/usr/bin/python2.7 /home/ubuntu/PycharmProjects/w2gm-EXP-FINAL/word2gm-master_weightedz/ste_d2vc_scws_similarity.py
/usr/lib/python2.7/dist-packages/sklearn/ensemble/weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.
  from numpy.core.umath_tests import inner1d
2021-07-14 06:52:44.732414: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
/usr/lib/python2.7/dist-packages/sklearn/feature_extraction/text.py:1015: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  if hasattr(X, 'dtype') and np.issubdtype(X.dtype, np.float):
Creating word-idf dictionary for Training set...
Returning with 'prob_wordvecs' dictionary
('Train News Covered : ', 1000)
('Train News Covered : ', 2000)
('Train News Covered : ', 3000)
('Train News Covered : ', 4000)
('Train News Covered : ', 5000)
('Train News Covered : ', 6000)
('Train News Covered : ', 7000)
('Train News Covered : ', 8000)
('Train News Covered : ', 9000)
('Train News Covered : ', 10000)
('Train News Covered : ', 11000)
('Test News Covered : ', 1000)
('Test News Covered : ', 2000)
('Test News Covered : ', 3000)
('Test News Covered : ', 4000)
('Test News Covered : ', 5000)
('Test News Covered : ', 6000)
('Test News Covered : ', 7000)
##########Print***Train***document**vector##############
('Train Document vector gwbowv:', array([[-0.00671967, -0.00157579,  0.0083263 , ..., -0.00047037,
         0.00924711,  0.00808237],
       [-0.00942505, -0.00244728,  0.00681253, ...,  0.00052145,
         0.01043856,  0.00792751],
       [-0.00597199, -0.00841104,  0.01014253, ...,  0.00060094,
         0.00785822,  0.00726783],
       ...,
       [-0.00665005, -0.00452198,  0.00657836, ..., -0.00420706,
         0.01144203,  0.00721412],
       [-0.0075092 , -0.00946011,  0.01359873, ..., -0.00418255,
         0.01262047,  0.00391075],
       [-0.0109271 ,  0.012833  ,  0.01232105, ..., -0.00497324,
         0.01029183,  0.00544472]], dtype=float32))
##########Print***Test***document**vector##############
('Test Document vector gwbowv:', array([[-0.00530167, -0.00605297,  0.0115642 , ...,  0.00133717,
         0.00915795,  0.00939436],
       [-0.00346684, -0.00271784,  0.00884042, ...,  0.00051374,
         0.00296905,  0.00515583],
       [ 0.00442618, -0.02957401,  0.01320041, ...,  0.00063304,
        -0.0010341 ,  0.00161021],
       ...,
       [-0.00362636, -0.00846389,  0.01234476, ...,  0.00406692,
         0.01077572,  0.0122564 ],
       [-0.00552996, -0.00548152,  0.00576873, ..., -0.00153271,
         0.00480329,  0.00110039],
       [ 0.00217831, -0.02804639,  0.01652036, ..., -0.00106111,
        -0.00734639,  0.00868065]], dtype=float32))
Fitting a SVM classifier on labeled training data...
('# Tuning hyper-parameters for', 'accuracy', '\n')
Best parameters set found on development set:

{'C': 6.900000000000001}
('Best value for ', 'accuracy', ':\n')
0.8659183312709917
Report
             precision    recall  f1-score   support

          0   0.718354  0.711599  0.714961       319
         10   0.685366  0.722365  0.703379       389
         20   0.690073  0.723350  0.706320       394
         30   0.679487  0.676020  0.677749       392
         40   0.770574  0.802597  0.786260       385
         50   0.796610  0.713924  0.753004       395
         60   0.797531  0.828205  0.812579       390
         70   0.870466  0.848485  0.859335       396
         80   0.927500  0.932161  0.929825       398
         90   0.933842  0.924433  0.929114       397
        100   0.948529  0.969925  0.959108       399
        110   0.905473  0.919192  0.912281       396
        120   0.712329  0.661578  0.686016       393
        130   0.875931  0.891414  0.883605       396
        140   0.879902  0.911168  0.895262       394
        150   0.768722  0.876884  0.819249       398
        160   0.713043  0.901099  0.796117       364
        170   0.934097  0.867021  0.899310       376
        180   0.753247  0.561290  0.643253       310
        190   0.668478  0.490040  0.565517       251

avg / total   0.805829  0.805762  0.803700      7532

/usr/lib/python2.7/dist-packages/sklearn/grid_search.py:418: ChangedBehaviorWarning: The long-standing behavior to use the estimator's score function in GridSearchCV.score has changed. The scoring parameter is now used.
  ChangedBehaviorWarning)
('Accuracy: ', 0.8057620817843866)
('Time taken:', 19434.412741184235, '\n')
('# Tuning hyper-parameters for', 'recall_micro', '\n')
Best parameters set found on development set:

{'C': 6.900000000000001}
('Best value for ', 'recall_micro', ':\n')
0.8659183312709917
Report
             precision    recall  f1-score   support

          0   0.718354  0.711599  0.714961       319
         10   0.685366  0.722365  0.703379       389
         20   0.690073  0.723350  0.706320       394
         30   0.679487  0.676020  0.677749       392
         40   0.770574  0.802597  0.786260       385
         50   0.796610  0.713924  0.753004       395
         60   0.797531  0.828205  0.812579       390
         70   0.870466  0.848485  0.859335       396
         80   0.927500  0.932161  0.929825       398
         90   0.933842  0.924433  0.929114       397
        100   0.948529  0.969925  0.959108       399
        110   0.905473  0.919192  0.912281       396
        120   0.712329  0.661578  0.686016       393
        130   0.875931  0.891414  0.883605       396
        140   0.879902  0.911168  0.895262       394
        150   0.768722  0.876884  0.819249       398
        160   0.713043  0.901099  0.796117       364
        170   0.934097  0.867021  0.899310       376
        180   0.753247  0.561290  0.643253       310
        190   0.668478  0.490040  0.565517       251

avg / total   0.805829  0.805762  0.803700      7532

('Accuracy: ', 0.8057620817843866)
('Time taken:', 19429.888288021088, '\n')
('# Tuning hyper-parameters for', 'f1_micro', '\n')
Best parameters set found on development set:

{'C': 6.900000000000001}
('Best value for ', 'f1_micro', ':\n')
0.8659183312709917
Report
             precision    recall  f1-score   support

          0   0.718354  0.711599  0.714961       319
         10   0.685366  0.722365  0.703379       389
         20   0.690073  0.723350  0.706320       394
         30   0.679487  0.676020  0.677749       392
         40   0.770574  0.802597  0.786260       385
         50   0.796610  0.713924  0.753004       395
         60   0.797531  0.828205  0.812579       390
         70   0.870466  0.848485  0.859335       396
         80   0.927500  0.932161  0.929825       398
         90   0.933842  0.924433  0.929114       397
        100   0.948529  0.969925  0.959108       399
        110   0.905473  0.919192  0.912281       396
        120   0.712329  0.661578  0.686016       393
        130   0.875931  0.891414  0.883605       396
        140   0.879902  0.911168  0.895262       394
        150   0.768722  0.876884  0.819249       398
        160   0.713043  0.901099  0.796117       364
        170   0.934097  0.867021  0.899310       376
        180   0.753247  0.561290  0.643253       310
        190   0.668478  0.490040  0.565517       251

avg / total   0.805829  0.805762  0.803700      7532

('Accuracy: ', 0.8057620817843866)
('Time taken:', 19490.19131207466, '\n')
('# Tuning hyper-parameters for', 'precision_micro', '\n')
