Move python files to root folder; remove built docs

miplearn/components/tests/test_primal.py

@@ -0,0 +1,99 @@
+#  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 unittest.mock import Mock
+
+import numpy as np
+from miplearn import PrimalSolutionComponent
+from miplearn.classifiers import Classifier
+from miplearn.tests import get_test_pyomo_instances
+
+
+def test_predict():
+    instances, models = get_test_pyomo_instances()
+    comp = PrimalSolutionComponent()
+    comp.fit(instances)
+    solution = comp.predict(instances[0])
+    assert "x" in solution
+    assert 0 in solution["x"]
+    assert 1 in solution["x"]
+    assert 2 in solution["x"]
+    assert 3 in solution["x"]
+
+
+def test_evaluate():
+    instances, models = get_test_pyomo_instances()
+    clf_zero = Mock(spec=Classifier)
+    clf_zero.predict_proba = Mock(return_value=np.array([
+        [0., 1.],  # x[0]
+        [0., 1.],  # x[1]
+        [1., 0.],  # x[2]
+        [1., 0.],  # x[3]
+    ]))
+    clf_one = Mock(spec=Classifier)
+    clf_one.predict_proba = Mock(return_value=np.array([
+        [1., 0.],  # x[0] instances[0]
+        [1., 0.],  # x[1] instances[0]
+        [0., 1.],  # x[2] instances[0]
+        [1., 0.],  # x[3] instances[0]
+    ]))
+    comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one],
+                                   threshold=0.50)
+    comp.fit(instances[:1])
+    assert comp.predict(instances[0]) == {"x": {0: 0,
+                                                1: 0,
+                                                2: 1,
+                                                3: None}}
+    assert instances[0].solution == {"x": {0: 1,
+                                           1: 0,
+                                           2: 1,
+                                           3: 1}}
+    ev = comp.evaluate(instances[:1])
+    assert ev == {'Fix one': {0: {'Accuracy': 0.5,
+                                  'Condition negative': 1,
+                                  'Condition negative (%)': 25.0,
+                                  'Condition positive': 3,
+                                  'Condition positive (%)': 75.0,
+                                  'F1 score': 0.5,
+                                  'False negative': 2,
+                                  'False negative (%)': 50.0,
+                                  'False positive': 0,
+                                  'False positive (%)': 0.0,
+                                  'Precision': 1.0,
+                                  'Predicted negative': 3,
+                                  'Predicted negative (%)': 75.0,
+                                  'Predicted positive': 1,
+                                  'Predicted positive (%)': 25.0,
+                                  'Recall': 0.3333333333333333,
+                                  'True negative': 1,
+                                  'True negative (%)': 25.0,
+                                  'True positive': 1,
+                                  'True positive (%)': 25.0}},
+                  'Fix zero': {0: {'Accuracy': 0.75,
+                                   'Condition negative': 3,
+                                   'Condition negative (%)': 75.0,
+                                   'Condition positive': 1,
+                                   'Condition positive (%)': 25.0,
+                                   'F1 score': 0.6666666666666666,
+                                   'False negative': 0,
+                                   'False negative (%)': 0.0,
+                                   'False positive': 1,
+                                   'False positive (%)': 25.0,
+                                   'Precision': 0.5,
+                                   'Predicted negative': 2,
+                                   'Predicted negative (%)': 50.0,
+                                   'Predicted positive': 2,
+                                   'Predicted positive (%)': 50.0,
+                                   'Recall': 1.0,
+                                   'True negative': 2,
+                                   'True negative (%)': 50.0,
+                                   'True positive': 1,
+                                   'True positive (%)': 25.0}}}
+
+
+def test_primal_parallel_fit():
+    instances, models = get_test_pyomo_instances()
+    comp = PrimalSolutionComponent()
+    comp.fit(instances, n_jobs=2)
+    assert len(comp.classifiers) == 2