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repos:
- repo: https://github.com/ambv/black
rev: stable
hooks:
- id: black
args: ["--check"]

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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.benchmark</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.
import logging
import os
from copy import deepcopy
import pandas as pd
from tqdm.auto import tqdm
from miplearn.solvers.learning import LearningSolver
class BenchmarkRunner:
def __init__(self, solvers):
assert isinstance(solvers, dict)
for solver in solvers.values():
assert isinstance(solver, LearningSolver)
self.solvers = solvers
self.results = None
def solve(self, instances, tee=False):
for (solver_name, solver) in self.solvers.items():
for i in tqdm(range(len((instances)))):
results = solver.solve(deepcopy(instances[i]), tee=tee)
self._push_result(
results,
solver=solver,
solver_name=solver_name,
instance=i,
)
def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
results = solver.parallel_solve(
trials,
n_jobs=n_jobs,
label=&#34;Solve (%s)&#34; % solver_name,
discard_outputs=True,
)
for i in range(len(trials)):
idx = (i % len(instances)) + index_offset
self._push_result(
results[i],
solver=solver,
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger()
def raw_results(self):
return self.results
def save_results(self, filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename)
def load_results(self, filename):
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
def load_state(self, filename):
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)
def fit(self, training_instances):
for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances)
@staticmethod
def _compute_gap(ub, lb):
if lb is None or ub is None or lb * ub &lt; 0:
# solver did not find a solution and/or bound, use maximum gap possible
return 1.0
elif abs(ub - lb) &lt; 1e-6:
# avoid division by zero when ub = lb = 0
return 0.0
else:
# divide by max(abs(ub),abs(lb)) to ensure gap &lt;= 1
return (ub - lb) / max(abs(ub), abs(lb))
def _push_result(self, result, solver, solver_name, instance):
if self.results is None:
self.results = pd.DataFrame(
# Show the following columns first in the CSV file
columns=[
&#34;Solver&#34;,
&#34;Instance&#34;,
]
)
result[&#34;Solver&#34;] = solver_name
result[&#34;Instance&#34;] = instance
result[&#34;Gap&#34;] = self._compute_gap(
ub=result[&#34;Upper bound&#34;],
lb=result[&#34;Lower bound&#34;],
)
result[&#34;Mode&#34;] = solver.mode
self.results = self.results.append(pd.DataFrame([result]))
def _silence_miplearn_logger(self):
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING)
def _restore_miplearn_logger(self):
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
miplearn_logger.setLevel(self.prev_log_level)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.benchmark.BenchmarkRunner"><code class="flex name class">
<span>class <span class="ident">BenchmarkRunner</span></span>
<span>(</span><span>solvers)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BenchmarkRunner:
def __init__(self, solvers):
assert isinstance(solvers, dict)
for solver in solvers.values():
assert isinstance(solver, LearningSolver)
self.solvers = solvers
self.results = None
def solve(self, instances, tee=False):
for (solver_name, solver) in self.solvers.items():
for i in tqdm(range(len((instances)))):
results = solver.solve(deepcopy(instances[i]), tee=tee)
self._push_result(
results,
solver=solver,
solver_name=solver_name,
instance=i,
)
def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
results = solver.parallel_solve(
trials,
n_jobs=n_jobs,
label=&#34;Solve (%s)&#34; % solver_name,
discard_outputs=True,
)
for i in range(len(trials)):
idx = (i % len(instances)) + index_offset
self._push_result(
results[i],
solver=solver,
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger()
def raw_results(self):
return self.results
def save_results(self, filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename)
def load_results(self, filename):
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
def load_state(self, filename):
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)
def fit(self, training_instances):
for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances)
@staticmethod
def _compute_gap(ub, lb):
if lb is None or ub is None or lb * ub &lt; 0:
# solver did not find a solution and/or bound, use maximum gap possible
return 1.0
elif abs(ub - lb) &lt; 1e-6:
# avoid division by zero when ub = lb = 0
return 0.0
else:
# divide by max(abs(ub),abs(lb)) to ensure gap &lt;= 1
return (ub - lb) / max(abs(ub), abs(lb))
def _push_result(self, result, solver, solver_name, instance):
if self.results is None:
self.results = pd.DataFrame(
# Show the following columns first in the CSV file
columns=[
&#34;Solver&#34;,
&#34;Instance&#34;,
]
)
result[&#34;Solver&#34;] = solver_name
result[&#34;Instance&#34;] = instance
result[&#34;Gap&#34;] = self._compute_gap(
ub=result[&#34;Upper bound&#34;],
lb=result[&#34;Lower bound&#34;],
)
result[&#34;Mode&#34;] = solver.mode
self.results = self.results.append(pd.DataFrame([result]))
def _silence_miplearn_logger(self):
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING)
def _restore_miplearn_logger(self):
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
miplearn_logger.setLevel(self.prev_log_level)</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="miplearn.benchmark.BenchmarkRunner.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.load_results"><code class="name flex">
<span>def <span class="ident">load_results</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_results(self, filename):
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.load_state"><code class="name flex">
<span>def <span class="ident">load_state</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_state(self, filename):
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.parallel_solve"><code class="name flex">
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=1, n_trials=1, index_offset=0)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
results = solver.parallel_solve(
trials,
n_jobs=n_jobs,
label=&#34;Solve (%s)&#34; % solver_name,
discard_outputs=True,
)
for i in range(len(trials)):
idx = (i % len(instances)) + index_offset
self._push_result(
results[i],
solver=solver,
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger()</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.raw_results"><code class="name flex">
<span>def <span class="ident">raw_results</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def raw_results(self):
return self.results</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.save_results"><code class="name flex">
<span>def <span class="ident">save_results</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save_results(self, filename):
os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename)</code></pre>
</details>
</dd>
<dt id="miplearn.benchmark.BenchmarkRunner.solve"><code class="name flex">
<span>def <span class="ident">solve</span></span>(<span>self, instances, tee=False)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def solve(self, instances, tee=False):
for (solver_name, solver) in self.solvers.items():
for i in tqdm(range(len((instances)))):
results = solver.solve(deepcopy(instances[i]), tee=tee)
self._push_result(
results,
solver=solver,
solver_name=solver_name,
instance=i,
)</code></pre>
</details>
</dd>
</dl>
</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" href="index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4>
<ul class="two-column">
<li><code><a title="miplearn.benchmark.BenchmarkRunner.fit" href="#miplearn.benchmark.BenchmarkRunner.fit">fit</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_results" href="#miplearn.benchmark.BenchmarkRunner.load_results">load_results</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_state" href="#miplearn.benchmark.BenchmarkRunner.load_state">load_state</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.parallel_solve" href="#miplearn.benchmark.BenchmarkRunner.parallel_solve">parallel_solve</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.raw_results" href="#miplearn.benchmark.BenchmarkRunner.raw_results">raw_results</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.save_results" href="#miplearn.benchmark.BenchmarkRunner.save_results">save_results</a></code></li>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.solve" href="#miplearn.benchmark.BenchmarkRunner.solve">solve</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<title>miplearn.classifiers.adaptive API documentation</title>
<meta name="description" content="" />
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.classifiers.adaptive</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.
import logging
from copy import deepcopy
from typing import Any, Dict
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.classifiers.evaluator import ClassifierEvaluator
logger = logging.getLogger(__name__)
class AdaptiveClassifier(Classifier):
&#34;&#34;&#34;
A meta-classifier which dynamically selects what actual classifier to use
based on its cross-validation score on a particular training data set.
&#34;&#34;&#34;
def __init__(
self,
candidates: Dict[str, Any] = None,
evaluator: ClassifierEvaluator = ClassifierEvaluator(),
) -&gt; None:
&#34;&#34;&#34;
Initializes the meta-classifier.
&#34;&#34;&#34;
if candidates is None:
candidates = {
&#34;knn(100)&#34;: {
&#34;classifier&#34;: KNeighborsClassifier(n_neighbors=100),
&#34;min samples&#34;: 100,
},
&#34;logistic&#34;: {
&#34;classifier&#34;: make_pipeline(StandardScaler(), LogisticRegression()),
&#34;min samples&#34;: 30,
},
&#34;counting&#34;: {
&#34;classifier&#34;: CountingClassifier(),
&#34;min samples&#34;: 0,
},
}
self.candidates = candidates
self.evaluator = evaluator
self.classifier = None
def fit(self, x_train, y_train):
best_name, best_clf, best_score = None, None, -float(&#34;inf&#34;)
n_samples = x_train.shape[0]
for (name, clf_dict) in self.candidates.items():
if n_samples &lt; clf_dict[&#34;min samples&#34;]:
continue
clf = deepcopy(clf_dict[&#34;classifier&#34;])
clf.fit(x_train, y_train)
score = self.evaluator.evaluate(clf, x_train, y_train)
if score &gt; best_score:
best_name, best_clf, best_score = name, clf, score
logger.debug(&#34;Best classifier: %s (score=%.3f)&#34; % (best_name, best_score))
self.classifier = best_clf
def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier"><code class="flex name class">
<span>class <span class="ident">AdaptiveClassifier</span></span>
<span>(</span><span>candidates=None, evaluator=<miplearn.classifiers.evaluator.ClassifierEvaluator object>)</span>
</code></dt>
<dd>
<section class="desc"><p>A meta-classifier which dynamically selects what actual classifier to use
based on its cross-validation score on a particular training data set.</p>
<p>Initializes the meta-classifier.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class AdaptiveClassifier(Classifier):
&#34;&#34;&#34;
A meta-classifier which dynamically selects what actual classifier to use
based on its cross-validation score on a particular training data set.
&#34;&#34;&#34;
def __init__(
self,
candidates: Dict[str, Any] = None,
evaluator: ClassifierEvaluator = ClassifierEvaluator(),
) -&gt; None:
&#34;&#34;&#34;
Initializes the meta-classifier.
