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Functions99 in github.com/Cisco-Talos/fnc-1

⨍ Functions 99◇ Types & classes 14
↓ 21 callersMethodload
(self)
deep_learning_model/Vectors.py:67
↓ 10 callersMethodshared
(self)
deep_learning_model/Vectors.py:75
↓ 8 callersFunctionscore_submission
(gold_labels, test_labels)
tree_model/score.py:8
↓ 5 callersMethodtransform
(self, X, pad=0)
deep_learning_model/Vectors.py:78
↓ 4 callersFunctiondense
(inp, n_in,n_out, act=T.nnet.relu)
deep_learning_model/clf.py:104
↓ 4 callersFunctionopen_process_rewrite
(filename)
tree_model/cleanup.py:52
↓ 4 callersFunctionpreprocess_data
(line, token_pattern=token_pattern, exclude_stopword=True,
tree_model/helpers.py:18
↓ 4 callersMethodread
read the feature matrix from a pickle file
tree_model/FeatureGenerator.py:21
↓ 2 callersFunctionadd_newline_to_unicode
(text)
tree_model/cleanup.py:29
↓ 2 callersFunctionbuild_data
()
tree_model/xgb_train_cvBodyId.py:39
↓ 2 callersFunctiongetUnigram
Input: a list of words, e.g., ['I', 'am', 'Denny'] Output: a list of unigram
tree_model/ngram.py:18
↓ 2 callersMethodget_one
(self, ridx=None)
deep_learning_model/utils.py:113
↓ 2 callersFunctionget_repeats_df
(df, groupby_cols)
tree_model/test_train_splits.py:79
↓ 2 callersMethodgrad
(self, args, g_outs)
deep_learning_model/clf.py:132
↓ 2 callersFunctionmax_over_time
(out)
deep_learning_model/clf.py:17
↓ 2 callersFunctionnormalize_to_bytes
(text, encoding="utf-8")
tree_model/cleanup.py:14
↓ 2 callersFunctionnormalize_to_unicode
(text, encoding="utf-8")
tree_model/cleanup.py:3
↓ 2 callersFunctionperfect_score
(truth_y)
tree_model/xgb_train.py:76
↓ 2 callersFunctionperfect_score
(truth_y)
tree_model/xgb_train_cvBodyId.py:126
↓ 2 callersMethodread
(self, header='train')
tree_model/TfidfFeatureGenerator.py:97
↓ 2 callersFunctionreport_score
(actual,predicted)
tree_model/score.py:48
↓ 2 callersMethodsample
(self, n=16, ridx=None)
deep_learning_model/utils.py:124
↓ 2 callersFunctionscore
(pred, act)
deep_learning_model/clf.py:180
↓ 2 callersFunctionsplit_it
( df, percentage )
tree_model/test_train_splits.py:7
↓ 2 callersFunctiontrain
()
tree_model/xgb_train_cvBodyId.py:145
↓ 2 callersMethodvalidate
(self)
deep_learning_model/utils.py:153
↓ 1 callersFunctionAdam
(grads, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8)
deep_learning_model/clf.py:135
↓ 1 callersFunctionTrain
(cost=None, params=None, inputs=None, outputs=None, rho=.9, givens={}, clip=False, updates={},learning_rate=.9
deep_learning_model/clf.py:157
↓ 1 callersFunctionbreak_it
break_it( df, percentage ) df : data frame with 'Headline', and 'Body ID' cols percentage : targe percentage to split into ou
tree_model/test_train_splits.py:27
↓ 1 callersFunctionbuild_data
()
tree_model/xgb_train.py:28
↓ 1 callersFunctionbuild_test_data
()
tree_model/xgb_train_cvBodyId.py:79
↓ 1 callersFunctionconvert_windows1252_to_utf8
(text)
tree_model/cleanup.py:25
↓ 1 callersFunctioncv
()
tree_model/xgb_train.py:86
↓ 1 callersFunctionfind_all
( df, this_indx )
tree_model/test_train_splits.py:65
↓ 1 callersFunctionfscore
(pred_y, truth_y)
tree_model/xgb_train.py:60
↓ 1 callersFunctiongetBigram
Input: a list of words, e.g., ['I', 'am', 'Denny'] Output: a list of bigram, e.g., ['I_am', 'am_Denny'] I use _ as join_string for this e
tree_model/ngram.py:26
↓ 1 callersFunctiongetBiterm
Input: a list of words, e.g., ['I', 'am', 'Denny', 'boy'] Output: a list of biterm, e.g., ['I_am', 'I_Denny', 'I_boy', 'am_Denny', 'a
tree_model/ngram.py:84
↓ 1 callersFunctiongetTrigram
Input: a list of words, e.g., ['I', 'am', 'Denny'] Output: a list of trigram, e.g., ['I_am_Denny'] I use _ as join_string for this exampl
tree_model/ngram.py:47
↓ 1 callersFunctionhyperopt_wrapper
(param)
tree_model/average.py:126
↓ 1 callersFunctionloadTest
()
tree_model/average.py:33
↓ 1 callersFunctionprint_confusion_matrix
(cm)
tree_model/score.py:29
↓ 1 callersFunctionproc_bodies
(fn)
deep_learning_model/utils.py:78
↓ 1 callersFunctionprocess
()
tree_model/generateFeatures.py:13
↓ 1 callersFunctionprocess
(lines)
tree_model/cleanup.py:43
↓ 1 callersMethodprocess
input: data: pandas dataframe generate features and save them into a pickle file
tree_model/FeatureGenerator.py:13
