Add ScikitLearnRegressor; move sklean classes to their own file

miplearn/classifiers/__init__.py

@@ -3,10 +3,9 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 from abc import ABC, abstractmethod
-from typing import Optional, Any, cast
+from typing import Optional
 
 import numpy as np
-import sklearn
 
 
 class Classifier(ABC):
@@ -150,58 +149,3 @@ class Regressor(ABC):
         Returns an unfitted copy of this regressor with the same hyperparameters.
         """
         pass
-
-
-class ScikitLearnClassifier(Classifier):
-    """
-    Wrapper for ScikitLearn classifiers, which makes sure inputs and outputs have the
-    correct dimensions and types.
-    """
-
-    def __init__(self, clf: Any) -> None:
-        super().__init__()
-        self.inner_clf = clf
-        self.constant: Optional[np.ndarray] = None
-
-    def fit(self, x_train: np.ndarray, y_train: np.ndarray) -> None:
-        super().fit(x_train, y_train)
-        (n_samples, n_classes) = y_train.shape
-        assert n_classes == 2, (
-            f"Scikit-learn classifiers must have exactly two classes. "
-            f"{n_classes} classes were provided instead."
-        )
-
-        # When all samples belong to the same class, sklearn's predict_proba returns
-        # an array with a single column. The following check avoid this strange
-        # behavior.
-        mean = cast(np.ndarray, y_train.astype(float).mean(axis=0))
-        if mean.max() == 1.0:
-            self.constant = mean
-            return
-
-        self.inner_clf.fit(x_train, y_train[:, 1])
-
-    def predict_proba(self, x_test: np.ndarray) -> np.ndarray:
-        super().predict_proba(x_test)
-        n_samples = x_test.shape[0]
-        if self.constant is not None:
-            return np.array([self.constant for n in range(n_samples)])
-        sklearn_proba = self.inner_clf.predict_proba(x_test)
-        if isinstance(sklearn_proba, list):
-            assert len(sklearn_proba) == self.n_classes
-            for pb in sklearn_proba:
-                assert isinstance(pb, np.ndarray)
-                assert pb.dtype in [np.float16, np.float32, np.float64]
-                assert pb.shape == (n_samples, 2)
-            proba = np.hstack([pb[:, [1]] for pb in sklearn_proba])
-            assert proba.shape == (n_samples, self.n_classes)
-            return proba
-        else:
-            assert isinstance(sklearn_proba, np.ndarray)
-            assert sklearn_proba.shape == (n_samples, 2)
-            return sklearn_proba
-
-    def clone(self) -> "ScikitLearnClassifier":
-        return ScikitLearnClassifier(
-            clf=sklearn.base.clone(self.inner_clf),
-        )