&#34;&#34;&#34;
if candidates is None:
candidates = {
&#34;knn(100)&#34;: {
&#34;classifier&#34;: KNeighborsClassifier(n_neighbors=100),
&#34;min samples&#34;: 100,
},
&#34;logistic&#34;: {
&#34;classifier&#34;: make_pipeline(StandardScaler(), LogisticRegression()),
&#34;min samples&#34;: 30,
},
&#34;counting&#34;: {
&#34;classifier&#34;: CountingClassifier(),
&#34;min samples&#34;: 0,
},
}
self.candidates = candidates
self.evaluator = evaluator
self.classifier = None
def fit(self, x_train, y_train):
best_name, best_clf, best_score = None, None, -float(&#34;inf&#34;)
n_samples = x_train.shape[0]
for (name, clf_dict) in self.candidates.items():
if n_samples &lt; clf_dict[&#34;min samples&#34;]:
continue
clf = deepcopy(clf_dict[&#34;classifier&#34;])
clf.fit(x_train, y_train)
score = self.evaluator.evaluate(clf, x_train, y_train)
if score &gt; best_score:
best_name, best_clf, best_score = name, clf, score
logger.debug(&#34;Best classifier: %s (score=%.3f)&#34; % (best_name, best_score))
self.classifier = best_clf
def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, x_train, y_train):
best_name, best_clf, best_score = None, None, -float(&#34;inf&#34;)
n_samples = x_train.shape[0]
for (name, clf_dict) in self.candidates.items():
if n_samples &lt; clf_dict[&#34;min samples&#34;]:
continue
clf = deepcopy(clf_dict[&#34;classifier&#34;])
clf.fit(x_train, y_train)
score = self.evaluator.evaluate(clf, x_train, y_train)
if score &gt; best_score:
best_name, best_clf, best_score = name, clf, score
logger.debug(&#34;Best classifier: %s (score=%.3f)&#34; % (best_name, best_score))
self.classifier = best_clf</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
</dd>
</dl>
</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.classifiers" href="index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.fit" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.fit">fit</a></code></li>
<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba">predict_proba</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.classifiers.counting</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.
import numpy as np
from miplearn.classifiers import Classifier
class CountingClassifier(Classifier):
&#34;&#34;&#34;
A classifier that generates constant predictions, based only on the
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
this classifier always returns [0.66 0.33] for any x_test. It essentially
counts how many times each label appeared, hence the name.
&#34;&#34;&#34;
def __init__(self) -&gt; None:
self.mean = None
def fit(self, x_train, y_train):
self.mean = np.mean(y_train)
def predict_proba(self, x_test):
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
def __repr__(self):
return &#34;CountingClassifier(mean=%s)&#34; % self.mean</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.counting.CountingClassifier"><code class="flex name class">
<span>class <span class="ident">CountingClassifier</span></span>
</code></dt>
<dd>
<section class="desc"><p>A classifier that generates constant predictions, based only on the
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
this classifier always returns [0.66 0.33] for any x_test. It essentially
counts how many times each label appeared, hence the name.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CountingClassifier(Classifier):
&#34;&#34;&#34;
A classifier that generates constant predictions, based only on the
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
this classifier always returns [0.66 0.33] for any x_test. It essentially
counts how many times each label appeared, hence the name.
&#34;&#34;&#34;
def __init__(self) -&gt; None:
self.mean = None
def fit(self, x_train, y_train):
self.mean = np.mean(y_train)
def predict_proba(self, x_test):
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
def __repr__(self):
return &#34;CountingClassifier(mean=%s)&#34; % self.mean</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.counting.CountingClassifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, x_train, y_train):
self.mean = np.mean(y_train)</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.counting.CountingClassifier.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba(self, x_test):
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])</code></pre>
</details>
</dd>
</dl>
</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.classifiers" href="index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.counting.CountingClassifier" href="#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.fit" href="#miplearn.classifiers.counting.CountingClassifier.fit">fit</a></code></li>
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.predict_proba" href="#miplearn.classifiers.counting.CountingClassifier.predict_proba">predict_proba</a></code></li>
</ul>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.classifiers.cv</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.
import logging
from copy import deepcopy
import numpy as np
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
from miplearn.classifiers import Classifier
logger = logging.getLogger(__name__)
class CrossValidatedClassifier(Classifier):
&#34;&#34;&#34;
A meta-classifier that, upon training, evaluates the performance of another
classifier on the training data set using k-fold cross validation, then
either adopts the other classifier it if the cv-score is high enough, or
returns a constant label for every x_test otherwise.
The threshold is specified in comparison to a dummy classifier trained
on the same dataset. For example, a threshold of 0.0 indicates that any
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
indicates that only classifier with a perfect cross-validation score are
acceptable. Other numbers are a linear interpolation of these two extremes.
&#34;&#34;&#34;
def __init__(
self,
classifier=LogisticRegression(),
threshold=0.75,
constant=0.0,
cv=5,
scoring=&#34;accuracy&#34;,
):
self.classifier = None
self.classifier_prototype = classifier
self.constant = constant
self.threshold = threshold
self.cv = cv
self.scoring = scoring
def fit(self, x_train, y_train):
# Calculate dummy score and absolute score threshold
y_train_avg = np.average(y_train)
dummy_score = max(y_train_avg, 1 - y_train_avg)
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
# Calculate cross validation score and decide which classifier to use
clf = deepcopy(self.classifier_prototype)
cv_score = float(
np.mean(
cross_val_score(
clf,
x_train,
y_train,
cv=self.cv,
scoring=self.scoring,
)
)
)
if cv_score &gt;= absolute_threshold:
logger.debug(
&#34;cv_score is above threshold (%.2f &gt;= %.2f); keeping&#34;
% (cv_score, absolute_threshold)
)
self.classifier = clf
else:
logger.debug(
&#34;cv_score is below threshold (%.2f &lt; %.2f); discarding&#34;
% (cv_score, absolute_threshold)
)
self.classifier = DummyClassifier(
strategy=&#34;constant&#34;,
constant=self.constant,
)
# Train chosen classifier
self.classifier.fit(x_train, y_train)
def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier"><code class="flex name class">
<span>class <span class="ident">CrossValidatedClassifier</span></span>
<span>(</span><span>classifier=LogisticRegression(), threshold=0.75, constant=0.0, cv=5, scoring='accuracy')</span>
</code></dt>
<dd>
<section class="desc"><p>A meta-classifier that, upon training, evaluates the performance of another
classifier on the training data set using k-fold cross validation, then
either adopts the other classifier it if the cv-score is high enough, or
returns a constant label for every x_test otherwise.</p>
<p>The threshold is specified in comparison to a dummy classifier trained
on the same dataset. For example, a threshold of 0.0 indicates that any
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
indicates that only classifier with a perfect cross-validation score are
acceptable. Other numbers are a linear interpolation of these two extremes.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CrossValidatedClassifier(Classifier):
&#34;&#34;&#34;
A meta-classifier that, upon training, evaluates the performance of another
classifier on the training data set using k-fold cross validation, then
either adopts the other classifier it if the cv-score is high enough, or
returns a constant label for every x_test otherwise.
The threshold is specified in comparison to a dummy classifier trained
on the same dataset. For example, a threshold of 0.0 indicates that any
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
indicates that only classifier with a perfect cross-validation score are
acceptable. Other numbers are a linear interpolation of these two extremes.
&#34;&#34;&#34;
def __init__(
self,
classifier=LogisticRegression(),
threshold=0.75,
constant=0.0,
cv=5,
scoring=&#34;accuracy&#34;,
):
self.classifier = None
self.classifier_prototype = classifier
self.constant = constant
self.threshold = threshold
self.cv = cv
self.scoring = scoring
def fit(self, x_train, y_train):
# Calculate dummy score and absolute score threshold
y_train_avg = np.average(y_train)
dummy_score = max(y_train_avg, 1 - y_train_avg)
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
# Calculate cross validation score and decide which classifier to use
clf = deepcopy(self.classifier_prototype)
cv_score = float(
np.mean(
cross_val_score(
clf,
x_train,
y_train,
cv=self.cv,
scoring=self.scoring,
)
)
)
if cv_score &gt;= absolute_threshold:
logger.debug(
&#34;cv_score is above threshold (%.2f &gt;= %.2f); keeping&#34;
% (cv_score, absolute_threshold)
)
self.classifier = clf
else:
logger.debug(
&#34;cv_score is below threshold (%.2f &lt; %.2f); discarding&#34;
% (cv_score, absolute_threshold)
)
self.classifier = DummyClassifier(
strategy=&#34;constant&#34;,
constant=self.constant,
)
# Train chosen classifier
self.classifier.fit(x_train, y_train)
def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, x_train, y_train):
# Calculate dummy score and absolute score threshold
y_train_avg = np.average(y_train)
dummy_score = max(y_train_avg, 1 - y_train_avg)
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
# Calculate cross validation score and decide which classifier to use
clf = deepcopy(self.classifier_prototype)
cv_score = float(
np.mean(
cross_val_score(
clf,
x_train,
y_train,
cv=self.cv,
scoring=self.scoring,
)
)
)
if cv_score &gt;= absolute_threshold:
logger.debug(
&#34;cv_score is above threshold (%.2f &gt;= %.2f); keeping&#34;
% (cv_score, absolute_threshold)
)
self.classifier = clf
else:
logger.debug(
&#34;cv_score is below threshold (%.2f &lt; %.2f); discarding&#34;
% (cv_score, absolute_threshold)
)
self.classifier = DummyClassifier(
strategy=&#34;constant&#34;,
constant=self.constant,
)
# Train chosen classifier
self.classifier.fit(x_train, y_train)</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict_proba(self, x_test):
return self.classifier.predict_proba(x_test)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
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<ul></ul>
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.fit" href="#miplearn.classifiers.cv.CrossValidatedClassifier.fit">fit</a></code></li>
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba" href="#miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba">predict_proba</a></code></li>
</ul>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.classifiers.evaluator</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 sklearn.metrics import roc_auc_score
class ClassifierEvaluator:
def __init__(self) -&gt; None:
pass
def evaluate(self, clf, x_train, y_train):
# FIXME: use cross-validation
proba = clf.predict_proba(x_train)
return roc_auc_score(y_train, proba[:, 1])</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.evaluator.ClassifierEvaluator"><code class="flex name class">
<span>class <span class="ident">ClassifierEvaluator</span></span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ClassifierEvaluator:
def __init__(self) -&gt; None:
pass
def evaluate(self, clf, x_train, y_train):
# FIXME: use cross-validation
proba = clf.predict_proba(x_train)
return roc_auc_score(y_train, proba[:, 1])</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, clf, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, clf, x_train, y_train):
# FIXME: use cross-validation
proba = clf.predict_proba(x_train)
return roc_auc_score(y_train, proba[:, 1])</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
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<div class="toc">
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<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.evaluator.ClassifierEvaluator" href="#miplearn.classifiers.evaluator.ClassifierEvaluator">ClassifierEvaluator</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate" href="#miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate">evaluate</a></code></li>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.classifiers</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 abc import ABC, abstractmethod
import numpy as np
class Classifier(ABC):
@abstractmethod
def fit(self, x_train, y_train):
pass
@abstractmethod
def predict_proba(self, x_test):
pass
def predict(self, x_test):
proba = self.predict_proba(x_test)
assert isinstance(proba, np.ndarray)
assert proba.shape == (x_test.shape[0], 2)
return (proba[:, 1] &gt; 0.5).astype(float)
class Regressor(ABC):
@abstractmethod
def fit(self, x_train, y_train):
pass
@abstractmethod
def predict(self):
pass</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.Classifier"><code class="flex name class">
<span>class <span class="ident">Classifier</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Classifier(ABC):
@abstractmethod
def fit(self, x_train, y_train):
pass
@abstractmethod
def predict_proba(self, x_test):
pass
def predict(self, x_test):
proba = self.predict_proba(x_test)
assert isinstance(proba, np.ndarray)
assert proba.shape == (x_test.shape[0], 2)
return (proba[:, 1] &gt; 0.5).astype(float)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.counting.CountingClassifier" href="counting.html#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></li>
<li><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="adaptive.html#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></li>
