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celinelee
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python_fn_calls

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kwargs.pop("params", {})
_SERIALIZER.query("api_version", api_version, 'str')
kwargs.pop("headers", {})
_SERIALIZER.header("accept", accept, 'str')
PrivateLinkResourcesOperations(object)
__init__(self, client, config, serializer, deserializer)
cls(response)
kwargs.pop('cls', None)
error_map.update(kwargs.pop('error_map', {})
kwargs.pop('api_version', "2022-04-01")
_convert_request(request)
self._client.format_url(request.url)
map_error(status_code=response.status_code, response=response, error_map=error_map)
HttpResponseError(response=response, error_format=ARMErrorFormat)
self._deserialize('PrivateLinkResourcesListResult', pipeline_response)
cls(pipeline_response, deserialized, {})
Migration(migrations.Migration)
models.CharField(max_length=255)
default_data_setup(sender, **kwargs)
User.objects.get(username='ANONYMOUS_USER')
print('Adding ANONYMOUS_USER')
User.objects.create_user('ANONYMOUS_USER', 'anonymous_user@example.com')
anon.set_unusable_password()
anon.save()
RadioConfig(AppConfig)
ready(self)
post_migrate.connect(default_data_setup, sender=self)
or_list(booleans)
get_ngrams(D)
ngrams (aka a token containing a dollar sign ($)
set()
ngrams.add(w)
list(ngrams)
get_frequent_ngrams(text, n, stopword_list, threshold)
ngrams(text, n)
Counter(bigrams)
bigram_freq.most_common()
not (or_list([i in stopword_list for i in bigram])
frequent_bigrams.append('{}${}'.format(bigram[0], bigram[1])
ngrammize_text(text, ngrams)
len(text)
len(text)
bigrammized_text.append(term)
format(term, next_term)
bigrammized_text.append(test_bigram)
bigrammized_text.append(term)
get_dataset_ngrams(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None)
stopwords.words('english')
get_pp_pipeline(remove_stopwords=False)
sw_pp.clean_document(sw)
full_text.extend(doc)
get_frequent_ngrams(full_text, 2, sw, min_freq)
frequent_bigrams.extend(extra_bigrams)
ngrammize_text(full_text, frequent_bigrams)
get_frequent_ngrams(bigrammized_text, 2, sw, min_freq)
frequent_ngrams.extend(extra_ngrams)
insert_ngrams_flat_from_lists(docs, frequent_bigrams, frequent_ngrams)
range(0, len(docs)
ngrammize_text(doc, frequent_bigrams)
ngrammize_text(doc, frequent_ngrams)
insert_ngrams_flat(docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None)
get_dataset_ngrams(docs, min_freq, sw, extra_bigrams, extra_ngrams)
insert_ngrams_flat_from_lists(docs, fb, fn)
insert_ngrams_from_lists(date_doc_tuples, frequent_bigrams, frequent_ngrams)
range(0, len(date_doc_tuples)
ngrammize_text(doc, frequent_bigrams)
ngrammize_text(doc, frequent_ngrams)
insert_ngrams(date_docs, min_freq=1000, sw=None, extra_bigrams=None, extra_ngrams=None)
get_dataset_ngrams([x[1] for x in date_docs], min_freq, sw, extra_bigrams, extra_ngrams)
insert_ngrams_from_lists(date_docs, fb, fn)
collections.namedtuple('Results', 'model reward config time metadata')
logging.getLogger(__name__)
logger.setLevel(logging.WARNING)
create_scheduler(train_fn, scheduler, scheduler_options)
isinstance(scheduler, str)
scheduler.lower()
callable(scheduler)
copy.copy(scheduler_options)
scheduler_cls(train_fn, **scheduler_options)
BaseTask(object)
Dataset()
try_import_mxnet()
time.time()
create_scheduler(train_fn, search_strategy, scheduler_options)
scheduler.run()
scheduler.join_jobs()
scheduler.get_best_reward()
scheduler.get_best_config()
hasattr(args, 'epochs')
hasattr(args, 'final_fit_epochs')
train_fn.args.update({'final_fit':True})
train_fn.kwvars.update({'final_fit':True})
create_scheduler(train_fn, search_strategy, scheduler_options)
scheduler_final.run_with_config(best_config)
time.time()
in_ipynb()
replace('exp1.ag', 'plot_training_curves.png')
scheduler.get_training_curves(filename=plot_training_curves, plot=True, use_legend=False)
copy.deepcopy(args)
logger.warning('No valid results obtained with best config, the result may not be useful...')