def get_corpus_contend_thread(process_index,
file_list,
word2index,
write_path="/home1/yk/wikipedia_dataset/filter",
word_kind_limit=50,
remove_stopwords=False,
stem_words=True,
remove_html=True):
corpus_contend = []
for file_iter, file_name in enumerate(file_list):
tem_data = get_real_word_list(file_name, word2index, word_kind_limit,
remove_stopwords, stem_words,
remove_html)
# print(file_name, 'read ready~', len(tem_data))
corpus_contend.extend(tem_data)
if (file_iter + 1) % 10 == 0:
print((file_iter + 1), 'file done.')
if (file_iter + 1) % 100 == 0:
name = process_index + "process_" + str(file_iter +
1) + "iter_text.csv"
CsvUtility.write_norm_array2csv(corpus_contend, write_path,
name)
corpus_contend = []
print(process_index, 'finish~')
return corpus_contend
def __read_phi_alpha_theta_byname(self, name):
plsa = ""
percent = ""
if self.PLSA:
plsa = "_PLSA"
if self.corpus_percent != 1.0:
percent = "_" + str(self.corpus_percent) + "percent"
alpha = CsvUtility.read_array_from_csv(
self.doc_path,
name + 'alpha_' + str(self.topic_num) + plsa + percent + '.csv')
phi = CsvUtility.read_array_from_csv(
self.doc_path,
name + 'phi_' + str(self.topic_num) + plsa + percent + '.csv')
# theta = CsvUtility.read_array_from_csv(self.output_path, name+'theta_' + str(self.topic_num) + plsa + percent + '.csv')
return alpha, phi
def get_filter_data(path):
get_con = []
for process_index in range(1):
for file_iter in range(6):
name = str(process_index) + "process_" + str(file_iter +
1) + "00iter_text.csv"
content = CsvUtility.read_norm_array_csv(path, name)
# print(len(content))
get_con.extend(content)
print(" content number : ", len(get_con))
return get_con[:100000]
def __change_Movie_word_index__(self, gamma, word2index_pickle):
feature_word2id = CsvUtility.read_pickle(word2index_pickle, 'r')
print 'feature size: ', len(feature_word2id)
change_index_result = np.zeros((gamma.shape[0], len(feature_word2id)))
for j in range(gamma.shape[1]):
new_index = feature_word2id[self.dictionary.__getitem__(j)]
for i in range(gamma.shape[0]):
change_index_result[i][new_index] += gamma[i][j]
if j % 1000 == 0:
print j, 'line'
print 'after changing the size of result: ', change_index_result.shape
return change_index_result
def reload_mimic_embedding(train_percent=MIMICP.train_percent, valid=False, file_path=MIMICP.mimic_data_path, seq_num=MIMICP.seq_num, embedding_type=EMBEDP.embedding_type, veclen=EMBEDP.veclen, window=EMBEDP.window):
if embedding_type == 'sg_add_sgns' or embedding_type == 'sg_cancat_sgns':
embedding_name = 'lda_sgns' + str(veclen) + '_window' + str(window)
else:
embedding_name = embedding_type + str(veclen) + '_window' + str(window)
train_x = CsvUtility.read_array_from_csv(file_path, 'formal_train_x_seq_' + embedding_name + '.csv')
train_y = CsvUtility.read_array_from_csv(file_path, 'formal_train_y_seq_' + embedding_name + '.csv')
valid_x = CsvUtility.read_array_from_csv(file_path, 'formal_valid_x_seq_' + embedding_name + '.csv')
valid_y = CsvUtility.read_array_from_csv(file_path, 'formal_valid_y_seq_' + embedding_name + '.csv')
test_x = CsvUtility.read_array_from_csv(file_path, 'formal_test_x_seq_' + embedding_name + '.csv')
test_y = CsvUtility.read_array_from_csv(file_path, 'formal_test_y_seq_' + embedding_name + '.csv')
train_x = train_x.reshape((train_x.shape[0], seq_num, -1))
valid_x = valid_x.reshape((valid_x.shape[0], seq_num, -1))
test_x = test_x.reshape((test_x.shape[0], seq_num, -1))
if embedding_type == 'sg_add_sgns' or embedding_type == 'sg_cancat_sgns':
embedding_name = 'embedding_skipgram' + str(veclen) + '_window' + str(window)