<li><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="cv.html#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.Classifier.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def fit(self, x_train, y_train):
pass</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.Classifier.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, x_test):
proba = self.predict_proba(x_test)
assert isinstance(proba, np.ndarray)
assert proba.shape == (x_test.shape[0], 2)
return (proba[:, 1] &gt; 0.5).astype(float)</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.Classifier.predict_proba"><code class="name flex">
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def predict_proba(self, x_test):
pass</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.classifiers.Regressor"><code class="flex name class">
<span>class <span class="ident">Regressor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Regressor(ABC):
@abstractmethod
def fit(self, x_train, y_train):
pass
@abstractmethod
def predict(self):
pass</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.Regressor.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def fit(self, x_train, y_train):
pass</code></pre>
</details>
</dd>
<dt id="miplearn.classifiers.Regressor.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def predict(self):
pass</code></pre>
</details>
</dd>
</dl>
</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" href="../index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></li>
<li><code><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></li>
<li><code><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></li>
<li><code><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></li>
<li><code><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></li>
<li><code><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.Classifier" href="#miplearn.classifiers.Classifier">Classifier</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.Classifier.fit" href="#miplearn.classifiers.Classifier.fit">fit</a></code></li>
<li><code><a title="miplearn.classifiers.Classifier.predict" href="#miplearn.classifiers.Classifier.predict">predict</a></code></li>
<li><code><a title="miplearn.classifiers.Classifier.predict_proba" href="#miplearn.classifiers.Classifier.predict_proba">predict_proba</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.classifiers.Regressor" href="#miplearn.classifiers.Regressor">Regressor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.Regressor.fit" href="#miplearn.classifiers.Regressor.fit">fit</a></code></li>
<li><code><a title="miplearn.classifiers.Regressor.predict" href="#miplearn.classifiers.Regressor.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
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<header>
<h1 class="title">Module <code>miplearn.classifiers.tests</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.</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.classifiers.tests.test_counting" href="test_counting.html">miplearn.classifiers.tests.test_counting</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.tests.test_cv" href="test_cv.html">miplearn.classifiers.tests.test_cv</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.tests.test_evaluator" href="test_evaluator.html">miplearn.classifiers.tests.test_evaluator</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers.tests.test_threshold" href="test_threshold.html">miplearn.classifiers.tests.test_threshold</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
</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.classifiers" href="../index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.classifiers.tests.test_counting" href="test_counting.html">miplearn.classifiers.tests.test_counting</a></code></li>
<li><code><a title="miplearn.classifiers.tests.test_cv" href="test_cv.html">miplearn.classifiers.tests.test_cv</a></code></li>
<li><code><a title="miplearn.classifiers.tests.test_evaluator" href="test_evaluator.html">miplearn.classifiers.tests.test_evaluator</a></code></li>
<li><code><a title="miplearn.classifiers.tests.test_threshold" href="test_threshold.html">miplearn.classifiers.tests.test_threshold</a></code></li>
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<h1 class="title">Module <code>miplearn.classifiers.tests.test_counting</code></h1>
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<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.
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) &lt; E</code></pre>
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<span>def <span class="ident">test_counting</span></span>(<span>)</span>
</code></dt>
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<section class="desc"></section>
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<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">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) &lt; E</code></pre>
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<li><code><a title="miplearn.classifiers.tests.test_counting.test_counting" href="#miplearn.classifiers.tests.test_counting.test_counting">test_counting</a></code></li>
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<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.
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 &lt;= 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)) &lt; 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=&#34;poly&#34;, 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)) &lt; E</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.classifiers.tests.test_cv.test_cv"><code class="name flex">
<span>def <span class="ident">test_cv</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_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 &lt;= 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)) &lt; 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=&#34;poly&#34;, 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)) &lt; E</code></pre>
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<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.
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</code></pre>
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<h2 class="section-title" id="header-functions">Functions</h2>
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<dt id="miplearn.classifiers.tests.test_evaluator.test_evaluator"><code class="name flex">
<span>def <span class="ident">test_evaluator</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_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</code></pre>
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<h1 class="title">Module <code>miplearn.classifiers.tests.test_threshold</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
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</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic"><code class="name flex">
<span>def <span class="ident">test_threshold_dynamic</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_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</code></pre>
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<li><code><a title="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic" href="#miplearn.classifiers.tests.test_threshold.test_threshold_dynamic">test_threshold_dynamic</a></code></li>
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<h1 class="title">Module <code>miplearn.classifiers.threshold</code></h1>
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<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 abc import abstractmethod, ABC
import numpy as np
from sklearn.metrics._ranking import _binary_clf_curve
from miplearn.classifiers import Classifier
class DynamicThreshold(ABC):
@abstractmethod
def find(
self,
clf: Classifier,
x_train: np.ndarray,
y_train: np.ndarray,
) -&gt; float:
&#34;&#34;&#34;
Given a trained binary classifier `clf` and a training data set,
returns the numerical threshold (float) satisfying some criterea.
&#34;&#34;&#34;
pass
class MinPrecisionThreshold(DynamicThreshold):
&#34;&#34;&#34;
The smallest possible threshold satisfying a minimum acceptable true
positive rate (also known as precision).
&#34;&#34;&#34;
def __init__(self, min_precision: float) -&gt; None:
self.min_precision = min_precision
def find(self, clf, x_train, y_train):
proba = clf.predict_proba(x_train)
assert isinstance(proba, np.ndarray), &#34;classifier should return numpy array&#34;
assert proba.shape == (
x_train.shape[0],
2,
), &#34;classifier should return (%d,%d)-shaped array, not %s&#34; % (
x_train.shape[0],
2,
str(proba.shape),
)
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
precision = tps / (tps + fps)
for k in reversed(range(len(precision))):
if precision[k] &gt;= self.min_precision:
return thresholds[k]
return 2.0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.classifiers.threshold.DynamicThreshold"><code class="flex name class">
<span>class <span class="ident">DynamicThreshold</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class DynamicThreshold(ABC):
@abstractmethod
def find(
self,
clf: Classifier,
x_train: np.ndarray,
y_train: np.ndarray,
) -&gt; float:
&#34;&#34;&#34;
Given a trained binary classifier `clf` and a training data set,
returns the numerical threshold (float) satisfying some criterea.
&#34;&#34;&#34;
pass</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.classifiers.threshold.DynamicThreshold.find"><code class="name flex">
<span>def <span class="ident">find</span></span>(<span>self, clf, x_train, y_train)</span>
</code></dt>
<dd>
<section class="desc"><p>Given a trained binary classifier <code>clf</code> and a training data set,
returns the numerical threshold (float) satisfying some criterea.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def find(
self,
clf: Classifier,
x_train: np.ndarray,
y_train: np.ndarray,
) -&gt; float:
&#34;&#34;&#34;
Given a trained binary classifier `clf` and a training data set,
returns the numerical threshold (float) satisfying some criterea.
&#34;&#34;&#34;
pass</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.classifiers.threshold.MinPrecisionThreshold"><code class="flex name class">
<span>class <span class="ident">MinPrecisionThreshold</span></span>
<span>(</span><span>min_precision)</span>
</code></dt>
<dd>
<section class="desc"><p>The smallest possible threshold satisfying a minimum acceptable true
positive rate (also known as precision).</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MinPrecisionThreshold(DynamicThreshold):
&#34;&#34;&#34;
The smallest possible threshold satisfying a minimum acceptable true
positive rate (also known as precision).
&#34;&#34;&#34;
def __init__(self, min_precision: float) -&gt; None:
self.min_precision = min_precision
def find(self, clf, x_train, y_train):
proba = clf.predict_proba(x_train)
assert isinstance(proba, np.ndarray), &#34;classifier should return numpy array&#34;
assert proba.shape == (
x_train.shape[0],
2,
), &#34;classifier should return (%d,%d)-shaped array, not %s&#34; % (
x_train.shape[0],
2,
str(proba.shape),
)
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
precision = tps / (tps + fps)
for k in reversed(range(len(precision))):
if precision[k] &gt;= self.min_precision:
return thresholds[k]
return 2.0</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</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.classifiers" href="index.html">miplearn.classifiers</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></code></h4>
<ul class="">
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></code></h4>
</li>
</ul>
</li>
</ul>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.component</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 abc import ABC, abstractmethod
from typing import Any, List, Union, TYPE_CHECKING
from miplearn.instance import Instance
from miplearn.types import MIPSolveStats, TrainingSample
if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver
class Component(ABC):
&#34;&#34;&#34;
A Component is an object which adds functionality to a LearningSolver.
For better code maintainability, LearningSolver simply delegates most of its
functionality to Components. Each Component is responsible for exactly one ML
strategy.
&#34;&#34;&#34;
def before_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver before the problem is solved.
Parameters
----------
solver
The solver calling this method.
instance
The instance being solved.
model
The concrete optimization model being solved.
&#34;&#34;&#34;
return
@abstractmethod
def after_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver after the problem is solved to optimality.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file.
training_data: dict
A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are
free to add their own training data here. For example,
PrimalSolutionComponent adds the current primal solution. The data must
be pickable.
&#34;&#34;&#34;
pass
def fit(
self,
training_instances: Union[List[str], List[Instance]],
) -&gt; None:
return
def iteration_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; bool:
&#34;&#34;&#34;
Method called by LearningSolver at the end of each iteration.
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
giving them a chance to modify the problem and resolve it before the solution
process ends. For example, the lazy constraint component uses `iteration_cb`
to check that all lazy constraints are satisfied.
If `iteration_cb` returns False for all components, the solution process
ends. If it retunrs True for any component, the MIP is solved again.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
&#34;&#34;&#34;
return False
def lazy_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
return</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.component.Component"><code class="flex name class">
<span>class <span class="ident">Component</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
<p>For better code maintainability, LearningSolver simply delegates most of its
functionality to Components. Each Component is responsible for exactly one ML
strategy.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Component(ABC):
&#34;&#34;&#34;
A Component is an object which adds functionality to a LearningSolver.
For better code maintainability, LearningSolver simply delegates most of its
functionality to Components. Each Component is responsible for exactly one ML
strategy.
&#34;&#34;&#34;
def before_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver before the problem is solved.
Parameters
----------
solver
The solver calling this method.
instance
The instance being solved.
model
The concrete optimization model being solved.
&#34;&#34;&#34;
return
@abstractmethod
def after_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver after the problem is solved to optimality.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file.
training_data: dict
A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are
free to add their own training data here. For example,
PrimalSolutionComponent adds the current primal solution. The data must
be pickable.
&#34;&#34;&#34;
pass
def fit(
self,
training_instances: Union[List[str], List[Instance]],
) -&gt; None:
return
def iteration_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; bool:
&#34;&#34;&#34;
Method called by LearningSolver at the end of each iteration.
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
giving them a chance to modify the problem and resolve it before the solution
process ends. For example, the lazy constraint component uses `iteration_cb`
to check that all lazy constraints are satisfied.
If `iteration_cb` returns False for all components, the solution process
ends. If it retunrs True for any component, the MIP is solved again.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
&#34;&#34;&#34;
return False
def lazy_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.components.cuts.UserCutsComponent" href="cuts.html#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></li>
<li><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent" href="lazy_dynamic.html#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent">DynamicLazyConstraintsComponent</a></li>
<li><a title="miplearn.components.objective.ObjectiveValueComponent" href="objective.html#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></li>
<li><a title="miplearn.components.primal.PrimalSolutionComponent" href="primal.html#miplearn.components.primal.PrimalSolutionComponent">PrimalSolutionComponent</a></li>
<li><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="lazy_static.html#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></li>
<li><a title="miplearn.components.composite.CompositeComponent" href="composite.html#miplearn.components.composite.CompositeComponent">CompositeComponent</a></li>
<li><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="steps/drop_redundant.html#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></li>
<li><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="steps/convert_tight.html#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></li>
<li><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="steps/relax_integrality.html#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></li>
<li><a title="miplearn.components.relaxation.RelaxationComponent" href="relaxation.html#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.component.Component.after_solve"><code class="name flex">
<span>def <span class="ident">after_solve</span></span>(<span>self, solver, instance, model, stats, training_data)</span>
</code></dt>
<dd>
<section class="desc"><p>Method called by LearningSolver after the problem is solved to optimality.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>solver</code></strong> :&ensp;<code>LearningSolver</code></dt>
<dd>The solver calling this method.</dd>
<dt><strong><code>instance</code></strong> :&ensp;<code>Instance</code></dt>
<dd>The instance being solved.</dd>
<dt><strong><code>model</code></strong> :&ensp;<code>Any</code></dt>
<dd>The concrete optimization model being solved.</dd>
<dt><strong><code>stats</code></strong> :&ensp;<code>dict</code></dt>
<dd>A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file.</dd>
<dt><strong><code>training_data</code></strong> :&ensp;<code>dict</code></dt>
<dd>A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are
free to add their own training data here. For example,
PrimalSolutionComponent adds the current primal solution. The data must
be pickable.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def after_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver after the problem is solved to optimality.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file.
training_data: dict
A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are
free to add their own training data here. For example,
PrimalSolutionComponent adds the current primal solution. The data must
be pickable.
&#34;&#34;&#34;
pass</code></pre>
</details>
</dd>
<dt id="miplearn.components.component.Component.before_solve"><code class="name flex">
<span>def <span class="ident">before_solve</span></span>(<span>self, solver, instance, model)</span>
</code></dt>
<dd>
<section class="desc"><p>Method called by LearningSolver before the problem is solved.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>solver</code></strong></dt>
<dd>The solver calling this method.</dd>
<dt><strong><code>instance</code></strong></dt>
<dd>The instance being solved.</dd>
<dt><strong><code>model</code></strong></dt>
<dd>The concrete optimization model being solved.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def before_solve(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
&#34;&#34;&#34;
Method called by LearningSolver before the problem is solved.
Parameters
----------
solver
The solver calling this method.
instance
The instance being solved.
model
The concrete optimization model being solved.
&#34;&#34;&#34;
return</code></pre>
</details>
</dd>
<dt id="miplearn.components.component.Component.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(
self,
training_instances: Union[List[str], List[Instance]],
) -&gt; None:
return</code></pre>
</details>
</dd>
<dt id="miplearn.components.component.Component.iteration_cb"><code class="name flex">
<span>def <span class="ident">iteration_cb</span></span>(<span>self, solver, instance, model)</span>
</code></dt>
<dd>
<section class="desc"><p>Method called by LearningSolver at the end of each iteration.</p>
<p>After solving the MIP, LearningSolver calls <code>iteration_cb</code> of each component,
giving them a chance to modify the problem and resolve it before the solution
process ends. For example, the lazy constraint component uses <code>iteration_cb</code>
to check that all lazy constraints are satisfied.</p>
<p>If <code>iteration_cb</code> returns False for all components, the solution process
ends. If it retunrs True for any component, the MIP is solved again.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>solver</code></strong> :&ensp;<code>LearningSolver</code></dt>
<dd>The solver calling this method.</dd>
<dt><strong><code>instance</code></strong> :&ensp;<code>Instance</code></dt>
<dd>The instance being solved.</dd>
<dt><strong><code>model</code></strong> :&ensp;<code>Any</code></dt>
<dd>The concrete optimization model being solved.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def iteration_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; bool:
&#34;&#34;&#34;
Method called by LearningSolver at the end of each iteration.
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
giving them a chance to modify the problem and resolve it before the solution
process ends. For example, the lazy constraint component uses `iteration_cb`
to check that all lazy constraints are satisfied.
If `iteration_cb` returns False for all components, the solution process
ends. If it retunrs True for any component, the MIP is solved again.
Parameters
----------
solver: LearningSolver
The solver calling this method.
instance: Instance
The instance being solved.
model: Any
The concrete optimization model being solved.
&#34;&#34;&#34;
return False</code></pre>
</details>
</dd>
<dt id="miplearn.components.component.Component.lazy_cb"><code class="name flex">
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def lazy_cb(
self,
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
) -&gt; None:
return</code></pre>
</details>
</dd>
</dl>
</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.component.Component" href="#miplearn.components.component.Component">Component</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.component.Component.after_solve" href="#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.fit" href="#miplearn.components.component.Component.fit">fit</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
<li><code><a title="miplearn.components.component.Component.lazy_cb" href="#miplearn.components.component.Component.lazy_cb">lazy_cb</a></code></li>
</ul>
</li>
</ul>
</li>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.composite</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 miplearn.components.component import Component
class CompositeComponent(Component):
&#34;&#34;&#34;
A Component which redirects each method call to one or more subcomponents.
Useful for breaking down complex components into smaller classes. See
RelaxationComponent for a concrete example.
Parameters
----------
children : list[Component]
Subcomponents that compose this component.
&#34;&#34;&#34;
def __init__(self, children):
self.children = children
def before_solve(self, solver, instance, model):
for child in self.children:
child.before_solve(solver, instance, model)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
for child in self.children:
child.after_solve(solver, instance, model, stats, training_data)
def fit(self, training_instances):
for child in self.children:
child.fit(training_instances)
def lazy_cb(self, solver, instance, model):
for child in self.children:
child.lazy_cb(solver, instance, model)
def iteration_cb(self, solver, instance, model):
should_repeat = False
for child in self.children:
if child.iteration_cb(solver, instance, model):
should_repeat = True
return should_repeat</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.composite.CompositeComponent"><code class="flex name class">
<span>class <span class="ident">CompositeComponent</span></span>
<span>(</span><span>children)</span>
</code></dt>
<dd>
<section class="desc"><p>A Component which redirects each method call to one or more subcomponents.</p>
<p>Useful for breaking down complex components into smaller classes. See
RelaxationComponent for a concrete example.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>children</code></strong> :&ensp;<code>list</code>[<code>Component</code>]</dt>
<dd>Subcomponents that compose this component.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CompositeComponent(Component):
&#34;&#34;&#34;
A Component which redirects each method call to one or more subcomponents.
Useful for breaking down complex components into smaller classes. See
RelaxationComponent for a concrete example.
Parameters
----------
children : list[Component]
Subcomponents that compose this component.
&#34;&#34;&#34;
def __init__(self, children):
self.children = children
def before_solve(self, solver, instance, model):
for child in self.children:
child.before_solve(solver, instance, model)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
for child in self.children:
child.after_solve(solver, instance, model, stats, training_data)
def fit(self, training_instances):
for child in self.children:
child.fit(training_instances)
def lazy_cb(self, solver, instance, model):
for child in self.children:
child.lazy_cb(solver, instance, model)
def iteration_cb(self, solver, instance, model):
should_repeat = False
for child in self.children:
if child.iteration_cb(solver, instance, model):
should_repeat = True
return should_repeat</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.composite.CompositeComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
for child in self.children:
child.fit(training_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.components.composite.CompositeComponent.lazy_cb"><code class="name flex">
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def lazy_cb(self, solver, instance, model):
for child in self.children:
child.lazy_cb(solver, instance, model)</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.composite.CompositeComponent" href="#miplearn.components.composite.CompositeComponent">CompositeComponent</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.composite.CompositeComponent.fit" href="#miplearn.components.composite.CompositeComponent.fit">fit</a></code></li>
<li><code><a title="miplearn.components.composite.CompositeComponent.lazy_cb" href="#miplearn.components.composite.CompositeComponent.lazy_cb">lazy_cb</a></code></li>
</ul>
</li>
</ul>
</li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.cuts</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.
import logging
import sys
from copy import deepcopy
from typing import Any, Dict
import numpy as np
from tqdm.auto import tqdm
from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.extractors import InstanceFeaturesExtractor
logger = logging.getLogger(__name__)
class UserCutsComponent(Component):
&#34;&#34;&#34;
A component that predicts which user cuts to enforce.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = CountingClassifier(),
threshold: float = 0.05,
):
self.threshold: float = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
instance.found_violated_user_cuts = []
logger.info(&#34;Predicting violated user cuts...&#34;)
violations = self.predict(instance)
logger.info(&#34;Enforcing %d user cuts...&#34; % len(violations))
for v in violations:
cut = instance.build_user_cut(model, v)
solver.internal_solver.add_constraint(cut)
def after_solve(
self,
solver,
instance,
model,
results,
training_data,
):
pass
def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(training_instances):
if not hasattr(instance, &#34;found_violated_user_cuts&#34;):
continue
for v in instance.found_violated_user_cuts:
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (user cuts)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)
def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations
def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_user_cuts)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_user_cuts)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.cuts.UserCutsComponent"><code class="flex name class">
<span>class <span class="ident">UserCutsComponent</span></span>
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05)</span>
</code></dt>
<dd>
<section class="desc"><p>A component that predicts which user cuts to enforce.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class UserCutsComponent(Component):
&#34;&#34;&#34;
A component that predicts which user cuts to enforce.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = CountingClassifier(),
threshold: float = 0.05,
):
self.threshold: float = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
instance.found_violated_user_cuts = []
logger.info(&#34;Predicting violated user cuts...&#34;)
violations = self.predict(instance)
logger.info(&#34;Enforcing %d user cuts...&#34; % len(violations))
for v in violations:
cut = instance.build_user_cut(model, v)
solver.internal_solver.add_constraint(cut)
def after_solve(
self,
solver,
instance,
model,
results,
training_data,
):
pass
def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(training_instances):
if not hasattr(instance, &#34;found_violated_user_cuts&#34;):
continue
for v in instance.found_violated_user_cuts:
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (user cuts)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)
def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations
def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_user_cuts)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_user_cuts)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.cuts.UserCutsComponent.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_user_cuts)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_user_cuts)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
</dd>
<dt id="miplearn.components.cuts.UserCutsComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(training_instances):
if not hasattr(instance, &#34;found_violated_user_cuts&#34;):
continue
for v in instance.found_violated_user_cuts:
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (user cuts)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)</code></pre>
</details>
</dd>
<dt id="miplearn.components.cuts.UserCutsComponent.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.cuts.UserCutsComponent" href="#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.cuts.UserCutsComponent.evaluate" href="#miplearn.components.cuts.UserCutsComponent.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.cuts.UserCutsComponent.fit" href="#miplearn.components.cuts.UserCutsComponent.fit">fit</a></code></li>
<li><code><a title="miplearn.components.cuts.UserCutsComponent.predict" href="#miplearn.components.cuts.UserCutsComponent.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components</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.
def classifier_evaluation_dict(tp, tn, fp, fn):
p = tp + fn
n = fp + tn
d = {
&#34;Predicted positive&#34;: fp + tp,
&#34;Predicted negative&#34;: fn + tn,
&#34;Condition positive&#34;: p,
&#34;Condition negative&#34;: n,
&#34;True positive&#34;: tp,
&#34;True negative&#34;: tn,
&#34;False positive&#34;: fp,
&#34;False negative&#34;: fn,
&#34;Accuracy&#34;: (tp + tn) / (p + n),
&#34;F1 score&#34;: (2 * tp) / (2 * tp + fp + fn),
}
if p &gt; 0:
d[&#34;Recall&#34;] = tp / p
else:
d[&#34;Recall&#34;] = 1.0
if tp + fp &gt; 0:
d[&#34;Precision&#34;] = tp / (tp + fp)
else:
d[&#34;Precision&#34;] = 1.0
t = (p + n) / 100.0
d[&#34;Predicted positive (%)&#34;] = d[&#34;Predicted positive&#34;] / t
d[&#34;Predicted negative (%)&#34;] = d[&#34;Predicted negative&#34;] / t
d[&#34;Condition positive (%)&#34;] = d[&#34;Condition positive&#34;] / t
d[&#34;Condition negative (%)&#34;] = d[&#34;Condition negative&#34;] / t
d[&#34;True positive (%)&#34;] = d[&#34;True positive&#34;] / t
d[&#34;True negative (%)&#34;] = d[&#34;True negative&#34;] / t
d[&#34;False positive (%)&#34;] = d[&#34;False positive&#34;] / t
d[&#34;False negative (%)&#34;] = d[&#34;False negative&#34;] / t
return d</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.components.component" href="component.html">miplearn.components.component</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.composite" href="composite.html">miplearn.components.composite</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.cuts" href="cuts.html">miplearn.components.cuts</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.lazy_dynamic" href="lazy_dynamic.html">miplearn.components.lazy_dynamic</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.lazy_static" href="lazy_static.html">miplearn.components.lazy_static</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.objective" href="objective.html">miplearn.components.objective</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.primal" href="primal.html">miplearn.components.primal</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.relaxation" href="relaxation.html">miplearn.components.relaxation</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.steps" href="steps/index.html">miplearn.components.steps</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.tests" href="tests/index.html">miplearn.components.tests</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.classifier_evaluation_dict"><code class="name flex">
<span>def <span class="ident">classifier_evaluation_dict</span></span>(<span>tp, tn, fp, fn)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def classifier_evaluation_dict(tp, tn, fp, fn):
p = tp + fn
n = fp + tn
d = {
&#34;Predicted positive&#34;: fp + tp,
&#34;Predicted negative&#34;: fn + tn,
&#34;Condition positive&#34;: p,
&#34;Condition negative&#34;: n,
&#34;True positive&#34;: tp,
&#34;True negative&#34;: tn,
&#34;False positive&#34;: fp,
&#34;False negative&#34;: fn,
&#34;Accuracy&#34;: (tp + tn) / (p + n),
&#34;F1 score&#34;: (2 * tp) / (2 * tp + fp + fn),
}
if p &gt; 0:
d[&#34;Recall&#34;] = tp / p
else:
d[&#34;Recall&#34;] = 1.0
if tp + fp &gt; 0:
d[&#34;Precision&#34;] = tp / (tp + fp)
else:
d[&#34;Precision&#34;] = 1.0
t = (p + n) / 100.0
d[&#34;Predicted positive (%)&#34;] = d[&#34;Predicted positive&#34;] / t
d[&#34;Predicted negative (%)&#34;] = d[&#34;Predicted negative&#34;] / t
d[&#34;Condition positive (%)&#34;] = d[&#34;Condition positive&#34;] / t
d[&#34;Condition negative (%)&#34;] = d[&#34;Condition negative&#34;] / t
d[&#34;True positive (%)&#34;] = d[&#34;True positive&#34;] / t
d[&#34;True negative (%)&#34;] = d[&#34;True negative&#34;] / t
d[&#34;False positive (%)&#34;] = d[&#34;False positive&#34;] / t
d[&#34;False negative (%)&#34;] = d[&#34;False negative&#34;] / t
return d</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" href="../index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.components.component" href="component.html">miplearn.components.component</a></code></li>
<li><code><a title="miplearn.components.composite" href="composite.html">miplearn.components.composite</a></code></li>
<li><code><a title="miplearn.components.cuts" href="cuts.html">miplearn.components.cuts</a></code></li>
<li><code><a title="miplearn.components.lazy_dynamic" href="lazy_dynamic.html">miplearn.components.lazy_dynamic</a></code></li>
<li><code><a title="miplearn.components.lazy_static" href="lazy_static.html">miplearn.components.lazy_static</a></code></li>
<li><code><a title="miplearn.components.objective" href="objective.html">miplearn.components.objective</a></code></li>
<li><code><a title="miplearn.components.primal" href="primal.html">miplearn.components.primal</a></code></li>
<li><code><a title="miplearn.components.relaxation" href="relaxation.html">miplearn.components.relaxation</a></code></li>
<li><code><a title="miplearn.components.steps" href="steps/index.html">miplearn.components.steps</a></code></li>
<li><code><a title="miplearn.components.tests" href="tests/index.html">miplearn.components.tests</a></code></li>
</ul>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.lazy_dynamic</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.
import logging
import sys
from copy import deepcopy
from typing import Any, Dict
import numpy as np
from tqdm.auto import tqdm
from miplearn.classifiers import Classifier
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.extractors import InstanceFeaturesExtractor, InstanceIterator
logger = logging.getLogger(__name__)
class DynamicLazyConstraintsComponent(Component):
&#34;&#34;&#34;
A component that predicts which lazy constraints to enforce.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = CountingClassifier(),
threshold: float = 0.05,
):
self.threshold: float = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
instance.found_violated_lazy_constraints = []
logger.info(&#34;Predicting violated lazy constraints...&#34;)
violations = self.predict(instance)
logger.info(&#34;Enforcing %d lazy constraints...&#34; % len(violations))
for v in violations:
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
def iteration_cb(self, solver, instance, model):
logger.debug(&#34;Finding violated (dynamic) lazy constraints...&#34;)
violations = instance.find_violated_lazy_constraints(model)
if len(violations) == 0:
return False
instance.found_violated_lazy_constraints += violations
logger.debug(&#34; %d violations found&#34; % len(violations))
for v in violations:
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
return True
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
for v in instance.found_violated_lazy_constraints:
if isinstance(v, list):
v = tuple(v)
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)
def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations
def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_lazy_constraints)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_lazy_constraints)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent"><code class="flex name class">
<span>class <span class="ident">DynamicLazyConstraintsComponent</span></span>
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05)</span>
</code></dt>
<dd>
<section class="desc"><p>A component that predicts which lazy constraints to enforce.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class DynamicLazyConstraintsComponent(Component):
&#34;&#34;&#34;
A component that predicts which lazy constraints to enforce.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = CountingClassifier(),
threshold: float = 0.05,
):
self.threshold: float = threshold
self.classifier_prototype: Classifier = classifier
self.classifiers: Dict[Any, Classifier] = {}
def before_solve(self, solver, instance, model):
instance.found_violated_lazy_constraints = []
logger.info(&#34;Predicting violated lazy constraints...&#34;)
violations = self.predict(instance)
logger.info(&#34;Enforcing %d lazy constraints...&#34; % len(violations))
for v in violations:
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
def iteration_cb(self, solver, instance, model):
logger.debug(&#34;Finding violated (dynamic) lazy constraints...&#34;)
violations = instance.find_violated_lazy_constraints(model)
if len(violations) == 0:
return False
instance.found_violated_lazy_constraints += violations
logger.debug(&#34; %d violations found&#34; % len(violations))
for v in violations:
cut = instance.build_lazy_constraint(model, v)
solver.internal_solver.add_constraint(cut)
return True
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
for v in instance.found_violated_lazy_constraints:
if isinstance(v, list):
v = tuple(v)
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)
def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations
def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_lazy_constraints)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_lazy_constraints)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instances):
results = {}
all_violations = set()
for instance in instances:
all_violations |= set(instance.found_violated_lazy_constraints)
for idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
instance = instances[idx]
condition_positive = set(instance.found_violated_lazy_constraints)
condition_negative = all_violations - condition_positive
pred_positive = set(self.predict(instance)) &amp; all_violations
pred_negative = all_violations - pred_positive
tp = len(pred_positive &amp; condition_positive)
tn = len(pred_negative &amp; condition_negative)
fp = len(pred_positive &amp; condition_negative)
fn = len(pred_negative &amp; condition_positive)
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
return results</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
logger.debug(&#34;Fitting...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
self.classifiers = {}
violation_to_instance_idx = {}
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
for v in instance.found_violated_lazy_constraints:
if isinstance(v, list):
v = tuple(v)
if v not in self.classifiers:
self.classifiers[v] = deepcopy(self.classifier_prototype)
violation_to_instance_idx[v] = []
violation_to_instance_idx[v] += [idx]
for (v, classifier) in tqdm(
self.classifiers.items(),
desc=&#34;Fit (lazy)&#34;,
disable=not sys.stdout.isatty(),
):
logger.debug(&#34;Training: %s&#34; % (str(v)))
label = np.zeros(len(training_instances))
label[violation_to_instance_idx[v]] = 1.0
classifier.fit(features, label)</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, instance):
violations = []
features = InstanceFeaturesExtractor().extract([instance])
for (v, classifier) in self.classifiers.items():
proba = classifier.predict_proba(features)
if proba[0][1] &gt; self.threshold:
violations += [v]
return violations</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent">DynamicLazyConstraintsComponent</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit">fit</a></code></li>
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.lazy_static</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.
import logging
import sys
from copy import deepcopy
import numpy as np
from tqdm.auto import tqdm
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components.component import Component
logger = logging.getLogger(__name__)
class LazyConstraint:
def __init__(self, cid, obj):
self.cid = cid
self.obj = obj
class StaticLazyConstraintsComponent(Component):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.05,
use_two_phase_gap=True,
large_gap=1e-2,
violation_tolerance=-0.5,
):
self.threshold = threshold
self.classifier_prototype = classifier
self.classifiers = {}
self.pool = []
self.original_gap = None
self.large_gap = large_gap
self.is_gap_large = False
self.use_two_phase_gap = use_two_phase_gap
self.violation_tolerance = violation_tolerance
def before_solve(self, solver, instance, model):
self.pool = []
if not solver.use_lazy_cb and self.use_two_phase_gap:
logger.info(&#34;Increasing gap tolerance to %f&#34;, self.large_gap)
self.original_gap = solver.gap_tolerance
self.is_gap_large = True
solver.internal_solver.set_gap_tolerance(self.large_gap)
instance.found_violated_lazy_constraints = []
if instance.has_static_lazy_constraints():
self._extract_and_predict_static(solver, instance)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def iteration_cb(self, solver, instance, model):
if solver.use_lazy_cb:
return False
else:
should_repeat = self._check_and_add(instance, solver)
if should_repeat:
return True
else:
if self.is_gap_large:
logger.info(&#34;Restoring gap tolerance to %f&#34;, self.original_gap)
solver.internal_solver.set_gap_tolerance(self.original_gap)
self.is_gap_large = False
return True
else:
return False
def lazy_cb(self, solver, instance, model):
self._check_and_add(instance, solver)
def _check_and_add(self, instance, solver):
logger.debug(&#34;Finding violated lazy constraints...&#34;)
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(
c.obj, tol=self.violation_tolerance
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
if len(constraints_to_add) &gt; 0:
logger.info(
&#34;%8d lazy constraints added %8d in the pool&#34;
% (len(constraints_to_add), len(self.pool))
)
return True
else:
return False
def fit(self, training_instances):
training_instances = [
t
for t in training_instances
if hasattr(t, &#34;found_violated_lazy_constraints&#34;)
]
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(
x.keys(), desc=&#34;Fit (lazy)&#34;, disable=not sys.stdout.isatty()
):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
def predict(self, instance):
pass
def evaluate(self, instances):
pass
def _extract_and_predict_static(self, solver, instance):
x = {}
constraints = {}
logger.info(&#34;Extracting lazy constraints...&#34;)
for cid in solver.internal_solver.get_constraint_ids():
if instance.is_constraint_lazy(cid):
category = instance.get_constraint_category(cid)
if category not in x:
x[category] = []
constraints[category] = []
x[category] += [instance.get_constraint_features(cid)]
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
constraints[category] += [c]
self.pool.append(c)
logger.info(&#34;%8d lazy constraints extracted&#34; % len(self.pool))
logger.info(&#34;Predicting required lazy constraints...&#34;)
n_added = 0
for (category, x_values) in x.items():
if category not in self.classifiers:
continue
if isinstance(x_values[0], np.ndarray):
x[category] = np.array(x_values)
proba = self.classifiers[category].predict_proba(x[category])
for i in range(len(proba)):
if proba[i][1] &gt; self.threshold:
n_added += 1
c = constraints[category][i]
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
logger.info(
&#34;%8d lazy constraints added %8d in the pool&#34;
% (
n_added,
len(self.pool),
)
)
def _collect_constraints(self, train_instances):
constraints = {}
for instance in train_instances:
for cid in instance.found_violated_lazy_constraints:
category = instance.get_constraint_category(cid)
if category not in constraints:
constraints[category] = set()
constraints[category].add(cid)
for (category, cids) in constraints.items():
constraints[category] = sorted(list(cids))
return constraints
def x(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
result[category].append(instance.get_constraint_features(cid))
return result
def y(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
if cid in instance.found_violated_lazy_constraints:
result[category].append([0, 1])
else:
result[category].append([1, 0])
return result</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.lazy_static.LazyConstraint"><code class="flex name class">
<span>class <span class="ident">LazyConstraint</span></span>
<span>(</span><span>cid, obj)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LazyConstraint:
def __init__(self, cid, obj):
self.cid = cid
self.obj = obj</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent"><code class="flex name class">
<span>class <span class="ident">StaticLazyConstraintsComponent</span></span>
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05, use_two_phase_gap=True, large_gap=0.01, violation_tolerance=-0.5)</span>
</code></dt>
<dd>
<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
<p>For better code maintainability, LearningSolver simply delegates most of its
functionality to Components. Each Component is responsible for exactly one ML
strategy.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class StaticLazyConstraintsComponent(Component):
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.05,
use_two_phase_gap=True,
large_gap=1e-2,
violation_tolerance=-0.5,
):
self.threshold = threshold
self.classifier_prototype = classifier
self.classifiers = {}
self.pool = []
self.original_gap = None
self.large_gap = large_gap
self.is_gap_large = False
self.use_two_phase_gap = use_two_phase_gap
self.violation_tolerance = violation_tolerance
def before_solve(self, solver, instance, model):
self.pool = []
if not solver.use_lazy_cb and self.use_two_phase_gap:
logger.info(&#34;Increasing gap tolerance to %f&#34;, self.large_gap)
self.original_gap = solver.gap_tolerance
self.is_gap_large = True
solver.internal_solver.set_gap_tolerance(self.large_gap)
instance.found_violated_lazy_constraints = []
if instance.has_static_lazy_constraints():
self._extract_and_predict_static(solver, instance)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def iteration_cb(self, solver, instance, model):
if solver.use_lazy_cb:
return False
else:
should_repeat = self._check_and_add(instance, solver)
if should_repeat:
return True
else:
if self.is_gap_large:
logger.info(&#34;Restoring gap tolerance to %f&#34;, self.original_gap)
solver.internal_solver.set_gap_tolerance(self.original_gap)
self.is_gap_large = False
return True
else:
return False
def lazy_cb(self, solver, instance, model):
self._check_and_add(instance, solver)
def _check_and_add(self, instance, solver):
logger.debug(&#34;Finding violated lazy constraints...&#34;)
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(
c.obj, tol=self.violation_tolerance
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
if len(constraints_to_add) &gt; 0:
logger.info(
&#34;%8d lazy constraints added %8d in the pool&#34;
% (len(constraints_to_add), len(self.pool))
)
return True
else:
return False
def fit(self, training_instances):
training_instances = [
t
for t in training_instances
if hasattr(t, &#34;found_violated_lazy_constraints&#34;)
]
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(
x.keys(), desc=&#34;Fit (lazy)&#34;, disable=not sys.stdout.isatty()
):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
def predict(self, instance):
pass
def evaluate(self, instances):
pass
def _extract_and_predict_static(self, solver, instance):
x = {}
constraints = {}
logger.info(&#34;Extracting lazy constraints...&#34;)
for cid in solver.internal_solver.get_constraint_ids():
if instance.is_constraint_lazy(cid):
category = instance.get_constraint_category(cid)
if category not in x:
x[category] = []
constraints[category] = []
x[category] += [instance.get_constraint_features(cid)]
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
constraints[category] += [c]
self.pool.append(c)
logger.info(&#34;%8d lazy constraints extracted&#34; % len(self.pool))
logger.info(&#34;Predicting required lazy constraints...&#34;)
n_added = 0
for (category, x_values) in x.items():
if category not in self.classifiers:
continue
if isinstance(x_values[0], np.ndarray):
x[category] = np.array(x_values)
proba = self.classifiers[category].predict_proba(x[category])
for i in range(len(proba)):
if proba[i][1] &gt; self.threshold:
n_added += 1
c = constraints[category][i]
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
instance.found_violated_lazy_constraints += [c.cid]
logger.info(
&#34;%8d lazy constraints added %8d in the pool&#34;
% (
n_added,
len(self.pool),
)
)
def _collect_constraints(self, train_instances):
constraints = {}
for instance in train_instances:
for cid in instance.found_violated_lazy_constraints:
category = instance.get_constraint_category(cid)
if category not in constraints:
constraints[category] = set()
constraints[category].add(cid)
for (category, cids) in constraints.items():
constraints[category] = sorted(list(cids))
return constraints
def x(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
result[category].append(instance.get_constraint_features(cid))
return result
def y(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
if cid in instance.found_violated_lazy_constraints:
result[category].append([0, 1])
else:
result[category].append([1, 0])
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instances):
pass</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
training_instances = [
t
for t in training_instances
if hasattr(t, &#34;found_violated_lazy_constraints&#34;)
]
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(
x.keys(), desc=&#34;Fit (lazy)&#34;, disable=not sys.stdout.isatty()
):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb"><code class="name flex">
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def lazy_cb(self, solver, instance, model):
self._check_and_add(instance, solver)</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, instance):
pass</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x"><code class="name flex">
<span>def <span class="ident">x</span></span>(<span>self, train_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def x(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
result[category].append(instance.get_constraint_features(cid))
return result</code></pre>
</details>
</dd>
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y"><code class="name flex">
<span>def <span class="ident">y</span></span>(<span>self, train_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def y(self, train_instances):
result = {}
constraints = self._collect_constraints(train_instances)
for (category, cids) in constraints.items():
result[category] = []
for instance in train_instances:
for cid in cids:
if cid in instance.found_violated_lazy_constraints:
result[category].append([0, 1])
else:
result[category].append([1, 0])
return result</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.lazy_static.LazyConstraint" href="#miplearn.components.lazy_static.LazyConstraint">LazyConstraint</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></code></h4>
<ul class="two-column">
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit">fit</a></code></li>
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb">lazy_cb</a></code></li>
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict">predict</a></code></li>
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.x">x</a></code></li>
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.y">y</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.objective</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.
import logging
from copy import deepcopy
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.metrics import (
mean_squared_error,
explained_variance_score,
max_error,
mean_absolute_error,
r2_score,
)
from miplearn.classifiers import Regressor
from miplearn.components.component import Component
from miplearn.extractors import InstanceFeaturesExtractor, ObjectiveValueExtractor
logger = logging.getLogger(__name__)
class ObjectiveValueComponent(Component):
&#34;&#34;&#34;
A Component which predicts the optimal objective value of the problem.
&#34;&#34;&#34;
def __init__(
self,
regressor: Regressor = LinearRegression(),
) -&gt; None:
self.ub_regressor = None
self.lb_regressor = None
self.regressor_prototype = regressor
def before_solve(self, solver, instance, model):
if self.ub_regressor is not None:
logger.info(&#34;Predicting optimal value...&#34;)
lb, ub = self.predict([instance])[0]
instance.predicted_ub = ub
instance.predicted_lb = lb
logger.info(&#34;Predicted values: lb=%.2f, ub=%.2f&#34; % (lb, ub))
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if self.ub_regressor is not None:
stats[&#34;Predicted UB&#34;] = instance.predicted_ub
stats[&#34;Predicted LB&#34;] = instance.predicted_lb
else:
stats[&#34;Predicted UB&#34;] = None
stats[&#34;Predicted LB&#34;] = None
def fit(self, training_instances):
logger.debug(&#34;Extracting features...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
ub = ObjectiveValueExtractor(kind=&#34;upper bound&#34;).extract(training_instances)
lb = ObjectiveValueExtractor(kind=&#34;lower bound&#34;).extract(training_instances)
assert ub.shape == (len(training_instances), 1)
assert lb.shape == (len(training_instances), 1)
self.ub_regressor = deepcopy(self.regressor_prototype)
self.lb_regressor = deepcopy(self.regressor_prototype)
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.ub_regressor.fit(features, ub.ravel())
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.lb_regressor.fit(features, lb.ravel())
def predict(self, instances):
features = InstanceFeaturesExtractor().extract(instances)
lb = self.lb_regressor.predict(features)
ub = self.ub_regressor.predict(features)
assert lb.shape == (len(instances),)
assert ub.shape == (len(instances),)
return np.array([lb, ub]).T
def evaluate(self, instances):
y_pred = self.predict(instances)
y_true = np.array(
[
[
inst.training_data[0][&#34;Lower bound&#34;],
inst.training_data[0][&#34;Upper bound&#34;],
]
for inst in instances
]
)
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
ev = {
&#34;Lower bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_lb, y_pred_lb),
&#34;Explained variance&#34;: explained_variance_score(y_true_lb, y_pred_lb),
&#34;Max error&#34;: max_error(y_true_lb, y_pred_lb),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
&#34;R2&#34;: r2_score(y_true_lb, y_pred_lb),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
},
&#34;Upper bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_ub, y_pred_ub),
&#34;Explained variance&#34;: explained_variance_score(y_true_ub, y_pred_ub),
&#34;Max error&#34;: max_error(y_true_ub, y_pred_ub),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
&#34;R2&#34;: r2_score(y_true_ub, y_pred_ub),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
},
}
return ev</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.objective.ObjectiveValueComponent"><code class="flex name class">
<span>class <span class="ident">ObjectiveValueComponent</span></span>
<span>(</span><span>regressor=LinearRegression())</span>
</code></dt>
<dd>
<section class="desc"><p>A Component which predicts the optimal objective value of the problem.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ObjectiveValueComponent(Component):
&#34;&#34;&#34;
A Component which predicts the optimal objective value of the problem.
&#34;&#34;&#34;
def __init__(
self,
regressor: Regressor = LinearRegression(),
) -&gt; None:
self.ub_regressor = None
self.lb_regressor = None
self.regressor_prototype = regressor
def before_solve(self, solver, instance, model):
if self.ub_regressor is not None:
logger.info(&#34;Predicting optimal value...&#34;)
lb, ub = self.predict([instance])[0]
instance.predicted_ub = ub
instance.predicted_lb = lb
logger.info(&#34;Predicted values: lb=%.2f, ub=%.2f&#34; % (lb, ub))
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if self.ub_regressor is not None:
stats[&#34;Predicted UB&#34;] = instance.predicted_ub
stats[&#34;Predicted LB&#34;] = instance.predicted_lb
else:
stats[&#34;Predicted UB&#34;] = None
stats[&#34;Predicted LB&#34;] = None
def fit(self, training_instances):
logger.debug(&#34;Extracting features...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
ub = ObjectiveValueExtractor(kind=&#34;upper bound&#34;).extract(training_instances)
lb = ObjectiveValueExtractor(kind=&#34;lower bound&#34;).extract(training_instances)
assert ub.shape == (len(training_instances), 1)
assert lb.shape == (len(training_instances), 1)
self.ub_regressor = deepcopy(self.regressor_prototype)
self.lb_regressor = deepcopy(self.regressor_prototype)
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.ub_regressor.fit(features, ub.ravel())
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.lb_regressor.fit(features, lb.ravel())
def predict(self, instances):
features = InstanceFeaturesExtractor().extract(instances)
lb = self.lb_regressor.predict(features)
ub = self.ub_regressor.predict(features)
assert lb.shape == (len(instances),)
assert ub.shape == (len(instances),)
return np.array([lb, ub]).T
def evaluate(self, instances):
y_pred = self.predict(instances)
y_true = np.array(
[
[
inst.training_data[0][&#34;Lower bound&#34;],
inst.training_data[0][&#34;Upper bound&#34;],
]
for inst in instances
]
)
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
ev = {
&#34;Lower bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_lb, y_pred_lb),
&#34;Explained variance&#34;: explained_variance_score(y_true_lb, y_pred_lb),
&#34;Max error&#34;: max_error(y_true_lb, y_pred_lb),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
&#34;R2&#34;: r2_score(y_true_lb, y_pred_lb),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
},
&#34;Upper bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_ub, y_pred_ub),
&#34;Explained variance&#34;: explained_variance_score(y_true_ub, y_pred_ub),
&#34;Max error&#34;: max_error(y_true_ub, y_pred_ub),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
&#34;R2&#34;: r2_score(y_true_ub, y_pred_ub),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
},
}
return ev</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.objective.ObjectiveValueComponent.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instances):
y_pred = self.predict(instances)
y_true = np.array(
[
[
inst.training_data[0][&#34;Lower bound&#34;],
inst.training_data[0][&#34;Upper bound&#34;],
]
for inst in instances
]
)
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
ev = {
&#34;Lower bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_lb, y_pred_lb),
&#34;Explained variance&#34;: explained_variance_score(y_true_lb, y_pred_lb),
&#34;Max error&#34;: max_error(y_true_lb, y_pred_lb),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
&#34;R2&#34;: r2_score(y_true_lb, y_pred_lb),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_lb, y_pred_lb),
},
&#34;Upper bound&#34;: {
&#34;Mean squared error&#34;: mean_squared_error(y_true_ub, y_pred_ub),
&#34;Explained variance&#34;: explained_variance_score(y_true_ub, y_pred_ub),
&#34;Max error&#34;: max_error(y_true_ub, y_pred_ub),
&#34;Mean absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
&#34;R2&#34;: r2_score(y_true_ub, y_pred_ub),
&#34;Median absolute error&#34;: mean_absolute_error(y_true_ub, y_pred_ub),
},
}
return ev</code></pre>
</details>
</dd>
<dt id="miplearn.components.objective.ObjectiveValueComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
logger.debug(&#34;Extracting features...&#34;)
features = InstanceFeaturesExtractor().extract(training_instances)
ub = ObjectiveValueExtractor(kind=&#34;upper bound&#34;).extract(training_instances)
lb = ObjectiveValueExtractor(kind=&#34;lower bound&#34;).extract(training_instances)
assert ub.shape == (len(training_instances), 1)
assert lb.shape == (len(training_instances), 1)
self.ub_regressor = deepcopy(self.regressor_prototype)
self.lb_regressor = deepcopy(self.regressor_prototype)
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.ub_regressor.fit(features, ub.ravel())
logger.debug(&#34;Fitting ub_regressor...&#34;)
self.lb_regressor.fit(features, lb.ravel())</code></pre>
</details>
</dd>
<dt id="miplearn.components.objective.ObjectiveValueComponent.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, instances):
features = InstanceFeaturesExtractor().extract(instances)
lb = self.lb_regressor.predict(features)
ub = self.ub_regressor.predict(features)
assert lb.shape == (len(instances),)
assert ub.shape == (len(instances),)
return np.array([lb, ub]).T</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.objective.ObjectiveValueComponent" href="#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.evaluate" href="#miplearn.components.objective.ObjectiveValueComponent.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.fit" href="#miplearn.components.objective.ObjectiveValueComponent.fit">fit</a></code></li>
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.predict" href="#miplearn.components.objective.ObjectiveValueComponent.predict">predict</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.primal</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.
