markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
|---|---|---|---|---|---|
標籤選擇器 選擇元素 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#印出物包含外框標籤
print(soup.title)
print(type(soup.title)) #回傳一個tag
print(soup.head)
print... | <title>NTU Mail-臺灣大學電子郵件系統</title>
<class 'bs4.element.Tag'>
<head>
<meta content="text/html; charset=utf-8" http-equiv="Content-Type"/>
<title>NTU Mail-臺灣大學電子郵件系統</title>
<link href="images/style.css" rel="stylesheet" type="text/css"/>
</head>
<a href="http://www.ntu.edu.tw/">臺大首頁 NTU Home</a>
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
獲取名稱、內容 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.title.name) #列印tag名稱
print(soup.title.string) #列印tag裡面的內容 | title
NTU Mail-臺灣大學電子郵件系統
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
獲取屬性 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#列印attribute
print(soup.img.attrs['src'])
print(soup.img['src']) | images/mail20.png
images/mail20.png
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
嵌套選擇 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.head.title.string) #選擇head裡的title的文本 | NTU Mail-臺灣大學電子郵件系統
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
子節點、子孫節點 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#獲取所有子節點，返回list
print(soup.head.contents) #把head裡的文本按照行數讀取出來
#引入requests好爬取html檔案給bs... | <generator object descendants at 0x00000290B724A0A0>
0
1 <meta content="text/html; charset=utf-8" http-equiv="Content-Type"/>
2
3 <title>NTU Mail-臺灣大學電子郵件系統</title>
4 NTU Mail-臺灣大學電子郵件系統
5
6 <link href="images/style.css" rel="stylesheet" type="text/css"/>
7
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
父節點、祖父節點 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#獲取所有父節點
print(soup.img.parent)
#引入requests好爬取html檔案給bs4使用
import requests
response =... | [(0, <div id="imgcss"><img src="images/mail20.png"/></div>), (1, <div id="mail">
<div id="imgcss"><img src="images/mail20.png"/></div>
<div id="content">
<h1><a href="https://mail.ntu.edu.tw/">NTU Mail 2.0</a></h1>
<ul>
<li><img align="absmiddle" src="images/face01-01.gif"/> 服務對象
<ol>
<li>教職員帳號 \ Faculty ... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
兄弟節點 | #引入requests好爬取html檔案給bs4使用
import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#獲取兄弟節點
print(list(enumerate(soup.div.next_siblings)))
print(list(enumerate(soup.div.... | [(0, '\n'), (1, <div id="wrapper">
<div id="banner"></div>
<div id="mail">
<div id="imgcss"><img src="images/mail20.png"/></div>
<div id="content">
<h1><a href="https://mail.ntu.edu.tw/">NTU Mail 2.0</a></h1>
<ul>
<li><img align="absmiddle" src="images/face01-01.gif"/> 服務對象
<ol>
<li>教職員帳號 \ Faculty Accoun... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
標準選擇器 find_all(name, attrs, recursive, text, **kwargs)，找全部元素 可以根據標籤名稱，屬性內容查找文檔 name | import requests
response = requests.get('http://www.pythonscraping.com/pages/page3.html')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.find_all('td'))
print(soup.find_all('td')[0])
import requests
response = requests.g... | []
[]
[]
[<img src="../img/gifts/img1.jpg"/>]
[]
[]
[]
[<img src="../img/gifts/img2.jpg"/>]
[]
[]
[]
[<img src="../img/gifts/img3.jpg"/>]
[]
[]
[]
[<img src="../img/gifts/img4.jpg"/>]
[]
[]
[]
[<img src="../img/gifts/img6.jpg"/>]
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
attrs | import requests
response = requests.get('http://www.pythonscraping.com/pages/page3.html')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.find_all(attrs={'id':'gift1'}))
print(soup.find_all(attrs={'class':'gift'}))
import... | [<tr class="gift" id="gift1"><td>
Vegetable Basket
</td><td>
This vegetable basket is the perfect gift for your health conscious (or overweight) friends!
<span class="excitingNote">Now with super-colorful bell peppers!</span>
</td><td>
$15.00
</td><td>
<img src="../img/gifts/img1.jpg"/>
</td></tr>]
[<tr class="gift" id... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
text | import requests
response = requests.get('http://www.pythonscraping.com/pages/page3.html')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.find_all(text='trained monkeys'))
#不知道為什麼找不到 | []
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
find(name, attrs, recursive, text, **kwargs)，返回第一個元素 | import requests
response = requests.get('http://www.pythonscraping.com/pages/page3.html')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
#特殊屬性可以直接使用
print(soup.find(id='gift1'))
print(soup.find(class_='gift')) | <tr class="gift" id="gift1"><td>
Vegetable Basket
</td><td>
This vegetable basket is the perfect gift for your health conscious (or overweight) friends!
<span class="excitingNote">Now with super-colorful bell peppers!</span>
</td><td>
$15.00
</td><td>
<img src="../img/gifts/img1.jpg"/>
</td></tr>
<tr class="gift" id="g... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
find_parents()返回所有祖先節點, find_parent()返回父節點 find_next_siblings(), find_next_sibling() find_previous_siblings(), find_previous_sibling() find_all_next(), find_next() find_all_previous(), find_previous() CSS選擇器 select()可以直接傳入CSS選擇器即可完成 | import requests
response = requests.get('http://www.pythonscraping.com/pages/page3.html')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
print(soup.select('.gift')) #class前面加.
print(soup.select('#gift1'))#id前面加#
print(soup.select('... | []
[<td>
Vegetable Basket
</td>, <td>
This vegetable basket is the perfect gift for your health conscious (or overweight) friends!
<span class="excitingNote">Now with super-colorful bell peppers!</span>
</td>, <td>
$15.00
</td>, <td>
<img src="../img/gifts/img1.jpg"/>
</td>]
[<td>
Russian Nesting Dolls
</td>, <td>
Hand... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
獲取屬性 | import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
for div in soup.select('div'):
print(div['id'])
print(div.attrs['id']) | top
top
wrapper
wrapper
banner
banner
mail
mail
imgcss
imgcss
content
content
webmail
webmail
imgcss
imgcss
content
content
footer
footer
| MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
獲取文本內容 | import requests
response = requests.get('http://ntumail.cc.ntu.edu.tw')
response.encoding = 'UTF-8' #加入encoding的方法避免中文亂碼
html = response.text
from bs4 import BeautifulSoup
soup = BeautifulSoup(html,'lxml')
for li in soup.select('li'):
print(li.get_text()) | 服務對象
教職員帳號 \ Faculty Account
公務、計畫、及短期帳號 \ Project and Short Term Account
所有在學學生帳號 \ Internal Student Account
教職員帳號 \ Faculty Account
公務、計畫、及短期帳號 \ Project and Short Term Account
所有在學學生帳號 \ Internal Student Account
立即前往 Go to Mail 2.0
Mail 2.0 FAQ
服務對象
校友帳號 \ Alumni Account
醫院員工帳號 \ ... | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
總結 | 推薦使用lxml解析庫，必要時使用html.parser
標籤選擇篩選功能弱，但是速度快
建議使用find(), find_all() 查詢匹配單個結果或多個結果
如果對CSS選擇器熟悉則用select()
記住常用的獲取attrs和text方法 | _____no_output_____ | MIT | BeautifulSoup.ipynb | Pytoddler/Web-scraping |
Toggl Reports Downloader Script to Extract from Toggl API and create CSV Export of **Latest and Complete Timelogs** as as well as separate exports of Clients, Projects, Workspace Lists. Useful for back up purposes or additional data analysis. ---- Add Dependencies | import pandas as pd
from datetime import datetime
from dateutil.parser import parse
import time
import pytz
# Toggl Wrapper API
# https://github.com/matthewdowney/TogglPy
import TogglPy | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
---- Authentication | import json
with open("credentials.json", "r") as file:
credentials = json.load(file)
toggl_cr = credentials['toggl']
APIKEY = toggl_cr['APIKEY']
toggl = TogglPy.Toggl()
toggl.setAPIKey(APIKEY) | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- User Data | user = toggl.request("https://www.toggl.com/api/v8/me")
user_id = user['data']['id']
user['data']['fullname']
join_date = parse(user['data']['created_at'])
join_date
# today = datetime.now()
def utcnow():
return datetime.now(tz=pytz.utc)
today = utcnow()
dates = list(pd.date_range(join_date, today))
print("Days Sin... | Days Since Joining: 2058
| MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- Clients | user_clients = toggl.request("https://www.toggl.com/api/v8/clients")
clients = pd.DataFrame()
for i in list(range(0, len(user_clients))):
clients_df_temp = pd.DataFrame.from_dict(user_clients)
clients = pd.concat([clients_df_temp, clients])
clients.to_csv('data/toggl-clients.csv') | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- Workplaces API Ref: https://github.com/toggl/toggl_api_docs/blob/master/chapters/workspaces.mdget-workspaces | workspaces_list = toggl.request("https://www.toggl.com/api/v8/workspaces")
len(workspaces_list)
workspaces = pd.DataFrame.from_dict(workspaces_list)
workspaces_dict = dict(zip(workspaces.id, workspaces.name))
workspaces.to_csv('data/toggl-workspaces.csv') | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
---- Workplace Projects * API Ref: https://github.com/toggl/toggl_api_docs/blob/master/chapters/workspaces.mdget-workspace-projects* Endpoint: https://www.toggl.com/api/v8/workspaces/{workspace_id}/projects | projects = pd.DataFrame()
for i in list(range(0, len(workspaces_list))):
projects_list = toggl.request("https://www.toggl.com/api/v8/workspaces/" + str(workspaces_list[i]['id']) + "/projects")
projects_df_temp = pd.DataFrame.from_dict(projects_list)
projects = pd.concat([projects_df_temp, projects])
len(pro... | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
---- Collect Yearly Export of Detailed Timelogs | def get_detailed_reports(wid, since, until): # max 365 days
uid = user_id
param = {
'workspace_id': wid,
'since': since,
'until': until,
'uid': uid
}
#print(str(workspace_id) + " " + since)
toggl.getDetailedReportCSV(param, "data/detailed/toggl-detailed-report-" + wi... | Generating CSV... for Workspace: 341257 from 2013-01-01 until 2013-12-31
Generating CSV... for Workspace: 341257 from 2014-01-01 until 2014-12-31
Generating CSV... for Workspace: 341257 from 2015-01-01 until 2015-12-31
Generating CSV... for Workspace: 341257 from 2016-01-01 until 2016-12-31
Generating CSV... for Worksp... | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- Log of Latest Time Entries for that User * API Ref: https://github.com/toggl/toggl_api_docs/blob/master/chapters/time_entries.mdget-time-entries-started-in-a-specific-time-range* Endpoint: https://www.toggl.com/api/v8/time_entries * Note: start_date and end_date must be ISO 8601 date and time strings. | # latest_time_entries from last 9 days
