis that the population is normal for the %s %s. \"\n \"Therefore, we assume that not all populations are \"\n \"normal.\" % (population_term, create_population_string(not_normal, pop_pvals=pvals)))\n\n if result.omnibus == 'bayes':\n if result.all_normal:\n central_tendency = 'mean value'\n central_tendency_long = 'mean value (M)'\n variability = 'standard deviation (SD)'\n effect_size = 'd'\n else:\n central_tendency = 'median'\n central_tendency_long = 'median (MD)'\n variability = 'median absolute deviation (MAD)'\n effect_size = 'gamma'\n print(\n \"We used a bayesian signed rank test to determine differences between the mean values of the \"\n \"populations and report the %s and the %s for each population. We distinguish \"\n \"between populations being pair-wise smaller, equal, or larger and make a decision for one \"\n \"of these cases if we estimate that the posterior probability is at least \"\n \"alpha=%.*f.\" % (central_tendency_long, variability, decimal_places, result.alpha))\n if result.rope_mode == 'effsize':\n print(\n 'We used the effect size to define the region of practical equivalence (ROPE) around the %s '\n 'dynamically as %.*f*%s.' % (central_tendency, decimal_places, result.rope, effect_size))\n else:\n print(\n 'We used a fixed value of %.*f to define the region of practical equivalence (ROPE) around the '\n '%s.' % (decimal_places, result.rope, central_tendency))\n decision_set = set(result.rankdf['decision'])\n decision_set.remove('NA')\n if {'inconclusive'} == decision_set:\n print(\"We failed to find any conclusive evidence for differences between the populations \"\n \"%s.\" % create_population_string(result.rankdf.index, with_stats=True))\n elif {'equal'} == decision_set:\n print(\n \"All populations are equal, i.e., the are no significant and practically relevant differences \"\n \"between the populations %s.\" % create_population_string(result.rankdf.index,\n with_stats=True))\n elif {'equal', 'inconclusive'} == decision_set:\n print(\n \"The populations %s are all either equal or the results of the analysis are inconclusive.\")\n print(result.decision_matrix)\n else:\n print(\"We found significant and practically relevant differences between the populations \"\n \"%s.\" % create_population_string(result.rankdf.index, with_stats=True))\n for i in range(len(result.rankdf)):\n if len(result.rankdf.index[result.decision_matrix.iloc[i, :] == 'smaller']) > 0:\n print('The %s of the population %s is larger than of the populations '\n '%s.' % (central_tendency, result.rankdf.index[i],\n create_population_string(\n result.rankdf.index[\n result.decision_matrix.iloc[i, :] == 'smaller'])))\n equal_pairs = []\n for i in range(len(result.rankdf)):\n for j in range(i + 1, len(result.rankdf)):\n if result.decision_matrix.iloc[i, j] == 'equal':\n equal_pairs.append(result.rankdf.index[i] + ' and ' + result.rankdf.index[j])\n if len(equal_pairs) > 0:\n equal_pairs_str = create_population_string(equal_pairs).replace(',', ';')\n print('The following pairs of populations are equal: %s.' % equal_pairs_str)\n if 'inconclusive' in set(result.rankdf['decision']):\n print('All other differences are inconclusive.')\n elif len(result.rankdf) == 2:\n print(\"No check for homogeneity was required because we only have two populations.\")\n if result.effect_size == 'cohen_d':\n effect_size = 'd'\n elif result.effect_size == 'cliff_delta':\n effect_size = 'delta'\n elif result.effect_size == 'akinshin_gamma':\n effect_size = 'gamma'\n else:\n raise ValueError('unknown effect size method, this should not be possible: %s' % result.effect_size)\n if result.omnibus == 'ttest':\n larger = np.argmax(result.rankdf['mean'].values)\n smaller = int(bool(larger - 1))\n if result.all_normal:\n print(\"Because we have only two populations and both populations are normal, we use the t-test to \"\n \"determine differences between the mean values of the populations and report the mean value (M)\"\n \"and the standard deviation (SD) for each population. \")\n else:\n if len(not_normal) == 1:\n notnormal_str = 'one of them is'\n else:\n notnormal_str = 'both of them are'\n print(\"Because we have only two populations and %s not normal, we use should Wilcoxon's signed rank \"\n \"test to determine the differences in the central tendency and report the median (MD) and the \"\n \"median absolute deviation (MAD) for each population. However, the user decided to force the \"\n \"use of the t-test which assumes normality of all populations and we report the mean value (M) \"\n \"and the standard deviation (SD) for each population.\" % notnormal_str)\n if result.pvalue >= result.alpha:\n print(\"We failed to reject the null hypothesis (p=%.*f) of the paired t-test that the mean values of \"\n \"the populations %s are are equal. Therefore, we \"\n \"assume that there is no statistically significant difference between the mean values of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True)))\n else:\n print(\"We reject the null hypothesis (p=%.*f) of the paired t-test that the mean values of the \"\n \"populations %s are \"\n \"equal. Therefore, we assume that the mean value of %s is \"\n \"significantly larger than the mean value of %s with a %s effect size (%s=%.*f).\"\n % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True),\n result.rankdf.index[larger], result.rankdf.index[smaller],\n result.rankdf.magnitude[larger], effect_size, decimal_places, result.rankdf.effect_size[larger]))\n elif result.omnibus == 'wilcoxon':\n larger = np.argmax(result.rankdf['median'].values)\n smaller = int(bool(larger - 1))\n if result.all_normal:\n print(\"Because we have only two populations and both populations are normal, we should use the t-test \"\n \"to determine differences between the mean values of the populations and report the mean value \"\n \"(M) and the standard deviation (SD) for each population. However, the user decided to force the \"\n \"use of the less powerful Wilcoxon signed rank test and we report the median (MD) and the median \"\n \"absolute devivation (MAD) for each population.\")\n else:\n if len(not_normal) == 1:\n notnormal_str = 'one of them is'\n else:\n notnormal_str = 'both of them are'\n print(\"Because we have only two populations and %s not normal, we use Wilcoxon's signed rank test to \"\n \"determine the differences in the central tendency and report the median (MD) and the median \"\n \"absolute deviation (MAD) for each population.\" % notnormal_str)\n if result.pvalue >= result.alpha:\n print(\"We failed to reject the null hypothesis (p=%.*f) of Wilcoxon's signed rank test that \"\n \"population %s is not greater than population %s . Therefore, we \"\n \"assume that there is no statistically significant difference between the medians of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index[larger], with_stats=True),\n create_population_string(result.rankdf.index[smaller], with_stats=True)))\n else:\n print(\"We reject the null hypothesis (p=%.*f) of Wilcoxon's signed rank test that population \"\n \"%s is not greater than population %s. Therefore, we assume \"\n \"that the median of %s is \"\n \"significantly larger than the median value of %s with a %s effect size (%s=%.*f).\"\n % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index[larger], with_stats=True),\n create_population_string(result.rankdf.index[smaller], with_stats=True),\n result.rankdf.index[larger], result.rankdf.index[smaller],\n result.rankdf.magnitude[larger], effect_size, decimal_places, result.rankdf.effect_size[larger]))\n else:\n raise ValueError('Unknown omnibus test for difference in the central tendency: %s' % result.omnibus)\n else:\n if result.all_normal:\n if result.homoscedastic:\n print(\"We applied Bartlett's test for homogeneity and failed to reject the null hypothesis \"\n \"(p=%.*f) that the data is homoscedastic. Thus, we assume that our data is \"\n \"homoscedastic.\" % (decimal_places, result.pval_homogeneity))\n else:\n print(\"We applied Bartlett's test for homogeneity and reject the null hypothesis (p=%.*f) that the\"\n \"data is homoscedastic. Thus, we assume that our data is \"\n \"heteroscedastic.\" % (decimal_places, result.pval_homogeneity))\n\n if result.omnibus == 'anova':\n if result.all_normal and result.homoscedastic:\n print(\"Because we have more than two populations and all populations are normal and homoscedastic, we \"\n \"use repeated measures ANOVA as omnibus \"\n \"test to determine if there are any significant differences between the mean values of the \"\n \"populations. If the results of the ANOVA test are significant, we use the post-hoc Tukey HSD \"\n \"test to infer which differences are significant. We report the mean value (M) and the standard \"\n \"deviation (SD) for each population. Populations are significantly different if their confidence \"\n \"intervals are not overlapping.\")\n else:\n if result.all_normal:\n print(\n \"Because we have more than two populations and the populations are normal but heteroscedastic, \"\n \"we should use the non-parametric Friedman test \"\n \"as omnibus test to determine if there are any significant differences between the mean values \"\n \"of the populations. However, the user decided to force the use of \"\n \"repeated measures ANOVA as omnibus test which assume homoscedascity to determine if there are \"\n \"any significant difference between the mean values of the populations. If the results of the \"\n \"ANOVA test are significant, we use the post-hoc Tukey HSD test to infer which differences are \"\n \"significant. We report the mean value (M) and the standard deviation (SD) for each \"\n \"population. Populations are significantly different if their confidence intervals are not \"\n \"overlapping.\")\n else:\n if len(not_normal) == 1:\n notnormal_str = 'one of them is'\n else:\n notnormal_str = 'some of them are'\n print(\"Because we have more than two populations and the populations and %s not normal, \"\n \"we should use the non-parametric Friedman test \"\n \"as omnibus test to determine if there are any significant differences between the median \"\n \"values of the populations and report the median (MD) and the median absolute deviation \"\n \"(MAD). However, the user decided to force the use of repeated measures ANOVA as omnibus \"\n \"test which assume homoscedascity to determine if there are any significant difference \"\n \"between the mean values of the populations. If the results of the ANOVA test are \"\n \"significant, we use the post-hoc Tukey HSD test to infer which differences are \"\n \"significant. We report the mean value (M) and the standard deviation (SD) for each \"\n \"population. Populations are significantly different if their confidence intervals are not \"\n \"overlapping.\" % (notnormal_str))\n if result.pvalue >= result.alpha:\n print(\"We failed to reject the null hypothesis (p=%.*f) of the repeated measures ANOVA that there is \"\n \"a difference between the mean values of the populations %s. Therefore, we \"\n \"assume that there is no statistically significant difference between the mean values of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True)))\n else:\n print(\"We reject the null hypothesis (p=%.*f) of the repeated measures ANOVA that there is \"\n \"a difference between the mean values of the populations %s. Therefore, we \"\n \"assume that there is a statistically significant difference between the mean values of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True)))\n meanranks, names, groups = get_sorted_rank_groups(result, False)\n if len(groups) == 0:\n print(\"Based on post-hoc Tukey HSD test, we assume that all differences between the populations \"\n \"are significant.\")\n else:\n groupstrs = []\n for group_range in groups:\n group = range(group_range[0], group_range[1] + 1)\n if len(group) == 1:\n cur_groupstr = names[group[0]]\n elif len(group) == 2:\n cur_groupstr = \" and \".join([names[pop] for pop in group])\n else:\n cur_groupstr = \", \".join([names[pop] for pop in group[:-1]]) + \", and \" + names[group[-1]]\n groupstrs.append(cur_groupstr)\n print(\"Based post-hoc Tukey HSD test, we assume that there are no significant differences within \"\n \"the following groups: %s. All other differences are significant.\" % (\"; \".join(groupstrs)))\n print()\n elif result.omnibus == 'friedman':\n if result.all_normal and result.homoscedastic:\n print(\"Because we have more than two populations and all populations are normal and homoscedastic, we \"\n \"should use repeated measures ANOVA as omnibus \"\n \"test to determine if there are any significant differences between the mean values of the \"\n \"populations. However, the user decided to force the use of the less powerful Friedman test as \"\n \"omnibus test to determine if there are any significant differences between the mean values \"\n \"of the populations. We report the mean value (M), the standard deviation (SD) and the mean rank \"\n \"(MR) among all populations over the samples. Differences between populations are significant, \"\n \"if the difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \"\n \"test.\" % (decimal_places, result.cd))\n elif result.all_normal:\n print(\"Because we have more than two populations and the populations are normal but heteroscedastic, \"\n \"we use the non-parametric Friedman test \"\n \"as omnibus test to determine if there are any significant differences between the mean values \"\n \"of the populations. We use the post-hoc Nemenyi test to infer which differences are \"\n \"significant. We report the mean value (M), the standard deviation (SD) and the mean rank (MR) \"\n \"among all populations over the samples. Differences between populations are significant, if the \"\n \"difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \"\n \"test.\" % (decimal_places, result.cd))\n else:\n if len(not_normal) == 1:\n notnormal_str = 'one of them is'\n else:\n notnormal_str = 'some of them are'\n print(\"Because we have more than two populations and the populations and %s not normal, \"\n \"we use the non-parametric Friedman test \"\n \"as omnibus test to determine if there are any significant differences between the median values \"\n \"of the populations. We use the post-hoc Nemenyi test to infer which differences are \"\n \"significant. We report the median (MD), the median absolute deviation (MAD) and the mean rank \"\n \"(MR) among all populations over the samples. Differences between populations are significant, \"\n \"if the difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \"\n \"test.\" % (notnormal_str, decimal_places, result.cd))\n if result.pvalue >= result.alpha:\n print(\"We failed to reject the null hypothesis (p=%.*f) of the Friedman test that there is no \"\n \"difference in the central tendency of the populations %s. Therefore, we \"\n \"assume that there is no statistically significant difference between the median values of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True, with_rank=True)))\n else:\n print(\"We reject the null hypothesis (p=%.*f) of the Friedman test that there is no \"\n \"difference in the central tendency of the populations %s. Therefore, we \"\n \"assume that there is a statistically significant difference between the median values of the \"\n \"populations.\" % (decimal_places, result.pvalue,\n create_population_string(result.rankdf.index, with_stats=True, with_rank=True)))\n meanranks, names, groups = get_sorted_rank_groups(result, False)\n if len(groups) == 0:\n print(\"Based on the post-hoc Nemenyi test, we assume that all differences between the populations \"\n \"are significant.\")\n else:\n groupstrs = []\n for group_range in groups:\n group = range(group_range[0], group_range[1] + 1)\n if len(group) == 1:\n cur_groupstr = names[group[0]]\n elif len(group) == 2:\n cur_groupstr = \" and \".join([names[pop] for pop in group])\n else:\n cur_groupstr = \", \".join([names[pop] for pop in group[:-1]]) + \", and \" + names[group[-1]]\n groupstrs.append(cur_groupstr)\n print(\"Based on the post-hoc Nemenyi test, we assume that there are no significant differences \"\n \"within the following groups: %s. All other differences are \"\n \"significant.\" % (\"; \".join(groupstrs)))\n else:\n raise ValueError('Unknown omnibus test for difference in the central tendency: %s' % result.omnibus)","def create_risk_groups(model, X, use_log = True, num_bins = 50, \r\n figure_size = (20, 8), **kwargs):\r\n\r\n # Ensuring that the input data has the right format\r\n X = utils.check_data(X)\r\n\r\n # Computing the risk scores\r\n risk = model.predict_risk(X)\r\n if use_log:\r\n risk = np.log(risk)\r\n \r\n # Displaying simple histogram\r\n if len(kwargs) == 0:\r\n \r\n # Initializing the chart\r\n fig, ax1 = plt.subplots(figsize=figure_size)\r\n risk_groups = None\r\n \r\n # Applying any color coding\r\n else:\r\n # Initializing the results\r\n risk_groups = {}\r\n \r\n # Initializing the chart\r\n fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=figure_size)\r\n \r\n # Displaying simple histogram with risk groups\r\n nums_per_bins, bins, patches = ax2.hist(risk, bins=num_bins)\r\n ax2.set_title('Risk groups with colors', fontsize=15)\r\n\r\n # Number of group definitions\r\n num_group_def = len(kwargs.values())\r\n\r\n # Extracting the bounds values\r\n bounds = {}\r\n colors_ = {}\r\n indexes = {}\r\n group_names = []\r\n handles = []\r\n\r\n # we need to check that the boundaries match the bins\r\n is_not_valid = 0\r\n for group_name, group_def in kwargs.items():\r\n\r\n # by ensuring that the bounds are not outside\r\n # the bins values\r\n min_bin, max_bin = min(bins), max(bins)\r\n if (group_def['lower_bound'] < min_bin and \\\r\n group_def['upper_bound'] < min_bin) or \\\r\n (group_def['lower_bound'] > max_bin and \\\r\n group_def['upper_bound'] > max_bin) :\r\n is_not_valid += 1\r\n\r\n # Extracting the bounds\r\n bounds[group_name] = (group_def['lower_bound'], \r\n group_def['upper_bound'])\r\n\r\n # Extracting the colors\r\n colors_[group_name] = group_def['color']\r\n\r\n # Creating index placeholders\r\n indexes[group_name] = []\r\n group_names.append( group_name )\r\n color_indv = group_def['color']\r\n handles.append(Rectangle((0,0),1,1, color=color_indv, ec=\"k\"))\r\n\r\n if is_not_valid >= num_group_def :\r\n error_msg = \"The boundaries definitions {} do not match\"\r\n error_msg += \", the values of the risk scores.\"\r\n error_msg = error_msg.format(list(bounds.values()))\r\n raise ValueError(error_msg)\r\n\r\n # Assigning each rectangle/bin to its group definition\r\n # and color\r\n colored_patches = []\r\n bin_index = {}\r\n for i, bin_, patch_ in zip(range(num_bins), bins, patches):\r\n\r\n # Check if the bin belongs to this bound def\r\n for grp_name, bounds_ in bounds.items():\r\n\r\n if bounds_[0] <= bin_ < bounds_[-1] :\r\n bin_index[i] = grp_name\r\n \r\n # Extracting color\r\n color_ = colors_[grp_name]\r\n if color_ not in colors.CSS4_COLORS :\r\n error_msg = '{} is not a valid color'\r\n error_msg = error_msg.format(colors_[grp_name])\r\n raise ValueError(error_msg)\r\n \r\n patch_.set_facecolor( color_ )\r\n\r\n # Saving the rectangles\r\n colored_patches.append(patch_)\r\n\r\n # Assigning each sample to its group\r\n risk_bins = np.minimum(np.digitize(risk, bins, True), num_bins-1) \r\n for i, r in enumerate(risk_bins):\r\n\r\n # Extracting the right group_name\r\n group_name = bin_index[r]\r\n indexes[group_name].append(i)\r\n \r\n\r\n # Displaying the original distribution\r\n ax1.hist(risk, bins=num_bins, color = 'black', alpha=0.5) \r\n ax1.set_title('Risk Score Distribution', fontsize=15)\r\n\r\n # Show everything\r\n plt.show()\r\n \r\n # Returning results\r\n if risk_groups is not None:\r\n for group_name in group_names:\r\n result = (colors_[group_name], indexes[group_name]) \r\n risk_groups[group_name] = result\r\n\r\n return risk_groups","def perSaccPerGroup(addExp2 = True, trim = True, spacing = .5, addSimulation = True, \\\n\t\texclOverlap = True):\n\n\tfig = plt.figure(figsize = (10,10))\n\ttitle = \"Average towards-handle landings per group - exclOverlap = %s\" % exclOverlap\n\tplt.title(title)\n\t\n\t\n\t# Exp 1:\n\tdm1 = getDM.getDM(exp = \"004A\", excludeErrors = True, driftCorr = True)\n\t\n\t\n\tsubPlot = 1\n\t\n\tfor group in dm1.unique(\"corrDirection\"):\n\n\t\t# New subplot:\n\t\tplt.subplot(1,2,subPlot)\n\t\tplt.title(group)\n\t\t_dm1 = dm1.select(\"corrDirection == '%s'\" % group)\n\n\t\tlLandingsAbs = []\n\t\tlLandingsCorr = []\n\t\t\n\t\tfor sacc in [\"1\", \"2\", \"3\"]:\n\t\t\t\n\t\t\t# This is the same for corrected landing positions (the saccade\n\t\t\t# doesn't change; only the reference point does)\n\t\t\t_dm = _dm1.select(\"endX%sNorm != ''\" % sacc)\n\t\t\t_dm = _dm.select(\"endX%sNorm > -.5\" % sacc)\n\t\t\t_dm = _dm.select(\"endX%sNorm < .5\" % sacc)\n\t\t\t\n\t\t\tif trim:\n\t\t\t\t_dmAbs = _dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sNormToHandle\" % sacc)\n\t\t\t\t_dmCorr = _dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sCorrNormToHandle\" % sacc)\n\t\t\t\n\t\t\t# Determine avg landing position:\n\t\t\tavgAbs = _dmAbs[\"endX%sNormToHandle\" % sacc].mean()\n\t\t\tavgCorr = _dmCorr[\"endX%sCorrNormToHandle\" % sacc].mean()\n\t\t\t\n\t\t\t# TODO: determine error bars:\n\t\t\t\n\t\t\t\n\t\t\tlLandingsAbs.append(avgAbs)\n\t\t\tlLandingsCorr.append(avgCorr)\n\t\t\n\t\tplt.plot(lLandingsAbs, color = \"#f57900\", linewidth = 2, marker = \"o\")\n\t\tplt.plot(lLandingsCorr, color = \"#73d216\", linewidth = 2, marker = \"o\")\n\t\t\n\t\t# The other 2 experiments can be treated equally:\n\t\tcolList = [\"#ef2929\", \"#3465a4\"]\n\t\t\n\t\tfor exp in [\"004B\", \"004C\"]:\n\t\t\t\n\t\t\tif not addExp2:\n\t\t\t\tif exp == \"004B\":\n\t\t\t\t\tcontinue\n\t\t\tif not addSimulation:\n\t\t\t\tif exp == \"004C\":\n\t\t\t\t\tcontinue\n\n\t\t\tdm = getDM.getDM(exp = exp, excludeErrors = True, driftCorr = True)\n\t\t\t_dm = dm.select(\"corrDirection == '%s'\" % group)\n\n\t\t\tif exp == \"004C\":\n\t\t\t\tif exclOverlap:\n\t\t\t\t\t_dm = _dm.select(\"gap == 'zero'\")\n\n\t\t\t\n\t\t\tlLandingsAbs = []\n\t\t\t\n\t\t\tfor sacc in [\"1\", \"2\", \"3\"]:\n\t\t\t\t\n\t\t\t\t# TODO: how to filter only on-object saccades exp 2??\n\t\t\t\tsacc_dm = _dm.select(\"endX%sNorm != ''\" % sacc)\n\t\t\t\tsacc_dm = sacc_dm.select(\"endX%sNorm > -.5\" % sacc)\n\t\t\t\tsacc_dm = sacc_dm.select(\"endX%sNorm < .5\" % sacc)\n\t\t\t\t\n\t\t\t\tif trim:\n\t\t\t\t\tsacc_dm = sacc_dm.selectByStdDev(keys = [\"file\"], dv = \"endX%sNormToHandle\" % sacc)\n\t\t\t\t\n\t\t\t\t# Determine avg landing position:\n\t\t\t\tavgAbs = sacc_dm[\"endX%sNormToHandle\" % sacc].mean()\n\t\t\t\t\n\t\t\t\t# TODO: determine error bars:\n\t\t\t\t\n\t\t\t\tlLandingsAbs.append(avgAbs)\n\t\t\tcol = colList.pop()\n\t\t\tplt.plot(lLandingsAbs, color = col, linewidth = 2, marker = \"o\")\n\t\t\t\n\t\t# Modify plot:\n\t\tplt.legend([\"Exp1 abs\", \"Exp1 corr\", \"Exp2\", \"Sim\"])\n\t\t\t\n\t\tplt.axhline(0, color = \"#888a85\", linestyle = \"--\", linewidth = 2)\n\t\txLabels = [\"sacc 1\", \"sacc 2\", \"sacc 3\"]\n\t\txTicks = range(0,3)\n\t\tplt.xticks(xTicks, xLabels, rotation = .5)\n\t\tplt.ylim([-.5, .5])\n\t\tplt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)\n\t\t\n\t\tsubPlot +=1\n\t\t\n\tplt.savefig(\"%s.png\" % title)","def group_action_by_gamma_coord(f, gamma):\n n, T = f.shape\n fn = zeros((n, T))\n\n for j in range(0, n):\n s = interp1d(linspace(0, 1, T), f[j, :])\n fn[j, :] = s(gamma)\n\n return (fn)","def Morris_Measure_Groups(NumFact, Sample, OutFact, Output, p=4,\n Group=[], Diagnostic=False):\n try:\n NumGroups = Group.shape[1]\n if Diagnostic: print('{:d} Groups are used'.format(NumGroups))\n except:\n NumGroups = 0\n if Diagnostic: print('No Groups are used')\n\n Delt = p / (2. * (p - 1.))\n\n if NumGroups != 0:\n sizea = NumGroups\n GroupMat = Group\n GroupMat = GroupMat.transpose()\n if Diagnostic: print('NumGroups', NumGroups)\n else:\n sizea = NumFact\n sizeb = sizea + 1\n\n # r = Sample.shape[0]/sizeb\n r = Sample.shape[0] // sizeb\n\n try:\n NumOutp = Output.shape[1]\n except:\n NumOutp = 1\n Output = Output.reshape((Output.size, 1))\n\n # For each Output\n if NumGroups == 0:\n # for every output: every factor is a line, columns are mu*, mu and std\n OutMatrix = np.zeros((NumOutp * NumFact, 3))\n else:\n OutMatrix = np.zeros((NumOutp * NumFact, 1)) # for every output: every factor is a line, column is mu*\n\n SAmeas_out = np.zeros((NumOutp * NumFact, r))\n\n for k in range(NumOutp):\n OutValues = Output[:, k]\n\n # For each trajectory\n SAmeas = np.zeros((NumFact, r)) #vorm afhankelijk maken van group of niet...\n for i in range(r):\n # For each step j in the trajectory\n # Read the orientation matrix fact for the r-th sampling\n # Read the corresponding output values\n # Read the line of changing factors\n Single_Sample = Sample[i * sizeb:(i + 1) * sizeb, :]\n Single_OutValues = OutValues[i * sizeb:(i + 1) * sizeb]\n Single_Facts = np.array(OutFact[i * sizeb:(i + 1) * sizeb], dtype=np.int) # gives factor in change (or group)\n\n A = (Single_Sample[1:sizeb, :] - Single_Sample[:sizea, :]).transpose()\n Delta = A[np.where(A)] # AAN TE PASSEN?\n # If upper bound==lower bound then A==0 in all trajectories.\n # Delta will have not the right dimensions then because these are filtered out with where.\n # Fill in Delta==0 for these cases.\n ii = np.where(np.sum(A, axis=0) == 0.)[0]\n if ii.size > 0: Delta = np.insert(Delta, ii, 0.)\n\n if Diagnostic:\n print('A: ', A)\n print('Delta: ', Delta)\n print('Single_Facts: ', Single_Facts)\n\n # For each point of the fixed trajectory, i.e. for each factor, compute the values of the Morris function.\n for j in range(sizea):\n if NumGroups != 0: #work with groups\n Auxfind = A[:, j]\n Change_factor = np.where(np.abs(Auxfind) > 1e-010)[0]\n for gk in Change_factor:\n SAmeas[gk, i] = np.abs(\n (Single_OutValues[j] - Single_OutValues[j + 1]) /\n Delt) #nog niet volledig goe\n else:\n if Delta[j] > 0.0:\n SAmeas[Single_Facts[j], i] = (Single_OutValues[j + 1] - Single_OutValues[j]) / Delt\n else:\n SAmeas[Single_Facts[j], i] = (Single_OutValues[j] - Single_OutValues[j + 1]) / Delt\n\n # Compute Mu AbsMu and StDev\n if np.isnan(SAmeas).any():\n AbsMu = np.zeros(NumFact)\n Mu = np.zeros(NumFact)\n Stdev = np.zeros(NumFact)\n\n for j in range(NumFact):\n SAm = SAmeas[j, :]\n SAm = SAm[~np.isnan(SAm)]\n rr = np.float(SAm.size)\n AbsMu[j] = np.sum(np.abs(SAm)) / rr\n if NumGroups == 0:\n Mu[j] = SAm.mean()\n if SAm.size > 1:\n Stdev[j] = np.std(SAm, ddof=1)\n else:\n Stdev[j] = 0.\n else:\n AbsMu = np.sum(np.abs(SAmeas), axis=1) / r\n if NumGroups == 0:\n Mu = SAmeas.mean(axis=1)\n if SAmeas.shape[1] > 1:\n Stdev = np.std(SAmeas, ddof=1, axis=1)\n else:\n Stdev = np.zeros(SAmeas.shape[0])\n else:\n Mu = np.zeros(NumFact)\n Stdev = np.zeros(NumFact)\n\n OutMatrix[k * NumFact:k * NumFact + NumFact, 0] = AbsMu\n if NumGroups == 0:\n OutMatrix[k * NumFact:k * NumFact + NumFact, 1] = Mu\n OutMatrix[k * NumFact:k * NumFact + NumFact, 2] = Stdev\n\n SAmeas_out[k * NumFact:k * NumFact + NumFact, :] = SAmeas\n\n return SAmeas_out, OutMatrix","def groupby_grade(master_dataframe):\n\tno_name_df = master_dataframe.drop(['last_name', 'first_name', 'email'], axis=1)\n\tgrouped = list(no_name_df.groupby(['grade']))\n\tnine, ten, eleven, twelve = grouped[0][1], grouped[1][1], grouped[2][1], grouped[3][1]\n\n\treturn nine, ten, eleven, twelve","def group_action_by_gamma(q, gamma):\n n, T = q.shape\n gammadot = gradient(gamma, 1. / T)\n qn = zeros((n, T))\n\n for j in range(0, n):\n s = InterpolatedUnivariateSpline(linspace(0, 1, T), q[j, :], k=3)\n qn[j, :] = s(gamma) * sqrt(gammadot)\n\n qn = qn / sqrt(innerprod_q2(qn, qn))\n\n return (qn)","def gamma(self, predictor):\n pred = predictor(self.X)\n self.tags[_PREDICTION] = pred\n expect_event = self.tags.groupby(_EVENT).mean()\n expect_group_event = self.tags.groupby(\n [_EVENT, _GROUP_ID]).mean()\n\n num_grp = len(self.error_rate)\n tprs = [0 for _ in range(num_grp)]\n # print(expect_group_event)\n for i in range(num_grp):\n tprs[i] = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == i)].groupby([_EVENT]).mean()\n expect_group_event.loc[('label=1', i), 'pred'] = (1 - self.error_rate[i][0]) * tprs[i].loc['label=1', 'pred'] + self.error_rate[i][0] * tprs[i].loc['label=0', 'pred']\n expect_group_event.loc[('label=0', i), 'pred'] = (1 - self.error_rate[i][1]) * tprs[i].loc['label=0', 'pred'] + self.error_rate[i][1] * tprs[i].loc['label=1', 'pred']\n\n # neg = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == 0.0)].groupby([_EVENT]).mean()\n # pos = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == 1.0)].groupby([_EVENT]).mean()\n\n # expect_group_event.loc[('label=1.0', 1), 'pred'] = (1 - self.error_rate[1][0]) * pos.loc['label=1.0', 'pred'] + self.error_rate[1][1] * pos.loc['label=0.0', 'pred']\n # expect_group_event.loc[('label=0.0', 1), 'pred'] = (1 - self.error_rate[1][1]) * pos.loc['label=0.0', 'pred'] + self.error_rate[1][0] * pos.loc['label=1.0', 'pred']\n\n # expect_group_event.loc[('label=1.0', 0), 'pred'] = (1 - self.error_rate[0][0]) * neg.loc['label=1.0', 'pred'] + self.error_rate[0][1] * neg.loc['label=0.0', 'pred']\n # expect_group_event.loc[('label=0.0', 0), 'pred'] = (1 - self.error_rate[0][1]) * neg.loc['label=0.0', 'pred'] + self.error_rate[0][0] * neg.loc['label=1.0', 'pred']\n\n expect_event = expect_group_event.groupby(_EVENT).mean()\n expect_group_event[_DIFF] = expect_group_event[_PREDICTION] - expect_event[_PREDICTION]\n\n # expect_group_event[_DIFF] = expect_group_event[_PREDICTION] - expect_event[_PREDICTION]\n g_unsigned = expect_group_event[_DIFF]\n g_signed = pd.concat([g_unsigned, -g_unsigned],\n keys=[\"+\", \"-\"],\n names=[_SIGN, _EVENT, _GROUP_ID])\n self._gamma_descr = str(expect_group_event[[_PREDICTION, _DIFF]])\n return g_signed","def calculateGammaFactors(self):\n return (self.time/self.expectedDuration)**self.beta","def all_conditions_visualized(self):\n # group dataframe based on standard deviation and effect size\n grouped_df = self.df.groupby([\"es\", \"sd1\", \"samp2\"])\n results = self.helpers.get_mean_df(grouped_df, False)\n print(results.sample())\n\n # seperate groups based on typeI or typeII error\n dfs = {\"typeI_df\": results[:8*7],\n \"ef_0_2\": results[8*7:8*7*2],\n \"ef_0_5\": results[8*7*2:8*7*3],\n \"ef_0_8\": results[8*7*3:]}\n\n for i in dfs:\n sdI = self.get_sd_list(dfs[i], False)\n dictI = {\"xlabel\": 'Group sizes', \"ylabel\": \"TypeI error\",\n \"title\": \"Mean typeI error per group size\",\n \"xtickslabels\": sdI}\n self.multiple_bars(dfs[i], 2, 2, dictI)","def test_properties_stats_group_by_group_by_get(self):\n pass","def do_grouping(prefixes, grouping_keys, xaxis_key='Age_At_IMGExam',\n plots='regressions',\n atlas='desikan', username=None, passwd=None,\n data_dir='data', output_dir='.', output_type='matplotlib'):\n\n data = get_all_data(atlas, username=username, passwd=passwd, data_dir=data_dir)\n data.filter(lambda k, v: 'fuzzy' not in k) # Remove 'fuzzy'\n data.filter([partial(lambda k, v, p: (k.startswith(p) or\n k in grouping_keys or\n k == xaxis_key),\n p=p)\n for p in prefixes])\n\n # Process & plot the data.\n measure_keys = [(k) for k in data.data_dict.keys()\n if k.startswith(p) and k.endswith('_AI')]\n for pi, yaxis_key in enumerate(sorted(measure_keys)):\n print(\"Comparing %d (%s)...\" % (pi, yaxis_key))\n\n kwargs = dict(xaxis_key=xaxis_key, yaxis_key=yaxis_key)\n group_names, group_x, group_y = compute_group_asymmetry(data, grouping_keys=grouping_keys, **kwargs)\n\n kwargs.update(dict(group_names=group_names, group_x=group_x, group_y=group_y))\n if 'regressions' in plots:\n fh = plot_regressions(**kwargs)\n fh.name = '%s-%s-regressions' % (yaxis_key, '__'.join(grouping_keys))\n\n if 'distributions' in plots:\n plot_distributions(**kwargs)\n if 'stats' in plots:\n do_stats(group_names, group_x, group_y)\n\n # Outside of loop\n if 'regression_stats' in plots:\n dump_regressions_csv(regressions,\n group_names=group_names,\n measure_names=measure_keys)\n\n plot_regressions_scatter(regressions,\n group_names=group_names,\n measure_names=measure_keys)\n\n if 'stat_distributions' in plots:\n plot_stat_distributions(stats, group_names=group_names)\n\n show_plots(plotengine=output_type, output_dir=output_dir)","def _parse_groupped_data(self):\n for i, val in enumerate(self.values.keys()):\n xy = self.values[val]\n self._set_and_get(\"x_\", val, xy[:, 0])\n self._set_and_get(\"y_\", val, xy[:, 1])","def get_contribution_dataframe_groups(self):\n pargrp_dict = {}\n par = self.pst.parameter_data\n groups = par.groupby(\"pargp\").groups\n for grp,idxs in groups.items():\n pargrp_dict[grp] = list(par.loc[idxs,\"parnme\"])\n return self.get_contribution_dataframe(pargrp_dict)","def values_per_gamma():\n\n keys, data = parse(\"data/treeNodePolicyIncludingN=1.csv\")\n gammas = [1] + [round(0.01*i,2) for i in range(100)]\n\n with open('output/discounted_values.csv', mode='w') as out_file:\n out_writer = csv.writer(out_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n out_writer.writerow(['world', 'node', 'child', 'value'] + ['dv_'+str(gamma) for gamma in gammas[1:]])\n\n for d in data:\n values = [round(node_value(d[keys['world']], d[keys['child']], gamma),3) for gamma in gammas]\n out_writer.writerow([d[keys['world']], d[keys['node']], d[keys['child']]] + values)","def big_analysis(beta0s=[0.5, 0.8, 1.1, 1.4, 1.7], ks=range(6), betaps=[1.2, 1.5, 2, 3]):","def recalculate_groups(dynamic_list):\n new_final_list = []\n for page in dynamic_list:\n page_list = []\n for group in page:\n new_dict = {}\n lowest_x = 100\n highest_x = 0\n for line in group:\n # find if x is the lowest or highest\n if line['bbox'][0] < lowest_x:\n lowest_x = line['bbox'][0]\n if line['bbox'][2] > highest_x:\n highest_x = line['bbox'][2]\n x_one = lowest_x\n x_two = highest_x\n y_one = group[0]['bbox'][1]\n y_two = h.get_biggest_y(group)\n new_bbox = [x_one, y_one, x_two, y_two]\n new_dict['bbox'] = new_bbox\n new_dict['font'] = h.find_font_in_group(group)\n new_dict['size'] = h.find_font_size_in_group(group)\n new_dict['page'] = group[0]['page']\n new_dict['lines'] = group\n\n page_list.append(new_dict)\n\n new_final_list.append(page_list)\n return new_final_list","def params_groups(request):\n return np.array([-0.2, -0.1])","def makeGroupsFromCutFile(self):\n if self.cutfile == None:\n print \"Cannot make groups without a cuts file\"\n return ([],[])\n else:\n groups = []\n labels = []\n yields = []\n all_cols = self.qie.columns.values\n # For each predefined group\n for grouplist in cut_groups:\n labels.append(grouplist[0])\n g = None\n # For each cut in that group\n for cut in grouplist[1]:\n # Get min and max values for main cuts (TODO: handle marginal cuts)\n cut_min = self.cuts[cut][0]\n cut_max = self.cuts[cut][1]\n # For each df column corresponding to that cut (sometimes more than one measurement)\n for col in all_cols:\n if col.split(\"_\")[0] == cut:\n g_tmp = (self.qie[col] < cut_min) | (self.qie[col] > cut_max)\n if 'NoneType' in str(type(g)) :\n g = g_tmp\n else: \n g = g | g_tmp\n # Make exclusive groups\n if len(groups) > 0:\n g = g & (self.NotGroup(groups))\n groups.append(g)\n yields.append(g.sum())\n # Make final group containing all other chips\n groups.append(self.NotGroup(groups))\n labels.append(\"Good\")\n yields.append(groups[-1].sum())\n self.makeYieldsTable(yields, labels)\n # Add column to data frame containing \"Good\" (1), \"bad\" (0), \"marginal\" (2,..) info\n self.qie[\"Sorting\"] = np.where(groups[-1], 1, 0)\n print sum(self.qie[\"Sorting\"])\n #print self.qie\n self.makeSortingFile()\n return (groups, labels)","def build_group_catalog(cat, mfac=2.0, dmax=10.0/60.0):\n from pydl.pydlutils.spheregroup import spheregroup\n\n ## RAF\n ## REPLACING ASTROMETRY FUNCTION WITH ASTROPY \n #from astrometry.util.starutil_numpy import degrees_between\n #print('Starting spheregrouping.')\n\n nchar = np.max([len(gg) for gg in cat['GALAXY']])+6 # add six characters for \"_GROUP\"\n \n t0 = time.time()\n cat.add_column(Column(name='GROUP_ID', data=np.zeros(len(cat), dtype=np.int)-1))\n cat.add_column(Column(name='GROUP_NAME', length=len(cat), dtype='<U{}'.format(nchar)))\n cat.add_column(Column(name='GROUP_MULT', data=np.zeros(len(cat), dtype=np.int16)))\n cat.add_column(Column(name='GROUP_PRIMARY', data=np.zeros(len(cat), dtype=bool)))\n cat.add_column(Column(name='GROUP_RA', length=len(cat), dtype='f8')) # diameter-weighted center\n cat.add_column(Column(name='GROUP_DEC', length=len(cat), dtype='f8'))\n cat.add_column(Column(name='GROUP_DIAMETER', length=len(cat), dtype='f4'))\n\n #ww = np.where((parent['RA'] > 177) * (parent['RA'] < 178) * (parent['DEC'] > -1.5) * (parent['DEC'] < -0.5))[0]\n #ww = np.where((parent['RA'] > 200) * (parent['RA'] < 240) * (parent['DEC'] > 20))[0]\n #ww = np.where((parent['RA'] > 193) * (parent['RA'] < 196) * (parent['DEC'] > 26) * (parent['DEC'] < 30))[0]\n \n # Initialize a unique group number for each galaxy\n gnum = np.arange(len(cat)).astype(np.int)\n mgrp = np.ones(len(cat)).astype(np.int16)\n \n # First group galaxies within 10 arcmin, setting those to have the same\n # group number\n t0 = time.time()\n print('Spheregrouping took...', end='')\n ingroup, group_mult, firstgroup, nextgroup = spheregroup(cat['RA'], cat['DEC'], dmax)\n\n ngroup = np.count_nonzero(firstgroup != -1)\n for ii in np.arange(ngroup):\n #print(ii, ngroup)\n nn = group_mult[ii] # number of galaxies in this group\n if nn > 1:\n # Build INDX as the indices of all objects in this grouping\n indx = np.zeros(nn, dtype=int)\n indx[0] = firstgroup[ii]\n for jj in np.arange(nn-1):\n indx[jj+1] = nextgroup[indx[jj]]\n # Look at all pairs within this grouping to see if they should be connected.\n for jj in np.arange(nn-1):\n for kk in np.arange(jj, nn):\n dd = degrees_between(cat['RA'][indx[jj]], cat['DEC'][indx[jj]], cat['RA'][indx[kk]], cat['DEC'][indx[kk]])\n #dd = c1.separate(c2)\n # If these two galaxies should be connected, make GNUM the\n # same for them...\n #print(dd, mfac * (cat['D25'][indx[jj]] / 60. + cat['D25'][indx[kk]] / 60.))\n if dd < (0.5 * mfac * (cat['D25'][indx[jj]] / 60. + cat['D25'][indx[kk]] / 60.)):\n jndx = np.where(np.logical_or(gnum[indx]==gnum[indx[jj]], gnum[indx]==gnum[indx[kk]]))[0]\n gnum[indx[jndx]] = gnum[indx[jndx[0]]]\n mgrp[indx[jndx]] = len(jndx)\n #print(ii, ngroup, gnum[indx], mgrp[indx])\n\n # Special-case the largest galaxies, looking for neighbhors\n ibig = np.where(cat['D25'] / 60. > dmax)[0]\n if len(ibig) > 0:\n for ii in np.arange(len(ibig)):\n dd = degrees_between(cat['RA'][ibig[ii]], cat['DEC'][ibig[ii]], cat['RA'], cat['DEC'])\n inear = np.where(dd < 0.5*(cat[ibig[ii]]['D25'] + cat['D25']) / 60.)[0]\n if len(inear) > 0:\n for jj in np.arange(len(inear)):\n indx = np.where(np.logical_or(gnum==gnum[ibig[ii]], gnum==gnum[inear[jj]]))[0]\n gnum[indx] = gnum[indx[0]]\n mgrp[indx] = len(indx)\n print('...{:.3f} min'.format((time.time() - t0)/60))\n\n npergrp, _ = np.histogram(gnum, bins=len(gnum), range=(0, len(gnum)))\n\n print('Found {} total groups, including:'.format(len(set(gnum))))\n print(' {} groups with 1 member'.format(np.sum( (npergrp == 1) ).astype('int')))\n print(' {} groups with 2 members'.format(np.sum( (npergrp == 2) ).astype('int')))\n print(' {} group(s) with 3-5 members'.format(np.sum( (npergrp >= 3)*(npergrp <= 5) ).astype('int')))\n print(' {} group(s) with 6-10 members'.format(np.sum( (npergrp >= 6)*(npergrp <= 10) ).astype('int')))\n print(' {} group(s) with >10 members'.format(np.sum( (npergrp > 10) ).astype('int')))\n\n cat['GROUP_ID'] = gnum\n cat['GROUP_MULT'] = mgrp\n\n I = np.where(cat['GROUP_MULT'] == 1)[0]\n if len(I) > 0:\n cat['GROUP_RA'][I] = cat['RA'][I]\n cat['GROUP_DEC'][I] = cat['DEC'][I]\n cat['GROUP_DIAMETER'][I] = cat['D25'][I]\n cat['GROUP_NAME'][I] = cat['GALAXY'][I]\n cat['GROUP_PRIMARY'][I] = True\n\n more = np.where(cat['GROUP_MULT'] > 1)[0]\n for group in set(cat['GROUP_ID'][more]):\n I = np.where(cat['GROUP_ID'] == group)[0]\n # Compute the D25-weighted RA, Dec of the group:\n weight = cat[I]['D25']\n cat['GROUP_RA'][I] = np.sum(weight * cat[I]['RA']) / np.sum(weight)\n cat['GROUP_DEC'][I] = np.sum(weight * cat[I]['DEC']) / np.sum(weight)\n # Get the diameter of the group as the distance between the center of\n # the group and the outermost galaxy (plus the diameter of that galaxy,\n # in case it's a big one!).\n dd = degrees_between(cat['RA'][I], cat['DEC'][I], cat['GROUP_RA'][I[0]], cat['GROUP_DEC'][I[0]])\n pad = dd + cat['D25'][I] / 60.0\n cat['GROUP_DIAMETER'][I] = np.max(pad) * 60 # [arcmin]\n if cat['GROUP_DIAMETER'][I[0]] < np.max(cat['D25'][I]):\n print('Should not happen!')\n pdb.set_trace()\n\n # Assign the group name based on its largest member and also make this\n # galaxy \"primary\".\n primary = np.argmax(cat['D25'][I])\n cat['GROUP_NAME'][I] = '{}_GROUP'.format(cat['GALAXY'][I][primary])\n cat['GROUP_PRIMARY'][I[primary]] = True\n\n print('Building a group catalog took {:.3f} min'.format((time.time() - t0)/60))\n \n return cat","def multiple(x, y, alpha, k, diag=False):\n #- Create data array:\n data = np.concatenate((x, y))\n n_group = np.size(x) #number of x and number of y\n \n \n #- Calculate difference in mean of first and last n_group values for 10000\n # randomizations of data. The first n_group values represents x values\n # and the second n_group values represents y values. Note use of negative\n # index slicing:\n\n results = np.zeros(10000, dtype='d')\n for i in range(np.size(results)):\n np.random.shuffle(data) #done in-place\n results[i] = np.mean(data[:n_group]) - np.mean(data[-n_group:])\n \n \n #- Calculate and print original data difference in mean of x and y, the\n # p-value for where the absolute value of the difference is larger than\n # the absolute value of the difference, since we're doing:\n\n diff_orig = np.mean(x) - np.mean(y)\n p_value_orig = np.sum(np.absolute(results) >= abs(diff_orig)) / \\\n np.size(results)\n\n bonferroni = 1 - (1 - alpha**k)\n \n \n #- Printing diagnostics:\n\n if diag:\n print('p_value_orig: ' + str(p_value_orig))\n \n return p_value_orig, bonferroni","def _groups_reduce(\n group_stats: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]\n) -> Dict[str, torch.Tensor]:\n return {f\"group_{group}\": torch.stack(stats) / torch.stack(stats).sum() for group, stats in enumerate(group_stats)}","def batchAnalysis(groupfil):\n groups = []\n with open(groupfil, 'r') as fIn:\n for line in fIn:\n groups.append(line.strip().split(','))\n \n checks = ['maxV', 'maxDerivV', 'maxDerivdV', 'minDerivV',\n 'minDerivdV', 'preMinV', 'postMinV', 'preMaxCurveV',\n 'preMaxCurveK', 'postMaxCurveV', 'postMaxCurveK',\n 'height', 'repolarizationV', 'intervals', 'frequencies']\n props = {ch: {gr: {} for gr in list(set([g[1] for g in groups]))}\n for ch in checks} # A dict of dicts\n # props [properties] [group name] [cell name]\n cells = [f[0].split('/')[-1].split('_')[0] for f in groups]\n \n # Add a few more keys\n props['activity'] = {gr: {} for gr in list(set([g[1] for g in groups]))}\n \n # Assign all the properties to the props dict\n for g in groups:\n df = pd.read_csv(g[0])\n df = df.drop('Unnamed: 33', 1) # Garbage\n df = df.drop('freq', 1) # These are downsampled\n df = df.dropna() # Dropna\n \n # If there are multiple clusters, add them in order\n if max(df.clust_inds) == 1: # Two clusters\n numClusts = int(max(df.clust_inds)+1)\n for ch in checks:\n for clust in range(numClusts):\n try:\n props[ch][g[1]][cells[groups.index(g)]].append(df[df['clust_inds']==clust][ch].dropna().values)\n except:\n props[ch][g[1]][cells[groups.index(g)]] = [df[df['clust_inds']==clust][ch].dropna().values]\n else: # Just one cluster\n for ch in checks:\n props[ch][g[1]][cells[groups.index(g)]] = [df[ch].dropna().values]\n # Get activity profile\n tIn, cBouts = timeInClusters(df)\n props['activity'][g[1]][cells[groups.index(g)]] = [tIn, cBouts]\n \n return props","def extract_format_results_test(self):\n assert len(self.results.keys()) != 0\n TESTS = [\n {\n \"output\": [\n {\n \"file_name\": \"BSA1.mzML\",\n \"spec_id\": 1337,\n \"formula\": \"C(37)H(59)N(9)O(16)\",\n \"scaling_factor\": 100,\n \"score\": 1,\n \"charge\": 2,\n },\n {\n \"file_name\": \"BSA1.mzML\",\n \"spec_id\": 1338,\n \"formula\": \"C(37)H(59)N(9)O(16)\",\n \"scaling_factor\": 100,\n \"score\": 0.9,\n \"charge\": 2,\n },\n {\n \"file_name\": \"BSA2.mzML\",\n \"spec_id\": 1337,\n \"formula\": \"C(43)H(75)N(15)O(17)S(2)\",\n \"scaling_factor\": 10,\n \"score\": 1,\n \"charge\": 3,\n },\n ]\n }\n ]\n for test_dict in TESTS:\n values = self.results.format_all_results()\n\n assert isinstance(values, pd.DataFrame)\n\n for out_data in test_dict[\"output\"]:\n result = values.loc[\n (values[\"file_name\"] == out_data[\"file_name\"])\n & (values[\"spec_id\"] == out_data[\"spec_id\"])\n ]\n assert (result[\"formula\"] == out_data[\"formula\"]).all()\n assert (result[\"scaling_factor\"] == out_data[\"scaling_factor\"]).all()\n assert (result[\"score\"] == out_data[\"score\"]).all()\n assert (result[\"charge\"] == out_data[\"charge\"]).all()","def yield_stats(go_analysis):\n for i in xrange(go_analysis.nrow()):\n yield go_analysis[0][i], go_analysis[1][i], go_analysis[2][i], go_analysis[3][i], p_value_from_r(go_analysis[4][i]), p_value_from_r(go_analysis[5][i])","def _alpha_stats(self, trace):\n mean = np.mean(trace['alpha'])\n sd = np.std(trace['alpha'], ddof=1)\n zscore = mean / sd\n return mean, sd, zscore","def create_mia_report(attack_output: dict) -> FPDF:\n # dummy_metrics = attack_output[\"dummy_attack_metrics\"]\n dummy_metrics = []\n mia_metrics = [\n v\n for _, v in attack_output[\"attack_experiment_logger\"][\n \"attack_instance_logger\"\n ].items()\n ]\n # mia_metrics = attack_output[\"attack_metrics\"]\n metadata = attack_output[\"metadata\"]\n if dummy_metrics is None or len(dummy_metrics) == 0:\n do_dummy = False\n else:\n do_dummy = True\n\n dest_log_roc = (\n os.path.join(\n metadata[\"experiment_details\"][\"output_dir\"],\n metadata[\"experiment_details\"][\"report_name\"],\n )\n + \"_log_roc.png\"\n )\n _roc_plot(mia_metrics, dummy_metrics, dest_log_roc)\n\n pdf = FPDF()\n pdf.add_page()\n pdf.set_xy(0, 0)\n title(pdf, \"WorstCase MIA attack result report\")\n subtitle(pdf, \"Introduction\")\n line(pdf, INTRODUCTION)\n subtitle(pdf, \"Experiment summary\")\n for key, value in metadata[\"experiment_details\"].items():\n line(pdf, f\"{key:>30s}: {str(value):30s}\", font=\"courier\")\n subtitle(pdf, \"Global metrics\")\n for key, value in metadata[\"global_metrics\"].items():\n line(pdf, f\"{key:>30s}: {str(value):30s}\", font=\"courier\")\n if do_dummy:\n subtitle(pdf, \"Baseline global metrics\")\n for key, value in metadata[\"baseline_global_metrics\"].items():\n line(pdf, f\"{key:>30s}: {str(value):30s}\", font=\"courier\")\n\n subtitle(pdf, \"Metrics\")\n line(\n pdf,\n \"The following show summaries of the attack metrics over the repetitions\",\n font=\"arial\",\n )\n for metric in DISPLAY_METRICS:\n vals = np.array([m[metric] for m in mia_metrics])\n if metric in MAPPINGS:\n vals = np.array([MAPPINGS[metric](v) for v in vals])\n text = (\n f\"{metric:>12} mean = {vals.mean():.2f}, var = {vals.var():.4f}, \"\n f\"min = {vals.min():.2f}, max = {vals.max():.2f}\"\n )\n line(pdf, text, font=\"courier\")\n\n if do_dummy:\n subtitle(pdf, \"Baseline metrics\")\n line(\n pdf,\n (\n \"The following show summaries of the attack metrics over the \"\n \"repetitions where there is no statistical difference between \"\n \"predictions in the training and test sets. Simulation was done \"\n \"with training and test set sizes equal to the real ones\"\n ),\n font=\"arial\",\n )\n for metric in DISPLAY_METRICS:\n vals = np.array([m[metric] for m in dummy_metrics])\n if metric in MAPPINGS:\n vals = np.array([MAPPINGS[metric](v) for v in vals])\n text = (\n f\"{metric:>12} mean = {vals.mean():.2f}, var = {vals.var():.4f}, \"\n f\"min = {vals.min():.2f}, max = {vals.max():.2f}\"\n )\n line(pdf, text, font=\"courier\")\n\n _add_log_roc_to_page(dest_log_roc, pdf)\n line(pdf, LOGROC_CAPTION)\n\n pdf.add_page()\n title(pdf, \"Glossary\")\n _write_dict(pdf, GLOSSARY)\n\n return pdf","def split_json_report(data):\n\n def split_assertions(entries, assertions):\n \"\"\"Remove assertions from report and place them in a dictionary.\"\"\"\n for entry in entries:\n if entry.get(\"category\") == ReportCategories.TESTCASE:\n assertions[entry[\"uid\"]] = entry[\"entries\"]\n entry[\"entries\"] = []\n elif \"entries\" in entry:\n split_assertions(entry[\"entries\"], assertions)\n\n meta, structure, assertions = data, data[\"entries\"], {}\n meta[\"entries\"] = []\n split_assertions(structure, assertions)\n return meta, structure, assertions","def data_for_grouping():\n return RaggedArray(\n [[1, 0], [1, 0], [], [], [0, 0], [0, 0], [1, 0], [2, 0]])","def movies(self):\n return self.data.groupby('Parameters')","def format_data(self, bands, branches):\n self.new_dict = self.__gen_dict\n order = [] # list to store high-symmetry point\n band_index = {} # dict to store band index info corresponding to its high-symmetry point e.g. \"X\": 18\n formatted_bands = []\n zero_matrix = np.zeros(np.shape(bands))\n \"\"\"\n zero_matrix is for: if one configuration does not have some high-symmetry points listed in __generic_dict\n then fill zeros in those columns \n \"\"\"\n\n for i in range(len(branches)):\n order.append(branches[i][\"name\"])\n spilt = re.split('-', order[i])\n\n band_index[spilt[0]] = branches[i]['start_index']\n band_index[spilt[1]] = branches[i]['end_index']\n\n # print('>>>>>>>>>>>>>>>>>>', band_index)\n # iterate all keys in band_index, and if exists, give value to new_dict, if not, pass\n for hs_point in band_index:\n if hs_point in self.new_dict:\n self.new_dict[hs_point] = band_index[hs_point]\n # print('>>>>>>>>>>>>>>>>>', BandsData.