From 92c51169640163112949ccbf39717b88c7a26caa Mon Sep 17 00:00:00 2001 From: "Alinson S. Xavier" Date: Fri, 22 Jan 2021 07:43:55 -0600 Subject: [PATCH] Update 0.2 docs --- 0.2/api/miplearn/classifiers/index.html | 5 - 0.2/api/miplearn/classifiers/tests/index.html | 88 -- .../classifiers/tests/test_counting.html | 101 --- .../miplearn/classifiers/tests/test_cv.html | 161 ---- .../classifiers/tests/test_evaluator.html | 107 --- .../classifiers/tests/test_threshold.html | 141 ---- 0.2/api/miplearn/components/index.html | 5 - 0.2/api/miplearn/components/steps/index.html | 5 - .../components/steps/tests/index.html | 70 -- .../steps/tests/test_convert_tight.html | 385 --------- .../steps/tests/test_drop_redundant.html | 764 ------------------ 0.2/api/miplearn/components/tests/index.html | 93 --- .../components/tests/test_composite.html | 179 ---- .../components/tests/test_lazy_dynamic.html | 365 --------- .../components/tests/test_lazy_static.html | 538 ------------ .../components/tests/test_objective.html | 174 ---- .../components/tests/test_primal.html | 306 ------- 0.2/api/miplearn/index.html | 5 - 0.2/api/miplearn/instance.html | 3 - 0.2/api/miplearn/problems/index.html | 5 - 0.2/api/miplearn/problems/tests/index.html | 83 -- .../problems/tests/test_knapsack.html | 115 --- .../miplearn/problems/tests/test_stab.html | 191 ----- 0.2/api/miplearn/problems/tests/test_tsp.html | 242 ------ 0.2/api/miplearn/solvers/index.html | 5 - 0.2/api/miplearn/solvers/tests/index.html | 250 ------ .../solvers/tests/test_internal_solver.html | 543 ------------- .../miplearn/solvers/tests/test_lazy_cb.html | 119 --- .../solvers/tests/test_learning_solver.html | 395 --------- 0.2/api/miplearn/tests/index.html | 138 ---- 0.2/api/miplearn/tests/test_benchmark.html | 134 --- 0.2/api/miplearn/tests/test_extractors.html | 201 ----- 0.2/css/custom.css | 4 + 0.2/index.html | 2 +- 0.2/sitemap.xml.gz | Bin 196 -> 196 bytes 35 files changed, 5 insertions(+), 5917 deletions(-) delete mode 100644 0.2/api/miplearn/classifiers/tests/index.html delete mode 100644 0.2/api/miplearn/classifiers/tests/test_counting.html delete mode 100644 0.2/api/miplearn/classifiers/tests/test_cv.html delete mode 100644 0.2/api/miplearn/classifiers/tests/test_evaluator.html delete mode 100644 0.2/api/miplearn/classifiers/tests/test_threshold.html delete mode 100644 0.2/api/miplearn/components/steps/tests/index.html delete mode 100644 0.2/api/miplearn/components/steps/tests/test_convert_tight.html delete mode 100644 0.2/api/miplearn/components/steps/tests/test_drop_redundant.html delete mode 100644 0.2/api/miplearn/components/tests/index.html delete mode 100644 0.2/api/miplearn/components/tests/test_composite.html delete mode 100644 0.2/api/miplearn/components/tests/test_lazy_dynamic.html delete mode 100644 0.2/api/miplearn/components/tests/test_lazy_static.html delete mode 100644 0.2/api/miplearn/components/tests/test_objective.html delete mode 100644 0.2/api/miplearn/components/tests/test_primal.html delete mode 100644 0.2/api/miplearn/problems/tests/index.html delete mode 100644 0.2/api/miplearn/problems/tests/test_knapsack.html delete mode 100644 0.2/api/miplearn/problems/tests/test_stab.html delete mode 100644 0.2/api/miplearn/problems/tests/test_tsp.html delete mode 100644 0.2/api/miplearn/solvers/tests/index.html delete mode 100644 0.2/api/miplearn/solvers/tests/test_internal_solver.html delete mode 100644 0.2/api/miplearn/solvers/tests/test_lazy_cb.html delete mode 100644 0.2/api/miplearn/solvers/tests/test_learning_solver.html delete mode 100644 0.2/api/miplearn/tests/index.html delete mode 100644 0.2/api/miplearn/tests/test_benchmark.html delete mode 100644 0.2/api/miplearn/tests/test_extractors.html diff --git a/0.2/api/miplearn/classifiers/index.html b/0.2/api/miplearn/classifiers/index.html index 7caff94..f93376c 100644 --- a/0.2/api/miplearn/classifiers/index.html +++ b/0.2/api/miplearn/classifiers/index.html @@ -78,10 +78,6 @@ class Regressor(ABC):
-
miplearn.classifiers.tests
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miplearn.classifiers.threshold
@@ -253,7 +249,6 @@ def predict(self):
  • miplearn.classifiers.counting
  • miplearn.classifiers.cv
  • miplearn.classifiers.evaluator
    • -
  • miplearn.classifiers.tests
  • miplearn.classifiers.threshold
    • diff --git a/0.2/api/miplearn/classifiers/tests/index.html b/0.2/api/miplearn/classifiers/tests/index.html deleted file mode 100644 index b7b5b42..0000000 --- a/0.2/api/miplearn/classifiers/tests/index.html +++ /dev/null @@ -1,88 +0,0 @@ - - - - - - -miplearn.classifiers.tests API documentation - - - - - - - - - -
    -
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    Module miplearn.classifiers.tests

    -
    -
    -
    - -Expand source code - -
    #  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.
    -
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    Sub-modules

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    miplearn.classifiers.tests.test_counting
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    miplearn.classifiers.tests.test_cv
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    miplearn.classifiers.tests.test_evaluator
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    Module miplearn.classifiers.tests.test_counting

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    - -Expand source code - -
    #  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 numpy.linalg import norm
-
-from miplearn.classifiers.counting import CountingClassifier
-
-E = 0.1
-
-
-def test_counting():
-    clf = CountingClassifier()
-    clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
-    expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
-    actual_proba = clf.predict_proba(np.zeros((2, 25)))
-    assert norm(actual_proba - expected_proba) < E
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    Functions

    -
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    -def test_counting() -
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    - -Expand source code - -
    def test_counting():
-    clf = CountingClassifier()
-    clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
-    expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
-    actual_proba = clf.predict_proba(np.zeros((2, 25)))
-    assert norm(actual_proba - expected_proba) < E
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    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/classifiers/tests/test_cv.html b/0.2/api/miplearn/classifiers/tests/test_cv.html deleted file mode 100644 index 6cef61b..0000000 --- a/0.2/api/miplearn/classifiers/tests/test_cv.html +++ /dev/null @@ -1,161 +0,0 @@ - - - - - - -miplearn.classifiers.tests.test_cv API documentation - - - - - - - - - -
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    Module miplearn.classifiers.tests.test_cv

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    - -Expand source code - -
    #  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 numpy.linalg import norm
-from sklearn.preprocessing import StandardScaler
-from sklearn.svm import SVC
-
-from miplearn.classifiers.cv import CrossValidatedClassifier
-
-E = 0.1
-
-
-def test_cv():
-    # Training set: label is true if point is inside a 2D circle
-    x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
-    x_train = StandardScaler().fit_transform(x_train)
-    n_samples = x_train.shape[0]
-
-    y_train = np.array(
-        [
-            1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
-            for x1 in range(-10, 11)
-            for x2 in range(-10, 11)
-        ]
-    )
-
-    # Support vector machines with linear kernels do not perform well on this
-    # data set, so predictor should return the given constant.
-    clf = CrossValidatedClassifier(
-        classifier=SVC(probability=True, random_state=42),
-        threshold=0.90,
-        constant=0.0,
-        cv=30,
-    )
-    clf.fit(x_train, y_train)
-    assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
-
-    # Support vector machines with quadratic kernels perform almost perfectly
-    # on this data set, so predictor should return their prediction.
-    clf = CrossValidatedClassifier(
-        classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
-        threshold=0.90,
-        cv=30,
-    )
-    clf.fit(x_train, y_train)
-    print(y_train - clf.predict(x_train))
-    assert norm(y_train - clf.predict(x_train)) < E
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    -def test_cv() -
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    - -Expand source code - -
    def test_cv():
-    # Training set: label is true if point is inside a 2D circle
-    x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
-    x_train = StandardScaler().fit_transform(x_train)
-    n_samples = x_train.shape[0]
-
-    y_train = np.array(
-        [
-            1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
-            for x1 in range(-10, 11)
-            for x2 in range(-10, 11)
-        ]
-    )
-
-    # Support vector machines with linear kernels do not perform well on this
-    # data set, so predictor should return the given constant.
-    clf = CrossValidatedClassifier(
-        classifier=SVC(probability=True, random_state=42),
-        threshold=0.90,
-        constant=0.0,
-        cv=30,
-    )
-    clf.fit(x_train, y_train)
-    assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
-
-    # Support vector machines with quadratic kernels perform almost perfectly
-    # on this data set, so predictor should return their prediction.
-    clf = CrossValidatedClassifier(
-        classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
-        threshold=0.90,
-        cv=30,
-    )
-    clf.fit(x_train, y_train)
-    print(y_train - clf.predict(x_train))
-    assert norm(y_train - clf.predict(x_train)) < E
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    -
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    Module miplearn.classifiers.tests.test_evaluator

    -
    -
    -
    - -Expand source code - -
    #  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 sklearn.neighbors import KNeighborsClassifier
-
-from miplearn.classifiers.evaluator import ClassifierEvaluator
-
-
-def test_evaluator():
-    clf_a = KNeighborsClassifier(n_neighbors=1)
-    clf_b = KNeighborsClassifier(n_neighbors=2)
-    x_train = np.array([[0, 0], [1, 0]])
-    y_train = np.array([0, 1])
-    clf_a.fit(x_train, y_train)
-    clf_b.fit(x_train, y_train)
-    ev = ClassifierEvaluator()
-    assert ev.evaluate(clf_a, x_train, y_train) == 1.0
-    assert ev.evaluate(clf_b, x_train, y_train) == 0.5
    -
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    Functions

    -
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    -def test_evaluator() -
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    - -Expand source code - -
    def test_evaluator():
-    clf_a = KNeighborsClassifier(n_neighbors=1)
-    clf_b = KNeighborsClassifier(n_neighbors=2)
-    x_train = np.array([[0, 0], [1, 0]])
-    y_train = np.array([0, 1])
-    clf_a.fit(x_train, y_train)
-    clf_b.fit(x_train, y_train)
-    ev = ClassifierEvaluator()
-    assert ev.evaluate(clf_a, x_train, y_train) == 1.0
-    assert ev.evaluate(clf_b, x_train, y_train) == 0.5
    -
    -
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    -
    -
    -

    Module miplearn.classifiers.tests.test_threshold

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock
-
-import numpy as np
-
-from miplearn.classifiers import Classifier
-from miplearn.classifiers.threshold import MinPrecisionThreshold
-
-
-def test_threshold_dynamic():
-    clf = Mock(spec=Classifier)
-    clf.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.10, 0.90],
-                [0.10, 0.90],
-                [0.20, 0.80],
-                [0.30, 0.70],
-            ]
-        )
-    )
-    x_train = np.array([0, 1, 2, 3])
-    y_train = np.array([1, 1, 0, 0])
-
-    threshold = MinPrecisionThreshold(min_precision=1.0)
-    assert threshold.find(clf, x_train, y_train) == 0.90
-
-    threshold = MinPrecisionThreshold(min_precision=0.65)
-    assert threshold.find(clf, x_train, y_train) == 0.80
-
-    threshold = MinPrecisionThreshold(min_precision=0.50)
-    assert threshold.find(clf, x_train, y_train) == 0.70
-
-    threshold = MinPrecisionThreshold(min_precision=0.00)
-    assert threshold.find(clf, x_train, y_train) == 0.70
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    Functions

    -
    -
    -def test_threshold_dynamic() -
    -
    -
    -
    - -Expand source code - -
    def test_threshold_dynamic():
-    clf = Mock(spec=Classifier)
-    clf.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.10, 0.90],
-                [0.10, 0.90],
-                [0.20, 0.80],
-                [0.30, 0.70],
-            ]
-        )
-    )
-    x_train = np.array([0, 1, 2, 3])
-    y_train = np.array([1, 1, 0, 0])
-
-    threshold = MinPrecisionThreshold(min_precision=1.0)
-    assert threshold.find(clf, x_train, y_train) == 0.90
-
-    threshold = MinPrecisionThreshold(min_precision=0.65)
-    assert threshold.find(clf, x_train, y_train) == 0.80
-
-    threshold = MinPrecisionThreshold(min_precision=0.50)
-    assert threshold.find(clf, x_train, y_train) == 0.70
-
-    threshold = MinPrecisionThreshold(min_precision=0.00)
-    assert threshold.find(clf, x_train, y_train) == 0.70
    -
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    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/index.html b/0.2/api/miplearn/components/index.html index c911eb9..25634da 100644 --- a/0.2/api/miplearn/components/index.html +++ b/0.2/api/miplearn/components/index.html @@ -106,10 +106,6 @@ def classifier_evaluation_dict(tp, tn, fp, fn):
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    miplearn.components.tests
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    @@ -191,7 +187,6 @@ def classifier_evaluation_dict(tp, tn, fp, fn):
  • miplearn.components.primal
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  • miplearn.components.tests