train_x_sg = CsvUtility.read_array_from_csv(file_path, 'formal_train_x_seq_' + embedding_name + '.csv')
valid_x_sg = CsvUtility.read_array_from_csv(file_path, 'formal_valid_x_seq_' + embedding_name + '.csv')
test_x_sg = CsvUtility.read_array_from_csv(file_path, 'formal_test_x_seq_' + embedding_name + '.csv')
train_x_sg = train_x_sg.reshape((train_x_sg.shape[0], seq_num, -1))
valid_x_sg = valid_x_sg.reshape((valid_x_sg.shape[0], seq_num, -1))
test_x_sg = test_x_sg.reshape((test_x_sg.shape[0], seq_num, -1))
if embedding_type == 'sg_add_sgns':
train_x = train_x + train_x_sg
valid_x = valid_x + valid_x_sg
test_x = test_x + test_x_sg
if embedding_type == 'sg_cancat_sgns':
train_x = np.concatenate((train_x, train_x_sg), axis=2)
valid_x = np.concatenate((valid_x, valid_x_sg), axis=2)
test_x = np.concatenate((test_x, test_x_sg), axis=2)
if valid:
test_x = valid_x
test_y = valid_y
else:
train_x = np.concatenate((train_x, valid_x), axis=0)
train_y = np.concatenate((train_y, valid_y), axis=0)
if train_percent < 0.8:
new_training_size = int((train_x.shape[0] + test_x.shape[0]) * train_percent)
train_x = train_x[:new_training_size]
train_y = train_y[:new_training_size]
return train_x, train_y, test_x, test_y
def save_results(net, sita, result_epoch, time_epochs, run_p):
save_path = run_p.save_path
save_name = run_p.save_name
time_code = '#' + time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime()) + '#_'
used_params = [run_p]
if run_p.mimic0_movie1_wiki2 == 0:
used_params.append(MIMICP)
else:
used_params.append(MOVIEP)
if run_p.onehot0_embedding != 0:
used_params.append(EMBEDP)
if run_p.lm_lda_l2 == 0:
used_params.append(LSTMP)
used_params.append(LDAP)
used_params.append(ldaregP)
elif run_p.lm_lda_l2 == 1:
used_params.append(LSTMP)
elif run_p.lm_lda_l2 == 2:
used_params.append(MLPP)
used_params.append(LDAP)
used_params.append(ldaregP)
else:
used_params.append(MLPP)
for param_item in used_params:
param_item.save_self(save_path,
time_code + 'params_' + save_name + '.csv')
# save net
torch.save(net, save_path + time_code + 'lstm_model_' + save_name + '.pkl')
# save topic distribution
if sita.ndim > 1:
CsvUtility.write_array2csv(sita, save_path,
time_code + 'sita_' + save_name + '.csv')
# save results
metric_result, aucs = __split_metrics(result_epoch)
CsvUtility.write_list2csv(metric_result, save_path,
time_code + 'metrics_' + save_name + '.csv')
if len(aucs) > 0:
CsvUtility.write_array2csv(aucs, save_path,
time_code + 'aucs_' + save_name + '.csv')
# save time consuming
CsvUtility.write_array2csv(time_epochs, save_path,
time_code + 'time_epochs_' + save_name + '.csv')
def _load_and_process_metadata(sentence_dir,
movie_review_path,
num_processor=8):
# Extract the filenames.
sentence_filenames = glob.glob(os.path.join(sentence_dir, "*/*"))
print(len(sentence_filenames))
# print(sentence_filenames[-10:])
# sentence_filenames = sentence_filenames[-20:]
word2index = CsvUtility.read_pickle(
movie_review_path + '/new_word2index.pkl', 'r')
index2word = CsvUtility.read_pickle(
movie_review_path + '/new_index2word.pkl', 'r')
# Break the files into num_threads batches.
spacing = np.linspace(0, len(sentence_filenames),
num_processor + 1).astype(np.int)
ranges = []
for i in xrange(len(spacing) - 1):
ranges.append([spacing[i], spacing[i + 1]])
p = Pool(num_processor)
res = []
for i in range(num_processor):
start = ranges[i][0]
end = ranges[i][1]
res.append(
p.apply_async(get_corpus_contend_thread,
args=(str(i), sentence_filenames[start:end],
word2index)))
print(str(i) + ' processor started !')
