Rewrite PrimalSolutionComponent.sample_xy

miplearn/components/primal.py

@@ -20,7 +20,7 @@ from miplearn.classifiers.adaptive import AdaptiveClassifier
 from miplearn.classifiers.threshold import MinPrecisionThreshold, Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.component import Component
-from miplearn.features import TrainingSample, Features
+from miplearn.features import TrainingSample, Features, Sample
 from miplearn.instance.base import Instance
 from miplearn.types import (
     LearningSolveStats,
@@ -179,6 +179,50 @@ class PrimalSolutionComponent(Component):
                 y[category] += [[opt_value < 0.5, opt_value >= 0.5]]
         return x, y
 
+    @overrides
+    def sample_xy(
+        self,
+        sample: Sample,
+    ) -> Tuple[Dict[Category, List[List[float]]], Dict[Category, List[List[float]]]]:
+        x: Dict = {}
+        y: Dict = {}
+        assert sample.after_load is not None
+        assert sample.after_load.variables is not None
+        for (var_name, var) in sample.after_load.variables.items():
+            # Initialize categories
+            category = var.category
+            if category is None:
+                continue
+            if category not in x.keys():
+                x[category] = []
+                y[category] = []
+
+            # Features
+            sf = sample.after_load
+            if sample.after_lp is not None:
+                sf = sample.after_lp
+            assert sf.instance is not None
+            features = list(sf.instance.to_list())
+            assert sf.variables is not None
+            assert sf.variables[var_name] is not None
+            features.extend(sf.variables[var_name].to_list())
+            x[category].append(features)
+
+            # Labels
+            if sample.after_mip is not None:
+                assert sample.after_mip.variables is not None
+                assert sample.after_mip.variables[var_name] is not None
+                opt_value = sample.after_mip.variables[var_name].value
+                assert opt_value is not None
+                assert 0.0 - 1e-5 <= opt_value <= 1.0 + 1e-5, (
+                    f"Variable {var_name} has non-binary value {opt_value} in the "
+                    "optimal solution. Predicting values of non-binary "
+                    "variables is not currently supported. Please set its "
+                    "category to None."
+                )
+                y[category].append([opt_value < 0.5, opt_value >= 0.5])
+        return x, y
+
     @overrides
     def sample_evaluate_old(
         self,

tests/components/test_primal.py

@@ -5,6 +5,7 @@ from typing import cast
 from unittest.mock import Mock
 
 import numpy as np
+import pytest
 from numpy.testing import assert_array_equal
 from scipy.stats import randint
 
@@ -12,13 +13,83 @@ from miplearn.classifiers import Classifier
 from miplearn.classifiers.threshold import Threshold
 from miplearn.components import classifier_evaluation_dict
 from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.features import TrainingSample, Variable, Features
+from miplearn.features import (
+    TrainingSample,
+    Variable,
+    Features,
+    Sample,
+    InstanceFeatures,
+)
 from miplearn.instance.base import Instance
 from miplearn.problems.tsp import TravelingSalesmanGenerator
 from miplearn.solvers.learning import LearningSolver
 
 
-def test_xy() -> None:
+@pytest.fixture
+def sample() -> Sample:
+    sample = Sample(
+        after_load=Features(
+            variables={
+                "x[0]": Variable(category="default"),
+                "x[1]": Variable(category=None),
+                "x[2]": Variable(category="default"),
+                "x[3]": Variable(category="default"),
+            },
+        ),
+        after_lp=Features(
+            instance=InstanceFeatures(),
+            variables={
+                "x[0]": Variable(),
+                "x[1]": Variable(),
+                "x[2]": Variable(),
+                "x[3]": Variable(),
+            },
+        ),
+        after_mip=Features(
+            variables={
+                "x[0]": Variable(value=0.0),
+                "x[1]": Variable(value=0.0),
+                "x[2]": Variable(value=1.0),
+                "x[3]": Variable(value=0.0),
+            }
+        ),
+    )
+    sample.after_lp.instance.to_list = Mock(return_value=[5.0])  # type: ignore
+    sample.after_lp.variables["x[0]"].to_list = Mock(  # type: ignore
+        return_value=[0.0, 0.0]
+    )
+    sample.after_lp.variables["x[2]"].to_list = Mock(  # type: ignore
+        return_value=[1.0, 0.0]
+    )
+    sample.after_lp.variables["x[3]"].to_list = Mock(  # type: ignore
+        return_value=[1.0, 1.0]
+    )
+    return sample
+
+
+def test_xy(sample: Sample) -> None:
+    x_expected = {
+        "default": [
+            [5.0, 0.0, 0.0],
+            [5.0, 1.0, 0.0],
+            [5.0, 1.0, 1.0],
+        ]
+    }
+    y_expected = {
+        "default": [
+            [True, False],
+            [False, True],
+            [True, False],
+        ]
+    }
+    xy = PrimalSolutionComponent().sample_xy(sample)
+    assert xy is not None
+    x_actual, y_actual = xy
+    assert x_actual == x_expected
+    assert y_actual == y_expected
+
+
+def test_xy_old() -> None:
     features = Features(
         variables={
             "x[0]": Variable(