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Fix calculation of score for pytorch models when a single column of probabilities is used for binary classification (#91)

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Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailgold 2024-01-21 06:56:57 -05:00 • committed by GitHub
parent a8f5326572
commit cb70ca10e6
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1 changed files with 9 additions and 2 deletions
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apt/utils/models/pytorch_model.py
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@ -408,9 +408,16 @@ class PyTorchClassifier(PyTorchModel):
:type test_data: `PytorchData`
:return: the score as float (between 0 and 1)
"""
y = check_and_transform_label_format(test_data.get_labels(), self._art_model.nb_classes)
y = test_data.get_labels()
predicted = self.predict(test_data)
return np.count_nonzero(np.argmax(y, axis=1) == np.argmax(predicted, axis=1)) / predicted.shape[0]
# binary classification, single column of probabilities
if self._art_model.nb_classes == 2 and (len(predicted.shape) == 1 or predicted.shape[1] == 1):
if len(predicted.shape) > 1:
y = check_and_transform_label_format(y, self._art_model.nb_classes, return_one_hot=False)
return np.count_nonzero(y == (predicted > 0.5)) / predicted.shape[0]
else:
y = check_and_transform_label_format(y, self._art_model.nb_classes)
return np.count_nonzero(np.argmax(y, axis=1) == np.argmax(predicted, axis=1)) / predicted.shape[0]
def load_checkpoint_state_dict_by_path(self, model_name: str, path: str = None):
"""