train = count_vect.transform(train_list)
test = count_vect.transform(test_list)
return train, test, count_vect.get_feature_names()
set = 1
if __name__ == '__main__':
if set == 1:
articleList, commentList, parentList, commentCount = read_comments(comment_data_path + 'trainTestDataSet.txt', skip_mtn=False)
elif set == 2:
articleList, commentList, parentList, commentCount = read_toy_comments(comment_data_path + 'trainTestDataSet.txt', comment_data_path + 'toyComments.csv')
elif set == 3:
articleList, commentList, commentCount = read_slashdot_comments(comment_data_path + 'slashdotDataSet.txt', limit=100000)
# Values
y = extract_values(articleList, commentList, commentCount, set)
sss = StratifiedShuffleSplit(y, 1, test_size=0.95, random_state=42)
y_train = []
y_test = []
for train, test in sss:
np.save('train_vect', train)
np.save('test_vect', test)
y_train = y[train]
y_test = y[test]
processed_comment_list = extract_global_bag_of_words_processed(commentList)
train_v, test_v = np.load('train_vect.npy'), np.load('test_vect.npy')
train_list = []
test_list = []
for v in train_v:
train_list.append(processed_comment_list[v])
def extractSaveValues(df_comments, filename, datatype):
valueVector = extract_values(df_comments, datatype)
print "Extracted values"
save_numpy_matrix(filename, valueVector)
def extractSaveValues(articleList, commentList, commentCount, filename,
datatype):
valueVector = extract_values(articleList, commentList, commentCount,
datatype)
print "Extracted values"
save_numpy_matrix(filename, valueVector)
set = 1
if __name__ == '__main__':
if set == 1:
articleList, commentList, parentList, commentCount = read_comments(
comment_data_path + 'trainTestDataSet.txt', skip_mtn=False)
elif set == 2:
articleList, commentList, parentList, commentCount = read_toy_comments(
comment_data_path + 'trainTestDataSet.txt',
comment_data_path + 'toyComments.csv')
elif set == 3:
articleList, commentList, commentCount = read_slashdot_comments(
comment_data_path + 'slashdotDataSet.txt', limit=100000)
# Values
y = extract_values(articleList, commentList, commentCount, set)
sss = StratifiedShuffleSplit(y, 1, test_size=0.95, random_state=42)
y_train = []
y_test = []
for train, test in sss:
np.save('train_vect', train)
np.save('test_vect', test)
y_train = y[train]
y_test = y[test]
processed_comment_list = extract_global_bag_of_words_processed(commentList)
train_v, test_v = np.load('train_vect.npy'), np.load('test_vect.npy')
train_list = []
test_list = []
for v in train_v:
train_list.append(processed_comment_list[v])
def extractSaveValues(articleList, commentList, commentCount, filename, datatype):
valueVector = extract_values(articleList, commentList, commentCount, datatype)
print "Extracted values"
save_numpy_matrix(filename, valueVector)
test = count_vect.transform(test_list)
#print count_vect.get_feature_names()[1000:1010]
#print count_vect.get_feature_names()
print "Train:", train.shape
print "Test:", test.shape
print count_vect.vocabulary_
return train, test, count_vect.get_feature_names()
if __name__ == '__main__':
articleList, commentList, parentList, commentCount = read_comments(comment_data_path + 'trainTestDataSet.txt')
# Values
y = extract_values(articleList, commentList, parentList, commentCount)[:, 3]
sss = StratifiedShuffleSplit(y, 1, test_size=0.40, random_state=42)
y_train = []
y_test = []
for train, test in sss:
print train
np.save('train_vect', train)
np.save('test_vect', test)
y_train = y[train]
y_test = y[test]
processed_comment_list = extract_global_bag_of_words_processed(commentList)
train_v, test_v = np.load('train_vect.npy'), np.load('test_vect.npy')
train_list = []
test_list = []
for v in train_v:
#print count_vect.get_feature_names()[1000:1010]
#print count_vect.get_feature_names()
print "Train:", train.shape
print "Test:", test.shape
print count_vect.vocabulary_
return train, test, count_vect.get_feature_names()
if __name__ == '__main__':
articleList, commentList, parentList, commentCount = read_comments(
comment_data_path + 'trainTestDataSet.txt')
# Values
y = extract_values(articleList, commentList, parentList, commentCount)[:,
3]
sss = StratifiedShuffleSplit(y, 1, test_size=0.40, random_state=42)
y_train = []
y_test = []
for train, test in sss:
print train
np.save('train_vect', train)
np.save('test_vect', test)
y_train = y[train]
y_test = y[test]
processed_comment_list = extract_global_bag_of_words_processed(commentList)
train_v, test_v = np.load('train_vect.npy'), np.load('test_vect.npy')
train_list = []
test_list = []
for v in train_v:
