__author__ = "hs"
__author__ = "hs"
__author__ = "NLP-PC"
import feature_generating
import classifiers
import analysis
from load_data import load_train_data, load_processed_data
from load_data import load_test_data
from save_data import dump_picle
from vectorizers import TFIDF_estimator, anew_estimator
from analysis import analysis_result
from classifiers import mNB
from load_data import load_selected_data
print("Start")
vectorizer = TFIDF_estimator()
texts, train_labels = load_selected_data(data_type="train")
transformed_train = vectorizer.fit_transform(texts)
testdata, true_labels = load_selected_data(data_type="test")
transformed_test = vectorizer.transform(testdata)
predict = mNB(transformed_train, train_labels, transformed_test)
analysis_result(predict, true_labels)
occur_times = (word_count >= 1).sum()
if occur_times > 0:
avg = np.average(np.array(valence)[word_count >= 1])
else:
avg = -1
texts_scores.append(avg)
return texts_scores
if __name__ == '__main__':
from load_data import load_selected_data, load_anew, load_extend_anew
# # print(count_matching(texts, words))
texts, labels = load_selected_data(data_type='train', stem=False)
words, valence, _ = load_anew()
# select_matching(texts,labels, words)
# exit()
words, valence = np.array(words), np.array(valence)
words_pos, valence_pos = words[valence > np.average(valence)], valence[
valence > np.average(valence)] # avg = 5.1511713456
words_neg, valence_neg = words[valence < np.average(valence)], valence[
valence < np.average(valence)] # avg = 5.1511713456
pos = avg_valence(texts, words_pos, valence_pos)
neg = avg_valence(texts, words_neg, valence_neg)
from visualization import draw_scatter_with_color
word_count = np.array(word_count)
occur_times = (word_count >= 1).sum()
if occur_times > 0:
avg = np.average(np.array(valence)[word_count >= 1])
else:
avg = -1
texts_scores.append(avg)
return texts_scores
if __name__ == '__main__':
from load_data import load_selected_data, load_anew, load_extend_anew
# # print(count_matching(texts, words))
texts, labels = load_selected_data(data_type='train', stem=False)
words, valence, _ = load_anew()
# select_matching(texts,labels, words)
# exit()
words, valence = np.array(words), np.array(valence)
words_pos, valence_pos = words[valence > np.average(valence)], valence[
valence > np.average(valence)] # avg = 5.1511713456
words_neg, valence_neg = words[valence < np.average(valence)], valence[
valence < np.average(valence)] # avg = 5.1511713456
pos = avg_valence(texts, words_pos, valence_pos)
neg = avg_valence(texts, words_neg, valence_neg)
from visualization import draw_scatter_with_color
__author__ = 'hs'
__author__ = 'hs'
__author__ = 'NLP-PC'
import feature_generating
import classifiers
import analysis
from load_data import load_train_data, load_processed_data
from load_data import load_test_data
from save_data import dump_picle
from vectorizers import TFIDF_estimator, anew_estimator
from analysis import analysis_result
from classifiers import mNB
from load_data import load_selected_data
print('Start')
vectorizer = TFIDF_estimator()
texts, train_labels = load_selected_data(data_type='train')
transformed_train = vectorizer.fit_transform(texts)
testdata, true_labels = load_selected_data(data_type='test')
transformed_test = vectorizer.transform(testdata)
predict = mNB(transformed_train, train_labels, transformed_test)
analysis_result(predict, true_labels)
