import sys
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import f1_score
root_path = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(root_path)
from lib.model.configs import cfg
from lib.model.fast_disan.model_fast_disan import ModelFastDiSAN
from data_test.ant.embedding import Embedding
from common.data_helper import DataHelper
emb = Embedding(cfg)
GPU = cfg.gpu
os.environ["CUDA_VISIBLE_DEVICES"] = GPU
logging.basicConfig(filename="ant_fast_disan.log" + cfg.log_name,
filemode="w",
format="%(asctime)s-%(name)s-%(levelname)s-%(message)s",
level=logging.INFO)
train_data = pd.read_csv(cfg.train_data, sep='\t')
x_train, y_train = emb.generate_sentence_token_ind(train_data)
train_data_emb = list(zip(x_train, y_train))
valid_data = pd.read_csv(cfg.validate_data, sep='\t')
x_valid, y_valid = emb.generate_sentence_token_ind(valid_data)
root_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
sys.path.append(root_path)
from data_test.ant.embedding import Embedding
GPU = '2'
os.environ["CUDA_VISIBLE_DEVICES"] = GPU
from common.data_helper import DataHelper
from lib.model.configs import cfg
from data_test.ant.util import get_model_list
# Parameters
# ==================================================
emb = Embedding(cfg)
if cfg.test_data is None:
print("test_data is empty.")
exit()
test_data = pd.read_csv(cfg.test_data, sep='\t')
x_test, y_test = emb.generate_sentence_token_ind(test_data)
x1_test, x2_test = zip(*x_test)
model_list = get_model_list(cfg.model_directory)
# print checkpoint_file
graph = tf.Graph()
with graph.as_default():
session_conf = tf.ConfigProto(