# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
print '4. 预测'
# -------------- code start : 开始 -------------
y_predict = map(rand_embedding_cnn.predict,test_X_feature)
test_data[u'Y_PRED'] = [index_to_label[item] for item in y_predict]
data_util.save_data(test_data,path=result_file_path)
quit()
rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))
print index_to_label[rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
y_pred, is_correct, accu,f1 = rand_embedding_cnn.accuracy((test_X_feature, test_y))
logging.debug('F1(macro)为：%f'%(np.average(f1[:-1])))
print 'F1(macro)为：%f'%(np.average(f1[:-1]))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
data_util.save_data(test_data,
path=result_file_path)
# -------------- region start : 生成深度特征编码 -------------
if verbose > 2:
logging.debug('-' * 20)
print '-' * 20
# -------------- region end : 3. 初始化CNN模型并训练 ---------------
# -------------- region start : 4. 预测 -------------
if verbose > 1:
logging.debug('-' * 20)
print '-' * 20
logging.debug('4. 预测')
print '4. 预测'
# -------------- code start : 开始 -------------
print index_to_label[rand_embedding_cnn.predict(feature_encoder.transform_sentence('你好吗'))]
y_pred, is_correct, accu,f1 = rand_embedding_cnn.accuracy((map(feature_encoder.transform_sentence, test_X), test_y))
logging.debug('F1(macro)为：%f'%(np.average(f1[:-1])))
print 'F1(macro)为：%f'%(np.average(f1[:-1]))
test_data[u'IS_CORRECT'] = is_correct
test_data[u'PREDICT'] = [index_to_label[item] for item in y_pred]
# data_util.save_data(test_data,'tmp.tmp')
# quit()
result_file_path = ''.join(config['result_file_path'])
data_util.save_data(test_data,
path=result_file_path)
# -------------- code start : 结束 -------------
if verbose > 1:
