corpus_name = get_file_path('cn_corpus')
logger.info(r"loading corpus from : " + corpus_name)
lexicon_name = get_file_path('lexicon')
logger.info(r"loading lexicon form : " + lexicon_name)
expand_name = get_file_path('neural_cand')
logger.info(r"loading expand_word from : " + expand_name)
mark_name = get_file_path('mark')
logger.info(r"loading mark from : " + mark_name)
corpus = load_corpus(corpus_name)
lexicon = load_lexicon(lexicon_name)
mark = load_mark(mark_name)
# log_state('use extend lexicon')
lexicon = combine_lexicon(lexicon_name, expand_name)
log_state('mean')
evaluate_mean(corpus, lexicon, mark)
log_state('tf_mean')
evaluate_tf_mean(corpus, lexicon, mark)
log_state('tfidf_mean')
evaluate_tfidf_mean(corpus, lexicon, mark)
log_state('geo')
evaluate_geo(corpus, lexicon, mark)
log_state('tfidf_geo')
evaluate_tfidf_geo(corpus, lexicon, mark)
log_state('tf_geo')
vocab = get_vocab(corpus)
dump_picle(vocab, get_file_path('CVAT_Vocab'))
print('Dump CVAT vocab OK')
# vocab = load_pickle(get_file_path('CVAT_Vocab'))
for i in vocab:
print(i)
print(len(vocab))
W, word_idx_map = build_embedding_matrix(load_embeddings('zh_tw'), vocab, k=400)
dump_picle(word_idx_map, get_file_path('word_idx_map_CVAT'))
print('dump word_idx_map successful')
dump_picle(W, './data/tmp/embedding_matrix_CVAT.p')
print('OK')
# word_idx_map = load_pickle(get_file_path('word_idx_map_CVAT'))
mark = load_mark(get_file_path('mark'))
valence, arousal = gold_valence_arousal(corpus, mark)
idx_data = make_idx_data(corpus, word_idx_map, max_len=200, kernel_size=5)
dump_picle([idx_data, valence, arousal], get_file_path('CVAT_processed_data'))
# idx_data, valence, arousal = load_pickle(get_file_path('CVAT_processed_data'))
print(idx_data.shape)
exit()
word_vecs = load_embeddings('zh_tw')
dim = len(word_vecs['我們']) # 400
embedding_matrix, idx_map = build_embedding_matrix(word_vecs, k=dim)
print(embedding_matrix[1])
print(idx_map['我們'])
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(data, target, test_size=0.1, random_state=10)
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='ordinary_least_squares')
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='Ridge_Regression')
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='Bayesian_Regression')
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='SVR')
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='KNN_Reg')
if __name__ == '__main__':
normalize = True
corpus = load_corpus(get_file_path('cn_corpus'))
# lexicon = load_lexicon(get_file_path('lexicon'))
mark = load_mark(get_file_path('mark'))
lexicon = combine_lexicon(get_file_path('lexicon'), get_file_path('neural_cand'))
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion(corpus, lexicon, mark)
print('start.....')
cv(valence_mean, valence_true, multivariant=False)
cv(arousal_mean, arousal_true, multivariant=False)
print('OK')
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_sqr(corpus, lexicon, mark)
print('start.....')
def cv(data, target, multivariant=False):
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(
data, target, test_size=0.2, random_state=0)
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train,
X_test,
Y_train,
Y_test,
cost_fun='Ridge_Regression')
normalize = True
corpus = load_corpus(get_file_path('cn_corpus'))
lexicon = load_lexicon(get_file_path('normalized_onezero_lexicon'))
mark = load_mark(get_file_path('normalized_onezero_mark'))
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_sqr(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = nonlinear_max_fusion(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tf(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tfidf(corpus, lexicon, mark)
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo(
corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tf(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tfidf(corpus, lexicon, mark)
from positive_negative_split import get_pos_neg_va
def cv(data, target, multivariant=False):
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(data, target, test_size=0.2,
random_state=0)
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun='Ridge_Regression')
normalize = True
corpus = load_corpus(get_file_path('cn_corpus'))
lexicon = load_lexicon(get_file_path('normalized_onezero_lexicon'))
mark = load_mark(get_file_path('normalized_onezero_mark'))
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_sqr(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = nonlinear_max_fusion(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tf(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_tfidf(corpus, lexicon, mark)
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tf(corpus, lexicon, mark)
# valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_geo_tfidf(corpus, lexicon, mark)
X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(data, target, test_size=0.1, random_state=10)
if multivariant is False:
linear_regression(X_train, X_test, Y_train, Y_test, plot=False)
else:
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun="ordinary_least_squares")
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun="Ridge_Regression")
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun="Bayesian_Regression")
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun="SVR")
linear_regression_multivariant(X_train, X_test, Y_train, Y_test, cost_fun="KNN_Reg")
if __name__ == "__main__":
normalize = True
corpus = load_corpus(get_file_path("cn_corpus"))
# lexicon = load_lexicon(get_file_path('lexicon'))
mark = load_mark(get_file_path("mark"))
lexicon = combine_lexicon(get_file_path("lexicon"), get_file_path("neural_cand"))
# # the following could use to check the same words in corpus and lexicon
# from visualization import show_common_term
# show_common_term(corpus, lexicon)
# exit()
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion(corpus, lexicon, mark)
print("start.....")
cv(valence_mean, valence_true, multivariant=False)
cv(arousal_mean, arousal_true, multivariant=False)
print("OK")
valence_mean, valence_true, arousal_mean, arousal_true = linear_fusion_sqr(corpus, lexicon, mark)
print("start.....")
corpus_name = get_file_path('cn_corpus')
logger.info(r"loading corpus from : " + corpus_name)
lexicon_name = get_file_path('lexicon')
logger.info(r"loading lexicon form : " + lexicon_name)
expand_name = get_file_path('neural_cand')
logger.info(r"loading expand_word from : " + expand_name)
mark_name = get_file_path('mark')
logger.info(r"loading mark from : " + mark_name)
corpus = load_corpus(corpus_name)
lexicon = load_lexicon(lexicon_name)
mark = load_mark(mark_name)
# log_state('use extend lexicon')
lexicon = combine_lexicon(lexicon_name, expand_name)
log_state('mean')
evaluate_mean(corpus, lexicon, mark)
log_state('tf_mean')
evaluate_tf_mean(corpus, lexicon, mark)
log_state('tfidf_mean')
evaluate_tfidf_mean(corpus, lexicon, mark)
log_state('geo')
evaluate_geo(corpus, lexicon, mark)
log_state('tfidf_geo')
evaluate_tfidf_geo(corpus, lexicon, mark)
log_state('tf_geo')
