-
Notifications
You must be signed in to change notification settings - Fork 0
/
newsdataclassification.py
547 lines (421 loc) · 17.6 KB
/
newsdataclassification.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
# -*- coding: utf-8 -*-
"""NewsDataClassification.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1qPKrhra33j-b3CtlFX5ILsbd9YoxvPyZ
Download the repo for original and raw csv data
"""
!git clone https://github.com/omkarsk98/NewsDataClassification.git
"""Go the directory and checkout to the required folder"""
# Commented out IPython magic to ensure Python compatibility.
# %cd NewsDataClassification
!git checkout development
"""Download trained model and unzip it."""
!wget -c "https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz"
!gunzip GoogleNews-vectors-negative300.bin.gz
"""Download nltk libraries and dependencies"""
import nltk
nltk.download('punkt')
nltk.download('stopwords')
"""import all required libraries"""
import pandas as pd
from gensim import models
from sklearn.linear_model import LogisticRegression
import numpy as np
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.metrics import accuracy_score ,confusion_matrix
import time
from sklearn.manifold import TSNE
from google.colab import files
# Load word2vec model (trained on Google's corpus)
model = models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary = True)
# Check dimension of word vectors
print("Dimensions of the model",model.vector_size)
"""Read the raw data and set max records to be used"""
# read csv
main_data = pd.read_csv('News_Final.csv')
# read titles from it
article_titles = main_data['TITLE']
labels = main_data["CATEGORY"]
# Create a list of strings, one for each title
titles_list = [title for title in article_titles]
# form a single string fro the list of strings
big_title_string = ' '.join(titles_list)
# define total records to be considered for analysis
total = 50000
# 422178 total records as max value
"""Tokenise all words and get stop words for english"""
# Tokenize the string into words
tokens = word_tokenize(big_title_string)
# Remove non-alphabetic tokens, such as punctuation
words = [word.lower() for word in tokens if word.isalpha()]
stop_words = set(stopwords.words('english'))
"""Define all the function that can be used for later stage"""
def document_vector(word2vec_model, doc):
# remove out-of-vocabulary words
doc = [word for word in doc if word in model.vocab]
return np.mean(model[doc], axis=0)
# Our earlier preprocessing was done when we were dealing only with word vectors
# Here, we need each document to remain a document
def preprocess(text):
text = text.lower()
doc = word_tokenize(text)
doc = [word for word in doc if word not in stop_words]
doc = [word for word in doc if word.isalpha()]
return doc
# Function that will help us drop documents that have no word vectors in word2vec
def has_vector_representation(word2vec_model, doc):
"""check if at least one word of the document is in the
word2vec dictionary"""
return not all(word not in word2vec_model.vocab for word in doc)
# Filter out documents
def filter_docs(corpus, texts, labels, condition_on_doc):
"""
Filter corpus and texts given the function condition_on_doc which takes a doc. The document doc is kept if condition_on_doc(doc) is true.
"""
number_of_docs = len(corpus)
if texts is not None:
texts = [text for (text, doc) in zip(texts, corpus)
if condition_on_doc(doc)]
corpus = [doc for doc in corpus if condition_on_doc(doc)]
final_labels = []
for i in range(len(corpus)):
if condition_on_doc(corpus[i]):
final_labels.append(labels[i])
print("{} docs removed".format(number_of_docs - len(corpus)))
return (corpus, texts, final_labels)
"""Remove stop words, non vocab words, empty docs and prepare vector for each title"""
# # Preprocess the corpus to get list of documents with stop words removed and containing only the words that are present in the vocab
corpus = [preprocess(title) for title in titles_list]
# # still contains all the documents, nothing is filtered
# # Remove docs that don't include any words in W2V's vocab
corpus, titles_list, labels = filter_docs(corpus, titles_list, labels, lambda doc: has_vector_representation(model, doc))