__gen_dict)\n\n # iterate all keys in new_dict, export bands (not arranged in bands dimension)\n for hs_point in self.new_dict:\n hs_value = self.new_dict[hs_point]\n if self.new_dict[hs_point] is None:\n # fill zeros in bands\n formatted_bands.append(zero_matrix[:, 0])\n else:\n formatted_bands.append(bands[:, hs_value])\n\n # transpose of formatted_bands\n formatted_bands = np.transpose(formatted_bands)\n\n return formatted_bands, self.new_dict","def groups(self):\n\n return ('train', 'dev', 'eval')","def respuesta(response):\n for report in response.get('reports', []):\n\n columnHeader = report.get('columnHeader', {})\n dimensionHeaders = columnHeader.get('dimensions', [])\n metricHeaders = columnHeader.get(\n 'metricHeader', {}).get('metricHeaderEntries', [])\n\n return_data = []\n\n for row in report.get('data', {}).get('rows', []):\n\n dimensions = row.get('dimensions', [])\n dateRangeValues = row.get('metrics', [])\n pipeline_insert = {}\n for header, dimension in zip(dimensionHeaders, dimensions):\n pipeline_insert[header] = dimension\n\n for i, values in enumerate(dateRangeValues):\n\n for metricHeader, value in zip(metricHeaders, values.get('values')):\n pipeline_insert[metricHeader.get('name')] = value\n return_data.append(pipeline_insert)\n\n return return_data","def eachDigitGMM(data, cfg):\r\n\r\n models = {}\r\n for j in range(len(data)):\r\n train_set = data[j][0]\r\n for i in range(1, len(data[j])):\r\n train_set = np.concatenate((train_set, data[j][i]), axis=0)\r\n\r\n estimator = GaussianMixture(n_components=cfg['components'], max_iter=cfg['max_iterations'],\r\n tol=cfg['tolerance'], covariance_type=cfg['covariance_type'])\r\n models[j] = estimator.fit(train_set)\r\n\r\n return models","def mc_get_card_group_hist(hand, remainder, num_samples=1000, hist_res=100):\n lst = {}\n remainder = list(filter(lambda x: x not in hand, remainder))\n for i in range(num_samples):\n res = choice(remainder)\n group = get_card_group(res)\n hs = hand_strength_evaluator.hand_strength(hand, res)\n if group not in lst:\n lst[group] = {str(hand): []}\n lst[group][str(hand)].append(hs)\n return lst","def gamma_pdf(a,b):\n df = DataFrame(columns=['Day','Gamma_Values'])\n for day in range(181):\n df = df.append({'Day': int(day), 'Gamma_Values': float(gamma.pdf(day,a,0,b))}, ignore_index=True)\n return df","def get_results(self):\n d = {}\n# r = {}\n for analyser in self.xml_tree.getroot():\n for child in analyser:\n if child.tag == 'all-records':\n for record in child:\n attributes = record.attrib\n sample = attributes['sampleId']\n assay_id = attributes['assayId']\n genotype = attributes['genotypeId']\n quality = attributes['description'].split('.')[0]\n if re.match(r'rs\\d+', assay_id):\n if sample in d:\n if assay_id in d[sample]:\n for allele in list(genotype):\n if allele not in d[sample][assay_id]['genotype']:\n d[sample][assay_id]['genotype'] += allele\n if quality not in d[sample][assay_id]['quality']:\n d[sample][assay_id]['quality'].append(quality)\n else:\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\n else:\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\n# if sample in r:\n# if assay_id in r[sample]:\n# for allele in list(genotype):\n# if allele not in r[sample][assay_id]:\n# r[sample][assay_id] += allele\n# else:\n# r[sample][assay_id] = genotype\n# else:\n# r[sample] = {assay_id: genotype}\n# for k, v in r.items():\n# for k1, v1, in v.items():\n# if len(v1) == 1:\n# v[k1] += v1\n# pprint.pprint(r)\n# df = pd.DataFrame.from_dict(r).transpose()\n# print(df)\n# df.to_excel('snpcheck.xlsx')\n return d","def set_up_groups(self):\n groups = []\n groups.append({'groupname': 'th',\n 'grouptitle': 'TH',\n 'path': '/'})\n groups.append({'groupname': 'neutronics',\n 'grouptitle': 'Neutronics',\n 'path': '/'})\n groups.append({'groupname': 'metadata',\n 'grouptitle': 'Simulation Metadata',\n 'path': '/'})\n return groups","def group_normalization(x, gamma, beta, groups=32, eps=1e-5):\n return GroupNormalization(groups, eps).apply((x, gamma, beta))[0]","def report(res, _c, _s, gamma):\n _res = res.copy()\n # smooth\n for _, _scores in enumerate(_res):\n if _ == 0: continue\n\n _res[_, :] = _res[_, :] * gamma + _res[_-1, :] * (1 - gamma)\n\n reports = np.argmax(_res, axis=1)\n scores = np.max(_res, axis=1)\n\n pre_r = 0\n cum = 0\n cums = []\n alarms = []\n\n for r in reports:\n alarm = [0] * _res.shape[1]\n if r != 0 and (r == pre_r or _c == 0):\n if cum == _c:\n alarm[r] = 1\n cum = -_s\n else:\n alarm[0] = 1\n cum += 1\n else:\n alarm[0] = 1\n cum = max(0, cum-1)\n\n alarms.append(alarm)\n cums.append(cum)\n pre_r = r\n \n alarms = np.array(alarms)\n cums = np.array(cums)\n\n return alarms, cums","def prepare_data(groups):\n all_dicts = []\n for idx, group in groups:\n res_dict = {'organism': group.organism.iloc[0]}\n for g_idx, row in group.iterrows():\n if pd.notna(row.label):\n res_dict[row.cmp_name] = {'label': row.label, 'mic': row.MIC}\n else:\n res_dict[row.cmp_name] = {'label': '', 'mic': row.MIC}\n all_dicts.append(res_dict)\n return all_dicts","def data_collect(gamertag):\n k_d = []\n for page in range(0, 2):\n # Used an f string literal to apply the input argument and for loop to the weblink which can be used by\n # any gamertag and page number\n page_url = f'http://halotracker.com/h5/games/{gamertag}?page={page}&mode=custom'\n res = requests.get(page_url)\n soup = bs4.BeautifulSoup(res.text, 'lxml')\n for value in soup.select('.game-stat-value'):\n k_d.append(float(value.text.strip()))\n k_d = k_d[1::2]\n return k_d","def calc_groups(sensors_rad_clean, sensors_metadata_cat):\n # calculate number of groups as number of optimal combinations.\n groups_ob = sensors_metadata_cat.groupby(['CATB', 'CATGB', 'CATteta_z']) # group the sensors by categories\n prop_observers = groups_ob.mean().reset_index()\n prop_observers = pd.DataFrame(prop_observers)\n total_area_installed_module = groups_ob['area_installed_module'].sum().reset_index()['area_installed_module']\n prop_observers['total_area_module'] = total_area_installed_module\n number_groups = groups_ob.size().count()\n sensors_list = groups_ob.groups.values()\n\n # calculate mean hourly radiation of sensors in each group\n rad_group_mean = np.empty(shape=(number_groups,8760))\n number_points = np.empty(shape=(number_groups,1))\n for x in range(0, number_groups):\n sensors_rad_group = sensors_rad_clean[sensors_list[x]]\n rad_mean = sensors_rad_group.mean(axis=1).as_matrix().T\n rad_group_mean[x] = rad_mean\n number_points[x] = len(sensors_list[x])\n hourlydata_groups = pd.DataFrame(rad_group_mean).T\n\n return number_groups, hourlydata_groups, number_points, prop_observers","def permutation(data, dataLabel=None, nperm=10000, decimals=4):\n\n # test calling values\n if data is None or not isinstance(data, dict) or len(data.keys()) != 2:\n raise ValueError('RSTATS.permutation: data must be'\n + ' a dictionary with at exactly 2 keys'\n + '\\nUse KW (anova) for more than 2 groups')\n\n k = list(data.keys())\n\n g1 = data[k[0]]\n g2 = data[k[1]]\n # (w1, p1) = Stats.shapiro(g1, a=None, reta=False)\n # (w2, p2) = Stats.shapiro(g2, a=None, reta=False)\n\n combined = np.concatenate((g1, g2))\n diffobs = np.mean(g2)-np.mean(g1)\n diffs = np.zeros(nperm)\n nperm = nperm\n index = range(0, combined.shape[0])\n for i in range(nperm):\n # draw from combined data set without replacement\n #shuff = np.random.randint(combined.shape[0], size=combined.shape[0])\n shuff = np.random.permutation(index)\n ar = combined[shuff[0:len(g1)]]\n br = combined[shuff[len(g1):]]\n diffs[i] = np.mean(br) - np.mean(ar)\n pvalue = np.sum(np.abs(diffs) >= np.abs(diffobs)) / float(nperm)\n if dataLabel is not None:\n print ('\\n%s: Permutation Test (Nperm = %d)' % (dataLabel, nperm))\n # if p1 < 0.05 and p2 < 0.05:\n # print(u' Both data sets appear normally distributed: Shapiro-Wilk Group 1 p = {:6.3f}, Group2 p = {:6.3f}'.format(p1, p2))\n # else:\n # print(u' ****At least one Data set is NOT normally distributed****\\n Shapiro-Wilk Group 1 p = {:6.3f}, Group2 p = {:6.3f}'.format(p1, p2))\n # print (u' (Permutation test does not depend on distribution)')\n \n n = max([len(l) for l in k])\n print(u' {:s}={:8.{pc}f} \\u00B1{:.{pc}f}, {:d} (mean, SD, N)'.\n format(k[0].rjust(n), np.mean(g1), np.std(g1, ddof=1),\n len(g1), pc=decimals))\n print(u' {:s}={:8.{pc}f} \\u00B1{:.{pc}f}, {:d} (mean, SD, N)'.\n format(k[1].rjust(n), np.mean(g2), np.std(g2, ddof=1),\n len(g2), pc=decimals))\n summarizeData(data, decimals=decimals)\n # iqr1 = np.subtract(*np.percentile(g1, [75, 25]))\n # iqr2 = np.subtract(*np.percentile(g2, [75, 25]))\n # print(u' {:s}: median={:8.4f} IQR={:8.4f}'.format(k[0].rjust(n), np.median(g1), iqr1))\n # print(u' {:s}: median={:8.4f} IQR={:8.4f}'.format(k[1].rjust(n), np.median(g2), iqr2))\n print(u' Observed difference: {:8.4f}'.format(diffobs))\n print(u' p={:8.6f}, Nperm={:8d}\\n'.format(float(pvalue), int(nperm)))\n return(pvalue, nperm)","def selectedComparisons(samples_dict):\n sectors=\"peripherals\",\"intermediaries\",\"hubs\"\n ks_measures={}\n for analyses_type in samples_dict[sectors[0]]:\n ks_measures[analyses_type]={}\n for analysis_grouping in samples_dict[sectors[0]][analyses_type]:\n ks_measures[analyses_type][analysis_grouping]={}\n if samples_dict[sectors[0]][analyses_type][analysis_grouping]==dict:\n ks_measures[analyses_type][analysis_grouping]={}\n for analysis in samples_dict[sectors[0]][analyses_type][analysis_grouping]:\n for var in samples_dict[sectors[0]][analyses_type][analysis_grouping][analysis]:\n samples_peripherals=samples_dict[sectors[0]][analyses_type][analysis_grouping][analysis]\n samples_intermediaries=samples_dict[sectors[1]][analyses_type][analysis_grouping][analysis]\n samples_hubs=samples_dict[sectors[2]][analyses_type][analysis_grouping][analysis]\n ks_measures[analysis][\"peripherals_intermediaries\"]=P.kolmogorovSmirnovTest(samples_peripherals,samples_intermediaries)\n ks_measures[analysis][\"peripherals_hubs\"]=P.kolmogorovSmirnovTest(samples_peripherals,samples_hubs)\n ks_measures[analysis][\"hubs_intermediaries\"]=P.kolmogorovSmirnovTest(samples_hubs,samples_intermediaries)\n else:\n for var in samples_dict[sectors[0]][analyses_type][analysis_grouping]:\n samples_peripherals=samples_dict[sectors[0]][analyses_type][analysis_grouping]\n samples_intermediaries=samples_dict[sectors[1]][analyses_type][analysis_grouping]\n samples_hubs=samples_dict[sectors[2]][analyses_type][analysis_grouping]\n\n\n\n samples[sector][analyses][analysis_grouping]=updateDict(samples[sector][analyses][analysis_grouping],getSamples(authors_analysis[analyses][author][analysis_grouping]))","def grouping_cols(df, cat_percentage = 0.05, checking_itr = 10):","def _group_samples_by_channel(self):\n samples_acquired_per_channel = len(self.all_samples_list) / self.num_channels\n\n #make a list with each element being a numpy array for the results from each channel\n channel_data = []\n for i in range(self.num_channels):\n channel_data.append(numpy.zeros(samples_acquired_per_channel,dtype=float))\n\n scan_num = 0\n channel_index = 0\n for sample in range(len(self.all_samples_list)): #each sample in interleaved list\n channel_data[channel_index][scan_num] = self.all_samples_list[sample]\n if channel_index < self.num_channels-1:\n channel_index += 1\n else:\n channel_index = 0\n scan_num += 1\n\n if self.num_channels == 1: #if only one channel put the numpy array on the queue\n self.result_queue.put(channel_data[0])\n else: #if more than one channel put the list of arrays on the queue\n self.result_queue.put(channel_data)","def generate_groups():\n groups = group_elements(\n generate_examples(file_name),\n cfg.tfrecord_size)\n\n # pairing groups to unique numbers and \n # filtering nulls from zip_longest\n groups = (\n list(filter(is_not_none, group))\n for group in groups\n )\n\n yield from groups","def test_grouped_cumul_div(self):\n gfile = grades.writers.GradesFile(self.fname)\n gfile.table.compute_cumul()\n gfile.table.compute_grouped_mean('Group')\n gfile.table_format = 'org'\n self.check_output(self.output_str, gfile, div_on=('Group',))","def _process_recording(args):\n recording, filter_ = args\n out = [recording.subject, recording.session, filter_]\n n_channels = recording.info[\"nchan\"]\n recording.preprocess(filter_[0], filter_[1])\n recording.gfp()\n out.append(len(recording.gfp_peaks))\n for n_states in NO_STATES_RANGE:\n recording.run_microstates(n_states=n_states, n_inits=N_INITS)\n out += [\n pm_variance_test(\n gev=1.0 - recording.gev_tot,\n no_states=n_states,\n n_channels=n_channels,\n ),\n pm_variance_test(\n gev=1.0 - recording.gev_gfp,\n no_states=n_states,\n n_channels=n_channels,\n ),\n ]\n assert len(out) == 4 + 2 * len(NO_STATES_RANGE)\n return out","def G12(x, a):\n alpha, a, beta, b = a\n pi26 = np.pi ** 2 / 6.0\n G = (pi26 + x) * np.exp(-x)\n tmp = 1 + b * x ** beta\n g = 1. / (a * x ** alpha / tmp + 1.)\n return G * g","def test_get_groupings_between_tiny_dataset(self):\r\n self.assertEqual(_get_groupings(self.tiny_dist_matrix_header,\r\n self.tiny_dist_matrix, self.tiny_groups, within=False), [])"],"string":"[\n \"def compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf,\\n alpha=3, thresh=0):\\n if utool.DEBUG2:\\n from ibeis.model.hots.smk import smk_debug\\n smk_debug.rrr()\\n smk_debug.check_wx2(wx2_rvecs=wx2_rvecs, wx2_aids=wx2_aids)\\n wx_sublist = pdh.ensure_values(pdh.ensure_index(wx2_rvecs))\\n if utool.VERBOSE:\\n print('[smk_index] Compute Gamma alpha=%r, thresh=%r: ' % (alpha, thresh))\\n mark1, end1_ = utool.log_progress(\\n '[smk_index] Gamma group (by word): ', len(wx_sublist),\\n flushfreq=100, writefreq=50, with_totaltime=True)\\n # Get list of aids and rvecs w.r.t. words\\n aids_list = pdh.ensure_values_subset(wx2_aids, wx_sublist)\\n rvecs_list1 = pdh.ensure_values_subset(wx2_rvecs, wx_sublist)\\n # Group by daids first and then by word index\\n daid2_wx2_drvecs = utool.ddict(lambda: utool.ddict(list))\\n for wx, aids, rvecs in zip(wx_sublist, aids_list, rvecs_list1):\\n group_aids, groupxs = clustertool.group_indicies(aids)\\n rvecs_group = clustertool.apply_grouping(rvecs, groupxs) # 2.9 ms\\n for aid, rvecs_ in zip(group_aids, rvecs_group):\\n daid2_wx2_drvecs[aid][wx] = rvecs_\\n\\n if utool.VERBOSE:\\n end1_()\\n\\n # For every daid, compute its gamma using pregrouped rvecs\\n # Summation over words for each aid\\n if utool.VERBOSE:\\n mark2, end2_ = utool.log_progress(\\n '[smk_index] Gamma Sum (over daid): ', len(daid2_wx2_drvecs),\\n flushfreq=100, writefreq=25, with_totaltime=True)\\n # Get lists w.r.t daids\\n aid_list = list(daid2_wx2_drvecs.keys())\\n # list of mappings from words to rvecs foreach daid\\n # [wx2_aidrvecs_1, ..., wx2_aidrvecs_nDaids,]\\n _wx2_aidrvecs_list = list(daid2_wx2_drvecs.values())\\n _aidwxs_iter = (list(wx2_aidrvecs.keys()) for wx2_aidrvecs in _wx2_aidrvecs_list)\\n aidrvecs_list = [list(wx2_aidrvecs.values()) for wx2_aidrvecs in _wx2_aidrvecs_list]\\n aididf_list = [[wx2_idf[wx] for wx in aidwxs] for aidwxs in _aidwxs_iter]\\n\\n #gamma_list = []\\n if utool.DEBUG2:\\n try:\\n for count, (idf_list, rvecs_list) in enumerate(zip(aididf_list, aidrvecs_list)):\\n assert len(idf_list) == len(rvecs_list), 'one list for each word'\\n #gamma = smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)\\n except Exception as ex:\\n utool.printex(ex)\\n utool.embed()\\n raise\\n gamma_list = [smk_core.gamma_summation2(rvecs_list, idf_list, alpha, thresh)\\n for idf_list, rvecs_list in zip(aididf_list, aidrvecs_list)]\\n\\n if WITH_PANDAS:\\n daid2_gamma = pdh.IntSeries(gamma_list, index=aid_list, name='gamma')\\n else:\\n daid2_gamma = dict(zip(aid_list, gamma_list))\\n if utool.VERBOSE:\\n end2_()\\n\\n return daid2_gamma\",\n \"def metrics_group():\",\n \"def test_add_alpha_diversity_values_to_mapping_file(self):\\r\\n\\r\\n # regular case no special cases for avg method\\r\\n expected_mapping_file_data = MAPPING_FILE_DATA_WITH_ALPHA_A\\r\\n expected_mapping_file_headers = ['SampleID', 'BarcodeSequence',\\r\\n 'LinkerPrimerSequence', 'Treatment', 'DOB', 'Description',\\r\\n 'chao1_alpha', 'chao1_normalized_alpha', 'chao1_alpha_label',\\r\\n 'PD_whole_tree_alpha', 'PD_whole_tree_normalized_alpha',\\r\\n 'PD_whole_tree_alpha_label']\\r\\n\\r\\n out_mapping_file_data, out_mapping_file_headers =\\\\\\r\\n add_alpha_diversity_values_to_mapping_file(self.metrics,\\r\\n self.sample_ids, self.alpha_diversity_data,\\r\\n self.mapping_file_headers, self.mapping_file_data, 4, 'equal')\\r\\n\\r\\n self.assertEquals(out_mapping_file_data, expected_mapping_file_data)\\r\\n self.assertEquals(\\r\\n out_mapping_file_headers,\\r\\n expected_mapping_file_headers)\\r\\n\\r\\n # regular case no special cases for quantile method\\r\\n expected_mapping_file_data = MAPPING_FILE_DATA_WITH_ALPHA_B\\r\\n out_mapping_file_data, out_mapping_file_headers =\\\\\\r\\n add_alpha_diversity_values_to_mapping_file(self.metrics,\\r\\n self.sample_ids, self.alpha_diversity_data,\\r\\n self.mapping_file_headers, self.mapping_file_data, 4, 'quantile')\\r\\n\\r\\n self.assertEquals(out_mapping_file_data, expected_mapping_file_data)\\r\\n self.assertEquals(\\r\\n out_mapping_file_headers,\\r\\n expected_mapping_file_headers)\",\n \"def gamma_h_subgroups(self):\\n from .all import GammaH\\n N = self.level()\\n R = IntegerModRing(N)\\n return [GammaH(N, H) for H in R.multiplicative_subgroups()]\",\n \"def data_grouping(self):\\n group_container, film_container, plank_container = [[] for a in range(self.tot_conditions)], \\\\\\n [[] for a in range(self.tot_conditions)], \\\\\\n [[] for a in range(self.tot_conditions)]\\n\\n for i in self.data_labels:\\n group = int(i[:-1])\\n group_container[group - 1].append(i)\\n film_container[group - 1].append(self.film_count[self.data_labels.index(i)])\\n plank_container[group - 1].append(self.plank_count[self.data_labels.index(i)])\\n\\n return group_container, film_container, plank_container\",\n \"def test_group(self):\\n obs_group, obs_nogroup = group(self.seqstruct, 0.75)\\n exp_group = {'cluster_337': ['cluster_343', 'cluster_345',\\n 'cluster_339'],\\n 'cluster_347': ['cluster_338'],\\n 'cluster_344': ['cluster_340']}\\n exp_nogroup = [self.seqstruct[6], self.seqstruct[8]]\\n\\n self.assertEqual(obs_group, exp_group)\\n self.assertEqual(obs_nogroup, exp_nogroup)\",\n \"def data_group():\\n ...\",\n \"def test_group_by_hardware_info(self):\\n self._test_group_by('Hardware Info', [1, 1, 2, 1, 1])\",\n \"def data_for_grouping() -> NoReturn:\\n raise NotImplementedError\",\n \"def data_for_grouping() -> NoReturn:\\n raise NotImplementedError\",\n \"def group_data_by_gs(data_table):\\n gene_data = collections.defaultdict(lambda: collections.defaultdict(list))\\n for _idx, row in data_table.iterrows():\\n samp = row['sample']\\n gene = row['gene']\\n gene_data[gene][samp].append({\\n 'muttype': row['type'].strip(),\\n 'normalized': row['Normalized'], # NMAF in the manuscript\\n 'consequence': row['MissenseConsequence'].strip(),\\n })\\n return gene_data\",\n \"def divisor_subgroups(self):\\n return [Gamma0_constructor(M) for M in self.level().divisors()]\",\n \"def get_filter_stats(data: AnnData) -> Tuple[pd.DataFrame, pd.DataFrame]:\\n\\n # cell stats\\n gb1 = data.obs.groupby(\\\"Channel\\\")\\n df_before = gb1.median()\\n df_before = df_before.assign(total=gb1.size())\\n df_before.rename(\\n columns={\\n \\\"n_genes\\\": \\\"median_n_genes_before\\\",\\n \\\"n_counts\\\": \\\"median_n_umis_before\\\",\\n \\\"percent_mito\\\": \\\"median_percent_mito_before\\\",\\n },\\n inplace=True,\\n )\\n\\n data = data[data.obs[\\\"passed_qc\\\"]] # focusing only on filtered cells\\n\\n gb2 = data.obs.groupby(\\\"Channel\\\")\\n df_after = gb2.median()\\n df_after = df_after.assign(kept=gb2.size())\\n df_after.rename(\\n columns={\\n \\\"n_genes\\\": \\\"median_n_genes\\\",\\n \\\"n_counts\\\": \\\"median_n_umis\\\",\\n \\\"percent_mito\\\": \\\"median_percent_mito\\\",\\n },\\n inplace=True,\\n )\\n df_cells = pd.concat((df_before, df_after), axis=1, sort=False)\\n df_cells.fillna(0, inplace=True)\\n df_cells[\\\"kept\\\"] = df_cells[\\\"kept\\\"].astype(int)\\n df_cells[\\\"filt\\\"] = df_cells[\\\"total\\\"] - df_cells[\\\"kept\\\"]\\n df_cells = df_cells[\\n [\\n \\\"kept\\\",\\n \\\"median_n_genes\\\",\\n \\\"median_n_umis\\\",\\n \\\"median_percent_mito\\\",\\n \\\"filt\\\",\\n \\\"total\\\",\\n \\\"median_n_genes_before\\\",\\n \\\"median_n_umis_before\\\",\\n \\\"median_percent_mito_before\\\",\\n ]\\n ]\\n df_cells.sort_values(\\\"kept\\\", inplace=True)\\n\\n # gene stats\\n idx = data.var[\\\"robust\\\"] == False\\n df_genes = pd.DataFrame(\\n {\\n \\\"n_cells\\\": data.var.loc[idx, \\\"n_cells\\\"],\\n \\\"percent_cells\\\": data.var.loc[idx, \\\"percent_cells\\\"],\\n }\\n )\\n df_genes.index.name = \\\"gene\\\"\\n df_genes.sort_values(\\\"n_cells\\\", ascending=False, inplace=True)\\n\\n return df_cells, df_genes\",\n \"def group_mae(outputs: torch.Tensor, targets: torch.Tensor) -> List[Tuple[int, int, int, float, str]]:\\n # groups = [\\n # (-1, 1800, \\\"0-0.5h\\\"),\\n # (1800, 3600, \\\"0.5-1h\\\"),\\n # (3600, 7200, \\\"1-2h\\\"),\\n # (7200, 10800, \\\"2-3h\\\"),\\n # (10800, 14400, \\\"3-4h\\\"),\\n # (14400, 18000, \\\"4-5h\\\"),\\n # (18000, 21600, \\\"5-6h\\\"),\\n # (21600, 25200, \\\"6-7h\\\"),\\n # (25200, 28800, \\\"7-8h\\\"),\\n # (28800, 32400, \\\"8-9h\\\"),\\n # (32400, 36000, \\\"9-10h\\\"),\\n # (36000, 39600, \\\"10-11h\\\"),\\n # (39600, 43200, \\\"11-12\\\"),\\n # (43200, 86400, \\\"12h - 1 day\\\"),\\n # (86400, 172800, \\\"1 day - 2 days\\\"),\\n # (172800, 259200, \\\"2 days - 3 days\\\"),\\n # (259200, 345600, \\\"3 days - 4 days\\\"),\\n # (345600, 432000, \\\"4 days - 5 days\\\"),\\n # (432000, 518400, \\\"5 days - 6 days\\\"),\\n # (518400, 604800, \\\"6 days - 1 week\\\"),\\n # (604800, 155520000, \\\"1 week - 1 month\\\"),\\n # (155520000, int(data_ranges[\\\"label\\\"][\\\"max\\\"]), \\\"> 1 month\\\")\\n # ]\\n groups = [\\n (-1, 1800, \\\"0-0.5h\\\"),\\n (1800, 3600, \\\"0.5-1h\\\"),\\n (3600, 7200, \\\"1-2h\\\"),\\n (7200, 10800, \\\"2-3h\\\"),\\n (10800, 14400, \\\"3-4h\\\"),\\n (14400, 21600, \\\"4-6h\\\"),\\n (21600, 28800, \\\"6-8h\\\"),\\n (28800, 36000, \\\"8-10h\\\"),\\n (36000, 43200, \\\"10-12h\\\"),\\n (43200, 50400, \\\"12-16h\\\"),\\n (50400, 64800, \\\"16-20h\\\"),\\n (64800, 86400, \\\"20-24h\\\"),\\n (86400, 172800, \\\"1-2d\\\"),\\n (172800, 259200, \\\"2-3d\\\"),\\n (259200, 345600, \\\"3-4d\\\"),\\n (345600, 432000, \\\"4-5d\\\"),\\n (432000, 518400, \\\"5-6d\\\"),\\n (518400, 604800, \\\"6-7d\\\"),\\n (604800, 1209600, \\\"1-2w\\\"),\\n (1209600, 2419200, \\\"2-4w\\\"),\\n (2419200, int(data_ranges[\\\"label\\\"][\\\"max\\\"]), \\\"> 4w\\\")\\n ]\\n\\n def scale(seconds: int) -> float:\\n # half_range = (data_ranges[\\\"label\\\"][\\\"max\\\"] - data_ranges[\\\"label\\\"][\\\"min\\\"]) / 2\\n # result = seconds / half_range\\n # return -1 + result if seconds < half_range else result\\n label_range = data_ranges[\\\"label\\\"][\\\"max\\\"]\\n return seconds / label_range\\n\\n def process_group(x: torch.Tensor, y: torch.Tensor, group: Tuple[int, int, str]) -> Tuple[int, int, int, float,\\n str]:\\n criterion = nn.L1Loss(reduction=\\\"mean\\\")\\n mask = (y > scale(group[0])) & (y <= scale(group[1]))\\n # mask = (y > group[0]) & (y <= group[1])\\n x = x[mask]\\n y = y[mask]\\n mae = 0.\\n num_data = x.shape[0]\\n if num_data > 0:\\n loss = criterion(x, y)\\n mae = loss.item()\\n return group[0], group[1], num_data, mae, group[2]\\n\\n mae_groups = [process_group(outputs, targets, group) for group in groups]\\n return mae_groups\",\n \"def test_grouped(self):\\n gfile = grades.writers.GradesFile(self.fname)\\n gfile.table.compute_grouped_mean('Group')\\n gfile.table_format = 'org'\\n self.check_output(self.output_str2, gfile)\",\n \"def get_report(dataset):\\n\\n dataset = dataset.round(2)\\n print('Overall results (mean): ')\\n display(dataset[['classifier', 'preprocessor', 'f1', 'precision', 'recall']].groupby(['preprocessor', 'classifier'])\\n .mean().round(2))\\n print('Overall results (max): ')\\n display(dataset[['classifier', 'preprocessor', 'f1', 'precision', 'recall']].groupby(['preprocessor', 'classifier'])\\n .max().round(2))\\n print('Grouped by Preprocessor (mean):')\\n display(dataset[['preprocessor', 'f1', 'precision', 'recall']].groupby('preprocessor').mean().round(2))\\n print('Grouped by Classifier (mean):')\\n display(dataset[['classifier', 'f1', 'precision', 'recall']].groupby('classifier').mean().round(2))\\n\\n preprocessors = dataset['preprocessor'].unique()\\n metrics = ['f1', 'precision', 'recall']\\n\\n # For each metric, display top 10 rounds.\\n for m in metrics:\\n print(f'Top 10 by {m}:')\\n display(dataset.sort_values(m, ascending=False).head(10).round(2))\\n\\n for p in preprocessors:\\n for m in metrics:\\n d = dataset[dataset['preprocessor'] == p]\\n for c in dataset['classifier'].unique():\\n plt.plot(d[d['classifier'] == c]['prior'].unique(), d[d['classifier'] == c].groupby('prior').mean()[m],\\n label=str(c))\\n plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\\n plt.title(m + ' - ' + str(p))\\n plt.show()\",\n \"def test_color_groups(self):\\r\\n data_colors = color_dict_to_objects(self.data_color_hsv)\\r\\n\\r\\n exp = None\\r\\n obs = color_groups(self.groups, data_colors, self.data_color_order)\\r\\n\\r\\n self.assertEqual(obs, exp)\",\n \"def perSaccAcrossGroups(addExp2 = True, trim = True, spacing = .5, \\\\\\n\\t\\taddSimulation = True, exclOverlap = True, exclY = True):\\n\\t\\n\\tfig = plt.figure(figsize = (5,10))\\n\\ttitle = \\\"Average towards-handle landings across groups - exclOverlap = %s - exclY = %s\\\" % (exclOverlap, exclY)\\n\\tplt.suptitle(title)\\n\\t\\n\\t# The first experiment has to be treated differently, because\\n\\t# it contains two dependent variables: absolute and corrected\\n\\tdm1 = getDM.getDM(exp = \\\"004A\\\", excludeErrors = True, driftCorr = True)\\n\\tlLandingsAbs = []\\n\\tlLandingsCorr = []\\n\\t\\n\\tfor sacc in [\\\"1\\\", \\\"2\\\", \\\"3\\\"]:\\n\\t\\t\\n\\t\\t# This is the same for corrected landing positions (the saccade\\n\\t\\t# doesn't change; only the reference point does)\\n\\t\\t_dm = dm1.select(\\\"endX%sNorm != ''\\\" % sacc)\\n\\t\\t_dm = _dm.select(\\\"endX%sNorm > -.5\\\" % sacc)\\n\\t\\t_dm = _dm.select(\\\"endX%sNorm < .5\\\" % sacc)\\n\\t\\t\\n\\t\\tif exclY:\\n\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm != ''\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm > -.5\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm < .5\\\" % sacc)\\n\\t\\t\\n\\t\\t\\n\\t\\tif trim:\\n\\t\\t\\t_dmAbs = _dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sNormToHandle\\\" % sacc)\\n\\t\\t\\t_dmCorr = _dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sCorrNormToHandle\\\" % sacc)\\n\\t\\t\\n\\t\\t# Determine avg landing position:\\n\\t\\tavgAbs = _dmAbs[\\\"endX%sNormToHandle\\\" % sacc].mean()\\n\\t\\tavgCorr = _dmCorr[\\\"endX%sCorrNormToHandle\\\" % sacc].mean()\\n\\t\\t\\n\\t\\t# TODO: determine error bars:\\n\\n\\t\\tlLandingsAbs.append(avgAbs)\\n\\t\\tlLandingsCorr.append(avgCorr)\\n\\t\\n\\tplt.plot(lLandingsAbs, color = \\\"#f57900\\\", linewidth = 2, marker = \\\"o\\\")\\n\\tplt.plot(lLandingsCorr, color = \\\"#73d216\\\", linewidth = 2, marker = \\\"o\\\")\\n\\t\\n\\t# The other 2 experiments can be treated equally:\\n\\tcolList = [\\\"#ef2929\\\", \\\"#3465a4\\\"]\\n\\n\\tfor exp in [\\\"004B\\\", \\\"004C\\\"]:\\n\\t\\t\\n\\t\\tcontinue\\n\\t\\t\\n\\t\\tif not addExp2:\\n\\t\\t\\tif exp == \\\"004B\\\":\\n\\t\\t\\t\\tcontinue\\n\\t\\tif not addSimulation:\\n\\t\\t\\tif exp == \\\"004C\\\":\\n\\t\\t\\t\\tcontinue\\n\\n\\t\\tdm = getDM.getDM(exp = exp, excludeErrors = True, driftCorr = True)\\n\\t\\t\\n\\t\\tif exp == \\\"004C\\\":\\n\\t\\t\\tprint 'xxx'\\n\\t\\t\\tif exclOverlap:\\n\\t\\t\\t\\tdm = dm.select(\\\"gap == 'zero'\\\")\\n\\n\\t\\tlLandingsAbs = []\\n\\t\\t\\n\\t\\tfor sacc in [\\\"1\\\", \\\"2\\\", \\\"3\\\", '4']:\\n\\t\\t\\t\\n\\t\\t\\t# TODO: how to filter only on-object saccades exp 2??\\n\\t\\t\\t_dm = dm.select(\\\"endX%sNorm != ''\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endX%sNorm > -.5\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endX%sNorm < .5\\\" % sacc)\\n\\t\\t\\t\\n\\t\\t\\tif exclY:\\n\\t\\t\\t\\n\\t\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm != ''\\\" % sacc)\\n\\t\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm > -.5\\\" % sacc)\\n\\t\\t\\t\\t_dm = _dm.select(\\\"endY%sNorm < .5\\\" % sacc)\\n\\n\\t\\t\\tif trim:\\n\\t\\t\\t\\t_dm = _dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sNormToHandle\\\" % sacc)\\n\\t\\t\\t\\n\\t\\t\\t# Determine avg landing position:\\n\\t\\t\\tavgAbs = _dm[\\\"endX%sNormToHandle\\\" % sacc].mean()\\n\\t\\t\\tlLandingsAbs.append(avgAbs)\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\tcol = colList.pop()\\n\\t\\tplt.plot(lLandingsAbs, color = col, linewidth = 2, marker = \\\"o\\\")\\n\\t\\n\\t# Modify plot:\\n\\tplt.legend([\\\"Exp1 abs\\\", \\\"Exp1 corr\\\", \\\"Exp2\\\", \\\"Sim\\\"])\\n\\t\\t\\n\\tplt.axhline(0, color = \\\"#888a85\\\", linestyle = \\\"--\\\", linewidth = 2)\\n\\txLabels = [\\\"sacc 1\\\", \\\"sacc 2\\\", \\\"sacc 3\\\"]\\n\\txTicks = range(0,3)\\n\\tplt.xticks(xTicks, xLabels, rotation = .5)\\n\\tplt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)\\n\\t\\n\\tplt.ylim([-.5, .5])\\n\\t\\n\\tplt.savefig(\\\"%s.png\\\" % title)\",\n \"def group_data(self):\\n groups = []\\n bug_map = self.bug_map()\\n union_map = self.union_map(list(bug_map.keys()))\\n # test_id grouping\\n for union_id in set(union_map.values()):\\n group = []\\n for k, v in union_map.items():\\n if v == union_id:\\n group.extend(bug_map[k])\\n if len(group) > 1:\\n groups.append(group)\\n return groups\",\n \"def group_data(data):\\n\\n data_grouped = dict()\\n\\n for data_pt in data:\\n resonance_id = data_pt.par['resonance_id']\\n\\n assignment = parse_assignment(resonance_id)\\n index = int(assignment[0][0])\\n\\n data_grouped.setdefault((index, resonance_id), []).append(data_pt)\\n\\n return data_grouped\",\n \"def profile_group(func, args, kwargs, func_result):\\n (collection, key, condition, initial, reduce) = args[:5]\\n report_kvs = _profile_query(collection)\\n\\n if key:\\n report_kvs['Group_Key'] = _to_json(key)\\n\\n if condition:\\n report_kvs['Group_Condition'] = _to_json(condition)\\n\\n if initial:\\n report_kvs['Group_Initial'] = _to_json(initial)\\n\\n if reduce:\\n report_kvs['Group_Reduce'] = reduce\\n\\n return report_kvs\",\n \"def compare_alpha_diversities(rarefaction_lines, mapping_lines, category,\\r\\n depth=None, test_type='nonparametric', num_permutations=999):\\r\\n if test_type == 'nonparametric' and num_permutations < 1:\\r\\n raise ValueError(\\\"Invalid number of permutations: %d. Must be greater \\\"\\r\\n \\\"than zero.\\\" % num_permutations)\\r\\n\\r\\n rarefaction_data = parse_rarefaction(rarefaction_lines)\\r\\n mapping_data = parse_mapping_file_to_dict(mapping_lines)[0]\\r\\n # samid_pairs, treatment_pairs are in the same order\\r\\n samid_pairs, treatment_pairs = sampleId_pairs(mapping_data,\\r\\n rarefaction_data, category)\\r\\n\\r\\n ps_avg_div = get_per_sample_average_diversities(rarefaction_data, depth)\\r\\n\\r\\n ttest_results, ad_avgs = {}, {}\\r\\n for sid_pair, treatment_pair in zip(samid_pairs, treatment_pairs):\\r\\n # if there is only 1 sample for each treatment in a comparison, and mc\\r\\n # using mc method, will error (e.g. mc_t_two_sample([1],[1]).\\r\\n if len(sid_pair[0]) == 1 and len(sid_pair[1]) == 1:\\r\\n ttest_results[treatment_pair] = (None, None)\\r\\n # add alpha diversity averages and standard deviations. since their\\r\\n # is only a single sample if we are in this part of the loop, we can\\r\\n # just record the sample value as the avg and 0 as the std.\\r\\n ad_avgs[treatment_pair[0]] = (sid_pair[0][0], 0.)\\r\\n ad_avgs[treatment_pair[1]] = (sid_pair[1][0], 0.)\\r\\n else:\\r\\n i = array([ps_avg_div[x] for x in sid_pair[0]])\\r\\n j = array([ps_avg_div[x] for x in sid_pair[1]])\\r\\n # add alpha diversity averages and standard deviations.\\r\\n ad_avgs[treatment_pair[0]] = (i.mean(), i.std())\\r\\n ad_avgs[treatment_pair[1]] = (j.mean(), j.std())\\r\\n # conduct tests\\r\\n if isnan(np_min(i)) or isnan(np_min(j)):\\r\\n ttest_results[treatment_pair] = (None, None)\\r\\n continue\\r\\n if test_type == 'parametric':\\r\\n obs_t, p_val = t_two_sample(i, j)\\r\\n elif test_type == 'nonparametric':\\r\\n obs_t, _, _, p_val = mc_t_two_sample(i, j,\\r\\n permutations=num_permutations)\\r\\n if p_val is not None:\\r\\n p_val = float(format_p_value_for_num_iters(p_val,\\r\\n num_iters=num_permutations))\\r\\n elif p_val is None: # None will error in format_p_val\\r\\n obs_t, p_val = None, None\\r\\n else:\\r\\n raise ValueError(\\\"Invalid test type '%s'.\\\" % test_type)\\r\\n ttest_results[treatment_pair] = (obs_t, p_val)\\r\\n\\r\\n return ttest_results, ad_avgs\",\n \"def formatdata(data,Params):\\n\\tmndata = dict()\\n\\talltrials = np.array([])\\n\\tfor k in range(len(Params[\\\"conditions\\\"])):\\n\\t\\tconditionmean = data[0,k].mean(axis = 0)\\n\\t\\tmndata.update({Params[\\\"conditions\\\"][k]: {'data' : data[0,k].mean(axis = 0), 'cmax' : conditionmean.max(), 'cmin' : conditionmean.min()}})\\n\\treturn mndata\",\n \"def modelOnBetaGrid(sample,bins,N,l,u):\\r\\n\\r\\n betaGrid=np.linspace(l,u,N)\\r\\n traces=[]\\r\\n WAIC=dict()\\r\\n index=0\\r\\n\\r\\n for beta in betaGrid:\\r\\n trace=intensityLogGauss(sample,bins,beta)\\r\\n traces.append(trace['intensity'])\\r\\n WAIC[index]=trace\\r\\n index+=1\\r\\n\\r\\n df=pm.compare(WAIC,ic='WAIC')\\r\\n\\r\\n return betaGrid,df,traces\",\n \"def test_compare_alpha_diversities(self):\\r\\n # test 'Dose' at 480 inputs\\r\\n category = 'Dose'\\r\\n depth = 480\\r\\n test_type = 'parametric'\\r\\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\\r\\n self.rarefaction_file,\\r\\n self.mapping_file, category=category, depth=depth,\\r\\n test_type=test_type)\\r\\n\\r\\n # hardcoded order of the terms in the keys otherwise would comps fail\\r\\n exp_tcomps = \\\\\\r\\n {('Control', '2xDose'): (1.1746048668554037, 0.44899351189030801),\\r\\n ('1xDose', '2xDose'): (1.7650193854830403, 0.17574514418562981),\\r\\n ('Control', '1xDose'): (0.43618805086434992, 0.7052689260099092)}\\r\\n\\r\\n # test each key in expected results -- this won't catch if\\r\\n # obs_tcomps has extra entries, but test that via the next call\\r\\n for k in exp_tcomps:\\r\\n assert_almost_equal(exp_tcomps[k], obs_tcomps[k])\\r\\n self.assertEqual(set(exp_tcomps.keys()), set(obs_tcomps.keys()))\\r\\n\\r\\n # test that returned alpha diversity averages are correct\\r\\n # dose\\r\\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\\r\\n # Control = ['Sam5']\\r\\n exp_ad_avgs = {'1xDose': (3.2511951575216664, 0.18664627928763661),\\r\\n '2xDose': (2.7539647172550001, 0.30099438035250015),\\r\\n 'Control': (3.3663303519925001, 0.0)}\\r\\n for k in exp_ad_avgs:\\r\\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\\r\\n\\r\\n # test 'Dose' at 480 inputs with nonparametric test\\r\\n seed(0) # set the seed to reproduce random MC pvals\\r\\n category = 'Dose'\\r\\n depth = 480\\r\\n test_type = 'nonparametric'\\r\\n num_permutations = 100\\r\\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\\r\\n self.rarefaction_file,\\r\\n self.mapping_file, category=category, depth=depth,\\r\\n test_type=test_type, num_permutations=num_permutations)\\r\\n exp_tcomps = {('1xDose', '2xDose'): (1.7650193854830403, 0.13),\\r\\n ('Control', '1xDose'): (0.43618805086434992, 0.83), ('Control',\\r\\n '2xDose'): (1.1746048668554037, 0.62)}\\r\\n # test each key in expected results -- this won't catch if\\r\\n # obs_tcomps has extra entries, but test that via the next call\\r\\n for k in exp_tcomps:\\r\\n assert_almost_equal(exp_tcomps[k], obs_tcomps[k])\\r\\n self.assertEqual(set(exp_tcomps.keys()), set(obs_tcomps.keys()))\\r\\n\\r\\n # test that returned alpha diversity averages are correct\\r\\n # dose\\r\\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\\r\\n # Control = ['Sam5']\\r\\n exp_ad_avgs = {'Control': (\\r\\n 3.3663303519925001,\\r\\n 0.0),\\r\\n '1xDose': (3.2511951575216664,\\r\\n 0.18664627928763661),\\r\\n '2xDose': (2.7539647172550001,\\r\\n 0.30099438035250015)}\\r\\n\\r\\n for k in exp_ad_avgs:\\r\\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\\r\\n\\r\\n # test it works with NA values\\r\\n # test 'Dose' at 500 inputs with paramteric test\\r\\n category = 'Dose'\\r\\n depth = 500\\r\\n test_type = 'parametric'\\r\\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\\r\\n self.rarefaction_file,\\r\\n self.mapping_file, category=category, depth=depth,\\r\\n test_type=test_type)\\r\\n exp_tcomps = \\\\\\r\\n {('Control', '2xDose'): (-0.63668873339963239, 0.63906168713487699),\\r\\n ('1xDose', '2xDose'): (None, None),\\r\\n ('Control', '1xDose'): (None, None)}\\r\\n for obs, exp in izip(obs_tcomps, exp_tcomps):\\r\\n self.assertEqual(obs, exp)\\r\\n \\r\\n # test that it works with nonparametric test - this was erroring.\\r\\n seed(0)\\r\\n test_type = 'nonparametric'\\r\\n exp_tcomps = \\\\\\r\\n {('Control', '2xDose'): (-0.63668873339963239, 0.672),\\r\\n ('1xDose', '2xDose'): (None, None),\\r\\n ('Control', '1xDose'): (None, None)}\\r\\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\\r\\n self.rarefaction_file,\\r\\n self.mapping_file, category=category, depth=depth,\\r\\n test_type=test_type)\\r\\n for obs, exp in izip(obs_tcomps, exp_tcomps):\\r\\n self.assertEqual(obs, exp)\\r\\n\\r\\n # test that returned alpha diversity averages are correct\\r\\n # dose\\r\\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\\r\\n # Control = ['Sam5']\\r\\n # will fail on nan comparison so avoid this\\r\\n exp_ad_avgs = {'1xDose': (nan, nan),\\r\\n '2xDose': (3.1955144893699998, 0.84206819489000018),\\r\\n 'Control': (2.2669008538500002, 0.0)}\\r\\n for k in exp_ad_avgs:\\r\\n if k != '1xDose':\\r\\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\\r\\n if k == '1xDose':\\r\\n self.assertTrue(all(map(isnan, obs_ad_avgs[k])))\\r\\n\\r\\n # test that it works when no depth is passed\\r\\n category = 'Dose'\\r\\n depth = None # should return depth = 910\\r\\n test_type = 'parametric'\\r\\n obs_tcomps, obs_ad_avgs = compare_alpha_diversities(\\r\\n self.rarefaction_file,\\r\\n self.mapping_file, category=category, depth=depth,\\r\\n test_type=test_type)\\r\\n\\r\\n # hardcoded order of the terms in the keys otherwise would comps fail\\r\\n exp_tcomps = \\\\\\r\\n {('Control', '2xDose'): (3.3159701868634883, 0.1864642327553255),\\r\\n ('1xDose', '2xDose'): (-0.48227871733885291, 0.66260803238173183),\\r\\n ('Control', '1xDose'): (0.83283756452373126, 0.49255115337550748)}\\r\\n for obs, exp in izip(obs_tcomps, exp_tcomps):\\r\\n self.assertEqual(obs, exp)\\r\\n\\r\\n # test that returned alpha diversity averages are correct\\r\\n # dose\\r\\n # 1xDose = ['Sam1','Sam2','Sam6'], 2xDose = ['Sam3','Sam4'],\\r\\n # Control = ['Sam5']\\r\\n exp_ad_avgs = {'1xDose': (2.6763340901916668, 0.36025734786901326),\\r\\n '2xDose': (2.8358041871949999, 0.04611264137749993),\\r\\n 'Control': (3.1006488615725001, 0.0)}\\r\\n for k in exp_ad_avgs:\\r\\n assert_almost_equal(exp_ad_avgs[k], obs_ad_avgs[k])\",\n \"def index():\\n import numpy as np\\n import random\\n\\n total_gender = {}\\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\\n\\n groups = db(db.groups).select()\\n freq_groups = {}\\n grp_gender = {}\\n for g in groups:\\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\\n grp_gender[g.code] = {}\\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n experiments = db(db.experiments).select()\\n freq_experiments = {}\\n exp_gender = {}\\n for e in experiments:\\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\\n exp_gender[e.code] = {}\\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n grp_exp = {}\\n for e in experiments:\\n grp_exp[e.code] = {}\\n for g in groups:\\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\\n\\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)\",\n \"def _compute_group_stats():\\n group_stats = []\\n \\n wmt16_group = Group.objects.filter(name='WMT16')\\n wmt16_users = _get_active_users_for_group(wmt16_group)\\n \\n # Aggregate information about participating groups.\\n groups = set()\\n for user in wmt16_users:\\n for group in _identify_groups_for_user(user):\\n groups.add(group)\\n \\n # TODO: move this to property of evaluation group or add dedicated data model.\\n # GOAL: should be configurable from within the Django admin backend.\\n #\\n # MINIMAL: move to local_settings.py?\\n #\\n # The following dictionary defines the number of HITs each group should\\n # have completed during the WMT16 evaluation campaign.\\n \\n for group in groups:\\n _name = group.name\\n \\n _group_stats = HIT.compute_status_for_group(group)\\n _total = _group_stats[0]\\n \\n if _total > 0 and not _name in GROUP_HIT_REQUIREMENTS.keys():\\n _required = 0\\n elif _name in GROUP_HIT_REQUIREMENTS.keys():\\n _required = GROUP_HIT_REQUIREMENTS[_name]\\n _delta = _total - _required\\n _data = (_total, _required, _delta)\\n \\n if _data[0] > 0:\\n group_stats.append((_name, _data))\\n \\n # Sort by number of remaining HITs.\\n group_stats.sort(key=lambda x: x[1][2])\\n \\n # Add totals at the bottom.\\n global_total = sum([x[1][0] for x in group_stats])\\n global_required = sum([x[1][1] for x in group_stats])\\n global_delta = global_total - global_required\\n global_data = (global_total, global_required, global_delta)\\n group_stats.append((\\\"Totals\\\", global_data))\\n \\n return group_stats\",\n \"def Group(self) -> _n_5_t_0:\",\n \"def Group(self) -> _n_5_t_0:\",\n \"def aga_expression_entropies(adata):\\n from scipy.stats import entropy\\n groups_order, groups_masks = utils.select_groups(adata, smp='aga_groups')\\n entropies = []\\n for mask in groups_masks:\\n X_mask = adata.X[mask]\\n x_median = np.median(X_mask, axis=0)\\n x_probs = (x_median - np.min(x_median)) / (np.max(x_median) - np.min(x_median))\\n entropies.append(entropy(x_probs))\\n return entropies\",\n \"def _composition_handler(self):\\n return {\\n group : StockAnalyzer(data) \\\\\\n for group, data in self.data.groupby(self.group_by)\\n }\",\n \"def test_group_by_roles_and_hardware_info(self):\\n self._test_group_by('Roles and hardware info', [1, 2, 1, 1, 1])\",\n \"def gdcs_reporter(metadata, analysistype, reportpath):\\n # Initialise list to store all the GDCS genes, and genera in the analysis\\n gdcs = list()\\n genera = list()\\n for sample in metadata:\\n sample[analysistype].faidict = dict()\\n if sample.general.bestassemblyfile != 'NA':\\n if os.path.isdir(sample[analysistype].targetpath):\\n # Update the fai dict with all the genes in the analysis, rather than just those with baited hits\\n Reports.gdcs_fai(sample=sample,\\n analysistype=analysistype)\\n sample[analysistype].createreport = True\\n # Determine which genera are present in the analysis\\n if sample.general.closestrefseqgenus not in genera:\\n genera.append(sample.general.closestrefseqgenus)\\n try:\\n # Add all the GDCS genes to the list\\n for gene in sorted(sample[analysistype].faidict):\\n if gene not in gdcs:\\n gdcs.append(gene)\\n except AttributeError:\\n sample[analysistype].createreport = False\\n else:\\n sample[analysistype].createreport = False\\n else:\\n sample[analysistype].createreport = False\\n sample.general.incomplete = True\\n header = 'Strain,Genus,Matches,Pass/Fail,{},\\\\n'.format(','.join(sorted(gdcs)))\\n data = str()\\n with open(os.path.join(reportpath, '{}.csv'.format(analysistype)), 'w') as report:\\n # Sort the samples in the report based on the closest refseq genus e.g. all samples with the same genus\\n # will be grouped together in the report\\n for genus in genera:\\n for sample in metadata:\\n if sample.general.closestrefseqgenus == genus:\\n if sample[analysistype].createreport:\\n sample[analysistype].totaldepth = list()\\n # Add the sample to the report if it matches the current genus\\n data += '{},{},'.format(sample.name, genus)\\n # Initialise a variable to store the number of GDCS genes were matched\\n count = 0\\n # As I want the count to be in the report before all the gene results, this string will\\n # store the specific sample information, and will be added to data once count is known\\n specific = str()\\n for gene in sorted(gdcs):\\n # As there are different genes present in the GDCS databases for each organism of\\n # interest, genes that did not match because they're absent in the specific database are\\n # indicated using an X\\n if gene not in [result for result in sample[analysistype].faidict]:\\n specific += 'X,'\\n else:\\n try:\\n specific += '{p_id},'.format(p_id=sample[analysistype].blastresults[gene])\\n # Report the necessary information for each gene result\\n count += 1\\n # If the gene was missing from the results attribute, add a - to the cell\\n except (KeyError, AttributeError):\\n specific += '-,'\\n # Determine whether the sample pass the necessary quality criteria:\\n # Pass, all GDCS, mean coverage greater than 20X coverage;\\n # ?: Indeterminate value;\\n # -: Fail value\\n # Allow one missing GDCS to still be considered a pass\\n if count >= len(sample[analysistype].faidict) - 1:\\n quality = '+'\\n else:\\n quality = '-'\\n # Add the count, mean depth with standard deviation, the pass/fail determination,\\n # and the total number of GDCS genes as well as the results\\n data += '{hits}/{total},{fail},{gdcs}\\\\n'\\\\\\n .format(hits=str(count),\\n total=len(sample[analysistype].faidict),\\n fail=quality,\\n gdcs=specific)\\n # Any samples with a best assembly of 'NA' are considered incomplete.\\n else:\\n data += '{},{},,,-\\\\n'.format(sample.name, sample.general.closestrefseqgenus)\\n elif sample.general.closestrefseqgenus == 'NA':\\n data += '{}\\\\n'.format(sample.name)\\n # Write the header and data to file\\n report.write(header)\\n report.write(data)\\n # Return the updated metadata object\\n return metadata\",\n \"def data_averaging_and_cleaning(self):\\n groups, film, plank = self.data_grouping()\\n\\n for i in groups:\\n self.organized_names.append(input('Enter label name for condition ' + str(i)))\\n\\n self.organized_film.append(sum(film[groups.index(i)]) / len(film[groups.index(i)]))\\n try:\\n self.organized_plank.append(sum(film[groups.index(i)]) / (sum(film[groups.index(i)]) +\\n sum(plank[groups.index(i)])))\\n except ZeroDivisionError:\\n self.organized_plank.append(sum(film[groups.index(i)]) / 1)\",\n \"def add_alpha_diversity_values_to_mapping_file(metrics, alpha_sample_ids,\\r\\n alpha_data, mapping_file_headers,\\r\\n mapping_file_data, bins, method='equal',\\r\\n missing_value_name='N/A'):\\r\\n norm = lambda x, x_min, x_max: (x - x_min) / (x_max - x_min)\\r\\n\\r\\n # data will be modified and returned so get your own copy\\r\\n new_mapping_file_data = deepcopy(mapping_file_data)\\r\\n new_mapping_file_headers = deepcopy(mapping_file_headers)\\r\\n\\r\\n # regular levels assigned based on equally spaced bins\\r\\n overall_probs = [i / bins for i in range(1, bins)]\\r\\n\\r\\n # if we are using the average method the levels are equal to the probs list\\r\\n if method == 'equal':\\r\\n levels = overall_probs\\r\\n\\r\\n for index, metric in enumerate(metrics):\\r\\n # get the alpha diversity value for the metric being evaluated\\r\\n data = [[row[index]] for row in alpha_data]\\r\\n metric_max = max(data)[0]\\r\\n metric_min = min(data)[0]\\r\\n\\r\\n # add headers for each metric\\r\\n new_mapping_file_headers.append('{0}_alpha'.format(metric))\\r\\n new_mapping_file_headers.append('{0}_normalized_alpha'.format(metric))\\r\\n new_mapping_file_headers.append('{0}_alpha_label'.format(metric))\\r\\n\\r\\n # when using the quantile method the levels change depending on the\\r\\n # metric being used; hence the calculation and normalization of the\\r\\n # data\\r\\n if method == 'quantile':\\r\\n levels = quantile([norm(element[0], metric_min, metric_max)\\r\\n for element in data], overall_probs)\\r\\n\\r\\n # get the normalized value of diversity and the tag for each value\\r\\n for value in data:\\r\\n norm_value = norm(value[0], metric_min, metric_max)\\r\\n value.append(norm_value)\\r\\n value.append(_get_level(norm_value, levels, 'bin'))\\r\\n\\r\\n # iterate using the mapping file instead of using the alpha diversity\\r\\n # data because more often that you will like you will have more samples\\r\\n # in the mapping file than in your downstream analysis data\\r\\n for row in new_mapping_file_data:\\r\\n try:\\r\\n data_index = alpha_sample_ids.index(row[0])\\r\\n\\r\\n # data fields should be strings\\r\\n row.extend(map(str, data[data_index]))\\r\\n except ValueError:\\r\\n row.extend([missing_value_name, missing_value_name,\\r\\n missing_value_name])\\r\\n\\r\\n return new_mapping_file_data, new_mapping_file_headers\",\n \"def groups(self):\\n\\n\\t\\tprint \\\"completed minimization\\\"\\n\\t\\tcopy(self.rootdir+'counterions-minimized.gro',self.rootdir+'system.gro')\\n\\t\\tcopy(self.rootdir+'counterions.top',self.rootdir+'system.top')\\n\\t\\tif self.simscale == 'aamd': grouptype = 'standard'\\n\\t\\tif self.simscale == 'cgmd': grouptype = 'cgmd_water'\\n\\t\\tself.grouping(grouptype=grouptype)\",\n \"def group(simulations, groupby, sort=True, only_started=False, reverse=False):\\n\\n from collections import OrderedDict\\n from pencilnew.math import natural_sort\\n\\n sim_dict_grouped = {}\\n\\n if type(simulations) == type(['list']):\\n sim_list = simulations\\n #elif type(simulations) == Simulations:\\n# sim_list = simulations.sims\\n else:\\n print('!! ERROR: Dont know how to interprated simulations argument..')\\n return False\\n\\n # sort out simulations that has not started\\n if only_started == True: sim_list = [s for s in sim_list if s.started()]\\n\\n # special cases:\\n if groupby in ['Lx', 'Ly', 'Lz']:\\n if groupby[-1] == 'x': ii = 0\\n elif groupby[-1] == 'y': ii = 1\\n elif groupby[-1] == 'z': ii = 2\\n for sim in sim_list:\\n q = str(sim.param['lxyz'][ii])\\n if (not q in sim_dict_grouped.keys()):\\n sim_dict_grouped[q] = [sim]\\n else:\\n sim_dict_grouped[q].append(sim)\\n \\n elif groupby in ['nx', 'ny', 'nz']:\\n for sim in sim_list:\\n q = str(getattr(sim.dim, groupby))\\n if (not q in sim_dict_grouped.keys()):\\n sim_dict_grouped[q] = [sim]\\n else:\\n sim_dict_grouped[q].append(sim)\\n\\n # case the groupby-keyword can be found via __simulation__.get_value\\n elif sim_list[0].get_value(groupby) != None:\\n for sim in sim_list:\\n q = str(sim.get_value(groupby))\\n if (not q in sim_dict_grouped.keys()):\\n sim_dict_grouped[q] = [sim]\\n else:\\n sim_dict_grouped[q].append(sim)\\n\\n else:\\n print('!! ERROR: Coudnt group simulations, no fitting groupby-keyword has been found to match \\\"'+groupby+'\\\"!')\\n return False\\n\\n if sort:\\n sim_dict_grouped_n_sorted = OrderedDict()\\n keys = sim_dict_grouped.keys()\\n for key in natural_sort(keys, reverse=reverse):\\n sim_dict_grouped_n_sorted[key] = sim_dict_grouped[key]\\n\\n return sim_dict_grouped_n_sorted\\n\\n return sim_dict_grouped\",\n \"def get_stats(self, gene, groups=[\\\"SP\\\", \\\"SL06\\\", \\\"SL12\\\", \\\"SL24\\\",\\\"SL48\\\", \\\"SL96\\\",\\\"FP06\\\", \\\"FP12\\\", \\\"FP24\\\",\\\"FP48\\\", \\\"FP96\\\", \\\"FL\\\"]):\\n\\n try:\\n pos = self.gene_header.index(gene)\\n except:\\n return [],[]\\n\\n limits = [0] + self.reorder_matrix(groups=groups)\\n #print limits\\n intervals = zip(limits[:-1],limits[1:])\\n print \\\"Debugging: ave and stdev:-\\\"\\n #print intervals\\n #print self.data_matrix\\n #print self.data_matrix[:,pos]\\n #print [ (self.data_matrix[ i:j,pos ]) for i,j in intervals]\\n ave = [ numpy.average(self.data_matrix[ i:j,pos ]) for i,j in intervals]\\n print ave\\n stdev = [ numpy.std(self.data_matrix[ i:j,pos ]) for i,j in intervals]\\n print stdev\\n return ave, stdev\",\n \"def _process_data(self, data: Element) -> List[TestGroupReport]:\\n # NOTE: XML output contains skipped testcases which are ignored.