latest_time_entries = toggl.request("https://www.toggl.com/api/v8/time_entries")
len(latest_time_entries)
latest_time_entries[-1]
latest_timelog = pd.DataFrame.from_dict(latest_time_entries)
latest_timelog.tail()
latest_timelog.head()
latest_timelog.to_csv('data/toggl-timelog-late... | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- BONUS: Extract Times Entries for Every Single Day Using Toggl API **NOTE:** A bit of a hackish solution. But this is a possible approach to getting individual day logs. | extract_date_start = join_date.strftime("%Y-%m-%d") # join date
extract_date_end = today.strftime("%Y-%m-%d") # today
# UNCOMMENT TO Overide Full Extract
extract_date_start = "2018-05-23"
# extract_date_end = "2018-05-01".strftime("%Y-%m-%d")
# extract_date_end = today.strftime("%Y-%m-%d") # today
# Function that tu... | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- Simple Data Analysis (Using Exported CSV Logs) | import glob
import os
# import all days of time entries and create data frame
path = 'data/detailed/'
allFiles = glob.glob(path + "/*.csv")
timelogs = pd.DataFrame()
list_ = []
for file_ in allFiles:
df = pd.read_csv(file_,index_col=None, header=0)
list_.append(df)
timelog = pd.concat(list_)
timelog.head()
len(... | _____no_output_____ | MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
----- Combine to a Daily Project Time Number | # combine to daily number
daily_project_time = timelog.groupby(['Start date'])['seconds'].sum()
print('{:,} total project time data'.format(len(daily_project_time)))
daily_project_time.to_csv('data/daily_project_time.csv')
daily_project_time.tail(5) | 1,924 total project time data
| MIT | toggl/toggl_downloader.ipynb | Zackhardtoname/qs_ledger |
Market Basket Analysis IntroductionAttribution Chris Moffitt at http://pbpython.com/ Assiciationsanalys anses generellt tillhöra de oövervakade inlärningsmetoderna och kan exempelvis användas för att hitta gemensamma mönster bland stora datamängder med transaktionsdata. Ett applikationsområde blir därmed den så kallad... | import pandas as pd
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
df = pd.read_excel('http://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx')
df.head() | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Som vanligt börjar vi med att bekanta oss med det data vi har, vad är det för typ av data?Därefter behöver vi (som alltid) städa vårt data och se till att dess format passar den typ av analys vi ska genomföra. | # Städa upp mellanslag och ta bort rader som inte har ett giltligt kvitto.
df['Description'] = df['Description'].str.strip()
df.dropna(axis=0, subset=['InvoiceNo'], inplace=True)
#Ta bort kvitton från kreditkortstransaktioner
df['InvoiceNo'] = df['InvoiceNo'].astype('str')
df = df[~df['InvoiceNo'].str.contains('C')] | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
För att kunna köra våra algoritmer behöver vi också se till att ändra om vårt data så att varje rad representerar en transaktion och varje produkt har en egen kolumn. | #Vi startar också med att enbart analysera data från köp gjorda i Frankrike så att det inte blir alltför mycket data.
basket = (df[df['Country'] == "France"]
.groupby(['InvoiceNo', 'Description'])['Quantity']
.sum().unstack().reset_index().fillna(0)
.set_index('InvoiceNo')) | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Så här ser vårt dataset ut när vi format om det som vi vill ha det för vår associationsanalys. | basket.head() | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Hur många produkter säljer företaget i Frankrike? | # Titta på några av kolumnerna, vad är det vi ser?
basket.iloc[:,[0,1,2,3,4,5,6, 7]].head() | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Vi behöver också koda om med `one-hot encoding` så att en produkt som inhandlats i en viss transaktion representeras av 1 och frånvaron av en specifik produkt i en transaktion representeras av 0. Det medför att vårt dataset blir väldigt glest, varför? **OBS!** One hot encoding kan göras på olika sätt! | # Konvertera till 1 för produkt köpt och 0 för produkt inte köpt.
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
basket_sets = basket.applymap(encode_units)
# Ta bort onödig data
basket_sets.drop('POSTAGE', inplace=True, axis=1)
basket_sets.head() | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Att mäta associeringsregler För att ta reda på vilka associationsregler som är värdefulla krävs mycket domänkunskap. Det finns dock också några mätvärden som kan användas för att hjälpa till att avgöra kvaliteten på reglerna och för att veta hur mycket vikt vi bör lägga vid en specifik regel. Det finns tre huvudsaklig... | frequent_itemsets = apriori(basket_sets, min_support=0.07, use_colnames=True)
frequent_itemsets.head()
