c672738b39c8f739551da0d88991a2fedfba3e14,deeppavlov/models/ranking/metrics.py,,recall_at_k,#Any#Any#Any#,24
Before Change
predictions = np.argsort(predictions, -1)[:, :k]
flags = np.zeros_like(predictions)
for i in range(predictions.shape[0]):
for j in range(predictions.shape[1]):
if predictions[i][j] in np.arange(labels[i][j]):
flags[i][j] = 1.
return np.mean((np.sum(flags, -1) >= 1.).astype(float))
@register_metric("rank_response")
After Change
return recall_at_k(labels, predictions, k=10)
def recall_at_k(y_true, y_pred, k):
num_examples = float(len(y_pred))
predictions = np.array(y_pred)
predictions = np.flip(np.argsort(predictions, -1), -1)[:, :k]
num_correct = 0
for el in predictions:

In pattern: SUPERPATTERN
Frequency: 3
Non-data size: 3
Instances
Project Name: deepmipt/DeepPavlov
Commit Name: c672738b39c8f739551da0d88991a2fedfba3e14
Time: 2018-10-04
Author: puleon@mail.ru
File Name: deeppavlov/models/ranking/metrics.py
Class Name:
Method Name: recall_at_k
Project Name: deepmipt/DeepPavlov
Commit Name: c672738b39c8f739551da0d88991a2fedfba3e14
Time: 2018-10-04
Author: puleon@mail.ru
File Name: deeppavlov/models/ranking/metrics.py
Class Name:
Method Name: rank_response
Project Name: keras-team/keras
Commit Name: b938b91b527760cef3af29031e74ab181f2fc2b9
Time: 2019-07-07
Author: psv@google.com
File Name: keras/engine/training_generator.py
Class Name:
Method Name: evaluate_generator