  • Functions

    diff --git a/0.2/api/miplearn/components/steps/index.html b/0.2/api/miplearn/components/steps/index.html index 4bd1720..7542e24 100644 --- a/0.2/api/miplearn/components/steps/index.html +++ b/0.2/api/miplearn/components/steps/index.html @@ -36,10 +36,6 @@
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    • @@ -65,7 +61,6 @@
  • miplearn.components.steps.convert_tight
  • miplearn.components.steps.drop_redundant
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  • miplearn.components.steps.tests
    • diff --git a/0.2/api/miplearn/components/steps/tests/index.html b/0.2/api/miplearn/components/steps/tests/index.html deleted file mode 100644 index cb15ae9..0000000 --- a/0.2/api/miplearn/components/steps/tests/index.html +++ /dev/null @@ -1,70 +0,0 @@ - - - - - - -miplearn.components.steps.tests API documentation - - - - - - - - - -
    - - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/steps/tests/test_convert_tight.html b/0.2/api/miplearn/components/steps/tests/test_convert_tight.html deleted file mode 100644 index 0779727..0000000 --- a/0.2/api/miplearn/components/steps/tests/test_convert_tight.html +++ /dev/null @@ -1,385 +0,0 @@ - - - - - - -miplearn.components.steps.tests.test_convert_tight API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.steps.tests.test_convert_tight

    -
    -
    -
    - -Expand source code - -
    from unittest.mock import Mock
-
-from miplearn.classifiers import Classifier
-from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
-from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
-from miplearn.instance import Instance
-from miplearn.problems.knapsack import GurobiKnapsackInstance
-from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_convert_tight_usage():
-    instance = GurobiKnapsackInstance(
-        weights=[3.0, 5.0, 10.0],
-        prices=[1.0, 1.0, 1.0],
-        capacity=16.0,
-    )
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[
-            RelaxIntegralityStep(),
-            ConvertTightIneqsIntoEqsStep(),
-        ],
-    )
-
-    # Solve original problem
-    stats = solver.solve(instance)
-    original_upper_bound = stats["Upper bound"]
-
-    # Should collect training data
-    assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
-
-    # Fit and resolve
-    solver.fit([instance])
-    stats = solver.solve(instance)
-
-    # Objective value should be the same
-    assert stats["Upper bound"] == original_upper_bound
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 0
-
-
-class SampleInstance(Instance):
-    def to_model(self):
-        import gurobipy as grb
-
-        m = grb.Model("model")
-        x1 = m.addVar(name="x1")
-        x2 = m.addVar(name="x2")
-        m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
-        m.addConstr(x1 <= 2, name="c1")
-        m.addConstr(x2 <= 2, name="c2")
-        m.addConstr(x1 + x2 <= 3, name="c2")
-        return m
-
-
-def test_convert_tight_infeasibility():
-    comp = ConvertTightIneqsIntoEqsStep()
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 1
-    assert stats["ConvertTight: Subopt iterations"] == 0
-
-
-def test_convert_tight_suboptimality():
-    comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 1
-
-
-def test_convert_tight_optimal():
-    comp = ConvertTightIneqsIntoEqsStep()
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 0
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_convert_tight_infeasibility() -
    -
    -
    -
    - -Expand source code - -
    def test_convert_tight_infeasibility():
-    comp = ConvertTightIneqsIntoEqsStep()
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 1
-    assert stats["ConvertTight: Subopt iterations"] == 0
    -
    -
    -
    -def test_convert_tight_optimal() -
    -
    -
    -
    - -Expand source code - -
    def test_convert_tight_optimal():
-    comp = ConvertTightIneqsIntoEqsStep()
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 0
    -
    -
    -
    -def test_convert_tight_suboptimality() -
    -
    -
    -
    - -Expand source code - -
    def test_convert_tight_suboptimality():
-    comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
-    comp.classifiers = {
-        "c1": Mock(spec=Classifier),
-        "c2": Mock(spec=Classifier),
-        "c3": Mock(spec=Classifier),
-    }
-    comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
-    comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
-    comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
-
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[comp],
-        solve_lp_first=False,
-    )
-    instance = SampleInstance()
-    stats = solver.solve(instance)
-    assert stats["Upper bound"] == 5.0
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 1
    -
    -
    -
    -def test_convert_tight_usage() -
    -
    -
    -
    - -Expand source code - -
    def test_convert_tight_usage():
-    instance = GurobiKnapsackInstance(
-        weights=[3.0, 5.0, 10.0],
-        prices=[1.0, 1.0, 1.0],
-        capacity=16.0,
-    )
-    solver = LearningSolver(
-        solver=GurobiSolver,
-        components=[
-            RelaxIntegralityStep(),
-            ConvertTightIneqsIntoEqsStep(),
-        ],
-    )
-
-    # Solve original problem
-    stats = solver.solve(instance)
-    original_upper_bound = stats["Upper bound"]
-
-    # Should collect training data
-    assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
-
-    # Fit and resolve
-    solver.fit([instance])
-    stats = solver.solve(instance)
-
-    # Objective value should be the same
-    assert stats["Upper bound"] == original_upper_bound
-    assert stats["ConvertTight: Inf iterations"] == 0
-    assert stats["ConvertTight: Subopt iterations"] == 0
    -
    -
    -
    -
    -
    -

    Classes

    -
    -
    -class SampleInstance -
    -
    -

    Abstract class holding all the data necessary to generate a concrete model of the -problem.

    -

    In the knapsack problem, for example, this class could hold the number of items, -their weights and costs, as well as the size of the knapsack. Objects -implementing this class are able to convert themselves into a concrete -optimization model, which can be optimized by a solver, or into arrays of -features, which can be provided as inputs to machine learning models.

    -
    - -Expand source code - -
    class SampleInstance(Instance):
-    def to_model(self):
-        import gurobipy as grb
-
-        m = grb.Model("model")
-        x1 = m.addVar(name="x1")
-        x2 = m.addVar(name="x2")
-        m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
-        m.addConstr(x1 <= 2, name="c1")
-        m.addConstr(x2 <= 2, name="c2")
-        m.addConstr(x1 + x2 <= 3, name="c2")
-        return m
    -
    -

    Ancestors

    - -

    Inherited members

    - -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/steps/tests/test_drop_redundant.html b/0.2/api/miplearn/components/steps/tests/test_drop_redundant.html deleted file mode 100644 index 4d440b4..0000000 --- a/0.2/api/miplearn/components/steps/tests/test_drop_redundant.html +++ /dev/null @@ -1,764 +0,0 @@ - - - - - - -miplearn.components.steps.tests.test_drop_redundant API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.steps.tests.test_drop_redundant

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock, call
-
-import numpy as np
-
-from miplearn.classifiers import Classifier
-from miplearn.components.relaxation import DropRedundantInequalitiesStep
-from miplearn.instance import Instance
-from miplearn.solvers.internal import InternalSolver
-from miplearn.solvers.learning import LearningSolver
-
-
-def _setup():
-    solver = Mock(spec=LearningSolver)
-
-    internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
-    internal.get_inequality_slacks = Mock(
-        side_effect=lambda: {
-            "c1": 0.5,
-            "c2": 0.0,
-            "c3": 0.0,
-            "c4": 1.4,
-        }
-    )
-    internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
-    internal.is_constraint_satisfied = Mock(return_value=False)
-
-    instance = Mock(spec=Instance)
-    instance.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": np.array([1.0, 0.0]),
-            "c3": np.array([0.5, 0.5]),
-            "c4": np.array([1.0]),
-        }[cid]
-    )
-    instance.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-
-    classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    classifiers["type-a"].predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.20, 0.80],
-                [0.05, 0.95],
-            ]
-        )
-    )
-    classifiers["type-b"].predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.02, 0.98],
-            ]
-        )
-    )
-
-    return solver, internal, instance, classifiers
-
-
-def test_drop_redundant():
-    solver, internal, instance, classifiers = _setup()
-
-    component = DropRedundantInequalitiesStep()
-    component.classifiers = classifiers
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should query list of constraints
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should query category and features for each constraint in the model
-    assert instance.get_constraint_category.call_count == 4
-    instance.get_constraint_category.assert_has_calls(
-        [
-            call("c1"),
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # For constraint with non-null categories, should ask for features
-    assert instance.get_constraint_features.call_count == 3
-    instance.get_constraint_features.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask ML to predict whether constraint should be removed
-    type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
-    type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
-    np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
-    np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
-
-    # Should ask internal solver to remove constraints predicted as redundant
-    assert internal.extract_constraint.call_count == 2
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # LearningSolver calls after_solve
-    training_data = {}
-    component.after_solve(solver, instance, None, {}, training_data)
-
-    # Should query slack for all inequalities
-    internal.get_inequality_slacks.assert_called_once()
-
-    # Should store constraint slacks in instance object
-    assert training_data["slacks"] == {
-        "c1": 0.5,
-        "c2": 0.0,
-        "c3": 0.0,
-        "c4": 1.4,
-    }
-
-
-def test_drop_redundant_with_check_feasibility():
-    solver, internal, instance, classifiers = _setup()
-
-    component = DropRedundantInequalitiesStep(
-        check_feasibility=True,
-        violation_tolerance=1e-3,
-    )
-    component.classifiers = classifiers
-
-    # LearningSolver call before_solve
-    component.before_solve(solver, instance, None)
-
-    # Assert constraints are extracted
-    assert internal.extract_constraint.call_count == 2
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # LearningSolver calls iteration_cb (first time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-
-    # Should ask LearningSolver to repeat
-    assert should_repeat
-
-    # Should ask solver if removed constraints are satisfied (mock always returns false)
-    internal.is_constraint_satisfied.assert_has_calls(
-        [
-            call("<c3>", 1e-3),
-            call("<c4>", 1e-3),
-        ]
-    )
-
-    # Should add constraints back to LP relaxation
-    internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
-
-    # LearningSolver calls iteration_cb (second time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert not should_repeat
-
-
-def test_x_y_fit_predict_evaluate():
-    instances = [Mock(spec=Instance), Mock(spec=Instance)]
-    component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = Mock(
-        return_value=[
-            np.array([0.20, 0.80]),
-        ]
-    )
-    component.classifiers["type-b"].predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.50, 0.50],
-                [0.05, 0.95],
-            ]
-        )
-    )
-
-    # First mock instance
-    instances[0].training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.05,
-                "c3": 0.00,
-                "c4": 30.0,
-            }
-        }
-    ]
-    instances[0].get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[0].get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": np.array([1.0, 0.0]),
-            "c3": np.array([0.5, 0.5]),
-            "c4": np.array([1.0]),
-        }[cid]
-    )
-
-    # Second mock instance
-    instances[1].training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c3": 0.30,
-                "c4": 0.00,
-                "c5": 0.00,
-            }
-        }
-    ]
-    instances[1].get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instances[1].get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c3": np.array([0.3, 0.4]),
-            "c4": np.array([0.7]),
-            "c5": np.array([0.8]),
-        }[cid]
-    )
-
-    expected_x = {
-        "type-a": np.array(
-            [
-                [1.0, 0.0],
-                [0.5, 0.5],
-                [0.3, 0.4],
-            ]
-        ),
-        "type-b": np.array(
-            [
-                [1.0],
-                [0.7],
-                [0.8],
-            ]
-        ),
-    }
-    expected_y = {
-        "type-a": np.array([[0], [0], [1]]),
-        "type-b": np.array([[1], [0], [0]]),
-    }
-
-    # Should build X and Y matrices correctly
-    actual_x = component.x(instances)
-    actual_y = component.y(instances)
-    for category in ["type-a", "type-b"]:
-        np.testing.assert_array_equal(actual_x[category], expected_x[category])
-        np.testing.assert_array_equal(actual_y[category], expected_y[category])
-
-    # Should pass along X and Y matrices to classifiers
-    component.fit(instances)
-    for category in ["type-a", "type-b"]:
-        actual_x = component.classifiers[category].fit.call_args[0][0]
-        actual_y = component.classifiers[category].fit.call_args[0][1]
-        np.testing.assert_array_equal(actual_x, expected_x[category])
-        np.testing.assert_array_equal(actual_y, expected_y[category])
-
-    assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
-
-    ev = component.evaluate(instances[1])
-    assert ev["True positive"] == 1
-    assert ev["True negative"] == 1
-    assert ev["False positive"] == 1
-    assert ev["False negative"] == 0
-
-
-def test_x_multiple_solves():
-    instance = Mock(spec=Instance)
-    instance.training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.05,
-                "c3": 0.00,
-                "c4": 30.0,
-            }
-        },
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.00,
-                "c3": 1.00,
-                "c4": 0.0,
-            }
-        },
-    ]
-    instance.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-    instance.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": np.array([1.0, 0.0]),
-            "c3": np.array([0.5, 0.5]),
-            "c4": np.array([1.0]),
-        }[cid]
-    )
-
-    expected_x = {
-        "type-a": np.array(
-            [
-                [1.0, 0.0],
-                [0.5, 0.5],
-                [1.0, 0.0],
-                [0.5, 0.5],
-            ]
-        ),
-        "type-b": np.array(
-            [
-                [1.0],
-                [1.0],
-            ]
-        ),
-    }
-
-    expected_y = {
-        "type-a": np.array([[1], [0], [0], [1]]),
-        "type-b": np.array([[1], [0]]),
-    }
-
-    # Should build X and Y matrices correctly
-    component = DropRedundantInequalitiesStep()
-    actual_x = component.x([instance])
-    actual_y = component.y([instance])
-    print(actual_x)
-    for category in ["type-a", "type-b"]:
-        np.testing.assert_array_equal(actual_x[category], expected_x[category])
-        np.testing.assert_array_equal(actual_y[category], expected_y[category])
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_drop_redundant() -
    -
    -
    -
    - -Expand source code - -
    def test_drop_redundant():
-    solver, internal, instance, classifiers = _setup()
-
-    component = DropRedundantInequalitiesStep()
-    component.classifiers = classifiers
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should query list of constraints
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should query category and features for each constraint in the model
-    assert instance.get_constraint_category.call_count == 4
-    instance.get_constraint_category.assert_has_calls(
-        [
-            call("c1"),
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # For constraint with non-null categories, should ask for features
-    assert instance.get_constraint_features.call_count == 3
-    instance.get_constraint_features.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask ML to predict whether constraint should be removed
-    type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
-    type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
-    np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
-    np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
-
-    # Should ask internal solver to remove constraints predicted as redundant
-    assert internal.extract_constraint.call_count == 2
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # LearningSolver calls after_solve
-    training_data = {}
-    component.after_solve(solver, instance, None, {}, training_data)
-
-    # Should query slack for all inequalities
-    internal.get_inequality_slacks.assert_called_once()
-
-    # Should store constraint slacks in instance object
-    assert training_data["slacks"] == {
-        "c1": 0.5,
-        "c2": 0.0,
-        "c3": 0.0,
-        "c4": 1.4,
-    }
    -
    -
    -
    -def test_drop_redundant_with_check_feasibility() -
    -
    -
    -
    - -Expand source code - -
    def test_drop_redundant_with_check_feasibility():
-    solver, internal, instance, classifiers = _setup()
-
-    component = DropRedundantInequalitiesStep(
-        check_feasibility=True,
-        violation_tolerance=1e-3,
-    )
-    component.classifiers = classifiers
-
-    # LearningSolver call before_solve
-    component.before_solve(solver, instance, None)
-
-    # Assert constraints are extracted
-    assert internal.extract_constraint.call_count == 2
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # LearningSolver calls iteration_cb (first time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-
-    # Should ask LearningSolver to repeat
-    assert should_repeat
-
-    # Should ask solver if removed constraints are satisfied (mock always returns false)
-    internal.is_constraint_satisfied.assert_has_calls(
-        [
-            call("<c3>", 1e-3),
-            call("<c4>", 1e-3),
-        ]
-    )
-
-    # Should add constraints back to LP relaxation
-    internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
-
-    # LearningSolver calls iteration_cb (second time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert not should_repeat
    -
    -
    -
    -def test_x_multiple_solves() -
    -
    -
    -
    - -Expand source code - -
    def test_x_multiple_solves():
-    instance = Mock(spec=Instance)
-    instance.training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.05,
-                "c3": 0.00,
-                "c4": 30.0,
-            }
-        },
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.00,
-                "c3": 1.00,
-                "c4": 0.0,
-            }
-        },
-    ]
-    instance.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-    instance.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": np.array([1.0, 0.0]),
-            "c3": np.array([0.5, 0.5]),
-            "c4": np.array([1.0]),
-        }[cid]
-    )
-
-    expected_x = {
-        "type-a": np.array(
-            [
-                [1.0, 0.0],
-                [0.5, 0.5],
-                [1.0, 0.0],
-                [0.5, 0.5],
-            ]
-        ),
-        "type-b": np.array(
-            [
-                [1.0],
-                [1.0],
-            ]
-        ),
-    }
-
-    expected_y = {
-        "type-a": np.array([[1], [0], [0], [1]]),
-        "type-b": np.array([[1], [0]]),
-    }
-
-    # Should build X and Y matrices correctly
-    component = DropRedundantInequalitiesStep()
-    actual_x = component.x([instance])
-    actual_y = component.y([instance])
-    print(actual_x)
-    for category in ["type-a", "type-b"]:
-        np.testing.assert_array_equal(actual_x[category], expected_x[category])
-        np.testing.assert_array_equal(actual_y[category], expected_y[category])
    -
    -
    -
    -def test_x_y_fit_predict_evaluate() -
    -
    -
    -
    - -Expand source code - -
    def test_x_y_fit_predict_evaluate():
-    instances = [Mock(spec=Instance), Mock(spec=Instance)]
-    component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = Mock(
-        return_value=[
-            np.array([0.20, 0.80]),
-        ]
-    )
-    component.classifiers["type-b"].predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.50, 0.50],
-                [0.05, 0.95],
-            ]
-        )
-    )
-
-    # First mock instance
-    instances[0].training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c2": 0.05,
-                "c3": 0.00,
-                "c4": 30.0,
-            }
-        }
-    ]
-    instances[0].get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-    instances[0].get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": np.array([1.0, 0.0]),
-            "c3": np.array([0.5, 0.5]),
-            "c4": np.array([1.0]),
-        }[cid]
-    )
-
-    # Second mock instance
-    instances[1].training_data = [
-        {
-            "slacks": {
-                "c1": 0.00,
-                "c3": 0.30,
-                "c4": 0.00,
-                "c5": 0.00,
-            }
-        }
-    ]
-    instances[1].get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": None,
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instances[1].get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c3": np.array([0.3, 0.4]),
-            "c4": np.array([0.7]),
-            "c5": np.array([0.8]),
-        }[cid]
-    )
-
-    expected_x = {
-        "type-a": np.array(
-            [
-                [1.0, 0.0],
-                [0.5, 0.5],
-                [0.3, 0.4],
-            ]
-        ),
-        "type-b": np.array(
-            [
-                [1.0],
-                [0.7],
-                [0.8],
-            ]
-        ),
-    }
-    expected_y = {
-        "type-a": np.array([[0], [0], [1]]),
-        "type-b": np.array([[1], [0], [0]]),
-    }
-
-    # Should build X and Y matrices correctly
-    actual_x = component.x(instances)
-    actual_y = component.y(instances)
-    for category in ["type-a", "type-b"]:
-        np.testing.assert_array_equal(actual_x[category], expected_x[category])
-        np.testing.assert_array_equal(actual_y[category], expected_y[category])
-
-    # Should pass along X and Y matrices to classifiers
-    component.fit(instances)
-    for category in ["type-a", "type-b"]:
-        actual_x = component.classifiers[category].fit.call_args[0][0]
-        actual_y = component.classifiers[category].fit.call_args[0][1]
-        np.testing.assert_array_equal(actual_x, expected_x[category])
-        np.testing.assert_array_equal(actual_y, expected_y[category])
-
-    assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
-
-    ev = component.evaluate(instances[1])
-    assert ev["True positive"] == 1
-    assert ev["True negative"] == 1
-    assert ev["False positive"] == 1
-    assert ev["False negative"] == 0
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/index.html b/0.2/api/miplearn/components/tests/index.html deleted file mode 100644 index 0c6a22a..0000000 --- a/0.2/api/miplearn/components/tests/index.html +++ /dev/null @@ -1,93 +0,0 @@ - - - - - - -miplearn.components.tests API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests

    -
    -
    -
    - -Expand source code - -
    #  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.
    -
    -
    -
    -

    Sub-modules

    -
    -
    miplearn.components.tests.test_composite
    -
    -
    -
    -
    miplearn.components.tests.test_lazy_dynamic
    -
    -
    -
    -
    miplearn.components.tests.test_lazy_static
    -
    -
    -
    -
    miplearn.components.tests.test_objective
    -
    -
    -
    -
    miplearn.components.tests.test_primal
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/test_composite.html b/0.2/api/miplearn/components/tests/test_composite.html deleted file mode 100644 index 3e024d5..0000000 --- a/0.2/api/miplearn/components/tests/test_composite.html +++ /dev/null @@ -1,179 +0,0 @@ - - - - - - -miplearn.components.tests.test_composite API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests.test_composite

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock, call
-
-from miplearn.components.component import Component
-from miplearn.components.composite import CompositeComponent
-from miplearn.instance import Instance
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_composite():
-    solver, instance, model = (
-        Mock(spec=LearningSolver),
-        Mock(spec=Instance),
-        Mock(),
-    )
-
-    c1 = Mock(spec=Component)
-    c2 = Mock(spec=Component)
-    cc = CompositeComponent([c1, c2])
-
-    # Should broadcast before_solve
-    cc.before_solve(solver, instance, model)
-    c1.before_solve.assert_has_calls([call(solver, instance, model)])
-    c2.before_solve.assert_has_calls([call(solver, instance, model)])
-
-    # Should broadcast after_solve
-    cc.after_solve(solver, instance, model, {}, {})
-    c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
-    c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
-
-    # Should broadcast fit
-    cc.fit([1, 2, 3])
-    c1.fit.assert_has_calls([call([1, 2, 3])])
-    c2.fit.assert_has_calls([call([1, 2, 3])])
-
-    # Should broadcast lazy_cb
-    cc.lazy_cb(solver, instance, model)
-    c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
-    c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
-
-    # Should broadcast iteration_cb
-    cc.iteration_cb(solver, instance, model)
-    c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
-    c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
-
-    # If at least one child component returns true, iteration_cb should return True
-    c1.iteration_cb = Mock(return_value=True)
-    c2.iteration_cb = Mock(return_value=False)
-    assert cc.iteration_cb(solver, instance, model)
-
-    # If all children return False, iteration_cb should return False
-    c1.iteration_cb = Mock(return_value=False)
-    c2.iteration_cb = Mock(return_value=False)
-    assert not cc.iteration_cb(solver, instance, model)
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_composite() -
    -
    -
    -
    - -Expand source code - -
    def test_composite():
-    solver, instance, model = (
-        Mock(spec=LearningSolver),
-        Mock(spec=Instance),
-        Mock(),
-    )
-
-    c1 = Mock(spec=Component)
-    c2 = Mock(spec=Component)
-    cc = CompositeComponent([c1, c2])
-
-    # Should broadcast before_solve
-    cc.before_solve(solver, instance, model)
-    c1.before_solve.assert_has_calls([call(solver, instance, model)])
-    c2.before_solve.assert_has_calls([call(solver, instance, model)])
-
-    # Should broadcast after_solve
-    cc.after_solve(solver, instance, model, {}, {})
-    c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
-    c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
-
-    # Should broadcast fit
-    cc.fit([1, 2, 3])
-    c1.fit.assert_has_calls([call([1, 2, 3])])
-    c2.fit.assert_has_calls([call([1, 2, 3])])
-
-    # Should broadcast lazy_cb
-    cc.lazy_cb(solver, instance, model)
-    c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
-    c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
-
-    # Should broadcast iteration_cb
-    cc.iteration_cb(solver, instance, model)
-    c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
-    c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
-
-    # If at least one child component returns true, iteration_cb should return True
-    c1.iteration_cb = Mock(return_value=True)
-    c2.iteration_cb = Mock(return_value=False)
-    assert cc.iteration_cb(solver, instance, model)
-
-    # If all children return False, iteration_cb should return False
-    c1.iteration_cb = Mock(return_value=False)
-    c2.iteration_cb = Mock(return_value=False)
-    assert not cc.iteration_cb(solver, instance, model)
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/test_lazy_dynamic.html b/0.2/api/miplearn/components/tests/test_lazy_dynamic.html deleted file mode 100644 index 58b7306..0000000 --- a/0.2/api/miplearn/components/tests/test_lazy_dynamic.html +++ /dev/null @@ -1,365 +0,0 @@ - - - - - - -miplearn.components.tests.test_lazy_dynamic API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests.test_lazy_dynamic

