def valid_epoch(data_path, sess, model):
_costs = 0.0
predict_labels_list = list()
marked_labels_list = list()
# cnnout = None
for i in range(n_va_batches):
[X_batch, y_batch] = get_batch(data_path, i)
marked_labels_list.extend(y_batch)
y_batch = to_categorical(y_batch, settings.n_class)
_batch_size = len(y_batch)
if _batch_size != batch_size:
continue
fetches = [model.loss, model.y_pred, model.logits]
feed_dict = {
model.X_inputs: X_batch,
model.y_inputs: y_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
_cost, predict_labels, logits = sess.run(fetches, feed_dict)
_costs += _cost
predict_labels_list.extend(predict_labels)
print(max(predict_labels_list[0]), min(predict_labels_list[0]))
print(predict_labels_list[0])
print(logits[0])
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
return _costs, f1_micro, f1_macro, score12
def predict(sess, model, logger):
"""Test on the test data."""
time0 = time.time()
te_batches = os.listdir(data_test_path)
n_te_batches = len(te_batches)
predict_labels_list = list() # 所有的预测结果
marked_labels_list = list()
for i in tqdm(range(n_te_batches)):
X_batch, y_batch = get_batch(data_test_path, i)
_batch_size = len(X_batch)
marked_labels_list.extend(y_batch)
fetches = [model.y_pred]
feed_dict = {
model.X_inputs: X_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
predict_labels = sess.run(fetches, feed_dict)[0]
predict_labels_list.extend(predict_labels)
predict_scores_file = scores_path + model_name + '/' + 'predict.npy'
marked_scores_file = scores_path + model_name + '/' + 'origin.npy'
np.save(predict_scores_file, predict_labels_list)
np.save(marked_scores_file, marked_labels_list)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
print('f1_micro=%g, f1_macro=%g, score12=%g, time=%g s' %
(f1_micro, f1_macro, score12, time.time() - time0))
logger.info(
'\nTest predicting...\nEND:Global_step={}: f1_micro={}, f1_macro={}, score12={}, time=%g s'
.format(sess.run(model.global_step), f1_micro, f1_macro, score12,
time.time() - time0))
def predict_valid(sess, model, logger):
"""Test on the valid data."""
time0 = time.time()
predict_labels_list = list() # 所有的预测结果
marked_labels_list = list()
for i in tqdm(range(int(n_va_batches))):
[X_batch, y_batch] = get_batch(data_valid_path, i)
marked_labels_list.extend(y_batch)
_batch_size = len(X_batch)
fetches = [model.y_pred]
feed_dict = {
model.X_inputs: X_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
predict_labels = sess.run(fetches, feed_dict)[0]
predict_labels_list.extend(predict_labels)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
print('precision_micro=%g, recall_micro=%g, score12=%g, time=%g s' %
(f1_micro, f1_macro, score12, time.time() - time0))
logger.info(
'\nValid predicting...\nEND:Global_step={}: f1_micro={}, f1_macro={}, score12={}, time=%g s'
.format(sess.run(model.global_step), f1_micro, f1_macro, score12,
time.time() - time0))
def valid_epoch(data_path, sess, model):
va_batches = os.listdir(data_path)
n_va_batches = len(va_batches)
_costs = 0.0
predict_labels_list = list()
marked_labels_list = list()
for i in range(n_va_batches):
[X_batch, y_batch] = get_batch(data_path, i)
marked_labels_list.extend(y_batch)
y_batch = to_categorical(y_batch)
_batch_size = len(y_batch)
fetches = [model.loss, model.y_pred]
feed_dict = {
model.X_inputs: X_batch,
model.y_inputs: y_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
_cost, predict_labels = sess.run(fetches, feed_dict)
_costs += _cost
predict_labels_list.extend(predict_labels)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
return f1_micro, f1_macro, score12
def valid_epoch(data_path, sess, model):
va_batches = os.listdir(data_path)
n_va_batches = len(va_batches)
_costs = 0.0
predict_labels_list = list()
marked_labels_list = list()
for i in range(n_va_batches):
[X_batch, y_batch] = get_batch(data_path, i)
marked_labels_list.extend(y_batch)
y_batch = to_categorical(y_batch, cfg.n_class)
_batch_size = len(y_batch)
fetches = [model.loss, model.y_pred]
feed_dict = {model.token_seq: X_batch, model.gold_label: y_batch, model.is_train: False}
_cost, predict_labels = sess.run(fetches, feed_dict)
_costs += _cost
predict_labels_list.extend(predict_labels)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list, marked_labels_list)