\\n result = []\\n\\n for suite in data.getchildren():\\n suite_name = suite.attrib[\\\"name\\\"]\\n suite_report = TestGroupReport(\\n name=suite_name,\\n category=ReportCategories.TESTSUITE,\\n )\\n suite_has_run = False\\n\\n for testcase in suite.getchildren():\\n\\n testcase_name = testcase.attrib[\\\"name\\\"]\\n testcase_report = TestCaseReport(name=testcase_name)\\n\\n if not testcase.getchildren():\\n assertion_obj = RawAssertion(\\n description=\\\"Passed\\\",\\n content=\\\"Testcase {} passed\\\".format(testcase_name),\\n passed=True,\\n )\\n testcase_report.append(registry.serialize(assertion_obj))\\n else:\\n for entry in testcase.getchildren():\\n assertion_obj = RawAssertion(\\n description=entry.tag,\\n content=entry.text,\\n passed=entry.tag != \\\"failure\\\",\\n )\\n testcase_report.append(\\n registry.serialize(assertion_obj)\\n )\\n\\n testcase_report.runtime_status = RuntimeStatus.FINISHED\\n\\n if testcase.attrib[\\\"status\\\"] != \\\"notrun\\\":\\n suite_report.append(testcase_report)\\n suite_has_run = True\\n\\n if suite_has_run:\\n result.append(suite_report)\\n\\n return result\",\n \"def GroupLassoADMM(As, bs, lam, groups, rho, alpha, maxiter=1000, abstol=1e-4, reltol=1e-2):\\n \\n n,D = As[0].shape\\n m = len(As)\\n \\n Atbs = [A.T.dot(b) for (A,b) in zip(As,bs)]\\n \\n Ls = [Factor(A,rho) for A in As]\\n Us = [L.T for L in Ls]\\n \\n x = np.zeros((m*D,1))\\n z = 1e-5*np.random.randn(m*D,1)\\n u = 1e-5*np.random.randn(m*D,1)\\n \\n # Indices of x for each timestep. x[Ts[t]] is the coefficient vector for time t\\n Ts = [j*D + np.arange(D) for j in range(m)]\\n \\n history = {}\\n history['objval'] = []\\n history['gl_objval'] = []\\n history['r_norm'] = []\\n history['s_norm'] = []\\n history['eps_pri'] = []\\n history['eps_dual'] = []\\n \\n for k in range(maxiter):\\n \\n # x update\\n for j in range(m):\\n q = Atbs[j] + rho*(z[Ts[j]]-u[Ts[j]])\\n\\n if n >= D:\\n x[Ts[j]] = Solve(Us[j],Solve(Ls[j],q))\\n else:\\n x[Ts[j]] = q/rho-As[j].T.dot(Solve(Us[j],Solve(Ls[j],As[j].dot(q))))/rho**2\\n \\n # z update\\n zold = np.copy(z)\\n x_hat = alpha*x+(1-alpha)*zold\\n for g in groups:\\n z[g] = Shrinkage(x_hat[g]+u[g], lam/rho)\\n \\n u = u+(x_hat-z)\\n \\n # record history\\n history['objval'].append(ObjectiveADMM(As,bs,Ts,lam,groups,x,z))\\n history['gl_objval'].append(ObjectiveGLASSO_block(As,bs,Ts,lam,groups,x))\\n history['r_norm'].append(Norm(x-z))\\n history['s_norm'].append(Norm(rho*(z-zold)))\\n history['eps_pri'].append(np.sqrt(m)*abstol+reltol*np.max([Norm(x),Norm(z)]))\\n history['eps_dual'].append(np.sqrt(m)*abstol+reltol*Norm(rho*u))\\n \\n # check for termination\\n if (history['r_norm'][-1] < history['eps_pri'][-1]) and \\\\\\n (history['s_norm'][-1] < history['eps_dual'][-1]):\\n break\\n \\n # Return unbiased sparse predictor\\n z = z.reshape(D,m, order = 'F')\\n nz_coords = np.where(np.sum(abs(z), axis = 1) != 0)[0]\\n if len(nz_coords) != 0: \\n for j in range(m):\\n z[nz_coords,j] = np.linalg.lstsq(As[j][:, nz_coords], bs[j])[0][:,0]\\n \\n return z, history\",\n \"def test_properties_stats_group_by_group_by_and_sub_group_by_get(self):\\n pass\",\n \"def test_get_group_colors(self):\\r\\n\\r\\n data_colors = color_dict_to_objects(self.data_color_hsv)\\r\\n exp = (self.colors, data_colors, self.data_color_order)\\r\\n obs = get_group_colors(self.groups, self.colors, data_colors,\\r\\n self.data_color_order)\\r\\n\\r\\n self.assertEqual(obs, exp)\",\n \"def __init__(self, \\\\\\n model=\\\"IFBA\\\", \\\\\\n enrichment=4.0, \\\\\\n burnup=0.0 ):\\n\\n # Set characteristics used to define the group constants\\n # within the data module.\\n self.model = model\\n self.enrichment = enrichment\\n self.burnup = burnup \\n \\n # Get the group data object.\\n IFBA = 0\\n WABA = 0\\n GAD = 0\\n if (model == \\\"IFBA\\\"):\\n IFBA = 1\\n if (model == \\\"WABA\\\"):\\n WABA = 1 \\n if (model == \\\"GAD\\\"):\\n GAD = 1 \\n data = get_2g_parms(burnup, enrichment, IFBA, WABA, GAD)\\n\\n # Set the group data.\\n self.D1 = data.DIFF1\\n self.D2 = data.DIFF2\\n self.A1 = data.ABS1\\n self.A2 = data.ABS2\\n self.F1 = data.NUFISS1\\n self.F2 = data.NUFISS2\\n self.S12 = data.REMOV1 # Note, this is strange but is\\n self.R1 = data.REMOV1 + data.ABS1 # the way CASMO defines them.\\n self.KINF = data.K_INF_NO_XE\\n self.M2 = data.M2_XE\\n\\n # Peaking factor.\\n self.peak = 0.0\",\n \"def fairness_ranking(data, ranked_list, group_file, gamma=0.5, stop_prob=0.7, verbose=False):\\n author_exposure = {}\\n author_relevance = {}\\n\\n #calculate the exposure and relevance for each author\\n for query in ranked_list:\\n exposure = 1.0\\n for doc in query[1]:\\n relevance = doc[0]\\n\\n authors_id = data_combining.get_data(doc[1], data = data)['authors']['corpus_author_id']\\n\\n for a in authors_id:\\n if int(a) not in author_exposure:\\n author_exposure[int(a)] = exposure\\n else:\\n author_exposure[int(a)] += exposure\\n\\n if int(a) not in author_relevance:\\n author_relevance[int(a)] = stop_prob * relevance\\n else:\\n author_relevance[int(a)] += stop_prob * relevance\\n\\n exposure *= gamma\\n exposure *= (1 - stop_prob * relevance)\\n\\n #calculate group relevance and exposure\\n group_exposure_sum = {}\\n group_relevance_sum = {}\\n group = pd.read_csv(group_file)\\n for index, row in group.iterrows():\\n curr_author_exposure = author_exposure[row['author_id']] if row['author_id'] in author_exposure else 0\\n curr_author_relevance = author_relevance[row['author_id']] if row['author_id'] in author_relevance else 0\\n\\n\\n if row['gid'] not in group_exposure_sum:\\n group_exposure_sum[row['gid']] = curr_author_exposure\\n else:\\n group_exposure_sum[row['gid']] += curr_author_exposure\\n\\n if row['gid'] not in group_relevance_sum:\\n group_relevance_sum[row['gid']] = curr_author_relevance\\n else:\\n group_relevance_sum[row['gid']] += curr_author_relevance\\n\\n total_group_relevance = sum(group_relevance_sum.values())\\n total_group_exposure = sum(group_exposure_sum.values())\\n\\n squared_sum = 0\\n\\n group_exposure_and_relevance = {}\\n\\n for group, relevance in group_relevance_sum.items():\\n group_relevance = relevance / total_group_relevance\\n group_exposure = group_exposure_sum[group] / total_group_exposure\\n\\n group_exposure_and_relevance[group] = (group_relevance, group_exposure)\\n if verbose:\\n print('{}: rel {} | exp {}'.format(group, group_relevance, group_exposure))\\n\\n squared_sum += (group_exposure - group_relevance) ** 2\\n\\n if verbose:\\n print('fair exposure: {}'.format(math.sqrt(squared_sum)))\\n\\n return (math.sqrt(squared_sum), group_exposure_and_relevance)\",\n \"def generate_reports(self):\\n print(\\\"Generating reports.\\\")\\n # Input user memberships and utilities\\n input_mean_util = dict()\\n for g in range(self.num_groups):\\n input_mean_util[g] = self.group_distributions.get_group_distribution(g)[0]\\n input_mean_util[g] = input_mean_util[g]-input_mean_util[g][self.num_items-1]\\n input_mean_util = pd.DataFrame.from_dict(data=input_mean_util, orient='index')\\n input_mean_util.columns = [\\\"mean_input_util_item{0:03d}\\\".format(i) for i in range(self.num_items)]\\n input_mean_util.reset_index(inplace=True, drop=True)\\n\\n input_memb_prob = self.group_distributions.get_group_probabilities()\\n input_memb_prob = pd.DataFrame(data=input_memb_prob, columns=['input_memb_prob'])\\n input_memb_prob.reset_index(inplace=True, drop=True)\\n\\n # Drawn user membership probabilities\\n drawn_memb_prob = self.true_user_utilities.groupby(['user_group'])[['user_group']].count() / self.num_users\\n drawn_memb_prob.rename(columns={'user_group': 'sim_memb_prob'}, inplace=True)\\n drawn_memb_prob.reset_index(inplace=True, drop=True)\\n\\n # Drawn user utilities (mean)\\n drawn_mean_util = self.true_user_utilities.groupby(['user_group']).mean()\\n drawn_mean_util.drop(labels=['user_id'], axis=1, inplace=True)\\n drawn_mean_util.columns = [\\\"mean_sim_util_item{0:03d}\\\".format(i) for i in range(self.num_items)]\\n drawn_mean_util.reset_index(inplace=True, drop=True)\\n\\n user_summary = pd.concat(objs=[input_memb_prob, drawn_memb_prob, input_mean_util, drawn_mean_util], axis=1)\\n user_summary.to_csv(os.path.join(self.output_dir, 'drawn_users_summary.csv'), index=False)\\n\\n # Checking design balance per user\\n balance_per_user = self.data_set.groupby(['user_id']).sum()/(self.num_trips*self.shelf_size/self.num_items)\\n balance_per_user.drop(labels=['trip', 'choice'], axis=1, inplace=True)\\n balance_per_user.reset_index(inplace=True)\\n balance_per_user.to_csv(os.path.join(self.output_dir, 'item_balance_per_user.csv'), index=False)\\n\\n print(\\\"Done with generating reports.\\\")\",\n \"def GEEmacaGCMs(ptsFile,metric,timeStep,startYear,endYear,scenarios,buf,poly,models,\\n username,folderOut, scalePix = 4000):\\n \\n # load required libraries\\n import ee\\n \\n # Initialize the Earth Engine object, using the authentication credentials.\\n ee.Initialize()\\n\\n ID_field = \\\"geeID\\\"\\n\\n #load pts or poly file\\n pts1 = ee.FeatureCollection('users/' + username + '/' + str(ptsFile))\\n\\n time_d = {}\\n time_d['month'] = 'projm'\\n time_d['year'] = 'projy'\\n \\n for met in metric:\\n\\n for scenario in scenarios:\\n\\n for model in models:\\n\\n MACA = (ee.ImageCollection('IDAHO_EPSCOR/MACAv2_METDATA_MONTHLY')\\n .select(met)\\n .filterMetadata('model', 'equals', model)\\n .filterMetadata('scenario', 'equals', scenario))\\n\\n metL = [met]\\n \\n years = list(range(startYear, endYear + 1))\\n yearsEE = ee.List(years)\\n \\n if all([(timeStep == 'year'),any([(met == 'tasmin'),(met == 'tasmax'),\\n (met == 'huss'),(met == 'rsds'),\\n (met == 'was')])]):\\n\\n def map_m(i):\\n i = ee.Number(i).int()\\n image2 = (MACA\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .first())\\n filtered = (MACA\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .mean()\\n .copyProperties(image2,['system:time_start','system:time_end']))\\n return filtered\\n\\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\\n\\n elif (timeStep == 'month'):\\n \\n img_col = MACA.filter(ee.Filter.calendarRange(startYear, endYear, 'year'))\\n\\n elif all([(timeStep == 'year'),(met == 'pr')]):\\n\\n def map_m(i):\\n i = ee.Number(i).int()\\n image2 = (MACA\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .first())\\n filtered = (MACA\\n .filter(ee.Filter.calendarRange(i, i, 'year'))\\n .sum()\\n .copyProperties(image2,['system:time_start','system:time_end']))\\n return filtered\\n\\n img_col = ee.ImageCollection(yearsEE.map(map_m).flatten())\\n\\n #else:\\n #print(\\\"incorrect time step specified\\\")\\n \\n if buf > 0:\\n bufL = [buf]\\n def bufferPoly(feature):\\n return feature.buffer(bufL[0])\\n\\n ptsB = pts1.map(bufferPoly)\\n def table_m(image):\\n table = (image\\n .select(metL[0])\\n .reduceRegions(collection = ptsB.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_ptsB',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n \\n #print ('buffered pts by:' + str(buf) + ' for MACA: ' + met + ' ' + scenario + ' ' + model)\\n\\n elif poly > 0:\\n \\n def table_m(image):\\n table = (image\\n .select(metL[0])\\n .reduceRegions(collection = pts1.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_poly1',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n \\n #print ('spatial mean in poly: no buffer for MACA: ' + met + ' ' + scenario + ' ' + model)\\n\\n else:\\n def table_m(image):\\n table = (image\\n .select(metL[0])\\n .reduceRegions(collection = pts1.select([ID_field]),\\n reducer = ee.Reducer.mean(),\\n scale = scalePix))\\n \\n def table_add_date(f):\\n return f.set('startDate', ee.Date(image.get('system:time_start')))\\n\\n return table.map(table_add_date)\\n\\n triplets = img_col.map(table_m).flatten()\\n\\n task_tc = ee.batch.Export.table.toDrive(collection = triplets\\n .filter(ee.Filter.neq('mean', None))\\n .select(['.*'],None,False),\\n description = str(time_d[timeStep])+'_MACA_'+str(met)+'_'+scenario+'_'+model+'_'+str(years[0])+'_'+str(years[len(years)-1])+'_pts1',\\n folder = folderOut,\\n fileFormat = 'CSV')\\n task_tc.start()\\n \\n #print('value at point: no buffer for MACA: ' + met + ' ' + scenario + ' ' + model)\",\n \"def _group_energy_terms(ener_xvg):\\n with open(ener_xvg) as f:\\n all_lines = f.readlines()\\n energy_types = [line.split('\\\"')[1] for line in all_lines if line[:3] == '@ s']\\n energy_values = [float(x) * units.kilojoule_per_mole for x in all_lines[-1].split()[1:]]\\n e_out = OrderedDict(zip(energy_types, energy_values))\\n return e_out, ener_xvg\",\n \"def get_plotting_data(each_misfit_windows_collection, iterations_list, snr_threshold, event_depth_dict):\\n result = {}\\n phases_zr = [\\\"P\\\", \\\"pP\\\", \\\"sP\\\", \\\"PP\\\", \\\"S\\\", \\\"sS\\\", \\\"SS\\\"]\\n phases_t = [\\\"ScS\\\", \\\"S\\\", \\\"sS\\\", \\\"SS\\\"]\\n conditions = {\\n \\\"P\\\": {\\n \\\"exclude_p\\\": False,\\n \\\"exclude_s\\\": True\\n },\\n \\\"pP\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"sP\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"PP\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"S\\\": {\\n \\\"exclude_p\\\": False,\\n \\\"exclude_s\\\": False\\n },\\n \\\"sS\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"SS\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"ScS\\\": {\\n \\\"exclude_p\\\": True,\\n \\\"exclude_s\\\": True\\n },\\n \\\"surface_z\\\": {\\n \\\"exclude_p\\\": False,\\n \\\"exclude_s\\\": False\\n },\\n \\\"surface_r\\\": {\\n \\\"exclude_p\\\": False,\\n \\\"exclude_s\\\": False\\n },\\n \\\"surface_t\\\": {\\n \\\"exclude_p\\\": False,\\n \\\"exclude_s\\\": False\\n },\\n }\\n # we can exrtact the information from the misfit_windows in the order of the pdf output.\\n # order will be z,r,t[,surface_z,surface_r,surface_t]\\n rep_net_sta = sorted(event_depth_dict.keys())[0]\\n event_depth_this_event = event_depth_dict[rep_net_sta]\\n if (event_depth_this_event > SURFACE_THRESHOLD):\\n category_list = [\\\"z\\\", \\\"r\\\", \\\"t\\\"]\\n category_phases = [phases_zr, phases_zr, phases_t]\\n else:\\n category_list = [\\\"z\\\", \\\"r\\\", \\\"t\\\", \\\"surface_z\\\", \\\"surface_r\\\", \\\"surface_t\\\"]\\n category_phases = [phases_zr, phases_zr, phases_t,\\n [\\\"surface_z\\\"], [\\\"surface_r\\\"], [\\\"surface_t\\\"]]\\n for each_iteration in iterations_list:\\n result[each_iteration] = {}\\n for each_category, each_category_phases in zip(category_list, category_phases):\\n result[each_iteration][each_category] = []\\n for each_category_phase in each_category_phases:\\n phase_condition = conditions[each_category_phase]\\n cc = get_windows_cc(\\n each_misfit_windows_collection[each_iteration], phase_condition[\\n \\\"exclude_p\\\"], phase_condition[\\\"exclude_s\\\"],\\n each_category, snr_threshold, each_category_phase)\\n cc = cc[cc >= 0]\\n deltat = get_windows_deltat(\\n each_misfit_windows_collection[each_iteration], phase_condition[\\n \\\"exclude_p\\\"], phase_condition[\\\"exclude_s\\\"],\\n each_category, snr_threshold, each_category_phase)\\n deltat = deltat[np.abs(deltat) <= 10]\\n similarity = get_windows_similarity(\\n each_misfit_windows_collection[each_iteration], phase_condition[\\n \\\"exclude_p\\\"], phase_condition[\\\"exclude_s\\\"],\\n each_category, snr_threshold, each_category_phase)\\n similarity = similarity[similarity >= 0]\\n result[each_iteration][each_category].append(\\n {\\\"net_sta\\\": get_windows_net_sta(\\n each_misfit_windows_collection[each_iteration], phase_condition[\\n \\\"exclude_p\\\"], phase_condition[\\\"exclude_s\\\"],\\n each_category, snr_threshold, each_category_phase),\\n \\\"cc\\\": cc,\\n \\\"deltat\\\": deltat,\\n \\\"similarity\\\": similarity,\\n }\\n )\\n # result:dict->each_iteration:dict->each_category:list as the dict showed before, we should return the category_phases\\n # we should combine the surface wave phases to one page\\n if (len(category_phases) == 6):\\n for each_iteration in iterations_list:\\n category_phases = [phases_zr, phases_zr, phases_t,\\n [\\\"surface_z\\\", \\\"surface_r\\\", \\\"surface_t\\\"]]\\n category_list = [\\\"z\\\", \\\"r\\\", \\\"t\\\", \\\"surface\\\"]\\n result[each_iteration][\\\"surface\\\"] = []\\n result[each_iteration][\\\"surface\\\"].append(\\n result[each_iteration][\\\"surface_z\\\"][0])\\n result[each_iteration][\\\"surface\\\"].append(\\n result[each_iteration][\\\"surface_r\\\"][0])\\n result[each_iteration][\\\"surface\\\"].append(\\n result[each_iteration][\\\"surface_t\\\"][0])\\n del result[each_iteration][\\\"surface_z\\\"]\\n del result[each_iteration][\\\"surface_r\\\"]\\n del result[each_iteration][\\\"surface_t\\\"]\\n\\n return result, category_phases, category_list\",\n \"def create_report(result, *, decimal_places=3):\\n\\n # TODO add effect sizes to multiple comparisons.\\n def single_population_string(population, with_stats=False, pop_pval=None, with_rank=True):\\n if pop_pval is not None:\\n return \\\"%s (p=%.*f)\\\" % (population, decimal_places, pop_pval)\\n if with_stats:\\n halfwidth = (result.rankdf.at[population, 'ci_upper'] - result.rankdf.at[population, 'ci_lower']) / 2\\n mystats = []\\n if (result.force_mode is not None and result.force_mode=='parametric') or \\\\\\n (result.force_mode is None and result.all_normal):\\n mystats.append(\\\"M=%.*f+-%.*f\\\" % (decimal_places, result.rankdf.at[population, 'mean'],\\n decimal_places, halfwidth))\\n mystats.append(\\\"SD=%.*f\\\" % (decimal_places, result.rankdf.at[population, 'std']))\\n else:\\n mystats.append(\\\"MD=%.*f+-%.*f\\\" % (decimal_places, result.rankdf.at[population, 'median'],\\n decimal_places, halfwidth))\\n mystats.append(\\\"MAD=%.*f\\\" % (decimal_places, result.rankdf.at[population, 'mad']))\\n if with_rank:\\n mystats.append(\\\"MR=%.*f\\\" % (decimal_places, result.rankdf.at[population, 'meanrank']))\\n return \\\"%s (%s)\\\" % (population, \\\", \\\".join(mystats))\\n else:\\n return str(population)\\n\\n def create_population_string(populations, with_stats=False, pop_pvals=None, with_rank=False):\\n if isinstance(populations, str):\\n populations = [populations]\\n population_strings = []\\n for index, population in enumerate(populations):\\n if pop_pvals is not None:\\n cur_pval = pop_pvals[index]\\n else:\\n cur_pval = None\\n population_strings.append(single_population_string(population, with_stats, cur_pval, with_rank))\\n if len(populations) == 1:\\n popstr = population_strings[0]\\n elif len(populations) == 2:\\n popstr = \\\" and \\\".join(population_strings)\\n else:\\n popstr = \\\", \\\".join(population_strings[:-1]) + \\\", and \\\" + population_strings[-1]\\n return popstr\\n\\n if not isinstance(result, RankResult):\\n raise TypeError(\\\"result must be of type RankResult and should be the outcome of calling the autorank function.\\\")\\n\\n print(\\\"The statistical analysis was conducted for %i populations with %i paired samples.\\\" % (len(result.rankdf),\\n result.num_samples))\\n print(\\\"The family-wise significance level of the tests is alpha=%.*f.\\\" % (decimal_places, result.alpha))\\n\\n if result.all_normal:\\n not_normal = []\\n min_pvalue = min(result.pvals_shapiro)\\n print(\\\"We failed to reject the null hypothesis that the population is normal for all populations \\\"\\n \\\"(minimal observed p-value=%.*f). Therefore, we assume that all populations are \\\"\\n \\\"normal.\\\" % (decimal_places, min_pvalue))\\n else:\\n not_normal = []\\n pvals = []\\n normal = []\\n for i, pval in enumerate(result.pvals_shapiro):\\n if pval < result.alpha_normality:\\n not_normal.append(result.rankdf.index[i])\\n pvals.append(pval)\\n else:\\n normal.append(result.rankdf.index[i])\\n if len(not_normal) == 1:\\n population_term = 'population'\\n else:\\n population_term = 'populations'\\n print(\\\"We rejected the null hypothesis that the population is normal for the %s %s. \\\"\\n \\\"Therefore, we assume that not all populations are \\\"\\n \\\"normal.\\\" % (population_term, create_population_string(not_normal, pop_pvals=pvals)))\\n\\n if result.omnibus == 'bayes':\\n if result.all_normal:\\n central_tendency = 'mean value'\\n central_tendency_long = 'mean value (M)'\\n variability = 'standard deviation (SD)'\\n effect_size = 'd'\\n else:\\n central_tendency = 'median'\\n central_tendency_long = 'median (MD)'\\n variability = 'median absolute deviation (MAD)'\\n effect_size = 'gamma'\\n print(\\n \\\"We used a bayesian signed rank test to determine differences between the mean values of the \\\"\\n \\\"populations and report the %s and the %s for each population. We distinguish \\\"\\n \\\"between populations being pair-wise smaller, equal, or larger and make a decision for one \\\"\\n \\\"of these cases if we estimate that the posterior probability is at least \\\"\\n \\\"alpha=%.*f.\\\" % (central_tendency_long, variability, decimal_places, result.alpha))\\n if result.rope_mode == 'effsize':\\n print(\\n 'We used the effect size to define the region of practical equivalence (ROPE) around the %s '\\n 'dynamically as %.*f*%s.' % (central_tendency, decimal_places, result.rope, effect_size))\\n else:\\n print(\\n 'We used a fixed value of %.*f to define the region of practical equivalence (ROPE) around the '\\n '%s.' % (decimal_places, result.rope, central_tendency))\\n decision_set = set(result.rankdf['decision'])\\n decision_set.remove('NA')\\n if {'inconclusive'} == decision_set:\\n print(\\\"We failed to find any conclusive evidence for differences between the populations \\\"\\n \\\"%s.\\\" % create_population_string(result.rankdf.index, with_stats=True))\\n elif {'equal'} == decision_set:\\n print(\\n \\\"All populations are equal, i.e., the are no significant and practically relevant differences \\\"\\n \\\"between the populations %s.\\\" % create_population_string(result.rankdf.index,\\n with_stats=True))\\n elif {'equal', 'inconclusive'} == decision_set:\\n print(\\n \\\"The populations %s are all either equal or the results of the analysis are inconclusive.\\\")\\n print(result.decision_matrix)\\n else:\\n print(\\\"We found significant and practically relevant differences between the populations \\\"\\n \\\"%s.\\\" % create_population_string(result.rankdf.index, with_stats=True))\\n for i in range(len(result.rankdf)):\\n if len(result.rankdf.index[result.decision_matrix.iloc[i, :] == 'smaller']) > 0:\\n print('The %s of the population %s is larger than of the populations '\\n '%s.' % (central_tendency, result.rankdf.index[i],\\n create_population_string(\\n result.rankdf.index[\\n result.decision_matrix.iloc[i, :] == 'smaller'])))\\n equal_pairs = []\\n for i in range(len(result.rankdf)):\\n for j in range(i + 1, len(result.rankdf)):\\n if result.decision_matrix.iloc[i, j] == 'equal':\\n equal_pairs.append(result.rankdf.index[i] + ' and ' + result.rankdf.index[j])\\n if len(equal_pairs) > 0:\\n equal_pairs_str = create_population_string(equal_pairs).replace(',', ';')\\n print('The following pairs of populations are equal: %s.' % equal_pairs_str)\\n if 'inconclusive' in set(result.rankdf['decision']):\\n print('All other differences are inconclusive.')\\n elif len(result.rankdf) == 2:\\n print(\\\"No check for homogeneity was required because we only have two populations.\\\")\\n if result.effect_size == 'cohen_d':\\n effect_size = 'd'\\n elif result.effect_size == 'cliff_delta':\\n effect_size = 'delta'\\n elif result.effect_size == 'akinshin_gamma':\\n effect_size = 'gamma'\\n else:\\n raise ValueError('unknown effect size method, this should not be possible: %s' % result.effect_size)\\n if result.omnibus == 'ttest':\\n larger = np.argmax(result.rankdf['mean'].values)\\n smaller = int(bool(larger - 1))\\n if result.all_normal:\\n print(\\\"Because we have only two populations and both populations are normal, we use the t-test to \\\"\\n \\\"determine differences between the mean values of the populations and report the mean value (M)\\\"\\n \\\"and the standard deviation (SD) for each population. \\\")\\n else:\\n if len(not_normal) == 1:\\n notnormal_str = 'one of them is'\\n else:\\n notnormal_str = 'both of them are'\\n print(\\\"Because we have only two populations and %s not normal, we use should Wilcoxon's signed rank \\\"\\n \\\"test to determine the differences in the central tendency and report the median (MD) and the \\\"\\n \\\"median absolute deviation (MAD) for each population. However, the user decided to force the \\\"\\n \\\"use of the t-test which assumes normality of all populations and we report the mean value (M) \\\"\\n \\\"and the standard deviation (SD) for each population.\\\" % notnormal_str)\\n if result.pvalue >= result.alpha:\\n print(\\\"We failed to reject the null hypothesis (p=%.*f) of the paired t-test that the mean values of \\\"\\n \\\"the populations %s are are equal. Therefore, we \\\"\\n \\\"assume that there is no statistically significant difference between the mean values of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True)))\\n else:\\n print(\\\"We reject the null hypothesis (p=%.*f) of the paired t-test that the mean values of the \\\"\\n \\\"populations %s are \\\"\\n \\\"equal. Therefore, we assume that the mean value of %s is \\\"\\n \\\"significantly larger than the mean value of %s with a %s effect size (%s=%.*f).\\\"\\n % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True),\\n result.rankdf.index[larger], result.rankdf.index[smaller],\\n result.rankdf.magnitude[larger], effect_size, decimal_places, result.rankdf.effect_size[larger]))\\n elif result.omnibus == 'wilcoxon':\\n larger = np.argmax(result.rankdf['median'].values)\\n smaller = int(bool(larger - 1))\\n if result.all_normal:\\n print(\\\"Because we have only two populations and both populations are normal, we should use the t-test \\\"\\n \\\"to determine differences between the mean values of the populations and report the mean value \\\"\\n \\\"(M) and the standard deviation (SD) for each population. However, the user decided to force the \\\"\\n \\\"use of the less powerful Wilcoxon signed rank test and we report the median (MD) and the median \\\"\\n \\\"absolute devivation (MAD) for each population.\\\")\\n else:\\n if len(not_normal) == 1:\\n notnormal_str = 'one of them is'\\n else:\\n notnormal_str = 'both of them are'\\n print(\\\"Because we have only two populations and %s not normal, we use Wilcoxon's signed rank test to \\\"\\n \\\"determine the differences in the central tendency and report the median (MD) and the median \\\"\\n \\\"absolute deviation (MAD) for each population.\\\" % notnormal_str)\\n if result.pvalue >= result.alpha:\\n print(\\\"We failed to reject the null hypothesis (p=%.*f) of Wilcoxon's signed rank test that \\\"\\n \\\"population %s is not greater than population %s . Therefore, we \\\"\\n \\\"assume that there is no statistically significant difference between the medians of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index[larger], with_stats=True),\\n create_population_string(result.rankdf.index[smaller], with_stats=True)))\\n else:\\n print(\\\"We reject the null hypothesis (p=%.*f) of Wilcoxon's signed rank test that population \\\"\\n \\\"%s is not greater than population %s. Therefore, we assume \\\"\\n \\\"that the median of %s is \\\"\\n \\\"significantly larger than the median value of %s with a %s effect size (%s=%.*f).\\\"\\n % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index[larger], with_stats=True),\\n create_population_string(result.rankdf.index[smaller], with_stats=True),\\n result.rankdf.index[larger], result.rankdf.index[smaller],\\n result.rankdf.magnitude[larger], effect_size, decimal_places, result.rankdf.effect_size[larger]))\\n else:\\n raise ValueError('Unknown omnibus test for difference in the central tendency: %s' % result.omnibus)\\n else:\\n if result.all_normal:\\n if result.homoscedastic:\\n print(\\\"We applied Bartlett's test for homogeneity and failed to reject the null hypothesis \\\"\\n \\\"(p=%.*f) that the data is homoscedastic. Thus, we assume that our data is \\\"\\n \\\"homoscedastic.\\\" % (decimal_places, result.pval_homogeneity))\\n else:\\n print(\\\"We applied Bartlett's test for homogeneity and reject the null hypothesis (p=%.*f) that the\\\"\\n \\\"data is homoscedastic. Thus, we assume that our data is \\\"\\n \\\"heteroscedastic.\\\" % (decimal_places, result.pval_homogeneity))\\n\\n if result.omnibus == 'anova':\\n if result.all_normal and result.homoscedastic:\\n print(\\\"Because we have more than two populations and all populations are normal and homoscedastic, we \\\"\\n \\\"use repeated measures ANOVA as omnibus \\\"\\n \\\"test to determine if there are any significant differences between the mean values of the \\\"\\n \\\"populations. If the results of the ANOVA test are significant, we use the post-hoc Tukey HSD \\\"\\n \\\"test to infer which differences are significant. We report the mean value (M) and the standard \\\"\\n \\\"deviation (SD) for each population. Populations are significantly different if their confidence \\\"\\n \\\"intervals are not overlapping.\\\")\\n else:\\n if result.all_normal:\\n print(\\n \\\"Because we have more than two populations and the populations are normal but heteroscedastic, \\\"\\n \\\"we should use the non-parametric Friedman test \\\"\\n \\\"as omnibus test to determine if there are any significant differences between the mean values \\\"\\n \\\"of the populations. However, the user decided to force the use of \\\"\\n \\\"repeated measures ANOVA as omnibus test which assume homoscedascity to determine if there are \\\"\\n \\\"any significant difference between the mean values of the populations. If the results of the \\\"\\n \\\"ANOVA test are significant, we use the post-hoc Tukey HSD test to infer which differences are \\\"\\n \\\"significant. We report the mean value (M) and the standard deviation (SD) for each \\\"\\n \\\"population. Populations are significantly different if their confidence intervals are not \\\"\\n \\\"overlapping.\\\")\\n else:\\n if len(not_normal) == 1:\\n notnormal_str = 'one of them is'\\n else:\\n notnormal_str = 'some of them are'\\n print(\\\"Because we have more than two populations and the populations and %s not normal, \\\"\\n \\\"we should use the non-parametric Friedman test \\\"\\n \\\"as omnibus test to determine if there are any significant differences between the median \\\"\\n \\\"values of the populations and report the median (MD) and the median absolute deviation \\\"\\n \\\"(MAD). However, the user decided to force the use of repeated measures ANOVA as omnibus \\\"\\n \\\"test which assume homoscedascity to determine if there are any significant difference \\\"\\n \\\"between the mean values of the populations. If the results of the ANOVA test are \\\"\\n \\\"significant, we use the post-hoc Tukey HSD test to infer which differences are \\\"\\n \\\"significant. We report the mean value (M) and the standard deviation (SD) for each \\\"\\n \\\"population. Populations are significantly different if their confidence intervals are not \\\"\\n \\\"overlapping.\\\" % (notnormal_str))\\n if result.pvalue >= result.alpha:\\n print(\\\"We failed to reject the null hypothesis (p=%.*f) of the repeated measures ANOVA that there is \\\"\\n \\\"a difference between the mean values of the populations %s. Therefore, we \\\"\\n \\\"assume that there is no statistically significant difference between the mean values of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True)))\\n else:\\n print(\\\"We reject the null hypothesis (p=%.*f) of the repeated measures ANOVA that there is \\\"\\n \\\"a difference between the mean values of the populations %s. Therefore, we \\\"\\n \\\"assume that there is a statistically significant difference between the mean values of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True)))\\n meanranks, names, groups = get_sorted_rank_groups(result, False)\\n if len(groups) == 0:\\n print(\\\"Based on post-hoc Tukey HSD test, we assume that all differences between the populations \\\"\\n \\\"are significant.\\\")\\n else:\\n groupstrs = []\\n for group_range in groups:\\n group = range(group_range[0], group_range[1] + 1)\\n if len(group) == 1:\\n cur_groupstr = names[group[0]]\\n elif len(group) == 2:\\n cur_groupstr = \\\" and \\\".join([names[pop] for pop in group])\\n else:\\n cur_groupstr = \\\", \\\".join([names[pop] for pop in group[:-1]]) + \\\", and \\\" + names[group[-1]]\\n groupstrs.append(cur_groupstr)\\n print(\\\"Based post-hoc Tukey HSD test, we assume that there are no significant differences within \\\"\\n \\\"the following groups: %s. All other differences are significant.\\\" % (\\\"; \\\".join(groupstrs)))\\n print()\\n elif result.omnibus == 'friedman':\\n if result.all_normal and result.homoscedastic:\\n print(\\\"Because we have more than two populations and all populations are normal and homoscedastic, we \\\"\\n \\\"should use repeated measures ANOVA as omnibus \\\"\\n \\\"test to determine if there are any significant differences between the mean values of the \\\"\\n \\\"populations. However, the user decided to force the use of the less powerful Friedman test as \\\"\\n \\\"omnibus test to determine if there are any significant differences between the mean values \\\"\\n \\\"of the populations. We report the mean value (M), the standard deviation (SD) and the mean rank \\\"\\n \\\"(MR) among all populations over the samples. Differences between populations are significant, \\\"\\n \\\"if the difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \\\"\\n \\\"test.\\\" % (decimal_places, result.cd))\\n elif result.all_normal:\\n print(\\\"Because we have more than two populations and the populations are normal but heteroscedastic, \\\"\\n \\\"we use the non-parametric Friedman test \\\"\\n \\\"as omnibus test to determine if there are any significant differences between the mean values \\\"\\n \\\"of the populations. We use the post-hoc Nemenyi test to infer which differences are \\\"\\n \\\"significant. We report the mean value (M), the standard deviation (SD) and the mean rank (MR) \\\"\\n \\\"among all populations over the samples. Differences between populations are significant, if the \\\"\\n \\\"difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \\\"\\n \\\"test.\\\" % (decimal_places, result.cd))\\n else:\\n if len(not_normal) == 1:\\n notnormal_str = 'one of them is'\\n else:\\n notnormal_str = 'some of them are'\\n print(\\\"Because we have more than two populations and the populations and %s not normal, \\\"\\n \\\"we use the non-parametric Friedman test \\\"\\n \\\"as omnibus test to determine if there are any significant differences between the median values \\\"\\n \\\"of the populations. We use the post-hoc Nemenyi test to infer which differences are \\\"\\n \\\"significant. We report the median (MD), the median absolute deviation (MAD) and the mean rank \\\"\\n \\\"(MR) among all populations over the samples. Differences between populations are significant, \\\"\\n \\\"if the difference of the mean rank is greater than the critical distance CD=%.*f of the Nemenyi \\\"\\n \\\"test.\\\" % (notnormal_str, decimal_places, result.cd))\\n if result.pvalue >= result.alpha:\\n print(\\\"We failed to reject the null hypothesis (p=%.*f) of the Friedman test that there is no \\\"\\n \\\"difference in the central tendency of the populations %s. Therefore, we \\\"\\n \\\"assume that there is no statistically significant difference between the median values of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True, with_rank=True)))\\n else:\\n print(\\\"We reject the null hypothesis (p=%.*f) of the Friedman test that there is no \\\"\\n \\\"difference in the central tendency of the populations %s. Therefore, we \\\"\\n \\\"assume that there is a statistically significant difference between the median values of the \\\"\\n \\\"populations.\\\" % (decimal_places, result.pvalue,\\n create_population_string(result.rankdf.index, with_stats=True, with_rank=True)))\\n meanranks, names, groups = get_sorted_rank_groups(result, False)\\n if len(groups) == 0:\\n print(\\\"Based on the post-hoc Nemenyi test, we assume that all differences between the populations \\\"\\n \\\"are significant.\\\")\\n else:\\n groupstrs = []\\n for group_range in groups:\\n group = range(group_range[0], group_range[1] + 1)\\n if len(group) == 1:\\n cur_groupstr = names[group[0]]\\n elif len(group) == 2:\\n cur_groupstr = \\\" and \\\".join([names[pop] for pop in group])\\n else:\\n cur_groupstr = \\\", \\\".join([names[pop] for pop in group[:-1]]) + \\\", and \\\" + names[group[-1]]\\n groupstrs.append(cur_groupstr)\\n print(\\\"Based on the post-hoc Nemenyi test, we assume that there are no significant differences \\\"\\n \\\"within the following groups: %s. All other differences are \\\"\\n \\\"significant.\\\" % (\\\"; \\\".join(groupstrs)))\\n else:\\n raise ValueError('Unknown omnibus test for difference in the central tendency: %s' % result.omnibus)\",\n \"def create_risk_groups(model, X, use_log = True, num_bins = 50, \\r\\n figure_size = (20, 8), **kwargs):\\r\\n\\r\\n # Ensuring that the input data has the right format\\r\\n X = utils.check_data(X)\\r\\n\\r\\n # Computing the risk scores\\r\\n risk = model.predict_risk(X)\\r\\n if use_log:\\r\\n risk = np.log(risk)\\r\\n \\r\\n # Displaying simple histogram\\r\\n if len(kwargs) == 0:\\r\\n \\r\\n # Initializing the chart\\r\\n fig, ax1 = plt.subplots(figsize=figure_size)\\r\\n risk_groups = None\\r\\n \\r\\n # Applying any color coding\\r\\n else:\\r\\n # Initializing the results\\r\\n risk_groups = {}\\r\\n \\r\\n # Initializing the chart\\r\\n fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=figure_size)\\r\\n \\r\\n # Displaying simple histogram with risk groups\\r\\n nums_per_bins, bins, patches = ax2.hist(risk, bins=num_bins)\\r\\n ax2.set_title('Risk groups with colors', fontsize=15)\\r\\n\\r\\n # Number of group definitions\\r\\n num_group_def = len(kwargs.values())\\r\\n\\r\\n # Extracting the bounds values\\r\\n bounds = {}\\r\\n colors_ = {}\\r\\n indexes = {}\\r\\n group_names = []\\r\\n handles = []\\r\\n\\r\\n # we need to check that the boundaries match the bins\\r\\n is_not_valid = 0\\r\\n for group_name, group_def in kwargs.items():\\r\\n\\r\\n # by ensuring that the bounds are not outside\\r\\n # the bins values\\r\\n min_bin, max_bin = min(bins), max(bins)\\r\\n if (group_def['lower_bound'] < min_bin and \\\\\\r\\n group_def['upper_bound'] < min_bin) or \\\\\\r\\n (group_def['lower_bound'] > max_bin and \\\\\\r\\n group_def['upper_bound'] > max_bin) :\\r\\n is_not_valid += 1\\r\\n\\r\\n # Extracting the bounds\\r\\n bounds[group_name] = (group_def['lower_bound'], \\r\\n group_def['upper_bound'])\\r\\n\\r\\n # Extracting the colors\\r\\n colors_[group_name] = group_def['color']\\r\\n\\r\\n # Creating index placeholders\\r\\n indexes[group_name] = []\\r\\n group_names.append( group_name )\\r\\n color_indv = group_def['color']\\r\\n handles.append(Rectangle((0,0),1,1, color=color_indv, ec=\\\"k\\\"))\\r\\n\\r\\n if is_not_valid >= num_group_def :\\r\\n error_msg = \\\"The boundaries definitions {} do not match\\\"\\r\\n error_msg += \\\", the values of the risk scores.\\\"\\r\\n error_msg = error_msg.format(list(bounds.values()))\\r\\n raise ValueError(error_msg)\\r\\n\\r\\n # Assigning each rectangle/bin to its group definition\\r\\n # and color\\r\\n colored_patches = []\\r\\n bin_index = {}\\r\\n for i, bin_, patch_ in zip(range(num_bins), bins, patches):\\r\\n\\r\\n # Check if the bin belongs to this bound def\\r\\n for grp_name, bounds_ in bounds.items():\\r\\n\\r\\n if bounds_[0] <= bin_ < bounds_[-1] :\\r\\n bin_index[i] = grp_name\\r\\n \\r\\n # Extracting color\\r\\n color_ = colors_[grp_name]\\r\\n if color_ not in colors.CSS4_COLORS :\\r\\n error_msg = '{} is not a valid color'\\r\\n error_msg = error_msg.format(colors_[grp_name])\\r\\n raise ValueError(error_msg)\\r\\n \\r\\n patch_.set_facecolor( color_ )\\r\\n\\r\\n # Saving the rectangles\\r\\n colored_patches.append(patch_)\\r\\n\\r\\n # Assigning each sample to its group\\r\\n risk_bins = np.minimum(np.digitize(risk, bins, True), num_bins-1) \\r\\n for i, r in enumerate(risk_bins):\\r\\n\\r\\n # Extracting the right group_name\\r\\n group_name = bin_index[r]\\r\\n indexes[group_name].append(i)\\r\\n \\r\\n\\r\\n # Displaying the original distribution\\r\\n ax1.hist(risk, bins=num_bins, color = 'black', alpha=0.5) \\r\\n ax1.set_title('Risk Score Distribution', fontsize=15)\\r\\n\\r\\n # Show everything\\r\\n plt.show()\\r\\n \\r\\n # Returning results\\r\\n if risk_groups is not None:\\r\\n for group_name in group_names:\\r\\n result = (colors_[group_name], indexes[group_name]) \\r\\n risk_groups[group_name] = result\\r\\n\\r\\n return risk_groups\",\n \"def perSaccPerGroup(addExp2 = True, trim = True, spacing = .5, addSimulation = True, \\\\\\n\\t\\texclOverlap = True):\\n\\n\\tfig = plt.figure(figsize = (10,10))\\n\\ttitle = \\\"Average towards-handle landings per group - exclOverlap = %s\\\" % exclOverlap\\n\\tplt.title(title)\\n\\t\\n\\t\\n\\t# Exp 1:\\n\\tdm1 = getDM.getDM(exp = \\\"004A\\\", excludeErrors = True, driftCorr = True)\\n\\t\\n\\t\\n\\tsubPlot = 1\\n\\t\\n\\tfor group in dm1.unique(\\\"corrDirection\\\"):\\n\\n\\t\\t# New subplot:\\n\\t\\tplt.subplot(1,2,subPlot)\\n\\t\\tplt.title(group)\\n\\t\\t_dm1 = dm1.select(\\\"corrDirection == '%s'\\\" % group)\\n\\n\\t\\tlLandingsAbs = []\\n\\t\\tlLandingsCorr = []\\n\\t\\t\\n\\t\\tfor sacc in [\\\"1\\\", \\\"2\\\", \\\"3\\\"]:\\n\\t\\t\\t\\n\\t\\t\\t# This is the same for corrected landing positions (the saccade\\n\\t\\t\\t# doesn't change; only the reference point does)\\n\\t\\t\\t_dm = _dm1.select(\\\"endX%sNorm != ''\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endX%sNorm > -.5\\\" % sacc)\\n\\t\\t\\t_dm = _dm.select(\\\"endX%sNorm < .5\\\" % sacc)\\n\\t\\t\\t\\n\\t\\t\\tif trim:\\n\\t\\t\\t\\t_dmAbs = _dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sNormToHandle\\\" % sacc)\\n\\t\\t\\t\\t_dmCorr = _dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sCorrNormToHandle\\\" % sacc)\\n\\t\\t\\t\\n\\t\\t\\t# Determine avg landing position:\\n\\t\\t\\tavgAbs = _dmAbs[\\\"endX%sNormToHandle\\\" % sacc].mean()\\n\\t\\t\\tavgCorr = _dmCorr[\\\"endX%sCorrNormToHandle\\\" % sacc].mean()\\n\\t\\t\\t\\n\\t\\t\\t# TODO: determine error bars:\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\tlLandingsAbs.append(avgAbs)\\n\\t\\t\\tlLandingsCorr.append(avgCorr)\\n\\t\\t\\n\\t\\tplt.plot(lLandingsAbs, color = \\\"#f57900\\\", linewidth = 2, marker = \\\"o\\\")\\n\\t\\tplt.plot(lLandingsCorr, color = \\\"#73d216\\\", linewidth = 2, marker = \\\"o\\\")\\n\\t\\t\\n\\t\\t# The other 2 experiments can be treated equally:\\n\\t\\tcolList = [\\\"#ef2929\\\", \\\"#3465a4\\\"]\\n\\t\\t\\n\\t\\tfor exp in [\\\"004B\\\", \\\"004C\\\"]:\\n\\t\\t\\t\\n\\t\\t\\tif not addExp2:\\n\\t\\t\\t\\tif exp == \\\"004B\\\":\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\tif not addSimulation:\\n\\t\\t\\t\\tif exp == \\\"004C\\\":\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\tdm = getDM.getDM(exp = exp, excludeErrors = True, driftCorr = True)\\n\\t\\t\\t_dm = dm.select(\\\"corrDirection == '%s'\\\" % group)\\n\\n\\t\\t\\tif exp == \\\"004C\\\":\\n\\t\\t\\t\\tif exclOverlap:\\n\\t\\t\\t\\t\\t_dm = _dm.select(\\\"gap == 'zero'\\\")\\n\\n\\t\\t\\t\\n\\t\\t\\tlLandingsAbs = []\\n\\t\\t\\t\\n\\t\\t\\tfor sacc in [\\\"1\\\", \\\"2\\\", \\\"3\\\"]:\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# TODO: how to filter only on-object saccades exp 2??\\n\\t\\t\\t\\tsacc_dm = _dm.select(\\\"endX%sNorm != ''\\\" % sacc)\\n\\t\\t\\t\\tsacc_dm = sacc_dm.select(\\\"endX%sNorm > -.5\\\" % sacc)\\n\\t\\t\\t\\tsacc_dm = sacc_dm.select(\\\"endX%sNorm < .5\\\" % sacc)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tif trim:\\n\\t\\t\\t\\t\\tsacc_dm = sacc_dm.selectByStdDev(keys = [\\\"file\\\"], dv = \\\"endX%sNormToHandle\\\" % sacc)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Determine avg landing position:\\n\\t\\t\\t\\tavgAbs = sacc_dm[\\\"endX%sNormToHandle\\\" % sacc].mean()\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# TODO: determine error bars:\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tlLandingsAbs.append(avgAbs)\\n\\t\\t\\tcol = colList.pop()\\n\\t\\t\\tplt.plot(lLandingsAbs, color = col, linewidth = 2, marker = \\\"o\\\")\\n\\t\\t\\t\\n\\t\\t# Modify plot:\\n\\t\\tplt.legend([\\\"Exp1 abs\\\", \\\"Exp1 corr\\\", \\\"Exp2\\\", \\\"Sim\\\"])\\n\\t\\t\\t\\n\\t\\tplt.axhline(0, color = \\\"#888a85\\\", linestyle = \\\"--\\\", linewidth = 2)\\n\\t\\txLabels = [\\\"sacc 1\\\", \\\"sacc 2\\\", \\\"sacc 3\\\"]\\n\\t\\txTicks = range(0,3)\\n\\t\\tplt.xticks(xTicks, xLabels, rotation = .5)\\n\\t\\tplt.ylim([-.5, .5])\\n\\t\\tplt.xlim(min(xTicks)-spacing, max(xTicks)+spacing)\\n\\t\\t\\n\\t\\tsubPlot +=1\\n\\t\\t\\n\\tplt.savefig(\\\"%s.png\\\" % title)\",\n \"def group_action_by_gamma_coord(f, gamma):\\n n, T = f.shape\\n fn = zeros((n, T))\\n\\n for j in range(0, n):\\n s = interp1d(linspace(0, 1, T), f[j, :])\\n fn[j, :] = s(gamma)\\n\\n return (fn)\",\n \"def Morris_Measure_Groups(NumFact, Sample, OutFact, Output, p=4,\\n Group=[], Diagnostic=False):\\n try:\\n NumGroups = Group.shape[1]\\n if Diagnostic: print('{:d} Groups are used'.format(NumGroups))\\n except:\\n NumGroups = 0\\n if Diagnostic: print('No Groups are used')\\n\\n Delt = p / (2. * (p - 1.))\\n\\n if NumGroups != 0:\\n sizea = NumGroups\\n GroupMat = Group\\n GroupMat = GroupMat.transpose()\\n if Diagnostic: print('NumGroups', NumGroups)\\n else:\\n sizea = NumFact\\n sizeb = sizea + 1\\n\\n # r = Sample.shape[0]/sizeb\\n r = Sample.shape[0] // sizeb\\n\\n try:\\n NumOutp = Output.shape[1]\\n except:\\n NumOutp = 1\\n Output = Output.reshape((Output.size, 1))\\n\\n # For each Output\\n if NumGroups == 0:\\n # for every output: every factor is a line, columns are mu*, mu and std\\n OutMatrix = np.zeros((NumOutp * NumFact, 3))\\n else:\\n OutMatrix = np.zeros((NumOutp * NumFact, 1)) # for every output: every factor is a line, column is mu*\\n\\n SAmeas_out = np.zeros((NumOutp * NumFact, r))\\n\\n for k in range(NumOutp):\\n OutValues = Output[:, k]\\n\\n # For each trajectory\\n SAmeas = np.zeros((NumFact, r)) #vorm afhankelijk maken van group of niet...\\n for i in range(r):\\n # For each step j in the trajectory\\n # Read the orientation matrix fact for the r-th sampling\\n # Read the corresponding output values\\n # Read the line of changing factors\\n Single_Sample = Sample[i * sizeb:(i + 1) * sizeb, :]\\n Single_OutValues = OutValues[i * sizeb:(i + 1) * sizeb]\\n Single_Facts = np.array(OutFact[i * sizeb:(i + 1) * sizeb], dtype=np.int) # gives factor in change (or group)\\n\\n A = (Single_Sample[1:sizeb, :] - Single_Sample[:sizea, :]).transpose()\\n Delta = A[np.where(A)] # AAN TE PASSEN?\\n # If upper bound==lower bound then A==0 in all trajectories.\\n # Delta will have not the right dimensions then because these are filtered out with where.\\n # Fill in Delta==0 for these cases.\\n ii = np.where(np.sum(A, axis=0) == 0.)[0]\\n if ii.size > 0: Delta = np.insert(Delta, ii, 0.)\\n\\n if Diagnostic:\\n print('A: ', A)\\n print('Delta: ', Delta)\\n print('Single_Facts: ', Single_Facts)\\n\\n # For each point of the fixed trajectory, i.e. for each factor, compute the values of the Morris function.\\n for j in range(sizea):\\n if NumGroups != 0: #work with groups\\n Auxfind = A[:, j]\\n Change_factor = np.where(np.abs(Auxfind) > 1e-010)[0]\\n for gk in Change_factor:\\n SAmeas[gk, i] = np.abs(\\n (Single_OutValues[j] - Single_OutValues[j + 1]) /\\n Delt) #nog niet volledig goe\\n else:\\n if Delta[j] > 0.0:\\n SAmeas[Single_Facts[j], i] = (Single_OutValues[j + 1] - Single_OutValues[j]) / Delt\\n else:\\n SAmeas[Single_Facts[j], i] = (Single_OutValues[j] - Single_OutValues[j + 1]) / Delt\\n\\n # Compute Mu AbsMu and StDev\\n if np.isnan(SAmeas).any():\\n AbsMu = np.zeros(NumFact)\\n Mu = np.zeros(NumFact)\\n Stdev = np.zeros(NumFact)\\n\\n for j in range(NumFact):\\n SAm = SAmeas[j, :]\\n SAm = SAm[~np.isnan(SAm)]\\n rr = np.float(SAm.size)\\n AbsMu[j] = np.sum(np.abs(SAm)) / rr\\n if NumGroups == 0:\\n Mu[j] = SAm.mean()\\n if SAm.size > 1:\\n Stdev[j] = np.std(SAm, ddof=1)\\n else:\\n Stdev[j] = 0.\\n else:\\n AbsMu = np.sum(np.abs(SAmeas), axis=1) / r\\n if NumGroups == 0:\\n Mu = SAmeas.mean(axis=1)\\n if SAmeas.shape[1] > 1:\\n Stdev = np.std(SAmeas, ddof=1, axis=1)\\n else:\\n Stdev = np.zeros(SAmeas.shape[0])\\n else:\\n Mu = np.zeros(NumFact)\\n Stdev = np.zeros(NumFact)\\n\\n OutMatrix[k * NumFact:k * NumFact + NumFact, 0] = AbsMu\\n if NumGroups == 0:\\n OutMatrix[k * NumFact:k * NumFact + NumFact, 1] = Mu\\n OutMatrix[k * NumFact:k * NumFact + NumFact, 2] = Stdev\\n\\n SAmeas_out[k * NumFact:k * NumFact + NumFact, :] = SAmeas\\n\\n return SAmeas_out, OutMatrix\",\n \"def groupby_grade(master_dataframe):\\n\\tno_name_df = master_dataframe.drop(['last_name', 'first_name', 'email'], axis=1)\\n\\tgrouped = list(no_name_df.groupby(['grade']))\\n\\tnine, ten, eleven, twelve = grouped[0][1], grouped[1][1], grouped[2][1], grouped[3][1]\\n\\n\\treturn nine, ten, eleven, twelve\",\n \"def group_action_by_gamma(q, gamma):\\n n, T = q.shape\\n gammadot = gradient(gamma, 1. / T)\\n qn = zeros((n, T))\\n\\n for j in range(0, n):\\n s = InterpolatedUnivariateSpline(linspace(0, 1, T), q[j, :], k=3)\\n qn[j, :] = s(gamma) * sqrt(gammadot)\\n\\n qn = qn / sqrt(innerprod_q2(qn, qn))\\n\\n return (qn)\",\n \"def gamma(self, predictor):\\n pred = predictor(self.X)\\n self.tags[_PREDICTION] = pred\\n expect_event = self.tags.groupby(_EVENT).mean()\\n expect_group_event = self.tags.groupby(\\n [_EVENT, _GROUP_ID]).mean()\\n\\n num_grp = len(self.error_rate)\\n tprs = [0 for _ in range(num_grp)]\\n # print(expect_group_event)\\n for i in range(num_grp):\\n tprs[i] = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == i)].groupby([_EVENT]).mean()\\n expect_group_event.loc[('label=1', i), 'pred'] = (1 - self.error_rate[i][0]) * tprs[i].loc['label=1', 'pred'] + self.error_rate[i][0] * tprs[i].loc['label=0', 'pred']\\n expect_group_event.loc[('label=0', i), 'pred'] = (1 - self.error_rate[i][1]) * tprs[i].loc['label=0', 'pred'] + self.error_rate[i][1] * tprs[i].loc['label=1', 'pred']\\n\\n # neg = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == 0.0)].groupby([_EVENT]).mean()\\n # pos = expect_group_event.loc[(expect_group_event.index.get_level_values(_GROUP_ID) == 1.0)].groupby([_EVENT]).mean()\\n\\n # expect_group_event.loc[('label=1.0', 1), 'pred'] = (1 - self.error_rate[1][0]) * pos.loc['label=1.0', 'pred'] + self.error_rate[1][1] * pos.loc['label=0.0', 'pred']\\n # expect_group_event.loc[('label=0.0', 1), 'pred'] = (1 - self.error_rate[1][1]) * pos.loc['label=0.0', 'pred'] + self.error_rate[1][0] * pos.loc['label=1.0', 'pred']\\n\\n # expect_group_event.loc[('label=1.0', 0), 'pred'] = (1 - self.error_rate[0][0]) * neg.loc['label=1.0', 'pred'] + self.error_rate[0][1] * neg.loc['label=0.0', 'pred']\\n # expect_group_event.loc[('label=0.0', 0), 'pred'] = (1 - self.error_rate[0][1]) * neg.loc['label=0.0', 'pred'] + self.error_rate[0][0] * neg.loc['label=1.0', 'pred']\\n\\n expect_event = expect_group_event.groupby(_EVENT).mean()\\n expect_group_event[_DIFF] = expect_group_event[_PREDICTION] - expect_event[_PREDICTION]\\n\\n # expect_group_event[_DIFF] = expect_group_event[_PREDICTION] - expect_event[_PREDICTION]\\n g_unsigned = expect_group_event[_DIFF]\\n g_signed = pd.concat([g_unsigned, -g_unsigned],\\n keys=[\\\"+\\\", \\\"-\\\"],\\n names=[_SIGN, _EVENT, _GROUP_ID])\\n self._gamma_descr = str(expect_group_event[[_PREDICTION, _DIFF]])\\n return g_signed\",\n \"def calculateGammaFactors(self):\\n return (self.time/self.expectedDuration)**self.beta\",\n \"def all_conditions_visualized(self):\\n # group dataframe based on standard deviation and effect size\\n grouped_df = self.df.groupby([\\\"es\\\", \\\"sd1\\\", \\\"samp2\\\"])\\n results = self.helpers.get_mean_df(grouped_df, False)\\n print(results.sample())\\n\\n # seperate groups based on typeI or typeII error\\n dfs = {\\\"typeI_df\\\": results[:8*7],\\n \\\"ef_0_2\\\": results[8*7:8*7*2],\\n \\\"ef_0_5\\\": results[8*7*2:8*7*3],\\n \\\"ef_0_8\\\": results[8*7*3:]}\\n\\n for i in dfs:\\n sdI = self.get_sd_list(dfs[i], False)\\n dictI = {\\\"xlabel\\\": 'Group sizes', \\\"ylabel\\\": \\\"TypeI error\\\",\\n \\\"title\\\": \\\"Mean typeI error per group size\\\",\\n \\\"xtickslabels\\\": sdI}\\n self.multiple_bars(dfs[i], 2, 2, dictI)\",\n \"def test_properties_stats_group_by_group_by_get(self):\\n pass\",\n \"def do_grouping(prefixes, grouping_keys, xaxis_key='Age_At_IMGExam',\\n plots='regressions',\\n atlas='desikan', username=None, passwd=None,\\n data_dir='data', output_dir='.', output_type='matplotlib'):\\n\\n data = get_all_data(atlas, username=username, passwd=passwd, data_dir=data_dir)\\n data.filter(lambda k, v: 'fuzzy' not in k) # Remove 'fuzzy'\\n data.filter([partial(lambda k, v, p: (k.startswith(p) or\\n k in grouping_keys or\\n k == xaxis_key),\\n p=p)\\n for p in prefixes])\\n\\n # Process & plot the data.