Create initial version of wrappers for models (#1)

* New wrapper classes for models

apt/utils/models/sklearn_model.py

@@ -0,0 +1,142 @@
+import numpy as np
+from sklearn.preprocessing import OneHotEncoder
+
+from apt.utils.models import Model, ModelWithLoss, SingleOutputModel
+
+from art.estimators.classification.scikitlearn import SklearnClassifier as ArtSklearnClassifier
+from art.estimators.regression.scikitlearn import ScikitlearnRegressor
+
+
+class SklearnModel(Model):
+    """
+    Wrapper class for scikitlearn models.
+    """
+    def score(self, x: np.ndarray, y: np.ndarray, **kwargs):
+        """
+        Score the model using test data `(x, y)`.
+
+        :param x: Test data.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :param y: True labels.
+        :type y: `np.ndarray` or `pandas.DataFrame`
+        """
+        return self.model.score(x, y, **kwargs)
+
+
+class SklearnClassifier(SklearnModel):
+    """
+    Wrapper class for scikitlearn classification models.
+    """
+    def __init__(self, model, **kwargs):
+        """
+        Initialize a `SklearnClassifier` wrapper object.
+
+        :param model: The original sklearn model object
+        """
+        super().__init__(model, **kwargs)
+        self._art_model = ArtSklearnClassifier(model)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the model using the training data `(x, y)`.
+
+        :param x: Training data.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :param y: True labels.
+        :type y: `np.ndarray` or `pandas.DataFrame`
+        """
+        encoder = OneHotEncoder(sparse=False)
+        y_encoded = encoder.fit_transform(y.reshape(-1, 1))
+        self._art_model.fit(x, y_encoded, **kwargs)
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform predictions using the model for input `x`.
+
+        :param x: Input samples.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :return: Predictions from the model.
+        """
+        return self._art_model.predict(x, **kwargs)
+
+
+class SklearnRegressor(SklearnModel, SingleOutputModel, ModelWithLoss):
+    """
+    Wrapper class for scikitlearn regression models.
+    """
+    def __init__(self, model, **kwargs):
+        """
+        Initialize a `SklearnRegressor` wrapper object.
+
+        :param model: The original sklearn model object
+        """
+        super().__init__(model, **kwargs)
+        self._art_model = ScikitlearnRegressor(model)
+
+    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+        """
+        Fit the model using the training data `(x, y)`.
+
+        :param x: Training data.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :param y: True labels.
+        :type y: `np.ndarray` or `pandas.DataFrame`
+        """
+        self._art_model.fit(x, y, **kwargs)
+
+    def predict(self, x: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Perform predictions using the model for input `x`.
+
+        :param x: Input samples.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :return: Predictions from the model.
+        """
+        return self._art_model.predict(x, **kwargs)
+
+    def loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
+        """
+        Compute the loss of the model for samples `x`.
+
+        :param x: Input samples.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :param y: True labels.
+        :type y: `np.ndarray` or `pandas.DataFrame`
+        :return: Loss values.
+        """
+        return self._art_model.compute_loss(x, y, **kwargs)
+
+
+# Probably not needed for now, as we will not be using these wrappers directly in ART.
+# class SklearnDecisionTreeClassifier(SklearnClassifier, MultipleOutputModel):
+#     """
+#     Wrapper class for scikitlearn decision tree classifier models.
+#     """
+#     def __init__(self, model):
+#         """
+#         Initialize a `DecisionTreeClassifier` wrapper object.
+#
+#         :param model: The original sklearn decision tree model object
+#         """
+#         super().__init__(model)
+#         self._art_model = ScikitlearnDecisionTreeClassifier(model)
+#
+#     def get_decision_path(self, x: np.ndarray) -> np.ndarray:
+#         """
+#         Returns the nodes along the path taken in the tree when classifying x. Last node is the leaf, first node is the
+#         root node.
+#
+#         :param x: Input samples.
+#         :type x: `np.ndarray` or `pandas.DataFrame`
+#         :return: The indices of the nodes in the array structure of the tree.
+#         """
+#         return self._art_model.get_decision_path(x)
+#
+#     def get_samples_at_node(self, node_id: int) -> int:
+#         """
+#         Returns the number of training samples mapped to a node.
+#
+#         :param node_id: The ID of the node.
+#         :return: Number of samples mapped this node.
+#         """
+#         return self._art_model.get_samples_at_node(node_id)