def classify(classifier_type,
X_train,
Y_train=None,
X_test=None,
Y_test=None,
keywords=None,
class_map=None,
is_X_text=True):
_, Y_train = utility.binarize_data(Y_train, class_mapping=class_map)
_, Y_test = utility.binarize_data(Y_test, class_mapping=class_map)
svc_class = supervised.SupervisedClassifier.SvcClassifier()
Y_pred, train_acc, test_acc = svc_class.classify(X_train, Y_train, X_test,
Y_test)
return Y_pred, train_acc, test_acc
def keyword_driver(classifier_type,X_train,Y_train,num_of_keywords=50):
if all(isinstance(n, list) for n in X_train):
X_train = utility.get_str_from_list(X_train)
binarizer = utility.get_multilabel_binarizer(lrf_config.get_class_map())
Y_train_binary = utility.binarize_data(Y_train)
tfidf_vectorizer = utility.get_tf_idf_vectorizer(X_train)
X_tfidf = tfidf_vectorizer.transform(X_train)
model = classifier.get_classification_model(classifier_type, X_tfidf, Y_train_binary[1])
keywords = get_keywords(X_train, model, binarizer,num_of_keywords=num_of_keywords)
return keywords
def main():
fs = FeatureSelection()
data = datasets.get_news_data('keyword_data',
'annotator_data_dump_with_text')
X_data, Y_data = data['text'].values, data['category'].values
tf_idf_vectorizer = utility.get_tf_idf_vectorizer(X_data)
X_data_tf = tf_idf_vectorizer.transform(X_data)
Y_binary = utility.binarize_data(data=Y_data)
res = fs.ChiSquare.chiSquare(X_data_tf, Y_binary, 50)
for i in range(20):
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%')
print(res[0])
def main():
print("main_code goes here")
classifier_type = 'svc'
news_data = datasets.get_news_data('keyword_data',
'annotator_data_dump_with_text')
train_data, test_data = utility.split_data(news_data)
X_train = train_data['text']
Y_train = train_data['category']
binarizer = utility.get_multilabel_binarizer(lrf_config.get_class_map())
Y_train_binary = utility.binarize_data(Y_train)
tfidf_vectorizer = utility.get_tf_idf_vectorizer(X_train)
X_tfidf = tfidf_vectorizer.transform(X_train)
model = classifier.get_classification_model(classifier_type, X_tfidf,
Y_train_binary)
h = get_keywords(X_train, model, binarizer)
print(h)
def validation_model():
## Classifier Name
classifier_name = 'svc'
keyword_classifier_name = 'svc'
train_data_src = 'tweets_and_news'
num_splits = 5
## News Data
news_data = datasets.get_news_data(
folder_name='keyword_data', file_name='annotator_data_dump_with_text')
## Tweet Data
tweet_data = datasets.get_tweet_data(file_type='txt',
file_name='tweet_truth.txt')
if train_data_src == 'news':
data = news_data
field_names = ['text', 'category']
elif train_data_src == 'tweets':
data = tweet_data
field_names = ['tweet_cmplt', 'class_annotated']
elif train_data_src == 'tweets_and_news':
data = tweet_data
data_extra = news_data
field_names = ['tweet_cmplt', 'class_annotated']
field_names_extra = ['text', 'category']
kf = KFold(n_splits=num_splits)
kf.get_n_splits(data)
train_acc = []
test_acc = []
pos_f_measure = []
both_f_measure = []
pos_acc_list = []
both_acc_list = []
for train_index, test_index in kf.split(data):
X_train = data[field_names[0]].iloc[train_index]
Y_train = data[field_names[1]].iloc[train_index]
X_test = data[field_names[0]].iloc[test_index]
Y_test = data[field_names[1]].iloc[test_index]
if train_data_src == 'tweets_and_news':
X_extra = data_extra[field_names_extra[0]]
Y_extra = data_extra[field_names_extra[1]]
X_train = X_train.append(X_extra)
Y_train = Y_train.append(Y_extra)
if classifier_name in ['svc', 'lr', 'ada_boost']:
Y_predicted, curr_train_acc, curr_test_acc = classifier.classify(
classifier_name, X_train, Y_train, X_test, Y_test)
train_acc.append(curr_train_acc)
test_acc.append(curr_test_acc)
elif classifier_name == 'cosineSim':
keywords = keyword_generator.keyword_driver(
keyword_classifier_name, X_train, Y_train, num_of_keywords=50)
Y_predicted_pos, Y_predicted_both = classifier.classify(
classifier_name, X_test, keywords=keywords)
Y_test_list = []
Y_pred_both_list = []
Y_pred_pos_list = []
for i in Y_test.keys():
Y_test_list.append(Y_test.get_value(i))
Y_pred_pos_list.append(Y_predicted_pos[i])
Y_pred_both_list.append(Y_predicted_both[i])
Y_test_binary = utility.binarize_data(Y_test_list)