\\n measure_keys = [(k) for k in data.data_dict.keys()\\n if k.startswith(p) and k.endswith('_AI')]\\n for pi, yaxis_key in enumerate(sorted(measure_keys)):\\n print(\\\"Comparing %d (%s)...\\\" % (pi, yaxis_key))\\n\\n kwargs = dict(xaxis_key=xaxis_key, yaxis_key=yaxis_key)\\n group_names, group_x, group_y = compute_group_asymmetry(data, grouping_keys=grouping_keys, **kwargs)\\n\\n kwargs.update(dict(group_names=group_names, group_x=group_x, group_y=group_y))\\n if 'regressions' in plots:\\n fh = plot_regressions(**kwargs)\\n fh.name = '%s-%s-regressions' % (yaxis_key, '__'.join(grouping_keys))\\n\\n if 'distributions' in plots:\\n plot_distributions(**kwargs)\\n if 'stats' in plots:\\n do_stats(group_names, group_x, group_y)\\n\\n # Outside of loop\\n if 'regression_stats' in plots:\\n dump_regressions_csv(regressions,\\n group_names=group_names,\\n measure_names=measure_keys)\\n\\n plot_regressions_scatter(regressions,\\n group_names=group_names,\\n measure_names=measure_keys)\\n\\n if 'stat_distributions' in plots:\\n plot_stat_distributions(stats, group_names=group_names)\\n\\n show_plots(plotengine=output_type, output_dir=output_dir)\",\n \"def _parse_groupped_data(self):\\n for i, val in enumerate(self.values.keys()):\\n xy = self.values[val]\\n self._set_and_get(\\\"x_\\\", val, xy[:, 0])\\n self._set_and_get(\\\"y_\\\", val, xy[:, 1])\",\n \"def get_contribution_dataframe_groups(self):\\n pargrp_dict = {}\\n par = self.pst.parameter_data\\n groups = par.groupby(\\\"pargp\\\").groups\\n for grp,idxs in groups.items():\\n pargrp_dict[grp] = list(par.loc[idxs,\\\"parnme\\\"])\\n return self.get_contribution_dataframe(pargrp_dict)\",\n \"def values_per_gamma():\\n\\n keys, data = parse(\\\"data/treeNodePolicyIncludingN=1.csv\\\")\\n gammas = [1] + [round(0.01*i,2) for i in range(100)]\\n\\n with open('output/discounted_values.csv', mode='w') as out_file:\\n out_writer = csv.writer(out_file, delimiter=',', quotechar='\\\"', quoting=csv.QUOTE_MINIMAL)\\n out_writer.writerow(['world', 'node', 'child', 'value'] + ['dv_'+str(gamma) for gamma in gammas[1:]])\\n\\n for d in data:\\n values = [round(node_value(d[keys['world']], d[keys['child']], gamma),3) for gamma in gammas]\\n out_writer.writerow([d[keys['world']], d[keys['node']], d[keys['child']]] + values)\",\n \"def big_analysis(beta0s=[0.5, 0.8, 1.1, 1.4, 1.7], ks=range(6), betaps=[1.2, 1.5, 2, 3]):\",\n \"def recalculate_groups(dynamic_list):\\n new_final_list = []\\n for page in dynamic_list:\\n page_list = []\\n for group in page:\\n new_dict = {}\\n lowest_x = 100\\n highest_x = 0\\n for line in group:\\n # find if x is the lowest or highest\\n if line['bbox'][0] < lowest_x:\\n lowest_x = line['bbox'][0]\\n if line['bbox'][2] > highest_x:\\n highest_x = line['bbox'][2]\\n x_one = lowest_x\\n x_two = highest_x\\n y_one = group[0]['bbox'][1]\\n y_two = h.get_biggest_y(group)\\n new_bbox = [x_one, y_one, x_two, y_two]\\n new_dict['bbox'] = new_bbox\\n new_dict['font'] = h.find_font_in_group(group)\\n new_dict['size'] = h.find_font_size_in_group(group)\\n new_dict['page'] = group[0]['page']\\n new_dict['lines'] = group\\n\\n page_list.append(new_dict)\\n\\n new_final_list.append(page_list)\\n return new_final_list\",\n \"def params_groups(request):\\n return np.array([-0.2, -0.1])\",\n \"def makeGroupsFromCutFile(self):\\n if self.cutfile == None:\\n print \\\"Cannot make groups without a cuts file\\\"\\n return ([],[])\\n else:\\n groups = []\\n labels = []\\n yields = []\\n all_cols = self.qie.columns.values\\n # For each predefined group\\n for grouplist in cut_groups:\\n labels.append(grouplist[0])\\n g = None\\n # For each cut in that group\\n for cut in grouplist[1]:\\n # Get min and max values for main cuts (TODO: handle marginal cuts)\\n cut_min = self.cuts[cut][0]\\n cut_max = self.cuts[cut][1]\\n # For each df column corresponding to that cut (sometimes more than one measurement)\\n for col in all_cols:\\n if col.split(\\\"_\\\")[0] == cut:\\n g_tmp = (self.qie[col] < cut_min) | (self.qie[col] > cut_max)\\n if 'NoneType' in str(type(g)) :\\n g = g_tmp\\n else: \\n g = g | g_tmp\\n # Make exclusive groups\\n if len(groups) > 0:\\n g = g & (self.NotGroup(groups))\\n groups.append(g)\\n yields.append(g.sum())\\n # Make final group containing all other chips\\n groups.append(self.NotGroup(groups))\\n labels.append(\\\"Good\\\")\\n yields.append(groups[-1].sum())\\n self.makeYieldsTable(yields, labels)\\n # Add column to data frame containing \\\"Good\\\" (1), \\\"bad\\\" (0), \\\"marginal\\\" (2,..) info\\n self.qie[\\\"Sorting\\\"] = np.where(groups[-1], 1, 0)\\n print sum(self.qie[\\\"Sorting\\\"])\\n #print self.qie\\n self.makeSortingFile()\\n return (groups, labels)\",\n \"def build_group_catalog(cat, mfac=2.0, dmax=10.0/60.0):\\n from pydl.pydlutils.spheregroup import spheregroup\\n\\n ## RAF\\n ## REPLACING ASTROMETRY FUNCTION WITH ASTROPY \\n #from astrometry.util.starutil_numpy import degrees_between\\n #print('Starting spheregrouping.')\\n\\n nchar = np.max([len(gg) for gg in cat['GALAXY']])+6 # add six characters for \\\"_GROUP\\\"\\n \\n t0 = time.time()\\n cat.add_column(Column(name='GROUP_ID', data=np.zeros(len(cat), dtype=np.int)-1))\\n cat.add_column(Column(name='GROUP_NAME', length=len(cat), dtype='<U{}'.format(nchar)))\\n cat.add_column(Column(name='GROUP_MULT', data=np.zeros(len(cat), dtype=np.int16)))\\n cat.add_column(Column(name='GROUP_PRIMARY', data=np.zeros(len(cat), dtype=bool)))\\n cat.add_column(Column(name='GROUP_RA', length=len(cat), dtype='f8')) # diameter-weighted center\\n cat.add_column(Column(name='GROUP_DEC', length=len(cat), dtype='f8'))\\n cat.add_column(Column(name='GROUP_DIAMETER', length=len(cat), dtype='f4'))\\n\\n #ww = np.where((parent['RA'] > 177) * (parent['RA'] < 178) * (parent['DEC'] > -1.5) * (parent['DEC'] < -0.5))[0]\\n #ww = np.where((parent['RA'] > 200) * (parent['RA'] < 240) * (parent['DEC'] > 20))[0]\\n #ww = np.where((parent['RA'] > 193) * (parent['RA'] < 196) * (parent['DEC'] > 26) * (parent['DEC'] < 30))[0]\\n \\n # Initialize a unique group number for each galaxy\\n gnum = np.arange(len(cat)).astype(np.int)\\n mgrp = np.ones(len(cat)).astype(np.int16)\\n \\n # First group galaxies within 10 arcmin, setting those to have the same\\n # group number\\n t0 = time.time()\\n print('Spheregrouping took...', end='')\\n ingroup, group_mult, firstgroup, nextgroup = spheregroup(cat['RA'], cat['DEC'], dmax)\\n\\n ngroup = np.count_nonzero(firstgroup != -1)\\n for ii in np.arange(ngroup):\\n #print(ii, ngroup)\\n nn = group_mult[ii] # number of galaxies in this group\\n if nn > 1:\\n # Build INDX as the indices of all objects in this grouping\\n indx = np.zeros(nn, dtype=int)\\n indx[0] = firstgroup[ii]\\n for jj in np.arange(nn-1):\\n indx[jj+1] = nextgroup[indx[jj]]\\n # Look at all pairs within this grouping to see if they should be connected.\\n for jj in np.arange(nn-1):\\n for kk in np.arange(jj, nn):\\n dd = degrees_between(cat['RA'][indx[jj]], cat['DEC'][indx[jj]], cat['RA'][indx[kk]], cat['DEC'][indx[kk]])\\n #dd = c1.separate(c2)\\n # If these two galaxies should be connected, make GNUM the\\n # same for them...\\n #print(dd, mfac * (cat['D25'][indx[jj]] / 60. + cat['D25'][indx[kk]] / 60.))\\n if dd < (0.5 * mfac * (cat['D25'][indx[jj]] / 60. + cat['D25'][indx[kk]] / 60.)):\\n jndx = np.where(np.logical_or(gnum[indx]==gnum[indx[jj]], gnum[indx]==gnum[indx[kk]]))[0]\\n gnum[indx[jndx]] = gnum[indx[jndx[0]]]\\n mgrp[indx[jndx]] = len(jndx)\\n #print(ii, ngroup, gnum[indx], mgrp[indx])\\n\\n # Special-case the largest galaxies, looking for neighbhors\\n ibig = np.where(cat['D25'] / 60. > dmax)[0]\\n if len(ibig) > 0:\\n for ii in np.arange(len(ibig)):\\n dd = degrees_between(cat['RA'][ibig[ii]], cat['DEC'][ibig[ii]], cat['RA'], cat['DEC'])\\n inear = np.where(dd < 0.5*(cat[ibig[ii]]['D25'] + cat['D25']) / 60.)[0]\\n if len(inear) > 0:\\n for jj in np.arange(len(inear)):\\n indx = np.where(np.logical_or(gnum==gnum[ibig[ii]], gnum==gnum[inear[jj]]))[0]\\n gnum[indx] = gnum[indx[0]]\\n mgrp[indx] = len(indx)\\n print('...{:.3f} min'.format((time.time() - t0)/60))\\n\\n npergrp, _ = np.histogram(gnum, bins=len(gnum), range=(0, len(gnum)))\\n\\n print('Found {} total groups, including:'.format(len(set(gnum))))\\n print(' {} groups with 1 member'.format(np.sum( (npergrp == 1) ).astype('int')))\\n print(' {} groups with 2 members'.format(np.sum( (npergrp == 2) ).astype('int')))\\n print(' {} group(s) with 3-5 members'.format(np.sum( (npergrp >= 3)*(npergrp <= 5) ).astype('int')))\\n print(' {} group(s) with 6-10 members'.format(np.sum( (npergrp >= 6)*(npergrp <= 10) ).astype('int')))\\n print(' {} group(s) with >10 members'.format(np.sum( (npergrp > 10) ).astype('int')))\\n\\n cat['GROUP_ID'] = gnum\\n cat['GROUP_MULT'] = mgrp\\n\\n I = np.where(cat['GROUP_MULT'] == 1)[0]\\n if len(I) > 0:\\n cat['GROUP_RA'][I] = cat['RA'][I]\\n cat['GROUP_DEC'][I] = cat['DEC'][I]\\n cat['GROUP_DIAMETER'][I] = cat['D25'][I]\\n cat['GROUP_NAME'][I] = cat['GALAXY'][I]\\n cat['GROUP_PRIMARY'][I] = True\\n\\n more = np.where(cat['GROUP_MULT'] > 1)[0]\\n for group in set(cat['GROUP_ID'][more]):\\n I = np.where(cat['GROUP_ID'] == group)[0]\\n # Compute the D25-weighted RA, Dec of the group:\\n weight = cat[I]['D25']\\n cat['GROUP_RA'][I] = np.sum(weight * cat[I]['RA']) / np.sum(weight)\\n cat['GROUP_DEC'][I] = np.sum(weight * cat[I]['DEC']) / np.sum(weight)\\n # Get the diameter of the group as the distance between the center of\\n # the group and the outermost galaxy (plus the diameter of that galaxy,\\n # in case it's a big one!).\\n dd = degrees_between(cat['RA'][I], cat['DEC'][I], cat['GROUP_RA'][I[0]], cat['GROUP_DEC'][I[0]])\\n pad = dd + cat['D25'][I] / 60.0\\n cat['GROUP_DIAMETER'][I] = np.max(pad) * 60 # [arcmin]\\n if cat['GROUP_DIAMETER'][I[0]] < np.max(cat['D25'][I]):\\n print('Should not happen!')\\n pdb.set_trace()\\n\\n # Assign the group name based on its largest member and also make this\\n # galaxy \\\"primary\\\".\\n primary = np.argmax(cat['D25'][I])\\n cat['GROUP_NAME'][I] = '{}_GROUP'.format(cat['GALAXY'][I][primary])\\n cat['GROUP_PRIMARY'][I[primary]] = True\\n\\n print('Building a group catalog took {:.3f} min'.format((time.time() - t0)/60))\\n \\n return cat\",\n \"def multiple(x, y, alpha, k, diag=False):\\n #- Create data array:\\n data = np.concatenate((x, y))\\n n_group = np.size(x) #number of x and number of y\\n \\n \\n #- Calculate difference in mean of first and last n_group values for 10000\\n # randomizations of data. The first n_group values represents x values\\n # and the second n_group values represents y values. Note use of negative\\n # index slicing:\\n\\n results = np.zeros(10000, dtype='d')\\n for i in range(np.size(results)):\\n np.random.shuffle(data) #done in-place\\n results[i] = np.mean(data[:n_group]) - np.mean(data[-n_group:])\\n \\n \\n #- Calculate and print original data difference in mean of x and y, the\\n # p-value for where the absolute value of the difference is larger than\\n # the absolute value of the difference, since we're doing:\\n\\n diff_orig = np.mean(x) - np.mean(y)\\n p_value_orig = np.sum(np.absolute(results) >= abs(diff_orig)) / \\\\\\n np.size(results)\\n\\n bonferroni = 1 - (1 - alpha**k)\\n \\n \\n #- Printing diagnostics:\\n\\n if diag:\\n print('p_value_orig: ' + str(p_value_orig))\\n \\n return p_value_orig, bonferroni\",\n \"def _groups_reduce(\\n group_stats: List[Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]]\\n) -> Dict[str, torch.Tensor]:\\n return {f\\\"group_{group}\\\": torch.stack(stats) / torch.stack(stats).sum() for group, stats in enumerate(group_stats)}\",\n \"def batchAnalysis(groupfil):\\n groups = []\\n with open(groupfil, 'r') as fIn:\\n for line in fIn:\\n groups.append(line.strip().split(','))\\n \\n checks = ['maxV', 'maxDerivV', 'maxDerivdV', 'minDerivV',\\n 'minDerivdV', 'preMinV', 'postMinV', 'preMaxCurveV',\\n 'preMaxCurveK', 'postMaxCurveV', 'postMaxCurveK',\\n 'height', 'repolarizationV', 'intervals', 'frequencies']\\n props = {ch: {gr: {} for gr in list(set([g[1] for g in groups]))}\\n for ch in checks} # A dict of dicts\\n # props [properties] [group name] [cell name]\\n cells = [f[0].split('/')[-1].split('_')[0] for f in groups]\\n \\n # Add a few more keys\\n props['activity'] = {gr: {} for gr in list(set([g[1] for g in groups]))}\\n \\n # Assign all the properties to the props dict\\n for g in groups:\\n df = pd.read_csv(g[0])\\n df = df.drop('Unnamed: 33', 1) # Garbage\\n df = df.drop('freq', 1) # These are downsampled\\n df = df.dropna() # Dropna\\n \\n # If there are multiple clusters, add them in order\\n if max(df.clust_inds) == 1: # Two clusters\\n numClusts = int(max(df.clust_inds)+1)\\n for ch in checks:\\n for clust in range(numClusts):\\n try:\\n props[ch][g[1]][cells[groups.index(g)]].append(df[df['clust_inds']==clust][ch].dropna().values)\\n except:\\n props[ch][g[1]][cells[groups.index(g)]] = [df[df['clust_inds']==clust][ch].dropna().values]\\n else: # Just one cluster\\n for ch in checks:\\n props[ch][g[1]][cells[groups.index(g)]] = [df[ch].dropna().values]\\n # Get activity profile\\n tIn, cBouts = timeInClusters(df)\\n props['activity'][g[1]][cells[groups.index(g)]] = [tIn, cBouts]\\n \\n return props\",\n \"def extract_format_results_test(self):\\n assert len(self.results.keys()) != 0\\n TESTS = [\\n {\\n \\\"output\\\": [\\n {\\n \\\"file_name\\\": \\\"BSA1.mzML\\\",\\n \\\"spec_id\\\": 1337,\\n \\\"formula\\\": \\\"C(37)H(59)N(9)O(16)\\\",\\n \\\"scaling_factor\\\": 100,\\n \\\"score\\\": 1,\\n \\\"charge\\\": 2,\\n },\\n {\\n \\\"file_name\\\": \\\"BSA1.mzML\\\",\\n \\\"spec_id\\\": 1338,\\n \\\"formula\\\": \\\"C(37)H(59)N(9)O(16)\\\",\\n \\\"scaling_factor\\\": 100,\\n \\\"score\\\": 0.9,\\n \\\"charge\\\": 2,\\n },\\n {\\n \\\"file_name\\\": \\\"BSA2.mzML\\\",\\n \\\"spec_id\\\": 1337,\\n \\\"formula\\\": \\\"C(43)H(75)N(15)O(17)S(2)\\\",\\n \\\"scaling_factor\\\": 10,\\n \\\"score\\\": 1,\\n \\\"charge\\\": 3,\\n },\\n ]\\n }\\n ]\\n for test_dict in TESTS:\\n values = self.results.format_all_results()\\n\\n assert isinstance(values, pd.DataFrame)\\n\\n for out_data in test_dict[\\\"output\\\"]:\\n result = values.loc[\\n (values[\\\"file_name\\\"] == out_data[\\\"file_name\\\"])\\n & (values[\\\"spec_id\\\"] == out_data[\\\"spec_id\\\"])\\n ]\\n assert (result[\\\"formula\\\"] == out_data[\\\"formula\\\"]).all()\\n assert (result[\\\"scaling_factor\\\"] == out_data[\\\"scaling_factor\\\"]).all()\\n assert (result[\\\"score\\\"] == out_data[\\\"score\\\"]).all()\\n assert (result[\\\"charge\\\"] == out_data[\\\"charge\\\"]).all()\",\n \"def yield_stats(go_analysis):\\n for i in xrange(go_analysis.nrow()):\\n yield go_analysis[0][i], go_analysis[1][i], go_analysis[2][i], go_analysis[3][i], p_value_from_r(go_analysis[4][i]), p_value_from_r(go_analysis[5][i])\",\n \"def _alpha_stats(self, trace):\\n mean = np.mean(trace['alpha'])\\n sd = np.std(trace['alpha'], ddof=1)\\n zscore = mean / sd\\n return mean, sd, zscore\",\n \"def create_mia_report(attack_output: dict) -> FPDF:\\n # dummy_metrics = attack_output[\\\"dummy_attack_metrics\\\"]\\n dummy_metrics = []\\n mia_metrics = [\\n v\\n for _, v in attack_output[\\\"attack_experiment_logger\\\"][\\n \\\"attack_instance_logger\\\"\\n ].items()\\n ]\\n # mia_metrics = attack_output[\\\"attack_metrics\\\"]\\n metadata = attack_output[\\\"metadata\\\"]\\n if dummy_metrics is None or len(dummy_metrics) == 0:\\n do_dummy = False\\n else:\\n do_dummy = True\\n\\n dest_log_roc = (\\n os.path.join(\\n metadata[\\\"experiment_details\\\"][\\\"output_dir\\\"],\\n metadata[\\\"experiment_details\\\"][\\\"report_name\\\"],\\n )\\n + \\\"_log_roc.png\\\"\\n )\\n _roc_plot(mia_metrics, dummy_metrics, dest_log_roc)\\n\\n pdf = FPDF()\\n pdf.add_page()\\n pdf.set_xy(0, 0)\\n title(pdf, \\\"WorstCase MIA attack result report\\\")\\n subtitle(pdf, \\\"Introduction\\\")\\n line(pdf, INTRODUCTION)\\n subtitle(pdf, \\\"Experiment summary\\\")\\n for key, value in metadata[\\\"experiment_details\\\"].items():\\n line(pdf, f\\\"{key:>30s}: {str(value):30s}\\\", font=\\\"courier\\\")\\n subtitle(pdf, \\\"Global metrics\\\")\\n for key, value in metadata[\\\"global_metrics\\\"].items():\\n line(pdf, f\\\"{key:>30s}: {str(value):30s}\\\", font=\\\"courier\\\")\\n if do_dummy:\\n subtitle(pdf, \\\"Baseline global metrics\\\")\\n for key, value in metadata[\\\"baseline_global_metrics\\\"].items():\\n line(pdf, f\\\"{key:>30s}: {str(value):30s}\\\", font=\\\"courier\\\")\\n\\n subtitle(pdf, \\\"Metrics\\\")\\n line(\\n pdf,\\n \\\"The following show summaries of the attack metrics over the repetitions\\\",\\n font=\\\"arial\\\",\\n )\\n for metric in DISPLAY_METRICS:\\n vals = np.array([m[metric] for m in mia_metrics])\\n if metric in MAPPINGS:\\n vals = np.array([MAPPINGS[metric](v) for v in vals])\\n text = (\\n f\\\"{metric:>12} mean = {vals.mean():.2f}, var = {vals.var():.4f}, \\\"\\n f\\\"min = {vals.min():.2f}, max = {vals.max():.2f}\\\"\\n )\\n line(pdf, text, font=\\\"courier\\\")\\n\\n if do_dummy:\\n subtitle(pdf, \\\"Baseline metrics\\\")\\n line(\\n pdf,\\n (\\n \\\"The following show summaries of the attack metrics over the \\\"\\n \\\"repetitions where there is no statistical difference between \\\"\\n \\\"predictions in the training and test sets. Simulation was done \\\"\\n \\\"with training and test set sizes equal to the real ones\\\"\\n ),\\n font=\\\"arial\\\",\\n )\\n for metric in DISPLAY_METRICS:\\n vals = np.array([m[metric] for m in dummy_metrics])\\n if metric in MAPPINGS:\\n vals = np.array([MAPPINGS[metric](v) for v in vals])\\n text = (\\n f\\\"{metric:>12} mean = {vals.mean():.2f}, var = {vals.var():.4f}, \\\"\\n f\\\"min = {vals.min():.2f}, max = {vals.max():.2f}\\\"\\n )\\n line(pdf, text, font=\\\"courier\\\")\\n\\n _add_log_roc_to_page(dest_log_roc, pdf)\\n line(pdf, LOGROC_CAPTION)\\n\\n pdf.add_page()\\n title(pdf, \\\"Glossary\\\")\\n _write_dict(pdf, GLOSSARY)\\n\\n return pdf\",\n \"def split_json_report(data):\\n\\n def split_assertions(entries, assertions):\\n \\\"\\\"\\\"Remove assertions from report and place them in a dictionary.\\\"\\\"\\\"\\n for entry in entries:\\n if entry.get(\\\"category\\\") == ReportCategories.TESTCASE:\\n assertions[entry[\\\"uid\\\"]] = entry[\\\"entries\\\"]\\n entry[\\\"entries\\\"] = []\\n elif \\\"entries\\\" in entry:\\n split_assertions(entry[\\\"entries\\\"], assertions)\\n\\n meta, structure, assertions = data, data[\\\"entries\\\"], {}\\n meta[\\\"entries\\\"] = []\\n split_assertions(structure, assertions)\\n return meta, structure, assertions\",\n \"def data_for_grouping():\\n return RaggedArray(\\n [[1, 0], [1, 0], [], [], [0, 0], [0, 0], [1, 0], [2, 0]])\",\n \"def movies(self):\\n return self.data.groupby('Parameters')\",\n \"def format_data(self, bands, branches):\\n self.new_dict = self.__gen_dict\\n order = [] # list to store high-symmetry point\\n band_index = {} # dict to store band index info corresponding to its high-symmetry point e.g. \\\"X\\\": 18\\n formatted_bands = []\\n zero_matrix = np.zeros(np.shape(bands))\\n \\\"\\\"\\\"\\n zero_matrix is for: if one configuration does not have some high-symmetry points listed in __generic_dict\\n then fill zeros in those columns \\n \\\"\\\"\\\"\\n\\n for i in range(len(branches)):\\n order.append(branches[i][\\\"name\\\"])\\n spilt = re.split('-', order[i])\\n\\n band_index[spilt[0]] = branches[i]['start_index']\\n band_index[spilt[1]] = branches[i]['end_index']\\n\\n # print('>>>>>>>>>>>>>>>>>>', band_index)\\n # iterate all keys in band_index, and if exists, give value to new_dict, if not, pass\\n for hs_point in band_index:\\n if hs_point in self.new_dict:\\n self.new_dict[hs_point] = band_index[hs_point]\\n # print('>>>>>>>>>>>>>>>>>', BandsData.__gen_dict)\\n\\n # iterate all keys in new_dict, export bands (not arranged in bands dimension)\\n for hs_point in self.new_dict:\\n hs_value = self.new_dict[hs_point]\\n if self.new_dict[hs_point] is None:\\n # fill zeros in bands\\n formatted_bands.append(zero_matrix[:, 0])\\n else:\\n formatted_bands.append(bands[:, hs_value])\\n\\n # transpose of formatted_bands\\n formatted_bands = np.transpose(formatted_bands)\\n\\n return formatted_bands, self.new_dict\",\n \"def groups(self):\\n\\n return ('train', 'dev', 'eval')\",\n \"def respuesta(response):\\n for report in response.get('reports', []):\\n\\n columnHeader = report.get('columnHeader', {})\\n dimensionHeaders = columnHeader.get('dimensions', [])\\n metricHeaders = columnHeader.get(\\n 'metricHeader', {}).get('metricHeaderEntries', [])\\n\\n return_data = []\\n\\n for row in report.get('data', {}).get('rows', []):\\n\\n dimensions = row.get('dimensions', [])\\n dateRangeValues = row.get('metrics', [])\\n pipeline_insert = {}\\n for header, dimension in zip(dimensionHeaders, dimensions):\\n pipeline_insert[header] = dimension\\n\\n for i, values in enumerate(dateRangeValues):\\n\\n for metricHeader, value in zip(metricHeaders, values.get('values')):\\n pipeline_insert[metricHeader.get('name')] = value\\n return_data.append(pipeline_insert)\\n\\n return return_data\",\n \"def eachDigitGMM(data, cfg):\\r\\n\\r\\n models = {}\\r\\n for j in range(len(data)):\\r\\n train_set = data[j][0]\\r\\n for i in range(1, len(data[j])):\\r\\n train_set = np.concatenate((train_set, data[j][i]), axis=0)\\r\\n\\r\\n estimator = GaussianMixture(n_components=cfg['components'], max_iter=cfg['max_iterations'],\\r\\n tol=cfg['tolerance'], covariance_type=cfg['covariance_type'])\\r\\n models[j] = estimator.fit(train_set)\\r\\n\\r\\n return models\",\n \"def mc_get_card_group_hist(hand, remainder, num_samples=1000, hist_res=100):\\n lst = {}\\n remainder = list(filter(lambda x: x not in hand, remainder))\\n for i in range(num_samples):\\n res = choice(remainder)\\n group = get_card_group(res)\\n hs = hand_strength_evaluator.hand_strength(hand, res)\\n if group not in lst:\\n lst[group] = {str(hand): []}\\n lst[group][str(hand)].append(hs)\\n return lst\",\n \"def gamma_pdf(a,b):\\n df = DataFrame(columns=['Day','Gamma_Values'])\\n for day in range(181):\\n df = df.append({'Day': int(day), 'Gamma_Values': float(gamma.pdf(day,a,0,b))}, ignore_index=True)\\n return df\",\n \"def get_results(self):\\n d = {}\\n# r = {}\\n for analyser in self.xml_tree.getroot():\\n for child in analyser:\\n if child.tag == 'all-records':\\n for record in child:\\n attributes = record.attrib\\n sample = attributes['sampleId']\\n assay_id = attributes['assayId']\\n genotype = attributes['genotypeId']\\n quality = attributes['description'].split('.')[0]\\n if re.match(r'rs\\\\d+', assay_id):\\n if sample in d:\\n if assay_id in d[sample]:\\n for allele in list(genotype):\\n if allele not in d[sample][assay_id]['genotype']:\\n d[sample][assay_id]['genotype'] += allele\\n if quality not in d[sample][assay_id]['quality']:\\n d[sample][assay_id]['quality'].append(quality)\\n else:\\n d[sample][assay_id] = {'genotype': genotype, 'quality': [quality]}\\n else:\\n d[sample] = {assay_id: {'genotype': genotype, 'quality': [quality]}}\\n# if sample in r:\\n# if assay_id in r[sample]:\\n# for allele in list(genotype):\\n# if allele not in r[sample][assay_id]:\\n# r[sample][assay_id] += allele\\n# else:\\n# r[sample][assay_id] = genotype\\n# else:\\n# r[sample] = {assay_id: genotype}\\n# for k, v in r.items():\\n# for k1, v1, in v.items():\\n# if len(v1) == 1:\\n# v[k1] += v1\\n# pprint.pprint(r)\\n# df = pd.DataFrame.from_dict(r).transpose()\\n# print(df)\\n# df.to_excel('snpcheck.xlsx')\\n return d\",\n \"def set_up_groups(self):\\n groups = []\\n groups.append({'groupname': 'th',\\n 'grouptitle': 'TH',\\n 'path': '/'})\\n groups.append({'groupname': 'neutronics',\\n 'grouptitle': 'Neutronics',\\n 'path': '/'})\\n groups.append({'groupname': 'metadata',\\n 'grouptitle': 'Simulation Metadata',\\n 'path': '/'})\\n return groups\",\n \"def group_normalization(x, gamma, beta, groups=32, eps=1e-5):\\n return GroupNormalization(groups, eps).apply((x, gamma, beta))[0]\",\n \"def report(res, _c, _s, gamma):\\n _res = res.copy()\\n # smooth\\n for _, _scores in enumerate(_res):\\n if _ == 0: continue\\n\\n _res[_, :] = _res[_, :] * gamma + _res[_-1, :] * (1 - gamma)\\n\\n reports = np.argmax(_res, axis=1)\\n scores = np.max(_res, axis=1)\\n\\n pre_r = 0\\n cum = 0\\n cums = []\\n alarms = []\\n\\n for r in reports:\\n alarm = [0] * _res.shape[1]\\n if r != 0 and (r == pre_r or _c == 0):\\n if cum == _c:\\n alarm[r] = 1\\n cum = -_s\\n else:\\n alarm[0] = 1\\n cum += 1\\n else:\\n alarm[0] = 1\\n cum = max(0, cum-1)\\n\\n alarms.append(alarm)\\n cums.append(cum)\\n pre_r = r\\n \\n alarms = np.array(alarms)\\n cums = np.array(cums)\\n\\n return alarms, cums\",\n \"def prepare_data(groups):\\n all_dicts = []\\n for idx, group in groups:\\n res_dict = {'organism': group.organism.iloc[0]}\\n for g_idx, row in group.iterrows():\\n if pd.notna(row.label):\\n res_dict[row.cmp_name] = {'label': row.label, 'mic': row.MIC}\\n else:\\n res_dict[row.cmp_name] = {'label': '', 'mic': row.MIC}\\n all_dicts.append(res_dict)\\n return all_dicts\",\n \"def data_collect(gamertag):\\n k_d = []\\n for page in range(0, 2):\\n # Used an f string literal to apply the input argument and for loop to the weblink which can be used by\\n # any gamertag and page number\\n page_url = f'http://halotracker.com/h5/games/{gamertag}?page={page}&mode=custom'\\n res = requests.get(page_url)\\n soup = bs4.BeautifulSoup(res.text, 'lxml')\\n for value in soup.select('.game-stat-value'):\\n k_d.append(float(value.text.strip()))\\n k_d = k_d[1::2]\\n return k_d\",\n \"def calc_groups(sensors_rad_clean, sensors_metadata_cat):\\n # calculate number of groups as number of optimal combinations.\\n groups_ob = sensors_metadata_cat.groupby(['CATB', 'CATGB', 'CATteta_z']) # group the sensors by categories\\n prop_observers = groups_ob.mean().reset_index()\\n prop_observers = pd.DataFrame(prop_observers)\\n total_area_installed_module = groups_ob['area_installed_module'].sum().reset_index()['area_installed_module']\\n prop_observers['total_area_module'] = total_area_installed_module\\n number_groups = groups_ob.size().count()\\n sensors_list = groups_ob.groups.values()\\n\\n # calculate mean hourly radiation of sensors in each group\\n rad_group_mean = np.empty(shape=(number_groups,8760))\\n number_points = np.empty(shape=(number_groups,1))\\n for x in range(0, number_groups):\\n sensors_rad_group = sensors_rad_clean[sensors_list[x]]\\n rad_mean = sensors_rad_group.mean(axis=1).as_matrix().T\\n rad_group_mean[x] = rad_mean\\n number_points[x] = len(sensors_list[x])\\n hourlydata_groups = pd.DataFrame(rad_group_mean).T\\n\\n return number_groups, hourlydata_groups, number_points, prop_observers\",\n \"def permutation(data, dataLabel=None, nperm=10000, decimals=4):\\n\\n # test calling values\\n if data is None or not isinstance(data, dict) or len(data.keys()) != 2:\\n raise ValueError('RSTATS.permutation: data must be'\\n + ' a dictionary with at exactly 2 keys'\\n + '\\\\nUse KW (anova) for more than 2 groups')\\n\\n k = list(data.keys())\\n\\n g1 = data[k[0]]\\n g2 = data[k[1]]\\n # (w1, p1) = Stats.shapiro(g1, a=None, reta=False)\\n # (w2, p2) = Stats.shapiro(g2, a=None, reta=False)\\n\\n combined = np.concatenate((g1, g2))\\n diffobs = np.mean(g2)-np.mean(g1)\\n diffs = np.zeros(nperm)\\n nperm = nperm\\n index = range(0, combined.shape[0])\\n for i in range(nperm):\\n # draw from combined data set without replacement\\n #shuff = np.random.randint(combined.shape[0], size=combined.shape[0])\\n shuff = np.random.permutation(index)\\n ar = combined[shuff[0:len(g1)]]\\n br = combined[shuff[len(g1):]]\\n diffs[i] = np.mean(br) - np.mean(ar)\\n pvalue = np.sum(np.abs(diffs) >= np.abs(diffobs)) / float(nperm)\\n if dataLabel is not None:\\n print ('\\\\n%s: Permutation Test (Nperm = %d)' % (dataLabel, nperm))\\n # if p1 < 0.05 and p2 < 0.05:\\n # print(u' Both data sets appear normally distributed: Shapiro-Wilk Group 1 p = {:6.3f}, Group2 p = {:6.3f}'.format(p1, p2))\\n # else:\\n # print(u' ****At least one Data set is NOT normally distributed****\\\\n Shapiro-Wilk Group 1 p = {:6.3f}, Group2 p = {:6.3f}'.format(p1, p2))\\n # print (u' (Permutation test does not depend on distribution)')\\n \\n n = max([len(l) for l in k])\\n print(u' {:s}={:8.{pc}f} \\\\u00B1{:.{pc}f}, {:d} (mean, SD, N)'.\\n format(k[0].rjust(n), np.mean(g1), np.std(g1, ddof=1),\\n len(g1), pc=decimals))\\n print(u' {:s}={:8.{pc}f} \\\\u00B1{:.{pc}f}, {:d} (mean, SD, N)'.\\n format(k[1].rjust(n), np.mean(g2), np.std(g2, ddof=1),\\n len(g2), pc=decimals))\\n summarizeData(data, decimals=decimals)\\n # iqr1 = np.subtract(*np.percentile(g1, [75, 25]))\\n # iqr2 = np.subtract(*np.percentile(g2, [75, 25]))\\n # print(u' {:s}: median={:8.4f} IQR={:8.4f}'.format(k[0].rjust(n), np.median(g1), iqr1))\\n # print(u' {:s}: median={:8.4f} IQR={:8.4f}'.format(k[1].rjust(n), np.median(g2), iqr2))\\n print(u' Observed difference: {:8.4f}'.format(diffobs))\\n print(u' p={:8.6f}, Nperm={:8d}\\\\n'.format(float(pvalue), int(nperm)))\\n return(pvalue, nperm)\",\n \"def selectedComparisons(samples_dict):\\n sectors=\\\"peripherals\\\",\\\"intermediaries\\\",\\\"hubs\\\"\\n ks_measures={}\\n for analyses_type in samples_dict[sectors[0]]:\\n ks_measures[analyses_type]={}\\n for analysis_grouping in samples_dict[sectors[0]][analyses_type]:\\n ks_measures[analyses_type][analysis_grouping]={}\\n if samples_dict[sectors[0]][analyses_type][analysis_grouping]==dict:\\n ks_measures[analyses_type][analysis_grouping]={}\\n for analysis in samples_dict[sectors[0]][analyses_type][analysis_grouping]:\\n for var in samples_dict[sectors[0]][analyses_type][analysis_grouping][analysis]:\\n samples_peripherals=samples_dict[sectors[0]][analyses_type][analysis_grouping][analysis]\\n samples_intermediaries=samples_dict[sectors[1]][analyses_type][analysis_grouping][analysis]\\n samples_hubs=samples_dict[sectors[2]][analyses_type][analysis_grouping][analysis]\\n ks_measures[analysis][\\\"peripherals_intermediaries\\\"]=P.kolmogorovSmirnovTest(samples_peripherals,samples_intermediaries)\\n ks_measures[analysis][\\\"peripherals_hubs\\\"]=P.kolmogorovSmirnovTest(samples_peripherals,samples_hubs)\\n ks_measures[analysis][\\\"hubs_intermediaries\\\"]=P.kolmogorovSmirnovTest(samples_hubs,samples_intermediaries)\\n else:\\n for var in samples_dict[sectors[0]][analyses_type][analysis_grouping]:\\n samples_peripherals=samples_dict[sectors[0]][analyses_type][analysis_grouping]\\n samples_intermediaries=samples_dict[sectors[1]][analyses_type][analysis_grouping]\\n samples_hubs=samples_dict[sectors[2]][analyses_type][analysis_grouping]\\n\\n\\n\\n samples[sector][analyses][analysis_grouping]=updateDict(samples[sector][analyses][analysis_grouping],getSamples(authors_analysis[analyses][author][analysis_grouping]))\",\n \"def grouping_cols(df, cat_percentage = 0.05, checking_itr = 10):\",\n \"def _group_samples_by_channel(self):\\n samples_acquired_per_channel = len(self.all_samples_list) / self.num_channels\\n\\n #make a list with each element being a numpy array for the results from each channel\\n channel_data = []\\n for i in range(self.num_channels):\\n channel_data.append(numpy.zeros(samples_acquired_per_channel,dtype=float))\\n\\n scan_num = 0\\n channel_index = 0\\n for sample in range(len(self.all_samples_list)): #each sample in interleaved list\\n channel_data[channel_index][scan_num] = self.all_samples_list[sample]\\n if channel_index < self.num_channels-1:\\n channel_index += 1\\n else:\\n channel_index = 0\\n scan_num += 1\\n\\n if self.num_channels == 1: #if only one channel put the numpy array on the queue\\n self.result_queue.put(channel_data[0])\\n else: #if more than one channel put the list of arrays on the queue\\n self.result_queue.put(channel_data)\",\n \"def generate_groups():\\n groups = group_elements(\\n generate_examples(file_name),\\n cfg.tfrecord_size)\\n\\n # pairing groups to unique numbers and \\n # filtering nulls from zip_longest\\n groups = (\\n list(filter(is_not_none, group))\\n for group in groups\\n )\\n\\n yield from groups\",\n \"def test_grouped_cumul_div(self):\\n gfile = grades.writers.GradesFile(self.fname)\\n gfile.table.compute_cumul()\\n gfile.table.compute_grouped_mean('Group')\\n gfile.table_format = 'org'\\n self.check_output(self.output_str, gfile, div_on=('Group',))\",\n \"def _process_recording(args):\\n recording, filter_ = args\\n out = [recording.subject, recording.session, filter_]\\n n_channels = recording.info[\\\"nchan\\\"]\\n recording.preprocess(filter_[0], filter_[1])\\n recording.gfp()\\n out.append(len(recording.gfp_peaks))\\n for n_states in NO_STATES_RANGE:\\n recording.run_microstates(n_states=n_states, n_inits=N_INITS)\\n out += [\\n pm_variance_test(\\n gev=1.0 - recording.gev_tot,\\n no_states=n_states,\\n n_channels=n_channels,\\n ),\\n pm_variance_test(\\n gev=1.0 - recording.gev_gfp,\\n no_states=n_states,\\n n_channels=n_channels,\\n ),\\n ]\\n assert len(out) == 4 + 2 * len(NO_STATES_RANGE)\\n return out\",\n \"def G12(x, a):\\n alpha, a, beta, b = a\\n pi26 = np.pi ** 2 / 6.0\\n G = (pi26 + x) * np.exp(-x)\\n tmp = 1 + b * x ** beta\\n g = 1. / (a * x ** alpha / tmp + 1.)\\n return G * g\",\n \"def test_get_groupings_between_tiny_dataset(self):\\r\\n self.assertEqual(_get_groupings(self.tiny_dist_matrix_header,\\r\\n self.tiny_dist_matrix, self.tiny_groups, within=False), [])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5615865","0.5525419","0.5523908","0.54661447","0.536697","0.53608376","0.5330283","0.52438","0.5227983","0.5227983","0.5154076","0.5146968","0.51379746","0.51017696","0.5098285","0.50540596","0.5026247","0.5020847","0.50154185","0.49952942","0.4989816","0.4977468","0.4965934","0.49614936","0.4958199","0.4956973","0.49567375","0.49454173","0.49454173","0.4933719","0.49305514","0.4915775","0.49109048","0.4909276","0.49080214","0.49019256","0.4898978","0.488749","0.48780167","0.48694465","0.48674688","0.48526776","0.4850488","0.48484924","0.4842696","0.4834959","0.48332834","0.48204798","0.48078698","0.47938454","0.47913727","0.47898677","0.47879225","0.47828168","0.47790435","0.4773329","0.47727054","0.47688589","0.47543666","0.47500157","0.47488353","0.47488108","0.47399196","0.4738776","0.47378355","0.4737094","0.473634","0.47350484","0.47320268","0.4720338","0.47192058","0.47086465","0.46993887","0.46927175","0.4690177","0.46856388","0.46798742","0.46776572","0.4676354","0.4673323","0.4671362","0.46658954","0.46573457","0.46530506","0.4647807","0.46417955","0.46354523","0.4630319","0.46301562","0.46191078","0.46144482","0.4607634","0.46017498","0.4592021","0.45892906","0.45885614","0.45878047","0.45854458","0.45846796","0.4584023"],"string":"[\n \"0.5615865\",\n \"0.5525419\",\n \"0.5523908\",\n \"0.54661447\",\n \"0.536697\",\n \"0.53608376\",\n \"0.5330283\",\n \"0.52438\",\n \"0.5227983\",\n \"0.5227983\",\n \"0.5154076\",\n \"0.5146968\",\n \"0.51379746\",\n \"0.51017696\",\n \"0.5098285\",\n \"0.50540596\",\n \"0.5026247\",\n \"0.5020847\",\n \"0.50154185\",\n \"0.49952942\",\n \"0.4989816\",\n \"0.4977468\",\n \"0.4965934\",\n \"0.49614936\",\n \"0.4958199\",\n \"0.4956973\",\n \"0.49567375\",\n \"0.49454173\",\n \"0.49454173\",\n \"0.4933719\",\n \"0.49305514\",\n \"0.4915775\",\n \"0.49109048\",\n \"0.4909276\",\n \"0.49080214\",\n \"0.49019256\",\n \"0.4898978\",\n \"0.488749\",\n \"0.48780167\",\n \"0.48694465\",\n \"0.48674688\",\n \"0.48526776\",\n \"0.4850488\",\n \"0.48484924\",\n \"0.4842696\",\n \"0.4834959\",\n \"0.48332834\",\n \"0.48204798\",\n \"0.48078698\",\n \"0.47938454\",\n \"0.47913727\",\n \"0.47898677\",\n \"0.47879225\",\n \"0.47828168\",\n \"0.47790435\",\n \"0.4773329\",\n \"0.47727054\",\n \"0.47688589\",\n \"0.47543666\",\n \"0.47500157\",\n \"0.47488353\",\n \"0.47488108\",\n \"0.47399196\",\n \"0.4738776\",\n \"0.47378355\",\n \"0.4737094\",\n \"0.473634\",\n \"0.47350484\",\n \"0.47320268\",\n \"0.4720338\",\n \"0.47192058\",\n \"0.47086465\",\n \"0.46993887\",\n \"0.46927175\",\n \"0.4690177\",\n \"0.46856388\",\n \"0.46798742\",\n \"0.46776572\",\n \"0.4676354\",\n \"0.4673323\",\n \"0.4671362\",\n \"0.46658954\",\n \"0.46573457\",\n \"0.46530506\",\n \"0.4647807\",\n \"0.46417955\",\n \"0.46354523\",\n \"0.4630319\",\n \"0.46301562\",\n \"0.46191078\",\n \"0.46144482\",\n \"0.4607634\",\n \"0.46017498\",\n \"0.4592021\",\n \"0.45892906\",\n \"0.45885614\",\n \"0.45878047\",\n \"0.45854458\",\n \"0.45846796\",\n \"0.4584023\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6480124"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":9999,"cells":{"query":{"kind":"string","value":"Abstract method which provides a set of hyperparameters. This method will get called when the framework is about to launch a new trial,"},"document":{"kind":"string","value":"def generate_parameters(self, parameter_id, **kwargs):\n # FIXME: some tuners raise NoMoreTrialError when they are waiting for more trial results\n # we need to design a new exception for this purpose\n raise NotImplementedError('Tuner: generate_parameters not implemented')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_hyperparams(self):","def _get_current_hyperparameters(self):","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def hyperparameters(self):\n hyperparameters = super(TensorFlow, self).hyperparameters()\n\n self.checkpoint_path = self.checkpoint_path or self._default_s3_path('checkpoints')\n mpi_enabled = False\n\n if self._script_mode_enabled():\n additional_hyperparameters = {}\n\n if 'parameter_server' in self.distributions:\n ps_enabled = self.distributions['parameter_server'].get('enabled', False)\n additional_hyperparameters[self.LAUNCH_PS_ENV_NAME] = ps_enabled\n\n if 'mpi' in self.distributions:\n mpi_dict = self.distributions['mpi']\n mpi_enabled = mpi_dict.get('enabled', False)\n additional_hyperparameters[self.LAUNCH_MPI_ENV_NAME] = mpi_enabled\n additional_hyperparameters[self.MPI_NUM_PROCESSES_PER_HOST] = mpi_dict.get('processes_per_host', 1)\n additional_hyperparameters[self.MPI_CUSTOM_MPI_OPTIONS] = mpi_dict.get('custom_mpi_options', '')\n\n self.model_dir = self.model_dir or self._default_s3_path('model', mpi=mpi_enabled)\n additional_hyperparameters['model_dir'] = self.model_dir\n else:\n additional_hyperparameters = {'checkpoint_path': self.checkpoint_path,\n 'training_steps': self.training_steps,\n 'evaluation_steps': self.evaluation_steps,\n 'sagemaker_requirements': self.requirements_file}\n\n hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))\n return hyperparameters","def set_hyperparams(self, params):","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 1e-7\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [4096, 4096, 10]\n activation = 'relu'\n lr = 0.3\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ##### END YOUR CODE HERE ########\n #################################\n hyper_params = {\n 'layers_size': layers_size,\n 'activation': activation,\n 'lr': lr,\n 'epochs': epochs,\n 'init_kind': init_kind,\n 'dropout_rate': dropout_rate,\n }\n return hyper_params","def hyperparameters(self):\n return self._hyperparameters","def hyperparameters(self) -> dict[str, Any]:\n return self.config[\"hyperparameters\"]","def Params(cls):\n p = hyperparams.InstantiableParams(cls)\n p.Define('task_dict', None, 'dataset_name -> task params')\n p.Define('task_name', None, 'High level task name')\n p.Define('logdir', None, 'Log directory')\n p.Define('train_program', None, 'Train program params')\n p.Define('train_executions_per_eval', 1, '')\n p.Define('eval_programs', [], 'List of eval program params.')\n p.Define('num_splits_per_client', None, '')\n p.Define('dataset_names', [], 'List of all dataset names.')\n p.Define('emails', [], 'List of emails to send metrics.')\n p.Define('summary_exporter', None, 'The summary exporter Params.')\n p.Define('async_postprocess', True,\n 'whether to CPU postprocess asynchronously with TPU train')\n p.Define(\n 'checkpoint_to_load', None,\n 'If set, the program will initially load from this checkpoint, '\n 'ignoring train_dir. Typically used for oneoff decode.')\n\n # TODO(blee): Clean these up.\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\n mlp = p.ml_perf\n mlp.Define('submission_metadata', None,\n 'A dictionary of static submission metadata')\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\n mlp.Define('decoder_metric_name', None,\n 'Name of the decoder metric to report for compliance log.')\n mlp.Define('decoder_metric_success_threshold', None,\n 'Benchmark run must exceed this value to succeed.')\n mlp.Define('max_steps_to_train', None,\n 'Maximum number of steps to reach target accuracy')\n return p","def Params(cls):\n p = hyperparams.InstantiableParams(cls)\n\n p.Define('task_dict', None, 'dataset_name -> task params')\n p.Define('task_name', None, 'High level task name')\n p.Define('logdir', None, 'Log directory')\n p.Define('train_program', None, 'Train program params')\n p.Define('train_executions_per_eval', 1, '')\n p.Define('dataset_names', [], 'List of all dataset names.')\n p.Define('num_splits_per_client', None, '')\n\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\n\n mlp = p.ml_perf\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\n mlp.Define('decoder_metric_name', None,\n 'Name of the decoder metric to report for compliance log.')\n mlp.Define('decoder_metric_success_threshold', None,\n 'Benchmark run must exceed this value to succeed.')\n mlp.Define('max_steps_to_train', None,\n 'Maximum number of steps to reach target accuracy')\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\n mlp.Define('global_batch_size', None, 'Global batch size.')\n mlp.Define('max_sequence_length', None, 'Maximum sequence length.')\n mlp.Define('optimizer_name', None, 'Optimizer used.')\n mlp.Define('base_learning_rate', None, 'Base learning rate.')\n mlp.Define('warmup_steps', None, 'Number of warm-up steps.')\n\n return p","def define_parameters(self):","def Params(cls):\n p = hyperparams.InstantiableParams(cls)\n p.Define('task', None, 'Underlying task')\n p.Define('logdir', None, 'Log directory')\n p.Define('num_splits_per_client', None, '')\n p.Define('steps_per_loop', None, 'Number of steps to run.')\n p.Define('dataset_name', None,\n 'Dataset the program is operating on, eg: \"Test\"')\n p.Define('name', 'base_program', 'Program name.')\n p.Define('task_name', None,\n 'If multi-task, what the high-level task name is')\n p.Define('num_threads', 1, 'Number of threads in multiprocessing pool.')\n p.Define('spmd', False, 'Whether program is running under SPMD mode.')\n p.Define('write_train_input_stats', False,\n 'Whether to write input data stats during training.')\n p.Define('max_metrics', 256, 'Overrides TpuEvalMetrics.max_metrics')\n p.Define('ml_perf', None, 'MLPerf config')\n return p","def set_hyperparams(use_defaults):\n if use_defaults:\n n_neurons, n_hidden, n_steps, k_prob = default_hyperparams()\n return n_neurons, n_hidden, n_steps, k_prob\n\n print (\"Select number of neurons in recurrent layer (default \" +\n \"100):\")\n n_neurons = int(input())\n print (\"Select number of hidden neurons in fully connected \" +\n \"layer (default 100):\")\n n_hidden = int(input())\n print (\"Select n_steps; the max number of words to be read \" +\n \"from each abstract (default 50):\")\n n_steps = int(input())\n print (\"Select k_prob; the dropout probability (default 0.5):\")\n k_prob = float(input())\n\n return n_neurons, n_hidden, n_steps, k_prob","def Params(cls):\n return hyperparams.InstantiableParams(cls)","def get_params(self):\n return {'threshold': self.threshold,\n 'subsample': self.subsample,\n 'estimator': self.estimator,\n 'n_folds': self.n_folds,\n 'stratify': self.stratify,\n 'random_state': self.random_state,\n 'n_jobs': self.n_jobs}","def parameters(self):","def parameters(self):\n raise NotImplementedError('Abstract method \"parameters\" must be '\n 'specialised!')","def _starting_hparams():\n hparams = contrib_training.HParams()\n hparams.add_hparam('batch_style', 'bucket')\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\n hparams.add_hparam('learning_rate', 0.0005)\n hparams.add_hparam('lr_decay_rate', .997)\n hparams.add_hparam('lr_decay_steps', 1000)\n hparams.add_hparam('lr_warmup_steps', 3000)\n hparams.add_hparam('model_type', 'cnn')\n hparams.add_hparam('resnet_bottleneck_factor', 0.5)\n hparams.add_hparam('decision_threshold', 0.5)\n hparams.add_hparam('denominator_power', 1.0) # Standard mean-pooling.\n return hparams","def build(self):\n return self.hyperparams.items()","def parameters(self):\n pass","def hyperparams():\n H = 6\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)","def set_params(self):\n raise NotImplementedError","def init_hyperparameters():\n alpha = .8\n alpha2 = 1\n\n return alpha, alpha2","def parameters(self) -> Dict[str, Any]:\n return self.trainer_parameters","def gen_parameters(self):\n\n print \"\\t* Adding parameters to compute template\"\n # get all the server client\n servers = self.novaclient.servers.list()\n\n # add all key_pair_names\n self.gen_key_name_parameters(servers)\n\n # add all images\n self.gen_image_parameters(servers)\n\n # add all flavors\n self.gen_flavor_parameters(servers)\n\n # add all networks\n self.gen_network_parameters()","def set_params(self):\r\n pass","def test_base_hyper_parameters_reg(self):\n hyper_parameter_set = modelgen.generate_base_hyper_parameter_set()\n assert 'regularization_rate' in hyper_parameter_set.keys()","def __init__(self, params, data):\n super(HyperOptNoTraining, self).__init__(params, data)\n self.objective = None\n self.trial_losses = None\n self.best_trial = None\n self.trial_list = None","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rbk[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd_emu[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rbk,Rdy,Rqcd_emu\")","def get_params(self):","def Parameters():\n\n raise NotImplementedError()","def parameters(self):\n return self.trainer_parameters","def parameters(self):\n return self.trainer_parameters","def set_params(self, **params):\n if('threshold' in params.keys()):\n self.threshold = params['threshold']\n if('subsample' in params.keys()):\n self.subsample = params['subsample']\n if('estimator' in params.keys()):\n self.estimator = params['estimator']\n if('n_folds' in params.keys()):\n self.n_folds = params['n_folds']\n if('stratify' in params.keys()):\n self.stratify = params['stratify']\n if('random_state' in params.keys()):\n self.random_state = params['random_state']\n if('n_jobs' in params.keys()):\n self.n_jobs = params['n_jobs']","def Params(cls):\n p = super().Params()\n p.Define('train_task', None, 'Underlying task')\n p.Define('decode_task', None, 'Underlying task')\n p.Define('train_dataset_name', None, '')\n p.Define('decode_dataset_name', None, '')\n p.Define('train_steps_per_loop', 0, '')\n p.Define('decode_steps_per_loop', 0, '')\n return p","def get_parameters(self):\n # Get the parameters from the parent class\n params = super(LPEvalSplit, self).get_parameters()\n\n # Add the LP specific parameters\n params.update({\"owa\": self._owa, \"fe_ratio\": self._fe_ratio})\n return params","def __init__(self, **kwargs):\n # Register the hyperparameters and their type in _hparam_types.\n # _hparam_types maps the parameter name to a tuple (type, bool).\n # The type value is the type of the parameter for scalar hyperparameters,\n # or the type of the list elements for multidimensional hyperparameters.\n # The bool value is True if the value is a list, False otherwise.