Add ScikitLearnRegressor; move sklean classes to their own file

tests/classifiers/test_adaptive.py

@@ -1,7 +1,6 @@
 #  MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
-from typing import cast
 
 from numpy.linalg import norm
 from sklearn.svm import SVC

tests/classifiers/test_cv.py

@@ -6,7 +6,7 @@ import numpy as np
 from numpy.linalg import norm
 from sklearn.svm import SVC
 
-from miplearn.classifiers import ScikitLearnClassifier
+from miplearn.classifiers.sklearn import ScikitLearnClassifier
 from miplearn.classifiers.cv import CrossValidatedClassifier
 from tests.classifiers import _build_circle_training_data
 

tests/classifiers/test_sklearn.py

@@ -4,37 +4,30 @@
 
 import numpy as np
 from numpy.testing import assert_array_equal
+from sklearn.linear_model import LinearRegression
 from sklearn.neighbors import KNeighborsClassifier
 
-from miplearn import ScikitLearnClassifier
+from miplearn.classifiers.sklearn import ScikitLearnClassifier, ScikitLearnRegressor
 
 
 def test_constant_prediction():
-    x_train = np.array(
-        [
-            [0.0, 1.0],
-            [1.0, 0.0],
-        ]
-    )
-    y_train = np.array(
-        [
-            [True, False],
-            [True, False],
-        ]
-    )
-    clf = ScikitLearnClassifier(
-        KNeighborsClassifier(
-            n_neighbors=1,
-        )
-    )
+    x_train = np.array([[0.0, 1.0], [1.0, 0.0]])
+    y_train = np.array([[True, False], [True, False]])
+    clf = ScikitLearnClassifier(KNeighborsClassifier(n_neighbors=1))
     clf.fit(x_train, y_train)
     proba = clf.predict_proba(x_train)
     assert_array_equal(
         proba,
-        np.array(
-            [
-                [1.0, 0.0],
-                [1.0, 0.0],
-            ]
-        ),
+        np.array([[1.0, 0.0], [1.0, 0.0]]),
     )
+
+
+def test_regressor():
+    x_train = np.array([[0.0, 1.0], [1.0, 4.0], [2.0, 2.0]])
+    y_train = np.array([[1.0], [5.0], [4.0]])
+    x_test = np.array([[4.0, 4.0], [0.0, 0.0]])
+    clf = ScikitLearnRegressor(LinearRegression())
+    clf.fit(x_train, y_train)
+    y_test_actual = clf.predict(x_test)
+    y_test_expected = np.array([[8.0], [0.0]])
+    assert_array_equal(np.round(y_test_actual, 2), y_test_expected)