Redesign component.evaluate

tests/components/test_primal.py

@@ -1,7 +1,7 @@
 #  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 Dict
 from unittest.mock import Mock
 
 import numpy as np
@@ -10,6 +10,7 @@ from scipy.stats import randint
 
 from miplearn import Classifier, LearningSolver
 from miplearn.classifiers.threshold import Threshold
+from miplearn.components import classifier_evaluation_dict
 from miplearn.components.primal import PrimalSolutionComponent
 from miplearn.problems.tsp import TravelingSalesmanGenerator
 from miplearn.types import TrainingSample, Features
@@ -69,7 +70,7 @@ def test_xy() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent.xy(features, sample)
+    xy = PrimalSolutionComponent.sample_xy(features, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -122,7 +123,7 @@ def test_xy_without_lp_solution() -> None:
             [True, False],
         ]
     }
-    xy = PrimalSolutionComponent.xy(features, sample)
+    xy = PrimalSolutionComponent.sample_xy(features, sample)
     assert xy is not None
     x_actual, y_actual = xy
     assert x_actual == x_expected
@@ -169,11 +170,11 @@ def test_predict() -> None:
             }
         }
     }
-    x, _ = PrimalSolutionComponent.xy(features, sample)
+    x, _ = PrimalSolutionComponent.sample_xy(features, sample)
     comp = PrimalSolutionComponent()
     comp.classifiers = {"default": clf}
     comp.thresholds = {"default": thr}
-    solution_actual = comp.predict(features, sample)
+    solution_actual = comp.sample_predict(features, sample)
     clf.predict_proba.assert_called_once()
     assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
     thr.predict.assert_called_once()
@@ -229,3 +230,43 @@ def test_usage():
     assert stats["Primal: Free"] == 0
     assert stats["Primal: One"] + stats["Primal: Zero"] == 10
     assert stats["Lower bound"] == stats["Warm start value"]
+
+
+def test_evaluate() -> None:
+    comp = PrimalSolutionComponent()
+    comp.sample_predict = lambda _, __: {  # type: ignore
+        "x": {
+            0: 1.0,
+            1: 0.0,
+            2: 0.0,
+            3: None,
+            4: 1.0,
+        }
+    }
+    features: Features = {
+        "Variables": {
+            "x": {
+                0: {},
+                1: {},
+                2: {},
+                3: {},
+                4: {},
+            }
+        }
+    }
+    sample: TrainingSample = {
+        "Solution": {
+            "x": {
+                0: 1.0,
+                1: 1.0,
+                2: 0.0,
+                3: 1.0,
+                4: 1.0,
+            }
+        }
+    }
+    ev = comp.sample_evaluate(features, sample)
+    assert ev == {
+        0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),
+        1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2),
+    }