\n self._hparam_types = {}\n for name, value in six.iteritems(kwargs):\n self.add_hparam(name, value)","def get_params(self):\n raise NotImplementedError","def __init__(self):\n self.param_names = []\n self.param_values = []\n self.param_settings = []\n self.result = []\n self.best_params = None\n self.best_score = None\n self.max_reps = 5\n self.num_values = False\n self.algorithm_done = False","def setup_parameters(self):\n structure = self.ctx.structure_initial_primitive\n ecutwfc = []\n ecutrho = []\n\n for kind in structure.get_kind_names():\n try:\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\n cutrho = dual * cutoff\n ecutwfc.append(cutoff)\n ecutrho.append(cutrho)\n except KeyError as exception:\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\n\n natoms = len(structure.sites)\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\n\n self.ctx.inputs['parameters'] = {\n 'CONTROL': {\n 'restart_mode': 'from_scratch',\n 'tstress': self.ctx.protocol['tstress'],\n },\n 'SYSTEM': {\n 'ecutwfc': max(ecutwfc),\n 'ecutrho': max(ecutrho),\n 'smearing': self.ctx.protocol['smearing'],\n 'degauss': self.ctx.protocol['degauss'],\n 'occupations': self.ctx.protocol['occupations'],\n },\n 'ELECTRONS': {\n 'conv_thr': conv_thr,\n }\n }","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Rdy[1.,0.0,10.0]\");\n self.modelBuilder.doVar(\"Rqcd[1,0.0,10.0]\");\n self.modelBuilder.doSet(\"POI\",\"Rdy,Rqcd\")","def set_params(self, params: Dict):\n\n if params['training_instances'] is not None:\n self.training_instances = params['training_instances']\n if params['n'] is not None:\n self.n = params['n']\n if params['lda'] is not None:\n self.lda = params['lda']\n if params['verbose'] is not None:\n self.verbose = params['verbose']\n\n self.num_features = self.training_instances[0].get_feature_count()\n self.w = None\n self.b = None","def params():\n raise NotImplementedError","def getParams(self):\n return self.trainError, self.trainAcc, self.w","def __init__(self, hyperparameters):\n self._hyperparameters = numpy.copy(hyperparameters)","def hyperparameter_tune(X_train_all, y_train_all, X_val_all, y_val_all, hyperparameters, train_size=None):\n # Initialize train_size to be the full train data if no parameters passed\n if train_size is None:\n train_size = len(X_train_all)\n\n # Select Subset of Data\n X_train = X_train_all[:train_size]\n X_val = X_val_all[:train_size]\n y_train = y_train_all[:train_size]\n y_val = y_val_all[:train_size]\n\n # Create Grid of hyperparameters\n grid = cartesian_product(hyperparameters)\n\n # Loop through hyperparameters \n best_score = 0\n for hyperparameter in grid:\n # Initialize Model\n model = svm.SVC(kernel='linear', **hyperparameter)\n\n # Fit Model\n print(f\"Training using hyperparameters: {hyperparameter}\")\n model.fit(X_train, y_train)\n\n # Predict Values on Validation Set\n val_pred = model.predict(X_val)\n\n # Get Accuracy\n score = accuracy_score(y_val, val_pred)\n print(f\"Accuracy Score: {score}\")\n\n if score > best_score:\n best_score = score\n best_parameters = hyperparameter\n \n return best_score, best_parameters","def set_param_grid(self):\n\n # Interpret the request data based on the expected row and column structure\n row_template = ['strData', 'strData', 'strData']\n col_headers = ['model_name', 'estimator_args', 'grid_search_args']\n \n # Create a Pandas Data Frame for the request data\n self.request_df = utils.request_df(self.request, row_template, col_headers)\n \n # Initialize the persistent model\n self.model = PersistentModel()\n \n # Get the model name from the request dataframe\n self.model.name = self.request_df.loc[0, 'model_name']\n \n # Get the estimator's hyperparameter grid from the request dataframe\n param_grid = self.request_df.loc[:, 'estimator_args']\n\n # Get the grid search arguments from the request dataframe\n grid_search_args = self.request_df.loc[0, 'grid_search_args']\n\n # Get the model from cache or disk\n self._get_model()\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(3)\n\n self._set_grid_params(param_grid, grid_search_args)\n \n # Persist the model to disk\n self.model = self.model.save(self.model.name, self.path, overwrite=self.model.overwrite, compress=self.model.compress)\n \n # Update the cache to keep this model in memory\n self._update_cache()\n \n # Prepare the output\n message = [[self.model.name, 'Hyperparameter grid successfully saved to disk',\\\n time.strftime('%X %x %Z', time.localtime(self.model.state_timestamp))]]\n self.response = pd.DataFrame(message, columns=['model_name', 'result', 'time_stamp'])\n \n # Send the reponse table description to Qlik\n self._send_table_description(\"setup\")\n \n # Debug information is printed to the terminal and logs if the paramater debug = true\n if self.model.debug:\n self._print_log(4)\n \n # Finally send the response\n return self.response","def learnHyperparams(self, report=None):\n \n self.theta = crossValidate(self.k, self.cvEpochs, self.hpRanges, self.trainSet, \\\n self.train, self.test, report);","def __init__(self):\n # super(MultiEmbedding,self).__init__()\n HyperParameters.__init__(self)","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['range'] = self.range\n return paramDict\n # no other additional parameters required","def __init__(self, epsilon=0.05,gamma=0.8,alpha=0.2, numTraining=0, **args):\n args['epsilon'] = epsilon\n args['gamma'] = gamma\n args['alpha'] = alpha\n args['numTraining'] = numTraining\n QLearningAgent.__init__(self, **args)","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['apex' ] = self.apex\n paramDict['min' ] = self.min\n paramDict['max' ] = self.max\n return paramDict","def get_test_params(cls, parameter_set=\"default\"):\n params1 = {\n \"n_clusters\": 3,\n \"n_init\": 2,\n \"max_iter\": 2,\n \"tol\": 1e-3,\n \"verbose\": False,\n \"random_state\": 2,\n }\n params2 = {\n \"n_clusters\": 2,\n \"init_algorithm\": \"random\",\n \"n_init\": 1,\n \"max_iter\": 1,\n \"tol\": 1e-4,\n \"verbose\": False,\n \"random_state\": 1,\n }\n return [params1, params2]","def set_params(self, **kwargs):\n\n kw_keys = list(kwargs)\n\n if 'alpha' in kw_keys:\n self.alpha = kwargs['alpha']\n\n if 'beta' in kw_keys:\n self.beta = kwargs['beta']\n\n if 'gamma' in kw_keys: \n \tself.gamma = kwargs['gamma']\n\n if 'epsilon' in kw_keys:\n self.epsilon = kwargs['epsilon']\n \n self.nact = self.highbound-self.lowbound\n self.actions = np.arange(self.nact)","def init_parameters(obj, hyperparameters):\n # Initialize Global Configuration Parameter\n params = hyperparameters['global']\n setattr(obj, 'param', params)\n\n # Initialize Attributes (Pre-Checked Parameters)\n setattr(obj, 'learning_rate', params['learning_rate'])\n setattr(obj, 'loss', params['loss'])\n setattr(obj, 'max_iter', params['max_iter'])\n\n if params['loss'] == 'least_squares':\n setattr(obj, 'num_classes', 1)\n elif params['loss'] in ['binary_crossentropy', 'categorical_crossentropy', 'auto']:\n setattr(obj, 'num_classes', params['num_classes'])\n\n # Initialize Attributes (Optional Values - Based on Default Parameters)\n if 'l2_regularization' not in params or params['l2_regularization'] is None:\n setattr(obj, 'l2_regularization', 0)\n else:\n setattr(obj, 'l2_regularization', params['l2_regularization'])\n\n if 'max_bins' not in params:\n setattr(obj, 'max_bins', 255)\n else:\n setattr(obj, 'max_bins', params['max_bins'])\n\n if 'max_depth' not in params or params['max_depth'] is None:\n setattr(obj, 'max_depth', None)\n else:\n setattr(obj, 'max_depth', params['max_depth'])\n\n if 'max_leaf_nodes' not in params or params['max_leaf_nodes'] is None:\n setattr(obj, 'max_leaf_nodes', 31)\n else:\n setattr(obj, 'max_leaf_nodes', params['max_leaf_nodes'])\n\n if 'min_samples_leaf' not in params or params['min_samples_leaf'] is None:\n setattr(obj, 'min_samples_leaf', 20)\n else:\n setattr(obj, 'min_samples_leaf', params['min_samples_leaf'])\n\n if 'random_state' in params:\n setattr(obj, 'random_state', params['random_state'])\n else:\n setattr(obj, 'random_state', None)\n\n if 'scoring' in params:\n setattr(obj, 'scoring', params['scoring'])\n else:\n setattr(obj, 'scoring', None)\n\n if 'verbose' not in params or params['verbose'] is None:\n setattr(obj, 'verbose', False)\n else:\n setattr(obj, 'verbose', True)\n\n return obj","def get_hyperparameters(self):\n return deepcopy(self._hyperparameters)","def test_get_params():\n\n kwargs = {\n 'population_size': 500,\n 'generations': 1000,\n 'verbosity': 1\n }\n\n tpot_obj = TPOTClassifier(**kwargs)\n\n # Get default parameters of TPOT and merge with our specified parameters\n initializer = inspect.getargspec(TPOTBase.__init__)\n default_kwargs = dict(zip(initializer.args[1:], initializer.defaults))\n default_kwargs.update(kwargs)\n\n assert tpot_obj.get_params() == default_kwargs","def get_params(self):\n pass","def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['low' ] = self.low\n paramDict['high' ] = self.high\n paramDict['alpha'] = self.alpha\n paramDict['beta' ] = self.beta\n return paramDict","def _sample_hyperparameters(self):\n\t\tconfig = {}\n\t\tfor attr, option in self._config_options.items():\n\t\t\tprint('Sampling', attr)\n\t\t\tconfig[attr] = option.sample()\n\t\treturn config","def get_parameters(self):\r\n raise Exception(\"Not implemented (server-side parameter initialization)\")","def supported_parameters(cls):\n raise NotImplementedError()","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['low'] = self.low\n paramDict['alpha'] = self.alpha\n paramDict['beta'] = self.beta\n return paramDict","def set_parameters(self, **kwargs):\n self.__select_k_best.set_params(**kwargs)","def init_params():\n p = {}\n \n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\n \n p['noPolicySim'] = False\n p['multiprocessing'] = True\n p['numberProcessors'] = 9\n p['numRepeats'] = 3\n \n p['startYear'] = 1860\n p['endYear'] = 2040\n p['thePresent'] = 2012\n p['statsCollectFrom'] = 1990\n p['regressionCollectFrom'] = 1960 \n p['implementPoliciesFromYear'] = 2020\n p['yearOutcome'] = 2015\n \n p['favouriteSeed'] = 123\n p['loadFromFile'] = False\n p['verboseDebugging'] = False\n p['singleRunGraphs'] = False\n p['saveChecks'] = True\n p['getCheckVariablesAtYear'] = 2015\n # To change through command-line arguments\n\n p['numberPolicyParameters'] = 2\n p['valuesPerParam'] = 1\n p['numberScenarios'] = 3\n \n ############ Policy Parameters #######################\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\n p['taxBreakRate'] = 0.0\n p['ageOfRetirement'] = 65\n p['socialSupportLevel'] = 5\n # p['educationCosts']\n #############################################################\n p['socialCareCreditShare'] = 0.0\n p['maxWtWChildAge'] = 5\n # The basics: starting population and year, etc.\n \n p['discountingFactor'] = 0.03\n \n \n p['initialPop'] = 600 \n \n p['minStartAge'] = 24\n p['maxStartAge'] = 45\n p['numberClasses'] = 5\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\n p['unemploymentAgeBandParam'] = 0.3\n \n # doDeath function parameters\n p['mortalityBias'] = 0.85 # After 1950\n p['careNeedBias'] = 0.9\n p['unmetCareNeedBias'] = 0.5\n p['baseDieProb'] = 0.0001\n p['babyDieProb'] = 0.005\n p['maleAgeScaling'] = 14.0\n p['maleAgeDieProb'] = 0.00021\n p['femaleAgeScaling'] = 15.5\n p['femaleAgeDieProb'] = 0.00019\n \n p['orphansRelocationParam'] = 0.5\n \n # doBirths function parameters\n p['minPregnancyAge'] = 17\n p['maxPregnancyAge'] = 42\n p['growingPopBirthProb'] = 0.215\n p['fertilityCorrector'] = 1.0\n p['fertilityBias'] = 0.9\n \n # careTransitions function parameters\n p['zeroYearCare'] = 80.0\n p['childcareDecreaseRate'] = 0.25\n p['personCareProb'] = 0.0008\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\n p['baseCareProb'] = 0.0002\n p['careBias'] = 0.9\n p['careTransitionRate'] = 0.7\n\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\n \n p['numCareLevels'] = 5\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\n p['quantumCare'] = 4.0\n \n # careSupplies getCare and probSuppliers function parameters\n \n ######## Key parameter 1 ##############\n \n \n p['weeklyHours'] = 40.0\n \n \n p['priceChildCare'] = 0.76 # 6 \n p['schoolAge'] = 5\n p['maxFormalChildcareHours'] = 48\n p['schoolHours'] = 30\n p['freeChildcareHours'] = 15\n p['workingParentsFreeChildcareHours'] = 30\n p['minAgeStartChildCareSupport'] = 3\n p['minAgeStartChildCareSupportByIncome'] = 2\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\n \n ######## Key parameter 2 ##############\n # 5: No public supply \n \n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\n p['formalCareDiscountFactor'] = 0.5\n \n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\n p['networkDistanceParam'] = 2.0\n p['socialCareWeightBias'] = 1.0\n p['unmetCareNeedDiscountParam'] = 0.5\n p['shareUnmetNeedDiscountParam'] = 0.5\n # p['pastShareUnmetNeedWeight'] = 0.5\n \n \n \n p['networkSizeParam'] = 10.0 # 1.0\n \n p['careSupplyBias'] = 0.5\n p['careIncomeParam'] = 0.001\n \n # Hospitalization Costs\n p['qalyBeta'] = 0.18\n p['qalyAlpha'] = 1.5\n p['qalyDiscountRate'] = 0.035\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\n p['unmetCareHealthParam'] = 0.1\n p['hospitalizationParam'] = 0.5\n p['needLevelParam'] = 2.0\n p['unmetSocialCareParam'] = 2.0\n p['costHospitalizationPerDay'] = 400\n \n # ageTransitions, enterWorkForce and marketWage functions parameters\n p['ageTeenagers'] = 12\n p['minWorkingAge'] = 16\n \n ######## Key parameter 3 ##############\n \n p['careBankingSchemeOn'] = False\n p['socialCareBankingAge'] = 65\n \n p['absoluteCreditQuantity'] = False\n p['quantityYearlyIncrease'] = 0.0\n p['socialCareCreditQuantity'] = 0\n p['kinshipNetworkCarePropension'] = 0.5\n p['volunteersCarePropensionCoefficient'] = 0.01\n p['pensionContributionRate'] = 0.05\n \n p['hillHealthLevelThreshold'] = 3\n p['seriouslyHillSupportRate'] = 0.5\n \n ### Prices ####\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\n p['priceSocialCare'] = 17.0 # [2.29] # 18\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\n p['taxBandsNumber'] = 3\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\n # Tax Break Policy\n\n \n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \n \n # Priced growth #####\n p['wageGrowthRate'] = 1.0 # 1.01338 # \n\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\n \n # SES inter-generational mobility parameters\n p['leaveHomeStudentsProb'] = 0.5\n \n p['eduWageSensitivity'] = 0.2 # 0.5\n p['eduRankSensitivity'] = 3.0 # 5.0\n p['costantIncomeParam'] = 80.0 # 20.0\n p['costantEduParam'] = 10.0 # 10.0\n p['careEducationParam'] = 0.005 # 0.04\n \n \n \n # p['incEduExp'] = 0.25\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\n p['workingAge'] = [16, 18, 20, 22, 24]\n \n # doDivorce function parameters\n p['basicDivorceRate'] = 0.06\n p['variableDivorce'] = 0.06\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n p['divorceBias'] = 1.0\n \n # doMarriages function parameters\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\n p['incomeMarriageParam'] = 0.025\n p['studentFactorParam'] = 0.5\n ######## Key parameter 4 ##############\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\n \n p['betaSocExp'] = 2.0\n p['rankGenderBias'] = 0.5\n p['basicMaleMarriageProb'] = 0.9\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\n \n # jobMarket, updateWork and unemploymentRate functions parameters\n p['unemploymentClassBias'] = 0.75\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\n p['numberAgeBands'] = 6\n p['jobMobilitySlope'] = 0.004\n p['jobMobilityIntercept'] = 0.05\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\n p['deltaIncomeExp'] = 0.05\n p['unemployedCareBurdernParam'] = 0.025\n # Potential key parameter\n p['relocationCareLossExp'] = 1.0 # 40.0 # \n p['incomeSocialCostRelativeWeight'] = 0.5\n \n p['firingParam'] = 0.2\n p['wageVar'] = 0.06\n p['workDiscountingTime'] = 0.75 # 0.8\n p['sizeWeightParam'] = 0.7\n p['minClassWeightParam'] = 1.0\n p['incomeDiscountingExponent'] = 4.0\n p['discountingMultiplier'] = 2.0\n #p['incomeDiscountingParam'] = 2.0\n \n # relocationPensioners function parameters\n p['agingParentsMoveInWithKids'] = 0.1\n p['variableMoveBack'] = 0.1\n p['retiredRelocationParam'] = 0.001 # 0.005\n \n # houseMap function parameters\n p['geoDistanceSensitivityParam'] = 2.0\n p['socDistanceSensitivityParam'] = 2.0\n p['classAffinityWeight'] = 4.0\n p['distanceSensitivityParam'] = 0.5\n \n # relocationProb function parameters\n p['baseRelocatingProb'] = 0.05\n p['relocationParameter'] = 1.0 \n p['apprenticesRelocationProb'] = 0.5\n #p['expReloc'] = 1.0\n \n # computeRelocationCost and relocation Propensity functions parameters\n p['yearsInTownSensitivityParam'] = 0.5\n \n ######## Key parameter 5 ##############\n p['relocationCostParam'] = 0.5 # 1.0 \n \n ######## Key parameter 6 ##############\n p['propensityRelocationParam'] = 2.0 # 2.0 \n p['denRelocationWeight'] = 0.5\n \n \n ## Description of the map, towns, and houses\n p['mapGridXDimension'] = 8\n p['mapGridYDimension'] = 12 \n p['townGridDimension'] = 70\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\n p['mapDensityModifier'] = 0.6\n # p['numHouseClasses'] = 3\n # p['houseClasses'] = ['small','medium','large']\n \n ## Graphical interface details\n p['interactiveGraphics'] = False #True\n p['delayTime'] = 0.0\n p['screenWidth'] = 1300\n p['screenHeight'] = 700\n p['bgColour'] = 'black'\n p['mainFont'] = 'Helvetica 18'\n p['fontColour'] = 'white'\n p['dateX'] = 70\n p['dateY'] = 20\n p['popX'] = 70\n p['popY'] = 50\n p['pixelsInPopPyramid'] = 2000\n p['num5YearAgeClasses'] = 28\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\n p['houseSizeColour'] = ['brown','purple','yellow']\n p['pixelsPerTown'] = 56\n p['maxTextUpdateList'] = 22\n \n # p['eduEduSensitivity'] = 0.5\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\n\n ## Transitions to care statistics\n \n ## Availability of care statistics\n \n #p['childHours'] = 5.0\n # p['employedHours'] = 12.0\n #p['homeAdultHours'] = 30.0\n #p['workingAdultHours'] = 25.0\n #p['maxEmployedHours'] = 60.0\n \n #p['lowCareHandicap'] = 0.5\n #p['hourlyCostOfCare'] = 20.0\n \n ## Fertility statistics\n \n # p['steadyPopBirthProb'] = 0.13\n # p['transitionYear'] = 1965\n \n ## Class and employment statistics\n # p['numClasses'] = 5\n # p['occupationClasses'] = ['lower','intermediate','higher']\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\n\n ## Age transition statistics\n # p['ageOfAdulthood'] = 17\n \n ## Marriage function parameters\n \n # p['basicFemaleMarriageProb'] = 0.25\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\n \n ## Leaving home and moving around statistics\n # p['probApartWillMoveTogether'] = 0.3\n # p['coupleMovesToExistingHousehold'] = 0.3\n # p['basicProbAdultMoveOut'] = 0.22\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbSingleMove'] = 0.05\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\n # p['basicProbFamilyMove'] = 0.03\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\n\n \n return p","def set_parameters(self):\n params = {}\n if self.modelname == 'SI':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after splot\n # Ts: Time from split to present, in 2*Na generation units\n names = ['N1', 'N2', 'Ts']\n values = [1, 1, 1]\n upper_bounds = [20, 20, 10]\n lower_bounds = [0.01, 0.01, 0]\n elif self.modelname == 'IM':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Ts: Time from split to present, in 2*Na generations\n names = ['N1', 'N2', 'm21', 'm12', 'Ts']\n values = [1, 1, 1, 1, 1]\n upper_bounds = [20, 20, 20, 20, 10]\n lower_bounds = [0.01, 0.01, 0, 0, 0]\n elif self.modelname == 'AM':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Tam: Time from end of anc migration to split, in 2*Na gens\n # Ts: Time from split to present, in 2*Na generations\n names = ['N1', 'N2', 'm21', 'm12', 'Tam', 'Ts']\n values = [1, 1, 1, 1, 0.1, 1]\n upper_bounds = [20, 20, 20, 20, 2, 10]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\n elif self.modelname == 'SC':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # Ts: Time from split to secondary contact, in 2*Na generations\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\n names = ['N1', 'N2', 'm21', 'm12', 'Ts', 'Tsc']\n values = [1, 1, 1, 1, 1, 0.1]\n upper_bounds = [20, 20, 20, 20, 10, 2]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\n elif self.modelname == 'IM2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Ts: Time from split to present, in 2*Na generations\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0.05]\n elif self.modelname == 'AM2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Tam: Time from end of anc migration to split, in 2*Na gens\n # Ts: Time from split to present, in 2*Na generations\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Tam', 'Ts', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 0.1, 1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 2, 10, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\n elif self.modelname == 'SC2M':\n # N1: Pop 1 size after split\n # N2: Pop 2 size after split\n # m21: Migration from 1 to 2 (2*Na*mm21)\n # m12: Migration from 2 to 1 (2*Na*m12)\n # mi21: Migration from 1 to 2 in \"islands\" (2*Na*mi21)\n # mi12: Migration from 1 to 2 in \"islands\" (2*Na*mi12)\n # Ts: Time from split to secondary contact, in 2*Na generations\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\n # p: Porpotion of genome evoloving in \"islands\"\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'Tsc', 'p']\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.1, 0.5]\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 2, 0.95]\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\n params['Names'] = names\n params['Values'] = values\n params['Upper'] = upper_bounds\n params['Lower'] = lower_bounds\n return params","def _set_cv_params(self):\n _base_estimator = self.init_params['base_estimator'] \n ada = {'n_estimators': randint(10, 1000),\n 'learning_rate': _uniform(0.01, 0.1)} \n \n if isinstance(_base_estimator, DecisionTreeClassifier().__class__):\n base = {\n 'base_estimator__criterion': ('gini', 'entropy'),\n 'base_estimator__max_depth': randint(1, 8), \n 'base_estimator__min_samples_leaf': randint(2, 20),\n 'base_estimator__max_features': (0.1, 'auto', 'log2'),\n 'base_estimator__class_weight': ('balanced', None) } \n \n elif isinstance(_base_estimator, LogisticRegression().__class__): \n base = {\n 'base_estimator__C': uniform(0, 1000),\n 'base_estimator__fit_intercept': (True, False),\n 'base_estimator__penalty': ('l1', 'l2') } \n else:\n base = {} \n ada.update(base) \n \n return [ada]","def __init__(self, grid, estimator, parameter_search, **kwargs):\n self.kwargs = kwargs\n self.grid = grid\n self.estimator = estimator\n self.parameter_search = parameter_search","def default_params():\n params = {}\n params['dataset'] = 'adult'\n params['engines'] = ['MD','RDA']\n params['iters'] = 10000\n params['epsilon'] = 1.0\n params['delta'] = 0.0\n params['bounded'] = True\n params['frequency'] = 1\n params['seed'] = 0\n params['save'] = None\n params['load'] = None\n params['plot'] = None\n\n return params","def feed_training_args(self):\n return {}","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['location'] = self.location\n paramDict['scale' ] = self.scale\n return paramDict","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['location'] = self.location\n paramDict['scale' ] = self.scale\n return paramDict","def get_parameters(self):\n # Get the parameters from the parent class\n params = super(NREvalSplit, self).get_parameters()\n\n # Add the LP specific parameters\n params.update({\"samp_frac\": self._samp_frac})\n return params","def setParameters(self):\n\n # Set the parameters\n self.taux = 24.2\n self.mu = 0.23\n self.G = 33.75\n self.alpha_0 = 0.05\n self.delta = 0.0075\n self.p = 0.50\n self.I0 = 9500.0\n self.kparam = 0.55","def get_params(self):\n return {'classifier': self.classifier,\n 'grid_param': self.grid_param,\n 'n_param_comb': self.n_param_comb,\n 'top_bagging': self.bagging,\n 'bagging_param': self.bagging_param,\n 'comb_seed': self.comb_seed}","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['p'] = self.p\n return paramDict","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['p'] = self.p\n return paramDict","def initializeParameters(self):\r\n\t\tself.input_raster.enabled = True\r\n\t\tself.approach.enabled = True\r\n\t\tself.predefined_pattern.enabled = False\r\n\t\tself.predefined_pattern.value = 'Mexican Hat wavelet'\r\n\t\tself.pattern_workspace.enabled = False\r\n\t\tself.point_matrix_size.enabled = False\r\n\t\tself.point_matrix_size.value = 3\r\n\t\tself.point_vectors.enabled = False\r\n\t\tself.mapping_field.enabled = False\r\n\t\tself.move_to_max.enabled = False\r\n\t\tself.move_to_max_distance.enabled = False\r\n\t\tself.move_to_max_distance.value = 3\r\n\t\tself.mh_iteration.enabled = False\r\n\t\tself.mh_dil_val.enabled = False\r\n\t\tself.mh_dil_val.value = 1\r\n\t\tself.mh_dil_start.value = 0.01\r\n\t\tself.mh_dil_stop.value = 1\r\n\t\tself.mh_dil_step.value = 0.1\r\n\t\tself.mh_dil_start.enabled = False\r\n\t\tself.mh_dil_stop.enabled = False\r\n\t\tself.mh_dil_step.enabled = False\r\n\t\tself.transform.enabled = False\r\n\t\tself.size_of_the_cell.enabled = False\r\n\t\tself.size_of_the_cell.value = 1\r\n\t\tself.output_sim_matrix.enabled = False\r\n\t\tself.output_table.enabled = False\r\n\t\tself.output_raster_workspace.enabled = False","def __init__(self, parameters, learning_rate):\n self.parameters = parameters\n self.learning_rate = learning_rate","def create_hparams(experiment):\n hparams = {}\n\n # General parameters.\n hparams['batch_size'] = 64\n hparams['eval_batch_size'] = 64\n hparams['learning_rate_warmup_steps'] = 2000\n hparams['learning_rate_constant'] = 1\n hparams['learning_rate'] = 0.001\n hparams['train_epoches'] = 200\n hparams['steps_per_epoch'] = 30\n hparams['train_steps'] = 1000 * 1000\n hparams['eval_steps'] = 100\n hparams['caption_optimizer'] = 't2t'\n hparams['clip_norm'] = 5.0\n hparams['train_files'] = ''\n hparams['eval_files'] = ''\n hparams['train_buffer_size'] = 2000\n hparams['eval_buffer_size'] = 500\n hparams['train_pixel_encoder'] = True\n hparams['debug'] = False\n hparams['distribution_strategy'] = 'mirrored'\n\n # Embedding parameters.\n hparams['embedding_file'] = ''\n hparams['word_vocab_path'] = ''\n hparams['glove_trainable'] = True\n hparams['vocab_size'] = 10000\n\n # View hierarchy encoder parameters.\n hparams['max_pixel_pos'] = 100\n hparams['max_dom_pos'] = 500\n hparams['screen_encoder'] = 'pixel_transformer'\n hparams['screen_embedding_feature'] = ['text', 'type', 'pos', 'click', 'dom']\n hparams['obj_text_aggregation'] = 'max'\n hparams['synthetic_screen_noise'] = 0.\n\n # General parameters.\n hparams['num_hidden_layers'] = 2\n hparams['hidden_size'] = 2\n hparams['filter_size'] = 2\n hparams['num_heads'] = 2\n hparams['dropout'] = 0.2\n hparams['layer_prepostprocess_dropout'] = 0.2\n hparams['attention_dropout'] = 0.2\n hparams['relu_dropout'] = 0.2\n\n transformer_hparams = model_params.BASE_PARAMS\n\n # Add parameters from transformer model.\n hparams.update(transformer_hparams)\n\n # Rewrite all the parameters from command-line flags.\n config = screen2words_experiment_config.experiments[experiment]\n hparams.update(config)\n\n return hparams","def getInitParams(self):\n paramDict = Distribution.getInitParams(self)\n paramDict['strategy'] = self.strategy\n paramDict['nPoints'] = self.nPoints\n return paramDict","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['n' ] = self.n\n paramDict['p' ] = self.p\n return paramDict","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"rAfb[1.0,-5.0, 5.0]\");\n self.modelBuilder.doVar(\"rA0[1.0, -5.0, 5.0]\");\n self.modelBuilder.doSet(\"POI\",\"rAfb,rA0\")\n self.modelBuilder.factory_('expr::mAfb(\"@0*@1\",eAfb,rAfb)')\n self.modelBuilder.factory_('expr::mA0(\"(@0*@1)\",eA0,rA0)')\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"Afb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"A0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"Afb,A0\")\n\n \n self.modelBuilder.factory_('expr::Alph(\"2.0*@0/(2.0-@0)\",A0)')\n self.modelBuilder.factory_('expr::Norm(\"3.0/4.0/(2.0+@0)\",Alph)')\n self.modelBuilder.factory_('expr::RAlph(\"@0*@1\",Alph,Norm)')\n self.modelBuilder.factory_('expr::Rpl(\"(@0+@1)\",Norm,Afb)')\n self.modelBuilder.factory_('expr::Rmn(\"(@0-@1)\",Norm,Afb)')","def get_test_params(cls, parameter_set=\"default\"):\n params1 = {}\n params2 = {\"p_threshold\": 0.1, \"regression\": \"ctt\", \"nlags\": 5}\n\n return [params1, params2]","def getInitParams(self):\n paramDict = BoostDistribution.getInitParams(self)\n paramDict['lambda'] = self.lambdaVar\n paramDict['k' ] = self.k\n paramDict['low' ] = self.low\n return paramDict","def __init__(self, parameter_dictionary):\n super().__init__(parameter_dictionary)\n\n self.model_string = \"gauss\"\n model_dictionary = self._get_model_dict(__class__.default_parameters)\n\n # wake expansion parameters\n self.ka = model_dictionary[\"ka\"]\n self.kb = model_dictionary[\"kb\"]\n\n # near wake / far wake boundary parameters\n self.alpha = model_dictionary[\"alpha\"]\n self.beta = model_dictionary[\"beta\"]\n\n # GCH Parameters\n self.calculate_VW_velocities = model_dictionary[\"calculate_VW_velocities\"]\n self.use_yaw_added_recovery = model_dictionary[\"use_yaw_added_recovery\"]\n self.eps_gain = model_dictionary[\"eps_gain\"]","def set_hyperparameters(self, hyperparameters):\n self._hyperparameters = numpy.copy(hyperparameters)","def __init__(self, parameter_set):\n self.parameter_set = parameter_set.to_parameterization('Meijaard2007')","def doParametersOfInterest(self):\n\n self.modelBuilder.doVar(\"eAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"eA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doVar(\"mAfb[0.6,-0.75,0.75]\");\n self.modelBuilder.doVar(\"mA0[0.05, -1.0, 1.0]\");\n self.modelBuilder.doSet(\"POI\",\"eAfb,mAfb\")\n\n \n self.modelBuilder.factory_('expr::eAlph(\"2.0*@0/(2.0-@0)\",eA0)')\n self.modelBuilder.factory_('expr::eNorm(\"3.0/4.0/(2.0+@0)\",eAlph)')\n self.modelBuilder.factory_('expr::eRAlph(\"@0*@1\",eAlph,eNorm)')\n self.modelBuilder.factory_('expr::eRpl(\"(@0+@1)\",eNorm,eAfb)')\n self.modelBuilder.factory_('expr::eRmn(\"(@0-@1)\",eNorm,eAfb)')\n\n self.modelBuilder.factory_('expr::mAlph(\"2.0*@0/(2.0-@0)\",mA0)')\n self.modelBuilder.factory_('expr::mNorm(\"3.0/4.0/(2.0+@0)\",mAlph)')\n self.modelBuilder.factory_('expr::mRAlph(\"@0*@1\",mAlph,mNorm)')\n self.modelBuilder.factory_('expr::mRpl(\"(@0+@1)\",mNorm,mAfb)')\n self.modelBuilder.factory_('expr::mRmn(\"(@0-@1)\",mNorm,mAfb)')","def test_text_classifier_set_params(self):\n pass","def _get_fitted_params(self):\n return {}","def _generate(self, **kwargs):\n self._create_parameter_set_hashes()\n self._create_parameter_set_names()\n self._create_parameter_study()\n if self.previous_parameter_study:\n self._merge_parameter_studies()","def get_hparams():\n hparams = registry.get_registered_hparams_set(FLAGS.hparams_set)\n hparams.add_hparam(\"inputs_vocab_size\", FLAGS.inputs_vocab_size) \n hparams.add_hparam(\"targets_vocab_size\", FLAGS.targets_vocab_size) \n hparams.parse(FLAGS.hparams)\n return hparams","def __init__(self, temboo_session):\n super(DescribeEvaluations, self).__init__(temboo_session, '/Library/Amazon/MachineLearning/DescribeEvaluations')"],"string":"[\n \"def get_hyperparams(self):\",\n \"def _get_current_hyperparameters(self):\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def hyperparameters(self):\\n hyperparameters = super(TensorFlow, self).hyperparameters()\\n\\n self.checkpoint_path = self.checkpoint_path or self._default_s3_path('checkpoints')\\n mpi_enabled = False\\n\\n if self._script_mode_enabled():\\n additional_hyperparameters = {}\\n\\n if 'parameter_server' in self.distributions:\\n ps_enabled = self.distributions['parameter_server'].get('enabled', False)\\n additional_hyperparameters[self.LAUNCH_PS_ENV_NAME] = ps_enabled\\n\\n if 'mpi' in self.distributions:\\n mpi_dict = self.distributions['mpi']\\n mpi_enabled = mpi_dict.get('enabled', False)\\n additional_hyperparameters[self.LAUNCH_MPI_ENV_NAME] = mpi_enabled\\n additional_hyperparameters[self.MPI_NUM_PROCESSES_PER_HOST] = mpi_dict.get('processes_per_host', 1)\\n additional_hyperparameters[self.MPI_CUSTOM_MPI_OPTIONS] = mpi_dict.get('custom_mpi_options', '')\\n\\n self.model_dir = self.model_dir or self._default_s3_path('model', mpi=mpi_enabled)\\n additional_hyperparameters['model_dir'] = self.model_dir\\n else:\\n additional_hyperparameters = {'checkpoint_path': self.checkpoint_path,\\n 'training_steps': self.training_steps,\\n 'evaluation_steps': self.evaluation_steps,\\n 'sagemaker_requirements': self.requirements_file}\\n\\n hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))\\n return hyperparameters\",\n \"def set_hyperparams(self, params):\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 1e-7\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 5e-4\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def get_hyper_params():\\n #################################\\n ##### INSERT YOUR CODE HERE #####\\n layers_size = [4096, 4096, 10]\\n activation = 'relu'\\n lr = 0.3\\n epochs = 30\\n dropout_rate = 0.2\\n init_kind = 'xavier'\\n ##### END YOUR CODE HERE ########\\n #################################\\n hyper_params = {\\n 'layers_size': layers_size,\\n 'activation': activation,\\n 'lr': lr,\\n 'epochs': epochs,\\n 'init_kind': init_kind,\\n 'dropout_rate': dropout_rate,\\n }\\n return hyper_params\",\n \"def hyperparameters(self):\\n return self._hyperparameters\",\n \"def hyperparameters(self) -> dict[str, Any]:\\n return self.config[\\\"hyperparameters\\\"]\",\n \"def Params(cls):\\n p = hyperparams.InstantiableParams(cls)\\n p.Define('task_dict', None, 'dataset_name -> task params')\\n p.Define('task_name', None, 'High level task name')\\n p.Define('logdir', None, 'Log directory')\\n p.Define('train_program', None, 'Train program params')\\n p.Define('train_executions_per_eval', 1, '')\\n p.Define('eval_programs', [], 'List of eval program params.')\\n p.Define('num_splits_per_client', None, '')\\n p.Define('dataset_names', [], 'List of all dataset names.')\\n p.Define('emails', [], 'List of emails to send metrics.')\\n p.Define('summary_exporter', None, 'The summary exporter Params.')\\n p.Define('async_postprocess', True,\\n 'whether to CPU postprocess asynchronously with TPU train')\\n p.Define(\\n 'checkpoint_to_load', None,\\n 'If set, the program will initially load from this checkpoint, '\\n 'ignoring train_dir. Typically used for oneoff decode.')\\n\\n # TODO(blee): Clean these up.\\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\\n mlp = p.ml_perf\\n mlp.Define('submission_metadata', None,\\n 'A dictionary of static submission metadata')\\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\\n mlp.Define('decoder_metric_name', None,\\n 'Name of the decoder metric to report for compliance log.')\\n mlp.Define('decoder_metric_success_threshold', None,\\n 'Benchmark run must exceed this value to succeed.')\\n mlp.Define('max_steps_to_train', None,\\n 'Maximum number of steps to reach target accuracy')\\n return p\",\n \"def Params(cls):\\n p = hyperparams.InstantiableParams(cls)\\n\\n p.Define('task_dict', None, 'dataset_name -> task params')\\n p.Define('task_name', None, 'High level task name')\\n p.Define('logdir', None, 'Log directory')\\n p.Define('train_program', None, 'Train program params')\\n p.Define('train_executions_per_eval', 1, '')\\n p.Define('dataset_names', [], 'List of all dataset names.')\\n p.Define('num_splits_per_client', None, '')\\n\\n p.Define('ml_perf', hyperparams.Params(), 'MlPerf configuration.')\\n\\n mlp = p.ml_perf\\n mlp.Define('benchmark_name', None, 'Benchmark name for compliance log.')\\n mlp.Define('decoder_metric_name', None,\\n 'Name of the decoder metric to report for compliance log.')\\n mlp.Define('decoder_metric_success_threshold', None,\\n 'Benchmark run must exceed this value to succeed.')\\n mlp.Define('max_steps_to_train', None,\\n 'Maximum number of steps to reach target accuracy')\\n mlp.Define('steps_per_epoch', None, 'Number of training steps per epoch.')\\n mlp.Define('global_batch_size', None, 'Global batch size.')\\n mlp.Define('max_sequence_length', None, 'Maximum sequence length.')\\n mlp.Define('optimizer_name', None, 'Optimizer used.')\\n mlp.Define('base_learning_rate', None, 'Base learning rate.')\\n mlp.Define('warmup_steps', None, 'Number of warm-up steps.')\\n\\n return p\",\n \"def define_parameters(self):\",\n \"def Params(cls):\\n p = hyperparams.InstantiableParams(cls)\\n p.Define('task', None, 'Underlying task')\\n p.Define('logdir', None, 'Log directory')\\n p.Define('num_splits_per_client', None, '')\\n p.Define('steps_per_loop', None, 'Number of steps to run.')\\n p.Define('dataset_name', None,\\n 'Dataset the program is operating on, eg: \\\"Test\\\"')\\n p.Define('name', 'base_program', 'Program name.')\\n p.Define('task_name', None,\\n 'If multi-task, what the high-level task name is')\\n p.Define('num_threads', 1, 'Number of threads in multiprocessing pool.')\\n p.Define('spmd', False, 'Whether program is running under SPMD mode.')\\n p.Define('write_train_input_stats', False,\\n 'Whether to write input data stats during training.')\\n p.Define('max_metrics', 256, 'Overrides TpuEvalMetrics.max_metrics')\\n p.Define('ml_perf', None, 'MLPerf config')\\n return p\",\n \"def set_hyperparams(use_defaults):\\n if use_defaults:\\n n_neurons, n_hidden, n_steps, k_prob = default_hyperparams()\\n return n_neurons, n_hidden, n_steps, k_prob\\n\\n print (\\\"Select number of neurons in recurrent layer (default \\\" +\\n \\\"100):\\\")\\n n_neurons = int(input())\\n print (\\\"Select number of hidden neurons in fully connected \\\" +\\n \\\"layer (default 100):\\\")\\n n_hidden = int(input())\\n print (\\\"Select n_steps; the max number of words to be read \\\" +\\n \\\"from each abstract (default 50):\\\")\\n n_steps = int(input())\\n print (\\\"Select k_prob; the dropout probability (default 0.5):\\\")\\n k_prob = float(input())\\n\\n return n_neurons, n_hidden, n_steps, k_prob\",\n \"def Params(cls):\\n return hyperparams.InstantiableParams(cls)\",\n \"def get_params(self):\\n return {'threshold': self.threshold,\\n 'subsample': self.subsample,\\n 'estimator': self.estimator,\\n 'n_folds': self.n_folds,\\n 'stratify': self.stratify,\\n 'random_state': self.random_state,\\n 'n_jobs': self.n_jobs}\",\n \"def parameters(self):\",\n \"def parameters(self):\\n raise NotImplementedError('Abstract method \\\"parameters\\\" must be '\\n 'specialised!')\",\n \"def _starting_hparams():\\n hparams = contrib_training.HParams()\\n hparams.add_hparam('batch_style', 'bucket')\\n hparams.add_hparam('gradient_clipping_decay', 0.9999)\\n hparams.add_hparam('learning_rate', 0.0005)\\n hparams.add_hparam('lr_decay_rate', .997)\\n hparams.add_hparam('lr_decay_steps', 1000)\\n hparams.add_hparam('lr_warmup_steps', 3000)\\n hparams.add_hparam('model_type', 'cnn')\\n hparams.add_hparam('resnet_bottleneck_factor', 0.5)\\n hparams.add_hparam('decision_threshold', 0.5)\\n hparams.add_hparam('denominator_power', 1.0) # Standard mean-pooling.\\n return hparams\",\n \"def build(self):\\n return self.hyperparams.items()\",\n \"def parameters(self):\\n pass\",\n \"def hyperparams():\\n H = 6\\n return Munch(N=500, H=H, D=(H // 2) ** 2, batch_size=10, precision=to.float32)\",\n \"def set_params(self):\\n raise NotImplementedError\",\n \"def init_hyperparameters():\\n alpha = .8\\n alpha2 = 1\\n\\n return alpha, alpha2\",\n \"def parameters(self) -> Dict[str, Any]:\\n return self.trainer_parameters\",\n \"def gen_parameters(self):\\n\\n print \\\"\\\\t* Adding parameters to compute template\\\"\\n # get all the server client\\n servers = self.novaclient.servers.list()\\n\\n # add all key_pair_names\\n self.gen_key_name_parameters(servers)\\n\\n # add all images\\n self.gen_image_parameters(servers)\\n\\n # add all flavors\\n self.gen_flavor_parameters(servers)\\n\\n # add all networks\\n self.gen_network_parameters()\",\n \"def set_params(self):\\r\\n pass\",\n \"def test_base_hyper_parameters_reg(self):\\n hyper_parameter_set = modelgen.generate_base_hyper_parameter_set()\\n assert 'regularization_rate' in hyper_parameter_set.keys()\",\n \"def __init__(self, params, data):\\n super(HyperOptNoTraining, self).__init__(params, data)\\n self.objective = None\\n self.trial_losses = None\\n self.best_trial = None\\n self.trial_list = None\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rbk[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd_emu[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rbk,Rdy,Rqcd_emu\\\")\",\n \"def get_params(self):\",\n \"def Parameters():\\n\\n raise NotImplementedError()\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def parameters(self):\\n return self.trainer_parameters\",\n \"def set_params(self, **params):\\n if('threshold' in params.keys()):\\n self.threshold = params['threshold']\\n if('subsample' in params.keys()):\\n self.subsample = params['subsample']\\n if('estimator' in params.keys()):\\n self.estimator = params['estimator']\\n if('n_folds' in params.keys()):\\n self.n_folds = params['n_folds']\\n if('stratify' in params.keys()):\\n self.stratify = params['stratify']\\n if('random_state' in params.keys()):\\n self.random_state = params['random_state']\\n if('n_jobs' in params.keys()):\\n self.n_jobs = params['n_jobs']\",\n \"def Params(cls):\\n p = super().Params()\\n p.Define('train_task', None, 'Underlying task')\\n p.Define('decode_task', None, 'Underlying task')\\n p.Define('train_dataset_name', None, '')\\n p.Define('decode_dataset_name', None, '')\\n p.Define('train_steps_per_loop', 0, '')\\n p.Define('decode_steps_per_loop', 0, '')\\n return p\",\n \"def get_parameters(self):\\n # Get the parameters from the parent class\\n params = super(LPEvalSplit, self).get_parameters()\\n\\n # Add the LP specific parameters\\n params.update({\\\"owa\\\": self._owa, \\\"fe_ratio\\\": self._fe_ratio})\\n return params\",\n \"def __init__(self, **kwargs):\\n # Register the hyperparameters and their type in _hparam_types.\\n # _hparam_types maps the parameter name to a tuple (type, bool).\\n # The type value is the type of the parameter for scalar hyperparameters,\\n # or the type of the list elements for multidimensional hyperparameters.\\n # The bool value is True if the value is a list, False otherwise.\\n self._hparam_types = {}\\n for name, value in six.iteritems(kwargs):\\n self.add_hparam(name, value)\",\n \"def get_params(self):\\n raise NotImplementedError\",\n \"def __init__(self):\\n self.param_names = []\\n self.param_values = []\\n self.param_settings = []\\n self.result = []\\n self.best_params = None\\n self.best_score = None\\n self.max_reps = 5\\n self.num_values = False\\n self.algorithm_done = False\",\n \"def setup_parameters(self):\\n structure = self.ctx.structure_initial_primitive\\n ecutwfc = []\\n ecutrho = []\\n\\n for kind in structure.get_kind_names():\\n try:\\n dual = self.ctx.protocol['pseudo_data'][kind]['dual']\\n cutoff = self.ctx.protocol['pseudo_data'][kind]['cutoff']\\n cutrho = dual * cutoff\\n ecutwfc.append(cutoff)\\n ecutrho.append(cutrho)\\n except KeyError as exception:\\n self.abort_nowait('failed to retrieve the cutoff or dual factor for {}'.format(kind))\\n\\n natoms = len(structure.sites)\\n conv_thr = self.ctx.protocol['convergence_threshold'] * natoms\\n\\n self.ctx.inputs['parameters'] = {\\n 'CONTROL': {\\n 'restart_mode': 'from_scratch',\\n 'tstress': self.ctx.protocol['tstress'],\\n },\\n 'SYSTEM': {\\n 'ecutwfc': max(ecutwfc),\\n 'ecutrho': max(ecutrho),\\n 'smearing': self.ctx.protocol['smearing'],\\n 'degauss': self.ctx.protocol['degauss'],\\n 'occupations': self.ctx.protocol['occupations'],\\n },\\n 'ELECTRONS': {\\n 'conv_thr': conv_thr,\\n }\\n }\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Rdy[1.,0.0,10.0]\\\");\\n self.modelBuilder.doVar(\\\"Rqcd[1,0.0,10.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Rdy,Rqcd\\\")\",\n \"def set_params(self, params: Dict):\\n\\n if params['training_instances'] is not None:\\n self.training_instances = params['training_instances']\\n if params['n'] is not None:\\n self.n = params['n']\\n if params['lda'] is not None:\\n self.lda = params['lda']\\n if params['verbose'] is not None:\\n self.verbose = params['verbose']\\n\\n self.num_features = self.training_instances[0].get_feature_count()\\n self.w = None\\n self.b = None\",\n \"def params():\\n raise NotImplementedError\",\n \"def getParams(self):\\n return self.trainError, self.trainAcc, self.w\",\n \"def __init__(self, hyperparameters):\\n self._hyperparameters = numpy.copy(hyperparameters)\",\n \"def hyperparameter_tune(X_train_all, y_train_all, X_val_all, y_val_all, hyperparameters, train_size=None):\\n # Initialize train_size to be the full train data if no parameters passed\\n if train_size is None:\\n train_size = len(X_train_all)\\n\\n # Select Subset of Data\\n X_train = X_train_all[:train_size]\\n X_val = X_val_all[:train_size]\\n y_train = y_train_all[:train_size]\\n y_val = y_val_all[:train_size]\\n\\n # Create Grid of hyperparameters\\n grid = cartesian_product(hyperparameters)\\n\\n # Loop through hyperparameters \\n best_score = 0\\n for hyperparameter in grid:\\n # Initialize Model\\n model = svm.SVC(kernel='linear', **hyperparameter)\\n\\n # Fit Model\\n print(f\\\"Training using hyperparameters: {hyperparameter}\\\")\\n model.fit(X_train, y_train)\\n\\n # Predict Values on Validation Set\\n val_pred = model.predict(X_val)\\n\\n # Get Accuracy\\n score = accuracy_score(y_val, val_pred)\\n print(f\\\"Accuracy Score: {score}\\\")\\n\\n if score > best_score:\\n best_score = score\\n best_parameters = hyperparameter\\n \\n return best_score, best_parameters\",\n \"def set_param_grid(self):\\n\\n # Interpret the request data based on the expected row and column structure\\n row_template = ['strData', 'strData', 'strData']\\n col_headers = ['model_name', 'estimator_args', 'grid_search_args']\\n \\n # Create a Pandas Data Frame for the request data\\n self.request_df = utils.request_df(self.request, row_template, col_headers)\\n \\n # Initialize the persistent model\\n self.model = PersistentModel()\\n \\n # Get the model name from the request dataframe\\n self.model.name = self.request_df.loc[0, 'model_name']\\n \\n # Get the estimator's hyperparameter grid from the request dataframe\\n param_grid = self.request_df.loc[:, 'estimator_args']\\n\\n # Get the grid search arguments from the request dataframe\\n grid_search_args = self.request_df.loc[0, 'grid_search_args']\\n\\n # Get the model from cache or disk\\n self._get_model()\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(3)\\n\\n self._set_grid_params(param_grid, grid_search_args)\\n \\n # Persist the model to disk\\n self.model = self.model.save(self.model.name, self.path, overwrite=self.model.overwrite, compress=self.model.compress)\\n \\n # Update the cache to keep this model in memory\\n self._update_cache()\\n \\n # Prepare the output\\n message = [[self.model.name, 'Hyperparameter grid successfully saved to disk',\\\\\\n time.strftime('%X %x %Z', time.localtime(self.model.state_timestamp))]]\\n self.response = pd.DataFrame(message, columns=['model_name', 'result', 'time_stamp'])\\n \\n # Send the reponse table description to Qlik\\n self._send_table_description(\\\"setup\\\")\\n \\n # Debug information is printed to the terminal and logs if the paramater debug = true\\n if self.model.debug:\\n self._print_log(4)\\n \\n # Finally send the response\\n return self.response\",\n \"def learnHyperparams(self, report=None):\\n \\n self.theta = crossValidate(self.k, self.cvEpochs, self.hpRanges, self.trainSet, \\\\\\n self.train, self.test, report);\",\n \"def __init__(self):\\n # super(MultiEmbedding,self).__init__()\\n HyperParameters.__init__(self)\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['range'] = self.range\\n return paramDict\\n # no other additional parameters required\",\n \"def __init__(self, epsilon=0.05,gamma=0.8,alpha=0.2, numTraining=0, **args):\\n args['epsilon'] = epsilon\\n args['gamma'] = gamma\\n args['alpha'] = alpha\\n args['numTraining'] = numTraining\\n QLearningAgent.__init__(self, **args)\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['apex' ] = self.apex\\n paramDict['min' ] = self.min\\n paramDict['max' ] = self.max\\n return paramDict\",\n \"def get_test_params(cls, parameter_set=\\\"default\\\"):\\n params1 = {\\n \\\"n_clusters\\\": 3,\\n \\\"n_init\\\": 2,\\n \\\"max_iter\\\": 2,\\n \\\"tol\\\": 1e-3,\\n \\\"verbose\\\": False,\\n \\\"random_state\\\": 2,\\n }\\n params2 = {\\n \\\"n_clusters\\\": 2,\\n \\\"init_algorithm\\\": \\\"random\\\",\\n \\\"n_init\\\": 1,\\n \\\"max_iter\\\": 1,\\n \\\"tol\\\": 1e-4,\\n \\\"verbose\\\": False,\\n \\\"random_state\\\": 1,\\n }\\n return [params1, params2]\",\n \"def set_params(self, **kwargs):\\n\\n kw_keys = list(kwargs)\\n\\n if 'alpha' in kw_keys:\\n self.alpha = kwargs['alpha']\\n\\n if 'beta' in kw_keys:\\n self.beta = kwargs['beta']\\n\\n if 'gamma' in kw_keys: \\n \\tself.gamma = kwargs['gamma']\\n\\n if 'epsilon' in kw_keys:\\n self.epsilon = kwargs['epsilon']\\n \\n self.nact = self.highbound-self.lowbound\\n self.actions = np.arange(self.nact)\",\n \"def init_parameters(obj, hyperparameters):\\n # Initialize Global Configuration Parameter\\n params = hyperparameters['global']\\n setattr(obj, 'param', params)\\n\\n # Initialize Attributes (Pre-Checked Parameters)\\n setattr(obj, 'learning_rate', params['learning_rate'])\\n setattr(obj, 'loss', params['loss'])\\n setattr(obj, 'max_iter', params['max_iter'])\\n\\n if params['loss'] == 'least_squares':\\n setattr(obj, 'num_classes', 1)\\n elif params['loss'] in ['binary_crossentropy', 'categorical_crossentropy', 'auto']:\\n setattr(obj, 'num_classes', params['num_classes'])\\n\\n # Initialize Attributes (Optional Values - Based on Default Parameters)\\n if 'l2_regularization' not in params or params['l2_regularization'] is None:\\n setattr(obj, 'l2_regularization', 0)\\n else:\\n setattr(obj, 'l2_regularization', params['l2_regularization'])\\n\\n if 'max_bins' not in params:\\n setattr(obj, 'max_bins', 255)\\n else:\\n setattr(obj, 'max_bins', params['max_bins'])\\n\\n if 'max_depth' not in params or params['max_depth'] is None:\\n setattr(obj, 'max_depth', None)\\n else:\\n setattr(obj, 'max_depth', params['max_depth'])\\n\\n if 'max_leaf_nodes' not in params or params['max_leaf_nodes'] is None:\\n setattr(obj, 'max_leaf_nodes', 31)\\n else:\\n setattr(obj, 'max_leaf_nodes', params['max_leaf_nodes'])\\n\\n if 'min_samples_leaf' not in params or params['min_samples_leaf'] is None:\\n setattr(obj, 'min_samples_leaf', 20)\\n else:\\n setattr(obj, 'min_samples_leaf', params['min_samples_leaf'])\\n\\n if 'random_state' in params:\\n setattr(obj, 'random_state', params['random_state'])\\n else:\\n setattr(obj, 'random_state', None)\\n\\n if 'scoring' in params:\\n setattr(obj, 'scoring', params['scoring'])\\n else:\\n setattr(obj, 'scoring', None)\\n\\n if 'verbose' not in params or params['verbose'] is None:\\n setattr(obj, 'verbose', False)\\n else:\\n setattr(obj, 'verbose', True)\\n\\n return obj\",\n \"def get_hyperparameters(self):\\n return deepcopy(self._hyperparameters)\",\n \"def test_get_params():\\n\\n kwargs = {\\n 'population_size': 500,\\n 'generations': 1000,\\n 'verbosity': 1\\n }\\n\\n tpot_obj = TPOTClassifier(**kwargs)\\n\\n # Get default parameters of TPOT and merge with our specified parameters\\n initializer = inspect.getargspec(TPOTBase.__init__)\\n default_kwargs = dict(zip(initializer.args[1:], initializer.defaults))\\n default_kwargs.update(kwargs)\\n\\n assert tpot_obj.get_params() == default_kwargs\",\n \"def get_params(self):\\n pass\",\n \"def __init__(self, parameters: ParametersList, algorithm: ClassVar, algorithm_data: AlgorithmData):\\n super(GreedyTrain, self).__init__(parameters, algorithm, algorithm_data)\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['low' ] = self.low\\n paramDict['high' ] = self.high\\n paramDict['alpha'] = self.alpha\\n paramDict['beta' ] = self.beta\\n return paramDict\",\n \"def _sample_hyperparameters(self):\\n\\t\\tconfig = {}\\n\\t\\tfor attr, option in self._config_options.items():\\n\\t\\t\\tprint('Sampling', attr)\\n\\t\\t\\tconfig[attr] = option.sample()\\n\\t\\treturn config\",\n \"def get_parameters(self):\\r\\n raise Exception(\\\"Not implemented (server-side parameter initialization)\\\")\",\n \"def supported_parameters(cls):\\n raise NotImplementedError()\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['low'] = self.low\\n paramDict['alpha'] = self.alpha\\n paramDict['beta'] = self.beta\\n return paramDict\",\n \"def set_parameters(self, **kwargs):\\n self.