Y_pred_pos_binary = utility.binarize_data(Y_pred_pos_list)
Y_pred_both_binary = utility.binarize_data(Y_pred_both_list)
both_acc_list.append(
ca.calculate_accuracy(Y_predicted_both, Y_test))
both_f_measure.append(
cf.calculate_f_measure(Y_test_binary[1],
Y_pred_both_binary[1]))
pos_acc_list.append(ca.calculate_accuracy(Y_predicted_pos, Y_test))
pos_f_measure.append(
cf.calculate_f_measure(Y_test_binary[1], Y_pred_pos_binary[1]))
if classifier_name == 'svc':
print('SVC train Acc : ', mean(train_acc))
print('SVC test Acc : ', mean(test_acc))
elif classifier_name == 'cosineSim':
print('cosineSim POS Acc : ', mean(pos_acc_list))
print('cosineSim BOTH Acc : ', mean(both_acc_list))
print('cosineSim POS F : ', mean(pos_f_measure))
print('cosineSim BOTH F : ', mean(both_f_measure))
print("main_code goes here")
classifier_type = 'svc'
<<<<<<< HEAD
news_data = datasets.get_news_data('keyword_data', 'annotator_data_dump_with_text')
=======
news_data = datasets.get_news_data('keyword_data','annotator_data_dump_with_text')
>>>>>>> 9af9d4b1e982bbca90d4f5989640d5baa4cc6377
train_data,test_data = utility.split_data(news_data)
X_train = train_data['text']
Y_train = train_data['category']
binarizer = utility.get_multilabel_binarizer(lrf_config.get_class_map())
Y_train_binary = utility.binarize_data(Y_train)
tfidf_vectorizer = utility.get_tf_idf_vectorizer(X_train)
X_tfidf = tfidf_vectorizer.transform(X_train)
<<<<<<< HEAD
model = classifier.get_classification_model(classifier_type, X_tfidf, Y_train_binary)
=======
model = classifier.get_classification_model(classifier_type,X_tfidf,Y_train_binary)
>>>>>>> 9af9d4b1e982bbca90d4f5989640d5baa4cc6377
h = get_keywords(X_train,model,binarizer)
print(h)
#############################
if __name__=='__main__':
def classify_bkp(classifier_type,
X_train,
Y_train=None,
X_test=None,
Y_test=None,
keywords=None,
class_map=None,
is_X_text=True):
if (classifier_type in ['svc', 'lr', 'ada_boost']):
if Y_train is None:
raise ValueError(
classifier_type,
' is a Supervised Algorithm, pass training labels ...')
elif X_test is None and Y_test is None:
train_data = zip(X_train, Y_train)
train_data, test_data = sklearn.model_selection.train_test_split(
pd.DataFrame.from_records(train_data))
X_train, Y_train = train_data[0], train_data[1]
X_test, Y_test = test_data[0], test_data[1]
print(
'Since no TEST Data provided, splitting given data into train and test'
)
X_train = utility.get_str_from_list(X_train)
X_test = utility.get_str_from_list(X_test)
if class_map is not None:
fitted_binarizer, Y_train_binary = utility.binarize_data(
Y_train, class_mapping=class_map)
else:
fitted_binarizer, Y_train_binary = utility.binarize_data(Y_train)
if Y_test is not None:
f, Y_test_binary = utility.binarize_data(Y_test,
class_mapping=class_map)
if is_X_text == True:
tf_idf_vectorizer = utility.get_tf_idf_vectorizer(X_train)
X_train_tf_idf = tf_idf_vectorizer.transform(X_train)
X_test_tf_idf = tf_idf_vectorizer.transform(X_test)
else:
X_train_tf_idf = X_train
X_test_tf_idf = X_test
if classifier_type == 'svc':
svc_class = supervised.SupervisedClassifier.SvcClassifier()
if Y_test is not None:
Y_pred, train_acc, test_acc = svc_class.classify(
X_train_tf_idf, Y_train_binary, X_test_tf_idf,
Y_test_binary)
return Y_pred, train_acc, test_acc
else:
Y_pred, train_acc = svc_class.classify(X_train_tf_idf,
Y_train_binary,
X_test_tf_idf)
return Y_pred, train_acc
return fitted_binarizer.inverse_transform(
Y_pred), train_acc, test_acc
elif classifier_type == 'lr':
lr_class = supervised.SupervisedClassifier.LogisticRClassifier()
if Y_test is not None:
Y_pred, train_acc, test_acc = lr_class.classify(
X_train_tf_idf, Y_train_binary, X_test_tf_idf,
Y_test_binary)
return Y_pred, train_acc, test_acc
else:
Y_pred, train_acc = lr_class.classify(X_train_tf_idf,
Y_train_binary,
X_test_tf_idf)
return Y_pred, train_acc
elif classifier_type == 'ada_boost':
ada_boost_class = supervised.SupervisedClassifier.AdaBoostClassifier(
)
if Y_test is not None:
Y_pred, train_acc, test_acc = ada_boost_class.classify(
X_train_tf_idf, Y_train_binary, X_test_tf_idf,
Y_test_binary)
return Y_pred, train_acc, test_acc
else:
Y_pred, train_acc = ada_boost_class.classify(
X_train_tf_idf, Y_train_binary, X_test_tf_idf)
return Y_pred, train_acc
elif classifier_type == 'cosineSim':
cosine_sim_class = unsupervised.UnsupervisedClassifiers.CosineSimilarity(
)
Y_pred_pos, Y_pred_both = cosine_sim_class.classify(
X_train, keywords, vector_type='word_embeddings')
return Y_pred_pos, Y_pred_both
def main():
news_dict = datasets.get_news_data(
folder_name='keyword_data', file_name='annotator_data_dump_with_text')
# train_data, test_data = utility.split_data(news_dict)
category_names = ['text', 'category']
category_names_news = ['text', 'category']
# X = news_dict[category_names[0]]
# y = news_dict[category_names[1]]
twitter_dict = datasets.get_tweet_data('txt', 'tweet_truth.txt')
kf = KFold(n_splits=5)
kf.get_n_splits(twitter_dict)
some_dict = {}
train_acc = []
test_acc = []
acc_both = []
f_both = []
acc_pos = []
f_pos = []
ada_test_list = []
ada_train_list = []
for train_index, test_index in kf.split(twitter_dict):
print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')
X_train = twitter_dict['tweet_cmplt'].iloc[train_index]
Y_train = twitter_dict['class_annotated'].iloc[train_index]
X_test = twitter_dict['tweet_cmplt'].iloc[test_index]
Y_test = twitter_dict['class_annotated'].iloc[test_index]
news_train = news_dict['text']
news_class = news_dict['category']
some_dict['tweet_cmplt'] = X_train.append(news_train)
some_dict['class_annotated'] = Y_train.append(news_class)
ada_predicted, ada_train_acc, ada_test_acc = classify(
'ada_boost', some_dict['tweet_cmplt'],
some_dict['class_annotated'], X_test, Y_test)
ada_train_list.append(ada_train_acc)
ada_test_list.append(ada_test_acc)
print('ada_train_list : ', ada_train_list)
print('ada_test_list : ', ada_test_list)
keywords = keyword_generator.keyword_driver(
'svc',
some_dict, ['tweet_cmplt', 'class_annotated'],
num_of_keywords=50)
for item in keywords:
print(item, ' : ', keywords[item])
predicted, curr_train_acc, curr_test_acc = classify(
'svc', some_dict['tweet_cmplt'], some_dict['class_annotated'],
X_test, Y_test)
train_acc.append(curr_train_acc)
test_acc.append(curr_test_acc)
exit(0)
print('train_acc SVC: ', train_acc)
print('test_acc SVC: ', test_acc)
# Y_pred_pos, Y_pred_both = classify('cosineSim', X_test ,keywords = keywords)
Y_test_list = []
Y_pred_both_list = []
Y_pred_pos_list = []
for i in Y_test.keys():
Y_test_list.append(Y_test.get_value(i))
Y_pred_pos_list.append(Y_pred_pos[i])
Y_pred_both_list.append(Y_pred_both[i])
Y_test_binary = utility.binarize_data(Y_test_list)
Y_pred_pos_binary = utility.binarize_data(Y_pred_pos_list)
Y_pred_both_binary = utility.binarize_data(Y_pred_both_list)
acc_both.append(ca.calculate_accuracy(Y_pred_both, Y_test))
f_both.append(
cf.calculate_f_measure(Y_test_binary[1], Y_pred_both_binary[1]))
acc_pos.append(ca.calculate_accuracy(Y_pred_pos, Y_test))
f_pos.append(
cf.calculate_f_measure(Y_test_binary[1], Y_pred_pos_binary[1]))
print('################################ BOTH')
print('acc_both : ', mean(acc_both))
print('f_both : ', mean(f_both))
print('################################ POS')
print('acc_pos : ', mean(acc_pos))
print('f_pos : ', mean(f_pos))
print('############################### SVC')
print('Train_Accuracy : ', mean(train_acc))
print('Test_Accuracy : ', mean(test_acc))
print('############################### ADA_Boost')
print('Train_Accuracy : ', mean(ada_train_list))
print('Test_Accuracy : ', mean(ada_test_list))
exit(0)
# TWEET DATA
twitter_dict = datasets.get_tweet_data('txt', 'tweet_truth.txt')
train_data, test_data = utility.split_data(twitter_dict)
category_names = ['tweet_cmplt', 'class_annotated']
#category_names_tweet = ['tweet_word_list', 'class_annotated']
predicted_data, train_acc, test_acc = classify(
'lr', train_data[category_names[0]], train_data[category_names[1]],
test_data[category_names[0]], test_data[category_names[1]])
#predicted_data, train_acc, test_acc = classify('svc', news_dict[category_names_news[0]], news_dict[category_names_news[1]],
# twitter_dict[category_names_tweet[0]], twitter_dict[category_names_tweet[1]])
# print(predicted_data)
print('train_acc : ', train_acc)
print('test_acc : ', test_acc)