Reorganize directories

miplearn/tests/test_extractors.py

@@ -1,62 +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.
-
-from miplearn.problems.knapsack import KnapsackInstance
-from miplearn import (LearningSolver,
-                      SolutionExtractor,
-                      InstanceFeaturesExtractor,
-                      VariableFeaturesExtractor,
-                     )
-import numpy as np
-import pyomo.environ as pe
-
-
-def _get_instances():
-    instances = [
-        KnapsackInstance(weights=[1., 2., 3.],
-                         prices=[10., 20., 30.],
-                         capacity=2.5,
-                        ),
-        KnapsackInstance(weights=[3., 4., 5.],
-                         prices=[20., 30., 40.],
-                         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_solution_extractor():
-    instances, models = _get_instances()
-    features = SolutionExtractor().extract(instances)
-    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.,
-    ]
-
-    
-def test_instance_features_extractor():
-    instances, models = _get_instances()
-    features = InstanceFeaturesExtractor().extract(instances)
-    assert features.shape == (2,3)
-    
-    
-def test_variable_features_extractor():
-    instances, models = _get_instances()
-    features = VariableFeaturesExtractor().extract(instances)
-    assert isinstance(features, dict)
-    assert "default" in features
-    assert features["default"].shape == (6,5)
-