# Skapa själva reglerna varvid de olika mätvärdena också beräknas.
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
rules
#Beräkna antal antecendant för varje regel
rules["num_antecedents"... | _____no_output_____ | Apache-2.0 | Market_Basket_Intro.ipynb | UU-IM-EU/Code_along4 |
Load an image from a URL | from geodatatool import visual
visual.load_image_from_url("https://upload.wikimedia.org/wikipedia/commons/6/61/Remote_Sensing_Illustration.jpg") | _____no_output_____ | MIT | docs/notebooks/visual_intro.ipynb | clancygeodata/geodatatool |
Display a YouTube video | from geodatatool import visual
visual.display_youtube("Ezn1ne2Fj6Y") | _____no_output_____ | MIT | docs/notebooks/visual_intro.ipynb | clancygeodata/geodatatool |
**Day 3 - Task 1**: Plot the boxplot (column imdb_score) of the colored and bw films | imdb_has_attr_color = imdb.dropna(subset=['color'])
sns.boxplot(data = imdb_has_attr_color, x ="color", y="imdb_score")
plt.gcf().set_size_inches(3, 6) | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 2**: In the graph (budget x gross), we have a point with a high gross value (close to 2.5) and also a high loss, find this movie | imdb = imdb.drop_duplicates()
imdb_usa = imdb.query("country == 'USA'")
sns.scatterplot(x="budget", y="gross", data = imdb_usa)
imdb_usa.query('budget > 250000000 & gross < 100000000')['movie_title'] | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 4**: What are the films that came before the 2WW decade and have high gains | imdb_usa['earnings'] = imdb_usa['gross'] - imdb_usa['budget']
sns.scatterplot(x="title_year", y="earnings", data = imdb_usa)
imdb_usa.query('title_year > 1935 & title_year < 1940 & earnings > 150000000')[['movie_title', 'title_year', 'gross']] | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 5**: In the graph (movies_per_director x gross), we have some strange points between 15 and 20. Confirm Paulo's theory that the director is Woody Allen | movies_director = imdb_usa.groupby('director_name')['director_name'].count().rename('movies_director')
gross_director_movies = imdb_usa[['director_name', 'gross', 'movie_title']].merge(movies_director, on='director_name')
sns.scatterplot(x="movies_director", y="gross", data = gross_director_movies)
gross_director_movie... | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 7**: Calculate the correlation of films only after the 2000s | imdb_usa_af2000 = imdb_usa.query('title_year > 2000')
imdb_usa_af2000[["gross", "budget", "earnings", "title_year"]].corr() | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 8**: Try to find a graph that looks like a line | sns.lineplot(data = imdb_usa.query('title_year > 2005').groupby('title_year')['gross'].mean()) | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
**Day 3 - Task 9**: Show the correlation between other variables present in the dataframe. Counting revisions per year can also be a resource. | imdb_usa[["num_user_for_reviews", "num_voted_users"]].corr()
imdb_usa[["actor_1_facebook_likes", "cast_total_facebook_likes"]].corr()
sns.lineplot(data = imdb_usa.groupby('title_year')['num_voted_users'].sum()) | _____no_output_____ | MIT | ImdbTasks.ipynb | cardosorrenan/alura-QuarentenaDados |
Most examples work across multiple plotting backends, this example is also available for:* [Matplotlib Directed Airline Routes](../matplotlib/directed_airline_routes.ipynb) | import networkx as nx
import holoviews as hv
from holoviews import opts
from holoviews.element.graphs import layout_nodes
from bokeh.sampledata.airport_routes import routes, airports
hv.extension('bokeh') | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/directed_airline_routes.ipynb | ppwadhwa/holoviews |
Declare data | # Create dataset indexed by AirportID and with additional value dimension
airports = hv.Dataset(airports, ['AirportID'], ['Name', 'IATA', 'City'])
label = 'Alaska Airline Routes'
# Select just Alaska Airline routes
as_graph = hv.Graph((routes[routes.Airline=='AS'], airports), ['SourceID', "DestinationID"], 'Airline',... | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/directed_airline_routes.ipynb | ppwadhwa/holoviews |
Plot | (as_graph * labels).opts(
opts.Graph(directed=True, node_size=8, bgcolor='gray', xaxis=None, yaxis=None,
edge_line_color='white', edge_line_width=1, width=800, height=800, arrowhead_length=0.01,
node_fill_color='white', node_nonselection_fill_color='black'),
opts.Labels(xoffset=-0.... | _____no_output_____ | BSD-3-Clause | examples/gallery/demos/bokeh/directed_airline_routes.ipynb | ppwadhwa/holoviews |
Starbucks Capstone Challenge IntroductionThis data set contains simulated data that mimics customer behavior on the Starbucks rewards mobile app. Once every few days, Starbucks sends out an offer to users of the mobile app. An offer can be merely an advertisement for a drink or an actual offer such as a discount or BO... | import pandas as pd
import numpy as np
import math
import json
% matplotlib inline
# read in the json files
portfolio = pd.read_json('data/portfolio.json', orient='records', lines=True)
profile = pd.read_json('data/profile.json', orient='records', lines=True)
transcript = pd.read_json('data/transcript.json', orient='r... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Reading The Datasets | portfolio.head(10)
portfolio.shape[0]
portfolio.shape[1]
print('portfolio: rows = {} ,columns = {}'.format((portfolio.shape[0]),(portfolio.shape[1])))
portfolio.describe()
portfolio.info()
portfolio.offer_type.value_counts()
portfolio.reward.value_counts()
import matplotlib.pyplot as plt
plt.figure(figsize=[6,6])
fig, ... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Discount and bogo are equally given and on maximum times | plt.figure(figsize=[6,6])
fig, ax = plt.subplots()
y_counts = portfolio['duration'].value_counts()
y_counts.plot(kind='barh').invert_yaxis()
for i, v in enumerate(y_counts):
ax.text(v, i, str(v), color='black', fontsize=14)
plt.title('Different offer types\' duartion') | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Here we can see that most of the offers are for the duration of 7 days Profile | profile.head(8)
print('profile: rows = {} ,columns = {}'.format((profile.shape[0]),(profile.shape[1])))
profile.describe()
profile.isnull().sum()
profile.shape
import seaborn as sns
plt.figure(figsize=[6,6])
fig, ax = plt.subplots()
y_counts = profile['gender'].value_counts()
y_counts.plot(kind='bar... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Mostly male are interested in the offers and they are the major ones Transcript | transcript.head(9)
transcript.describe()
transcript.info()
print('transcript: rows = {} ,columns = {}'.format((profile.shape[0]),(profile.shape[1]))) | transcript: rows = 17000 ,columns = 5
| MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Cleaning The Datasets PortfolioRenaming 'id' to 'offer_id' | portfolio.columns = ['channels', 'difficulty', 'duration', 'offer_id', 'offer_type', 'reward']
portfolio.columns
portfolio.head() | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
ProfileRenaming 'id' to 'customer_id' , filling the missing values of age and income with mean value , filling the missing values of gender with mode | profile.columns
profile.columns = ['age', 'became_member_on', 'gender', 'customer_id', 'income']
profile.columns
profile['age'].fillna(profile['age'].mean()) #filling missing age with average age
profile['income'].fillna(profile['income'].mean()) #filling missing income with average income
profile['gender'].fillna(prof... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
So there is not any missing value remaining in the profile dataframe TranscriptRenaming 'person' to 'customer_id' , splitting the 'value' column based on its keys anddropping the unnecessary columns | transcript.columns
transcript.columns = ['event', 'customer_id', 'time', 'value'] #changing the column name
transcript.head()
transcript.value.astype('str').value_counts().to_dict() #converting the values in the column 'value' to dictionary
transcript['offer_id'] = transcript.value.apply(lambda x: x.get('offer_id')) #s... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Exploratory Data Analysis Now we will merge the dataframes | merge_df = pd.merge(portfolio, transcript, on='offer_id')#merging portfolio and transcript dataframes on the basis of 'offer_id'
final_df = pd.merge(merge_df, profile, on='customer_id')#merging the merged dataframe of portfolio and transcript with profile dataframe on the basis of 'customer-id'
#Exploring the final mer... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Now we will see the different offer types and their counts | final_df['offer_type'].value_counts().plot.barh(title = 'Offer types with their counts') | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
So,we can see that discount and bogo are thr most given offer types Now we will see the different events and their counts | final_df['event'].value_counts().plot.barh(title = 'Different events and their counts') | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
So,in most of the cases offer is received by the user and it is not completed by him/her,means most of the people just ignore the offers they receive Now we will analyse this data on the basis of the age of the customers | sns.distplot(final_df['age'] , bins = 50 , hist_kws = {'alpha' : 0.4}); | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
As we can see that the people after the age of 100 are just acting as outliers,so we will remove them | final_df = final_df[final_df['age']<=100]
# Now seeing the distortion plot of age
sns.distplot(final_df['age'] , bins = 50 , hist_kws = {'alpha' : 0.4}); | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
We can observe that most of the customers are within the age group of 45-60 are the most frequent customers and more than any other group,this is quite interesting. Now,we will analyse this data on the basis of income of the customers | sns.distplot(final_df['income'] , bins = 50 , hist_kws = {'alpha' : 0.4});
final_df['income'].mean() | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Now we can see that most people who are the customers of Starbucks have their income within the range of 55k - 75k with a mean income of 66413.35 Now,we will see how our final dataframe is depedent on the 'gender' feature | final_df['gender'].value_counts().plot.barh(title = 'Analysing the gender of customers') | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
So,we can see that most of the customers are male We will analyse the dataframe on the basis of 'offer_type' on the basis of gender | sns.countplot(x = 'offer_type' , hue = 'gender' , data = final_df) | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
We can see that the count of gender weather it is male or female is approximately equal in the bogo and discount offers Now,we will see the relation between gender and events | sns.countplot(x = 'event' , hue = 'gender' , data = final_df) | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
So,from the exploratory data analysis we can see that most of the customers just receive the offers and they do not view them and the people who complete the offers they receive is quite less and most of the offers made by Starbuks are BOGO and Discount and most of the people that are the customers are within the age g... | final_df | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