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock
-
-import numpy as np
-from numpy.linalg import norm
-
-from miplearn.classifiers import Classifier
-from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
-from miplearn.solvers.internal import InternalSolver
-from miplearn.solvers.learning import LearningSolver
-from miplearn.tests import get_test_pyomo_instances
-
-E = 0.1
-
-
-def test_lazy_fit():
-    instances, models = get_test_pyomo_instances()
-    instances[0].found_violated_lazy_constraints = ["a", "b"]
-    instances[1].found_violated_lazy_constraints = ["b", "c"]
-    classifier = Mock(spec=Classifier)
-    component = DynamicLazyConstraintsComponent(classifier=classifier)
-
-    component.fit(instances)
-
-    # Should create one classifier for each violation
-    assert "a" in component.classifiers
-    assert "b" in component.classifiers
-    assert "c" in component.classifiers
-
-    # Should provide correct x_train to each classifier
-    expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
-    actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
-    actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
-    assert norm(expected_x_train_a - actual_x_train_a) < E
-    assert norm(expected_x_train_b - actual_x_train_b) < E
-    assert norm(expected_x_train_c - actual_x_train_c) < E
-
-    # Should provide correct y_train to each classifier
-    expected_y_train_a = np.array([1.0, 0.0])
-    expected_y_train_b = np.array([1.0, 1.0])
-    expected_y_train_c = np.array([0.0, 1.0])
-    actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
-    actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
-    actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
-    assert norm(expected_y_train_a - actual_y_train_a) < E
-    assert norm(expected_y_train_b - actual_y_train_b) < E
-    assert norm(expected_y_train_c - actual_y_train_c) < E
-
-
-def test_lazy_before():
-    instances, models = get_test_pyomo_instances()
-    instances[0].build_lazy_constraint = Mock(return_value="c1")
-    solver = LearningSolver()
-    solver.internal_solver = Mock(spec=InternalSolver)
-    component = DynamicLazyConstraintsComponent(threshold=0.10)
-    component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
-    component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
-
-    component.before_solve(solver, instances[0], models[0])
-
-    # Should ask classifier likelihood of each constraint being violated
-    expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
-    expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
-    actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
-    actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
-    assert norm(expected_x_test_a - actual_x_test_a) < E
-    assert norm(expected_x_test_b - actual_x_test_b) < E
-
-    # Should ask instance to generate cut for constraints whose likelihood
-    # of being violated exceeds the threshold
-    instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
-
-    # Should ask internal solver to add generated constraint
-    solver.internal_solver.add_constraint.assert_called_once_with("c1")
-
-
-def test_lazy_evaluate():
-    instances, models = get_test_pyomo_instances()
-    component = DynamicLazyConstraintsComponent()
-    component.classifiers = {
-        "a": Mock(spec=Classifier),
-        "b": Mock(spec=Classifier),
-        "c": Mock(spec=Classifier),
-    }
-    component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-    component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-
-    instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
-    instances[1].found_violated_lazy_constraints = ["b", "d"]
-    assert component.evaluate(instances) == {
-        0: {
-            "Accuracy": 0.75,
-            "F1 score": 0.8,
-            "Precision": 1.0,
-            "Recall": 2 / 3.0,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 3,
-            "Condition negative": 1,
-            "False negative": 1,
-            "False positive": 0,
-            "True negative": 1,
-            "True positive": 2,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 75.0,
-            "Condition negative (%)": 25.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 50.0,
-        },
-        1: {
-            "Accuracy": 0.5,
-            "F1 score": 0.5,
-            "Precision": 0.5,
-            "Recall": 0.5,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 2,
-            "Condition negative": 2,
-            "False negative": 1,
-            "False positive": 1,
-            "True negative": 1,
-            "True positive": 1,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 50.0,
-            "Condition negative (%)": 50.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 25.0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 25.0,
-        },
-    }
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_lazy_before() -
    -
    -
    -
    - -Expand source code - -
    def test_lazy_before():
-    instances, models = get_test_pyomo_instances()
-    instances[0].build_lazy_constraint = Mock(return_value="c1")
-    solver = LearningSolver()
-    solver.internal_solver = Mock(spec=InternalSolver)
-    component = DynamicLazyConstraintsComponent(threshold=0.10)
-    component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
-    component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
-
-    component.before_solve(solver, instances[0], models[0])
-
-    # Should ask classifier likelihood of each constraint being violated
-    expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
-    expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
-    actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
-    actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
-    assert norm(expected_x_test_a - actual_x_test_a) < E
-    assert norm(expected_x_test_b - actual_x_test_b) < E
-
-    # Should ask instance to generate cut for constraints whose likelihood
-    # of being violated exceeds the threshold
-    instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
-
-    # Should ask internal solver to add generated constraint
-    solver.internal_solver.add_constraint.assert_called_once_with("c1")
    -
    -
    -
    -def test_lazy_evaluate() -
    -
    -
    -
    - -Expand source code - -
    def test_lazy_evaluate():
-    instances, models = get_test_pyomo_instances()
-    component = DynamicLazyConstraintsComponent()
-    component.classifiers = {
-        "a": Mock(spec=Classifier),
-        "b": Mock(spec=Classifier),
-        "c": Mock(spec=Classifier),
-    }
-    component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
-    component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-    component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
-
-    instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
-    instances[1].found_violated_lazy_constraints = ["b", "d"]
-    assert component.evaluate(instances) == {
-        0: {
-            "Accuracy": 0.75,
-            "F1 score": 0.8,
-            "Precision": 1.0,
-            "Recall": 2 / 3.0,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 3,
-            "Condition negative": 1,
-            "False negative": 1,
-            "False positive": 0,
-            "True negative": 1,
-            "True positive": 2,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 75.0,
-            "Condition negative (%)": 25.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 50.0,
-        },
-        1: {
-            "Accuracy": 0.5,
-            "F1 score": 0.5,
-            "Precision": 0.5,
-            "Recall": 0.5,
-            "Predicted positive": 2,
-            "Predicted negative": 2,
-            "Condition positive": 2,
-            "Condition negative": 2,
-            "False negative": 1,
-            "False positive": 1,
-            "True negative": 1,
-            "True positive": 1,
-            "Predicted positive (%)": 50.0,
-            "Predicted negative (%)": 50.0,
-            "Condition positive (%)": 50.0,
-            "Condition negative (%)": 50.0,
-            "False negative (%)": 25.0,
-            "False positive (%)": 25.0,
-            "True negative (%)": 25.0,
-            "True positive (%)": 25.0,
-        },
-    }
    -
    -
    -
    -def test_lazy_fit() -
    -
    -
    -
    - -Expand source code - -
    def test_lazy_fit():
-    instances, models = get_test_pyomo_instances()
-    instances[0].found_violated_lazy_constraints = ["a", "b"]
-    instances[1].found_violated_lazy_constraints = ["b", "c"]
-    classifier = Mock(spec=Classifier)
-    component = DynamicLazyConstraintsComponent(classifier=classifier)
-
-    component.fit(instances)
-
-    # Should create one classifier for each violation
-    assert "a" in component.classifiers
-    assert "b" in component.classifiers
-    assert "c" in component.classifiers
-
-    # Should provide correct x_train to each classifier
-    expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
-    actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
-    actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
-    actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
-    assert norm(expected_x_train_a - actual_x_train_a) < E
-    assert norm(expected_x_train_b - actual_x_train_b) < E
-    assert norm(expected_x_train_c - actual_x_train_c) < E
-
-    # Should provide correct y_train to each classifier
-    expected_y_train_a = np.array([1.0, 0.0])
-    expected_y_train_b = np.array([1.0, 1.0])
-    expected_y_train_c = np.array([0.0, 1.0])
-    actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
-    actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
-    actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
-    assert norm(expected_y_train_a - actual_y_train_a) < E
-    assert norm(expected_y_train_b - actual_y_train_b) < E
-    assert norm(expected_y_train_c - actual_y_train_c) < E
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/test_lazy_static.html b/0.2/api/miplearn/components/tests/test_lazy_static.html deleted file mode 100644 index ac77440..0000000 --- a/0.2/api/miplearn/components/tests/test_lazy_static.html +++ /dev/null @@ -1,538 +0,0 @@ - - - - - - -miplearn.components.tests.test_lazy_static API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests.test_lazy_static

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock, call
-
-from miplearn.classifiers import Classifier
-from miplearn.components.lazy_static import StaticLazyConstraintsComponent
-from miplearn.instance import Instance
-from miplearn.solvers.internal import InternalSolver
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_usage_with_solver():
-    solver = Mock(spec=LearningSolver)
-    solver.use_lazy_cb = False
-    solver.gap_tolerance = 1e-4
-
-    internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
-    internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
-    internal.is_constraint_satisfied = Mock(return_value=False)
-
-    instance = Mock(spec=Instance)
-    instance.has_static_lazy_constraints = Mock(return_value=True)
-    instance.is_constraint_lazy = Mock(
-        side_effect=lambda cid: {
-            "c1": False,
-            "c2": True,
-            "c3": True,
-            "c4": True,
-        }[cid]
-    )
-    instance.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": [1.0, 0.0],
-            "c3": [0.5, 0.5],
-            "c4": [1.0],
-        }[cid]
-    )
-    instance.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-
-    component = StaticLazyConstraintsComponent(
-        threshold=0.90,
-        use_two_phase_gap=False,
-        violation_tolerance=1.0,
-    )
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = Mock(
-        return_value=[
-            [0.20, 0.80],
-            [0.05, 0.95],
-        ]
-    )
-    component.classifiers["type-b"].predict_proba = Mock(
-        return_value=[
-            [0.02, 0.98],
-        ]
-    )
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should ask if instance has static lazy constraints
-    instance.has_static_lazy_constraints.assert_called_once()
-
-    # Should ask internal solver for a list of constraints in the model
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should ask if each constraint in the model is lazy
-    instance.is_constraint_lazy.assert_has_calls(
-        [
-            call("c1"),
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # For the lazy ones, should ask for features
-    instance.get_constraint_features.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should also ask for categories
-    assert instance.get_constraint_category.call_count == 3
-    instance.get_constraint_category.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask internal solver to remove constraints identified as lazy
-    assert internal.extract_constraint.call_count == 3
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask ML to predict whether each lazy constraint should be enforced
-    component.classifiers["type-a"].predict_proba.assert_called_once_with(
-        [[1.0, 0.0], [0.5, 0.5]]
-    )
-    component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
-
-    # For the ones that should be enforced, should ask solver to re-add them
-    # to the formulation. The remaining ones should remain in the pool.
-    assert internal.add_constraint.call_count == 2
-    internal.add_constraint.assert_has_calls(
-        [
-            call("<c3>"),
-            call("<c4>"),
-        ]
-    )
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (first time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert should_repeat
-
-    # Should ask internal solver to verify if constraints in the pool are
-    # satisfied and add the ones that are not
-    internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
-    internal.is_constraint_satisfied.reset_mock()
-    internal.add_constraint.assert_called_once_with("<c2>")
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (second time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert not should_repeat
-
-    # The lazy constraint pool should be empty by now, so no calls should be made
-    internal.is_constraint_satisfied.assert_not_called()
-    internal.add_constraint.assert_not_called()
-
-    # Should update instance object
-    assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
-
-
-def test_fit():
-    instance_1 = Mock(spec=Instance)
-    instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
-    instance_1.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instance_1.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c1": [1, 1],
-            "c2": [1, 2],
-            "c3": [1, 3],
-            "c4": [1, 4, 0],
-            "c5": [1, 5, 0],
-        }[cid]
-    )
-
-    instance_2 = Mock(spec=Instance)
-    instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
-    instance_2.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instance_2.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c1": [2, 1],
-            "c2": [2, 2],
-            "c3": [2, 3],
-            "c4": [2, 4, 0],
-            "c5": [2, 5, 0],
-        }[cid]
-    )
-
-    instances = [instance_1, instance_2]
-    component = StaticLazyConstraintsComponent()
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-
-    expected_constraints = {
-        "type-a": ["c1", "c2", "c3"],
-        "type-b": ["c4", "c5"],
-    }
-    expected_x = {
-        "type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
-        "type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
-    }
-    expected_y = {
-        "type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
-        "type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
-    }
-    assert component._collect_constraints(instances) == expected_constraints
-    assert component.x(instances) == expected_x
-    assert component.y(instances) == expected_y
-
-    component.fit(instances)
-    component.classifiers["type-a"].fit.assert_called_once_with(
-        expected_x["type-a"],
-        expected_y["type-a"],
-    )
-    component.classifiers["type-b"].fit.assert_called_once_with(
-        expected_x["type-b"],
-        expected_y["type-b"],
-    )
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_fit() -
    -
    -
    -
    - -Expand source code - -
    def test_fit():
-    instance_1 = Mock(spec=Instance)
-    instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
-    instance_1.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instance_1.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c1": [1, 1],
-            "c2": [1, 2],
-            "c3": [1, 3],
-            "c4": [1, 4, 0],
-            "c5": [1, 5, 0],
-        }[cid]
-    )
-
-    instance_2 = Mock(spec=Instance)
-    instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
-    instance_2.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c1": "type-a",
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-            "c5": "type-b",
-        }[cid]
-    )
-    instance_2.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c1": [2, 1],
-            "c2": [2, 2],
-            "c3": [2, 3],
-            "c4": [2, 4, 0],
-            "c5": [2, 5, 0],
-        }[cid]
-    )
-
-    instances = [instance_1, instance_2]
-    component = StaticLazyConstraintsComponent()
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-
-    expected_constraints = {
-        "type-a": ["c1", "c2", "c3"],
-        "type-b": ["c4", "c5"],
-    }
-    expected_x = {
-        "type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
-        "type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
-    }
-    expected_y = {
-        "type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
-        "type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
-    }
-    assert component._collect_constraints(instances) == expected_constraints
-    assert component.x(instances) == expected_x
-    assert component.y(instances) == expected_y
-
-    component.fit(instances)
-    component.classifiers["type-a"].fit.assert_called_once_with(
-        expected_x["type-a"],
-        expected_y["type-a"],
-    )
-    component.classifiers["type-b"].fit.assert_called_once_with(
-        expected_x["type-b"],
-        expected_y["type-b"],
-    )
    -
    -
    -
    -def test_usage_with_solver() -
    -
    -
    -
    - -Expand source code - -
    def test_usage_with_solver():
-    solver = Mock(spec=LearningSolver)
-    solver.use_lazy_cb = False
-    solver.gap_tolerance = 1e-4
-
-    internal = solver.internal_solver = Mock(spec=InternalSolver)
-    internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
-    internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
-    internal.is_constraint_satisfied = Mock(return_value=False)
-
-    instance = Mock(spec=Instance)
-    instance.has_static_lazy_constraints = Mock(return_value=True)
-    instance.is_constraint_lazy = Mock(
-        side_effect=lambda cid: {
-            "c1": False,
-            "c2": True,
-            "c3": True,
-            "c4": True,
-        }[cid]
-    )
-    instance.get_constraint_features = Mock(
-        side_effect=lambda cid: {
-            "c2": [1.0, 0.0],
-            "c3": [0.5, 0.5],
-            "c4": [1.0],
-        }[cid]
-    )
-    instance.get_constraint_category = Mock(
-        side_effect=lambda cid: {
-            "c2": "type-a",
-            "c3": "type-a",
-            "c4": "type-b",
-        }[cid]
-    )
-
-    component = StaticLazyConstraintsComponent(
-        threshold=0.90,
-        use_two_phase_gap=False,
-        violation_tolerance=1.0,
-    )
-    component.classifiers = {
-        "type-a": Mock(spec=Classifier),
-        "type-b": Mock(spec=Classifier),
-    }
-    component.classifiers["type-a"].predict_proba = Mock(
-        return_value=[
-            [0.20, 0.80],
-            [0.05, 0.95],
-        ]
-    )
-    component.classifiers["type-b"].predict_proba = Mock(
-        return_value=[
-            [0.02, 0.98],
-        ]
-    )
-
-    # LearningSolver calls before_solve
-    component.before_solve(solver, instance, None)
-
-    # Should ask if instance has static lazy constraints
-    instance.has_static_lazy_constraints.assert_called_once()
-
-    # Should ask internal solver for a list of constraints in the model
-    internal.get_constraint_ids.assert_called_once()
-
-    # Should ask if each constraint in the model is lazy
-    instance.is_constraint_lazy.assert_has_calls(
-        [
-            call("c1"),
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # For the lazy ones, should ask for features
-    instance.get_constraint_features.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should also ask for categories
-    assert instance.get_constraint_category.call_count == 3
-    instance.get_constraint_category.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask internal solver to remove constraints identified as lazy
-    assert internal.extract_constraint.call_count == 3
-    internal.extract_constraint.assert_has_calls(
-        [
-            call("c2"),
-            call("c3"),
-            call("c4"),
-        ]
-    )
-
-    # Should ask ML to predict whether each lazy constraint should be enforced
-    component.classifiers["type-a"].predict_proba.assert_called_once_with(
-        [[1.0, 0.0], [0.5, 0.5]]
-    )
-    component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
-
-    # For the ones that should be enforced, should ask solver to re-add them
-    # to the formulation. The remaining ones should remain in the pool.
-    assert internal.add_constraint.call_count == 2
-    internal.add_constraint.assert_has_calls(
-        [
-            call("<c3>"),
-            call("<c4>"),
-        ]
-    )
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (first time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert should_repeat
-
-    # Should ask internal solver to verify if constraints in the pool are
-    # satisfied and add the ones that are not
-    internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
-    internal.is_constraint_satisfied.reset_mock()
-    internal.add_constraint.assert_called_once_with("<c2>")
-    internal.add_constraint.reset_mock()
-
-    # LearningSolver calls after_iteration (second time)
-    should_repeat = component.iteration_cb(solver, instance, None)
-    assert not should_repeat
-
-    # The lazy constraint pool should be empty by now, so no calls should be made
-    internal.is_constraint_satisfied.assert_not_called()
-    internal.add_constraint.assert_not_called()
-
-    # Should update instance object
-    assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/test_objective.html b/0.2/api/miplearn/components/tests/test_objective.html deleted file mode 100644 index 55a17e6..0000000 --- a/0.2/api/miplearn/components/tests/test_objective.html +++ /dev/null @@ -1,174 +0,0 @@ - - - - - - -miplearn.components.tests.test_objective API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests.test_objective