↓ 1 callersMethodread
(self, header='train')
tree_model/CountFeatureGenerator.py:141
↓ 1 callersFunctionsingle_item_process_funky
(text)
tree_model/cleanup.py:37
↓ 1 callersFunctionsingle_item_process_standard
(text)
tree_model/cleanup.py:32
↓ 1 callersFunctionstack_cv
(param)
tree_model/average.py:88
↓ 1 callersFunctionstack_test
()
tree_model/average.py:51
↓ 1 callersFunctionstem_tokens
(tokens, stemmer)
tree_model/helpers.py:11
↓ 1 callersFunctionvectorize
(text, vocab={})
deep_learning_model/Vectors.py:12
Method__call__
(self, inp)
deep_learning_model/clf.py:45
Method__call__
(self, inp)
deep_learning_model/clf.py:80
Method__call__
(self, inp)
deep_learning_model/clf.py:100
Method__init__
(self, name)
tree_model/FeatureGenerator.py:7
Method__init__
(self, name='sentimentFeatureGenerator')
tree_model/SentimentFeatureGenerator.py:13
Method__init__
(self, name='countFeatureGenerator')
tree_model/CountFeatureGenerator.py:13
Method__init__
(self, name='alignmentFeatureGenerator')
tree_model/AlignmentFeatureGenerator.py:20
Method__init__
(self, name='word2vecFeatureGenerator')
tree_model/Word2VecFeatureGenerator.py:13
Method__init__
(self, name='targetFeatureGenerator')
tree_model/TargetFeatureGenerator.py:13
Method__init__
(self, name='tfidfFeatureGenerator')
tree_model/TfidfFeatureGenerator.py:12
Method__init__
(self, name='svdFeatureGenerator')
tree_model/SvdFeatureGenerator.py:13
Method__init__
(self, stances='train_stances.csv',bodies='train_bodies.csv',vecs=None)
deep_learning_model/utils.py:92
Method__init__
(self, path='./GoogleNews-vectors-negative300.bin')
deep_learning_model/Vectors.py:61
Method__init__
(self, n_in=1, n_out=1, width=1, pad=1, stride=1, dilate=1, act=T.nnet.relu)
deep_learning_model/clf.py:25
Method__init__
(self, n_in=1, n_out=1, width=1, pad=1, stride=1, act=T.nnet.relu,rng=None,drop_prob=.5)
deep_learning_model/clf.py:57
Method__init__
(self, size=2)
deep_learning_model/clf.py:97
Method__init__
(self, clip_lower_bound, clip_upper_bound)
deep_learning_model/clf.py:127
Functionbest_practice_load
(filename)
tree_model/cleanup.py:64
Functionbest_practice_load_with_pandas
(filename)
tree_model/cleanup.py:74
Methodcat_text
(x)
tree_model/TfidfFeatureGenerator.py:19
Methodcompute_sentiment
(sentences)
tree_model/SentimentFeatureGenerator.py:27
Functioncosine_sim
(x, y)
tree_model/helpers.py:42
Functioncv
()
tree_model/xgb_train_cvBodyId.py:204
Functioneval_metric
(yhat, dtrain)
tree_model/xgb_train_cvBodyId.py:135
Functionfscore
(pred_y, truth_y)
tree_model/xgb_train_cvBodyId.py:110
FunctiongetFourgram
Input: a list of words, e.g., ['I', 'am', 'Denny', 'boy'] Output: a list of trigram, e.g., ['I_am_Denny_boy'] I use _ as join
tree_model/ngram.py:67
FunctiongetTriterm
Input: a list of words, e.g., ['I', 'am', 'Denny'] Output: a list of triterm, e.g., ['I_am_Denny', 'I_Denny_am', 'am_I_Denny',
tree_model/ngram.py:102
Functionload_data
()
tree_model/average.py:10
Methodname
(self)
tree_model/FeatureGenerator.py:10
Functionpad_char
(text, padc=-1)
deep_learning_model/utils.py:37
Methodprocess
(self, df)
tree_model/SentimentFeatureGenerator.py:17
Methodprocess
(self, df)
tree_model/CountFeatureGenerator.py:17
Methodprocess
(self, df)
tree_model/AlignmentFeatureGenerator.py:24
Methodprocess
(self, df)
tree_model/Word2VecFeatureGenerator.py:17
Methodprocess
(self, df, header='train')
tree_model/TargetFeatureGenerator.py:17
Methodprocess
(self, df)
tree_model/TfidfFeatureGenerator.py:16
Methodprocess
(self, df)
tree_model/SvdFeatureGenerator.py:17
Methodread
(self, header='train')
tree_model/SentimentFeatureGenerator.py:95
Methodread
(self, header='train')
tree_model/AlignmentFeatureGenerator.py:62
Methodread
(self, header='train')
tree_model/Word2VecFeatureGenerator.py:117
Methodread
(self, header='train')
tree_model/TargetFeatureGenerator.py:28
Methodread
(self, header='train')
tree_model/SvdFeatureGenerator.py:100
Functionrun
()
tree_model/average.py:137
Functionshow_incorrect_pred
(actual, predicted, idx_valid)
tree_model/xgb_train_cvBodyId.py:368
Functionsplit
()
deep_learning_model/utils.py:49
Functiontransform
(text)
deep_learning_model/utils.py:33
Functiontry_divide
Try to divide two numbers
tree_model/helpers.py:33