We will now encode the categorical features like 'offer_type' , 'gender' , 'age' We will encode the offer_id and customer_id | final_df = pd.get_dummies(final_df , columns = ['offer_type' , 'gender' , 'age'])
#processing offer_id
offer_id = final_df['offer_id'].unique().tolist()
offer_map = dict( zip(offer_id,range(len(offer_id))) )
final_df.replace({'offer_id': offer_map},inplace=True)
#processing customer_id
customer_id = final_df['custom... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Now we will scale the numerical data including 'income' , 'difficulty' , 'duration' and many more... | from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
numerical_columns = ['income' , 'difficulty' , 'duration' , 'offer_reward' , 'time' , 'reward' , 'amount']
final_df[numerical_columns] = scaler.fit_transform(final_df[numerical_columns])
final_df.head() | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
We will encode the values in the 'event' column | final_df['event'] = final_df['event'].map({'offer received':1, 'offer viewed':2, 'offer completed':3})
final_df2 = final_df.drop('event' , axis = 1) | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Now encoding the channels column | final_df2['web'] = final_df2['channels'].apply(lambda x : 1 if 'web' in x else 0)
final_df2['mobile'] = final_df2['channels'].apply(lambda x : 1 if 'mobile' in x else 0)
final_df2['social'] = final_df2['channels'].apply(lambda x : 1 if 'social' in x else 0)
final_df2['email'] = final_df2['channels'].apply(lambda x : 1 ... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Training Our Dataset Now splitting our 'final_df' into training and test set | independent_variables = final_df2 #our dataset containing all the independent variables excluding the 'event'
dependent_variable = final_df['event'] #our final dataset containing the 'event'
from sklearn.model_selection import train_test_split
# splitting our dataset into training and test set and the test set being th... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Testing Our Dataset | # We will implement a number of classification machine learning methods and will determine which method is best for our model
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeCl... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Implementing the KNN Model | knn = KNeighborsClassifier()
f1_score_train_knn , f1_score_test_knn = train_test_f1(knn)#calculating the F1 scores | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Implementing the Logistic Regression | logistic = LogisticRegression()
f1_score_train_logistic , f1_score_test_logistic = train_test_f1(logistic)#calculating the F1 scores | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Implementing the Random Forest Classifier | random_forest = RandomForestClassifier()
f1_score_train_random , f1_score_test_random = train_test_f1(random_forest)#calculating the F1 scores | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Implementing the Decision Tree Classifier | decision_tree = DecisionTreeClassifier()
f1_score_train_decision , f1_score_test_decision = train_test_f1(decision_tree)#calculating the F1 scores | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Concluding from the above models and scores | f1_scores_models = {'model_name' : [knn.__class__.__name__ , logistic.__class__.__name__ , random_forest.__class__.__name__ , decision_tree.__class__.__name__]
, 'Training set F1 Score' : [f1_score_train_knn , f1_score_train_logistic , f1_score_train_random , f1_score_train_decision],
... | _____no_output_____ | MIT | Capstone/Starbucks_Capstone_notebook.ipynb | mahajan-abhay/Nanodegree |
Unit CommitmentKeywords: semi-continuous variables, cbc usage, gdp, disjunctive programming Imports | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import display, HTML
import shutil
import sys
import os.path
if not shutil.which("pyomo"):
!pip install -q pyomo
assert(shutil.which("pyomo"))
if not (shutil.which("cbc") or os.path.isfile("cbc")):
... | _____no_output_____ | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
Problem statementA set of $N$ electrical generating units are available to meet a required demand $d_t$ for time period $t \in 1, 2, \ldots, T$. The power generated by unit $n$ for time period $t$ is denoted $x_{n,t}$. Each generating unit is either off, $x_{n,t} = 0$ or else operating in a range $[p_n^{min}, p_n^{ma... | # demand
T = 20
T = np.array([t for t in range(0, T)])
d = np.array([100 + 100*np.random.uniform() for t in T])
fig, ax = plt.subplots(1,1)
ax.bar(T+1, d)
ax.set_xlabel('Time Period')
ax.set_title('Demand') | _____no_output_____ | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
Generating Units | # generating units
N = 5
pmax = 2*max(d)/N
pmin = 0.6*pmax
N = np.array([n for n in range(0, N)])
a = np.array([0.5 + 0.2*np.random.randn() for n in N])
b = np.array([10*np.random.uniform() for n in N])
p = np.linspace(pmin, pmax)
fig, ax = plt.subplots(1,1)
for n in N:
ax.plot(p, a[n]*p + b[n])
ax.set_xlim(0, p... | _____no_output_____ | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
Pyomo model 1: Conventional implementation for emi-continuous variables | def unit_commitment():
m = pyo.ConcreteModel()
m.N = pyo.Set(initialize=N)
m.T = pyo.Set(initialize=T)
m.x = pyo.Var(m.N, m.T, bounds = (0, pmax))
m.u = pyo.Var(m.N, m.T, domain=pyo.Binary)