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock
-
-import numpy as np
-
-from miplearn.classifiers import Regressor
-from miplearn.components.objective import ObjectiveValueComponent
-from miplearn.tests import get_test_pyomo_instances
-
-
-def test_usage():
-    instances, models = get_test_pyomo_instances()
-    comp = ObjectiveValueComponent()
-    comp.fit(instances)
-    assert instances[0].training_data[0]["Lower bound"] == 1183.0
-    assert instances[0].training_data[0]["Upper bound"] == 1183.0
-    assert np.round(comp.predict(instances), 2).tolist() == [
-        [1183.0, 1183.0],
-        [1070.0, 1070.0],
-    ]
-
-
-def test_obj_evaluate():
-    instances, models = get_test_pyomo_instances()
-    reg = Mock(spec=Regressor)
-    reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
-    comp = ObjectiveValueComponent(regressor=reg)
-    comp.fit(instances)
-    ev = comp.evaluate(instances)
-    assert ev == {
-        "Lower bound": {
-            "Explained variance": 0.0,
-            "Max error": 183.0,
-            "Mean absolute error": 126.5,
-            "Mean squared error": 19194.5,
-            "Median absolute error": 126.5,
-            "R2": -5.012843605607331,
-        },
-        "Upper bound": {
-            "Explained variance": 0.0,
-            "Max error": 183.0,
-            "Mean absolute error": 126.5,
-            "Mean squared error": 19194.5,
-            "Median absolute error": 126.5,
-            "R2": -5.012843605607331,
-        },
-    }
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_obj_evaluate() -
    -
    -
    -
    - -Expand source code - -
    def test_obj_evaluate():
-    instances, models = get_test_pyomo_instances()
-    reg = Mock(spec=Regressor)
-    reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
-    comp = ObjectiveValueComponent(regressor=reg)
-    comp.fit(instances)
-    ev = comp.evaluate(instances)
-    assert ev == {
-        "Lower bound": {
-            "Explained variance": 0.0,
-            "Max error": 183.0,
-            "Mean absolute error": 126.5,
-            "Mean squared error": 19194.5,
-            "Median absolute error": 126.5,
-            "R2": -5.012843605607331,
-        },
-        "Upper bound": {
-            "Explained variance": 0.0,
-            "Max error": 183.0,
-            "Mean absolute error": 126.5,
-            "Mean squared error": 19194.5,
-            "Median absolute error": 126.5,
-            "R2": -5.012843605607331,
-        },
-    }
    -
    -
    -
    -def test_usage() -
    -
    -
    -
    - -Expand source code - -
    def test_usage():
-    instances, models = get_test_pyomo_instances()
-    comp = ObjectiveValueComponent()
-    comp.fit(instances)
-    assert instances[0].training_data[0]["Lower bound"] == 1183.0
-    assert instances[0].training_data[0]["Upper bound"] == 1183.0
-    assert np.round(comp.predict(instances), 2).tolist() == [
-        [1183.0, 1183.0],
-        [1070.0, 1070.0],
-    ]
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/components/tests/test_primal.html b/0.2/api/miplearn/components/tests/test_primal.html deleted file mode 100644 index a7dc078..0000000 --- a/0.2/api/miplearn/components/tests/test_primal.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - -miplearn.components.tests.test_primal API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.components.tests.test_primal

    -
    -
    -
    - -Expand source code - -
    #  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 unittest.mock import Mock
-
-import numpy as np
-
-from miplearn.classifiers import Classifier
-from miplearn.components.primal import PrimalSolutionComponent
-from miplearn.tests import get_test_pyomo_instances
-
-
-def test_predict():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances)
-    solution = comp.predict(instances[0])
-    assert "x" in solution
-    assert 0 in solution["x"]
-    assert 1 in solution["x"]
-    assert 2 in solution["x"]
-    assert 3 in solution["x"]
-
-
-def test_evaluate():
-    instances, models = get_test_pyomo_instances()
-    clf_zero = Mock(spec=Classifier)
-    clf_zero.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.0, 1.0],  # x[0]
-                [0.0, 1.0],  # x[1]
-                [1.0, 0.0],  # x[2]
-                [1.0, 0.0],  # x[3]
-            ]
-        )
-    )
-    clf_one = Mock(spec=Classifier)
-    clf_one.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [1.0, 0.0],  # x[0] instances[0]
-                [1.0, 0.0],  # x[1] instances[0]
-                [0.0, 1.0],  # x[2] instances[0]
-                [1.0, 0.0],  # x[3] instances[0]
-            ]
-        )
-    )
-    comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
-    comp.fit(instances[:1])
-    assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
-    assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
-    ev = comp.evaluate(instances[:1])
-    assert ev == {
-        "Fix one": {
-            0: {
-                "Accuracy": 0.5,
-                "Condition negative": 1,
-                "Condition negative (%)": 25.0,
-                "Condition positive": 3,
-                "Condition positive (%)": 75.0,
-                "F1 score": 0.5,
-                "False negative": 2,
-                "False negative (%)": 50.0,
-                "False positive": 0,
-                "False positive (%)": 0.0,
-                "Precision": 1.0,
-                "Predicted negative": 3,
-                "Predicted negative (%)": 75.0,
-                "Predicted positive": 1,
-                "Predicted positive (%)": 25.0,
-                "Recall": 0.3333333333333333,
-                "True negative": 1,
-                "True negative (%)": 25.0,
-                "True positive": 1,
-                "True positive (%)": 25.0,
-            }
-        },
-        "Fix zero": {
-            0: {
-                "Accuracy": 0.75,
-                "Condition negative": 3,
-                "Condition negative (%)": 75.0,
-                "Condition positive": 1,
-                "Condition positive (%)": 25.0,
-                "F1 score": 0.6666666666666666,
-                "False negative": 0,
-                "False negative (%)": 0.0,
-                "False positive": 1,
-                "False positive (%)": 25.0,
-                "Precision": 0.5,
-                "Predicted negative": 2,
-                "Predicted negative (%)": 50.0,
-                "Predicted positive": 2,
-                "Predicted positive (%)": 50.0,
-                "Recall": 1.0,
-                "True negative": 2,
-                "True negative (%)": 50.0,
-                "True positive": 1,
-                "True positive (%)": 25.0,
-            }
-        },
-    }
-
-
-def test_primal_parallel_fit():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances, n_jobs=2)
-    assert len(comp.classifiers) == 2
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_evaluate() -
    -
    -
    -
    - -Expand source code - -
    def test_evaluate():
-    instances, models = get_test_pyomo_instances()
-    clf_zero = Mock(spec=Classifier)
-    clf_zero.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [0.0, 1.0],  # x[0]
-                [0.0, 1.0],  # x[1]
-                [1.0, 0.0],  # x[2]
-                [1.0, 0.0],  # x[3]
-            ]
-        )
-    )
-    clf_one = Mock(spec=Classifier)
-    clf_one.predict_proba = Mock(
-        return_value=np.array(
-            [
-                [1.0, 0.0],  # x[0] instances[0]
-                [1.0, 0.0],  # x[1] instances[0]
-                [0.0, 1.0],  # x[2] instances[0]
-                [1.0, 0.0],  # x[3] instances[0]
-            ]
-        )
-    )
-    comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
-    comp.fit(instances[:1])
-    assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
-    assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
-    ev = comp.evaluate(instances[:1])
-    assert ev == {
-        "Fix one": {
-            0: {
-                "Accuracy": 0.5,
-                "Condition negative": 1,
-                "Condition negative (%)": 25.0,
-                "Condition positive": 3,
-                "Condition positive (%)": 75.0,
-                "F1 score": 0.5,
-                "False negative": 2,
-                "False negative (%)": 50.0,
-                "False positive": 0,
-                "False positive (%)": 0.0,
-                "Precision": 1.0,
-                "Predicted negative": 3,
-                "Predicted negative (%)": 75.0,
-                "Predicted positive": 1,
-                "Predicted positive (%)": 25.0,
-                "Recall": 0.3333333333333333,
-                "True negative": 1,
-                "True negative (%)": 25.0,
-                "True positive": 1,
-                "True positive (%)": 25.0,
-            }
-        },
-        "Fix zero": {
-            0: {
-                "Accuracy": 0.75,
-                "Condition negative": 3,
-                "Condition negative (%)": 75.0,
-                "Condition positive": 1,
-                "Condition positive (%)": 25.0,
-                "F1 score": 0.6666666666666666,
-                "False negative": 0,
-                "False negative (%)": 0.0,
-                "False positive": 1,
-                "False positive (%)": 25.0,
-                "Precision": 0.5,
-                "Predicted negative": 2,
-                "Predicted negative (%)": 50.0,
-                "Predicted positive": 2,
-                "Predicted positive (%)": 50.0,
-                "Recall": 1.0,
-                "True negative": 2,
-                "True negative (%)": 50.0,
-                "True positive": 1,
-                "True positive (%)": 25.0,
-            }
-        },
-    }
    -
    -
    -
    -def test_predict() -
    -
    -
    -
    - -Expand source code - -
    def test_predict():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances)
-    solution = comp.predict(instances[0])
-    assert "x" in solution
-    assert 0 in solution["x"]
-    assert 1 in solution["x"]
-    assert 2 in solution["x"]
-    assert 3 in solution["x"]
    -
    -
    -
    -def test_primal_parallel_fit() -
    -
    -
    -
    - -Expand source code - -
    def test_primal_parallel_fit():
-    instances, models = get_test_pyomo_instances()
-    comp = PrimalSolutionComponent()
-    comp.fit(instances, n_jobs=2)
-    assert len(comp.classifiers) == 2
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/index.html b/0.2/api/miplearn/index.html index b6edbbf..707f019 100644 --- a/0.2/api/miplearn/index.html +++ b/0.2/api/miplearn/index.html @@ -93,10 +93,6 @@ from .solvers.pyomo.gurobi import GurobiPyomoSolver
    -
    miplearn.tests
    -
    -
    -
    miplearn.types
    @@ -126,7 +122,6 @@ from .solvers.pyomo.gurobi import GurobiPyomoSolver
  • miplearn.log
  • miplearn.problems
  • miplearn.solvers
    • -
  • miplearn.tests
  • miplearn.types
    • diff --git a/0.2/api/miplearn/instance.html b/0.2/api/miplearn/instance.html index cac032e..eedba99 100644 --- a/0.2/api/miplearn/instance.html +++ b/0.2/api/miplearn/instance.html @@ -373,13 +373,10 @@ features, which can be provided as inputs to machine learning models.

    Subclasses

    Methods

    diff --git a/0.2/api/miplearn/problems/index.html b/0.2/api/miplearn/problems/index.html index 5b35380..5b786be 100644 --- a/0.2/api/miplearn/problems/index.html +++ b/0.2/api/miplearn/problems/index.html @@ -40,10 +40,6 @@
    -
    miplearn.problems.tests
    -
    -
    -
    miplearn.problems.tsp
    @@ -72,7 +68,6 @@ diff --git a/0.2/api/miplearn/problems/tests/index.html b/0.2/api/miplearn/problems/tests/index.html deleted file mode 100644 index 6b14cd8..0000000 --- a/0.2/api/miplearn/problems/tests/index.html +++ /dev/null @@ -1,83 +0,0 @@ - - - - - - -miplearn.problems.tests API documentation - - - - - - - - - -
    - - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/problems/tests/test_knapsack.html b/0.2/api/miplearn/problems/tests/test_knapsack.html deleted file mode 100644 index 6a509ab..0000000 --- a/0.2/api/miplearn/problems/tests/test_knapsack.html +++ /dev/null @@ -1,115 +0,0 @@ - - - - - - -miplearn.problems.tests.test_knapsack API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.problems.tests.test_knapsack

    -
    -
    -
    - -Expand source code - -
    #  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 scipy.stats import uniform, randint
-
-from miplearn.problems.knapsack import MultiKnapsackGenerator
-
-
-def test_knapsack_generator():
-    gen = MultiKnapsackGenerator(
-        n=randint(low=100, high=101),
-        m=randint(low=30, high=31),
-        w=randint(low=0, high=1000),
-        K=randint(low=500, high=501),
-        u=uniform(loc=1.0, scale=1.0),
-        alpha=uniform(loc=0.50, scale=0.0),
-    )
-    instances = gen.generate(100)
-    w_sum = sum(instance.weights for instance in instances) / len(instances)
-    b_sum = sum(instance.capacities for instance in instances) / len(instances)
-    assert round(np.mean(w_sum), -1) == 500.0
-    assert round(np.mean(b_sum), -3) == 25000.0
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_knapsack_generator() -
    -
    -
    -
    - -Expand source code - -
    def test_knapsack_generator():
-    gen = MultiKnapsackGenerator(
-        n=randint(low=100, high=101),
-        m=randint(low=30, high=31),
-        w=randint(low=0, high=1000),
-        K=randint(low=500, high=501),
-        u=uniform(loc=1.0, scale=1.0),
-        alpha=uniform(loc=0.50, scale=0.0),
-    )
-    instances = gen.generate(100)
-    w_sum = sum(instance.weights for instance in instances) / len(instances)
-    b_sum = sum(instance.capacities for instance in instances) / len(instances)
-    assert round(np.mean(w_sum), -1) == 500.0
-    assert round(np.mean(b_sum), -3) == 25000.0
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/problems/tests/test_stab.html b/0.2/api/miplearn/problems/tests/test_stab.html deleted file mode 100644 index 08d1a53..0000000 --- a/0.2/api/miplearn/problems/tests/test_stab.html +++ /dev/null @@ -1,191 +0,0 @@ - - - - - - -miplearn.problems.tests.test_stab API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.problems.tests.test_stab