__select_k_best.set_params(**kwargs)\",\n \"def init_params():\\n p = {}\\n \\n # p['rootFolder'] = 'C:/Users/Umberto Gostoli/SPHSU/Social Care Model II'\\n # p['rootFolder'] = 'N:/Social Care Model Paper III'\\n \\n p['noPolicySim'] = False\\n p['multiprocessing'] = True\\n p['numberProcessors'] = 9\\n p['numRepeats'] = 3\\n \\n p['startYear'] = 1860\\n p['endYear'] = 2040\\n p['thePresent'] = 2012\\n p['statsCollectFrom'] = 1990\\n p['regressionCollectFrom'] = 1960 \\n p['implementPoliciesFromYear'] = 2020\\n p['yearOutcome'] = 2015\\n \\n p['favouriteSeed'] = 123\\n p['loadFromFile'] = False\\n p['verboseDebugging'] = False\\n p['singleRunGraphs'] = False\\n p['saveChecks'] = True\\n p['getCheckVariablesAtYear'] = 2015\\n # To change through command-line arguments\\n\\n p['numberPolicyParameters'] = 2\\n p['valuesPerParam'] = 1\\n p['numberScenarios'] = 3\\n \\n ############ Policy Parameters #######################\\n p['incomeCareParam'] = 0.0005 #[0.00025 - 0.001]\\n p['taxBreakRate'] = 0.0\\n p['ageOfRetirement'] = 65\\n p['socialSupportLevel'] = 5\\n # p['educationCosts']\\n #############################################################\\n p['socialCareCreditShare'] = 0.0\\n p['maxWtWChildAge'] = 5\\n # The basics: starting population and year, etc.\\n \\n p['discountingFactor'] = 0.03\\n \\n \\n p['initialPop'] = 600 \\n \\n p['minStartAge'] = 24\\n p['maxStartAge'] = 45\\n p['numberClasses'] = 5\\n p['socialClasses'] = ['unskilled', 'skilled', 'lower', 'middle', 'upper']\\n p['initialClassShares'] = [0.2, 0.25, 0.3, 0.2, 0.05]\\n p['initialUnemployment'] = [0.25, 0.2, 0.15, 0.1, 0.1]\\n p['unemploymentAgeBandParam'] = 0.3\\n \\n # doDeath function parameters\\n p['mortalityBias'] = 0.85 # After 1950\\n p['careNeedBias'] = 0.9\\n p['unmetCareNeedBias'] = 0.5\\n p['baseDieProb'] = 0.0001\\n p['babyDieProb'] = 0.005\\n p['maleAgeScaling'] = 14.0\\n p['maleAgeDieProb'] = 0.00021\\n p['femaleAgeScaling'] = 15.5\\n p['femaleAgeDieProb'] = 0.00019\\n \\n p['orphansRelocationParam'] = 0.5\\n \\n # doBirths function parameters\\n p['minPregnancyAge'] = 17\\n p['maxPregnancyAge'] = 42\\n p['growingPopBirthProb'] = 0.215\\n p['fertilityCorrector'] = 1.0\\n p['fertilityBias'] = 0.9\\n \\n # careTransitions function parameters\\n p['zeroYearCare'] = 80.0\\n p['childcareDecreaseRate'] = 0.25\\n p['personCareProb'] = 0.0008\\n p['maleAgeCareScaling'] = 18.0 # p['maleAgeCareProb'] = 0.0008\\n p['femaleAgeCareScaling'] = 19.0 # p['femaleAgeCareProb'] = 0.0008\\n p['baseCareProb'] = 0.0002\\n p['careBias'] = 0.9\\n p['careTransitionRate'] = 0.7\\n\\n p['unmetNeedExponent'] = 1.0 # 0.005 #[0.005 - 0.02]\\n \\n p['numCareLevels'] = 5\\n p['careLevelNames'] = ['none','low','moderate','substantial','critical']\\n p['careDemandInHours'] = [ 0.0, 8.0, 16.0, 32.0, 80.0 ]\\n p['quantumCare'] = 4.0\\n \\n # careSupplies getCare and probSuppliers function parameters\\n \\n ######## Key parameter 1 ##############\\n \\n \\n p['weeklyHours'] = 40.0\\n \\n \\n p['priceChildCare'] = 0.76 # 6 \\n p['schoolAge'] = 5\\n p['maxFormalChildcareHours'] = 48\\n p['schoolHours'] = 30\\n p['freeChildcareHours'] = 15\\n p['workingParentsFreeChildcareHours'] = 30\\n p['minAgeStartChildCareSupport'] = 3\\n p['minAgeStartChildCareSupportByIncome'] = 2\\n p['maxHouseholdIncomeChildCareSupport'] = 40 # 320\\n \\n ######## Key parameter 2 ##############\\n # 5: No public supply \\n \\n p['retiredHours'] = [48.0, 36.0, 20.0, 10.0] # 60.0\\n p['studentHours'] = [24.0, 16.0, 8.0, 4.0]\\n p['teenAgersHours'] = [16.0, 0.0, 0.0, 0.0]\\n p['unemployedHours'] = [32.0, 24.0, 16.0, 8.0]\\n p['employedHours'] = [28.0, 20.0, 12.0, 8.0]\\n p['formalCareDiscountFactor'] = 0.5\\n \\n p['socialNetworkDistances'] = [0.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0]\\n p['networkDistanceParam'] = 2.0\\n p['socialCareWeightBias'] = 1.0\\n p['unmetCareNeedDiscountParam'] = 0.5\\n p['shareUnmetNeedDiscountParam'] = 0.5\\n # p['pastShareUnmetNeedWeight'] = 0.5\\n \\n \\n \\n p['networkSizeParam'] = 10.0 # 1.0\\n \\n p['careSupplyBias'] = 0.5\\n p['careIncomeParam'] = 0.001\\n \\n # Hospitalization Costs\\n p['qalyBeta'] = 0.18\\n p['qalyAlpha'] = 1.5\\n p['qalyDiscountRate'] = 0.035\\n p['qalyIndexes'] = [1.0, 0.8, 0.6, 0.4, 0.2]\\n p['unmetCareHealthParam'] = 0.1\\n p['hospitalizationParam'] = 0.5\\n p['needLevelParam'] = 2.0\\n p['unmetSocialCareParam'] = 2.0\\n p['costHospitalizationPerDay'] = 400\\n \\n # ageTransitions, enterWorkForce and marketWage functions parameters\\n p['ageTeenagers'] = 12\\n p['minWorkingAge'] = 16\\n \\n ######## Key parameter 3 ##############\\n \\n p['careBankingSchemeOn'] = False\\n p['socialCareBankingAge'] = 65\\n \\n p['absoluteCreditQuantity'] = False\\n p['quantityYearlyIncrease'] = 0.0\\n p['socialCareCreditQuantity'] = 0\\n p['kinshipNetworkCarePropension'] = 0.5\\n p['volunteersCarePropensionCoefficient'] = 0.01\\n p['pensionContributionRate'] = 0.05\\n \\n p['hillHealthLevelThreshold'] = 3\\n p['seriouslyHillSupportRate'] = 0.5\\n \\n ### Prices ####\\n p['pricePublicSocialCare'] = 20.0 # [2.55] # 20\\n p['priceSocialCare'] = 17.0 # [2.29] # 18\\n p['taxBrackets'] = [663, 228, 0] # [28.16, 110.23] # [221, 865]\\n p['taxBandsNumber'] = 3\\n p['bandsTaxationRates'] = [0.4, 0.2, 0.0] # [0.0, 0.2, 0.4]\\n # Tax Break Policy\\n\\n \\n p['pensionWage'] = [5.0, 7.0, 10.0, 13.0, 18.0] # [0.64, 0.89, 1.27, 1.66, 2.29] # \\n p['incomeInitialLevels'] = [5.0, 7.0, 9.0, 11.0, 14.0] #[0.64, 0.89, 1.15, 1.40, 1.78] # \\n p['incomeFinalLevels'] = [10.0, 15.0, 22.0, 33.0, 50.0] #[1.27, 1.91, 2.80, 4.21, 6.37] # \\n p['educationCosts'] = [0.0, 100.0, 150.0, 200.0] #[0.0, 12.74, 19.12, 25.49] # \\n \\n # Priced growth #####\\n p['wageGrowthRate'] = 1.0 # 1.01338 # \\n\\n p['incomeGrowthRate'] = [0.4, 0.35, 0.35, 0.3, 0.25]\\n \\n # SES inter-generational mobility parameters\\n p['leaveHomeStudentsProb'] = 0.5\\n \\n p['eduWageSensitivity'] = 0.2 # 0.5\\n p['eduRankSensitivity'] = 3.0 # 5.0\\n p['costantIncomeParam'] = 80.0 # 20.0\\n p['costantEduParam'] = 10.0 # 10.0\\n p['careEducationParam'] = 0.005 # 0.04\\n \\n \\n \\n # p['incEduExp'] = 0.25\\n p['educationLevels'] = ['GCSE', 'A-Level', 'HND', 'Degree', 'Higher Degree']\\n p['workingAge'] = [16, 18, 20, 22, 24]\\n \\n # doDivorce function parameters\\n p['basicDivorceRate'] = 0.06\\n p['variableDivorce'] = 0.06\\n p['divorceModifierByDecade'] = [ 0.0, 1.0, 0.9, 0.5, 0.4, 0.2, 0.1, 0.03, 0.01, 0.001, 0.001, 0.001, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n p['divorceBias'] = 1.0\\n \\n # doMarriages function parameters\\n p['deltageProb'] = [0.0, 0.1, 0.25, 0.4, 0.2, 0.05]\\n p['incomeMarriageParam'] = 0.025\\n p['studentFactorParam'] = 0.5\\n ######## Key parameter 4 ##############\\n p['betaGeoExp'] = 2.0 #[1.0 - 4.0]\\n \\n p['betaSocExp'] = 2.0\\n p['rankGenderBias'] = 0.5\\n p['basicMaleMarriageProb'] = 0.9\\n p['maleMarriageModifierByDecade'] = [ 0.0, 0.16, 0.5, 1.0, 0.8, 0.7, 0.66, 0.5, 0.4, 0.2, 0.1, 0.05, 0.01, 0.0, 0.0, 0.0, 0.0 ]\\n \\n # jobMarket, updateWork and unemploymentRate functions parameters\\n p['unemploymentClassBias'] = 0.75\\n p['unemploymentAgeBias'] = [1.0, 0.55, 0.35, 0.25, 0.2, 0.2]\\n p['numberAgeBands'] = 6\\n p['jobMobilitySlope'] = 0.004\\n p['jobMobilityIntercept'] = 0.05\\n p['ageBiasParam'] = [7.0, 3.0, 1.0, 0.5, 0.35, 0.15]\\n p['deltaIncomeExp'] = 0.05\\n p['unemployedCareBurdernParam'] = 0.025\\n # Potential key parameter\\n p['relocationCareLossExp'] = 1.0 # 40.0 # \\n p['incomeSocialCostRelativeWeight'] = 0.5\\n \\n p['firingParam'] = 0.2\\n p['wageVar'] = 0.06\\n p['workDiscountingTime'] = 0.75 # 0.8\\n p['sizeWeightParam'] = 0.7\\n p['minClassWeightParam'] = 1.0\\n p['incomeDiscountingExponent'] = 4.0\\n p['discountingMultiplier'] = 2.0\\n #p['incomeDiscountingParam'] = 2.0\\n \\n # relocationPensioners function parameters\\n p['agingParentsMoveInWithKids'] = 0.1\\n p['variableMoveBack'] = 0.1\\n p['retiredRelocationParam'] = 0.001 # 0.005\\n \\n # houseMap function parameters\\n p['geoDistanceSensitivityParam'] = 2.0\\n p['socDistanceSensitivityParam'] = 2.0\\n p['classAffinityWeight'] = 4.0\\n p['distanceSensitivityParam'] = 0.5\\n \\n # relocationProb function parameters\\n p['baseRelocatingProb'] = 0.05\\n p['relocationParameter'] = 1.0 \\n p['apprenticesRelocationProb'] = 0.5\\n #p['expReloc'] = 1.0\\n \\n # computeRelocationCost and relocation Propensity functions parameters\\n p['yearsInTownSensitivityParam'] = 0.5\\n \\n ######## Key parameter 5 ##############\\n p['relocationCostParam'] = 0.5 # 1.0 \\n \\n ######## Key parameter 6 ##############\\n p['propensityRelocationParam'] = 2.0 # 2.0 \\n p['denRelocationWeight'] = 0.5\\n \\n \\n ## Description of the map, towns, and houses\\n p['mapGridXDimension'] = 8\\n p['mapGridYDimension'] = 12 \\n p['townGridDimension'] = 70\\n p['cdfHouseClasses'] = [ 0.6, 0.9, 5.0 ]\\n p['ukMap'] = [[ 0.0, 0.1, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.1, 0.1, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.2, 1.0, 0.5, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.4, 0.0, 0.2, 0.2, 0.4, 0.0, 0.0, 0.0 ],\\n [ 0.6, 0.0, 0.0, 0.3, 0.8, 0.2, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.6, 0.8, 0.4, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 1.0, 0.8, 0.6, 0.1, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.2, 1.0, 0.6, 0.3, 0.4 ],\\n [ 0.0, 0.0, 0.5, 0.7, 0.5, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.0, 0.2, 0.4, 0.6, 1.0, 1.0, 0.0 ],\\n [ 0.0, 0.2, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 ]]\\n p['ukClassBias'] = [[ 0.0, -0.05, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, -0.05, -0.05, 0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ -0.05, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, -0.05, -0.05, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, -0.05, 0.0, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, -0.05, 0.0, -0.05, 0.0, 0.0, 0.0 ],\\n [ 0.0, 0.0, 0.0, -0.05, 0.0, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.0, 0.0, 0.1, 0.2, 0.15, 0.0 ],\\n [ 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0 ] ]\\n p['mapDensityModifier'] = 0.6\\n # p['numHouseClasses'] = 3\\n # p['houseClasses'] = ['small','medium','large']\\n \\n ## Graphical interface details\\n p['interactiveGraphics'] = False #True\\n p['delayTime'] = 0.0\\n p['screenWidth'] = 1300\\n p['screenHeight'] = 700\\n p['bgColour'] = 'black'\\n p['mainFont'] = 'Helvetica 18'\\n p['fontColour'] = 'white'\\n p['dateX'] = 70\\n p['dateY'] = 20\\n p['popX'] = 70\\n p['popY'] = 50\\n p['pixelsInPopPyramid'] = 2000\\n p['num5YearAgeClasses'] = 28\\n p['careLevelColour'] = ['blue','green','yellow','orange','red']\\n p['houseSizeColour'] = ['brown','purple','yellow']\\n p['pixelsPerTown'] = 56\\n p['maxTextUpdateList'] = 22\\n \\n # p['eduEduSensitivity'] = 0.5\\n # p['mortalityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['fertilityBias'] = [1.0, 0.92, 0.84, 0.76, 0.68]\\n # p['divorceBias'] = [2.0, 1.5, 1.0, 0.75, 0.5]\\n\\n ## Transitions to care statistics\\n \\n ## Availability of care statistics\\n \\n #p['childHours'] = 5.0\\n # p['employedHours'] = 12.0\\n #p['homeAdultHours'] = 30.0\\n #p['workingAdultHours'] = 25.0\\n #p['maxEmployedHours'] = 60.0\\n \\n #p['lowCareHandicap'] = 0.5\\n #p['hourlyCostOfCare'] = 20.0\\n \\n ## Fertility statistics\\n \\n # p['steadyPopBirthProb'] = 0.13\\n # p['transitionYear'] = 1965\\n \\n ## Class and employment statistics\\n # p['numClasses'] = 5\\n # p['occupationClasses'] = ['lower','intermediate','higher']\\n # p['cdfOccupationClasses'] = [ 0.6, 0.9, 1.0 ]\\n\\n ## Age transition statistics\\n # p['ageOfAdulthood'] = 17\\n \\n ## Marriage function parameters\\n \\n # p['basicFemaleMarriageProb'] = 0.25\\n # p['femaleMarriageModifierByDecade'] = [ 0.0, 0.5, 1.0, 1.0, 1.0, 0.6, 0.5, 0.4, 0.1, 0.01, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['femaleMarriageProb'] = [0.01, 0.15, 0.3, 0.2, 0.1, 0.1, 0.06, 0.05, 0.02, 0.01, 0.01, 0.005]\\n # p['maleMarriageProb'] = [0.005, 0.08, 0.25, 0.25, 0.15, 0.1, 0.07, 0.05, 0.03, 0.02, 0.01, 0.005]\\n \\n ## Leaving home and moving around statistics\\n # p['probApartWillMoveTogether'] = 0.3\\n # p['coupleMovesToExistingHousehold'] = 0.3\\n # p['basicProbAdultMoveOut'] = 0.22\\n # p['probAdultMoveOutModifierByDecade'] = [ 0.0, 0.2, 1.0, 0.6, 0.3, 0.15, 0.03, 0.03, 0.01, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbSingleMove'] = 0.05\\n # p['probSingleMoveModifierByDecade'] = [ 0.0, 1.0, 1.0, 0.8, 0.4, 0.06, 0.04, 0.02, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ]\\n # p['basicProbFamilyMove'] = 0.03\\n # p['probFamilyMoveModifierByDecade'] = [ 0.0, 0.5, 0.8, 0.5, 0.2, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1 ]\\n\\n \\n return p\",\n \"def set_parameters(self):\\n params = {}\\n if self.modelname == 'SI':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after splot\\n # Ts: Time from split to present, in 2*Na generation units\\n names = ['N1', 'N2', 'Ts']\\n values = [1, 1, 1]\\n upper_bounds = [20, 20, 10]\\n lower_bounds = [0.01, 0.01, 0]\\n elif self.modelname == 'IM':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Ts: Time from split to present, in 2*Na generations\\n names = ['N1', 'N2', 'm21', 'm12', 'Ts']\\n values = [1, 1, 1, 1, 1]\\n upper_bounds = [20, 20, 20, 20, 10]\\n lower_bounds = [0.01, 0.01, 0, 0, 0]\\n elif self.modelname == 'AM':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Tam: Time from end of anc migration to split, in 2*Na gens\\n # Ts: Time from split to present, in 2*Na generations\\n names = ['N1', 'N2', 'm21', 'm12', 'Tam', 'Ts']\\n values = [1, 1, 1, 1, 0.1, 1]\\n upper_bounds = [20, 20, 20, 20, 2, 10]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\\n elif self.modelname == 'SC':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # Ts: Time from split to secondary contact, in 2*Na generations\\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\\n names = ['N1', 'N2', 'm21', 'm12', 'Ts', 'Tsc']\\n values = [1, 1, 1, 1, 1, 0.1]\\n upper_bounds = [20, 20, 20, 20, 10, 2]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0]\\n elif self.modelname == 'IM2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Ts: Time from split to present, in 2*Na generations\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0.05]\\n elif self.modelname == 'AM2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Tam: Time from end of anc migration to split, in 2*Na gens\\n # Ts: Time from split to present, in 2*Na generations\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Tam', 'Ts', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 0.1, 1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 2, 10, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\\n elif self.modelname == 'SC2M':\\n # N1: Pop 1 size after split\\n # N2: Pop 2 size after split\\n # m21: Migration from 1 to 2 (2*Na*mm21)\\n # m12: Migration from 2 to 1 (2*Na*m12)\\n # mi21: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi21)\\n # mi12: Migration from 1 to 2 in \\\"islands\\\" (2*Na*mi12)\\n # Ts: Time from split to secondary contact, in 2*Na generations\\n # Tsc: Time from secondary contact to presesnt, in 2*Na gens\\n # p: Porpotion of genome evoloving in \\\"islands\\\"\\n names = ['N1', 'N2', 'm21', 'm12', 'mi21', 'mi12', 'Ts', 'Tsc', 'p']\\n values = [1, 1, 5, 5, 0.5, 0.5, 1, 0.1, 0.5]\\n upper_bounds = [20, 20, 30, 30, 5, 5, 10, 2, 0.95]\\n lower_bounds = [0.01, 0.01, 0, 0, 0, 0, 0, 0, 0.05]\\n params['Names'] = names\\n params['Values'] = values\\n params['Upper'] = upper_bounds\\n params['Lower'] = lower_bounds\\n return params\",\n \"def _set_cv_params(self):\\n _base_estimator = self.init_params['base_estimator'] \\n ada = {'n_estimators': randint(10, 1000),\\n 'learning_rate': _uniform(0.01, 0.1)} \\n \\n if isinstance(_base_estimator, DecisionTreeClassifier().__class__):\\n base = {\\n 'base_estimator__criterion': ('gini', 'entropy'),\\n 'base_estimator__max_depth': randint(1, 8), \\n 'base_estimator__min_samples_leaf': randint(2, 20),\\n 'base_estimator__max_features': (0.1, 'auto', 'log2'),\\n 'base_estimator__class_weight': ('balanced', None) } \\n \\n elif isinstance(_base_estimator, LogisticRegression().__class__): \\n base = {\\n 'base_estimator__C': uniform(0, 1000),\\n 'base_estimator__fit_intercept': (True, False),\\n 'base_estimator__penalty': ('l1', 'l2') } \\n else:\\n base = {} \\n ada.update(base) \\n \\n return [ada]\",\n \"def __init__(self, grid, estimator, parameter_search, **kwargs):\\n self.kwargs = kwargs\\n self.grid = grid\\n self.estimator = estimator\\n self.parameter_search = parameter_search\",\n \"def default_params():\\n params = {}\\n params['dataset'] = 'adult'\\n params['engines'] = ['MD','RDA']\\n params['iters'] = 10000\\n params['epsilon'] = 1.0\\n params['delta'] = 0.0\\n params['bounded'] = True\\n params['frequency'] = 1\\n params['seed'] = 0\\n params['save'] = None\\n params['load'] = None\\n params['plot'] = None\\n\\n return params\",\n \"def feed_training_args(self):\\n return {}\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['location'] = self.location\\n paramDict['scale' ] = self.scale\\n return paramDict\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['location'] = self.location\\n paramDict['scale' ] = self.scale\\n return paramDict\",\n \"def get_parameters(self):\\n # Get the parameters from the parent class\\n params = super(NREvalSplit, self).get_parameters()\\n\\n # Add the LP specific parameters\\n params.update({\\\"samp_frac\\\": self._samp_frac})\\n return params\",\n \"def setParameters(self):\\n\\n # Set the parameters\\n self.taux = 24.2\\n self.mu = 0.23\\n self.G = 33.75\\n self.alpha_0 = 0.05\\n self.delta = 0.0075\\n self.p = 0.50\\n self.I0 = 9500.0\\n self.kparam = 0.55\",\n \"def get_params(self):\\n return {'classifier': self.classifier,\\n 'grid_param': self.grid_param,\\n 'n_param_comb': self.n_param_comb,\\n 'top_bagging': self.bagging,\\n 'bagging_param': self.bagging_param,\\n 'comb_seed': self.comb_seed}\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['p'] = self.p\\n return paramDict\",\n \"def initializeParameters(self):\\r\\n\\t\\tself.input_raster.enabled = True\\r\\n\\t\\tself.approach.enabled = True\\r\\n\\t\\tself.predefined_pattern.enabled = False\\r\\n\\t\\tself.predefined_pattern.value = 'Mexican Hat wavelet'\\r\\n\\t\\tself.pattern_workspace.enabled = False\\r\\n\\t\\tself.point_matrix_size.enabled = False\\r\\n\\t\\tself.point_matrix_size.value = 3\\r\\n\\t\\tself.point_vectors.enabled = False\\r\\n\\t\\tself.mapping_field.enabled = False\\r\\n\\t\\tself.move_to_max.enabled = False\\r\\n\\t\\tself.move_to_max_distance.enabled = False\\r\\n\\t\\tself.move_to_max_distance.value = 3\\r\\n\\t\\tself.mh_iteration.enabled = False\\r\\n\\t\\tself.mh_dil_val.enabled = False\\r\\n\\t\\tself.mh_dil_val.value = 1\\r\\n\\t\\tself.mh_dil_start.value = 0.01\\r\\n\\t\\tself.mh_dil_stop.value = 1\\r\\n\\t\\tself.mh_dil_step.value = 0.1\\r\\n\\t\\tself.mh_dil_start.enabled = False\\r\\n\\t\\tself.mh_dil_stop.enabled = False\\r\\n\\t\\tself.mh_dil_step.enabled = False\\r\\n\\t\\tself.transform.enabled = False\\r\\n\\t\\tself.size_of_the_cell.enabled = False\\r\\n\\t\\tself.size_of_the_cell.value = 1\\r\\n\\t\\tself.output_sim_matrix.enabled = False\\r\\n\\t\\tself.output_table.enabled = False\\r\\n\\t\\tself.output_raster_workspace.enabled = False\",\n \"def __init__(self, parameters, learning_rate):\\n self.parameters = parameters\\n self.learning_rate = learning_rate\",\n \"def create_hparams(experiment):\\n hparams = {}\\n\\n # General parameters.\\n hparams['batch_size'] = 64\\n hparams['eval_batch_size'] = 64\\n hparams['learning_rate_warmup_steps'] = 2000\\n hparams['learning_rate_constant'] = 1\\n hparams['learning_rate'] = 0.001\\n hparams['train_epoches'] = 200\\n hparams['steps_per_epoch'] = 30\\n hparams['train_steps'] = 1000 * 1000\\n hparams['eval_steps'] = 100\\n hparams['caption_optimizer'] = 't2t'\\n hparams['clip_norm'] = 5.0\\n hparams['train_files'] = ''\\n hparams['eval_files'] = ''\\n hparams['train_buffer_size'] = 2000\\n hparams['eval_buffer_size'] = 500\\n hparams['train_pixel_encoder'] = True\\n hparams['debug'] = False\\n hparams['distribution_strategy'] = 'mirrored'\\n\\n # Embedding parameters.\\n hparams['embedding_file'] = ''\\n hparams['word_vocab_path'] = ''\\n hparams['glove_trainable'] = True\\n hparams['vocab_size'] = 10000\\n\\n # View hierarchy encoder parameters.\\n hparams['max_pixel_pos'] = 100\\n hparams['max_dom_pos'] = 500\\n hparams['screen_encoder'] = 'pixel_transformer'\\n hparams['screen_embedding_feature'] = ['text', 'type', 'pos', 'click', 'dom']\\n hparams['obj_text_aggregation'] = 'max'\\n hparams['synthetic_screen_noise'] = 0.\\n\\n # General parameters.\\n hparams['num_hidden_layers'] = 2\\n hparams['hidden_size'] = 2\\n hparams['filter_size'] = 2\\n hparams['num_heads'] = 2\\n hparams['dropout'] = 0.2\\n hparams['layer_prepostprocess_dropout'] = 0.2\\n hparams['attention_dropout'] = 0.2\\n hparams['relu_dropout'] = 0.2\\n\\n transformer_hparams = model_params.BASE_PARAMS\\n\\n # Add parameters from transformer model.\\n hparams.update(transformer_hparams)\\n\\n # Rewrite all the parameters from command-line flags.\\n config = screen2words_experiment_config.experiments[experiment]\\n hparams.update(config)\\n\\n return hparams\",\n \"def getInitParams(self):\\n paramDict = Distribution.getInitParams(self)\\n paramDict['strategy'] = self.strategy\\n paramDict['nPoints'] = self.nPoints\\n return paramDict\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['n' ] = self.n\\n paramDict['p' ] = self.p\\n return paramDict\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"rAfb[1.0,-5.0, 5.0]\\\");\\n self.modelBuilder.doVar(\\\"rA0[1.0, -5.0, 5.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"rAfb,rA0\\\")\\n self.modelBuilder.factory_('expr::mAfb(\\\"@0*@1\\\",eAfb,rAfb)')\\n self.modelBuilder.factory_('expr::mA0(\\\"(@0*@1)\\\",eA0,rA0)')\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"Afb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"A0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"Afb,A0\\\")\\n\\n \\n self.modelBuilder.factory_('expr::Alph(\\\"2.0*@0/(2.0-@0)\\\",A0)')\\n self.modelBuilder.factory_('expr::Norm(\\\"3.0/4.0/(2.0+@0)\\\",Alph)')\\n self.modelBuilder.factory_('expr::RAlph(\\\"@0*@1\\\",Alph,Norm)')\\n self.modelBuilder.factory_('expr::Rpl(\\\"(@0+@1)\\\",Norm,Afb)')\\n self.modelBuilder.factory_('expr::Rmn(\\\"(@0-@1)\\\",Norm,Afb)')\",\n \"def get_test_params(cls, parameter_set=\\\"default\\\"):\\n params1 = {}\\n params2 = {\\\"p_threshold\\\": 0.1, \\\"regression\\\": \\\"ctt\\\", \\\"nlags\\\": 5}\\n\\n return [params1, params2]\",\n \"def getInitParams(self):\\n paramDict = BoostDistribution.getInitParams(self)\\n paramDict['lambda'] = self.lambdaVar\\n paramDict['k' ] = self.k\\n paramDict['low' ] = self.low\\n return paramDict\",\n \"def __init__(self, parameter_dictionary):\\n super().__init__(parameter_dictionary)\\n\\n self.model_string = \\\"gauss\\\"\\n model_dictionary = self._get_model_dict(__class__.default_parameters)\\n\\n # wake expansion parameters\\n self.ka = model_dictionary[\\\"ka\\\"]\\n self.kb = model_dictionary[\\\"kb\\\"]\\n\\n # near wake / far wake boundary parameters\\n self.alpha = model_dictionary[\\\"alpha\\\"]\\n self.beta = model_dictionary[\\\"beta\\\"]\\n\\n # GCH Parameters\\n self.calculate_VW_velocities = model_dictionary[\\\"calculate_VW_velocities\\\"]\\n self.use_yaw_added_recovery = model_dictionary[\\\"use_yaw_added_recovery\\\"]\\n self.eps_gain = model_dictionary[\\\"eps_gain\\\"]\",\n \"def set_hyperparameters(self, hyperparameters):\\n self._hyperparameters = numpy.copy(hyperparameters)\",\n \"def __init__(self, parameter_set):\\n self.parameter_set = parameter_set.to_parameterization('Meijaard2007')\",\n \"def doParametersOfInterest(self):\\n\\n self.modelBuilder.doVar(\\\"eAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"eA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doVar(\\\"mAfb[0.6,-0.75,0.75]\\\");\\n self.modelBuilder.doVar(\\\"mA0[0.05, -1.0, 1.0]\\\");\\n self.modelBuilder.doSet(\\\"POI\\\",\\\"eAfb,mAfb\\\")\\n\\n \\n self.modelBuilder.factory_('expr::eAlph(\\\"2.0*@0/(2.0-@0)\\\",eA0)')\\n self.modelBuilder.factory_('expr::eNorm(\\\"3.0/4.0/(2.0+@0)\\\",eAlph)')\\n self.modelBuilder.factory_('expr::eRAlph(\\\"@0*@1\\\",eAlph,eNorm)')\\n self.modelBuilder.factory_('expr::eRpl(\\\"(@0+@1)\\\",eNorm,eAfb)')\\n self.modelBuilder.factory_('expr::eRmn(\\\"(@0-@1)\\\",eNorm,eAfb)')\\n\\n self.modelBuilder.factory_('expr::mAlph(\\\"2.0*@0/(2.0-@0)\\\",mA0)')\\n self.modelBuilder.factory_('expr::mNorm(\\\"3.0/4.0/(2.0+@0)\\\",mAlph)')\\n self.modelBuilder.factory_('expr::mRAlph(\\\"@0*@1\\\",mAlph,mNorm)')\\n self.modelBuilder.factory_('expr::mRpl(\\\"(@0+@1)\\\",mNorm,mAfb)')\\n self.modelBuilder.factory_('expr::mRmn(\\\"(@0-@1)\\\",mNorm,mAfb)')\",\n \"def test_text_classifier_set_params(self):\\n pass\",\n \"def _get_fitted_params(self):\\n return {}\",\n \"def _generate(self, **kwargs):\\n self._create_parameter_set_hashes()\\n self._create_parameter_set_names()\\n self._create_parameter_study()\\n if self.previous_parameter_study:\\n self._merge_parameter_studies()\",\n \"def get_hparams():\\n hparams = registry.get_registered_hparams_set(FLAGS.hparams_set)\\n hparams.add_hparam(\\\"inputs_vocab_size\\\", FLAGS.inputs_vocab_size) \\n hparams.add_hparam(\\\"targets_vocab_size\\\", FLAGS.targets_vocab_size) \\n hparams.parse(FLAGS.hparams)\\n return hparams\",\n \"def __init__(self, temboo_session):\\n super(DescribeEvaluations, self).__init__(temboo_session, '/Library/Amazon/MachineLearning/DescribeEvaluations')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7449988","0.73030883","0.6955949","0.6952735","0.69313055","0.6916811","0.6915098","0.6915098","0.6910595","0.6755054","0.66564333","0.6618931","0.6612997","0.6604273","0.6575251","0.6572437","0.64946365","0.64934057","0.6481356","0.6410235","0.64025366","0.6378353","0.63699347","0.634505","0.6342496","0.6340465","0.6336997","0.63094246","0.62725425","0.6263189","0.6236359","0.6232195","0.6230287","0.6224537","0.6196678","0.6196678","0.6194202","0.6189905","0.6187274","0.6177748","0.6156086","0.6153618","0.61482465","0.6125404","0.61061543","0.60823566","0.6071135","0.6063121","0.60629845","0.606139","0.605653","0.60543066","0.6046539","0.6043526","0.60433084","0.60286695","0.602734","0.6020102","0.6017354","0.6010346","0.60097575","0.6003549","0.6002423","0.59985995","0.5987772","0.5984319","0.5983966","0.59754556","0.59733963","0.5951378","0.5948275","0.5942284","0.5939973","0.59241474","0.5924083","0.5924083","0.59233904","0.5915645","0.5912716","0.5908551","0.5908551","0.59020984","0.58998287","0.5897889","0.58953285","0.588954","0.5886451","0.5885741","0.5874442","0.5871034","0.5867426","0.58580935","0.58539104","0.58396155","0.58368343","0.5836128","0.5834444","0.58323747","0.58309287","0.58304477"],"string":"[\n \"0.7449988\",\n \"0.73030883\",\n \"0.6955949\",\n \"0.6952735\",\n \"0.69313055\",\n \"0.6916811\",\n \"0.6915098\",\n \"0.6915098\",\n \"0.6910595\",\n \"0.6755054\",\n \"0.66564333\",\n \"0.6618931\",\n \"0.6612997\",\n \"0.6604273\",\n \"0.6575251\",\n \"0.6572437\",\n \"0.64946365\",\n \"0.64934057\",\n \"0.6481356\",\n \"0.6410235\",\n \"0.64025366\",\n \"0.6378353\",\n \"0.63699347\",\n \"0.634505\",\n \"0.6342496\",\n \"0.6340465\",\n \"0.6336997\",\n \"0.63094246\",\n \"0.62725425\",\n \"0.6263189\",\n \"0.6236359\",\n \"0.6232195\",\n \"0.6230287\",\n \"0.6224537\",\n \"0.6196678\",\n \"0.6196678\",\n \"0.6194202\",\n \"0.6189905\",\n \"0.6187274\",\n \"0.6177748\",\n \"0.6156086\",\n \"0.6153618\",\n \"0.61482465\",\n \"0.6125404\",\n \"0.61061543\",\n \"0.60823566\",\n \"0.6071135\",\n \"0.6063121\",\n \"0.60629845\",\n \"0.606139\",\n \"0.605653\",\n \"0.60543066\",\n \"0.6046539\",\n \"0.6043526\",\n \"0.60433084\",\n \"0.60286695\",\n \"0.602734\",\n \"0.6020102\",\n \"0.6017354\",\n \"0.6010346\",\n \"0.60097575\",\n \"0.6003549\",\n \"0.6002423\",\n \"0.59985995\",\n \"0.5987772\",\n \"0.5984319\",\n \"0.5983966\",\n \"0.59754556\",\n \"0.59733963\",\n \"0.5951378\",\n \"0.5948275\",\n \"0.5942284\",\n \"0.5939973\",\n \"0.59241474\",\n \"0.5924083\",\n \"0.5924083\",\n \"0.59233904\",\n \"0.5915645\",\n \"0.5912716\",\n \"0.5908551\",\n \"0.5908551\",\n \"0.59020984\",\n \"0.58998287\",\n \"0.5897889\",\n \"0.58953285\",\n \"0.588954\",\n \"0.5886451\",\n \"0.5885741\",\n \"0.5874442\",\n \"0.5871034\",\n \"0.5867426\",\n \"0.58580935\",\n \"0.58539104\",\n \"0.58396155\",\n \"0.58368343\",\n \"0.5836128\",\n \"0.5834444\",\n \"0.58323747\",\n \"0.58309287\",\n \"0.58304477\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6258678"},"document_rank":{"kind":"string","value":"30"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":99,"numItemsPerPage":100,"numTotalItems":95772,"offset":9900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDM5ODI4Miwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzQ0MDE4ODIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.0XRa5f8eU9pcxIZzB6xslHLGWhhvNc3RJ7IEbpn91bu9FXcF9TONXA6fl5ekZDwVjXjGYb6akSNZgjofT7ruCA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Loads MR polarity data from files, splits the data into words and generates labels. Returns split sentences and labels. | def load_data_and_labels_without_shuffled():
# Load data from files
with codecs.open('./data/train_pos.txt', 'r+', 'utf-8') as f:
train_pos = f.readlines()
with codecs.open('./data/dev_pos.txt', 'r+', 'utf-8') as f:
dev_pos = f.readlines()
with codecs.open('./data/train_neg.txt', 'r+', 'utf-8') as f:
train_neg = f.readlines()
with codecs.open('./data/dev_neg.txt', 'r+', 'utf-8') as f:
dev_neg = f.readlines()
positive_examples1 = []
positive_examples2 = []
negative_examples1 = []
negative_examples2 = []
for i in train_pos:
item1, item2 = i.split('\t')
item1 = remove_stop_word(item1)
item2 = remove_stop_word(item2)
positive_examples1.append(item1)
positive_examples2.append(item2)
for i in train_neg:
item1, item2 = i.split('\t')
item1 = remove_stop_word(item1)
item2 = remove_stop_word(item2)
negative_examples1.append(item1)
negative_examples2.append(item2)
# Split by words
x_text_train1 = positive_examples1 + negative_examples1
x_text_train2 = positive_examples2 + negative_examples2
positive_dev1 = []
positive_dev2 = []
negative_dev1 = []
negative_dev2 = []
for i in dev_pos:
item1, item2 = i.split('\t')
item1 = remove_stop_word(item1)
item2 = remove_stop_word(item2)
positive_dev1.append(item1)
positive_dev2.append(item2)
for i in dev_neg:
item1, item2 = i.split('\t')
item1 = remove_stop_word(item1)
item2 = remove_stop_word(item2)
negative_dev1.append(item1)
negative_dev2.append(item2)
x_text_dev1 = positive_dev1 + negative_dev1
x_text_dev2 = positive_dev2 + negative_dev2
# Generate labels
train_positive_labels = [[0, 1] for _ in train_pos]
dev_positive_labels = [[0, 1] for _ in dev_pos]
train_negative_labels = [[1, 0] for _ in train_neg]
dev_negative_labels = [[1, 0] for _ in dev_neg]
y_train = np.concatenate([train_positive_labels, train_negative_labels], 0)
y_dev = np.concatenate([dev_positive_labels, dev_negative_labels], 0)
return [x_text_train1, x_text_train2, x_text_dev1, x_text_dev2, y_train, y_dev] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data_and_labels():\n # Load data from files\n positive_examples = list(\n open(\"./data/rt-polarity.pos\", \"r\", encoding='latin-1').readlines())\n positive_examples = [s.strip() for s in positive_examples]\n negative_examples = list(\n open(\"./data/rt-polarity.neg\", \"r\", en... | [
"0.7615511",
"0.7190876",
"0.68102527",
"0.67267877",
"0.6636343",
"0.65735227",
"0.6539939",
"0.6465734",
"0.64292496",
"0.6304949",
"0.6301084",
"0.6295304",
"0.6271314",
"0.6269045",
"0.61948806",
"0.6137916",
"0.6116728",
"0.6078847",
"0.6076159",
"0.6075476",
"0.605736",... | 0.66374993 | 4 |
Builds a vocabulary mapping from word to index based on the sentences. Returns vocabulary mapping and inverse vocabulary mapping. | def build_vocab(sentences):
# Build vocabulary
word_counts = Counter(itertools.chain(*sentences)) # 实际没用到
# Mapping from index to word
vocabulary_inv = [x[0] for x in word_counts.most_common()]
vocabulary_inv = list(sorted(vocabulary_inv))
# 加入 <UNK>
vocabulary_inv.insert(0, '</s>')
# Mapping from word to index
vocabulary = {x: i for i, x in enumerate(vocabulary_inv)}
return [vocabulary, vocabulary_inv] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vocab(self, sentences):\n\t\t# Build the vocab\n\t\tword_counts = collections.Counter(sentences)\n\n\t\t# Mapping from index to word (get the indices of most common words)\n\t\tvocab_inv = [x[0] for x in word_counts.most_common()] # Do we need this?\n\t\tvocab_inv = list(sorted(vocab_inv))\n\n\t\t# Mapp... | [
"0.79638803",
"0.7715133",
"0.7715133",
"0.7715133",
"0.76599747",
"0.7653059",
"0.7552094",
"0.74614453",
"0.69518715",
"0.6917704",
"0.68646234",
"0.68494624",
"0.67772037",
"0.6662719",
"0.6608852",
"0.65526927",
"0.65498227",
"0.6457259",
"0.6451463",
"0.6442439",
"0.6440... | 0.7844205 | 1 |
Pads all sentences to the same length. The length is defined by the longest sentence. Returns padded sentences. | def pad_sentences(sentences, padding_word="<PAD/>"):
# !!! 一定要注意这里会影响数据的形状,要与代码内的 sequence length 保持一致 !!!
sequence_length = 30
# sequence_length = max(len(x) for x in sentences)
padded_sentences = []
for i in range(len(sentences)):
sentence = sentences[i][:sequence_length]
num_padding = sequence_length - len(sentence)
new_sentence = sentence + [padding_word] * num_padding
padded_sentences.append(new_sentence)
return padded_sentences | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pad_sentences(sentences, padding_word=\"<PAD/>\"):\n sequence_length = max(len(x) for x in sentences)\n padded_sentences = []\n for i in range(len(sentences)):\n sentence = sentences[i]\n num_padding = sequence_length - len(sentence)\n new_sentence = sentence + [padding_word] * nu... | [
"0.8116531",
"0.8083582",
"0.8041561",
"0.7986152",
"0.79414433",
"0.7637198",
"0.75545436",
"0.7517852",
"0.7492193",
"0.7338666",
"0.7041751",
"0.7004114",
"0.69546854",
"0.6900307",
"0.68227386",
"0.6682252",
"0.6641073",
"0.6603725",
"0.6601419",
"0.6599529",
"0.65984535"... | 0.8030784 | 3 |
Maps sentencs and labels to vectors based on a vocabulary. | def build_input_data(sentences, vocabulary):
count = 0
seq2seq_sentences = []
for sentence in sentences:
seq2seq_sentence = []
for word in sentence:
try:
seq2seq_sentence.append(vocabulary[word])
except KeyError:
seq2seq_sentence.append(vocabulary['</s>'])
count += 1
seq2seq_sentences.append(seq2seq_sentence)
print count
return np.array(seq2seq_sentences) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_vocab(sentences):\n # Build vocabulary\n word_counts = Counter(itertools.chain(*sentences)) # 实际没用到\n # Mapping from index to word\n vocabulary_inv = [x[0] for x in word_counts.most_common()]\n vocabulary_inv = list(sorted(vocabulary_inv))\n # 加入 <UNK>\n vocabulary_inv.insert(0, '</... | [
"0.7294365",
"0.70796233",
"0.70616835",
"0.700586",
"0.700586",
"0.700586",
"0.69982046",
"0.69713277",
"0.68781686",
"0.6814168",
"0.679631",
"0.6701771",
"0.6628131",
"0.66187143",
"0.6590109",
"0.65756196",
"0.6565486",
"0.65587103",
"0.6492771",
"0.6482376",
"0.6426598",... | 0.6217245 | 42 |
Loads and preprocessed data for the MR dataset. Returns input vectors, labels, vocabulary, and inverse vocabulary. | def load_data():
# Load and preprocess data
x_text_train1, x_text_train2, x_text_dev1, x_text_dev2, y_train, y_dev = load_data_and_labels_without_shuffled()
x_text_train1 = split_sentence(x_text_train1)
x_text_train2 = split_sentence(x_text_train2)
x_text_dev1 = split_sentence(x_text_dev1)
x_text_dev2 = split_sentence(x_text_dev2)
x_text_train1 = pad_sentences(x_text_train1)
x_text_train2 = pad_sentences(x_text_train2)
x_text_dev1 = pad_sentences(x_text_dev1)
x_text_dev2 = pad_sentences(x_text_dev2)
# sentences = x_text_train1 + x_text_train2 + x_text_dev1 + x_text_dev2
# vocabulary, vocabulary_inv = build_vocab(sentences)
# x_text_train1 = build_input_data(x_text_train1, vocabulary)
# x_text_train2 = build_input_data(x_text_train2, vocabulary)
# x_text_dev1 = build_input_data(x_text_dev1, vocabulary)
# x_text_dev2 = build_input_data(x_text_dev2, vocabulary)
x_train1 = sentence_word2vec(x_text_train1)
x_train2 = sentence_word2vec(x_text_train2)
x_dev1 = sentence_word2vec(x_text_dev1)
x_dev2 = sentence_word2vec(x_text_dev2)
y_train = np.array(y_train)
y_dev = np.array(y_dev)
# return [x_text_train1, x_text_train2, x_text_dev1, x_text_dev2, y_train, y_dev, vocabulary, vocabulary_inv]
return [x_train1, x_train2, x_dev1, x_dev2, y_train, y_dev] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data():\n # Load and preprocess data\n sentences, labels = load_data_and_labels()\n sentences_padded = pad_sentences(sentences)\n vocabulary, vocabulary_inv = build_vocab(sentences_padded)\n x, y = build_input_data(sentences_padded, labels, vocabulary)\n return [x, y, vocabulary, vocabul... | [
"0.7270907",
"0.6811596",
"0.6781514",
"0.663366",
"0.6511913",
"0.64549154",
"0.6454384",
"0.6444618",
"0.6405498",
"0.6399058",
"0.6371277",
"0.6303841",
"0.63015515",
"0.62549603",
"0.61739635",
"0.6170799",
"0.61557263",
"0.6155556",
"0.6148963",
"0.60986376",
"0.6094318"... | 0.6727568 | 3 |
Estimate the true signal mean and interpolate bad channels. This function implements the functionality of the `performReference` function as part of the PREP pipeline on mne raw object. Notes This function calls robust_reference first Currently this function only implements the functionality of default settings, i.e., doRobustPost | def perform_reference(self):
# Phase 1: Estimate the true signal mean with robust referencing
self.robust_reference()
if self.noisy_channels["bad_all"]:
self.raw.info["bads"] = self.noisy_channels["bad_all"]
self.raw.interpolate_bads()
self.reference_signal = (
np.nanmean(self.raw.get_data(picks=self.reference_channels), axis=0) * 1e6
)
rereferenced_index = [
self.ch_names_eeg.index(ch) for ch in self.rereferenced_channels
]
self.EEG = self.remove_reference(
self.EEG, self.reference_signal, rereferenced_index
)
# Phase 2: Find the bad channels and interpolate
self.raw._data = self.EEG * 1e-6
noisy_detector = NoisyChannels(self.raw)
noisy_detector.find_all_bads(ransac=self.ransac)
# Record Noisy channels and EEG before interpolation
self.bad_before_interpolation = noisy_detector.get_bads(verbose=True)
self.EEG_before_interpolation = self.EEG.copy()
bad_channels = _union(self.bad_before_interpolation, self.unusable_channels)
self.raw.info["bads"] = bad_channels
self.raw.interpolate_bads()
reference_correct = (
np.nanmean(self.raw.get_data(picks=self.reference_channels), axis=0) * 1e6
)
self.EEG = self.raw.get_data() * 1e6
self.EEG = self.remove_reference(
self.EEG, reference_correct, rereferenced_index
)
# reference signal after interpolation
self.reference_signal_new = self.reference_signal + reference_correct
# MNE Raw object after interpolation
self.raw._data = self.EEG * 1e-6
# Still noisy channels after interpolation
self.interpolated_channels = bad_channels
noisy_detector = NoisyChannels(self.raw)
noisy_detector.find_all_bads(ransac=self.ransac)
self.still_noisy_channels = noisy_detector.get_bads()
self.raw.info["bads"] = self.still_noisy_channels
return self | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def robust_reference(self):\n raw = self.raw.copy()\n raw._data = removeTrend(raw.get_data(), sample_rate=self.sfreq)\n\n # Determine unusable channels and remove them from the reference channels\n noisy_detector = NoisyChannels(raw, do_detrend=False)\n noisy_detector.find_all_ba... | [
"0.72033346",
"0.51534164",
"0.51021534",
"0.5064311",
"0.50264406",
"0.50084466",
"0.49690974",
"0.49434143",
"0.4942057",
"0.4890532",
"0.4875339",
"0.4820547",
"0.4813191",
"0.47748223",
"0.47736692",
"0.47729418",
"0.47667706",
"0.4733884",
"0.47221825",
"0.47158694",
"0.... | 0.80031914 | 0 |
Detect bad channels and estimate the robust reference signal. This function implements the functionality of the `robustReference` function as part of the PREP pipeline on mne raw object. | def robust_reference(self):
raw = self.raw.copy()
raw._data = removeTrend(raw.get_data(), sample_rate=self.sfreq)
# Determine unusable channels and remove them from the reference channels
noisy_detector = NoisyChannels(raw, do_detrend=False)
noisy_detector.find_all_bads(ransac=self.ransac)
self.noisy_channels_original = {
"bad_by_nan": noisy_detector.bad_by_nan,
"bad_by_flat": noisy_detector.bad_by_flat,
"bad_by_deviation": noisy_detector.bad_by_deviation,
"bad_by_hf_noise": noisy_detector.bad_by_hf_noise,
"bad_by_correlation": noisy_detector.bad_by_correlation,
"bad_by_ransac": noisy_detector.bad_by_ransac,
"bad_all": noisy_detector.get_bads(),
}
self.noisy_channels = self.noisy_channels_original.copy()
logger.info("Bad channels: {}".format(self.noisy_channels))
self.unusable_channels = _union(
noisy_detector.bad_by_nan, noisy_detector.bad_by_flat
)
# unusable_channels = _union(unusable_channels, noisy_detector.bad_by_SNR)
self.reference_channels = _set_diff(
self.reference_channels, self.unusable_channels
)
# Get initial estimate of the reference by the specified method
signal = raw.get_data() * 1e6
self.reference_signal = (
np.nanmedian(raw.get_data(picks=self.reference_channels), axis=0) * 1e6
)
reference_index = [
self.ch_names_eeg.index(ch) for ch in self.reference_channels
]
signal_tmp = self.remove_reference(
signal, self.reference_signal, reference_index
)
# Remove reference from signal, iteratively interpolating bad channels
raw_tmp = raw.copy()
iterations = 0
noisy_channels_old = []
max_iteration_num = 4
while True:
raw_tmp._data = signal_tmp * 1e-6
noisy_detector = NoisyChannels(raw_tmp)
noisy_detector.find_all_bads(ransac=self.ransac)
self.noisy_channels["bad_by_nan"] = _union(
self.noisy_channels["bad_by_nan"], noisy_detector.bad_by_nan
)
self.noisy_channels["bad_by_flat"] = _union(
self.noisy_channels["bad_by_flat"], noisy_detector.bad_by_flat
)
self.noisy_channels["bad_by_deviation"] = _union(
self.noisy_channels["bad_by_deviation"], noisy_detector.bad_by_deviation
)
self.noisy_channels["bad_by_hf_noise"] = _union(
self.noisy_channels["bad_by_hf_noise"], noisy_detector.bad_by_hf_noise
)
self.noisy_channels["bad_by_correlation"] = _union(
self.noisy_channels["bad_by_correlation"],
noisy_detector.bad_by_correlation,
)
self.noisy_channels["bad_by_ransac"] = _union(
self.noisy_channels["bad_by_ransac"], noisy_detector.bad_by_ransac
)
self.noisy_channels["bad_all"] = _union(
self.noisy_channels["bad_all"], noisy_detector.get_bads()
)
logger.info("Bad channels: {}".format(self.noisy_channels))
if (
iterations > 1
and (
not self.noisy_channels["bad_all"]
or set(self.noisy_channels["bad_all"]) == set(noisy_channels_old)
)
or iterations > max_iteration_num
):
break
noisy_channels_old = self.noisy_channels["bad_all"].copy()
if raw_tmp.info["nchan"] - len(self.noisy_channels["bad_all"]) < 2:
raise ValueError(
"RobustReference:TooManyBad "
"Could not perform a robust reference -- not enough good channels"
)
if self.noisy_channels["bad_all"]:
raw_tmp._data = signal * 1e-6
raw_tmp.info["bads"] = self.noisy_channels["bad_all"]
raw_tmp.interpolate_bads()
signal_tmp = raw_tmp.get_data() * 1e6
else:
signal_tmp = signal
self.reference_signal = (
np.nanmean(raw_tmp.get_data(picks=self.reference_channels), axis=0)
* 1e6
)
signal_tmp = self.remove_reference(
signal, self.reference_signal, reference_index
)
iterations = iterations + 1
logger.info("Iterations: {}".format(iterations))
logger.info("Robust reference done")
return self.noisy_channels, self.reference_signal | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_reference(self):\n # Phase 1: Estimate the true signal mean with robust referencing\n self.robust_reference()\n if self.noisy_channels[\"bad_all\"]:\n self.raw.info[\"bads\"] = self.noisy_channels[\"bad_all\"]\n self.raw.interpolate_bads()\n self.refere... | [
"0.7477632",
"0.6277125",
"0.59613264",
"0.58670795",
"0.55491996",
"0.53819615",
"0.5299285",
"0.5188852",
"0.51496625",
"0.5102384",
"0.50921977",
"0.5008404",
"0.49466848",
"0.4929399",
"0.49203682",
"0.49124527",
"0.48961046",
"0.48734668",
"0.48694846",
"0.48172373",
"0.... | 0.79005593 | 0 |
Remove the reference signal from the original EEG signal. This function implements the functionality of the `removeReference` function as part of the PREP pipeline on mne raw object. | def remove_reference(signal, reference, index=None):
if np.ndim(signal) != 2:
raise ValueError(
"RemoveReference: EEG signal must be 2D array (channels * times)"
)
if np.ndim(reference) != 1:
raise ValueError("RemoveReference: Reference signal must be 1D array")
if np.shape(signal)[1] != np.shape(reference)[0]:
raise ValueError(
"RemoveReference: The second dimension of EEG signal must be "
"the same with the length of reference signal"
)
if index is None:
signal_referenced = signal - reference
else:
if not isinstance(index, list):
raise TypeError(
"RemoveReference: Expected type list, got {} instead".format(
type(index)
)
)
signal_referenced = signal.copy()
signal_referenced[np.asarray(index), :] = (
signal[np.asarray(index), :] - reference
)
return signal_referenced | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def removeReference(self, reference: ghidra.program.model.symbol.Reference) -> None:\n ...",
"def removeReferenceGlyph(self, *args):\n return _libsbml.GeneralGlyph_removeReferenceGlyph(self, *args)",
"def _remove_reference(self, target):\n assert target in self._referenced_nodes\n ... | [
"0.7395361",
"0.618159",
"0.6136284",
"0.6101331",
"0.59427154",
"0.58940864",
"0.58332074",
"0.57739156",
"0.5770895",
"0.5762816",
"0.5671317",
"0.56659806",
"0.5665375",
"0.5660799",
"0.5658297",
"0.5644026",
"0.5601565",
"0.5555541",
"0.5552319",
"0.5550075",
"0.5489779",... | 0.7985783 | 0 |
Sets the buy list for the board | def setBuyList(self, buyList):
parsedBuyList = []
for bought in buyList:
if hasattr(bought, "unitType"):
parsedBuyList.append(bought)
elif isinstance(bought, dict) and u'unitType' in bought and u'territory' in bought:
parsedBuyList.append(createBoughtUnitFromDict(bought, self.board.territories))
else:
raise Exception("Invalid buy list", buyList)
sumCost = self.costOfUnits(parsedBuyList)
if sumCost <= self.board.currentCountry.money:
self.board.buyList = parsedBuyList[:] # copy in buyList
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buys(self, buys):\n\n self._buys = buys",
"def set_buy_sell_deal_account(self, account_list):\n self.multiple_items_selection_from_kendo_dropdown(self.buy_sell_deal_account_dropdown_locator, account_list)\n self.wait_for_ajax_spinner_load()",
"def set_target_buy_list(self, item_name, i... | [
"0.6422962",
"0.6265",
"0.5775737",
"0.5630926",
"0.5596823",
"0.5562822",
"0.55483556",
"0.5545518",
"0.5418781",
"0.53955275",
"0.535245",
"0.5310174",
"0.5309824",
"0.53068644",
"0.5252064",
"0.52498454",
"0.5239531",
"0.5211751",
"0.5184596",
"0.51813936",
"0.5175984",
... | 0.6954832 | 0 |
Converts an object into a json string | def SerializeObject(self, data):
if isinstance(data,dict):
serializad_data = json.dumps(data)
else:
serializad_data = json.dumps(data.__dict__)
return serializad_data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def toJSON(object):\n\treturn json.dumps(object, ensure_ascii=False)",
"def toJSON(cls, obj):\n return json.dumps(obj)",
"def to_json_string(my_obj):\n return json.dumps(my_obj)",
"def to_json_string(my_obj):\n return json.dumps(my_obj)",
"def to_json_string(my_obj):\n return json.dumps(my_... | [
"0.83760744",
"0.83283734",
"0.81909037",
"0.81909037",
"0.81909037",
"0.81909037",
"0.81712186",
"0.81712186",
"0.81665546",
"0.81609154",
"0.8029213",
"0.7942642",
"0.79287285",
"0.79166585",
"0.78885394",
"0.77051455",
"0.7690964",
"0.76449054",
"0.76258",
"0.755187",
"0.7... | 0.7041254 | 42 |
Converts json string in related object object_to_serialize have to be an instace of the desired to convert object | def DeserializeJson(self, json_string, object_to_serialize):
object_to_serialize.__dict__ = json.loads(str(json_string))
return object_to_serialize | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_serializable(o: Any) -> Any:\n if isinstance(o, UUID):\n return str(o)\n if isinstance(o, datetime):\n return isoformat(o)\n if is_dataclass(o):\n return asdict(o)\n if hasattr(o, \"__json__\"):\n return o.__json__()\n if hasattr(o, \"to_dict\"):\n # api_cli... | [
"0.68756694",
"0.6863126",
"0.68572044",
"0.68323946",
"0.68267304",
"0.6792545",
"0.67513794",
"0.6714095",
"0.6697272",
"0.66942084",
"0.6680018",
"0.6673534",
"0.6665409",
"0.6663698",
"0.6646167",
"0.6644696",
"0.6639211",
"0.6623753",
"0.66212523",
"0.66212523",
"0.66212... | 0.7826462 | 0 |
Constructs a DVR object | def DVR(
domain=None,
divs=None,
classes=None,
potential_function=None,
g=None,
g_deriv=None,
scf=False,
potential_optimize=False,
**base_opts
):
return DVRConstructor.construct(
domain=domain,
divs=divs,
classes=classes,
potential_function=potential_function,
g=g,
g_deriv=g_deriv,
scf=scf,
potential_optimize=potential_optimize,
**base_opts
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, dzdt, v, e, D):\n self.V = dzdt\n self.v = v\n self.e = e\n self.D = D\n self.laminar_label = \"Laminar\"\n self.critical_label = \"Critical\"\n self.turbulent_label = \"Turbulent\"",
"def __init__(self, dr_ds: DatasetReader) -> None:\n s... | [
"0.61528945",
"0.60658884",
"0.599728",
"0.59843045",
"0.59223074",
"0.5918787",
"0.5913648",
"0.5847089",
"0.5769131",
"0.5743365",
"0.57272416",
"0.57149744",
"0.57125163",
"0.5688939",
"0.56877244",
"0.5670665",
"0.56602484",
"0.56357646",
"0.5623393",
"0.5620744",
"0.5612... | 0.7178919 | 0 |
Run the entire NBA_bet project. | def run_all():
db = DBInterface()
year = Config.get_property("league_year")
session = Session(bind=db.engine)
scraper.scrape_all(db, session, year)
session.commit()
bets.predict_all(db, session)
session.commit()
session.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n boba_blast_game.main()",
"def run():\n\n # Set up environment and agent\n e = Environment() # create environment (also adds some dummy traffic)\n a = e.create_agent(LearningAgent) # create agent\n e.set_primary_agent(a, enforce_deadline=False) # set agent to track\n\n # Now sim... | [
"0.66027266",
"0.6353985",
"0.6346927",
"0.6313131",
"0.62619185",
"0.62193763",
"0.62180185",
"0.6216404",
"0.6215654",
"0.61611444",
"0.6076099",
"0.6034337",
"0.602182",
"0.59690386",
"0.5968432",
"0.59557575",
"0.59223056",
"0.5915671",
"0.5899744",
"0.58864325",
"0.58760... | 0.5997161 | 13 |
the function takes an images and try to quantify the roundness of the photo generated plasma | def round_score(file_name, target_file_name, save_calibration = False, target_directory = "ScreenCaps_contour"):
try:
img = Image.open(file_name).convert('L')
except:
raise NameError("The script failed to open the image")
# (width, height) = img.size
img_arr = np.asarray(img).copy() # copy to avoid working on the original data
img_arrN = img_arr/255