# get_corpus_contend(sentence_filenames, word2index)
p.close()
p.join()
def read_topic_distrib(self):
if self.mimic_movie_wiki == 0:
name = "MIMIC"
elif self.mimic_movie_wiki == 1:
name = "MovieReview"
else:
name = "Wiki"
plsa = ""
if self.PLSA:
plsa = "_PLSA"
percent = ""
if self.corpus_percent != 1.0:
percent = "_" + str(self.corpus_percent) + "percent"
topic_distrib = CsvUtility.read_array_from_csv(
self.output_path, name + '_topic_distrib_' + str(self.topic_num) +
plsa + percent + '.csv')
return topic_distrib
def cluster_neurons(
neuron_labels,
base_path="/home1/yk/experiments_TKDE/major_revision/",
sita_file="#2020-11-21 03_33_04#_sita_mimic_mlp_ldareg_1layer_0.8.csv",
cluster_num=10):
sita = CsvUtility.read_array_from_csv(base_path, sita_file)
# print(sita[:3])
# print(sita.shape)
sc = SpectralClustering(cluster_num,
assign_labels='discretize',
random_state=random.randint(0, 10))
sc.fit(sita)
# print(sc.labels_)
label_cluster_matrix = np.zeros((80, cluster_num))
for i, cluster in enumerate(sc.labels_):
neuron_i_labels = neuron_labels[i]
for nil in neuron_i_labels:
label_cluster_matrix[nil][cluster] += 1
return label_cluster_matrix, sc.labels_
def cal_common_label_ratio(
neuron_labels,
base_path="/home1/yk/experiments_TKDE/major_revision/",
sita_file="#2020-11-21 03_33_04#_sita_mimic_mlp_ldareg_1layer_0.8.csv"
):
sita = CsvUtility.read_array_from_csv(base_path, sita_file)
sita_cos_sim = cosine_similarity(sita)
consistent_neurons_num = 0
for neuron_i, neuron_sims in enumerate(sita_cos_sim):
top_k, top_v = get_list_sort_index(neuron_sims, 4)
top_k_label_count = {}
for top_k_i in top_k:
k_i_labels = neuron_labels[top_k_i]
for l in k_i_labels:
top_k_label_count[l] = top_k_label_count.setdefault(l, 0) + 1
for label_count_val in top_k_label_count.values():
if label_count_val >= 3:
consistent_neurons_num += 1
break
return consistent_neurons_num, consistent_neurons_num * 1.0 / len(
neuron_labels)
def get_some_instance(file_path=MIMICP.mimic_data_path, seq_num=MIMICP.seq_num, num=10):
train_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_train_x_seq.npz')
train_x = train_x.reshape((train_x.shape[0], seq_num, -1))
valid_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_valid_x_seq.npz')
valid_x = valid_x.reshape((valid_x.shape[0], seq_num, -1))
test_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_test_x_seq.npz')
test_x = test_x.reshape((test_x.shape[0], seq_num, -1))
train_y = CsvUtility.read_array_from_csv(file_path, 'formal_train_y_seq.csv')
valid_y = CsvUtility.read_array_from_csv(file_path, 'formal_valid_y_seq.csv')
test_y = CsvUtility.read_array_from_csv(file_path, 'formal_test_y_seq.csv')
x_data = np.concatenate((train_x, valid_x, test_x), axis=0)
y_data = np.concatenate((train_y, valid_y, test_y), axis=0)
idx = np.random.permutation(x_data.shape[0])
x_data = x_data[idx]
y_data = y_data[idx]
return x_data[:num], y_data[:num]
def reload_mimic_seq(train_percent=MIMICP.train_percent, valid=False, file_path=MIMICP.mimic_data_path, seq_num=MIMICP.seq_num):
train_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_train_x_seq.npz')
train_x = train_x.reshape((train_x.shape[0], seq_num, -1))
valid_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_valid_x_seq.npz')
valid_x = valid_x.reshape((valid_x.shape[0], seq_num, -1))
test_x = CsvUtility.read_sparse_array_from_csv(file_path, 'sparse_formal_test_x_seq.npz')
test_x = test_x.reshape((test_x.shape[0], seq_num, -1))
train_y = CsvUtility.read_array_from_csv(file_path, 'formal_train_y_seq.csv')
valid_y = CsvUtility.read_array_from_csv(file_path, 'formal_valid_y_seq.csv')
test_y = CsvUtility.read_array_from_csv(file_path, 'formal_test_y_seq.csv')
if valid:
test_x = valid_x
test_y = valid_y
else:
train_x = np.concatenate((train_x, valid_x), axis=0)
train_y = np.concatenate((train_y, valid_y), axis=0)
if train_percent < 0.8:
new_training_size = int((train_x.shape[0] + test_x.shape[0]) * train_percent)
