Move tests to separate folder

2025-12-08 02:18:51 -06:00 · 2021-01-22 07:42:28 -06:00
parent e2048fc659
commit f90d78f802
25 changed files with 6 additions and 6 deletions
--- a/tests/components/test_primal.py
+++ b/tests/components/test_primal.py
@@ -0,0 +1,111 @@
+#  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.classifiers import Classifier
+from miplearn.components.primal import PrimalSolutionComponent
+from .. 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(
+            [
+                [0.0, 1.0],  # x[0]
+                [0.0, 1.0],  # x[1]
+                [1.0, 0.0],  # x[2]
+                [1.0, 0.0],  # x[3]
+            ]
+        )
+    )
+    clf_one = Mock(spec=Classifier)
+    clf_one.predict_proba = Mock(
+        return_value=np.array(
+            [
+                [1.0, 0.0],  # x[0] instances[0]
+                [1.0, 0.0],  # x[1] instances[0]
+                [0.0, 1.0],  # x[2] instances[0]
+                [1.0, 0.0],  # 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].training_data[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