# finding the maximum value of the array
max_value_of_array = 0
for i in img_arrN:
if max(i) > max_value_of_array:
max_value_of_array = max(i)
# renormalization with the maximum value
for i in range(len(img_arrN)):
for j in range(len(img_arrN[0])):
img_arrN[i, j] = img_arrN[i, j] / max_value_of_array
# we applied some filters
img_arrN = filters.gaussian(img_arrN, 2)
img_arrN = abs(sig.convolve(img_arrN, kernel_test))
# contrast
img_arrN = np.int8(img_arrN)
# countour detecting
crit = 0.02
ct = np.zeros(shape=img_arrN.shape)
for i in range(len(img_arrN) - 1):
for j in range(len(img_arrN[0]) - 1):
if abs(img_arrN[i+1, j] - img_arrN[i, j]) > crit:
ct[i, j] = 1
if abs(img_arrN[i, j+1] - img_arrN[i, j]) > crit:
ct[i, j] = 1
#find the true center
idx_h = np.sum(ct,axis=0)
idx_v = np.sum(ct,axis=1)
count = 0
total_h = 0
for x in idx_h:
count += 1
if x != 0:
total_h += count
if (np.count_nonzero(idx_h) == 0) or (np.count_nonzero(idx_v) == 0):
return 1
center_x = total_h/np.count_nonzero(idx_h)
total_v = 0
count = 0
for x in idx_v:
count += 1
if x != 0:
total_v += count
center_y = total_v/np.count_nonzero(idx_v)
center = [center_y, center_x]
# calculate the mean radius and the score
radius = []
for i in range(len(ct)):
for j in range(len(ct[0])):
if ct[i, j] == 1:
radius.append(np.sqrt((i - center[0])**2 + (j - center[1])**2))
radius_mean = np.mean(radius)
# radius /= radius_mean
score = 0
if radius_mean < 0.1:
return 1
for i in radius:
score += abs(i - radius_mean)/radius_mean
#print(score/len(radius))
# getting the cercle with the radius_mean as the radius
# for i in range(len(ct)):
# for j in range(len(ct[0])):
# if ct[i, j] != 1:
# if np.sqrt((i-center[0])**2 + (j-center[1])**2) <= radius_mean:
# ct[i, j] = 1
img_gs = Image.fromarray(np.uint8(ct)*255)
img_gs.save("{}/{}".format(target_directory, target_file_name))
if save_calibration:
calibration_visualisation(radius_mean, center, img_arrN.shape,
ct, target_file_name, target_directory)
return score/len(radius) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def quantize(image_patch, gray_levels=12, n_stddev=2):\n # compute gray level gaussian stats\n mean = np.mean(image_patch)\n stddev = np.std(image_patch)\n # logger.debug('mean: {!s}\\nstd dev: {!s}'.format(mean, stddev))\n bin_width = 2*n_stddev*stddev / (gray_levels-2)\n # logger.debug('bin_... | [
"0.60352147",
"0.5991346",
"0.59536624",
"0.59423345",
"0.5887183",
"0.58796984",
"0.5852654",
"0.5849112",
"0.58206713",
"0.5803636",
"0.5785182",
"0.57806164",
"0.57765096",
"0.576138",
"0.57505316",
"0.5739448",
"0.5710007",
"0.5701599",
"0.5683255",
"0.5672449",
"0.565078... | 0.0 | -1 |
Format the data. In derived classes, it is usually better idea to override ``_format_data()`` than this method. | def format(self, response):
res = self._prepare_response(response)
res.content = self._format_data(res.content, self.charset)
return self._finalize_response(res) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_data(self, data):",
"def format(self, data):",
"def formatted_data(self):\n return self._formatted_data",
"def _formatData(self):\r\n assert self._runData is not None\r\n\r\n # Getting Axes data into separate lists\r\n x=[]; y=[]; z=[]\r\n for i in range(len(self... | [
"0.8710108",
"0.8226497",
"0.73157656",
"0.69565845",
"0.6939006",
"0.69311357",
"0.69138837",
"0.6854443",
"0.67923564",
"0.6714074",
"0.6688871",
"0.66315264",
"0.6579256",
"0.65059084",
"0.64873344",
"0.6434863",
"0.6407318",
"0.6352616",
"0.6308234",
"0.6295145",
"0.62939... | 0.0 | -1 |
Parse the data. It is usually a better idea to override ``_parse_data()`` than this method in derived classes. | def parse(self, data, charset=None):
charset = charset or self.charset
return self._parse_data(data, charset) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse(self, data):\n raise NotImplementedError",
"def parse(cls, data):\n raise NotImplementedError",
"def parse_data(self):\n\t\traise NotImplementedError('%s: No parse function implemented!' % self.name)",
"def parse_dataset(self, data):\n pass",
"def parse(self, data):\n ... | [
"0.83383363",
"0.8124831",
"0.8103867",
"0.7813138",
"0.7209145",
"0.71461093",
"0.71073484",
"0.70829093",
"0.70819396",
"0.7063399",
"0.7004795",
"0.6928474",
"0.6914489",
"0.69009197",
"0.68570995",
"0.6790236",
"0.6790236",
"0.6790236",
"0.6790236",
"0.67341244",
"0.67255... | 0.669551 | 21 |
Format the data data the data (may be None) | def _format_data(self, data, charset):
return self._encode_data(data) if data else u'' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_data(self, data):",
"def format(self, data):",
"def format_data(self)->float: \n try:\n formatted = chr(self.data[0])\n for i in range(1, len(self.data)): \n formatted = formatted + (chr(self.data[i])) \n return str(formatted)\n... | [
"0.8829935",
"0.8364288",
"0.71631086",
"0.6922519",
"0.67930263",
"0.6727314",
"0.6676519",
"0.6615989",
"0.6539193",
"0.64204735",
"0.6417292",
"0.640719",
"0.63545674",
"0.6275988",
"0.6255942",
"0.6243175",
"0.6212364",
"0.6212364",
"0.62109625",
"0.61957896",
"0.6140141"... | 0.747908 | 2 |
Coerce response to devil's Response | def _prepare_response(self, response):
if not isinstance(response, Response):
return Response(0, response)
return response | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def adapt_response(self, response):\n return response",
"def adapt_response(self, response):\n return response",
"def parse_response(self, response: Any) -> Any:\n return response",
"def _parse_response(self, response):\n if response is not None:\n return response.string\n ... | [
"0.773039",
"0.773039",
"0.72027785",
"0.69940764",
"0.6856815",
"0.68360895",
"0.67848694",
"0.6754801",
"0.6601165",
"0.6534803",
"0.6463667",
"0.6463667",
"0.64454454",
"0.6439771",
"0.6422133",
"0.6422133",
"0.6421432",
"0.6416055",
"0.64027804",
"0.6400647",
"0.639998",
... | 0.6973536 | 4 |
Convert the ``Response`` object into django's ``HttpResponse`` | def _finalize_response(self, response):
res = HttpResponse(content=response.content,
content_type=self._get_content_type())
# status_code is set separately to allow zero
res.status_code = response.code
return res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def to_http_response(self) -> HttpResponse:\n response = (\n JsonResponse(self.body)\n if (self.headers or {}).get(\"Content-Type\") == \"application/json\"\n else HttpResponse(self.body)\n )\n response.headers = self.headers\n return response",
"def m... | [
"0.7379816",
"0.7109294",
"0.69839066",
"0.6952941",
"0.6952941",
"0.6925957",
"0.6925957",
"0.69120204",
"0.68229306",
"0.6809943",
"0.6809943",
"0.6740994",
"0.6736841",
"0.6683467",
"0.66039944",
"0.65061057",
"0.64141685",
"0.640344",
"0.6377596",
"0.634833",
"0.6296918",... | 0.759568 | 0 |
Return ContentType header with charset info. | def _get_content_type(self):
return '%s; charset=%s' % (self.content_type, self.charset) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def charset(self) -> Optional[str]:\n raw = self._headers.get(hdrs.CONTENT_TYPE) # type: ignore[attr-defined]\n if self._stored_content_type != raw:\n self._parse_content_type(raw)\n return self._content_dict.get(\"charset\") # type: ignore[union-attr]",
"def content_type_header... | [
"0.7331894",
"0.71138537",
"0.6978401",
"0.6755701",
"0.6664483",
"0.6629774",
"0.6557151",
"0.65387005",
"0.65190446",
"0.64697444",
"0.64657295",
"0.6446364",
"0.642422",
"0.6410978",
"0.63235193",
"0.6279511",
"0.6264188",
"0.623511",
"0.6197167",
"0.61729234",
"0.6166692"... | 0.74809164 | 0 |
Initialize the manager. The ``_datamappers`` dictionary is initialized here to make testing easier. | def __init__(self):
self._datamappers = {
'*/*': DataMapper()
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.data_set_loc = conf.config_section_mapper(\"filePath\").get(\"data_set_loc\")\n self.data_extractor = DataExtractor(self.data_set_loc)",
"def do_init(self):\n\n pass",
"def init(self, **kwargs):\n self._d = {}\n self._th = None\n self._run = ... | [
"0.6461124",
"0.6418458",
"0.6413563",
"0.6375085",
"0.6290806",
"0.6228817",
"0.6185347",
"0.6185347",
"0.6185347",
"0.61723155",
"0.61663306",
"0.6159628",
"0.6139021",
"0.6131483",
"0.61117107",
"0.6094118",
"0.6062833",
"0.6062833",
"0.6062833",
"0.6062833",
"0.6062833",
... | 0.7486393 | 0 |
Select appropriate formatter based on the request. | def select_formatter(self, request, resource):
# 1. get from resource
if resource.mapper:
return resource.mapper
# 2. get from url
mapper_name = self._get_name_from_url(request)
if mapper_name:
return self._get_mapper(mapper_name)
# 3. get from accept header
mapper_name = self._get_name_from_accept(request)
if mapper_name:
return self._get_mapper(mapper_name)
# 4. use resource's default
if resource.default_mapper:
return resource.default_mapper
# 5. use manager's default
return self._get_default_mapper() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _determine_format(self, request):\n return determine_format(request, self._meta.serializer, default_format=self._meta.default_format)",
"def determine_format(request, serializer, default_format='application/json'):\r\n # First, check if they forced the format.\r\n if request.GET.get('format'):\r... | [
"0.70516115",
"0.68860674",
"0.67620766",
"0.6718547",
"0.641668",
"0.62356836",
"0.6230665",
"0.61402184",
"0.61402184",
"0.6086643",
"0.6054365",
"0.59921783",
"0.59283495",
"0.5847288",
"0.58354276",
"0.5824539",
"0.57529914",
"0.5744481",
"0.5692539",
"0.569212",
"0.56478... | 0.7539723 | 0 |
Select appropriate parser based on the request. | def select_parser(self, request, resource):
# 1. get from resource
if resource.mapper:
return resource.mapper
# 2. get from content type
mapper_name = self._get_name_from_content_type(request)
if mapper_name:
return self._get_mapper(mapper_name)
# 3. get from url
mapper_name = self._get_name_from_url(request)
if mapper_name:
return self._get_mapper(mapper_name)
# 4. use resource's default
if resource.default_mapper:
return resource.default_mapper
# 5. use manager's default
return self._get_default_mapper() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_parser():\n\n try:\n select_texttools_parser()\n except ImportError:\n select_python_parser()",
"def get_parser(self):\n if self.vendor and self.platform and self.version:\n cls = self.profile.get_profile().get_parser(\n self.vendor.code, self.platform.name, se... | [
"0.67126334",
"0.6521535",
"0.6489922",
"0.640149",
"0.6139885",
"0.61379045",
"0.613299",
"0.61198676",
"0.6049748",
"0.602667",
"0.5946609",
"0.58708733",
"0.58408815",
"0.5837304",
"0.58118755",
"0.5710313",
"0.5667457",
"0.5613246",
"0.5606975",
"0.5493934",
"0.5479771",
... | 0.73894787 | 0 |
Returs mapper based on the content type. | def get_mapper_by_content_type(self, content_type):
content_type = util.strip_charset(content_type)
return self._get_mapper(content_type) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mapper(self, structure):\n type_ = structure['type']\n mapper = self.mappers[type_]\n return mapper",
"def mapper_for_type(self, type_):\n mapper = self.mappers[type_]\n return mapper",
"def get_mapping_type(cls):\n ...",
"def _get_mapper(obj):\n its_a_model =... | [
"0.6787792",
"0.6768891",
"0.62163997",
"0.5705611",
"0.56396294",
"0.55432177",
"0.5536067",
"0.5350777",
"0.53324276",
"0.53072",
"0.529081",
"0.5220667",
"0.5189422",
"0.5182988",
"0.51817083",
"0.50861496",
"0.50557506",
"0.5036861",
"0.50153947",
"0.49689895",
"0.4966391... | 0.77844703 | 0 |
Set the default mapper to be used, when no format is defined. This is the same as calling ``register_mapper`` with ``/`` with the exception of giving ``None`` as parameter. | def set_default_mapper(self, mapper):
mapper = mapper or DataMapper()
self._datamappers['*/*'] = mapper | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_default_mapper(self):\n\n return self._datamappers['*/*']",
"def set_mapper(obj, mapper):\n setattr(obj, MAPPER, mapper)\n return mapper",
"def _get_mapper(self, mapper_name):\n\n if mapper_name in self._datamappers:\n # mapper found\n return self._datamappers... | [
"0.7185841",
"0.5845222",
"0.5408889",
"0.5322639",
"0.5239758",
"0.5030067",
"0.4899394",
"0.48726788",
"0.4867522",
"0.48561874",
"0.48168156",
"0.4815694",
"0.48097968",
"0.48017895",
"0.47900453",
"0.4782826",
"0.47522974",
"0.47464633",
"0.4713747",
"0.46979213",
"0.4691... | 0.7901851 | 0 |
Return the default mapper. | def _get_default_mapper(self):
return self._datamappers['*/*'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_default_mapper(self, mapper):\n\n mapper = mapper or DataMapper()\n self._datamappers['*/*'] = mapper",
"def mapper(self):\n if not self._fitted:\n raise ValueError(\"Cannot get mapper if object has not been fitted.\")\n return self._mapper.copy()",
"def _get_mapp... | [
"0.6871667",
"0.6705098",
"0.66739136",
"0.65977365",
"0.6307456",
"0.62351674",
"0.6097825",
"0.5983813",
"0.59388465",
"0.57689005",
"0.5738587",
"0.57277805",
"0.57277805",
"0.5723523",
"0.56839365",
"0.562199",
"0.5607039",
"0.5599599",
"0.5549087",
"0.553436",
"0.5475148... | 0.8958518 | 0 |
Return the mapper based on the given name. | def _get_mapper(self, mapper_name):
if mapper_name in self._datamappers:
# mapper found
return self._datamappers[mapper_name]
else:
# unsupported format
return self._unknown_format(mapper_name) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lookup(self, name):\n try:\n return self._baseLookup(name)\n except ImportError:\n raise ImportError(\"No module named %r in mapper %r\" % (name, self))",
"def get(cls, name):\n cls.initialize()\n if isinstance(name, cls):\n return name\n el... | [
"0.69885236",
"0.6523901",
"0.64461076",
"0.63601327",
"0.6340254",
"0.6093008",
"0.60604954",
"0.60452425",
"0.59643584",
"0.5960165",
"0.59453994",
"0.589722",
"0.5801919",
"0.567288",
"0.5612674",
"0.5609351",
"0.55997974",
"0.559257",
"0.55712503",
"0.5496303",
"0.5479467... | 0.7992836 | 0 |
Get name from ContentType header | def _get_name_from_content_type(self, request):
content_type = request.META.get('CONTENT_TYPE', None)
if content_type:
# remove the possible charset-encoding info
return util.strip_charset(content_type)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def content_type_header(request: Request) -> str:\n return request.content_type",
"def gettype(self, failobj=None):\n missing = []\n value = self.get('content-type', missing)\n if value is missing:\n return failobj\n return re.split(r';\\s*', value.strip())[0].lower()",
... | [
"0.6892327",
"0.6575444",
"0.65093666",
"0.6481238",
"0.6447588",
"0.63857204",
"0.6333128",
"0.63329685",
"0.6310448",
"0.6281364",
"0.6262302",
"0.62129503",
"0.6198477",
"0.61856425",
"0.608376",
"0.60403216",
"0.5957448",
"0.5953572",
"0.59456986",
"0.5944716",
"0.5935226... | 0.78704923 | 0 |
Process the Accept HTTP header. Find the most suitable mapper that the client wants and we support. | def _get_name_from_accept(self, request):
accepts = util.parse_accept_header(request.META.get("HTTP_ACCEPT", ""))
if not accepts:
return None
for accept in accepts:
if accept[0] in self._datamappers:
return accept[0]
raise errors.NotAcceptable() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _accept_strategy(self, host, port, environ, supported_content_types):\n accept = Accept(environ.get('HTTP_ACCEPT', ''))\n\n app = None\n\n # Find the best match in the Accept header\n mime_type, params = accept.best_match(supported_content_types)\n if 'version' in params:\n ... | [
"0.7370754",
"0.6847089",
"0.6750999",
"0.6682592",
"0.6649257",
"0.65085864",
"0.6476922",
"0.6448833",
"0.6262016",
"0.61876094",
"0.6172913",
"0.6024761",
"0.5840581",
"0.5723091",
"0.56867343",
"0.5537604",
"0.5529102",
"0.55037165",
"0.5392582",
"0.5388047",
"0.5382728",... | 0.6449594 | 7 |
Determine short name for the mapper based on the URL. Short name can be either in query string (e.g. ?format=json) or as an extension to the URL (e.g. myresource.json). | def _get_name_from_url(self, request):
format = request.GET.get('format', None)
if not format:
match = self._format_query_pattern.match(request.path)
if match and match.group('format'):
format = match.group('format')
return format | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def url_name(request):\n url_name = False\n if request.resolver_match:\n url_name = request.resolver_match.url_name\n return {\"url_name\": url_name}",
"def shorten_url():\n return rh.shorten_url(request)",
"def _shortenUrl(self, url):\n posturi = \"https://www.googleapis.com/urlshort... | [
"0.6074111",
"0.6005944",
"0.5902819",
"0.58046716",
"0.5792287",
"0.5767634",
"0.57404774",
"0.57221323",
"0.57147455",
"0.5575753",
"0.5546919",
"0.5540886",
"0.553158",
"0.55257696",
"0.5518946",
"0.5516314",
"0.54784685",
"0.5469388",
"0.544259",
"0.54098177",
"0.5381609"... | 0.66325134 | 0 |
Deal with the situation when we don't support the requested format. | def _unknown_format(self, format):
raise errors.NotAcceptable('unknown data format: ' + format) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_format(self):\n raise NotImplementedError()",
"def _determine_format(self, request):\n return determine_format(request, self._meta.serializer, default_format=self._meta.default_format)",
"def validateWorkFormat(format):\n\n if not(format):\n return \"You must select a work ... | [
"0.7758894",
"0.6573002",
"0.6391546",
"0.6325112",
"0.62708557",
"0.6243621",
"0.6233522",
"0.6175671",
"0.6166886",
"0.61477107",
"0.61403495",
"0.60342926",
"0.60149646",
"0.6010532",
"0.5987411",
"0.5959781",
"0.5955787",
"0.5895473",
"0.58931667",
"0.58854645",
"0.588295... | 0.76091594 | 1 |
Check that the mapper has valid signature. | def _check_mapper(self, mapper):
if not hasattr(mapper, 'parse') or not callable(mapper.parse):
raise ValueError('mapper must implement parse()')
if not hasattr(mapper, 'format') or not callable(mapper.format):
raise ValueError('mapper must implement format()') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_signature(self, inputs, signature):\n pass",
"def verify_signature(self, inputs, signature):\n pass",
"def signature_check(dummy, *args, **kwargs):\n try:\n dummy(*args, **kwargs)\n return True\n\n except TypeError:\n return False",
"def _check_type(self):\... | [
"0.6831698",
"0.6831698",
"0.6462788",
"0.6245771",
"0.62307477",
"0.6047557",
"0.601151",
"0.5921455",
"0.59146875",
"0.5847448",
"0.58155686",
"0.57982296",
"0.577483",
"0.5725613",
"0.57252246",
"0.5719018",
"0.5713518",
"0.57050633",
"0.56824374",
"0.5622677",
"0.561941",... | 0.73222697 | 0 |
Set up the Zigbee Home Automation device tracker from config entry. | async def async_setup_entry(
hass: HomeAssistant,
config_entry: ConfigEntry,
async_add_entities: AddEntitiesCallback,
) -> None:
entities_to_create = hass.data[DATA_ZHA][Platform.DEVICE_TRACKER]
unsub = async_dispatcher_connect(
hass,
SIGNAL_ADD_ENTITIES,
functools.partial(
discovery.async_add_entities, async_add_entities, entities_to_create
),
)
config_entry.async_on_unload(unsub) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup_platform(\n hass: HomeAssistant,\n config: ConfigType,\n add_entities: AddEntitiesCallback,\n discovery_info: DiscoveryInfoType | None = None,\n) -> None:\n name = config.get(CONF_NAME)\n mac = config.get(CONF_MAC)\n _LOGGER.debug(\"Setting up\")\n\n mon = Monitor(hass, mac, name)... | [
"0.645116",
"0.6365515",
"0.6173198",
"0.6137033",
"0.6097791",
"0.60210615",
"0.5982656",
"0.5962991",
"0.5919882",
"0.5888324",
"0.5879143",
"0.5873886",
"0.58680475",
"0.5860574",
"0.5820853",
"0.58103406",
"0.5783399",
"0.57670003",
"0.5749506",
"0.57469505",
"0.5705554",... | 0.0 | -1 |
Initialize the ZHA device tracker. | def __init__(self, unique_id, zha_device, cluster_handlers, **kwargs):
super().__init__(unique_id, zha_device, cluster_handlers, **kwargs)
self._battery_cluster_handler = self.cluster_handlers.get(
CLUSTER_HANDLER_POWER_CONFIGURATION
)
self._connected = False
self._keepalive_interval = 60
self._battery_level = None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def init(self):\n logger.info(\"Init device: %s\", self._serial)\n self._callback(STATUS_INIT)\n\n self._init_binaries()\n self._init_apks()\n await self._init_forwards()\n\n await adb.shell(self._serial, \"/data/local/tmp/atx-agent server --stop\")\n await ad... | [
"0.65205735",
"0.6469632",
"0.6374689",
"0.6327194",
"0.6239735",
"0.62235254",
"0.62131935",
"0.61628926",
"0.61628926",
"0.61628926",
"0.6160946",
"0.61380124",
"0.61321",
"0.610218",
"0.60903937",
"0.6049556",
"0.60407215",
"0.6000517",
"0.59815305",
"0.596642",
"0.5956365... | 0.59739983 | 19 |
Run when about to be added to hass. | async def async_added_to_hass(self) -> None:
await super().async_added_to_hass()
if self._battery_cluster_handler:
self.async_accept_signal(
self._battery_cluster_handler,
SIGNAL_ATTR_UPDATED,
self.async_battery_percentage_remaining_updated,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def async_added_to_hass(self):\n\n def gpio_edge_listener(port):\n \"\"\"Update GPIO when edge change is detected.\"\"\"\n self.schedule_update_ha_state(True)\n\n def setup_entity():\n setup_input(self._port)\n edge_detect(self._port, gpio_edge_listen... | [
"0.68537086",
"0.6834448",
"0.6687171",
"0.6658029",
"0.6650727",
"0.657596",
"0.65477496",
"0.65373653",
"0.6497973",
"0.64905834",
"0.64446324",
"0.64446324",
"0.64271516",
"0.64118624",
"0.64040715",
"0.64040715",
"0.64040715",
"0.6399848",
"0.6393785",
"0.6379698",
"0.637... | 0.6008163 | 65 |
Return true if the device is connected to the network. | def is_connected(self):
return self._connected | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_connected():\n sta_if = network.WLAN(network.STA_IF)\n return sta_if.isconnected()",
"def is_connected(self):\n return self._port.is_connected()",
"def isconnected(self):\n return self._wlan.isconnected()",
"def is_connected(self):\n return self.hw_connected",
"def is_connecte... | [
"0.7903595",
"0.78890985",
"0.7884118",
"0.78780943",
"0.7753229",
"0.7727309",
"0.7695511",
"0.76544905",
"0.76394",
"0.7609661",
"0.75915796",
"0.7570455",
"0.7563875",
"0.75160927",
"0.75101984",
"0.749174",
"0.7488076",
"0.7486625",
"0.74687254",
"0.7448089",
"0.74446356"... | 0.73389804 | 43 |
Return the source type, eg gps or router, of the device. | def source_type(self) -> SourceType:
return SourceType.ROUTER | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def source_type(self):\n return SOURCE_TYPE_GPS",
"def source_type(self):\n return SOURCE_TYPE_GPS",
"def source_type(self):\n return SOURCE_TYPE_ROUTER",
"def source_type(self):\n return SOURCE_TYPE_ROUTER",
"def source_type(self) -> Optional[str]:\n return pulumi.get(se... | [
"0.79885626",
"0.79885626",
"0.7760277",
"0.7760277",
"0.7449024",
"0.72812986",
"0.71759343",
"0.66198623",
"0.6579725",
"0.64343446",
"0.6371079",
"0.62237245",
"0.6218695",
"0.6187604",
"0.6182113",
"0.61786586",
"0.61733663",
"0.61695445",
"0.6154849",
"0.6086145",
"0.608... | 0.7750512 | 5 |
Return the battery level of the device. Percentage from 0100. | def battery_level(self):
return self._battery_level | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def battery_level(self):\n return self.battery",
"def get_battery_level(self) -> int:\n\n try:\n self._serial.transmit(b'\\x51\\x00')\n response = self._get_reply(0x51, 1, 0.25)\n finally:\n self._gpio.sleep()\n\n return response[2]",
"def battery_le... | [
"0.85965866",
"0.8490694",
"0.8206418",
"0.8130491",
"0.8092713",
"0.80214363",
"0.79328454",
"0.7850373",
"0.7657598",
"0.760602",
"0.7397481",
"0.7355417",
"0.723275",
"0.7155458",
"0.70573586",
"0.70248145",
"0.69043136",
"0.6902873",
"0.68718886",
"0.68614537",
"0.6757102... | 0.84553003 | 2 |
Return an airport code input after validating it | def airportCodeInput(self, prompt):
while True:
code = input(prompt).upper()
if code not in self.travel_db.airports:
print("Invalid airport code")
else:
return code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validateAirport(self, code):\n print(code)\n if code in self.travel_db.airports:\n return True\n else:\n return False",
"def iata(code):\r\n if len(code) == 3:\r\n return code.upper()\r\n else:\r\n raise argparse.ArgumentTypeError(\"%s is not val... | [
"0.7340205",
"0.6232221",
"0.6139465",
"0.6118442",
"0.6065149",
"0.60150987",
"0.5987312",
"0.58300376",
"0.57871574",
"0.57339126",
"0.5719007",
"0.5665261",
"0.5665261",
"0.5665261",
"0.5651849",
"0.56237924",
"0.5594974",
"0.5574781",
"0.55745834",
"0.556082",
"0.55538803... | 0.81940216 | 0 |
Return a country name input after validating it | def countryInput(self, prompt):
while True:
name = input(prompt)
if name not in self.travel_db.countries:
print("Invalid country name. Please make sure name is capitalized.")
else:
return name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def country() -> str:",
"def valid_country(value: Any) -> str:\n value = cv.string(value)\n all_supported_countries = holidays.list_supported_countries()\n\n try:\n raw_value = value.encode(\"utf-8\")\n except UnicodeError as err:\n raise vol.Invalid(\n \"The country name or ... | [
"0.6980752",
"0.69086546",
"0.6828054",
"0.669586",
"0.66514415",
"0.66304183",
"0.6440515",
"0.64396936",
"0.6435864",
"0.6435098",
"0.6365185",
"0.63565147",
"0.6318801",
"0.6305565",
"0.62800944",
"0.6262626",
"0.6246364",
"0.6230827",
"0.62267274",
"0.62176657",
"0.607218... | 0.79892355 | 0 |
Return a currency code input after validaing it | def currencyInput(self, prompt):
while True:
code = input(prompt).upper()
if code not in self.travel_db.currencies:
print("Invalid currency code")
else:
return code | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_currency(currency_code):\n try:\n rate = rates.get_rates(currency_code)\n return 0\n except:\n flash(f'Error: {currency_code} is not a valid currency')\n return 1",
"def get_currency(test_loop_count=None) -> str:\n loop_count = 0\n while True:\n try:\n ... | [
"0.6704559",
"0.6646507",
"0.6486832",
"0.62412405",
"0.6107163",
"0.60303926",
"0.600023",
"0.59911394",
"0.5982588",
"0.59745014",
"0.5958056",
"0.5924218",
"0.587548",
"0.58722013",
"0.5858742",
"0.5832311",
"0.5812267",
"0.5781818",
"0.5777066",
"0.5729549",
"0.5705858",
... | 0.7700206 | 0 |
Return True if airport code valid, False otherwise. | def validateAirport(self, code):
print(code)
if code in self.travel_db.airports:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _is_valid_code(self, code):\r\n return code in COUNTRY_CODES",
"def is_valid(postal_code):\n return bool(re.match(UK_POST_CODE_REGEX, postal_code, re.VERBOSE)) if postal_code else False",
"def check_code(item_code):\r\n # RA matches\r\n if re.match(r'^MCRNC[0-9]{4}\\.T$', item_code):\r\n ... | [
"0.70381415",
"0.6726082",
"0.64569366",
"0.6375067",
"0.630065",
"0.6263566",
"0.6250235",
"0.62470305",
"0.6238505",
"0.62053096",
"0.6156845",
"0.61343706",
"0.61318254",
"0.6113142",
"0.6089113",
"0.6086316",
"0.6080452",
"0.6008818",
"0.5969722",
"0.59552896",
"0.5907283... | 0.8865747 | 0 |
Return True if country_name valid, False otherwise. | def validateCountry(self, country_name):
if country_name in self.travel_db.countries:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_valid_country():\n assert valid_country(\"Democratic Republic of Lungary\") is True\n assert valid_country(\"Kraznoviklandstan\") is True\n assert valid_country(\"kraznoviklandstan\") is True\n assert valid_country(\"KRAZNOVIKLANDSTAN\") is True\n\n assert valid_country(\"Democratic_Republi... | [
"0.7013575",
"0.700049",
"0.6939837",
"0.68034947",
"0.67918384",
"0.6622717",
"0.6601707",
"0.6583337",
"0.6476302",
"0.64751047",
"0.6429186",
"0.64257467",
"0.64110726",
"0.6369207",
"0.63588893",
"0.62856215",
"0.6275524",
"0.62537944",
"0.62458146",
"0.6229923",
"0.62293... | 0.8314883 | 0 |
Return True if currency_code valid, False otherwise. | def validateCurrency(self, currency_code):
if currency_code in self.travel_db.currencies:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_currency(currency_code):\n try:\n rate = rates.get_rates(currency_code)\n return 0\n except:\n flash(f'Error: {currency_code} is not a valid currency')\n return 1",
"def _is_valid_code(self, code):\r\n return code in COUNTRY_CODES",
"def is_valid(postal_cod... | [
"0.759342",
"0.70800674",
"0.6537341",
"0.62727094",
"0.62223756",
"0.619591",
"0.6130835",
"0.60933155",
"0.6079185",
"0.6059149",
"0.6046414",
"0.60261106",
"0.5977034",
"0.59302557",
"0.5910405",
"0.5875142",
"0.5865183",
"0.5845647",
"0.5834872",
"0.57638985",
"0.57544607... | 0.85613906 | 0 |
Return a dictionary of Currency objects, with key = currency code. Created from info stored in filename | def buildCurrencyDict(filename):
currencies = {}
with open(os.path.join("input", filename), "rt", encoding="utf8") as f:
reader = csv.reader(f)
for line in reader:
currencies[line[1]] = Currency(line[1], line[0], float(line[2]))
return currencies | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildCountryDict(filename, currencies_dict):\n # This function requires the currency dictionary to be built already.\n countries = {}\n with open(os.path.join(\"input\", filename), \"rt\", encoding=\"utf8\") as f:\n reader = csv.reader(f)\n for line in reader:\n ... | [
"0.70550233",
"0.6869403",
"0.6178886",
"0.61106163",
"0.59804064",
"0.59100264",
"0.5908569",
"0.59003896",
"0.58851796",
"0.58286935",
"0.58016914",
"0.57944894",
"0.57516325",
"0.5689539",
"0.5687478",
"0.56543136",
"0.56246847",
"0.56089044",
"0.56031054",
"0.5590987",
"0... | 0.84670454 | 0 |
Return a dictionary of Country objects, with key = country name. Created from info stored in filename | def buildCountryDict(filename, currencies_dict):
# This function requires the currency dictionary to be built already.
countries = {}
with open(os.path.join("input", filename), "rt", encoding="utf8") as f:
reader = csv.reader(f)
for line in reader:
try:
countries[line[0]] = Country(line[0], line[14], currencies_dict)
except KeyError: # If currency isn't found, country won't be added to the dictionary
continue
return countries | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildAirportDict(filename, countries_dict): \n # This function requires the country dictionary to be built already.\n airports = {}\n with open(os.path.join(\"input\", filename), \"rt\", encoding=\"utf8\") as f:\n reader = csv.reader(f)\n for line in reader:\n ... | [
"0.65897286",
"0.64826685",
"0.6389784",
"0.6383171",
"0.6335359",
"0.62931234",
"0.6252615",
"0.62445325",
"0.6109666",
"0.6080601",
"0.6028311",
"0.60078806",
"0.5918966",
"0.588614",
"0.5876146",
"0.5860082",
"0.5792945",
"0.576631",
"0.57317054",
"0.5713805",
"0.5710851",... | 0.74422234 | 0 |
Return a dictionary of Airport objects, with key = airport code. Created from info stored in filename | def buildAirportDict(filename, countries_dict):
# This function requires the country dictionary to be built already.
airports = {}
with open(os.path.join("input", filename), "rt", encoding="utf8") as f:
reader = csv.reader(f)
for line in reader:
try:
airports[line[4]] = Airport(line[4], line[1], line[3], line[2], float(line[6]), float(line[7]), countries_dict)
except KeyError: # If country isn't found, the airport won't be added to the dictionary
continue
return airports | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def triplify(self):\n airports = {}\n with open(self.source_file_path, 'r') as csvfile:\n reader = csv.reader(csvfile, delimiter=\",\")\n for i, row in enumerate(reader):\n if i != 0:\n # even if it says that data is encoded to latin-1, it actua... | [
"0.70951945",
"0.7048426",
"0.68105704",
"0.67958844",
"0.67303306",
"0.660992",
"0.6241538",
"0.6214554",
"0.60528135",
"0.5989924",
"0.59378284",
"0.5857952",
"0.58015823",
"0.55762887",
"0.55647373",
"0.554717",
"0.5545705",
"0.5532614",
"0.551982",
"0.5506063",
"0.5496964... | 0.7226677 | 0 |
Return a list of routes from a file, in the format [name, [airport code list]]. Return None if file not found. | def getRouteInputFile(filename):
if filename[-4:] != ".csv": # Make sure the filename is a .csv
return None
routes = []
try:
with open(os.path.join("input", filename), "rt", encoding="utf8") as f:
reader = csv.reader(f)
for line in reader:
try:
routes.append([line[0], line[1:]])
except (UnicodeDecodeError, IndexError):
# skip blank lines and lines with invalid characters
continue
except (FileNotFoundError, OSError):
return None
return routes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_routes_file(route_filename):\n\n list_route_descriptions = []\n tree = ET.parse(route_filename)\n for route in tree.iter(\"route\"):\n route_town = route.attrib['map']\n route_id = route.attrib['id']\n waypoint_list = [] # the list of waypoints that can be found on this rou... | [
"0.6682189",
"0.61099017",
"0.60007584",
"0.59836805",
"0.59614146",
"0.585794",
"0.58239037",
"0.57957166",
"0.5757466",
"0.5705301",
"0.5684137",
"0.5619704",
"0.5542456",
"0.55303",
"0.5512766",
"0.5511307",
"0.5509003",
"0.54854715",
"0.5457731",
"0.54531974",
"0.54459774... | 0.6995897 | 0 |
Create a csv input file, given a list of routes. Routes are lists of names and airport codes. | def writeRoutesCSV(filename, routes):
if filename[-4:] != ".csv": # Make sure the filename is a .csv
filename += ".csv"
try:
with open(os.path.join("input", filename), "w", newline='') as f:
writer = csv.writer(f, delimiter=",")
writer.writerows(routes)
except (OSError, FileNotFoundError):
return False
else:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def route_data(route):\n os.chdir(\"../Data/test\") #change to whatever directory your data files are stored in\n with open(\"../Sorted Data/\"+str(route)+\"_data.csv\",\"w\",newline=\"\") as result_file: #storing resulting data in csv file in different directory\n wr=csv.writer(result_file, dialect='... | [
"0.6899799",
"0.66672605",
"0.6458159",
"0.63670135",
"0.634914",
"0.63457423",
"0.63208055",
"0.6306807",
"0.6239996",
"0.62074065",
"0.6170774",
"0.60570943",
"0.6028385",
"0.6027313",
"0.60171574",
"0.60073507",
"0.59914494",
"0.59842396",
"0.5965804",
"0.59501034",
"0.591... | 0.7744267 | 0 |
Write output .csv file for list of itineraries. Output file shows cheapest route and its cost. | def writeItineraryOutput(filename, itins):
if filename[-4:] != ".csv": # Make sure the filename is a .csv
filename += ".csv"
try:
with open(os.path.join("output", filename), "w", newline='') as f:
writer = csv.writer(f, delimiter=",")
firstline = ["Name", "Cost", "Home", "Dest 1", "Dest 2", "Dest 3", "Dest 4", "Dest 5", "Dest 6"]
writer.writerow(firstline)
for itinerary in itins:
line = []
line.append(itinerary.name)
line.append(itinerary.cheapest_cost)
line = line + itinerary.cheapest_route.getCodeList()
writer.writerow(line)
except (FileNotFoundError, OSError):
return False
else:
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_csv(net, wires, net_id, chip_id, chip):\n with open('output/output.csv', 'w') as file:\n # Write first line\n output = csv.writer(file)\n output.writerow([\"net\", \"wires\"])\n\n # Index and fill the body\n for step in range(len(wires)):\n output.writerow(... | [
"0.654631",
"0.64892834",
"0.6398828",
"0.6333328",
"0.6306452",
"0.62436354",
"0.6238039",
"0.6226846",
"0.6123271",
"0.6066527",
"0.6055366",
"0.6047722",
"0.6046962",
"0.6041532",
"0.6025302",
"0.60197496",
"0.6000928",
"0.5996336",
"0.59933907",
"0.5990987",
"0.5989872",
... | 0.7681153 | 0 |
Create an input file with randomly generated routes for num_people. | def generateRandomInput(filename, num_people, travel_db):
import random
routes = []
for i in range(num_people):
route = travel_db.randomRoute()
route.insert(0,"Person " + str(i)) # Add a name for each route.
routes.append(route)
if FileHandler.writeRoutesCSV(filename,routes): # If it's successful writing the file
print("File {0} created successfully with {1} people.".format(filename, num_people))
else:
print("File {0} could not be created.".format(filename)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prepare_input_file(self, filename, numlines, maxvalue):\n with open(filename, 'a') as f:\n for _ in range(numlines):\n f.write(str(randrange(maxvalue)) + '\\n')\n self.filepath = f.name",
"def routes_gen(num) -> Generator[Route, None, None]:\n with open(f'data/route-... | [
"0.587769",
"0.5856231",
"0.56174666",
"0.55805063",
"0.55153143",
"0.5446747",
"0.54272777",
"0.54071355",
"0.5406519",
"0.5362641",
"0.5354671",
"0.53122556",
"0.5305677",
"0.5279175",
"0.5232032",
"0.5200919",
"0.51978534",
"0.518729",
"0.51755005",
"0.5140269",
"0.5115986... | 0.84894335 | 0 |
get access token for ohub API | def get_access_token(self):
payload = {
'grant_type': 'client_credentials',
'client_id': self.client_id,
'client_secret': self.client_secret,
'resource': self.resource
}
res = requests.post(self.auth_url, data=payload)
data = res.json()
if res.status_code == 200:
return data['access_token'], res
return False, res | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_token(): \n \n # Token url\n token_endpoint = \"https://api.signicat.io/oauth/connect/token\"\n # Setting the grant type to client_credentials\n data = {'grant_type':'client_credentials', 'scope':'identify'}\n # Posting to token url with HTTP basic authentication\n token = requests.pos... | [
"0.752597",
"0.736419",
"0.72990793",
"0.7212395",
"0.7182696",
"0.7124292",
"0.70727074",
"0.7070482",
"0.70286155",
"0.7016784",
"0.7008737",
"0.6973223",
"0.69727576",
"0.69727576",
"0.69727325",
"0.6971487",
"0.6961795",
"0.6953318",
"0.69116116",
"0.6904606",
"0.6902688"... | 0.6742485 | 40 |
Tests API call to onboard NS descriptor resources | def test_post_ns_descriptors(post_ns_descriptors_keys):
sonata_vnfpkgm = SONATAClient.VnfPkgm(HOST_URL)
sonata_nsd = SONATAClient.Nsd(HOST_URL)
sonata_auth = SONATAClient.Auth(HOST_URL)
_token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))
_token = json.loads(_token["data"])
Helpers._delete_test_nsd(_token=_token["token"]["access_token"])
sonata_vnfpkgm.post_vnf_packages(token=_token["token"]["access_token"],
package_path="tests/samples/vnfd_example.yml")
response = json.loads(sonata_nsd.post_ns_descriptors(
token=_token["token"]["access_token"],
package_path="tests/samples/nsd_example.yml"))
assert response['error'] == False
assert response['data'] != '' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_ns_descriptors_nsdinfoid():\r\n sonata_nsd = SONATAClient.Nsd(HOST_URL)\r\n sonata_auth = SONATAClient.Auth(HOST_URL)\r\n _token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))\r\n _token = json.loads(_token[\"data\"])\r\n _nsd_list = json.loads(sonata_nsd.get_ns_de... | [
"0.66410756",
"0.63118935",
"0.6108497",
"0.5946216",
"0.5842967",
"0.58171374",
"0.5727807",
"0.5709085",
"0.56833494",
"0.56741107",
"0.56662744",
"0.56547874",
"0.56290054",
"0.5608064",
"0.55975276",
"0.55833757",
"0.55814964",
"0.5580672",
"0.5555872",
"0.5532858",
"0.55... | 0.52910966 | 63 |
Tests API call to fetch multiple NS descriptor resources | def test_get_ns_descriptors(get_ns_descriptors_keys):
sonata_nsd = SONATAClient.Nsd(HOST_URL)
sonata_auth = SONATAClient.Auth(HOST_URL)
_token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))
_token = json.loads(_token["data"])
response = json.loads(sonata_nsd.get_ns_descriptors(
token=_token["token"]["access_token"], limit=1000))
response = json.loads(response["data"])
assert isinstance(response, list)
if len(response) > 0:
assert set(get_ns_descriptors_keys).issubset(
response[0].keys()), "All keys should be in the response" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_many_descriptors(self, uuids):",
"def test_multiple_gets(uris):\n\n for uri in uris:\n print('='*10 + ' Try uri : {uri} '.format(uri=uri) + '='*10)\n resp = get_api_url(uri)\n print(resp)\n try:\n pprint(resp.json())\n except Exception as e:\n p... | [
"0.6480714",
"0.64030355",
"0.63878626",
"0.633152",
"0.61416095",
"0.6027618",
"0.59268606",
"0.5893378",
"0.5883436",
"0.5867936",
"0.5860991",
"0.58218473",
"0.58013874",
"0.5790113",
"0.5780573",
"0.5772255",
"0.57513046",
"0.5746608",
"0.5731543",
"0.5729499",
"0.5722015... | 0.65850234 | 0 |
Tests API call to read information about an NS descriptor resources | def test_get_ns_descriptors_nsdinfoid():
sonata_nsd = SONATAClient.Nsd(HOST_URL)
sonata_auth = SONATAClient.Auth(HOST_URL)
_token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))
_token = json.loads(_token["data"])
_nsd_list = json.loads(sonata_nsd.get_ns_descriptors(
token=_token["token"]["access_token"]))
_nsd_list = json.loads(_nsd_list["data"])
Helpers._upload_test_nsd(_token=_token["token"]["access_token"])
for _n in _nsd_list:
if "sonata-demo" == _n['nsd']['name']:
_nsd = _n['uuid']
response = json.loads(sonata_nsd.get_ns_descriptors_nsdinfoid(
token=_token["token"]["access_token"], nsdinfoid=_nsd))
Helpers._delete_test_nsd(_token=_token["token"]["access_token"])
if response["error"]:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_ns_descriptors(get_ns_descriptors_keys):\r\n sonata_nsd = SONATAClient.Nsd(HOST_URL)\r\n sonata_auth = SONATAClient.Auth(HOST_URL)\r\n _token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))\r\n _token = json.loads(_token[\"data\"])\r\n\r\n response = json.loads(sona... | [
"0.64663666",
"0.608117",
"0.5999047",
"0.5969652",
"0.59466934",
"0.59190524",
"0.5892444",
"0.5845748",
"0.5732233",
"0.5668746",
"0.5648303",
"0.5639067",
"0.56217575",
"0.56203",
"0.56144845",
"0.5610858",
"0.5607481",
"0.560151",
"0.5600352",
"0.55998194",
"0.5585602",
... | 0.70114577 | 0 |
Tests API call to delete NS descriptor resources | def test_delete_ns_descriptors_nsdinfoid(delete_ns_descriptors_nsdinfoid_keys):
sonata_vnfpkgm = SONATAClient.VnfPkgm(HOST_URL)
sonata_nsd = SONATAClient.Nsd(HOST_URL)
sonata_auth = SONATAClient.Auth(HOST_URL)
_token = json.loads(sonata_auth.auth(username=USERNAME, password=PASSWORD))
_token = json.loads(_token["data"])
_nsd_list = json.loads(sonata_nsd.get_ns_descriptors(
token=_token["token"]["access_token"]))
_nsd_list = json.loads(_nsd_list["data"])
_nsd = None
for _n in _nsd_list:
if "sonata-demo" == _n['nsd']['name']:
_nsd = _n['uuid']
time.sleep(10) # Wait for NSD onboarding
response = json.loads(sonata_nsd.delete_ns_descriptors_nsdinfoid(
token=_token["token"]["access_token"],
nsdinfoid=_nsd))
assert isinstance(response, dict)
assert response["data"] == "{\"error\":\"The NSD ID None does not exist\"}"
time.sleep(2) #Wait for NSD onboarding
_vnfd_list = json.loads(sonata_vnfpkgm.get_vnf_packages(
token=_token["token"]["access_token"]))
_vnfd_list = json.loads(_vnfd_list["data"])
_vnfd = None
for _v in _vnfd_list:
if "vnfd_example" == _v['uuid']:
_vnfd = _v['uuid']
response = None
if _vnfd:
response = json.loads(sonata_vnfpkgm.delete_vnf_packages_vnfpkgid(
token=_token["token"]["access_token"], vnfPkgId=_vnfd))
assert isinstance(response, dict)
assert response["data"] == "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_delete_on_background_response_descriptor_projects_release_release_resource_spaces(self):\n pass",
"def test_delete_on_background_response_descriptor_projects_release_release_resource(self):\n pass",
"def test_delete_on_background_response_descriptor_subscriptions_subscription_subscriptio... | [
"0.70575756",
"0.68376297",
"0.67838246",
"0.66237175",
"0.64669347",
"0.64376366",
"0.64376366",
"0.6399817",
"0.6381159",
"0.63791645",
"0.63716006",
"0.6369237",
"0.6348905",
"0.6325135",
"0.6323571",
"0.62920225",
"0.62577426",
"0.62550163",
"0.6228073",
"0.62071025",
"0.... | 0.70335805 | 1 |
palindrome should fail when first input is not string | def teststring(self):
self.assertRaises(palindrome.NotStringError,palindrome.palin, 4) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_palindrome():",
"def check_palindrome(inp_string):\n if len(inp_string) <= 2:\n return False\n elif inp_string == inp_string[::-1]:\n return True\n else:\n return False",
"def is_palindrome(string):\n return",
"def ispalindrome(string):\n if isinstance(string, (s... | [
"0.8580645",
"0.7966129",
"0.79021037",
"0.7837178",
"0.77251107",
"0.7704955",
"0.76943594",
"0.7679945",
"0.7677962",
"0.76570374",
"0.7632073",
"0.7594772",
"0.7590463",
"0.7588587",
"0.7567943",
"0.7513122",
"0.7498341",
"0.7479257",
"0.7478958",
"0.74631226",
"0.7449308"... | 0.6526004 | 90 |
Reads DHalo data into memory | def read(self, simtype):
if simtype == 'original':
if self.filename.endswith(".pkl"):
logging.debug("Loading pickle file %s", self.filename)
data = pd.read_pickle(self.filename)
elif self.filename.endswith(".hdf5"):
logging.debug("Loading HDF5 file %s", self.filename)
with h5py.File(self.filename, "r") as data_file:
#print('treeIndex', data_file["treeIndex"].keys())
#print('haloTrees', data_file["haloTrees"].keys())
# Find dimensionality of keys
columns_1dim = []
columns_2dim = []
for column in self.columns:
if len(data_file["/haloTrees/%s" % column].shape) == 1:
columns_1dim.append(column)
else:
columns_2dim.append(column)
# 1D keys
data = pd.DataFrame(
{
column: data_file["/haloTrees/%s" % column].value
for column in columns_1dim
},
columns=columns_1dim
).set_index("nodeIndex")
del columns_1dim
# 2D keys
for column in columns_2dim:
if column == 'position':
pos = data_file["/haloTrees/%s" % column].value
data['X'] = pd.Series(pos[:, 0], index=data.index)
data['Y'] = pd.Series(pos[:, 1], index=data.index)
data['Z'] = pd.Series(pos[:, 2], index=data.index)
del columns_2dim
data.rename(index=str,
columns={"snapshotNumber": "snapnum"})
## eliminate fake elements with isIntegrated=1
#data = data[data.isInterpolated != 1]
else:
raise TypeError("Unknown filetype %s" % self.filename)
if simtype == 'EAGLE':
if self.filename.endswith(".pkl"):
logging.debug("Loading pickle file %s", self.filename)
data = pd.read_pickle(self.filename)
elif self.filename.endswith(".hdf5"):
logging.debug("Loading HDF5 file %s", self.filename)
data_file = h5py.File(self.filename, 'r')
column_mt = []
column_sh = []
for column in self.columns:
if column in data_file['MergerTree']:
column_mt.append(column)
else:
column_sh.append(column)
data = pd.DataFrame(
{
column: data_file["/MergerTree/%s" % column].value
for column in column_mt
},
columns=column_mt
).set_index("HaloID")
#.set_index(data_file["/Merger/HaloID"].value)
for column in column_sh:
data[column] = pd.Series(data_file["/Subhalo/%s" % column].value,
index=data.index)
data = data.rename(index=str,
columns={"SnapNum": "snapnum", #"HaloID": "nodeIndex",
"DescendantID" : "descendantIndex"})
else:
raise TypeError("Unknown filetype %s" % self.filename)
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_data(self):",
"def read_drt(self):\n data = Array('B')\n data = self.read(0, 0, 8)\n num_of_devices = drt_controller.get_number_of_devices(data)\n len_to_read = num_of_devices * 8\n\n data = self.read(0, 0, len_to_read + 8)\n self.drt_manager.set_drt(data)",
"def _read_data(self) -... | [
"0.67620295",
"0.6609676",
"0.6301889",
"0.62566316",
"0.6254608",
"0.62242556",
"0.61881506",
"0.6081291",
"0.6077486",
"0.6040958",
"0.6022732",
"0.5944303",
"0.5917045",
"0.588852",
"0.588852",
"0.588852",
"0.5851131",
"0.58243155",
"0.5814752",
"0.5798164",
"0.57805437",
... | 0.0 | -1 |
Returns halo (row of data) given a ``nodeIndex`` | def get_halo(self, index):
try:
halo = self.data.loc[index]
except KeyError:
raise IndexError(
"Halo id %d not found in %s" % (index, self.filename)
)
return halo | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_node(self, index):\r\n\t\tself._validate_index(index)\r\n\t\treturn self._traverse(lambda i, list: i < index)[\"node\"]",
"def halo_host(self, index):\n halo = self.get_halo(index)\n return (\n halo\n if halo.name == halo[\"hostIndex\"]\n else self.halo_hos... | [
"0.5902977",
"0.5608498",
"0.5608498",
"0.5394057",
"0.5361975",
"0.53427607",
"0.5313705",
"0.52938735",
"0.526928",
"0.5263665",
"0.5258102",
"0.5251677",
"0.5238716",
"0.52234507",
"0.52188444",
"0.52096754",
"0.51846796",
"0.51694787",
"0.51663357",
"0.516497",
"0.5148196... | 0.6535571 | 1 |
Finds indices of all progenitors of a halo, recursively. | def halo_progenitor_ids(self, index):
_progenitors = []
def rec(i):
_progenitor_ids = self.data[self.data["descendantHost"] == i][
"hostIndex"
].unique()
logging.debug("Progenitors recursion: %d > %d (%d progenitors)",
index,
i,
len(_progenitor_ids))
if len(_progenitor_ids) == 0:
return
for _progenitor_id in _progenitor_ids:
# if _progenitor_id not in _progenitors:
# TODO: this only eliminates fly-byes
_progenitors.append(_progenitor_id)
rec(_progenitor_id)
rec(index)
logging.info(
"%d progenitors found for halo %d", len(_progenitors), index
)
return _progenitors | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def halo_progenitor_ids(self, index):\n _progenitors = []\n\n def rec(i):\n _progenitor_ids = self.data[self.data[\"descendantHost\"] == i][\n \"hostIndex\"\n ].unique()\n logging.debug(\n \"Progenitors recursion: %d > %d (%d progenitors)... | [
"0.6863077",
"0.5833046",
"0.5638713",
"0.5561099",
"0.5542297",
"0.5519623",
"0.54976434",
"0.54312",
"0.537776",
"0.53390366",
"0.53045774",
"0.5278391",
"0.5255487",
"0.52501196",
"0.5239766",
"0.522312",
"0.52182275",
"0.5186579",
"0.5170028",
"0.51365596",
"0.5129794",
... | 0.6898143 | 0 |
Finds host of halo. Recursively continues until hits the main halo, in case of multiply embedded subhaloes. | def halo_host(self, index):
halo = self.get_halo(index)
return (
halo
if halo.name == halo["hostIndex"]
else self.halo_host(self.get_halo(halo["hostIndex"]).name)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def updateSubhalos(host,file, host2sub):\n if not (host.ID in host2sub):\n return\n g = open(file,'r')\n for posn in host2sub[host.ID]:\n g.seek(posn)\n line = g.readline()\n sub = MTH.MTHalo(line)\n if sub.pid != host.ID:\n print 'WARNING: ERROR: halo not sub... | [
"0.56107956",
"0.54044807",
"0.53122574",
"0.52616286",
"0.524105",
"0.51986015",
"0.5194676",
"0.5027002",
"0.49752408",
"0.49468756",
"0.49244332",
"0.49071574",
"0.48990795",
"0.4883024",
"0.48149812",
"0.47726056",
"0.47632203",
"0.47600392",
"0.47526672",
"0.47278097",
"... | 0.63591355 | 1 |
Finds mass of central halo and all subhaloes. | def halo_mass(self, index):
return self.data[self.data["hostIndex"] == index][
"particleNumber"
].sum() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_center_of_mass_allies(self,obs):",
"def center_of_mass(self, entity, geometric=False):\n\n # Structure, Model, Chain, Residue\n if isinstance(entity, Entity.Entity):\n atom_list = entity.get_atoms()\n # List of Atoms\n elif hasattr(entity, \"__iter__\") and [x for x... | [
"0.62661475",
"0.61996317",
"0.618251",
"0.6090707",
"0.60871905",
"0.5922938",
"0.5916971",
"0.5912088",
"0.59059227",
"0.5891885",
"0.5858639",
"0.5802172",
"0.5787271",
"0.578331",
"0.57702184",
"0.5754956",
"0.5754098",
"0.57494813",
"0.5742618",
"0.5713476",
"0.5711955",... | 0.6344772 | 1 |
Calculates mass assembly history for a given halo. Treebased approach has been abandoned for performace reasons. | def collapsed_mass_history(self, index, nfw_f):
logging.debug("Looking for halo %d", index)
halo = self.get_halo(index)
if halo["hostIndex"] != halo.name:
raise ValueError("Not a host halo!")
m_0 = self.halo_mass(index)
progenitors = pd.concat(
[
self.data.loc[index],
self.data.loc[self.halo_progenitor_ids(index)],
]
)
logging.debug(
"Built progenitor sub-table for halo %d of mass %d with %d members",
index,
m_0,
progenitors.size,
)
progenitors = progenitors[progenitors["particleNumber"] > nfw_f * m_0]
cmh = progenitors.groupby("snapshotNumber", as_index=False)[
"particleNumber"
].sum()
cmh["nodeIndex"] = index
logging.info(
"Aggregated masses of %d valid progenitors of halo %d",
progenitors.size,
index,
)
return cmh | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def collapsed_mass_history(self, index, nfw_f):\n\n logging.debug(\"Looking for halo %d\", index)\n halo = self.get_halo(index)\n if halo[\"hostIndex\"] != halo.name:\n raise ValueError(\"Not a host halo!\")\n m_0 = self.halo_mass(index)\n\n progenitors = pd.concat(\n ... | [
"0.5746956",
"0.53531224",
"0.51111126",
"0.5078",
"0.5021775",
"0.49615657",
"0.4913952",
"0.48098966",
"0.4727419",
"0.4717308",
"0.46857807",
"0.4666251",
"0.46613628",
"0.46605286",
"0.46548498",
"0.46313113",
"0.4621329",
"0.45925197",
"0.45755017",
"0.4556348",
"0.45549... | 0.565708 | 1 |
Find the all topnodes mtree(z1) (across different snapshots) of mergertrees with basenode in SH(z2) | def tot_num_of_progenitors_at_z(self, SH, mtree, z1, z2):
for ss in range(z1, z2+1):
print('redshift:', ss)
# nodes at redshift ss
ss_indx = np.where(mtree.data.snapshotNumber.values == ss)
nodeID = mtree.data.index.values[ss_indx]
nodeID_desc = mtree.data.descendantIndex.values[ss_indx]
# find number of progenitors for nodes at redshift ss
if ss != z1:
progcounts = np.zeros(len(nodeID), dtype=int)
for ii in range(len(nodeID_past_desc)):
if nodeID_past_desc[ii] in nodeID:
indx = np.where(nodeID == nodeID_past_desc[ii])
progcounts[indx] = count[ii]
nodeID_desc_unique, count = np.unique(nodeID_desc, return_counts=True)
nodeID_desc_unique=nodeID_desc_unique[1:]; count=count[1:]
# add progenitors of progenitors
if ss != z1:
for ii in range(len(nodeID)):
if progcounts[ii] > 1:
indx = np.where(nodeID_desc_unique == nodeID_desc[ii])
count[indx] += progcounts[ii] - 1
nodeID_past = nodeID
nodeID_past_desc = nodeID_desc_unique
return nodeID, progcounts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_progenitors_until_z_EAGLE(self, mtree, nodeID, z1, z2):\n snapcount = 0\n print(':Read MergerTree from %d until %d' % (z2, z1))\n for ss in range(z2, z1, -1):\n if ss == z2:\n df_target = pd.DataFrame({'nodeID':nodeID})\n _indx = np.where(mtree... | [
"0.6213814",
"0.59098613",
"0.57479274",
"0.55864424",
"0.55541563",
"0.55429643",
"0.55378467",
"0.5518064",
"0.5507714",
"0.54895484",
"0.54585093",
"0.5443105",
"0.5417853",
"0.53423256",
"0.53349715",
"0.53140324",
"0.53065985",
"0.530484",
"0.5277614",
"0.52477187",
"0.5... | 0.51368386 | 32 |
Number of progenitors at one given snapshot z1 | def find_progenitors_at_z(self, SH, mtree, z1, z2):
for ss in range(z1, z2):
# nodes at redshift ss
ss_indx = np.where(mtree.data.snapshotNumber.values == ss)
nodeID = mtree.data.index.values[ss_indx]
nodeID_desc = mtree.data.descendantIndex.values[ss_indx]
# find number of progenitors for nodes at redshift ss
if ss != z1:
_progcounts = np.zeros(len(nodeID))
for ii in range(len(nodeID_past_desc)):
if nodeID_past_desc[ii] in nodeID:
indx = np.where(nodeID == nodeID_past_desc[ii])
_progcounts[indx] = count[ii]
nodeID_desc_unique, count = np.unique(nodeID_desc, return_counts=True)
nodeID_desc_unique=nodeID_desc_unique[1:]; count=count[1:]
nodeID_past = nodeID
nodeID_past_desc = nodeID_desc_unique
if ss != z1:
_progcounts_past = _progcounts
print('_progcounts', _progcounts) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tot_num_of_progenitors_at_z(self, SH, mtree, z1, z2):\n \n for ss in range(z1, z2+1):\n print('redshift:', ss)\n # nodes at redshift ss\n ss_indx = np.where(mtree.data.snapshotNumber.values == ss)\n nodeID = mtree.data.index.values[ss_indx]\n ... | [
"0.7110273",
"0.6287449",
"0.5909766",
"0.57706916",
"0.57402396",
"0.5601302",
"0.55882317",
"0.5510065",
"0.5481831",
"0.54731274",
"0.54057765",
"0.5389035",
"0.53697664",
"0.53530174",
"0.5347362",
"0.53414553",
"0.5321019",
"0.53112924",
"0.53112924",
"0.53112924",
"0.53... | 0.6796147 | 1 |
Number of progenitors att all redshift between z1 and z2. | def find_progenitors_until_z(self, mtree, nodeID, z1, z2):
snapcount = 0
print('from %d until %d' % (z2, z1))
for ss in range(z2, z1, -1):
if ss == z2:
df_target = pd.DataFrame({'nodeID':nodeID})
_indx = np.where(mtree.data.snapshotNumber.values == ss-1)
nodeID_prog = mtree.data.index.values[_indx]
nodeID_prog_desc = mtree.data.descendantIndex.values[_indx]
_indx = np.where((nodeID_prog_desc < 1e15) &
(nodeID_prog_desc > 1e11))
nodeID_prog = nodeID_prog[_indx]
nodeID_prog_desc = nodeID_prog_desc[_indx]
df_prog = pd.DataFrame({'nodeID' : nodeID_prog,
'nodeID_target' : nodeID_prog_desc})
# Initiliaze Output Array
progcounts = np.zeros((df_target['nodeID'].size, z2-z1))
# nodeID_prog_desc_unic is sorted
nodeID_prog_desc_unic, count = np.unique(nodeID_prog_desc,
return_counts=True)
# remove -1's
nodeID_prog_desc_unic=nodeID_prog_desc_unic[1:]; count=count[1:]
# Nr. of progenitors for sub-&halos at snapshot z2
s = pd.Index(df_target['nodeID'].tolist())
_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
now_sort_indx = np.argsort(df_target['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(nodeID_prog_desc_unic)
progcounts[_indx_now[now_sort_indx], snapcount] = count[pro_sort_indx]
else:
df_now = df_prog
_indx = np.where(mtree.data.snapshotNumber.values == ss-1)
nodeID_prog = mtree.data.index.values[_indx]
nodeID_prog_desc = mtree.data.descendantIndex.values[_indx]
#_indx = np.where((nodeID_prog_desc < 1e15) &
# (nodeID_prog_desc > 1e10))
#nodeID_prog = nodeID_prog[_indx]
#nodeID_prog_desc = nodeID_prog_desc[_indx]
df_prog = pd.DataFrame({'nodeID' : nodeID_prog})
progcounts_local = np.zeros(df_now['nodeID'].size)
nodeID_prog_desc_unic, count = np.unique(nodeID_prog_desc,
return_counts=True)
# remove -1's
nodeID_prog_desc_unic=nodeID_prog_desc_unic[1:]; count=count[1:]
# progenitors for snapshot ss
s = pd.Index(df_now['nodeID'].tolist())
_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
now_sort_indx = np.argsort(df_now['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(nodeID_prog_desc_unic)
progcounts_local[_indx_now[now_sort_indx]] = count[pro_sort_indx]
df_now['progcount'] = pd.Series(progcounts_local,
index=df_now.index, dtype=int)
# Nr. of progenitors for sub-&halos at snapshot z2
df_inter = df_now.groupby(['nodeID_target'],
as_index=False)['progcount'].sum()
# only real progeniteurs
df_inter = df_inter[(df_inter['nodeID_target'] > 1e10) &
(df_inter['nodeID_target'] < 1e15)]
df_inter = df_inter.drop_duplicates(subset=['nodeID_target'],
keep='first')
s = pd.Index(df_target['nodeID'].tolist())
_indx_now = s.get_indexer(df_inter['nodeID_target'].tolist())
now_sort_indx = np.argsort(df_target['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(df_inter['nodeID_target'].values)
progcounts[_indx_now[now_sort_indx], snapcount] = df_inter['progcount'].values[pro_sort_indx]
# sort nodeID_prog to nodeID
#s = pd.Index(df_now['nodeID'].tolist())
#_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
#df_now['nodeID_target'].values[_indx_now]
obs_ref_local = np.zeros(df_prog['nodeID'].size)
for ii in range(len(nodeID_prog_desc_unic)):
tarID = df_now.loc[
df_now['nodeID'] == nodeID_prog_desc_unic[ii],
'nodeID_target'].values.astype(int)
if tarID:
_indx = np.where(
nodeID_prog_desc == nodeID_prog_desc_unic[ii])
obs_ref_local[_indx] = tarID
df_prog['nodeID_target'] = pd.Series(obs_ref_local,
index=df_prog.index)
snapcount += 1
del nodeID_prog_desc
del df_now, df_inter, df_prog
return np.asarray(df_target['nodeID'].tolist()), progcounts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tot_num_of_progenitors_at_z(self, SH, mtree, z1, z2):\n \n for ss in range(z1, z2+1):\n print('redshift:', ss)\n # nodes at redshift ss\n ss_indx = np.where(mtree.data.snapshotNumber.values == ss)\n nodeID = mtree.data.index.values[ss_indx]\n ... | [
"0.77634203",
"0.72745174",
"0.564701",
"0.5616065",
"0.552504",
"0.549008",
"0.5469204",
"0.53005636",
"0.5266585",
"0.52409446",
"0.5221631",
"0.5201175",
"0.5187531",
"0.5157279",
"0.5154978",
"0.51176065",
"0.510265",
"0.5062432",
"0.5053735",
"0.5025109",
"0.50141466",
... | 0.5992864 | 2 |
Number of progenitors att all redshift between z1 and z2. | def find_progenitors_until_z_EAGLE(self, mtree, nodeID, z1, z2):
snapcount = 0
print(':Read MergerTree from %d until %d' % (z2, z1))
for ss in range(z2, z1, -1):
if ss == z2:
df_target = pd.DataFrame({'nodeID':nodeID})
_indx = np.where(mtree.data.snapnum.values == ss-1)
nodeID_prog = mtree.data.index.values[_indx]
nodeID_prog_desc = mtree.data.descendantIndex.values[_indx]
_indx = np.where((nodeID_prog_desc < 1e15) &
(nodeID_prog_desc > 1e11))
nodeID_prog = nodeID_prog[_indx]
nodeID_prog_desc = nodeID_prog_desc[_indx]
df_prog = pd.DataFrame({'nodeID' : nodeID_prog,
'nodeID_target' : nodeID_prog_desc})
# Initiliaze Output Array
progcounts = np.zeros((df_target['nodeID'].size, z2-z1))
# nodeID_prog_desc_unic is sorted
nodeID_prog_desc_unic, count = np.unique(nodeID_prog_desc,
return_counts=True)
# remove -1's
nodeID_prog_desc_unic=nodeID_prog_desc_unic[1:]; count=count[1:]
# Nr. of progenitors for sub-&halos at snapshot z2
s = pd.Index(df_target['nodeID'].tolist())
_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
now_sort_indx = np.argsort(df_target['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(nodeID_prog_desc_unic)
progcounts[_indx_now[now_sort_indx], snapcount] = count[pro_sort_indx]
else:
df_now = df_prog
_indx = np.where(mtree.data.snapnum.values == ss-1)
nodeID_prog = mtree.data.index.values[_indx]
nodeID_prog_desc = mtree.data.descendantIndex.values[_indx]
#_indx = np.where((nodeID_prog_desc < 1e15) &
# (nodeID_prog_desc > 1e10))
#nodeID_prog = nodeID_prog[_indx]
#nodeID_prog_desc = nodeID_prog_desc[_indx]
df_prog = pd.DataFrame({'nodeID' : nodeID_prog})
progcounts_local = np.zeros(df_now['nodeID'].size)
nodeID_prog_desc_unic, count = np.unique(nodeID_prog_desc,
return_counts=True)
# remove -1's
nodeID_prog_desc_unic=nodeID_prog_desc_unic[1:]; count=count[1:]
# progenitors for snapshot ss
s = pd.Index(df_now['nodeID'].tolist())
_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
now_sort_indx = np.argsort(df_now['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(nodeID_prog_desc_unic)
progcounts_local[_indx_now[now_sort_indx]] = count[pro_sort_indx]
df_now['progcount'] = pd.Series(progcounts_local,
index=df_now.index, dtype=int)
# Nr. of progenitors for sub-&halos at snapshot z2
df_inter = df_now.groupby(['nodeID_target'],
as_index=False)['progcount'].sum()
# only real progeniteurs
df_inter = df_inter[(df_inter['nodeID_target'] > 1e10) &
(df_inter['nodeID_target'] < 1e15)]
df_inter = df_inter.drop_duplicates(subset=['nodeID_target'],
keep='first')
s = pd.Index(df_target['nodeID'].tolist())
_indx_now = s.get_indexer(df_inter['nodeID_target'].tolist())
now_sort_indx = np.argsort(df_target['nodeID'].values[_indx_now])
pro_sort_indx = np.argsort(df_inter['nodeID_target'].values)
progcounts[_indx_now[now_sort_indx], snapcount] = df_inter['progcount'].values[pro_sort_indx]
# sort nodeID_prog to nodeID
#s = pd.Index(df_now['nodeID'].tolist())
#_indx_now = s.get_indexer(list(nodeID_prog_desc_unic))
#df_now['nodeID_target'].values[_indx_now]
obs_ref_local = np.zeros(df_prog['nodeID'].size)
for ii in range(len(nodeID_prog_desc_unic)):
tarID = df_now.loc[
df_now['nodeID'] == nodeID_prog_desc_unic[ii],
'nodeID_target'].values.astype(int)
if tarID:
_indx = np.where(
nodeID_prog_desc == nodeID_prog_desc_unic[ii])
obs_ref_local[_indx] = tarID
df_prog['nodeID_target'] = pd.Series(obs_ref_local,
index=df_prog.index)
snapcount += 1
del nodeID_prog_desc
del df_now, df_inter, df_prog
return np.asarray(df_target['nodeID'].tolist()), progcounts | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tot_num_of_progenitors_at_z(self, SH, mtree, z1, z2):\n \n for ss in range(z1, z2+1):\n print('redshift:', ss)\n # nodes at redshift ss\n ss_indx = np.where(mtree.data.snapshotNumber.values == ss)\n nodeID = mtree.data.index.values[ss_indx]\n ... | [
"0.7762394",
"0.72740203",
"0.59918725",
"0.564635",
"0.56158906",
"0.5524609",
"0.5490007",
"0.5470048",
"0.53012586",
"0.52668864",
"0.5241456",
"0.5201391",
"0.5188126",
"0.51561224",
"0.51548773",
"0.5116847",
"0.510104",
"0.5062283",
"0.5053708",
"0.50249565",
"0.5013674... | 0.522115 | 11 |
Schedule WB category export on Scrapinghub. | def category_export(url: str, chat_id: int, spider='wb', priority=2) -> str:
logger.info(f'Export {url} for chat #{chat_id}')
client, project = init_scrapinghub()
scheduled_jobs = scheduled_jobs_count(project, spider)
max_scheduled_jobs = env('SCHEDULED_JOBS_THRESHOLD', cast=int, default=1)
if priority < 3 and scheduled_jobs > max_scheduled_jobs:
raise Exception('Spider wb has more than SCHEDULED_JOBS_THRESHOLD queued jobs')
job = project.jobs.run(spider, priority=priority, job_args={
'category_url': url,
'callback_url': env('WILDSEARCH_JOB_FINISHED_CALLBACK') + f'/{spider}_category_export',
'callback_params': f'chat_id={chat_id}',
})
logger.info(f'Export for category {url} will have job key {job.key}')
return 'https://app.scrapinghub.com/p/' + job.key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def daily_task():\n global CATEGORIES_PAGES\n global BROWSER\n global DATE\n global OBSERVATION\n log.info('Scraper started')\n # Refresh date\n DATE = str(datetime.date.today())\n OBSERVATION = 0\n # Initiate headless web browser\n log.info('Initializing browser')\n BROWSER = webd... | [
"0.62322336",
"0.5496554",
"0.5323388",
"0.5212413",
"0.51680547",
"0.5047314",
"0.50356627",
"0.5001191",
"0.4996804",
"0.4940439",
"0.49359438",
"0.48962823",
"0.48792583",
"0.48306614",
"0.47664198",
"0.47485566",
"0.4733813",
"0.47102794",
"0.46897477",
"0.4680858",
"0.46... | 0.5811483 | 1 |
Train a simple conv net img_h = sentence length (padded where necessary) img_w = word vector length (300 for word2vec) filter_hs = filter window sizes hidden_units = [x,y] x is the number of feature maps (per filter window), and y is the penultimate layer sqr_norm_lim = s^2 in the paper lr_decay = adadelta decay parameter | def train_conv_net(datasets,datasets_weights,
U, U_Topical,
img_w=300,
filter_hs=[3,4,5],
hidden_units=[100,2],
dropout_rate=[0.5],
shuffle_batch=True,
n_epochs=25,
batch_size=50,
lr_decay = 0.95,
conv_non_linear="relu",
use_valid_set=True,
show_states=False,
activations=[Iden],
sqr_norm_lim=9,
non_static=True):
rng = np.random.RandomState(3435)
img_h = len(datasets[0][0])-1
U_Topical.dtype = "float32"
(num_topics,topic_dim) = U_Topical.shape
word_w = img_w
img_w = int(img_w + num_topics*topic_dim)
filter_w = img_w
feature_maps = hidden_units[0]
filter_shapes = []
pool_sizes = []
for filter_h in filter_hs:
filter_shapes.append((feature_maps, 1, filter_h, filter_w)) # 100 1 3 300
pool_sizes.append((img_h-filter_h+1, img_w-filter_w+1)) # size of words samples one
parameters = [("image shape",img_h,img_w),("filter shape",filter_shapes), ("hidden_units",hidden_units),
("dropout", dropout_rate), ("batch_size",batch_size),("non_static", non_static),
("learn_decay",lr_decay), ("conv_non_linear", conv_non_linear), ("non_static", non_static)
,("sqr_norm_lim",sqr_norm_lim),("shuffle_batch",shuffle_batch)]
#print parameters
#define model architecture
index = T.lscalar()
x = T.matrix('x')
y = T.ivector('y')
x_topic = T.tensor3('x_topic')
Words = theano.shared(value = U, name = "Words")
Topics = theano.shared(value=U_Topical,name="Topics")
zero_vec_tensor = T.vector()
zero_vec = np.zeros(word_w, dtype='float32')
set_zero = theano.function([zero_vec_tensor], updates=[(Words, T.set_subtensor(Words[0,:], zero_vec_tensor))])
layer0_input_words = Words[T.cast(x.flatten(),dtype="int32")].reshape((x.shape[0],1,x.shape[1],Words.shape[1]))
layer0_inputs_topics = []
for i in range(num_topics):
sin_topic = x_topic[:,:,i]
Topic = Topics[i].reshape((1,Topics[i].shape[0]))
weights = sin_topic.flatten()
weights = weights.reshape((weights.shape[0],1))
layer0_inputs_topics.append(T.dot(weights, Topic))
layer0_input_topics = T.concatenate(layer0_inputs_topics,1)
layer0_input_topics = layer0_input_topics.reshape((x_topic.shape[0],1,x_topic.shape[1],num_topics*topic_dim))
layer0_input = T.concatenate([layer0_input_words,layer0_input_topics],3)
conv_layers = []
layer1_inputs = []
for i in xrange(len(filter_hs)):
filter_shape = filter_shapes[i]
pool_size = pool_sizes[i]
conv_layer = LeNetConvPoolLayer(rng, input=layer0_input,image_shape=(batch_size, 1, img_h, img_w),
filter_shape=filter_shape, poolsize=pool_size, non_linear=conv_non_linear)
layer1_input = conv_layer.output.flatten(2)
conv_layers.append(conv_layer)
layer1_inputs.append(layer1_input)
layer1_input = T.concatenate(layer1_inputs,1)
hidden_units[0] = feature_maps*len(filter_hs)
classifier = MLPDropout(rng, input=layer1_input, layer_sizes=hidden_units, activations=activations, dropout_rates=dropout_rate)
#define parameters of the model and update functions using adadelta
params = classifier.params
for conv_layer in conv_layers:
params += conv_layer.params
if non_static:
#if word vectors are allowed to change, add them as model parameters
params += [Words] #params are model parameters
params += [Topics] #Topics embedding are adjusted
cost = classifier.negative_log_likelihood(y)
dropout_cost = classifier.dropout_negative_log_likelihood(y)
grad_updates = sgd_updates_adadelta(params, dropout_cost, lr_decay, 1e-6, sqr_norm_lim)
#shuffle dataset and assign to mini batches. if dataset size is not a multiple of mini batches, replicate
#extra data (at random)
np.random.seed(3435)
if datasets[0].shape[0] % batch_size > 0:
extra_data_num = batch_size - datasets[0].shape[0] % batch_size
random_index = np.random.permutation(np.arange(datasets[0].shape[0]))
random_index.astype('int32')
train_set = datasets[0][random_index,:]
train_set_weights = datasets_weights[0][random_index,:,:]
extra_data = train_set[:extra_data_num]
extra_data_weights = train_set_weights[:extra_data_num]
new_data=np.append(datasets[0],extra_data,axis=0)
new_data_weights = np.append(datasets_weights[0],extra_data_weights,axis = 0)
else:
new_data = datasets[0]
new_data_weights = datasets_weights[0]
random_index = np.random.permutation(np.arange(new_data.shape[0]))
random_index.astype('int32')
new_data = new_data[random_index]
new_data_weights = new_data_weights[random_index]
n_batches = new_data.shape[0]/batch_size
n_train_batches = int(np.round(n_batches*0.9))
test_set_x = np.asarray(datasets[1][:,:img_h] ,"float32")
test_set_x_topic = np.asarray(datasets_weights[1][:,:img_h,:] ,"float32")
test_set_y = np.asarray(datasets[1][:,-1],"int32")
if use_valid_set:
train_set = new_data[:n_train_batches*batch_size,:]
train_set_weights = new_data_weights[:n_train_batches*batch_size,:,:]
val_set = new_data[n_train_batches*batch_size:,:]
val_set_weights = new_data_weights[n_train_batches*batch_size:,:,:]
train_set_x, train_set_x_topic, train_set_y = shared_dataset((train_set[:,:img_h],train_set_weights,train_set[:,-1]))
val_set_x, val_set_x_topic, val_set_y = shared_dataset((val_set[:,:img_h],val_set_weights,val_set[:,-1]))
n_val_batches = n_batches - n_train_batches
val_model = theano.function([index], classifier.errors(y),
givens={
x: val_set_x[index * batch_size: (index + 1) * batch_size],
x_topic: val_set_x_topic[index * batch_size: (index + 1) * batch_size],
y: val_set_y[index * batch_size: (index + 1) * batch_size]})
else:
train_set = new_data[:,:]
train_set_x, train_set_x_topic, train_set_y = shared_dataset((train_set[:,:img_h],train_set_weights,train_set[:,-1]))
#make theano functions to get train/val/test errors
test_model = theano.function([index], classifier.errors(y),
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size],
x_topic: train_set_x_topic[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index * batch_size: (index + 1) * batch_size]})
train_model = theano.function([index], cost, updates=grad_updates,
givens={
x: train_set_x[index*batch_size:(index+1)*batch_size],
x_topic: train_set_x_topic[index * batch_size: (index + 1) * batch_size],
y: train_set_y[index*batch_size:(index+1)*batch_size]})
test_pred_layers = []
test_size = test_set_x.shape[0]
test_layer0_input_words = Words[T.cast(x.flatten(),dtype="int32")].reshape((test_size,1,img_h,Words.shape[1]))
test_layer0_inputs_topics = []
for i in range(num_topics):
sin_topic = x_topic[:,:,i]
Topic = Topics[i].reshape((1,Topics[i].shape[0]))
weights = sin_topic.flatten()
weights = weights.reshape((weights.shape[0],1))
test_layer0_inputs_topics.append(T.dot(weights, Topic))
test_layer0_input_topics = T.concatenate(test_layer0_inputs_topics,1)
test_layer0_input_topics = test_layer0_input_topics.reshape((test_size,1,img_h,num_topics*topic_dim))
test_layer0_input = T.concatenate([test_layer0_input_words,test_layer0_input_topics],3)
for conv_layer in conv_layers:
test_layer0_output = conv_layer.predict(test_layer0_input, test_size)
test_pred_layers.append(test_layer0_output.flatten(2))
test_layer1_input = T.concatenate(test_pred_layers, 1)
test_y_pred = classifier.predict(test_layer1_input)
test_error = T.mean(T.neq(test_y_pred, y))
test_model_all = theano.function([x,x_topic,y], test_error)
#start training over mini-batches
print '... training'
epoch = 0
best_val_perf = 0
val_perf = 0
test_perf = 0
cost_epoch = 0
while (epoch < n_epochs):
epoch = epoch + 1
if shuffle_batch:
for minibatch_index in np.random.permutation(range(n_train_batches)):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
else:
for minibatch_index in xrange(n_train_batches):
cost_epoch = train_model(minibatch_index)
set_zero(zero_vec)
train_losses = [test_model(i) for i in xrange(n_train_batches)]
train_perf = 1 - np.mean(train_losses)
if use_valid_set:
val_losses = [val_model(i) for i in xrange(n_val_batches)]
val_perf = 1- np.mean(val_losses)
if val_perf >= best_val_perf:
params_conv = []
params_output = {}
test_loss = test_model_all(test_set_x,test_set_x_topic, test_set_y)
test_perf = 1- test_loss
best_val_perf = val_perf
for conv_layer in conv_layers:
params_conv.append(conv_layer.get_params())
params_output = classifier.get_params()
word_vec = Words.get_value()
Topic_vec = Topics.get_value()
else :
val_perf = 0
if show_states:
print('epoch %i, train perf %f %%, val perf %f' % (epoch, train_perf * 100., val_perf*100.))
if not use_valid_set:
params_conv = []
params_output = {}
test_loss = test_model_all(test_set_x,test_set_x_topic, test_set_y)
test_perf = 1- test_loss
for conv_layer in conv_layers:
params_conv.append(conv_layer.get_params())
params_output = classifier.get_params()
word_vec = Words.get_value()
Topic_vec = Topics.get_value()
return test_perf, [params_conv, params_output, word_vec,Topic_vec] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_cnn_for_text(input_data, conv_window, filter_num, pooling_window, activation_fn=tf.nn.relu,\n padding_vec=None, use_batch_norm=False, bias_initializer=tf.zeros_initializer,\n keep_prob=1, is_training=False):\n # Do padding\n input_shape = tf.shape(input_data)\n... | [
"0.6291026",
"0.61072165",
"0.6082616",
"0.59387344",
"0.59314805",
"0.5919466",
"0.5789336",
"0.5736913",
"0.5708138",
"0.5659221",
"0.5653504",
"0.5638298",
"0.5631338",
"0.5591712",
"0.55789256",
"0.55471337",
"0.5543536",
"0.5524808",
"0.550148",
"0.54923403",
"0.5481331"... | 0.6283863 | 1 |
Function that loads the dataset into shared variables The reason we store our dataset in shared variables is to allow Theano to copy it into the GPU memory (when code is run on GPU). Since copying data into the GPU is slow, copying a minibatch everytime is needed (the default behaviour if the data is not in a shared variable) would lead to a large decrease in performance. | def shared_dataset(data_xy, borrow=True):
data_x, data_x_topic, data_y = data_xy
shared_x = theano.shared(np.asarray(data_x,
dtype=theano.config.floatX),
borrow=borrow)
shared_x_topic = theano.shared(np.asarray(data_x_topic,
dtype=theano.config.floatX),
borrow=borrow)
shared_y = theano.shared(np.asarray(data_y,
dtype=theano.config.floatX),
borrow=borrow)
return shared_x, shared_x_topic, T.cast(shared_y, 'int32') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def shared_dataset(data_x, data_y, borrow=True):\n shared_x = theano.shared(np.asarray(data_x,\n dtype=theano.config.floatX),\n borrow=borrow)\n shared_y = theano.shared(np.asarray(data_y, dtype=np.int32),\n ... | [
"0.66170514",
"0.65900344",
"0.6475917",
"0.61465776",
"0.61301583",
"0.61217743",
"0.60805804",
"0.59559256",
"0.59458435",
"0.59417135",
"0.5921487",
"0.58830863",
"0.5878444",
"0.58709127",
"0.58622324",
"0.58391243",
"0.58236265",
"0.58061445",
"0.5792834",
"0.5765671",
"... | 0.60709625 | 7 |
adadelta update rule, mostly from | def sgd_updates_adadelta(params,cost,rho=0.95,epsilon=1e-6,norm_lim=9,word_vec_name='Words'):
updates = OrderedDict({})
exp_sqr_grads = OrderedDict({})
exp_sqr_ups = OrderedDict({})
gparams = []
for param in params:
empty = np.zeros_like(param.get_value())
exp_sqr_grads[param] = theano.shared(value=as_floatX(empty),name="exp_grad_%s" % param.name)
gp = T.grad(cost, param)
exp_sqr_ups[param] = theano.shared(value=as_floatX(empty), name="exp_grad_%s" % param.name)
gparams.append(gp)
for param, gp in zip(params, gparams):
exp_sg = exp_sqr_grads[param]
exp_su = exp_sqr_ups[param]
up_exp_sg = rho * exp_sg + (1 - rho) * T.sqr(gp)
updates[exp_sg] = up_exp_sg
step = -(T.sqrt(exp_su + epsilon) / T.sqrt(up_exp_sg + epsilon)) * gp
updates[exp_su] = rho * exp_su + (1 - rho) * T.sqr(step)
stepped_param = param + step
if (param.get_value(borrow=True).ndim == 2) and (param.name != 'Words') and (param.name != 'Topics'):
col_norms = T.sqrt(T.sum(T.sqr(stepped_param), axis=0))
desired_norms = T.clip(col_norms, 0, T.sqrt(norm_lim))
scale = desired_norms / (1e-7 + col_norms)
updates[param] = stepped_param * scale
else:
updates[param] = stepped_param
return updates | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_rule(self):\n pass",
"def update_rules():\n update_all_rules()\n return \"OK\"",
"def acUpdate(deltaT):#-------------------------------- AC UPDATE\n pass # -> Delete this line if you do something here !",
"def _UpdateAclRule(self, entry):\n\n print 'Update Acl rule: %s' % (... | [
"0.72158897",
"0.61915874",
"0.59953177",
"0.56383234",
"0.5619746",
"0.5614491",
"0.5574052",
"0.55433077",
"0.55414903",
"0.54987466",
"0.5419936",
"0.54156685",
"0.5361411",
"0.5350453",
"0.53490156",
"0.5341854",
"0.53329635",
"0.52209646",
"0.5166257",
"0.5149093",
"0.51... | 0.5070786 | 33 |
remake update dictionary for safe updating | def safe_update(dict_to, dict_from):
for key, val in dict(dict_from).iteritems():
if key in dict_to:
raise KeyError(key)
dict_to[key] = val
return dict_to | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_dict(new,old):",
"def _update_loose (self, dict):\n self.__dict__.update(dict)",
"def update(self, *args, **kwargs):\n super(ReadOnlyDict, self).update(*args, **kwargs) # pragma: no cover",
"def _update(value: Dict[str, Any], update: Dict[str, Any]):\n for key, val in update.item... | [
"0.8283676",
"0.7269931",
"0.6905765",
"0.6654361",
"0.66300076",
"0.6628225",
"0.6574362",
"0.65601563",
"0.6495813",
"0.6480724",
"0.64378697",
"0.64134395",
"0.6387203",
"0.6384412",
"0.6380623",
"0.63678986",
"0.62931854",
"0.6287735",
"0.62773114",
"0.6266723",
"0.623645... | 0.5890382 | 55 |
Transforms sentence into a list of indices. Pad with zeroes. | def get_idx_from_sent(sent, topic_weights, word_idx_map, max_l=51, k=300, filter_h=5):
x = []
t = []
num_topics = len(topic_weights[0])
pad = filter_h - 1
zero_weights = [0.0]*num_topics
for i in xrange(pad):
x.append(0)
t.append(zero_weights)
words = sent.split()
for index, word in enumerate(words):
if word in word_idx_map:
x.append(word_idx_map[word])
t.append(topic_weights[index])
while len(x) < max_l+2*pad:
x.append(0)
t.append(zero_weights)
return x,t | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sentence_to_indices(self, sentence):\n token_list = self.tokenize(sentence)\n indices = self.tokenizer.convert_tokens_to_ids(token_list)\n\n return indices",
"def sentence_to_indices(sentence, word_dict):\n return [word_dict.to_index(word) for word in word_tokenize(sentence)]",
"def... | [
"0.6450914",
"0.64385843",
"0.6401967",
"0.6338094",
"0.6316792",
"0.61736226",
"0.61721224",
"0.61721224",
"0.61499345",
"0.6140153",
"0.61389184",
"0.61104566",
"0.6093466",
"0.606643",
"0.60410887",
"0.5994239",
"0.5958273",
"0.5941653",
"0.5936649",
"0.5908101",
"0.590779... | 0.5256634 | 73 |
Transforms sentences into a 2d matrix. | def make_idx_data_cv(revs, lda_weights, word_idx_map, cv, max_l=56, k=300, filter_h=5):
train, test = [], []
train_weight, test_weight = [], []
for index, rev in enumerate(revs):
sent,sent_weight = get_idx_from_sent(rev["text"], lda_weights[index], word_idx_map, max_l, k, filter_h)
sent.append(rev["y"])
if rev["split"] == cv:
test.append(sent)
test_weight.append(sent_weight)
else:
train.append(sent)
train_weight.append(sent_weight)
train = np.array(train, dtype="int")
train_weight = np.array(train_weight, dtype="float32")
test_weight = np.array(test_weight, dtype="float32")
test = np.array(test, dtype="int")
return [train, test], [train_weight,test_weight] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lines_to_matrix(lines):\n\n for index, line in enumerate(lines):\n lines[index] = [char for char in line]\n\n return lines",
"def text_to_matrix(text):\n matrix = []\n for i in range(0, 8, 2):\n matrix.append([int(text[i] + text[i+1], 16), int(text[i+8] + text[i+9], 16),\n ... | [
"0.6663844",
"0.66369677",
"0.6621791",
"0.617014",
"0.61520714",
"0.61395663",
"0.6071079",
"0.6000583",
"0.5962202",
"0.5890576",
"0.58884305",
"0.58818334",
"0.58665574",
"0.5851002",
"0.5772027",
"0.56664914",
"0.5654367",
"0.5645098",
"0.5634978",
"0.56204677",
"0.560234... | 0.0 | -1 |
Get word matrix. W[i] is the vector for word indexed by i | def rand_TE(num_topics=5, k=20):
W = np.random.uniform(-0.25,0.25,(num_topics,k))
return W | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_matrix_of_vectors(wv_from_bin, required_words=['softball', 'technology','street','project','fellow','maps','view','fuel','summer','clubhouse','ball','steal','soccer','driving','motor','comedy']):\n import random\n words = list(wv_from_bin.vocab.keys())\n print(\"Shuffling words ...\")\n random.... | [
"0.7206552",
"0.70206374",
"0.6722377",
"0.6556378",
"0.6531552",
"0.6528248",
"0.6501242",
"0.64862776",
"0.64330167",
"0.6422995",
"0.64024043",
"0.63945264",
"0.6394137",
"0.6350247",
"0.6325137",
"0.63241976",
"0.6304245",
"0.6274398",
"0.6263698",
"0.62422335",
"0.623854... | 0.0 | -1 |
checks for equality beetwen two MyInts return False o True | def __eq__(self, number):
return int(self) != number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, oth):\n return int(self) != oth",
"def __ne__(self, oth):\n return int(self) == oth",
"def __eq__(self, other):\n return int(self) != int(other)",
"def __eq__(self, other):\n return int.__ne__(self, other)",
"def __ne__(self, other):\n return int(self) ==... | [
"0.76982534",
"0.7489979",
"0.7380747",
"0.7375535",
"0.7313602",
"0.7142088",
"0.6908504",
"0.68830556",
"0.68567646",
"0.6777183",
"0.6627412",
"0.6612154",
"0.66104484",
"0.65805703",
"0.65621144",
"0.6551062",
"0.6542485",
"0.65216887",
"0.65202266",
"0.65168554",
"0.6506... | 0.69736755 | 6 |
checks for equality beetwen two MyInts | def __ne__(self, number):
return int(self) == number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __eq__(self, oth):\n return int(self) != oth",
"def __eq__(self, other):\n return int(self) != int(other)",
"def __eq__(self, other):\n return int.__ne__(self, other)",
"def __ne__(self, oth):\n return int(self) == oth",
"def same(self, x: int, y: int):\n\n return sel... | [
"0.7517991",
"0.7299583",
"0.7024994",
"0.69548833",
"0.68217367",
"0.6819955",
"0.67301023",
"0.67204297",
"0.66224813",
"0.659244",
"0.6561572",
"0.65176463",
"0.6499448",
"0.6496064",
"0.64380145",
"0.64290977",
"0.6324286",
"0.6321902",
"0.62818635",
"0.6244796",
"0.62301... | 0.58893675 | 87 |
Return the absolute path to a valid plugins.cfg file. Copied from sf_OIS.py | def getPluginPath():
import sys
import os
import os.path
paths = [os.path.join(os.getcwd(), 'plugins.cfg'),
'/etc/OGRE/plugins.cfg',
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'plugins.cfg')]
for path in paths:
if os.path.exists(path):
return path
sys.stderr.write("\n"
"** Warning: Unable to locate a suitable plugins.cfg file.\n"
"** Warning: Please check your ogre installation and copy a\n"
"** Warning: working plugins.cfg file to the current directory.\n\n")
raise ogre.Exception(0, "can't locate the 'plugins.cfg' file", "") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_file_location():\n\n return './' + CONFIG_FILE_NAME",
"def config_file_and_path():\n return str(rmfriend_dir() / 'config.cfg')",
"def config_path(self):\n if os.path.exists(self._config_path):\n if pyhocon.ConfigFactory.parse_file(self._config_path):\n retu... | [
"0.7101111",
"0.7079233",
"0.70316",
"0.691988",
"0.6856361",
"0.6809086",
"0.67529494",
"0.6713169",
"0.6694192",
"0.6685862",
"0.6680839",
"0.6547461",
"0.64858216",
"0.6481523",
"0.6454455",
"0.64139074",
"0.6406523",
"0.64045656",
"0.6398177",
"0.63761204",
"0.635552",
... | 0.82794327 | 0 |
This shows the config dialog and returns the renderWindow. | def configure(ogre_root):
user_confirmation = ogre_root.showConfigDialog()
if user_confirmation:
return ogre_root.initialise(True, "OGRE Render Window")
else:
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def openRocConfig(self):\n self.rocConfig_Window = QtWidgets.QDialog()\n self.rocConfig_ui = Ui_rocConfigure()\n self.rocConfig_ui.setupUi(self.rocConfig_Window)\n self.rocConfig_Window.show()",
"def display_window(self):\n frame = tk.Frame(master=self.param_window)\n fr... | [
"0.6363973",
"0.6353406",
"0.62993693",
"0.6273288",
"0.62459344",
"0.62194985",
"0.6168433",
"0.61429256",
"0.6130523",
"0.6106466",
"0.60738796",
"0.6022143",
"0.59356743",
"0.5909631",
"0.58671",
"0.5833457",
"0.58322257",
"0.57948744",
"0.5749032",
"0.5740846",
"0.570118"... | 0.6725343 | 0 |
Read a DICOM file, raising an exception if the 'DICM' marker is not present at byte 128. dicom.read_file() does this as of pydicom 0.9.5. | def read_dicom_file(fname):
fo = open(fname)
try:
preamble = fo.read(128)
magic = fo.read(4)
if len(preamble) != 128 or magic != 'DICM':
raise InvalidDicomError
fo.seek(0)
do = dicom.read_file(fo)
finally:
fo.close()
return do | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read(path, desired_size):\n \n dcm = pydicom.dcmread(path)\n\n slope, intercept = dcm.RescaleSlope, dcm.RescaleIntercept\n \n try:\n img = (dcm.pixel_array * slope + intercept)\n except:\n img = np.zeros(desired_size[:2])-1\n \n if img.shape != desired_size[:2]:\n ... | [
"0.5621899",
"0.54997045",
"0.54964674",
"0.5454611",
"0.53653544",
"0.5280224",
"0.52707344",
"0.5212712",
"0.52099603",
"0.5170325",
"0.516826",
"0.51599985",
"0.51240665",
"0.51210904",
"0.5107606",
"0.51000935",
"0.50604916",
"0.5044146",
"0.5007753",
"0.5000708",
"0.4984... | 0.7051324 | 0 |
given our dicom_files and studies records and a patient ID, return a list of (datetime, study instance UID) ordered by date+time | def patient_studies(dicom_files, studies, patient_id):
ps = []
for uid in dicom_files[patient_id]:
datetime = '%s%s' % studies[uid]
ps.append([datetime, uid])
ps.sort(lambda a, b: cmp(a[0], b[0]))
for el in ps:
date_time_parts = (el[0][0:4],
el[0][4:6],
el[0][6:8],
el[0][8:10],
el[0][10:12],
el[0][12:14])
el[0] = '%s-%s-%s %s:%s:%s' % date_time_parts
return ps | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_all_dicom_images(self, verbose=True):\n if verbose: print(\"Loading dicom files ... This may take a moment.\")\n\n path = self.get_path_to_dicom_files()\n fnames = [fname for fname in os.listdir(path)\n if fname.endswith('.dcm') and not fname.startswith(\".\... | [
"0.60390854",
"0.5873085",
"0.5861059",
"0.5460806",
"0.5381554",
"0.53764325",
"0.5292173",
"0.52161545",
"0.5200111",
"0.51963425",
"0.5172361",
"0.516292",
"0.5146686",
"0.5142398",
"0.51141894",
"0.511357",
"0.5112972",
"0.510411",
"0.5102937",
"0.5091951",
"0.5091913",
... | 0.82577133 | 0 |
Create a new DICOM UID. Return an alreadycreated UID if the old UID has already been replaced. | def new_uid(old_uid):
if old_uid not in uids:
uids[old_uid] = '2.25.%d' % uuid.uuid1().int
return uids[old_uid] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def new_uid():\n return str(uuid.uuid1())[:30]",
"def create_uid():\n return random_string(5, string.hexdigits.lower())\n # return (\"%x\" % (int(time.time()) * 0x10 % 0x1000000000)\n # + random_string(7, string.hexdigits.lower()))",
"def generate_uid(prefix=PYDICOM_ROOT_UID, entropy_srcs=N... | [
"0.69686115",
"0.68575305",
"0.6833389",
"0.66276854",
"0.6467969",
"0.62508255",
"0.61379224",
"0.6119005",
"0.6028553",
"0.6014281",
"0.59217644",
"0.58923423",
"0.58923423",
"0.5885048",
"0.5780032",
"0.5770298",
"0.5749628",
"0.57420045",
"0.57420045",
"0.57278305",
"0.57... | 0.67064506 | 3 |
Check if a project/subject/session identifier is valid. Identifiers can only contain alphanumeric characters and underscores. | def _validate_identifier(self, identifier):
for c in identifier:
if c not in string.letters + string.digits + '_':
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IsProjectIDValid(project):\n if len(project) < 6 or len(project) > 30:\n return False\n return bool(re.match('^[a-z][a-z0-9\\\\-]*[a-z0-9]$', project))",
"def IsValidIdentifier(string):\n counter = 0\n if string in keyword.kwlist:\n feedback = (False, \"Invalid: can't use a keyword as your ... | [
"0.7817043",
"0.7263203",
"0.71657807",
"0.6915533",
"0.6915533",
"0.6901704",
"0.68005633",
"0.6792642",
"0.67050254",
"0.6685971",
"0.6454717",
"0.64318484",
"0.6427234",
"0.64037365",
"0.6375381",
"0.6367689",
"0.6350279",
"0.6343211",
"0.6312446",
"0.63092995",
"0.6287927... | 0.7744844 | 1 |
Scrapes for requested number of items and search word(s) as category. | def extract_etsy_items(number_of_items: int, category: str) -> pd.DataFrame:
print(f"Scrapping category \"{category}\"")
result = []
page_number = 1
headers = {"User-Agent": "Mozilla/5.0"}
while len(result) < number_of_items:
time.sleep(2)
url = f"https://www.etsy.com/search?q={make_url_compatible(category)}" \
f"&ref=pagination&page={page_number} "
source = requests.get(url, headers=headers)
soup = BeautifulSoup(source.content, "html.parser")
items = soup.select("li.wt-list-unstyled")
if len(items) == 0:
print(f'Total items of {category} was found less than requested: '
f'{len(result)}')
break
for item in items:
if len(result) >= number_of_items:
break
record = extract_item_data(item)
if record:
result.append(record)
page_number += 1
return pd.DataFrame(result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_item_list(client, word=''):\n\titem_group = client.search_metadata('collection_name:cooee')\n\titems = item_group.get_all()\n\titem_list_name = word + '_list'\n\titem_urls = []\n\tfor item in items:\n\t\tprimary_text = item.get_primary_text()\n\t\tif word in primary_text:\n\t\t\tprint item.url()\n\t\t\t... | [
"0.5936122",
"0.5875122",
"0.58417684",
"0.58384866",
"0.5813017",
"0.5788929",
"0.5743952",
"0.574124",
"0.5724943",
"0.5716384",
"0.57140964",
"0.5701603",
"0.5691337",
"0.56734043",
"0.5648546",
"0.5612714",
"0.5611656",
"0.55976135",
"0.55937296",
"0.55912596",
"0.5578013... | 0.58419687 | 2 |
Converts string to URL compatible. | def make_url_compatible(category: str) -> str:
return urllib.parse.quote(category) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def text_to_url(self, text):\r\n return QUrl(text)",
"def url(value: Any) -> str:\n url_in = str(value)\n\n if urlparse(url_in).scheme in [\"http\", \"https\"]:\n return cast(str, vol.Schema(vol.Url())(url_in))\n\n raise vol.Invalid(\"Invalid URL\")",
"def url_fix(s, charset='utf-8'):\n ... | [
"0.6706412",
"0.6616591",
"0.6346535",
"0.63381904",
"0.6220913",
"0.6202903",
"0.6197797",
"0.6176937",
"0.6167852",
"0.6164724",
"0.61420554",
"0.61230433",
"0.6118814",
"0.61146235",
"0.6109616",
"0.60942715",
"0.60720885",
"0.6057571",
"0.6017928",
"0.59949386",
"0.595964... | 0.5838849 | 35 |
Normalizes USD price with thousand separator into float value | def normalize_price(price: str) -> float:
return float(price.strip().replace(',', '')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_price(str_val):\n return float(str_val.replace('.', '').replace(',', '.'))",
"def clean_dollar_to_float(value):\n return (value.replace('$', '').replace(',', ''))",
"def convert_currency(val):\n new_val = val.replace(',','').replace('$', '')\n return float(new_val)",
"def clean_value(self... | [
"0.72919416",
"0.7071461",
"0.6978202",
"0.6896954",
"0.68778527",
"0.6861502",
"0.668687",
"0.66791004",
"0.6679073",
"0.6679073",
"0.6679073",
"0.6679073",
"0.6679073",
"0.6636291",
"0.6459509",
"0.64089173",
"0.64083546",
"0.63997513",
"0.6395582",
"0.6393109",
"0.6388328"... | 0.80026025 | 0 |
Take a list of categories from categories table in database. Scrapes website for all of them. Prepare data for inserting to the database. | def get_all_categories_items(number_of_items: int) -> pd.DataFrame:
all_items = pd.DataFrame()
for (category_id, category_name) in get_categories():
items = extract_etsy_items(number_of_items, category_name)
items['categoryId'] = category_id
items = items[['categoryId', 'title', 'price', 'itemURL', 'imgURL']]
items.set_index('categoryId', inplace=True)
all_items = all_items.append(items)
return all_items | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrapeCategories():\n page = requests.get(\"http://books.toscrape.com/index.html\")\n soup = BeautifulSoup(page.content, 'html.parser')\n content = soup.find(\"div\", {\"class\": \"side_categories\"}).findChild().find(\"ul\")\n categories = content.find_all(\"a\")\n for category in categories:\n... | [
"0.69870496",
"0.68652314",
"0.65082896",
"0.6431102",
"0.63733405",
"0.63554925",
"0.633605",
"0.632757",
"0.6321157",
"0.62610203",
"0.6253101",
"0.6226809",
"0.6174908",
"0.61444",
"0.6128838",
"0.6115741",
"0.6043684",
"0.6017082",
"0.5986148",
"0.5977991",
"0.5969803",
... | 0.5311808 | 82 |
Insert all items into the database. | def save_items_to_database(items: pd.DataFrame) -> None:
for item in items.itertuples():
insert_item(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insert(self, items=''):\n cur = self.conn.cursor()\n\n format_args = {'table': self.__name__,\n 'items': ', '.join(items.keys()),\n 'values': ', '.join([':'+key for key in items.keys()])}\n \n insert_query = \"\"\"\n INSERT INTO... | [
"0.738048",
"0.728863",
"0.7119373",
"0.69322",
"0.69140184",
"0.6894494",
"0.6738854",
"0.6694093",
"0.66766566",
"0.6634999",
"0.65382606",
"0.653699",
"0.6525922",
"0.6473785",
"0.6451338",
"0.6418449",
"0.63942665",
"0.63700026",
"0.63535374",
"0.6332911",
"0.62997335",
... | 0.7253259 | 2 |
Exports given file to csv. | def save_items_to_csv(items_data: pd.DataFrame):
with open('etsy_items.csv', 'w') as f:
writer = csv.writer(f)
writer.writerows(items_data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_csv(self):\n outputfile = tkinter.filedialog.asksaveasfilename(\n defaultextension=\".csv\",\n filetypes=((\"comma seperated values\", \"*.csv\"),\n (\"All Files\", \"*.*\")))\n if outputfile:\n tabledata = self.tabs.window.aistracker.... | [
"0.7477921",
"0.74023473",
"0.72890913",
"0.7163044",
"0.7147001",
"0.7147001",
"0.7116825",
"0.7090414",
"0.70343846",
"0.7002203",
"0.6979948",
"0.695002",
"0.6947416",
"0.69094193",
"0.6906419",
"0.68957627",
"0.686277",
"0.68464404",
"0.6839928",
"0.6741557",
"0.67377245"... | 0.0 | -1 |
Retrieve, update or delete a person instance. | def PrintVisitante(request, pk):
try:
visitante = Visitante.objects.get(pk=pk)
except Visitante.DoesNotExist:
return Response(status=status.HTTP_404_NOT_FOUND)
if request.method == 'GET':
serializer = VisitanteSerializer(visitante)
return Response(serializer.data)
elif request.method == 'PUT':
serializer = VisitanteSerializer(visitante, data=request.data)
if serializer.is_valid():
serializer.save()
return Response(serializer.data)
return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def person(self, person_id):\r\n return persons.Person(self, person_id)",
"def edit_person():\n # get person name from user\n responses = accept_inputs([\"Person's name\"])\n person_name = responses[\"Person's name\"]\n # check for existence\n results = query_with_results(\"select * from person where... | [
"0.6274317",
"0.6184409",
"0.6044379",
"0.60121447",
"0.60050964",
"0.5923112",
"0.59186417",
"0.5886551",
"0.58603877",
"0.5851857",
"0.58510053",
"0.58013856",
"0.5764969",
"0.57433015",
"0.5636165",
"0.5635824",
"0.5605609",
"0.5604004",
"0.55662686",
"0.5558843",
"0.55301... | 0.0 | -1 |
Does a country like Mexico or United States get a code assigned? | def test_country_code(self):
country_name = 'United States'
# population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
#Assert methods verifies result received matches expected one
self.assertEqual(code, 'usa') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def country(alpha_2_code: str) -> None:",
"def get_country_code(self):\n #method on the class InternationalMelonOrder\n\n return self.country_code\n # international has country code; domestic does not\n # WAI???",
"def get_country_code(country_name):\n for code, name in COUNTRIES... | [
"0.78264016",
"0.74500364",
"0.7422215",
"0.7363206",
"0.7362113",
"0.7261186",
"0.7252336",
"0.7212905",
"0.7122891",
"0.709406",
"0.7093551",
"0.7011302",
"0.6986172",
"0.69353557",
"0.69182456",
"0.68706924",
"0.6821515",
"0.6747886",
"0.6657928",
"0.6657928",
"0.6580908",... | 0.73427856 | 5 |
Reads the csv file CSV file should contain ['Question', 'Answer'] columns Remove NaN values Throw error if format is bad or file does not exist | def parse_csv_file(self, csv_file: str):
try:
df = pd.read_csv(csv_file)
if not set(['Question', 'Answer']).issubset(df.columns):
raise BadCSVFile(
"CSV file does not contain ['Question', 'Answer'] columns.")
df.dropna(inplace=True)
except Exception as e:
raise BadCSVFile(
"Error while reading the csv file. Please check the path of the file or the file might be curropted.")
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_csv():",
"def read_csv_file(self):\n pass",
"def test_csv(self, input_file_path: str, answer_file_path: List[Dict]):\n with open(attach_path(answer_file_path), 'r') as answer_file:\n csv_file = open(attach_path(input_file_path))\n assert str(read_csv(csv_file)) == a... | [
"0.7320947",
"0.6934107",
"0.6727932",
"0.6726128",
"0.6720706",
"0.6720139",
"0.67085135",
"0.66700244",
"0.65878505",
"0.6561937",
"0.64199805",
"0.64198834",
"0.6413396",
"0.6406278",
"0.64025426",
"0.63658756",
"0.63533485",
"0.63505536",
"0.63503546",
"0.63334334",
"0.63... | 0.77133363 | 0 |
Create a mechanism to vectorize the data Create vector for our current dataset | def build_model(self, documents):
self.vectorizer = TfidfVectorizer(
stop_words='english', lowercase=True).fit(documents)
self.vectors = self.vectorizer.transform(documents) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def vectorize(self):\n return vectorize(self)",
"def _vectorize_data(self, docs: []):\n print('Vectorizing data...')\n tfidf = TfidfVectorizer()\n encoded_data = tfidf.fit_transform(docs)\n return encoded_data",
"def get_data_vector(self) -> DataVector:\n raise NotImpl... | [
"0.7192277",
"0.66099465",
"0.6579369",
"0.65683573",
"0.6435415",
"0.6372212",
"0.63627356",
"0.636164",
"0.63602024",
"0.6243461",
"0.6200177",
"0.61812717",
"0.617205",
"0.6124774",
"0.60975635",
"0.60963786",
"0.6080632",
"0.6075216",
"0.6062782",
"0.6045074",
"0.60133845... | 0.0 | -1 |
Returns a vector for a given query | def get_vector(self, query: list):
if len(query) == 0:
raise BadQueryParameter("Query (list) can not be empty.")
return self.vectorizer.transform(query) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _to_full_vector(self, query_vector: List[Tuple[str, float]]) -> np.array:\n terms = list(self.index.get_terms())\n terms.sort()\n vector = np.zeros(len(terms))\n\n for (term, weight) in query_vector:\n index = terms.index(term)\n vector[index] = weight\n\n ... | [
"0.710231",
"0.6852143",
"0.6806123",
"0.6790017",
"0.65516216",
"0.6484472",
"0.6346682",
"0.62224436",
"0.62009585",
"0.61882883",
"0.6144658",
"0.6134512",
"0.61188674",
"0.6103813",
"0.6086374",
"0.6070219",
"0.60688305",
"0.5987167",
"0.59024227",
"0.5888981",
"0.5858321... | 0.7983144 | 0 |
Returns cosine similarity between the input question and our dataset questions | def get_cosine_similarity(self, query: list):
question_vector = self.get_vector(query)
return cosine_similarity(question_vector, self.vectors).flatten() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cosine_similarity(self, source_doc, input_doc):\n vectorizer = self.vectorizer or TfidfVectorizer(tokenizer=PlagiarismDetector.tokenize_and_stem, stop_words='english')\n tfidf = vectorizer.fit_transform([source_doc, input_doc])\n return ((tfidf * tfidf.T).A)[0, 1]",
"def calculate_cosine... | [
"0.77647394",
"0.77439165",
"0.7479039",
"0.7361782",
"0.72787845",
"0.7188363",
"0.7178258",
"0.7110796",
"0.7093376",
"0.7010269",
"0.6990073",
"0.6989606",
"0.6962196",
"0.6950967",
"0.6913966",
"0.6902417",
"0.68863827",
"0.6876185",
"0.68280274",
"0.67813444",
"0.6764831... | 0.7567193 | 2 |
API function that can be used by other codebase It takes query (string) and n top similar questions along with thier answers | def perform_query(self, query: str, n: int = 5):
query = [query]
cosine_similarities = self.get_cosine_similarity(query)
indices = cosine_similarities.argsort()[:-n-1:-1]
return self.data[
self.data.index.isin(indices)
].reset_index(drop=True).to_dict(orient='records') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def top_n_satisfy2(content, n):\n #print(n)\n sum_satisfy = 0.0\n query_num = 0.0\n for qid in content:\n label_sort = []\n score = []\n all_info = content[qid]\n num_label1 = 0\n for info in all_info:\n if info[0] > 0:\n num_label1 += 1\n ... | [
"0.6818519",
"0.6672054",
"0.66686153",
"0.6436746",
"0.6367996",
"0.63375914",
"0.63115406",
"0.6299758",
"0.6281875",
"0.6159052",
"0.61510074",
"0.61417896",
"0.6108233",
"0.61047775",
"0.6080384",
"0.60795337",
"0.60682553",
"0.60677445",
"0.60321194",
"0.60313493",
"0.60... | 0.54849684 | 63 |
Get most likely categorical variables (onehot) | def get_most_likely(self):
c_cat = self.theta.argmax(axis=1)
T = np.zeros((self.d, self.max))
for i, c in enumerate(c_cat):
T[i, c] = 1
return T | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def unhot(one_hot_labels):\n return np.argmax(one_hot_labels, axis=1)",
"def infer_categorical_variables_in_place(df: pd.DataFrame):\n # infer which variables are categorical\n MAX_UNIQUE_VALUES = 10\n for column in df.columns:\n if df[column].nunique() <= MAX_UNIQUE_VALUES:\n df[co... | [
"0.7186164",
"0.71264297",
"0.700594",
"0.6770333",
"0.66180325",
"0.6554686",
"0.65422523",
"0.6527132",
"0.6494277",
"0.6475236",
"0.64435905",
"0.64244854",
"0.64193946",
"0.64063036",
"0.6401098",
"0.63921547",
"0.63824326",
"0.63524544",
"0.63380814",
"0.6334604",
"0.632... | 0.0 | -1 |
Ranking Based Utility Transformation. w(f(x)) / lambda = 1/mu if rank(x) <= mu 0 if mu < rank(x) < lambda mu 1/mu if lambda mu <= rank(x) where rank(x) is the number of at least equally good points, including it self. The number of good and bad points, mu, is ceil(lambda/4). That is, mu = 1 if lambda = 2 mu = 1 if lambda = 4 mu = 2 if lambda = 6, etc. If there exist tie points, the utility values are equally distributed for these points. | def ranking_based_utility_transformation(losses, lam, rho=0.25, negative=True):
eps = 1e-14
idx = np.argsort(losses)
mu = int(np.ceil(lam * rho))
_w = np.zeros(lam)
_w[:mu] = 1 / mu
_w[lam - mu:] = -1 / mu if negative else 0
w = np.zeros(lam)
istart = 0
for i in range(losses.shape[0] - 1):
if losses[idx[i + 1]] - losses[idx[i]] < eps * losses[idx[i]]:
pass
elif istart < i:
w[istart:i + 1] = np.mean(_w[istart:i + 1])
istart = i + 1
else:
w[i] = _w[i]
istart = i + 1
w[istart:] = np.mean(_w[istart:])
return w, idx | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_good(self, m, n, rank, mu=2, ka=2):\n sr = random.random()\n s = []\n s.append(sr)\n for r in range(rank-1):\n newele = s[-1] * (1 + ka * random.random() / (rank-1))\n s.append(newele)\n s.reverse()\n \n # best_u = None\n # ... | [
"0.60227036",
"0.59967417",
"0.5658467",
"0.5579704",
"0.55585",
"0.55581784",
"0.55484015",
"0.5513262",
"0.5466028",
"0.54607856",
"0.54588825",
"0.5439738",
"0.5396137",
"0.5386086",
"0.5380749",
"0.53742325",
"0.5363156",
"0.53529656",
"0.5346669",
"0.5311985",
"0.5311851... | 0.51569587 | 42 |
Groups data in SimulationReport's by the value of alpha or gamma2 | def group_data(simulation_reports: List[SimulationReport]) -> Dict[float, SimulationTable]:
heat_maps: OrderedDict[float, SimulationTable] = OrderedDict()
for report in simulation_reports:
if report.param not in heat_maps:
param_name = "alpha" if report.growth_type == GrowthType.Polynomial else "gamma2"
simulation_table = heat_maps.setdefault(
report.param,
SimulationTable(report.growth_type, param_name, report.param, OrderedDict()),
)
else:
simulation_table = heat_maps[report.param]
errors_by_prefix = simulation_table.errors.setdefault(report.prefix_length, [])
errors_by_prefix.append((report.b0, report.error))
return heat_maps | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_data_gamma_(idx2_daid, wx2_rvecs, wx2_aids, wx2_idf,\n alpha=3, thresh=0):\n if utool.DEBUG2:\n from ibeis.model.hots.smk import smk_debug\n smk_debug.rrr()\n smk_debug.check_wx2(wx2_rvecs=wx2_rvecs, wx2_aids=wx2_aids)\n wx_sublist = pdh.ensure_values(p... | [
"0.5615865",
"0.5525419",
"0.5523908",
"0.54661447",
"0.536697",
"0.53608376",
"0.5330283",
"0.52438",
"0.5227983",
"0.5227983",
"0.5154076",
"0.5146968",
"0.51379746",
"0.51017696",
"0.5098285",
"0.50540596",
"0.5026247",
"0.5020847",
"0.50154185",
"0.49952942",
"0.4989816",... | 0.6480124 | 0 |
Abstract method which provides a set of hyperparameters. This method will get called when the framework is about to launch a new trial, | def generate_parameters(self, parameter_id, **kwargs):
# FIXME: some tuners raise NoMoreTrialError when they are waiting for more trial results
# we need to design a new exception for this purpose
raise NotImplementedError('Tuner: generate_parameters not implemented') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_hyperparams(self):",
"def _get_current_hyperparameters(self):",
"def get_hyper_params():\n #################################\n ##### INSERT YOUR CODE HERE #####\n layers_size = [10]\n activation = 'relu'\n lr = 5e-4\n epochs = 30\n dropout_rate = 0.2\n init_kind = 'xavier'\n ... | [
"0.7449988",
"0.73030883",
"0.6955949",
"0.6952735",
"0.69313055",
"0.6916811",
"0.6915098",
"0.6915098",
"0.6910595",
"0.6755054",
"0.66564333",
"0.6618931",
"0.6612997",
"0.6604273",
"0.6575251",
"0.6572437",
"0.64946365",
"0.64934057",
"0.6481356",
"0.6410235",
"0.64025366... | 0.6258678 | 30 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.