print("1st filter: Length of corpus:"+str(len(corpus))+", Length of titles_list:"+ str(len(titles_list))+", Length of labels:"+str(len(labels)))
# # Filter out any empty docs
corpus, titles_list, labels = filter_docs(corpus, titles_list, labels, lambda doc: (len(doc) != 0))
print("2nd filter: Length of corpus:"+str(len(corpus))+", Length of titles_list:"+ str(len(titles_list))+", Length of labels:"+str(len(labels)))
x = []
for doc in corpus: # append the vector for each document
x.append(document_vector(model, doc))
"""# **After removing stop words and empty docs** <br>
---
241 docs removed <br>
1st filter: Length of corpus:422178, Length of titles_list:422178, Length of labels:422178 <br>
0 docs removed <br>
2nd filter: Length of corpus:422178, Length of titles_list:422178, Length of labels:422178 <br>
"""
vectorsForEachDocument = np.array(x) # list to array
labels = np.array(labels)
labels = labels.reshape(labels.shape[0],1)
vectorsForEachDocument.shape, labels.shape
"""# **Vectors for each title**
---
A list of vectors of 300 dimensions each for all the titles and labels contain the respective labels<br>
Shape of these vectors is (422178, 300) <br>
Shape pf it respective labels (422178, 1)
# **Filter improper labels**
---
Filter out the data that has improper labels. Labels should only be of the following types. <br>
1. b: business
2. t: technology
3. e: entertainment
4. m: health
"""
# filter out data that has improper labels
possibleLabels = ["b","t","e","m"]
finalLabels = []
features = []
for i in range(len(labels)):
if labels[i] in possibleLabels:
finalLabels.append(labels[i])
features.append(vectorsForEachDocument[i])
if(len(finalLabels)==total):
break
features = np.array(features)
labels = np.array(finalLabels)
labels = labels.reshape(labels.shape[0],1)
features.shape, labels.shape
"""# **Create a dataframe to shuffle it**
---
Create dataframe to shuffle it and split it.
"""
finalData = pd.DataFrame.from_records(features)
finalData.columns = range(1,301)
finalData["labels"] = labels
# finalData.to_csv('FinalData.csv')
data = finalData.sample(frac=1) #shuffles the data
labels = data["labels"]
labels = np.array(labels)
labels = labels.reshape(labels.shape[0],1)
del data["labels"]
features = np.array(data)
features.shape, labels.shape
"""# Split the data
---
**Train Data**: Use 80% of the data for training purpose. <br>
**Test Data**: Use 20% of the data for testing purpose. <br>
**Features**: Use 300 dimensional vectors as features. It can be found in `vectorsForEachDocument`.<br>
**Labels**: Use the categories as labels. I can be found in `labels`.<br>
"""
train = int((80/100)*len(features))
trainFeatures, testFeatures = features[:train], features[train:]
trainLabels, testLabels = labels[:train], labels[train:]
trainFeatures.shape, trainLabels.shape, testFeatures.shape, testLabels.shape
"""**Shapes of the data** <br>
trainFeatures: (160000,300) <br>
trainLabels: (160000,1) <br>
testFeatures: (40000,300) <br>
testLabels: (40000,1) <br>
# **Train the logistic regression model** <br>
"""
tic = time.time()
logistic_Regression = LogisticRegression(multi_class="auto", solver="lbfgs", max_iter=1000)
logistic_Regression.fit(trainFeatures,trainLabels)
Y_predict = logistic_Regression.predict(testFeatures)
print(str((accuracy_score(testLabels,Y_predict)*100))+"%")
toc = time.time()
print("Time taken:"+str(toc-tic)+" seconds")
"""# **Outcomes of the training** <br>
---
|Train |Test |Dimensions |Accuracy |Time(sec) | Comments |
|---|---|---|---|---|---|
|40000|10000|300|86|12|Data randomly shuffled|
|80000|20000|300|78|53||
|40000|10000|300|74|12||
|80000|20000|300|78|53||# -*- coding: utf-8 -*-
"""NewsDataClassification.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/github/omkarsk98/NewsDataClassification/blob/development/NewsDataClassification.ipynb
Download the repo for original and raw csv data
"""
!git clone https://github.com/omkarsk98/NewsDataClassification.git
"""Go the directory and checkout to the required folder"""
# Commented out IPython magic to ensure Python compatibility.
# %cd NewsDataClassification
!git checkout development
"""Download trained model and unzip it."""
