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Faq output correction #1311

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Faq output correction #1311

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4 changes: 2 additions & 2 deletions deeppavlov/configs/faq/tfidf_logreg_autofaq.json
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@
},
{
"in": "y_pred_proba",
"out": "y_pred_ids",
"out": ["y_pred_ids", "y_pred_proba_max"],
"class_name": "proba2labels",
"max_proba": true
},
Expand All @@ -85,7 +85,7 @@
],
"out": [
"y_pred_answers",
"y_pred_proba"
"y_pred_proba_max"
]
},
"train": {
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4 changes: 2 additions & 2 deletions deeppavlov/configs/faq/tfidf_logreg_en_faq.json
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@
},
{
"in": "y_pred_proba",
"out": "y_pred_ids",
"out": ["y_pred_ids", "y_pred_proba_max"],
"class_name": "proba2labels",
"max_proba": true
},
Expand All @@ -84,7 +84,7 @@
],
"out": [
"y_pred_answers",
"y_pred_proba"
"y_pred_proba_max"
]
},
"train": {
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14 changes: 9 additions & 5 deletions deeppavlov/models/classifiers/proba2labels.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,7 @@
# limitations under the License.

from logging import getLogger
from typing import List, Union
from typing import List, Union, Tuple

import numpy as np

Expand Down Expand Up @@ -54,7 +54,7 @@ def __init__(self,
self.top_n = top_n

def __call__(self, data: Union[np.ndarray, List[List[float]], List[List[int]]],
*args, **kwargs) -> Union[List[List[int]], List[int]]:
*args, **kwargs) -> Tuple[Union[List[List[int]], List[int]], Union[List[List[int]], List[int]]]:
"""
Process probabilities to labels

Expand All @@ -63,14 +63,18 @@ def __call__(self, data: Union[np.ndarray, List[List[float]], List[List[int]]],

Returns:
list of labels (only label classification) or list of lists of labels (multi-label classification)
# add comment here
"""
if self.confident_threshold:
return [list(np.where(np.array(d) > self.confident_threshold)[0])
labels = [list(np.where(np.array(d) > self.confident_threshold)[0])
for d in data]
return labels, [[d[l] for l in label] for d, label in zip(data, labels)]
elif self.max_proba:
return [np.argmax(d) for d in data]
labels = [np.argmax(d) for d in data]
return labels, [[d[l] for l in label] for d, label in zip(data, labels)]
elif self.top_n:
return [np.argsort(d)[::-1][:self.top_n] for d in data]
labels = [np.argsort(d)[::-1][:self.top_n] for d in data]
return labels, [[d[l] for l in label] for d, label in zip(data, labels)]
else:
raise ConfigError("Proba2Labels requires one of three arguments: bool `max_proba` or "
"float `confident_threshold` for multi-label classification or"
Expand Down