# objective
m.cost = pyo.Objective(expr = sum(m.x[n,t]*a[n] + m.u[n,t]*b[n] for t in m.T for n ... | # ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# --------------------------------------------------... | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
Pyomo model 2: GDP implementation | def unit_commitment_gdp():
m = pyo.ConcreteModel()
m.N = pyo.Set(initialize=N)
m.T = pyo.Set(initialize=T)
m.x = pyo.Var(m.N, m.T, bounds = (0, pmax))
# demand
m.demand = pyo.Constraint(m.T, rule=lambda m, t: sum(m.x[n,t] for n in N) == d[t])
# representing the semicontinous vari... | # ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# --------------------------------------------------... | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
There is a problem here!Why are the results different? Somehow it appears values of the indicator variables are being ignored. | for n in N:
for t in T:
print("n = {0:2d} t = {1:2d} {2} {3} {4:5.2f}".format(n, t, m_gdp.sc1[n,t].indicator_var(), m_gdp.sc2[n,t].indicator_var(), m.x[n,t]())) | n = 0 t = 0 1.0 0.0 76.13
n = 0 t = 1 1.0 0.0 45.86
n = 0 t = 2 1.0 0.0 45.86
n = 0 t = 3 1.0 0.0 75.96
n = 0 t = 4 1.0 0.0 45.86
n = 0 t = 5 1.0 0.0 45.86
n = 0 t = 6 1.0 0.0 45.86
n = 0 t = 7 1.0 0.0 73.80
n = 0 t = 8 1.0 0.0 68.79
n = 0 t = 9 1.0 0.0 61.04
... | MIT | _build/html/_sources/notebooks/03/03.06-Unit-Commitment.ipynb | leonlan/MO-book |
Training Neural NetworksThe network we built in the previous part isn't so smart, it doesn't know anything about our handwritten digits. Neural networks with non-linear activations work like universal function approximators. There is some function that maps your input to the output. For example, images of handwritten ... | import torch
from torch import nn
import torch.nn.functional as F
from torchvision import datasets, transforms
# Define a transform to normalize the data
transform = transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,)),
])
# Downl... | tensor(2.3011, grad_fn=<NllLossBackward>)
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
In my experience it's more convenient to build the model with a log-softmax output using `nn.LogSoftmax` or `F.log_softmax` ([documentation](https://pytorch.org/docs/stable/nn.htmltorch.nn.LogSoftmax)). Then you can get the actual probabilites by taking the exponential `torch.exp(output)`. With a log-softmax output, yo... | ## Solution
# Build a feed-forward network
model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
# Define the loss
criterion = nn.NLLLos... | tensor(2.2987, grad_fn=<NllLossBackward>)
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
AutogradNow that we know how to calculate a loss, how do we use it to perform backpropagation? Torch provides a module, `autograd`, for automatically calculating the gradients of tensors. We can use it to calculate the gradients of all our parameters with respect to the loss. Autograd works by keeping track of operati... | x = torch.randn(2,2, requires_grad=True)
print(x)
y = x**2
print(y) | tensor([[0.0357, 0.2308],
[1.3125, 2.6173]], grad_fn=<PowBackward0>)
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
Below we can see the operation that created `y`, a power operation `PowBackward0`. | ## grad_fn shows the function that generated this variable
print(y.grad_fn) | <PowBackward0 object at 0x107e2e278>
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
The autograd module keeps track of these operations and knows how to calculate the gradient for each one. In this way, it's able to calculate the gradients for a chain of operations, with respect to any one tensor. Let's reduce the tensor `y` to a scalar value, the mean. | z = y.mean()
print(z) | tensor(1.0491, grad_fn=<MeanBackward0>)
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
You can check the gradients for `x` and `y` but they are empty currently. | print(x.grad) | None
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
To calculate the gradients, you need to run the `.backward` method on a Variable, `z` for example. This will calculate the gradient for `z` with respect to `x`$$\frac{\partial z}{\partial x} = \frac{\partial}{\partial x}\left[\frac{1}{n}\sum_i^n x_i^2\right] = \frac{x}{2}$$ | z.backward()
print(x.grad)
print(x/2) | tensor([[-0.0945, -0.2402],
[ 0.5728, 0.8089]])
tensor([[-0.0945, -0.2402],
[ 0.5728, 0.8089]], grad_fn=<DivBackward0>)
| MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
These gradients calculations are particularly useful for neural networks. For training we need the gradients of the weights with respect to the cost. With PyTorch, we run data forward through the network to calculate the loss, then, go backwards to calculate the gradients with respect to the loss. Once we have the grad... | # Build a feed-forward network
model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
images, labels = next(iter(... | Before backward pass:
None
After backward pass:
tensor([[ 2.9076e-04, 2.9076e-04, 2.9076e-04, ..., 2.9076e-04,
2.9076e-04, 2.9076e-04],
[ 1.8523e-03, 1.8523e-03, 1.8523e-03, ..., 1.8523e-03,
1.8523e-03, 1.8523e-03],
[-1.0316e-03, -1.0316e-03, -1.0316e-03, ..., -1.0316e... | MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
Training the network!There's one last piece we need to start training, an optimizer that we'll use to update the weights with the gradients. We get these from PyTorch's [`optim` package](https://pytorch.org/docs/stable/optim.html). For example we can use stochastic gradient descent with `optim.SGD`. You can see how to... | from torch import optim
# Optimizers require the parameters to optimize and a learning rate
optimizer = optim.SGD(model.parameters(), lr=0.01) | _____no_output_____ | MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
