def main():
opt = get_opt()
if opt.gpu > -1: torch.cuda.set_device(opt.gpu)
run_time_result = [[] for _ in range(opt.run_split_num)]
all_list = []
for iter_split_seed in range(opt.run_split_num):
target_data = load_processed_data(
opt,
opt.data_path,
opt.data_name,
shuffle_seed=opt.shuffle_seed_list[iter_split_seed])
setattr(opt, 'num_feature', target_data.num_features)
setattr(opt, 'num_class', target_data.num_classes)
adj = target_data.edge_index
for iter_init_seed in range(opt.run_init_num):
set_seed(seed_list[iter_init_seed], opt.gpu > -1)
model = get_model(opt)
best_model = train(model, opt, target_data, adj)
test_acc = test(opt, best_model, target_data, adj,
target_data.test_mask, 'test')
test_acc = round(test_acc, 4)
print(test_acc)
y2_test = np.vstack((y2_test, y2_test_tmp))
X_test = X_test[1:]
y1_test = y1_test[1:]
y2_test = y2_test[1:]
return X_test, (y1_test, y2_test), (label2class1, label2class2)
if __name__ == '__main__':
import matplotlib.pyplot as plt
from load_data import load_processed_data, set_data
dir_path = os.path.join('c:\\', 'Users', 'aviat', 'Google Drive', 'dl4us',
'prj')
data = load_processed_data(dir_path, cities=('nyc', 'kyoto'), verbose=1)
(X_train, X_valid,
X_test), (y1_train, y1_valid,
y1_test), (y2_train, y2_valid,
y2_test), (label2class1, label2class2) = set_data(
data, verbose=1)
train_gen, steps_per_epoch = create_generator(
X_train, (y1_train, y2_train), batch_size=32)
X_gen, (y1_gen, y2_gen) = next(train_gen)
we_city = zip(
list(map(lambda i: label2class1[i][1], np.argmax(y1_gen, axis=1))),
list(map(lambda i: label2class2[i][1], np.argmax(y2_gen, axis=1))))
for i, (w_or_e, city) in enumerate(we_city):
from sklearn.cross_validation import train_test_split
from gensim.models.word2vec import Word2Vec
from load_data import load_train_data, load_processed_data
import numpy as np
from sklearn.preprocessing import MinMaxScaler
# The following skills is useful
# train_test_split(np.array(texts), np.array(sentiemnt), test_size=0.2)
x_train, y_train = load_processed_data(stem=False)
x_test, y_test = load_processed_data(data_type='test', stem=False)
from preprocess import preprocessor as preprocess
n_dim = 300
scaling = False
# Build word vector for training set by using the average value of all word vectors in the tweet, then scale
from load_data import load_word_embedding
imdb_w2v = load_word_embedding()
def buildWordVector(text, size):
vec = np.zeros(size).reshape((1, size))
count = 0.
for word in text:
try:
vec += imdb_w2v[word].reshape((1, size))
count += 1.
from sklearn.cross_validation import train_test_split
from gensim.models.word2vec import Word2Vec
from load_data import load_train_data, load_processed_data
import numpy as np
from sklearn.preprocessing import MinMaxScaler
# The following skills is useful
# train_test_split(np.array(texts), np.array(sentiemnt), test_size=0.2)
x_train, y_train = load_processed_data(stem=False)
x_test, y_test = load_processed_data(data_type='test', stem=False)
from preprocess import preprocessor as preprocess
n_dim = 300
scaling = False
# Build word vector for training set by using the average value of all word vectors in the tweet, then scale
from load_data import load_word_embedding
imdb_w2v = load_word_embedding()
def buildWordVector(text, size):
vec = np.zeros(size).reshape((1, size))
count = 0.
for word in text:
try:
vec += imdb_w2v[word].reshape((1, size))
count += 1.
except KeyError:
from load_data import load_processed_data
from qrcode_generator import to_qrcode
import numpy as np
texts, labels = load_processed_data(data_type='train', stem=False)
feature_vec = []
i = 0
for text, label in zip(texts, labels):
text_qrcode = to_qrcode(text)
text_qrcode = np.array(list(text_qrcode.getdata()))
text_qrcode[text_qrcode > 0] = 1
feature_vec.append(np.append(label, text_qrcode))
from save_data import csv_save
csv_save(feature_vec, './data/traindata/qrcode_20000.csv')
__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
print('Start')
vectorizer = anew_estimator()
texts, train_labels = load_processed_data()
transformed_train = vectorizer.fit_transform(texts)
testdata, true_labels = load_processed_data(data_type='test')
transformed_test = vectorizer.transform(testdata)
predict = mNB(transformed_train, train_labels, transformed_test)
analysis_result(predict, true_labels)
print(
'returned: extracted_data\n - extracted_data: {} length tuple whose element\'s shape is (city_name, (pld_train, pld_test), (npld_train, npld_test))'
.format(len(cities)))
else:
print(
'returned: extracted_data\n - extracted_data: {} length tuple whose element\'s shape is (city_name, (pld_train, pld_test))'
.format(len(cities)))
return extracted_data # original_data
if __name__ == '__main__':
from load_data import load_processed_data
dir_path = os.path.join('c:\\', 'Users', 'aviat', 'Google Drive', 'dl4us',
'prj')
data = load_processed_data(dir_path, cities=('kyoto', ))
tmp_data = (('kyoto', data[0][1][:10]), )
extracted_data, original_data = extract(
tmp_data,
weights='places',
pooling='avg',
test_size=0.1,
random_state=42,
augment=True,
augment_mode=0,
augment_times=1,
)
__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
print('Start')
vectorizer = anew_estimator()
texts, train_labels = load_processed_data()
transformed_train = vectorizer.fit_transform(texts)
testdata, true_labels = load_processed_data(data_type='test')
transformed_test = vectorizer.transform(testdata)
predict = mNB(transformed_train, train_labels, transformed_test)
analysis_result(predict, true_labels)