train_x = train_x[:new_training_size]
train_y = train_y[:new_training_size]
return train_x, train_y, test_x, test_y
def save_phi_alpha_theta_topicdistrib(self):
plsa = ""
percent = ""
if self.PLSA:
plsa = "PLSA"
if self.corpus_percent != 1.0:
percent = "_" + str(self.corpus_percent) + "percent"
if self.mimic_movie_wiki == 0:
CsvUtility.write_array2csv(
self.get_alpha(), self.output_path,
'MIMIC_alpha_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_mimic_phi(MIMICP.feature_index_file),
self.output_path,
'MIMIC_phi_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_theta(), self.output_path,
'MIMIC_theta_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_topic_distrib_of_word(), self.output_path,
'MIMIC_topic_distrib_' + str(self.topic_num) + plsa + percent +
'.csv')
elif self.mimic_movie_wiki == 1:
CsvUtility.write_array2csv(
self.get_alpha(), self.output_path, 'MovieReview_alpha_' +
str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_movie_phi(MOVIEP.feature_index_file),
self.output_path, 'MovieReview_phi_' + str(self.topic_num) +
plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_theta(), self.output_path, 'MovieReview_theta_' +
str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_topic_distrib_of_word(), self.output_path,
'MovieReview_topic_distrib_' + str(self.topic_num) + plsa +
percent + '.csv')
else:
CsvUtility.write_array2csv(
self.get_alpha(), self.output_path,
'Wiki_alpha_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_movie_phi(MOVIEP.feature_index_file),
self.output_path,
'Wiki_phi_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_theta(), self.output_path,
'Wiki_theta_' + str(self.topic_num) + plsa + percent + '.csv')
CsvUtility.write_array2csv(
self.get_topic_distrib_of_word(), self.output_path,
'Wiki_topic_distrib_' + str(self.topic_num) + plsa + percent +
'.csv')
def get_mimic_sequence_data(data_pickle_path, word_dict_path, predict_dict_path, seq_max, vec_len, sgns_path, save_path, save=False):
all_events = CsvUtility.read_pickle(data_pickle_path, 'r')
word_dict = CsvUtility.read_pickle(word_dict_path, 'r')
predict_dict = CsvUtility.read_pickle(predict_dict_path, 'r')
# pprint(all_events[0])
print "word_dict:", len(word_dict), "predict_dict:", len(predict_dict), "all_events:", len(all_events)
feature_dict = WordIndexMap(list(word_dict))
pred_dict = WordIndexMap(list(predict_dict))
filter_event = __filter_events(all_events=all_events)
sgns_model = get_sgns_embedding('MIMIC', sgns_path)
feature_tensor = np.zeros((len(filter_event), seq_max, vec_len))
feature_count_tensor = np.zeros((len(filter_event), seq_max))
result_tensor = np.zeros((len(filter_event), len(predict_dict)))
find_nan = {}
for i_iter, event_line in enumerate(filter_event):
for seq_iter, sequence_item in enumerate(event_line[0]):
for event_code in sequence_item:
if event_code in sgns_model:
feature_tensor[i_iter][seq_iter] += sgns_model[event_code]
feature_count_tensor[i_iter][seq_iter] += 1
else:
if event_code in find_nan:
find_nan[event_code] += 1
else:
find_nan[event_code] = 1
for pred_item in event_line[1]:
result_tensor[i_iter][pred_dict.get_index_by_word(pred_item)] = 1
if i_iter % 1000 == 0:
print 'complete {0} of {1}'.format(i_iter, len(filter_event))
print 'words not in docs:', len(find_nan)
if save:
CsvUtility.write_dict2csv(feature_dict.get_word2index(), save_path, 'feature2index_seq_embedding'+str(vec_len)+'.csv')
CsvUtility.write_dict2csv(pred_dict.get_word2index(), save_path, 'predict2index_seq_embedding'+str(vec_len)+'.csv')
CsvUtility.write_array2csv(feature_tensor.reshape((feature_tensor.shape[0], -1)), save_path, 'feature_matrix_seq_embedding'+str(vec_len)+'.csv')
CsvUtility.write_array2csv(result_tensor.reshape((result_tensor.shape[0], -1)), save_path, 'result_matrix_seq_embedding'+str(vec_len)+'.csv')
return feature_tensor, feature_count_tensor, result_tensor