    -
    -
    -
    - -Expand source code - -
    #  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 networkx as nx
-import numpy as np
-from scipy.stats import uniform, randint
-
-from miplearn.problems.stab import MaxWeightStableSetInstance
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_stab():
-    graph = nx.cycle_graph(5)
-    weights = [1.0, 1.0, 1.0, 1.0, 1.0]
-    instance = MaxWeightStableSetInstance(graph, weights)
-    solver = LearningSolver()
-    stats = solver.solve(instance)
-    assert stats["Lower bound"] == 2.0
-
-
-def test_stab_generator_fixed_graph():
-    np.random.seed(42)
-    from miplearn.problems.stab import MaxWeightStableSetGenerator
-
-    gen = MaxWeightStableSetGenerator(
-        w=uniform(loc=50.0, scale=10.0),
-        n=randint(low=10, high=11),
-        p=uniform(loc=0.05, scale=0.0),
-        fix_graph=True,
-    )
-    instances = gen.generate(1_000)
-    weights = np.array([instance.weights for instance in instances])
-    weights_avg_actual = np.round(np.average(weights, axis=0))
-    weights_avg_expected = [55.0] * 10
-    assert list(weights_avg_actual) == weights_avg_expected
-
-
-def test_stab_generator_random_graph():
-    np.random.seed(42)
-    from miplearn.problems.stab import MaxWeightStableSetGenerator
-
-    gen = MaxWeightStableSetGenerator(
-        w=uniform(loc=50.0, scale=10.0),
-        n=randint(low=30, high=41),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=False,
-    )
-    instances = gen.generate(1_000)
-    n_nodes = [instance.graph.number_of_nodes() for instance in instances]
-    n_edges = [instance.graph.number_of_edges() for instance in instances]
-    assert np.round(np.mean(n_nodes)) == 35.0
-    assert np.round(np.mean(n_edges), -1) == 300.0
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_stab() -
    -
    -
    -
    - -Expand source code - -
    def test_stab():
-    graph = nx.cycle_graph(5)
-    weights = [1.0, 1.0, 1.0, 1.0, 1.0]
-    instance = MaxWeightStableSetInstance(graph, weights)
-    solver = LearningSolver()
-    stats = solver.solve(instance)
-    assert stats["Lower bound"] == 2.0
    -
    -
    -
    -def test_stab_generator_fixed_graph() -
    -
    -
    -
    - -Expand source code - -
    def test_stab_generator_fixed_graph():
-    np.random.seed(42)
-    from miplearn.problems.stab import MaxWeightStableSetGenerator
-
-    gen = MaxWeightStableSetGenerator(
-        w=uniform(loc=50.0, scale=10.0),
-        n=randint(low=10, high=11),
-        p=uniform(loc=0.05, scale=0.0),
-        fix_graph=True,
-    )
-    instances = gen.generate(1_000)
-    weights = np.array([instance.weights for instance in instances])
-    weights_avg_actual = np.round(np.average(weights, axis=0))
-    weights_avg_expected = [55.0] * 10
-    assert list(weights_avg_actual) == weights_avg_expected
    -
    -
    -
    -def test_stab_generator_random_graph() -
    -
    -
    -
    - -Expand source code - -
    def test_stab_generator_random_graph():
-    np.random.seed(42)
-    from miplearn.problems.stab import MaxWeightStableSetGenerator
-
-    gen = MaxWeightStableSetGenerator(
-        w=uniform(loc=50.0, scale=10.0),
-        n=randint(low=30, high=41),
-        p=uniform(loc=0.5, scale=0.0),
-        fix_graph=False,
-    )
-    instances = gen.generate(1_000)
-    n_nodes = [instance.graph.number_of_nodes() for instance in instances]
-    n_edges = [instance.graph.number_of_edges() for instance in instances]
-    assert np.round(np.mean(n_nodes)) == 35.0
-    assert np.round(np.mean(n_edges), -1) == 300.0
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/problems/tests/test_tsp.html b/0.2/api/miplearn/problems/tests/test_tsp.html deleted file mode 100644 index 1fbb5be..0000000 --- a/0.2/api/miplearn/problems/tests/test_tsp.html +++ /dev/null @@ -1,242 +0,0 @@ - - - - - - -miplearn.problems.tests.test_tsp API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.problems.tests.test_tsp

    -
    -
    -
    - -Expand source code - -
    #  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 numpy.linalg import norm
-from scipy.spatial.distance import pdist, squareform
-from scipy.stats import uniform, randint
-
-from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_generator():
-    instances = TravelingSalesmanGenerator(
-        x=uniform(loc=0.0, scale=1000.0),
-        y=uniform(loc=0.0, scale=1000.0),
-        n=randint(low=100, high=101),
-        gamma=uniform(loc=0.95, scale=0.1),
-        fix_cities=True,
-    ).generate(100)
-    assert len(instances) == 100
-    assert instances[0].n_cities == 100
-    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
-    d = [instance.distances[0, 1] for instance in instances]
-    assert np.std(d) > 0
-
-
-def test_instance():
-    n_cities = 4
-    distances = np.array(
-        [
-            [0.0, 1.0, 2.0, 1.0],
-            [1.0, 0.0, 1.0, 2.0],
-            [2.0, 1.0, 0.0, 1.0],
-            [1.0, 2.0, 1.0, 0.0],
-        ]
-    )
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    solver = LearningSolver()
-    stats = solver.solve(instance)
-    x = instance.training_data[0]["Solution"]["x"]
-    assert x[0, 1] == 1.0
-    assert x[0, 2] == 0.0
-    assert x[0, 3] == 1.0
-    assert x[1, 2] == 1.0
-    assert x[1, 3] == 0.0
-    assert x[2, 3] == 1.0
-    assert stats["Lower bound"] == 4.0
-    assert stats["Upper bound"] == 4.0
-
-
-def test_subtour():
-    n_cities = 6
-    cities = np.array(
-        [
-            [0.0, 0.0],
-            [1.0, 0.0],
-            [2.0, 0.0],
-            [3.0, 0.0],
-            [0.0, 1.0],
-            [3.0, 1.0],
-        ]
-    )
-    distances = squareform(pdist(cities))
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    solver = LearningSolver()
-    solver.solve(instance)
-    assert hasattr(instance, "found_violated_lazy_constraints")
-    assert hasattr(instance, "found_violated_user_cuts")
-    x = instance.training_data[0]["Solution"]["x"]
-    assert x[0, 1] == 1.0
-    assert x[0, 4] == 1.0
-    assert x[1, 2] == 1.0
-    assert x[2, 3] == 1.0
-    assert x[3, 5] == 1.0
-    assert x[4, 5] == 1.0
-    solver.fit([instance])
-    solver.solve(instance)
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_generator() -
    -
    -
    -
    - -Expand source code - -
    def test_generator():
-    instances = TravelingSalesmanGenerator(
-        x=uniform(loc=0.0, scale=1000.0),
-        y=uniform(loc=0.0, scale=1000.0),
-        n=randint(low=100, high=101),
-        gamma=uniform(loc=0.95, scale=0.1),
-        fix_cities=True,
-    ).generate(100)
-    assert len(instances) == 100
-    assert instances[0].n_cities == 100
-    assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
-    d = [instance.distances[0, 1] for instance in instances]
-    assert np.std(d) > 0
    -
    -
    -
    -def test_instance() -
    -
    -
    -
    - -Expand source code - -
    def test_instance():
-    n_cities = 4
-    distances = np.array(
-        [
-            [0.0, 1.0, 2.0, 1.0],
-            [1.0, 0.0, 1.0, 2.0],
-            [2.0, 1.0, 0.0, 1.0],
-            [1.0, 2.0, 1.0, 0.0],
-        ]
-    )
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    solver = LearningSolver()
-    stats = solver.solve(instance)
-    x = instance.training_data[0]["Solution"]["x"]
-    assert x[0, 1] == 1.0
-    assert x[0, 2] == 0.0
-    assert x[0, 3] == 1.0
-    assert x[1, 2] == 1.0
-    assert x[1, 3] == 0.0
-    assert x[2, 3] == 1.0
-    assert stats["Lower bound"] == 4.0
-    assert stats["Upper bound"] == 4.0
    -
    -
    -
    -def test_subtour() -
    -
    -
    -
    - -Expand source code - -
    def test_subtour():
-    n_cities = 6
-    cities = np.array(
-        [
-            [0.0, 0.0],
-            [1.0, 0.0],
-            [2.0, 0.0],
-            [3.0, 0.0],
-            [0.0, 1.0],
-            [3.0, 1.0],
-        ]
-    )
-    distances = squareform(pdist(cities))
-    instance = TravelingSalesmanInstance(n_cities, distances)
-    solver = LearningSolver()
-    solver.solve(instance)
-    assert hasattr(instance, "found_violated_lazy_constraints")
-    assert hasattr(instance, "found_violated_user_cuts")
-    x = instance.training_data[0]["Solution"]["x"]
-    assert x[0, 1] == 1.0
-    assert x[0, 4] == 1.0
-    assert x[1, 2] == 1.0
-    assert x[2, 3] == 1.0
-    assert x[3, 5] == 1.0
-    assert x[4, 5] == 1.0
-    solver.fit([instance])
-    solver.solve(instance)
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/solvers/index.html b/0.2/api/miplearn/solvers/index.html index e4f593b..2939c57 100644 --- a/0.2/api/miplearn/solvers/index.html +++ b/0.2/api/miplearn/solvers/index.html @@ -78,10 +78,6 @@ class _RedirectOutput:
    -
    miplearn.solvers.tests
    -
    -
    -
    @@ -108,7 +104,6 @@ class _RedirectOutput:
  • miplearn.solvers.internal
  • miplearn.solvers.learning
  • miplearn.solvers.pyomo
    • -
  • miplearn.solvers.tests
    • diff --git a/0.2/api/miplearn/solvers/tests/index.html b/0.2/api/miplearn/solvers/tests/index.html deleted file mode 100644 index de45e73..0000000 --- a/0.2/api/miplearn/solvers/tests/index.html +++ /dev/null @@ -1,250 +0,0 @@ - - - - - - -miplearn.solvers.tests API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.solvers.tests

    -
    -
    -
    - -Expand source code - -
    #  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 inspect import isclass
-from typing import List, Callable, Any
-
-from pyomo import environ as pe
-
-from miplearn.instance import Instance
-from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
-from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.internal import InternalSolver
-from miplearn.solvers.pyomo.base import BasePyomoSolver
-from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
-from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
-
-
-class InfeasiblePyomoInstance(Instance):
-    def to_model(self) -> pe.ConcreteModel:
-        model = pe.ConcreteModel()
-        model.x = pe.Var([0], domain=pe.Binary)
-        model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
-        model.eq = pe.Constraint(expr=model.x[0] >= 2)
-        return model
-
-
-class InfeasibleGurobiInstance(Instance):
-    def to_model(self) -> Any:
-        import gurobipy as gp
-        from gurobipy import GRB
-
-        model = gp.Model()
-        x = model.addVars(1, vtype=GRB.BINARY, name="x")
-        model.addConstr(x[0] >= 2)
-        model.setObjective(x[0])
-        return model
-
-
-def _is_subclass_or_instance(obj, parent_class):
-    return isinstance(obj, parent_class) or (
-        isclass(obj) and issubclass(obj, parent_class)
-    )
-
-
-def _get_knapsack_instance(solver):
-    if _is_subclass_or_instance(solver, BasePyomoSolver):
-        return KnapsackInstance(
-            weights=[23.0, 26.0, 20.0, 18.0],
-            prices=[505.0, 352.0, 458.0, 220.0],
-            capacity=67.0,
-        )
-    if _is_subclass_or_instance(solver, GurobiSolver):
-        return GurobiKnapsackInstance(
-            weights=[23.0, 26.0, 20.0, 18.0],
-            prices=[505.0, 352.0, 458.0, 220.0],
-            capacity=67.0,
-        )
-    assert False
-
-
-def _get_infeasible_instance(solver):
-    if _is_subclass_or_instance(solver, BasePyomoSolver):
-        return InfeasiblePyomoInstance()
-    if _is_subclass_or_instance(solver, GurobiSolver):
-        return InfeasibleGurobiInstance()
-
-
-def _get_internal_solvers() -> List[Callable[[], InternalSolver]]:
-    return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]
    -
    -
    -
    -

    Sub-modules

    -
    -
    miplearn.solvers.tests.test_internal_solver
    -
    -
    -
    -
    miplearn.solvers.tests.test_lazy_cb
    -
    -
    -
    -
    miplearn.solvers.tests.test_learning_solver
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -
    -

    Classes

    -
    -
    -class InfeasibleGurobiInstance -
    -
    -

    Abstract class holding all the data necessary to generate a concrete model of the -problem.

    -

    In the knapsack problem, for example, this class could hold the number of items, -their weights and costs, as well as the size of the knapsack. Objects -implementing this class are able to convert themselves into a concrete -optimization model, which can be optimized by a solver, or into arrays of -features, which can be provided as inputs to machine learning models.

    -
    - -Expand source code - -
    class InfeasibleGurobiInstance(Instance):
-    def to_model(self) -> Any:
-        import gurobipy as gp
-        from gurobipy import GRB
-
-        model = gp.Model()
-        x = model.addVars(1, vtype=GRB.BINARY, name="x")
-        model.addConstr(x[0] >= 2)
-        model.setObjective(x[0])
-        return model
    -
    -

    Ancestors

    - -

    Inherited members

    - -
    -
    -class InfeasiblePyomoInstance -
    -
    -

    Abstract class holding all the data necessary to generate a concrete model of the -problem.

    -

    In the knapsack problem, for example, this class could hold the number of items, -their weights and costs, as well as the size of the knapsack. Objects -implementing this class are able to convert themselves into a concrete -optimization model, which can be optimized by a solver, or into arrays of -features, which can be provided as inputs to machine learning models.

