Move components into submodule

miplearn/components/tests/test_branching.py

@@ -0,0 +1,49 @@
+# 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 import BranchPriorityComponent, LearningSolver
+from miplearn.problems.knapsack import KnapsackInstance
+import numpy as np
+import tempfile
+
+def _get_instances():
+    return [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+    ] * 2
+
+
+def test_branching():
+    instances = _get_instances()
+    component = BranchPriorityComponent()
+    for instance in instances:
+        component.after_solve(None, instance, None)
+    component.fit(None)
+    for key in ["default"]:
+        assert key in component.x_train.keys()
+        assert key in component.y_train.keys()
+        assert component.x_train[key].shape == (8,  4)
+        assert component.y_train[key].shape == (8,  1)
+        
+        
+def test_branch_priority_save_load():
+    state_file = tempfile.NamedTemporaryFile(mode="r")
+    solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
+    solver.parallel_solve(_get_instances(), n_jobs=2)
+    solver.fit()
+    comp = solver.components["branch-priority"]
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 1)
+    assert "default" in comp.predictors.keys()
+    solver.save_state(state_file.name)
+    
+    solver = LearningSolver(components={"branch-priority": BranchPriorityComponent()})
+    solver.load_state(state_file.name)
+    comp = solver.components["branch-priority"]
+    assert comp.x_train["default"].shape == (8, 4)
+    assert comp.y_train["default"].shape == (8, 1)
+    assert "default" in comp.predictors.keys()

miplearn/components/tests/test_warmstart.py

@@ -0,0 +1,37 @@
+# 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 import WarmStartComponent, LearningSolver
+from miplearn.problems.knapsack import KnapsackInstance
+import numpy as np
+import tempfile
+
+
+def _get_instances():
+    return [
+        KnapsackInstance(
+            weights=[23., 26., 20., 18.],
+            prices=[505., 352., 458., 220.],
+            capacity=67.,
+        ),
+    ] * 2
+
+
+def test_warm_start_save_load():
+    state_file = tempfile.NamedTemporaryFile(mode="r")
+    solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+    solver.parallel_solve(_get_instances(), n_jobs=2)
+    solver.fit()
+    comp = solver.components["warm-start"]
+    assert comp.x_train["default"].shape == (8, 6)
+    assert comp.y_train["default"].shape == (8, 2)
+    assert "default" in comp.predictors.keys()
+    solver.save_state(state_file.name)
+    
+    solver = LearningSolver(components={"warm-start": WarmStartComponent()})
+    solver.load_state(state_file.name)
+    comp = solver.components["warm-start"]
+    assert comp.x_train["default"].shape == (8, 6)
+    assert comp.y_train["default"].shape == (8, 2)
+    assert "default" in comp.predictors.keys()

miplearn/components/tests/test_warmstart_knn.py

@@ -0,0 +1,88 @@
+# 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 import KnnWarmStartPredictor
+from sklearn.metrics import accuracy_score, precision_score
+import numpy as np
+
+
+def test_knn_with_consensus():
+    x_train = np.array([
+        [0.0, 0.0],
+        [0.1, 0.0],
+        [0.0, 0.1],
+        [1.0, 1.0],
+    ])
+    y_train = np.array([
+        [0., 1.],
+        [0., 1.],
+        [0., 1.],
+        [1., 0.],
+    ])
+    ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
+    ws.fit(x_train, y_train)
+    
+    x_test = np.array([[0.0, 0.0]])
+    y_test = np.array([[0, 1]])
+    assert (ws.predict(x_test) == y_test).all()
+    
+def test_knn_without_consensus():
+    x_train = np.array([
+        [0.0, 0.0],
+        [0.1, 0.1],
+        [0.9, 0.9],
+        [1.0, 1.0],
+    ])
+    y_train = np.array([
+        [0., 1.],
+        [0., 1.],
+        [1., 0.],
+        [1., 0.],
+    ])
+    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
+    ws.fit(x_train, y_train)
+    
+    x_test = np.array([[0.5, 0.5]])
+    y_test = np.array([[0, 0]])
+    assert (ws.predict(x_test) == y_test).all()    
+    
+def test_knn_always_true():
+    x_train = np.array([
+        [0.0, 0.0],
+        [0.1, 0.1],
+        [0.9, 0.9],
+        [1.0, 1.0],
+    ])
+    y_train = np.array([
+        [1., 0.],
+        [1., 0.],
+        [1., 0.],
+        [1., 0.],
+    ])
+    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
+    ws.fit(x_train, y_train)
+    
+    x_test = np.array([[0.5, 0.5]])
+    y_test = np.array([[1, 0]])
+    assert (ws.predict(x_test) == y_test).all()       
+    
+def test_knn_always_false():
+    x_train = np.array([
+        [0.0, 0.0],
+        [0.1, 0.1],
+        [0.9, 0.9],
+        [1.0, 1.0],
+    ])
+    y_train = np.array([
+        [0., 1.],
+        [0., 1.],
+        [0., 1.],
+        [0., 1.],
+    ])
+    ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
+    ws.fit(x_train, y_train)
+    
+    x_test = np.array([[0.5, 0.5]])
+    y_test = np.array([[0, 1]])
+    assert (ws.predict(x_test) == y_test).all()           

miplearn/components/tests/test_warmstart_logistic.py

@@ -0,0 +1,64 @@
+# 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 import LogisticWarmStartPredictor
+from sklearn.metrics import accuracy_score, precision_score
+import numpy as np
+
+
+def _generate_dataset(ground_truth, n_samples=10_000):
+    x_train = np.random.rand(n_samples,5)
+    x_test = np.random.rand(n_samples,5)
+    y_train = ground_truth(x_train)
+    y_test = ground_truth(x_test)
+    return x_train, y_train, x_test, y_test
+
+
+def _is_sum_greater_than_two(x):
+    y = (np.sum(x, axis=1) > 2.0).astype(int)
+    return np.vstack([y, 1 - y]).transpose()
+
+
+def _always_zero(x):
+    y = np.zeros((1, x.shape[0]))
+    return np.vstack([y, 1 - y]).transpose()
+
+
+def _random_values(x):
+    y = np.random.randint(2, size=x.shape[0])
+    return np.vstack([y, 1 - y]).transpose()
+    
+    
+def test_logistic_ws_with_balanced_labels():
+    x_train, y_train, x_test, y_test = _generate_dataset(_is_sum_greater_than_two)
+    ws = LogisticWarmStartPredictor()
+    ws.fit(x_train, y_train)
+    y_pred = ws.predict(x_test)
+    assert accuracy_score(y_test[:,0], y_pred[:,0]) > 0.99
+    assert accuracy_score(y_test[:,1], y_pred[:,1]) > 0.99
+    
+    
+def test_logistic_ws_with_unbalanced_labels():
+    x_train, y_train, x_test, y_test = _generate_dataset(_always_zero)
+    ws = LogisticWarmStartPredictor()
+    ws.fit(x_train, y_train)
+    y_pred = ws.predict(x_test)
+    assert accuracy_score(y_test[:,0], y_pred[:,0]) == 1.0
+    assert accuracy_score(y_test[:,1], y_pred[:,1]) == 1.0
+
+    
+def test_logistic_ws_with_unpredictable_labels():
+    x_train, y_train, x_test, y_test = _generate_dataset(_random_values)
+    ws = LogisticWarmStartPredictor()
+    ws.fit(x_train, y_train)
+    y_pred = ws.predict(x_test)
+    assert np.sum(y_pred) == 0
+
+    
+def test_logistic_ws_with_small_sample_size():
+    x_train, y_train, x_test, y_test = _generate_dataset(_random_values, n_samples=3)
+    ws = LogisticWarmStartPredictor()
+    ws.fit(x_train, y_train)
+    y_pred = ws.predict(x_test)
+    assert np.sum(y_pred) == 0