# filter_contend = {}
# filter_index = 0
# for i in res:
# for a in i.get():
# filter_contend[str(filter_index)] = ' '.join(a)
# filter_index += 1
# CsvUtility.write_dict2csv(filter_contend, sentence_dir, 'selected_movie_review_docs4LDA.csv')
def get_filter_data(path):
get_con = []
for process_index in range(1):
for file_iter in range(6):
name = str(process_index) + "process_" + str(file_iter +
1) + "00iter_text.csv"
content = CsvUtility.read_norm_array_csv(path, name)
# print(len(content))
get_con.extend(content)
print(" content number : ", len(get_con))
return get_con[:100000]
# print(content[0])
if __name__ == '__main__':
# _load_and_process_metadata("/home1/yk/wikipedia_dataset/text", "/home1/yk/Movie_Review_data", num_processor=20)
contend = get_filter_data("/home1/yk/wikipedia_dataset/filter")
name = "wiki_text.csv"
CsvUtility.write_norm_array2csv(contend,
"/home1/yk/wikipedia_dataset/filter", name)
pass
def generate_train_test(base_path, seq_max, vec_len, sgns_path, save_path, seq_not, train_valid_perc=0.8, shuffle=False, save=False):
feature_tensor, feature_count_tensor, result_tensor = get_mimic_sequence_data(data_pickle_path=base_path+'after_instance_seq.pkl',
word_dict_path=base_path+'event_instance_dict_seq.pkl',
predict_dict_path=base_path+'predict_diags_dict_seq.pkl',
seq_max=seq_max,
vec_len=vec_len,
sgns_path=sgns_path,
save_path=save_path,
save=False)
feature_tensor = __get_aggregate_seq(feature_tensor, feature_count_tensor, seq_not)
x = feature_tensor.reshape((feature_tensor.shape[0], -1))
y = result_tensor.reshape((result_tensor.shape[0], -1))
train_size = int(x.shape[0] * train_valid_perc)
# for further extention
name_append = 'SGNS'
# shuffle the train set
if shuffle:
idx = np.random.permutation(x.shape[0])
CsvUtility.write_array2csv(idx, base_path, 'random_idx_seq_' + name_append + '.csv')
else:
idx = CsvUtility.read_array_from_csv(base_path, 'random_idx_seq_' + name_append + '.csv')
x_train = x[idx]
y_train = y[idx]
training_x = x_train[:train_size]
training_y = y_train[:train_size]
testing_x = x_train[train_size:]
testing_y = y_train[train_size:]
# print training_x.shape
# print training_y.shape
# print testing_x.shape
# print testing_y.shape
# print len(idx)
if save:
CsvUtility.write_array2csv(training_x, save_path, 'formal_train_valid_x_seq_'+name_append+'.csv')
CsvUtility.write_array2csv(training_y, save_path, 'formal_train_valid_y_seq_'+name_append+'.csv')
CsvUtility.write_array2csv(testing_x, save_path, 'formal_test_x_seq_'+name_append+'.csv')
CsvUtility.write_array2csv(testing_y, save_path, 'formal_test_y_seq_'+name_append+'.csv')
return training_x, training_y, testing_x, testing_y
def run():
base_path = "/home1/yk/experiments_TKDE/major_revision/"
# model_file = "#2020-11-21 03_33_04#_lstm_model_mimic_mlp_ldareg_1layer_0.8.pkl"
# sita_file = "#2020-11-21 03_33_04#_sita_mimic_mlp_ldareg_1layer_0.8.csv"
# time_prefix_list = ['#2020-11-29 15_28_25#', '#2020-11-29 15_26_23#', '#2020-11-29 15_26_31#', '#2020-11-29 15_28_09#', '#2020-11-29 15_30_14#']
# topic_list = ['20', '50', '100', '200', '500']
time_prefix_list = [
'#2020-11-30 22_36_35#', '#2020-11-30 22_36_56#',
'#2020-11-30 22_03_35#', '#2020-11-30 22_04_03#',
'#2020-11-30 22_09_12#'
]
topic_list = [20, 50, 100, 200, 500]
model_file_name = '_lstm_model_mimic_mlp_ldareg_1layer_0.8_topic'
sita_file_name = '_sita_mimic_mlp_ldareg_1layer_0.8_topic'
entropy_matrix = []
F1_matrix = []
# common_label_ratio_matrix = []
neuron_num = 10
mean_f1 = 0.0
while mean_f1 <= 0.599:
for i in [2]:
model_file = time_prefix_list[i] + model_file_name + str(
topic_list[i]) + '.pkl'
sita_file = time_prefix_list[i] + sita_file_name + str(
topic_list[i]) + '.csv'
# model_file = "#2020-11-21 03_33_04#_lstm_model_mimic_mlp_ldareg_1layer_0.8.pkl"
# sita_file = "#2020-11-21 03_33_04#_sita_mimic_mlp_ldareg_1layer_0.8.csv"
r1, r2 = get_neron_labels(base_path=base_path,
model_file=model_file,
top_n_label=2,
patient_num=100000,
neuron_num=neuron_num,
topic_num=topic_list[i])
while len(r1) < 128:
print("not cover all neurons !")
