Data and Model wrappers (#26)

* Squashed commit of wrappers:

    Wrapper minimizer

    * apply dataset wrapper on minimizer
    * apply changes on minimization notebook
    * add black_box_access and unlimited_queries params

    Dataset wrapper anonymizer

    Add features_names to ArrayDataset
    and allow providing features names in QI and Cat features not just indexes

    update notebooks

    categorical features and QI passed by indexes
    dataset include feature names and is_pandas param

    add pytorch Dataset

    Remove redundant code.
    Use data wrappers in model wrapper APIs.

    add generic dataset components 

    Create initial version of wrappers for models

* Fix handling of categorical features

apt/utils/models/sklearn_model.py

@@ -0,0 +1,112 @@
+from typing import Optional
+
+import numpy as np
+
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.base import BaseEstimator
+
+from apt.utils.models import Model, ModelOutputType
+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
+
+
+class SklearnModel(Model):
+    """
+    Wrapper class for scikitlearn models.
+    """
+    def score(self, test_data: Dataset, **kwargs):
+        """
+        Score the model using test data.
+
+        :param test_data: Test data.
+        :type train_data: `Dataset`
+        """
+        return self.model.score(test_data.get_samples(), test_data.get_labels(), **kwargs)
+
+
+class SklearnClassifier(SklearnModel):
+    """
+    Wrapper class for scikitlearn classification models.
+    """
+    def __init__(self, model: BaseEstimator, output_type: ModelOutputType, black_box_access: Optional[bool] = True,
+                 unlimited_queries: Optional[bool] = True, **kwargs):
+        """
+        Initialize a `SklearnClassifier` wrapper object.
+
+        :param model: The original sklearn model object.
+        :param output_type: The type of output the model yields (vector/label only for classifiers,
+                            value for regressors)
+        :param black_box_access: Boolean describing the type of deployment of the model (when in production).
+                                 Set to True if the model is only available via query (API) access, i.e.,
+                                 only the outputs of the model are exposed, and False if the model internals
+                                 are also available. Optional, Default is True.
+        :param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform
+                                  unlimited queries to the model API or whether there is a limit to the number of
+                                  queries that can be submitted. Optional, Default is True.
+        """
+        super().__init__(model, output_type, black_box_access, unlimited_queries, **kwargs)
+        self._art_model = ArtSklearnClassifier(model)
+
+    def fit(self, train_data: Dataset, **kwargs) -> None:
+        """
+        Fit the model using the training data.
+
+        :param train_data: Training data.
+        :type train_data: `Dataset`
+        """
+        encoder = OneHotEncoder(sparse=False)
+        y_encoded = encoder.fit_transform(train_data.get_labels().reshape(-1, 1))
+        self._art_model.fit(train_data.get_samples(), y_encoded, **kwargs)
+
+    def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE:
+        """
+        Perform predictions using the model for input `x`.
+
+        :param x: Input samples.
+        :type x: `np.ndarray` or `pandas.DataFrame`
+        :return: Predictions from the model (class probabilities, if supported).
+        """
+        return self._art_model.predict(x, **kwargs)
+
+
+class SklearnRegressor(SklearnModel):
+    """
+    Wrapper class for scikitlearn regression models.
+    """
+    def __init__(self, model: BaseEstimator, black_box_access: Optional[bool] = True,
+                 unlimited_queries: Optional[bool] = True, **kwargs):
+        """
+        Initialize a `SklearnRegressor` wrapper object.
+
+        :param model: The original sklearn model object.
+        :param black_box_access: Boolean describing the type of deployment of the model (when in production).
+                                 Set to True if the model is only available via query (API) access, i.e.,
+                                 only the outputs of the model are exposed, and False if the model internals
+                                 are also available. Optional, Default is True.
+        :param unlimited_queries: If black_box_access is True, this boolean indicates whether a user can perform
+                                  unlimited queries to the model API or whether there is a limit to the number of
+                                  queries that can be submitted. Optional, Default is True.
+        """
+        super().__init__(model, ModelOutputType.REGRESSOR_SCALAR, black_box_access, unlimited_queries, **kwargs)
+        self._art_model = ScikitlearnRegressor(model)
+
+    def fit(self, train_data: Dataset, **kwargs) -> None:
+        """
+        Fit the model using the training data.
+
+        :param train_data: Training data.
+        :type train_data: `Dataset`
+        """
+        self._art_model.fit(train_data.get_samples(), train_data.get_labels(), **kwargs)
+
+    def predict(self, x: Dataset, **kwargs) -> OUTPUT_DATA_ARRAY_TYPE:
+        """
+        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)