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

tests/test_model.py

@@ -0,0 +1,35 @@
+import pytest
+
+from apt.utils.models import SklearnClassifier, SklearnRegressor, ModelOutputType
+from apt.utils.datasets import ArrayDataset
+from apt.utils import dataset_utils
+
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.ensemble import RandomForestClassifier
+
+
+def test_sklearn_classifier():
+    (x_train, y_train), (x_test, y_test) = dataset_utils.get_iris_dataset()
+    underlying_model = RandomForestClassifier()
+    model = SklearnClassifier(underlying_model, ModelOutputType.CLASSIFIER_VECTOR)
+    train = ArrayDataset(x_train, y_train)
+    test = ArrayDataset(x_test, y_test)
+    model.fit(train)
+    pred = model.predict(x_test)
+    assert(pred.shape[0] == x_test.shape[0])
+
+    score = model.score(test)
+    assert(0.0 <= score <= 1.0)
+
+
+def test_sklearn_regressor():
+    (x_train, y_train), (x_test, y_test) = dataset_utils.get_diabetes_dataset()
+    underlying_model = DecisionTreeRegressor()
+    model = SklearnRegressor(underlying_model)
+    train = ArrayDataset(x_train, y_train)
+    test = ArrayDataset(x_test, y_test)
+    model.fit(train)
+    pred = model.predict(x_test)
+    assert (pred.shape[0] == x_test.shape[0])
+
+    score = model.score(test)