Move python files to root folder; remove built docs

miplearn/tests/__init__.py

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+#  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 import LearningSolver
+from miplearn.problems.knapsack import KnapsackInstance
+
+
+def get_test_pyomo_instances():
+    instances = [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+        KnapsackInstance(
+            weights=[25., 30., 22., 18.],
+            prices=[500., 365., 420., 150.],
+            capacity=70.,
+        ),
+    ]
+    models = [instance.to_model() for instance in instances]
+    solver = LearningSolver()
+    for i in range(len(instances)):
+        solver.solve(instances[i], models[i])
+    return instances, models

miplearn/tests/test_benchmark.py

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+#  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 os.path
+
+from miplearn import LearningSolver, BenchmarkRunner
+from miplearn.problems.stab import MaxWeightStableSetGenerator
+from scipy.stats import randint
+
+
+def test_benchmark():
+    # Generate training and test instances
+    train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)    
+    test_instances  = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3)
+
+    # Training phase...
+    training_solver = LearningSolver()
+    training_solver.parallel_solve(train_instances, n_jobs=10)
+
+    # Test phase...
+    test_solvers = {
+        "Strategy A": LearningSolver(),
+        "Strategy B": LearningSolver(),
+    }
+    benchmark = BenchmarkRunner(test_solvers)
+    benchmark.fit(train_instances)
+    benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
+    assert benchmark.raw_results().values.shape == (12,16)
+    
+    benchmark.save_results("/tmp/benchmark.csv")
+    assert os.path.isfile("/tmp/benchmark.csv")
+    
+    benchmark = BenchmarkRunner(test_solvers)
+    benchmark.load_results("/tmp/benchmark.csv")
+    assert benchmark.raw_results().values.shape == (12,16)

miplearn/tests/test_extractors.py

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+#  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)
+