Remove obsolete extractor classes

miplearn/__init__.py

@@ -22,7 +22,6 @@ from .components.primal import PrimalSolutionComponent
 from .components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
 from .components.steps.drop_redundant import DropRedundantInequalitiesStep
 from .components.steps.relax_integrality import RelaxIntegralityStep
-from .extractors import InstanceFeaturesExtractor
 from .instance import (
     Instance,
     PickleGzInstance,

miplearn/extractors.py

@@ -1,45 +0,0 @@
-#  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.
-
-import logging
-from abc import ABC, abstractmethod
-
-import numpy as np
-
-logger = logging.getLogger(__name__)
-
-
-class Extractor(ABC):
-    @abstractmethod
-    def extract(self, instances):
-        pass
-
-    @staticmethod
-    def split_variables(instance):
-        result = {}
-        lp_solution = instance.training_data[0].lp_solution
-        for var_name in lp_solution:
-            for index in lp_solution[var_name]:
-                category = instance.get_variable_category(var_name, index)
-                if category is None:
-                    continue
-                if category not in result:
-                    result[category] = []
-                result[category] += [(var_name, index)]
-        return result
-
-
-class InstanceFeaturesExtractor(Extractor):
-    def extract(self, instances):
-        return np.vstack(
-            [
-                np.hstack(
-                    [
-                        instance.get_instance_features(),
-                        instance.training_data[0].lp_value,
-                    ]
-                )
-                for instance in instances
-            ]
-        )

tests/test_extractors.py

@@ -1,34 +0,0 @@
-#  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.
-import numpy as np
-
-from miplearn.extractors import InstanceFeaturesExtractor
-from miplearn.problems.knapsack import KnapsackInstance
-from miplearn.solvers.learning import LearningSolver
-
-
-def _get_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[1.0, 2.0, 3.0],
-            prices=[10.0, 20.0, 30.0],
-            capacity=2.5,
-        ),
-        KnapsackInstance(
-            weights=[3.0, 4.0, 5.0],
-            prices=[20.0, 30.0, 40.0],
-            capacity=4.5,
-        ),
-    ]
-    models = [instance.to_model() for instance in instances]
-    solver = LearningSolver()
-    for (i, instance) in enumerate(instances):
-        solver.solve(instances[i], models[i])
-    return instances, models
-
-
-def test_instance_features_extractor():
-    instances, models = _get_instances()
-    features = InstanceFeaturesExtractor().extract(instances)
-    assert features.shape == (2, 3)