def run_eval(self, filename, num_ngs):
"""Evaluate the given file and returns some evaluation metrics.
Args:
filename (str): A file name that will be evaluated.
num_ngs (int): The number of negative sampling for a positive instance.
Returns:
dict: A dictionary that contains evaluation metrics.
"""
load_sess = self.sess
preds = []
labels = []
group_preds = []
group_labels = []
group = num_ngs + 1
for batch_data_input in self.iterator.load_data_from_file(
filename, min_seq_length=self.min_seq_length, batch_num_ngs=0):
if batch_data_input:
step_pred, step_labels = self.eval(load_sess, batch_data_input)
preds.extend(np.reshape(step_pred, -1))
labels.extend(np.reshape(step_labels, -1))
group_preds.extend(np.reshape(step_pred, (-1, group)))
group_labels.extend(np.reshape(step_labels, (-1, group)))
res = cal_metric(labels, preds, self.hparams.metrics)
res_pairwise = cal_metric(group_labels, group_preds,
self.hparams.pairwise_metrics)
res.update(res_pairwise)
return res
def run_eval(self, filename):
"""Evaluate the given file and returns some evaluation metrics.
Args:
filename (str): A file name that will be evaluated.
Returns:
dict: A dictionary that contains evaluation metrics.
"""
load_sess = self.sess
preds = []
labels = []
imp_indexs = []
for batch_data_input, imp_index, data_size in self.iterator.load_data_from_file(
filename):
step_pred, step_labels = self.eval(load_sess, batch_data_input)
preds.extend(np.reshape(step_pred, -1))
labels.extend(np.reshape(step_labels, -1))
imp_indexs.extend(np.reshape(imp_index, -1))
res = cal_metric(labels, preds, self.hparams.metrics)
if "pairwise_metrics" in self.hparams.values():
group_labels, group_preds = self.group_labels(
labels, preds, imp_indexs)
res_pairwise = cal_metric(group_labels, group_preds,
self.hparams.pairwise_metrics)
res.update(res_pairwise)
return res
def run_eval(self, filename):
"""Evaluate the given file and returns some evaluation metrics.
Args:
filename (str): A file name that will be evaluated.
Returns:
dict: A dictionary containing evaluation metrics.
"""
load_sess = self.sess
group_preds = []
group_labels = []
for (
batch_data_input,
newsid_list,
data_size,
) in self.iterator.load_data_from_file(filename):
if batch_data_input:
step_pred, step_labels = self.eval(load_sess, batch_data_input)
group_preds.extend(step_pred)
group_labels.extend(step_labels)
res = cal_metric(group_labels, group_preds,
self.hparams.pairwise_metrics)
return res
def run_eval(self, news_filename, behaviors_file):
"""Evaluate the given file and returns some evaluation metrics.
Args:
filename (str): A file name that will be evaluated.
Returns:
dict: A dictionary that contains evaluation metrics.
"""
if self.support_quick_scoring:
_, group_labels, group_preds = self.run_fast_eval(
news_filename, behaviors_file)
else:
_, group_labels, group_preds = self.run_slow_eval(
news_filename, behaviors_file)
res = cal_metric(group_labels, group_preds, self.hparams.metrics)
return res