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<dt><code class="name"><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></dt>
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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),
|
||||
"c2": Mock(spec=Classifier),
|
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"c3": Mock(spec=Classifier),
|
||||
}
|
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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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|
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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>
|
||||
<section class="desc"></section>
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||||
<details class="source">
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||||
<summary>
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||||
<span>Expand source code</span>
|
||||
</summary>
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||||
<pre><code class="python">def test_convert_tight_infeasibility():
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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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_optimal</span></span>(<span>)</span>
|
||||
</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_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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_suboptimality</span></span>(<span>)</span>
|
||||
</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_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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_usage</span></span>(<span>)</span>
|
||||
</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():
|
||||
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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.SampleInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">SampleInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>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.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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</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>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
<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>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<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>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.tests.test_drop_redundant</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># 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])</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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,
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant_with_check_feasibility</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_multiple_solves</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_y_fit_predict_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">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</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant">test_drop_redundant</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility">test_drop_redundant_with_check_feasibility</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves">test_x_multiple_solves</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate">test_x_y_fit_predict_evaluate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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