    -
    - -Expand source code - -
    class InfeasiblePyomoInstance(Instance):
-    def to_model(self) -> pe.ConcreteModel:
-        model = pe.ConcreteModel()
-        model.x = pe.Var([0], domain=pe.Binary)
-        model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
-        model.eq = pe.Constraint(expr=model.x[0] >= 2)
-        return model
    -
    -

    Ancestors

    - -

    Inherited members

    - -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/solvers/tests/test_internal_solver.html b/0.2/api/miplearn/solvers/tests/test_internal_solver.html deleted file mode 100644 index 437d37c..0000000 --- a/0.2/api/miplearn/solvers/tests/test_internal_solver.html +++ /dev/null @@ -1,543 +0,0 @@ - - - - - - -miplearn.solvers.tests.test_internal_solver API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.solvers.tests.test_internal_solver

    -
    -
    -
    - -Expand source code - -
    #  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 io import StringIO
-from warnings import warn
-
-import pyomo.environ as pe
-
-from miplearn.solvers import _RedirectOutput
-from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.pyomo.base import BasePyomoSolver
-from miplearn.solvers.tests import (
-    _get_knapsack_instance,
-    _get_internal_solvers,
-    _get_infeasible_instance,
-)
-
-logger = logging.getLogger(__name__)
-
-
-def test_redirect_output():
-    import sys
-
-    original_stdout = sys.stdout
-    io = StringIO()
-    with _RedirectOutput([io]):
-        print("Hello world")
-    assert sys.stdout == original_stdout
-    assert io.getvalue() == "Hello world\n"
-
-
-def test_internal_solver_warm_starts():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_knapsack_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-        solver.set_warm_start(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 0.0,
-                    2: 0.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        if stats["Warm start value"] is not None:
-            assert stats["Warm start value"] == 725.0
-        else:
-            warn(f"{solver_class.__name__} should set warm start value")
-
-        solver.set_warm_start(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 1.0,
-                    2: 1.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        assert stats["Warm start value"] is None
-
-        solver.fix(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 0.0,
-                    2: 0.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        assert stats["Lower bound"] == 725.0
-        assert stats["Upper bound"] == 725.0
-
-
-def test_internal_solver():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-
-        instance = _get_knapsack_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-
-        stats = solver.solve_lp()
-        assert not solver.is_infeasible()
-        assert round(stats["Optimal value"], 3) == 1287.923
-        assert len(stats["Log"]) > 100
-
-        solution = solver.get_solution()
-        assert round(solution["x"][0], 3) == 1.000
-        assert round(solution["x"][1], 3) == 0.923
-        assert round(solution["x"][2], 3) == 1.000
-        assert round(solution["x"][3], 3) == 0.000
-
-        stats = solver.solve(tee=True)
-        assert not solver.is_infeasible()
-        assert len(stats["Log"]) > 100
-        assert stats["Lower bound"] == 1183.0
-        assert stats["Upper bound"] == 1183.0
-        assert stats["Sense"] == "max"
-        assert isinstance(stats["Wallclock time"], float)
-
-        solution = solver.get_solution()
-        assert solution["x"][0] == 1.0
-        assert solution["x"][1] == 0.0
-        assert solution["x"][2] == 1.0
-        assert solution["x"][3] == 1.0
-
-        # Add a brand new constraint
-        if isinstance(solver, BasePyomoSolver):
-            model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
-            solver.add_constraint(model.cut)
-        elif isinstance(solver, GurobiSolver):
-            x = model.getVarByName("x[0]")
-            solver.add_constraint(x <= 0.0, name="cut")
-        else:
-            raise Exception("Illegal state")
-
-        # New constraint should affect solution and should be listed in
-        # constraint ids
-        assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-        stats = solver.solve()
-        assert stats["Lower bound"] == 1030.0
-
-        assert solver.get_sense() == "max"
-        assert solver.get_constraint_sense("cut") == "<"
-        assert solver.get_constraint_sense("eq_capacity") == "<"
-
-        # Verify slacks
-        assert solver.get_inequality_slacks() == {
-            "cut": 0.0,
-            "eq_capacity": 3.0,
-        }
-
-        if isinstance(solver, GurobiSolver):
-            # Extract the new constraint
-            cobj = solver.extract_constraint("cut")
-
-            # New constraint should no longer affect solution and should no longer
-            # be listed in constraint ids
-            assert solver.get_constraint_ids() == ["eq_capacity"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1183.0
-
-            # New constraint should not be satisfied by current solution
-            assert not solver.is_constraint_satisfied(cobj)
-
-            # Re-add constraint
-            solver.add_constraint(cobj)
-
-            # Constraint should affect solution again
-            assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1030.0
-
-            # New constraint should now be satisfied
-            assert solver.is_constraint_satisfied(cobj)
-
-            # Relax problem and make cut into an equality constraint
-            solver.relax()
-            solver.set_constraint_sense("cut", "=")
-            stats = solver.solve()
-            assert round(stats["Lower bound"]) == 1030.0
-            assert round(solver.get_dual("eq_capacity")) == 0.0
-
-
-def test_relax():
-    for solver_class in _get_internal_solvers():
-        instance = _get_knapsack_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        solver.relax()
-        stats = solver.solve()
-        assert round(stats["Lower bound"]) == 1288.0
-
-
-def test_infeasible_instance():
-    for solver_class in _get_internal_solvers():
-        instance = _get_infeasible_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        stats = solver.solve()
-
-        assert solver.is_infeasible()
-        assert solver.get_solution() is None
-        assert stats["Upper bound"] is None
-        assert stats["Lower bound"] is None
-
-        stats = solver.solve_lp()
-        assert solver.get_solution() is None
-        assert stats["Optimal value"] is None
-        assert solver.get_value("x", 0) is None
-
-
-def test_iteration_cb():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_knapsack_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        count = 0
-
-        def custom_iteration_cb():
-            nonlocal count
-            count += 1
-            return count < 5
-
-        solver.solve(iteration_cb=custom_iteration_cb)
-        assert count == 5
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_infeasible_instance() -
    -
    -
    -
    - -Expand source code - -
    def test_infeasible_instance():
-    for solver_class in _get_internal_solvers():
-        instance = _get_infeasible_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        stats = solver.solve()
-
-        assert solver.is_infeasible()
-        assert solver.get_solution() is None
-        assert stats["Upper bound"] is None
-        assert stats["Lower bound"] is None
-
-        stats = solver.solve_lp()
-        assert solver.get_solution() is None
-        assert stats["Optimal value"] is None
-        assert solver.get_value("x", 0) is None
    -
    -
    -
    -def test_internal_solver() -
    -
    -
    -
    - -Expand source code - -
    def test_internal_solver():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-
-        instance = _get_knapsack_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-
-        stats = solver.solve_lp()
-        assert not solver.is_infeasible()
-        assert round(stats["Optimal value"], 3) == 1287.923
-        assert len(stats["Log"]) > 100
-
-        solution = solver.get_solution()
-        assert round(solution["x"][0], 3) == 1.000
-        assert round(solution["x"][1], 3) == 0.923
-        assert round(solution["x"][2], 3) == 1.000
-        assert round(solution["x"][3], 3) == 0.000
-
-        stats = solver.solve(tee=True)
-        assert not solver.is_infeasible()
-        assert len(stats["Log"]) > 100
-        assert stats["Lower bound"] == 1183.0
-        assert stats["Upper bound"] == 1183.0
-        assert stats["Sense"] == "max"
-        assert isinstance(stats["Wallclock time"], float)
-
-        solution = solver.get_solution()
-        assert solution["x"][0] == 1.0
-        assert solution["x"][1] == 0.0
-        assert solution["x"][2] == 1.0
-        assert solution["x"][3] == 1.0
-
-        # Add a brand new constraint
-        if isinstance(solver, BasePyomoSolver):
-            model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
-            solver.add_constraint(model.cut)
-        elif isinstance(solver, GurobiSolver):
-            x = model.getVarByName("x[0]")
-            solver.add_constraint(x <= 0.0, name="cut")
-        else:
-            raise Exception("Illegal state")
-
-        # New constraint should affect solution and should be listed in
-        # constraint ids
-        assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-        stats = solver.solve()
-        assert stats["Lower bound"] == 1030.0
-
-        assert solver.get_sense() == "max"
-        assert solver.get_constraint_sense("cut") == "<"
-        assert solver.get_constraint_sense("eq_capacity") == "<"
-
-        # Verify slacks
-        assert solver.get_inequality_slacks() == {
-            "cut": 0.0,
-            "eq_capacity": 3.0,
-        }
-
-        if isinstance(solver, GurobiSolver):
-            # Extract the new constraint
-            cobj = solver.extract_constraint("cut")
-
-            # New constraint should no longer affect solution and should no longer
-            # be listed in constraint ids
-            assert solver.get_constraint_ids() == ["eq_capacity"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1183.0
-
-            # New constraint should not be satisfied by current solution
-            assert not solver.is_constraint_satisfied(cobj)
-
-            # Re-add constraint
-            solver.add_constraint(cobj)
-
-            # Constraint should affect solution again
-            assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
-            stats = solver.solve()
-            assert stats["Lower bound"] == 1030.0
-
-            # New constraint should now be satisfied
-            assert solver.is_constraint_satisfied(cobj)
-
-            # Relax problem and make cut into an equality constraint
-            solver.relax()
-            solver.set_constraint_sense("cut", "=")
-            stats = solver.solve()
-            assert round(stats["Lower bound"]) == 1030.0
-            assert round(solver.get_dual("eq_capacity")) == 0.0
    -
    -
    -
    -def test_internal_solver_warm_starts() -
    -
    -
    -
    - -Expand source code - -
    def test_internal_solver_warm_starts():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_knapsack_instance(solver_class)
-        model = instance.to_model()
-        solver = solver_class()
-        solver.set_instance(instance, model)
-        solver.set_warm_start(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 0.0,
-                    2: 0.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        if stats["Warm start value"] is not None:
-            assert stats["Warm start value"] == 725.0
-        else:
-            warn(f"{solver_class.__name__} should set warm start value")
-
-        solver.set_warm_start(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 1.0,
-                    2: 1.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        assert stats["Warm start value"] is None
-
-        solver.fix(
-            {
-                "x": {
-                    0: 1.0,
-                    1: 0.0,
-                    2: 0.0,
-                    3: 1.0,
-                }
-            }
-        )
-        stats = solver.solve(tee=True)
-        assert stats["Lower bound"] == 725.0
-        assert stats["Upper bound"] == 725.0
    -
    -
    -
    -def test_iteration_cb() -
    -
    -
    -
    - -Expand source code - -
    def test_iteration_cb():
-    for solver_class in _get_internal_solvers():
-        logger.info("Solver: %s" % solver_class)
-        instance = _get_knapsack_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        count = 0
-
-        def custom_iteration_cb():
-            nonlocal count
-            count += 1
-            return count < 5
-
-        solver.solve(iteration_cb=custom_iteration_cb)
-        assert count == 5
    -
    -
    -
    -def test_redirect_output() -
    -
    -
    -
    - -Expand source code - -
    def test_redirect_output():
-    import sys
-
-    original_stdout = sys.stdout
-    io = StringIO()
-    with _RedirectOutput([io]):
-        print("Hello world")
-    assert sys.stdout == original_stdout
-    assert io.getvalue() == "Hello world\n"
    -
    -
    -
    -def test_relax() -
    -
    -
    -
    - -Expand source code - -
    def test_relax():
-    for solver_class in _get_internal_solvers():
-        instance = _get_knapsack_instance(solver_class)
-        solver = solver_class()
-        solver.set_instance(instance)
-        solver.relax()
-        stats = solver.solve()
-        assert round(stats["Lower bound"]) == 1288.0
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/solvers/tests/test_lazy_cb.html b/0.2/api/miplearn/solvers/tests/test_lazy_cb.html deleted file mode 100644 index 2b2e805..0000000 --- a/0.2/api/miplearn/solvers/tests/test_lazy_cb.html +++ /dev/null @@ -1,119 +0,0 @@ - - - - - - -miplearn.solvers.tests.test_lazy_cb API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.solvers.tests.test_lazy_cb

    -
    -
    -
    - -Expand source code - -
    #  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 miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.tests import _get_knapsack_instance
-
-logger = logging.getLogger(__name__)
-
-
-def test_lazy_cb():
-    solver = GurobiSolver()
-    instance = _get_knapsack_instance(solver)
-    model = instance.to_model()
-
-    def lazy_cb(cb_solver, cb_model):
-        logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
-        cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
-        if not cb_solver.is_constraint_satisfied(cobj):
-            cb_solver.add_constraint(cobj)
-
-    solver.set_instance(instance, model)
-    solver.solve(lazy_cb=lazy_cb)
-    solution = solver.get_solution()
-    assert solution["x"][0] == 0.0
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_lazy_cb() -
    -
    -
    -
    - -Expand source code - -
    def test_lazy_cb():
-    solver = GurobiSolver()
-    instance = _get_knapsack_instance(solver)
-    model = instance.to_model()
-
-    def lazy_cb(cb_solver, cb_model):
-        logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
-        cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
-        if not cb_solver.is_constraint_satisfied(cobj):
-            cb_solver.add_constraint(cobj)
-
-    solver.set_instance(instance, model)
-    solver.solve(lazy_cb=lazy_cb)
-    solution = solver.get_solution()
-    assert solution["x"][0] == 0.0
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/solvers/tests/test_learning_solver.html b/0.2/api/miplearn/solvers/tests/test_learning_solver.html deleted file mode 100644 index 3cc83ec..0000000 --- a/0.2/api/miplearn/solvers/tests/test_learning_solver.html +++ /dev/null @@ -1,395 +0,0 @@ - - - - - - -miplearn.solvers.tests.test_learning_solver API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.solvers.tests.test_learning_solver