Now we know how to use all the individual parts so it's time to see how they work together. Let's consider just one learning step before looping through all the data. The general process with PyTorch:* Make a forward pass through the network * Use the network output to calculate the loss* Perform a backward pass throug... | print('Initial weights - ', model[0].weight)
images, labels = next(iter(trainloader))
images.resize_(64, 784)
# Clear the gradients, do this because gradients are accumulated
optimizer.zero_grad()
# Forward pass, then backward pass, then update weights
output = model(images)
loss = criterion(output, labels)
loss.bac... | Updated weights - Parameter containing:
tensor([[ 0.0134, 0.0305, 0.0163, ..., -0.0268, 0.0101, -0.0027],
[-0.0334, -0.0089, -0.0294, ..., 0.0047, -0.0106, -0.0214],
[-0.0068, -0.0275, -0.0132, ..., -0.0203, 0.0075, 0.0117],
...,
[-0.0147, 0.0041, 0.0312, ..., 0.0302, 0.01... | MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
Training for realNow we'll put this algorithm into a loop so we can go through all the images. Some nomenclature, one pass through the entire dataset is called an *epoch*. So here we're going to loop through `trainloader` to get our training batches. For each batch, we'll doing a training pass where we calculate the l... | model = nn.Sequential(nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 64),
nn.ReLU(),
nn.Linear(64, 10),
nn.LogSoftmax(dim=1))
criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.003)
epoch... | _____no_output_____ | MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
With the network trained, we can check out it's predictions. | %matplotlib inline
import helper
images, labels = next(iter(trainloader))
img = images[0].view(1, 784)
# Turn off gradients to speed up this part
with torch.no_grad():
logps = model(img)
# Output of the network are log-probabilities, need to take exponential for probabilities
ps = torch.exp(logps)
helper.view_cl... | _____no_output_____ | MIT | intro-to-pytorch/Part 3 - Training Neural Networks (Solution).ipynb | yangjue-han/deep-learning-v2-pytorch |
Cleaning / Sampling | def cleanDF (df):
r1 = re.compile('.*reporting')
r2 = re.compile('.*imputed')
cols_to_drop1 = list(filter((r1.match), df.columns))
cols_to_drop2 = list(filter((r2.match), df.columns))
cols_to_drop3 = ['admit_NICU']
cols_to_drop = cols_to_drop1 + cols_to_drop2 + cols_to_drop3
cols_to_keep =... | _____no_output_____ | FTL | notebooks/richardkim/RK_modeling.ipynb | ConnorHaas03/CDC_capstone |
Logistic Model Part 1 | sample_size_list = [100]
import warnings
warnings.filterwarnings('ignore')
#GLM with Cross Validation
for sample_per_year in sample_size_list:
dwnSmplDF = concat_df.groupby('birth_year',group_keys = False).apply(lambda x: x.sample(sample_per_year))
cl_df = dwnSmplDF[cols_to_keep]
encoded_target = dwnSm... | _____no_output_____ | FTL | notebooks/richardkim/RK_modeling.ipynb | ConnorHaas03/CDC_capstone |
Logistic Model Part 2 Sampled in a way that1. Unknowns in `admit_NICU` column was thrown away.2. There are equal number of `Y`'s and `N`'s in `admit_NICU` column. (balanced sampling) | cl_df = cleanDF(totDF)
nicu_allY = cl_df.loc[cl_df['admit_NICU']==1]
nicu_allN = cl_df.loc[cl_df['admit_NICU']==0]
#pure GLM with balanced sample (w/o stratified year)
sample_size_list = [100]
for sample_per_class in sample_size_list:
sampN = nicu_allN.sample(sample_per_class)
sampY = nicu_allY.sample(sample_... | sample size : 200
CPU times: user 79.9 ms, sys: 1.13 ms, total: 81 ms
Wall time: 108 ms
score : 0.774
--------------------------------------------------
| FTL | notebooks/richardkim/RK_modeling.ipynb | ConnorHaas03/CDC_capstone |
----- Session 06: OOP By: **Mohamed Fouad Fakhruldeen**, mohamed.fakhruldeen@epita.fr Class & Object | class ClassName:
attributes
methods | _____no_output_____ | MIT | Session 06 - OOP.ipynb | FOU4D/ITI-Python |
attributes | class My_Class:
my_attr = "old Attribute Value Here"
x = My_Class()
print(x)
print(x.my_attr)
x.my_attr = "New Attribute Value"
print(x.my_attr) | <__main__.My_Class object at 0x7f47c80e8130>
old Attribute Value Here
New Attribute Value
| MIT | Session 06 - OOP.ipynb | FOU4D/ITI-Python |
methods | class My_Class:
my_attr = "New Attribute Value Here" # class attribute
def my_method(self):
print("Print my method")
x = My_Class()
print(x)
print(x.my_attr)
x.my_method()
class My_Cars:
def __init__(self, brand, model, year, price): ## instance attributes
self.brand = brand
sel... | Toyota
Toyota Corolla made in 2016 with initial value 5000
Toyota Corolla made in 2016 with initial value 5000 has new value 3500.0
this only appears while printing
this only appears while printing
| MIT | Session 06 - OOP.ipynb | FOU4D/ITI-Python |
Inheritance Child classes can override or extend the attributes and methods of parent classes. can also specify attributes and methods that are unique to themselves. | class MainClass:
attr1 = "this is parent attribute"
class ChildClass(MainClass):
pass
x = ChildClass()
print(x.attr1)
class MainClass2:
attr12 = "this is parent attribute"
class ChildClass2(MainClass2):
attr12 = "This one from child"
x2 = ChildClass2()
print(x2.attr12) | This one from child
| MIT | Session 06 - OOP.ipynb | FOU4D/ITI-Python |
multiple inheretance | class Base1:
pass
class Base2:
pass
class MultiDerived(Base1, Base2):
pass
class Base:
pass
class Derived1(Base):
pass
class Derived2(Derived1):
pass | _____no_output_____ | MIT | Session 06 - OOP.ipynb | FOU4D/ITI-Python |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.