Implement UserFeaturesExtractor and SolutionExtractor

miplearn/extractors.py

@@ -0,0 +1,70 @@
+# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+import numpy as np
+from abc import ABC, abstractmethod
+from pyomo.core import Var
+
+
+class Extractor(ABC):
+    @abstractmethod
+    def extract(self, instances, models):
+        pass
+    
+    @staticmethod
+    def split_variables(instance, model):
+        result = {}
+        for var in model.component_objects(Var):
+            for index in var:
+                category = instance.get_variable_category(var, index)
+                if category is None:
+                    continue
+                if category not in result.keys():
+                    result[category] = []
+                result[category] += [(var, index)]
+        return result
+
+
+class UserFeaturesExtractor(Extractor):
+    def extract(self,
+                instances,
+                models=None,
+               ):
+        result = {}
+        if models is None:
+            models = [instance.to_model() for instance in instances]
+        for (index, instance) in enumerate(instances):
+            model = models[index]
+            instance_features = instance.get_instance_features()
+            var_split = self.split_variables(instance, model)
+            for (category, var_index_pairs) in var_split.items():
+                if category not in result.keys():
+                    result[category] = []
+                for (var, index) in var_index_pairs:
+                    result[category] += [np.hstack([
+                        instance_features,
+                        instance.get_variable_features(var, index),
+                    ])]
+        for category in result.keys():
+            result[category] = np.vstack(result[category])
+        return result
+
+
+class SolutionExtractor(Extractor):
+    def extract(self, instances, models):
+        result = {}
+        for (index, instance) in enumerate(instances):
+            model = models[index]
+            var_split = self.split_variables(instance, model)
+            for (category, var_index_pairs) in var_split.items():
+                if category not in result.keys():
+                    result[category] = []
+                for (var, index) in var_index_pairs:
+                    result[category] += [[
+                        1 - var[index].value,
+                        var[index].value,
+                    ]]
+        for category in result.keys():
+            result[category] = np.vstack(result[category])            
+        return result

miplearn/tests/test_extractors.py

@@ -0,0 +1,54 @@
+# MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
+# Copyright (C) 2019-2020 Argonne National Laboratory. All rights reserved.
+# Written by Alinson S. Xavier <axavier@anl.gov>
+
+from miplearn.problems.knapsack import KnapsackInstance
+from miplearn import (UserFeaturesExtractor,
+                      SolutionExtractor)
+import numpy as np
+import pyomo.environ as pe
+
+
+def _get_instances():
+    return [
+        KnapsackInstance(weights=[1., 2., 3.],
+                         prices=[10., 20., 30.],
+                         capacity=2.5,
+                        ),
+        KnapsackInstance(weights=[3., 4., 5.],
+                         prices=[20., 30., 40.],
+                         capacity=4.5,
+                        ),
+    ]
+
+
+def test_user_features():
+    instances = _get_instances()
+    extractor = UserFeaturesExtractor()
+    features = extractor.extract(instances)
+    assert isinstance(features, dict)
+    assert "default" in features.keys()
+    assert isinstance(features["default"], np.ndarray)
+    assert features["default"].shape == (6, 4)
+
+    
+def test_solution_extractor():
+    instances = _get_instances()
+    models = [instance.to_model() for instance in instances]
+    for model in models:
+        solver = pe.SolverFactory("cbc")
+        solver.solve(model)
+    extractor = SolutionExtractor()
+    features = extractor.extract(instances, models)
+    assert isinstance(features, dict)
+    assert "default" in features.keys()
+    assert isinstance(features["default"], np.ndarray)
+    assert features["default"].shape == (6, 2)
+    assert features["default"].ravel().tolist() == [
+        1., 0.,
+        0., 1.,
+        1., 0.,
+        1., 0.,
+        0., 1.,
+        1., 0.,
+    ]