    -
    -
    -
    - -Expand source code - -
    #  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
-import pickle
-import tempfile
-import os
-
-from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.learning import LearningSolver
-from miplearn.solvers.tests import _get_knapsack_instance, _get_internal_solvers
-
-logger = logging.getLogger(__name__)
-
-
-def test_learning_solver():
-    for mode in ["exact", "heuristic"]:
-        for internal_solver in _get_internal_solvers():
-            logger.info("Solver: %s" % internal_solver)
-            instance = _get_knapsack_instance(internal_solver)
-            solver = LearningSolver(
-                solver=internal_solver,
-                mode=mode,
-            )
-
-            solver.solve(instance)
-            data = instance.training_data[0]
-            assert data["Solution"]["x"][0] == 1.0
-            assert data["Solution"]["x"][1] == 0.0
-            assert data["Solution"]["x"][2] == 1.0
-            assert data["Solution"]["x"][3] == 1.0
-            assert data["Lower bound"] == 1183.0
-            assert data["Upper bound"] == 1183.0
-            assert round(data["LP solution"]["x"][0], 3) == 1.000
-            assert round(data["LP solution"]["x"][1], 3) == 0.923
-            assert round(data["LP solution"]["x"][2], 3) == 1.000
-            assert round(data["LP solution"]["x"][3], 3) == 0.000
-            assert round(data["LP value"], 3) == 1287.923
-            assert len(data["MIP log"]) > 100
-
-            solver.fit([instance])
-            solver.solve(instance)
-
-            # Assert solver is picklable
-            with tempfile.TemporaryFile() as file:
-                pickle.dump(solver, file)
-
-
-def test_solve_without_lp():
-    for internal_solver in _get_internal_solvers():
-        logger.info("Solver: %s" % internal_solver)
-        instance = _get_knapsack_instance(internal_solver)
-        solver = LearningSolver(
-            solver=internal_solver,
-            solve_lp_first=False,
-        )
-        solver.solve(instance)
-        solver.fit([instance])
-        solver.solve(instance)
-
-
-def test_parallel_solve():
-    for internal_solver in _get_internal_solvers():
-        instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
-        solver = LearningSolver(solver=internal_solver)
-        results = solver.parallel_solve(instances, n_jobs=3)
-        assert len(results) == 10
-        for instance in instances:
-            data = instance.training_data[0]
-            assert len(data["Solution"]["x"].keys()) == 4
-
-
-def test_solve_fit_from_disk():
-    for internal_solver in _get_internal_solvers():
-        # Create instances and pickle them
-        filenames = []
-        for k in range(3):
-            instance = _get_knapsack_instance(internal_solver)
-            with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
-                filenames += [file.name]
-                pickle.dump(instance, file)
-
-        # Test: solve
-        solver = LearningSolver(solver=internal_solver)
-        solver.solve(filenames[0])
-        with open(filenames[0], "rb") as file:
-            instance = pickle.load(file)
-            assert len(instance.training_data) > 0
-
-        # Test: parallel_solve
-        solver.parallel_solve(filenames)
-        for filename in filenames:
-            with open(filename, "rb") as file:
-                instance = pickle.load(file)
-                assert len(instance.training_data) > 0
-
-        # Test: solve (with specified output)
-        output = [f + ".out" for f in filenames]
-        solver.solve(
-            filenames[0],
-            output_filename=output[0],
-        )
-        assert os.path.isfile(output[0])
-
-        # Test: parallel_solve (with specified output)
-        solver.parallel_solve(
-            filenames,
-            output_filenames=output,
-        )
-        for filename in output:
-            assert os.path.isfile(filename)
-
-        # Delete temporary files
-        for filename in filenames:
-            os.remove(filename)
-        for filename in output:
-            os.remove(filename)
-
-
-def test_simulate_perfect():
-    internal_solver = GurobiSolver
-    instance = _get_knapsack_instance(internal_solver)
-    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
-        pickle.dump(instance, tmp)
-        tmp.flush()
-        solver = LearningSolver(
-            solver=internal_solver,
-            simulate_perfect=True,
-        )
-        stats = solver.solve(tmp.name)
-        assert stats["Lower bound"] == stats["Predicted LB"]
-
-
-def test_gap():
-    assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
-    assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
-    assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
-    assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
-    assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
-    assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
-    assert LearningSolver._compute_gap(ub=None, lb=None) is None
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_gap() -
    -
    -
    -
    - -Expand source code - -
    def test_gap():
-    assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
-    assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
-    assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
-    assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
-    assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
-    assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
-    assert LearningSolver._compute_gap(ub=None, lb=None) is None
    -
    -
    -
    -def test_learning_solver() -
    -
    -
    -
    - -Expand source code - -
    def test_learning_solver():
-    for mode in ["exact", "heuristic"]:
-        for internal_solver in _get_internal_solvers():
-            logger.info("Solver: %s" % internal_solver)
-            instance = _get_knapsack_instance(internal_solver)
-            solver = LearningSolver(
-                solver=internal_solver,
-                mode=mode,
-            )
-
-            solver.solve(instance)
-            data = instance.training_data[0]
-            assert data["Solution"]["x"][0] == 1.0
-            assert data["Solution"]["x"][1] == 0.0
-            assert data["Solution"]["x"][2] == 1.0
-            assert data["Solution"]["x"][3] == 1.0
-            assert data["Lower bound"] == 1183.0
-            assert data["Upper bound"] == 1183.0
-            assert round(data["LP solution"]["x"][0], 3) == 1.000
-            assert round(data["LP solution"]["x"][1], 3) == 0.923
-            assert round(data["LP solution"]["x"][2], 3) == 1.000
-            assert round(data["LP solution"]["x"][3], 3) == 0.000
-            assert round(data["LP value"], 3) == 1287.923
-            assert len(data["MIP log"]) > 100
-
-            solver.fit([instance])
-            solver.solve(instance)
-
-            # Assert solver is picklable
-            with tempfile.TemporaryFile() as file:
-                pickle.dump(solver, file)
    -
    -
    -
    -def test_parallel_solve() -
    -
    -
    -
    - -Expand source code - -
    def test_parallel_solve():
-    for internal_solver in _get_internal_solvers():
-        instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
-        solver = LearningSolver(solver=internal_solver)
-        results = solver.parallel_solve(instances, n_jobs=3)
-        assert len(results) == 10
-        for instance in instances:
-            data = instance.training_data[0]
-            assert len(data["Solution"]["x"].keys()) == 4
    -
    -
    -
    -def test_simulate_perfect() -
    -
    -
    -
    - -Expand source code - -
    def test_simulate_perfect():
-    internal_solver = GurobiSolver
-    instance = _get_knapsack_instance(internal_solver)
-    with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
-        pickle.dump(instance, tmp)
-        tmp.flush()
-        solver = LearningSolver(
-            solver=internal_solver,
-            simulate_perfect=True,
-        )
-        stats = solver.solve(tmp.name)
-        assert stats["Lower bound"] == stats["Predicted LB"]
    -
    -
    -
    -def test_solve_fit_from_disk() -
    -
    -
    -
    - -Expand source code - -
    def test_solve_fit_from_disk():
-    for internal_solver in _get_internal_solvers():
-        # Create instances and pickle them
-        filenames = []
-        for k in range(3):
-            instance = _get_knapsack_instance(internal_solver)
-            with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
-                filenames += [file.name]
-                pickle.dump(instance, file)
-
-        # Test: solve
-        solver = LearningSolver(solver=internal_solver)
-        solver.solve(filenames[0])
-        with open(filenames[0], "rb") as file:
-            instance = pickle.load(file)
-            assert len(instance.training_data) > 0
-
-        # Test: parallel_solve
-        solver.parallel_solve(filenames)
-        for filename in filenames:
-            with open(filename, "rb") as file:
-                instance = pickle.load(file)
-                assert len(instance.training_data) > 0
-
-        # Test: solve (with specified output)
-        output = [f + ".out" for f in filenames]
-        solver.solve(
-            filenames[0],
-            output_filename=output[0],
-        )
-        assert os.path.isfile(output[0])
-
-        # Test: parallel_solve (with specified output)
-        solver.parallel_solve(
-            filenames,
-            output_filenames=output,
-        )
-        for filename in output:
-            assert os.path.isfile(filename)
-
-        # Delete temporary files
-        for filename in filenames:
-            os.remove(filename)
-        for filename in output:
-            os.remove(filename)
    -
    -
    -
    -def test_solve_without_lp() -
    -
    -
    -
    - -Expand source code - -
    def test_solve_without_lp():
-    for internal_solver in _get_internal_solvers():
-        logger.info("Solver: %s" % internal_solver)
-        instance = _get_knapsack_instance(internal_solver)
-        solver = LearningSolver(
-            solver=internal_solver,
-            solve_lp_first=False,
-        )
-        solver.solve(instance)
-        solver.fit([instance])
-        solver.solve(instance)
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/tests/index.html b/0.2/api/miplearn/tests/index.html deleted file mode 100644 index aba8185..0000000 --- a/0.2/api/miplearn/tests/index.html +++ /dev/null @@ -1,138 +0,0 @@ - - - - - - -miplearn.tests API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.tests

    -
    -
    -
    - -Expand source code - -
    #  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.solvers.learning import LearningSolver
-
-
-def get_test_pyomo_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[23.0, 26.0, 20.0, 18.0],
-            prices=[505.0, 352.0, 458.0, 220.0],
-            capacity=67.0,
-        ),
-        KnapsackInstance(
-            weights=[25.0, 30.0, 22.0, 18.0],
-            prices=[500.0, 365.0, 420.0, 150.0],
-            capacity=70.0,
-        ),
-    ]
-    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
    -
    -
    -
    -

    Sub-modules

    -
    -
    miplearn.tests.test_benchmark
    -
    -
    -
    -
    miplearn.tests.test_extractors
    -
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def get_test_pyomo_instances() -
    -
    -
    -
    - -Expand source code - -
    def get_test_pyomo_instances():
-    instances = [
-        KnapsackInstance(
-            weights=[23.0, 26.0, 20.0, 18.0],
-            prices=[505.0, 352.0, 458.0, 220.0],
-            capacity=67.0,
-        ),
-        KnapsackInstance(
-            weights=[25.0, 30.0, 22.0, 18.0],
-            prices=[500.0, 365.0, 420.0, 150.0],
-            capacity=70.0,
-        ),
-    ]
-    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
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/tests/test_benchmark.html b/0.2/api/miplearn/tests/test_benchmark.html deleted file mode 100644 index 9c5ec62..0000000 --- a/0.2/api/miplearn/tests/test_benchmark.html +++ /dev/null @@ -1,134 +0,0 @@ - - - - - - -miplearn.tests.test_benchmark API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.tests.test_benchmark

    -
    -
    -
    - -Expand source code - -
    #  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.benchmark import BenchmarkRunner
-from miplearn.problems.stab import MaxWeightStableSetGenerator
-from scipy.stats import randint
-
-from miplearn.solvers.learning import LearningSolver
-
-
-def test_benchmark():
-    # Generate training and test instances
-    generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
-    train_instances = generator.generate(5)
-    test_instances = generator.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.results.values.shape == (12, 14)
-
-    benchmark.write_csv("/tmp/benchmark.csv")
-    assert os.path.isfile("/tmp/benchmark.csv")
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_benchmark() -
    -
    -
    -
    - -Expand source code - -
    def test_benchmark():
-    # Generate training and test instances
-    generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
-    train_instances = generator.generate(5)
-    test_instances = generator.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.results.values.shape == (12, 14)
-
-    benchmark.write_csv("/tmp/benchmark.csv")
-    assert os.path.isfile("/tmp/benchmark.csv")
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/api/miplearn/tests/test_extractors.html b/0.2/api/miplearn/tests/test_extractors.html deleted file mode 100644 index cf8fe6c..0000000 --- a/0.2/api/miplearn/tests/test_extractors.html +++ /dev/null @@ -1,201 +0,0 @@ - - - - - - -miplearn.tests.test_extractors API documentation - - - - - - - - - -
    -
    -
    -

    Module miplearn.tests.test_extractors

    -
    -
    -
    - -Expand source code - -
    #  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 (
-    SolutionExtractor,
-    InstanceFeaturesExtractor,
-    VariableFeaturesExtractor,
-)
-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_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,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-        1.0,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-    ]
-
-
-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)
    -
    -
    -
    -
    -
    -
    -
    -

    Functions

    -
    -
    -def test_instance_features_extractor() -
    -
    -
    -
    - -Expand source code - -
    def test_instance_features_extractor():
-    instances, models = _get_instances()
-    features = InstanceFeaturesExtractor().extract(instances)
-    assert features.shape == (2, 3)
    -
    -
    -
    -def test_solution_extractor() -
    -
    -
    -
    - -Expand source code - -
    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,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-        1.0,
-        0.0,
-        0.0,
-        1.0,
-        1.0,
-        0.0,
-    ]
    -
    -
    -
    -def test_variable_features_extractor() -
    -
    -
    -
    - -Expand source code - -
    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)
    -
    -
    -
    -
    -
    -
    -
    - -
    - - - - - \ No newline at end of file diff --git a/0.2/css/custom.css b/0.2/css/custom.css index 366aa46..babb687 100644 --- a/0.2/css/custom.css +++ b/0.2/css/custom.css @@ -14,6 +14,10 @@ a, .navbar-default a { } .navbar-default a:hover, +.navbar-default a:focus { + background-color: #f4f4f4 !important; +} + .navbar-default .active, .active > a { background-color: #f0f0f0 !important; diff --git a/0.2/index.html b/0.2/index.html index 6814d99..d62cadc 100644 --- a/0.2/index.html +++ b/0.2/index.html @@ -286,5 +286,5 @@ Unlike conventional MIP solvers, MIPLearn can take full advantage of very specif diff --git a/0.2/sitemap.xml.gz b/0.2/sitemap.xml.gz index 771057b9dc1006a56cf0c495b9c482f2b658fec5..1026f4922f26855ded75dd0248b0da70fa761ac5 100644 GIT binary patch delta 14 VcmX@Yc!ZHnzMF$%(zS_f`v4-)1qc8D delta 14 VcmX@Yc!ZHnzMF$X?b1ZHeE=Y&1egE-