def evaluate():
vocab_map, _ = dataset.read_map('corpus/mapping')
sess = tf.Session()
Model = create_model(sess, 'test')
Model.batch_size = 1
df = pd.read_csv('corpus/SAD.csv', header=None)
df = df.dropna()
#df=df.head()
idx = list(df.index)
random.seed(SEED)
random.shuffle(idx)
df = df.ix[idx]
cut_by = int(0.9 * df.shape[0])
train_df = df.iloc[:cut_by]
val_df = df.iloc[cut_by:]
for df in [train_df, val_df]:
sentences = df[3]
answers = df[1]
scores = []
for i, sentence in enumerate(sentences):
if i % 1000 == 0:
print(i)
token_ids = dataset.convert_to_token(sentence, vocab_map)
encoder_input, encoder_length, _ = Model.get_batch(
[(0, token_ids)], shuffle=False)
score = Model.step(sess, encoder_input, encoder_length)
#print(i,score)
scores.append(score)
scores = [s[0][0] for s in scores]
auc = roc_auc_score(answers, scores)
yield auc
def evaluate(cut_mode):
if cut_mode == "word":
import jieba_fast as jieba
jieba.load_userdict("dict_fasttext.txt")
vocab_map, _ = dataset.read_map('corpus/mapping')
sess = tf.Session()
Model = create_model(sess, 'test')
Model.batch_size = 1
sys.stdout.write('>')
sys.stdout.flush()
sentence = sys.stdin.readline()
sentence = sentence_cutter(sentence, cut_mode)
while (sentence):
print('sentence: ', sentence)
token_ids = dataset.convert_to_token(sentence, vocab_map)
print('toekn_ids: ', token_ids)
encoder_input, encoder_length, _ = Model.get_batch([(0, token_ids)])
print('encoder_input: ', encoder_input, encoder_input.shape)
print('encoder_length: ', encoder_length)
score = Model.step(sess, encoder_input, encoder_length)
print('Score: ', score[0][0])
print('>', end='')
sys.stdout.flush()
sentence = sys.stdin.readline()
sentence = sentence_cutter(sentence, cut_mode)
def evaluate():
vocab_map, _ = dataset.read_map('corpus/mapping')
sess = tf.Session()
Model = create_model(sess, 'test')
Model.batch_size = 1
sys.stdout.write('>')
sys.stdout.flush()
sentence = sys.stdin.readline()
while(sentence):
token_ids = dataset.convert_to_token(sentence, vocab_map)
encoder_input, encoder_length, _ = Model.get_batch([(0, token_ids)])
score = Model.step(sess, encoder_input, encoder_length)
print('Score: ' + str(score[0][0]))
print('>', end = '')
sys.stdout.flush()
sentence = sys.stdin.readline()