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New model wrappers (#32)

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* keras wrapper + blackbox classifier wrapper (fix #7)

* fix error in NCP calculation

* Update notebooks

* Fix #25 (incorrect attack_feature indexes for social feature in notebook)

* Consistent naming of internal parameters
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abigailgold 2022-05-12 15:44:29 +03:00 • committed by GitHub
parent fd6be8e778
commit fe676fa426
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15 changed files with 1407 additions and 656 deletions
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15
apt/utils/models/sklearn_model.py
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@ -1,7 +1,5 @@
from typing import Optional
import numpy as np
from sklearn.preprocessing import OneHotEncoder
from sklearn.base import BaseEstimator
@ -10,6 +8,7 @@ from apt.utils.datasets import Dataset, OUTPUT_DATA_ARRAY_TYPE
from art.estimators.classification.scikitlearn import SklearnClassifier as ArtSklearnClassifier
from art.estimators.regression.scikitlearn import ScikitlearnRegressor
from art.utils import check_and_transform_label_format
class SklearnModel(Model):
@ -54,12 +53,14 @@ class SklearnClassifier(SklearnModel):
"""
Fit the model using the training data.
:param train_data: Training data.
:param train_data: Training data. Labels are expected to either be one-hot encoded or a 1D-array of categorical
labels (consecutive integers starting at 0).
:type train_data: `Dataset`
:return: None
"""
encoder = OneHotEncoder(sparse=False)
y_encoded = encoder.fit_transform(train_data.get_labels().reshape(-1, 1))
y = train_data.get_labels()
self.nb_classes = self.get_nb_classes(y)
y_encoded = check_and_transform_label_format(y, nb_classes=self.nb_classes)
self._art_model.fit(train_data.get_samples(), y_encoded, **kwargs)
def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE:
@ -70,7 +71,7 @@ class SklearnClassifier(SklearnModel):
:type x: `Dataset`
:return: Predictions from the model as numpy array (class probabilities, if supported).
"""
return self._art_model.predict(x, **kwargs)
return self._art_model.predict(x.get_samples(), **kwargs)
class SklearnRegressor(SklearnModel):
@ -112,4 +113,4 @@ class SklearnRegressor(SklearnModel):
:type x: `Dataset`
:return: Predictions from the model as numpy array.
"""
return self._art_model.predict(x, **kwargs)
return self._art_model.predict(x.get_samples(), **kwargs)