!wget -c "https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz"
!gunzip GoogleNews-vectors-negative300.bin.gz
"""Download nltk libraries and dependencies"""
import nltk
nltk.download('punkt')
nltk.download('stopwords')
"""import all required libraries"""
import pandas as pd
from gensim import models
from sklearn.linear_model import LogisticRegression
import numpy as np
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.metrics import accuracy_score ,confusion_matrix
import time
from sklearn.manifold import TSNE
from google.colab import files
# Load word2vec model (trained on Google's corpus)
model = models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary = True)
# Check dimension of word vectors
print("Dimensions of the model",model.vector_size)
"""Read the raw data and set max records to be used"""
# read csv
main_data = pd.read_csv('News_Final.csv')
# read titles from it
article_titles = main_data['TITLE']
labels = main_data["CATEGORY"]
# Create a list of strings, one for each title
titles_list = [title for title in article_titles]
# form a single string fro the list of strings
big_title_string = ' '.join(titles_list)
# define total records to be considered for analysis
total = 100000
# 422178 total records as max value
"""Tokenise all words and get stop words for english"""
# Tokenize the string into words
tokens = word_tokenize(big_title_string)
# Remove non-alphabetic tokens, such as punctuation
words = [word.lower() for word in tokens if word.isalpha()]
stop_words = set(stopwords.words('english'))
"""Define all the function that can be used for later stage"""
def document_vector(word2vec_model, doc):
# remove out-of-vocabulary words
doc = [word for word in doc if word in model.vocab]
return np.mean(model[doc], axis=0)
# Our earlier preprocessing was done when we were dealing only with word vectors
# Here, we need each document to remain a document
def preprocess(text):
text = text.lower()
doc = word_tokenize(text)
doc = [word for word in doc if word not in stop_words]
doc = [word for word in doc if word.isalpha()]
return doc
# Function that will help us drop documents that have no word vectors in word2vec
def has_vector_representation(word2vec_model, doc):
"""check if at least one word of the document is in the
word2vec dictionary"""
return not all(word not in word2vec_model.vocab for word in doc)
# Filter out documents
def filter_docs(corpus, texts, labels, condition_on_doc):
"""
Filter corpus and texts given the function condition_on_doc which takes a doc. The document doc is kept if condition_on_doc(doc) is true.
"""
number_of_docs = len(corpus)
if texts is not None:
texts = [text for (text, doc) in zip(texts, corpus)
if condition_on_doc(doc)]
corpus = [doc for doc in corpus if condition_on_doc(doc)]
final_labels = []
for i in range(len(corpus)):
if condition_on_doc(corpus[i]):
final_labels.append(labels[i])
print("{} docs removed".format(number_of_docs - len(corpus)))
return (corpus, texts, final_labels)
"""Remove stop words, non vocab words, empty docs and prepare vector for each title"""
# # Preprocess the corpus to get list of documents with stop words removed and containing only the words that are present in the vocab
corpus = [preprocess(title) for title in titles_list]
# # still contains all the documents, nothing is filtered
# # Remove docs that don't include any words in W2V's vocab
corpus, titles_list, labels = filter_docs(corpus, titles_list, labels, lambda doc: has_vector_representation(model, doc))
print("1st filter: Length of corpus:"+str(len(corpus))+", Length of titles_list:"+ str(len(titles_list))+", Length of labels:"+str(len(labels)))
# # Filter out any empty docs
corpus, titles_list, labels = filter_docs(corpus, titles_list, labels, lambda doc: (len(doc) != 0))
print("2nd filter: Length of corpus:"+str(len(corpus))+", Length of titles_list:"+ str(len(titles_list))+", Length of labels:"+str(len(labels)))
x = []
for doc in corpus: # append the vector for each document
x.append(document_vector(model, doc))
"""# **After removing stop words and empty docs** <br>
---
241 docs removed <br>
1st filter: Length of corpus:422178, Length of titles_list:422178, Length of labels:422178 <br>
0 docs removed <br>
2nd filter: Length of corpus:422178, Length of titles_list:422178, Length of labels:422178 <br>
"""
vectorsForEachDocument = np.array(x) # list to array
labels = np.array(labels)
labels = labels.reshape(labels.shape[0],1)
vectorsForEachDocument.shape, labels.shape
"""# **Vectors for each title**
---
A list of vectors of 300 dimensions each for all the titles and labels contain the respective labels<br>
Shape of these vectors is (422178, 300) <br>
Shape pf it respective labels (422178, 1)
# **Filter improper labels**
---
Filter out the data that has improper labels. Labels should only be of the following types. <br>
1. b: business
2. t: technology
3. e: entertainment
4. m: health
"""
# filter out data that has improper labels
possibleLabels = ["b","t","e","m"]
finalLabels = []
features = []
for i in range(len(labels)):
if labels[i] in possibleLabels:
finalLabels.append(labels[i])
features.append(vectorsForEachDocument[i])
if(len(finalLabels)==total):
break
features = np.array(features)
labels = np.array(finalLabels)
labels = labels.reshape(labels.shape[0],1)
features.shape, labels.shape
"""# **Create a dataframe to shuffle it**
---
Create dataframe to shuffle it and split it.
