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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.components.steps.tests.test_convert_tight</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python">from unittest.mock import Mock
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from miplearn.classifiers import Classifier
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from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
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from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
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from miplearn.instance import Instance
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from miplearn.problems.knapsack import GurobiKnapsackInstance
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from miplearn.solvers.gurobi import GurobiSolver
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from miplearn.solvers.learning import LearningSolver
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def test_convert_tight_usage():
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instance = GurobiKnapsackInstance(
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weights=[3.0, 5.0, 10.0],
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prices=[1.0, 1.0, 1.0],
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capacity=16.0,
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)
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[
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RelaxIntegralityStep(),
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ConvertTightIneqsIntoEqsStep(),
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],
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)
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# Solve original problem
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stats = solver.solve(instance)
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original_upper_bound = stats["Upper bound"]
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# Should collect training data
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assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
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# Fit and resolve
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solver.fit([instance])
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stats = solver.solve(instance)
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# Objective value should be the same
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assert stats["Upper bound"] == original_upper_bound
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0
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class SampleInstance(Instance):
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def to_model(self):
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import gurobipy as grb
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m = grb.Model("model")
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x1 = m.addVar(name="x1")
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x2 = m.addVar(name="x2")
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m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
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m.addConstr(x1 <= 2, name="c1")
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m.addConstr(x2 <= 2, name="c2")
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m.addConstr(x1 + x2 <= 3, name="c2")
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return m
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def test_convert_tight_infeasibility():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 1
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assert stats["ConvertTight: Subopt iterations"] == 0
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def test_convert_tight_suboptimality():
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comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 1
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def test_convert_tight_optimal():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dl>
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<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility"><code class="name flex">
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<span>def <span class="ident">test_convert_tight_infeasibility</span></span>(<span>)</span>
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</code></dt>
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<dd>
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<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
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|
<span>Expand source code</span>
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</summary>
|
|
<pre><code class="python">def test_convert_tight_infeasibility():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 1
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assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
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|
</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal"><code class="name flex">
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|
<span>def <span class="ident">test_convert_tight_optimal</span></span>(<span>)</span>
|
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</code></dt>
|
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<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_convert_tight_optimal():
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comp = ConvertTightIneqsIntoEqsStep()
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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solver = LearningSolver(
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solver=GurobiSolver,
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components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
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</details>
|
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</dd>
|
|
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality"><code class="name flex">
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<span>def <span class="ident">test_convert_tight_suboptimality</span></span>(<span>)</span>
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</code></dt>
|
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<dd>
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<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_convert_tight_suboptimality():
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comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
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comp.classifiers = {
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"c1": Mock(spec=Classifier),
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"c2": Mock(spec=Classifier),
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"c3": Mock(spec=Classifier),
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}
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comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
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comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
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comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
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|
|
|
solver = LearningSolver(
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|
solver=GurobiSolver,
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|
components=[comp],
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solve_lp_first=False,
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)
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instance = SampleInstance()
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|
stats = solver.solve(instance)
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assert stats["Upper bound"] == 5.0
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 1</code></pre>
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</details>
|
|
</dd>
|
|
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage"><code class="name flex">
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|
<span>def <span class="ident">test_convert_tight_usage</span></span>(<span>)</span>
|
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</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def test_convert_tight_usage():
|
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instance = GurobiKnapsackInstance(
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|
weights=[3.0, 5.0, 10.0],
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prices=[1.0, 1.0, 1.0],
|
|
capacity=16.0,
|
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)
|
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solver = LearningSolver(
|
|
solver=GurobiSolver,
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components=[
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RelaxIntegralityStep(),
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ConvertTightIneqsIntoEqsStep(),
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],
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)
|
|
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# Solve original problem
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stats = solver.solve(instance)
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original_upper_bound = stats["Upper bound"]
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|
|
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# Should collect training data
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assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
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|
|
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# Fit and resolve
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solver.fit([instance])
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stats = solver.solve(instance)
|
|
|
|
# Objective value should be the same
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assert stats["Upper bound"] == original_upper_bound
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assert stats["ConvertTight: Inf iterations"] == 0
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assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
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<section>
|
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.components.steps.tests.test_convert_tight.SampleInstance"><code class="flex name class">
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<span>class <span class="ident">SampleInstance</span></span>
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</code></dt>
|
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<dd>
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<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
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problem.</p>
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<p>In the knapsack problem, for example, this class could hold the number of items,
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their weights and costs, as well as the size of the knapsack. Objects
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implementing this class are able to convert themselves into a concrete
|
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optimization model, which can be optimized by a solver, or into arrays of
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features, which can be provided as inputs to machine learning models.</p></section>
|
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<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
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</summary>
|
|
<pre><code class="python">class SampleInstance(Instance):
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|
def to_model(self):
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import gurobipy as grb
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|
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m = grb.Model("model")
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x1 = m.addVar(name="x1")
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x2 = m.addVar(name="x2")
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m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
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m.addConstr(x1 <= 2, name="c1")
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m.addConstr(x2 <= 2, name="c2")
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m.addConstr(x1 + x2 <= 3, name="c2")
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return m</code></pre>
|
|
</details>
|
|
<h3>Ancestors</h3>
|
|
<ul class="hlist">
|
|
<li><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
|
<li>abc.ABC</li>
|
|
</ul>
|
|
<h3>Inherited members</h3>
|
|
<ul class="hlist">
|
|
<li><code><b><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
|
<ul class="hlist">
|
|
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
|
<li><code><a title="miplearn.instance.Instance.to_model" href="../../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
|
</ul>
|
|
</li>
|
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</ul>
|
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</dd>
|
|
</dl>
|
|
</section>
|
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</article>
|
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
|
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<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
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</ul>
|
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</li>
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<li><h3><a href="#header-functions">Functions</a></h3>
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<ul class="">
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility">test_convert_tight_infeasibility</a></code></li>
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal">test_convert_tight_optimal</a></code></li>
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality">test_convert_tight_suboptimality</a></code></li>
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage">test_convert_tight_usage</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></code></h4>
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</li>
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