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Wrappers no train (#40)

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1) Handle train None in Data
2) Update BB Classifier to handle None either for train or test (x or y)
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Shlomit Shachor 2022-06-26 14:43:22 +03:00 • committed by GitHub
parent dfa684da6b
commit 1c4b963add
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3 changed files with 103 additions and 10 deletions
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37
apt/utils/models/model.py
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@ -153,19 +153,36 @@ class BlackboxClassifier(Model):
def __init__(self, model: Data, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
unlimited_queries: Optional[bool] = True, **kwargs):
super().__init__(model, output_type, black_box_access=True, unlimited_queries=False, **kwargs)
x = model.get_train_samples()
y = model.get_train_labels()
self.nb_classes = self.get_nb_classes(y)
y = check_and_transform_label_format(y, nb_classes=self.nb_classes)
x_train_pred = model.get_train_samples()
y_train_pred = model.get_train_labels()
x_test_pred = model.get_test_samples()
y_test_pred = model.get_test_labels()
if model.get_test_samples() is not None and type(x) == np.ndarray:
x = np.vstack((x, model.get_test_samples()))
if x_train_pred is not None and y_train_pred is not None and x_test_pred is not None and y_test_pred is not None:
if type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray \
or type(y_train_pred) != np.ndarray or type(y_test_pred) != np.ndarray:
raise NotImplementedError("X/Y Data should be np ndarray")
if model.get_test_labels() is not None and type(y) == np.ndarray:
y = np.vstack((y, check_and_transform_label_format(model.get_test_labels(), nb_classes=self.nb_classes)))
self.nb_classes = self.get_nb_classes(y_train_pred)
y_train_pred = check_and_transform_label_format(y_train_pred, nb_classes=self.nb_classes)
y_test_pred = check_and_transform_label_format(y_test_pred, nb_classes=self.nb_classes)
x_pred = np.vstack((x_train_pred, x_test_pred))
y_pred = np.vstack((y_train_pred, y_test_pred))
elif x_test_pred is not None and y_test_pred is not None:
self.nb_classes = self.get_nb_classes(y_test_pred)
y_test_pred = check_and_transform_label_format(y_test_pred, nb_classes=self.nb_classes)
x_pred = x_test_pred
y_pred = y_test_pred
elif x_train_pred is not None and y_train_pred is not None:
self.nb_classes = self.get_nb_classes(y_train_pred)
y_train_pred = check_and_transform_label_format(y_train_pred, nb_classes=self.nb_classes)
x_pred = x_train_pred
y_pred = y_train_pred
else:
raise NotImplementedError("Invalid data - None")
predict_fn = (x, y)
self._art_model = BlackBoxClassifier(predict_fn, x.shape[1:], self.nb_classes, fuzzy_float_compare=True)
predict_fn = (x_pred, y_pred)
self._art_model = BlackBoxClassifier(predict_fn, x_pred.shape[1:], self.nb_classes, fuzzy_float_compare=True)
def fit(self, train_data: Dataset, **kwargs) -> None:
"""