"""
finalData = pd.DataFrame.from_records(features)
finalData.columns = range(1,301)
finalData["labels"] = labels
# finalData.to_csv('FinalData.csv')
data = finalData.sample(frac=1) #shuffles the data
labels = data["labels"]
labels = np.array(labels)
labels = labels.reshape(labels.shape[0],1)
del data["labels"]
features = np.array(data)
features.shape, labels.shape
"""# Split the data
---
**Train Data**: Use 80% of the data for training purpose. <br>
**Test Data**: Use 20% of the data for testing purpose. <br>
**Features**: Use 300 dimensional vectors as features. It can be found in `vectorsForEachDocument`.<br>
**Labels**: Use the categories as labels. I can be found in `labels`.<br>
"""
train = int((80/100)*len(features))
trainFeatures, testFeatures = features[:train], features[train:]
trainLabels, testLabels = labels[:train], labels[train:]
trainFeatures.shape, trainLabels.shape, testFeatures.shape, testLabels.shape
"""**Shapes of the data** <br>
trainFeatures: (160000,300) <br>
trainLabels: (160000,1) <br>
testFeatures: (40000,300) <br>
testLabels: (40000,1) <br>
# **Train the logistic regression model** <br>
"""
tic = time.time()
logistic_Regression = LogisticRegression(multi_class="auto", solver="lbfgs", max_iter=1000)
logistic_Regression.fit(trainFeatures,trainLabels)
Y_predict = logistic_Regression.predict(testFeatures)
print(str((accuracy_score(testLabels,Y_predict)*100))+"%")
toc = time.time()
print("Time taken:"+str(toc-tic)+" seconds")
"""# **Outcomes of the training** <br>
---
|Train |Test |Dimensions |Accuracy |Time(sec) | Comments |
|---|---|---|---|---|---|
|40000|10000|300|86|12|Data randomly shuffled|
|80000|20000|300|78|53||
|40000|10000|300|74|12||
|80000|20000|300|78|53||
|24000|6000|300|73|11||
|160000|40000|300|72|53||
|80000|20000|300|82.8|19.4|Data randomly shuffled|
**Precision and recall**
---
Calculating precision and recall using sklearn.metrics.precision_recall_fscore_support.<br>
Precision is the fraction of relevant instances among the retrieved instances, while recall is the fraction of the total amount of relevant instances that were actually retrieved.
"""
from sklearn.metrics import precision_recall_fscore_support as score
precision, recall, fscore, support = score(testLabels, Y_predict, labels=possibleLabels)
print("For labels in sequence as",possibleLabels)
print('precision: {}'.format(precision*100))
print('recall: {}'.format(recall*100))
"""**Result of precision and recall**
---
For labels in sequence as ['b', 't', 'e', 'm'] <br>
precision: [82.94729775 83.25883787 89.44781729 87.46594005] <br>
recall: [84.79752917 80.92417062 91.4843288 82.16723549] <br>
"""
from sklearn import metrics
from sklearn.multiclass import OneVsRestClassifier
from sklearn.metrics import roc_curve, auc
from sklearn.preprocessing import label_binarize
import matplotlib.pyplot as plt
testLabelsBinary = label_binarize(testLabels, classes=possibleLabels)
trainLabelsBinary = label_binarize(trainLabels, classes=possibleLabels)
# classifier
clf = OneVsRestClassifier(LogisticRegression(solver='sag'), n_jobs=1)
y_score = clf.fit(trainFeatures, trainLabelsBinary).decision_function(testFeatures)
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(4):
fpr[i], tpr[i], _ = roc_curve(testLabelsBinary[:, i], y_score[:, i])
roc_auc[i] = auc(fpr[i], tpr[i])
for i in range(4):
plt.figure()
plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f)' % roc_auc[i])
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
title = 'ROC for '+possibleLabels[i]
plt.title(title)
plt.legend(loc="lower right")
plt.show()
|24000|6000|300|73|11||
|160000|40000|300|72|53||
"""