return f1_micro, f1_macro, score12
def valid_epoch(sess, model):
_costs = 0.0
predict_labels_list = list()
marked_labels_list = list()
for i in tqdm(range(int(n_va_batches))):
[X_batch, y_batch] = get_batch(data_valid_path, i)
marked_labels_list.extend(y_batch)
_batch_size = len(y_batch)
fetches = [model.y_pred]
feed_dict = {
model.X_inputs: X_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
predict_labels = sess.run(fetches, feed_dict)
predict_labels_list.extend(predict_labels)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
print(f1_micro, f1_macro, score12)
def predict(sess, model, logger):
time0 = time.time()
te_batches = os.listdir(data_test_path)
n_te_batches = len(te_batches)
predict_labels_list = list() # 所有的预测结果
marked_labels_list = list()
id_sample = 0
for i in tqdm(range(n_te_batches)):
X_batch, y_batch, sent_len, length = get_batch(data_test_path, i)
_batch_size = len(X_batch)
marked_labels_list.extend(y_batch)
fetches = [model.y_pred]
feed_dict = {
model.X_inputs: X_batch,
model.wNum: sent_len,
model.sNum: length,
model.batch_size: _batch_size,
model.tst: True,
model.is_train: False
}
predict_labels = sess.run(fetches, feed_dict)[0]
predict_labels_list.extend(predict_labels)
predict_scores_file = scores_path + model_name + '/' + 'predict.npy'
marked_scores_file = scores_path + model_name + '/' + 'origin.npy'
np.save(predict_scores_file, predict_labels_list)
np.save(marked_scores_file, marked_labels_list)
print('save predict_labels_list', predict_scores_file)
f1_micro, f1_macro, score12 = cail_evaluator(predict_labels_list,
marked_labels_list)
print('cail_evaluator: f1_micro=%g, f1_macro=%g, score12=%g, time=%g s' %
(f1_micro, f1_macro, score12, time.time() - time0))
logger.info(
'\nTest predicting...\ncail_evaluator--END:Global_step={}: f1_micro={}, f1_macro={}, score12={}, time={}s'
.format(sess.run(model.global_step), f1_micro, f1_macro, score12,
time.time() - time0))
def valid_epoch(data_path, sess, model, logger, global_step):
va_batches = os.listdir(data_path)
n_va_batches = len(va_batches)
_costs = 0.0
acc_predict_onehot = list()
acc_marked_labels = list()
law_marked_labels = list()
law_predict_onehot = list()
death_marked_labels = list()
death_predict_onehot = list()
imp_marked_labels = list()
imp_predict_onehot = list()
lif_marked_predict = list()
lif_predict_onehot = list()
for i in range(n_va_batches):
[X_batch, acc, law, death, imp, lif] = get_batch(data_path, i)
acc_marked_labels.extend(acc)
law_marked_labels.extend(law)
death_marked_labels.extend(death)
imp_marked_labels.extend(imp)
lif_marked_predict.extend(lif)
acc_batch = to_categorical(acc, 202)
law_batch = to_categorical(law, 183)
death_batch = to_categorical_death(death, 2)
imp_batch = np.expand_dims(imp, axis=1)
lif_batch = to_categorical_death(lif, 2)
_batch_size = len(acc_batch)
fetches = [
model.loss, model.accu_pred, model.law_pred, model.death_pred,
model.imp_pred, model.lif_pred
]
feed_dict = {
model.X_inputs: X_batch,
model.acc_y: acc_batch,
model.article_y: law_batch,
model.death_y: death_batch,
model.imp_y: imp_batch,
model.lif_y: lif_batch,
model.batch_size: _batch_size,
model.tst: True,
model.keep_prob: 1.0
}
_cost, accu_pred, law_pred, death_pred, imp_pred, lif_pred = sess.run(
fetches, feed_dict)
_costs += _cost
acc_predict_onehot.extend(accu_pred)
law_predict_onehot.extend(law_pred)
death_predict_onehot.extend(death_pred)
imp_predict_onehot.extend(imp_pred)
lif_predict_onehot.extend(lif_pred)
accu_f1_micro, accu_f1_macro, accu_score12 = cail_evaluator(
acc_predict_onehot, acc_marked_labels)
law_f1_micro, law_f1_macro, law_score12 = cail_evaluator(
law_predict_onehot, law_marked_labels)
imp_score = cail_imprisonment_evaluator(
death_predict_onehot, death_marked_labels, imp_predict_onehot,
imp_marked_labels, lif_predict_onehot, lif_marked_predict)
# score12 = accu_score12 + law_score12 + imp_score
score12 = accu_score12
logger.info('Global_step={}'.format(global_step, ))
logger.info('accu: f1_micro={}, f1_macro={}, score12={}'.format(
accu_f1_micro, accu_f1_macro, accu_score12))
logger.info('law: f1_micro={}, f1_macro={}, score12={}'.format(
law_f1_micro, law_f1_macro, law_score12))
logger.info('imprisonment: score={}'.format(imp_score))
return score12