neuron_num += 2
r1, r2 = get_neron_labels(base_path=base_path,
model_file=model_file,
top_n_label=2,
patient_num=100000,
neuron_num=neuron_num,
topic_num=topic_list[i])
entropy_cluster = [topic_list[i]]
f1_cluster = [topic_list[i]]
common_label_ratio = [topic_list[i]]
for tn in [2, 5, 10, 20, 30]:
label_cluster_matrix, cluster_re = cluster_neurons(
neuron_labels=r1,
base_path=base_path,
sita_file=sita_file,
cluster_num=tn)
cce = cal_class_entropy(
label_cluster_matrix=label_cluster_matrix, neuron_num=128)
cf = cal_F1(label_cluster_matrix=label_cluster_matrix,
neuron_num=128,
cluster_re=cluster_re)
entropy_cluster.append(cce)
f1_cluster.append(cf)
mean_f1 += cf
entropy_matrix.append(entropy_cluster)
mean_f1 /= 5.0
F1_matrix.append(f1_cluster)
# common_count, common_ratio = cal_common_label_ratio(neuron_labels=r1, base_path=base_path, sita_file=sita_file)
# common_label_ratio.append(common_count)
# common_label_ratio.append(common_ratio)
# common_label_ratio_matrix.append(common_label_ratio)
time_code = '#' + time.strftime("%Y-%m-%d %H:%M:%S",
time.localtime()) + '#_'
CsvUtility.write_array2csv(
entropy_matrix, base_path,
time_code + "class_entropy_pa100000_topneu10_toplab2_tuneTopic100.csv")
CsvUtility.write_array2csv(
F1_matrix, base_path,
time_code + "f1_pa100000_topneu10_toplab2_tuneTopic100.csv")
def get_train_validation_test_seq(base_path, seq_max, vec_len, sgns_path, save_path, seq_not, train_perc=0.8, shuffle=False, save=False):
training_x, training_y, testing_x, testing_y = generate_train_test(base_path, seq_max, vec_len, sgns_path, save_path, seq_not, train_perc, shuffle, save)
training_size = int(training_x.shape[0] * 0.8)
formal_training_x = training_x[:training_size]
formal_training_y = training_y[:training_size]
validation_x = training_x[training_size:]
validation_y = training_y[training_size:]
print formal_training_x.shape
print formal_training_y.shape
print validation_x.shape
print validation_y.shape
print testing_x.shape
print testing_y.shape
# for further extention
embedding_append = 'lda_sgns500_window50'
CsvUtility.write_array2csv(formal_training_x, save_path, 'formal_train_x_seq_' + embedding_append + '.csv')
CsvUtility.write_array2csv(formal_training_y, save_path, 'formal_train_y_seq_' + embedding_append + '.csv')
CsvUtility.write_array2csv(validation_x, save_path, 'formal_valid_x_seq_' + embedding_append + '.csv')
CsvUtility.write_array2csv(validation_y, save_path, 'formal_valid_y_seq_' + embedding_append + '.csv')
CsvUtility.write_array2csv(testing_x, save_path, 'formal_test_x_seq_' + embedding_append + '.csv')
CsvUtility.write_array2csv(testing_y, save_path, 'formal_test_y_seq_' + embedding_append + '.csv')
return training_x, training_y, validation_x, validation_y, testing_x, testing_y