import logging
from copy import deepcopy
from typing import Union, Dict, Any
import numpy as np
from tqdm.auto import tqdm
from miplearn.classifiers import Classifier
from miplearn.classifiers.adaptive import AdaptiveClassifier
from miplearn.classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.extractors import VariableFeaturesExtractor, SolutionExtractor, Extractor
logger = logging.getLogger(__name__)
class PrimalSolutionComponent(Component):
&#34;&#34;&#34;
A component that predicts primal solutions.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = AdaptiveClassifier(),
mode: str = &#34;exact&#34;,
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
) -&gt; None:
self.mode = mode
self.classifiers: Dict[Any, Classifier] = {}
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
self.threshold_prototype = threshold
self.classifier_prototype = classifier
def before_solve(self, solver, instance, model):
logger.info(&#34;Predicting primal solution...&#34;)
solution = self.predict(instance)
if self.mode == &#34;heuristic&#34;:
solver.internal_solver.fix(solution)
else:
solver.internal_solver.set_warm_start(solution)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def x(self, training_instances):
return VariableFeaturesExtractor().extract(training_instances)
def y(self, training_instances):
return SolutionExtractor().extract(training_instances)
def fit(self, training_instances, n_jobs=1):
logger.debug(&#34;Extracting features...&#34;)
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(
features.keys(),
desc=&#34;Fit (primal)&#34;,
):
x_train = features[category]
for label in [0, 1]:
y_train = solutions[category][:, label].astype(int)
# If all samples are either positive or negative, make constant
# predictions
y_avg = np.average(y_train)
if y_avg &lt; 0.001 or y_avg &gt;= 0.999:
self.classifiers[category, label] = round(y_avg)
self.thresholds[category, label] = 0.50
continue
# Create a copy of classifier prototype and train it
if isinstance(self.classifier_prototype, list):
clf = deepcopy(self.classifier_prototype[label])
else:
clf = deepcopy(self.classifier_prototype)
clf.fit(x_train, y_train)
# Find threshold (dynamic or static)
if isinstance(self.threshold_prototype, DynamicThreshold):
self.thresholds[category, label] = self.threshold_prototype.find(
clf,
x_train,
y_train,
)
else:
self.thresholds[category, label] = deepcopy(
self.threshold_prototype
)
self.classifiers[category, label] = clf
def predict(self, instance):
solution = {}
x_test = VariableFeaturesExtractor().extract([instance])
var_split = Extractor.split_variables(instance)
for category in var_split.keys():
n = len(var_split[category])
for (i, (var, index)) in enumerate(var_split[category]):
if var not in solution.keys():
solution[var] = {}
solution[var][index] = None
for label in [0, 1]:
if (category, label) not in self.classifiers.keys():
continue
clf = self.classifiers[category, label]
if isinstance(clf, float) or isinstance(clf, int):
ws = np.array([[1 - clf, clf] for _ in range(n)])
else:
ws = clf.predict_proba(x_test[category])
assert ws.shape == (n, 2), &#34;ws.shape should be (%d, 2) not %s&#34; % (
n,
ws.shape,
)
for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] &gt;= self.thresholds[category, label]:
solution[var][index] = label
return solution
def evaluate(self, instances):
ev = {&#34;Fix zero&#34;: {}, &#34;Fix one&#34;: {}}
for instance_idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (primal)&#34;,
):
instance = instances[instance_idx]
solution_actual = instance.training_data[0][&#34;Solution&#34;]
solution_pred = self.predict(instance)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()
for (varname, var_dict) in solution_actual.items():
if varname not in solution_pred.keys():
continue
for (idx, value) in var_dict.items():
vars_all.add((varname, idx))
if value &gt; 0.5:
vars_one.add((varname, idx))
else:
vars_zero.add((varname, idx))
if solution_pred[varname][idx] is not None:
if solution_pred[varname][idx] &gt; 0.5:
pred_one_positive.add((varname, idx))
else:
pred_zero_positive.add((varname, idx))
pred_one_negative = vars_all - pred_one_positive
pred_zero_negative = vars_all - pred_zero_positive
tp_zero = len(pred_zero_positive &amp; vars_zero)
fp_zero = len(pred_zero_positive &amp; vars_one)
tn_zero = len(pred_zero_negative &amp; vars_one)
fn_zero = len(pred_zero_negative &amp; vars_zero)
tp_one = len(pred_one_positive &amp; vars_one)
fp_one = len(pred_one_positive &amp; vars_zero)
tn_one = len(pred_one_negative &amp; vars_zero)
fn_one = len(pred_one_negative &amp; vars_one)
ev[&#34;Fix zero&#34;][instance_idx] = classifier_evaluation_dict(
tp_zero, tn_zero, fp_zero, fn_zero
)
ev[&#34;Fix one&#34;][instance_idx] = classifier_evaluation_dict(
tp_one, tn_one, fp_one, fn_one
)
return ev</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.primal.PrimalSolutionComponent"><code class="flex name class">
<span>class <span class="ident">PrimalSolutionComponent</span></span>
<span>(</span><span>classifier=<miplearn.classifiers.adaptive.AdaptiveClassifier object>, mode='exact', threshold=<miplearn.classifiers.threshold.MinPrecisionThreshold object>)</span>
</code></dt>
<dd>
<section class="desc"><p>A component that predicts primal solutions.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class PrimalSolutionComponent(Component):
&#34;&#34;&#34;
A component that predicts primal solutions.
&#34;&#34;&#34;
def __init__(
self,
classifier: Classifier = AdaptiveClassifier(),
mode: str = &#34;exact&#34;,
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
) -&gt; None:
self.mode = mode
self.classifiers: Dict[Any, Classifier] = {}
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
self.threshold_prototype = threshold
self.classifier_prototype = classifier
def before_solve(self, solver, instance, model):
logger.info(&#34;Predicting primal solution...&#34;)
solution = self.predict(instance)
if self.mode == &#34;heuristic&#34;:
solver.internal_solver.fix(solution)
else:
solver.internal_solver.set_warm_start(solution)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
pass
def x(self, training_instances):
return VariableFeaturesExtractor().extract(training_instances)
def y(self, training_instances):
return SolutionExtractor().extract(training_instances)
def fit(self, training_instances, n_jobs=1):
logger.debug(&#34;Extracting features...&#34;)
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(
features.keys(),
desc=&#34;Fit (primal)&#34;,
):
x_train = features[category]
for label in [0, 1]:
y_train = solutions[category][:, label].astype(int)
# If all samples are either positive or negative, make constant
# predictions
y_avg = np.average(y_train)
if y_avg &lt; 0.001 or y_avg &gt;= 0.999:
self.classifiers[category, label] = round(y_avg)
self.thresholds[category, label] = 0.50
continue
# Create a copy of classifier prototype and train it
if isinstance(self.classifier_prototype, list):
clf = deepcopy(self.classifier_prototype[label])
else:
clf = deepcopy(self.classifier_prototype)
clf.fit(x_train, y_train)
# Find threshold (dynamic or static)
if isinstance(self.threshold_prototype, DynamicThreshold):
self.thresholds[category, label] = self.threshold_prototype.find(
clf,
x_train,
y_train,
)
else:
self.thresholds[category, label] = deepcopy(
self.threshold_prototype
)
self.classifiers[category, label] = clf
def predict(self, instance):
solution = {}
x_test = VariableFeaturesExtractor().extract([instance])
var_split = Extractor.split_variables(instance)
for category in var_split.keys():
n = len(var_split[category])
for (i, (var, index)) in enumerate(var_split[category]):
if var not in solution.keys():
solution[var] = {}
solution[var][index] = None
for label in [0, 1]:
if (category, label) not in self.classifiers.keys():
continue
clf = self.classifiers[category, label]
if isinstance(clf, float) or isinstance(clf, int):
ws = np.array([[1 - clf, clf] for _ in range(n)])
else:
ws = clf.predict_proba(x_test[category])
assert ws.shape == (n, 2), &#34;ws.shape should be (%d, 2) not %s&#34; % (
n,
ws.shape,
)
for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] &gt;= self.thresholds[category, label]:
solution[var][index] = label
return solution
def evaluate(self, instances):
ev = {&#34;Fix zero&#34;: {}, &#34;Fix one&#34;: {}}
for instance_idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (primal)&#34;,
):
instance = instances[instance_idx]
solution_actual = instance.training_data[0][&#34;Solution&#34;]
solution_pred = self.predict(instance)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()
for (varname, var_dict) in solution_actual.items():
if varname not in solution_pred.keys():
continue
for (idx, value) in var_dict.items():
vars_all.add((varname, idx))
if value &gt; 0.5:
vars_one.add((varname, idx))
else:
vars_zero.add((varname, idx))
if solution_pred[varname][idx] is not None:
if solution_pred[varname][idx] &gt; 0.5:
pred_one_positive.add((varname, idx))
else:
pred_zero_positive.add((varname, idx))
pred_one_negative = vars_all - pred_one_positive
pred_zero_negative = vars_all - pred_zero_positive
tp_zero = len(pred_zero_positive &amp; vars_zero)
fp_zero = len(pred_zero_positive &amp; vars_one)
tn_zero = len(pred_zero_negative &amp; vars_one)
fn_zero = len(pred_zero_negative &amp; vars_zero)
tp_one = len(pred_one_positive &amp; vars_one)
fp_one = len(pred_one_positive &amp; vars_zero)
tn_one = len(pred_one_negative &amp; vars_zero)
fn_one = len(pred_one_negative &amp; vars_one)
ev[&#34;Fix zero&#34;][instance_idx] = classifier_evaluation_dict(
tp_zero, tn_zero, fp_zero, fn_zero
)
ev[&#34;Fix one&#34;][instance_idx] = classifier_evaluation_dict(
tp_one, tn_one, fp_one, fn_one
)
return ev</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.primal.PrimalSolutionComponent.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instances):
ev = {&#34;Fix zero&#34;: {}, &#34;Fix one&#34;: {}}
for instance_idx in tqdm(
range(len(instances)),
desc=&#34;Evaluate (primal)&#34;,
):
instance = instances[instance_idx]
solution_actual = instance.training_data[0][&#34;Solution&#34;]
solution_pred = self.predict(instance)
vars_all, vars_one, vars_zero = set(), set(), set()
pred_one_positive, pred_zero_positive = set(), set()
for (varname, var_dict) in solution_actual.items():
if varname not in solution_pred.keys():
continue
for (idx, value) in var_dict.items():
vars_all.add((varname, idx))
if value &gt; 0.5:
vars_one.add((varname, idx))
else:
vars_zero.add((varname, idx))
if solution_pred[varname][idx] is not None:
if solution_pred[varname][idx] &gt; 0.5:
pred_one_positive.add((varname, idx))
else:
pred_zero_positive.add((varname, idx))
pred_one_negative = vars_all - pred_one_positive
pred_zero_negative = vars_all - pred_zero_positive
tp_zero = len(pred_zero_positive &amp; vars_zero)
fp_zero = len(pred_zero_positive &amp; vars_one)
tn_zero = len(pred_zero_negative &amp; vars_one)
fn_zero = len(pred_zero_negative &amp; vars_zero)
tp_one = len(pred_one_positive &amp; vars_one)
fp_one = len(pred_one_positive &amp; vars_zero)
tn_one = len(pred_one_negative &amp; vars_zero)
fn_one = len(pred_one_negative &amp; vars_one)
ev[&#34;Fix zero&#34;][instance_idx] = classifier_evaluation_dict(
tp_zero, tn_zero, fp_zero, fn_zero
)
ev[&#34;Fix one&#34;][instance_idx] = classifier_evaluation_dict(
tp_one, tn_one, fp_one, fn_one
)
return ev</code></pre>
</details>
</dd>
<dt id="miplearn.components.primal.PrimalSolutionComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances, n_jobs=1)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances, n_jobs=1):
logger.debug(&#34;Extracting features...&#34;)
features = VariableFeaturesExtractor().extract(training_instances)
solutions = SolutionExtractor().extract(training_instances)
for category in tqdm(
features.keys(),
desc=&#34;Fit (primal)&#34;,
):
x_train = features[category]
for label in [0, 1]:
y_train = solutions[category][:, label].astype(int)
# If all samples are either positive or negative, make constant
# predictions
y_avg = np.average(y_train)
if y_avg &lt; 0.001 or y_avg &gt;= 0.999:
self.classifiers[category, label] = round(y_avg)
self.thresholds[category, label] = 0.50
continue
# Create a copy of classifier prototype and train it
if isinstance(self.classifier_prototype, list):
clf = deepcopy(self.classifier_prototype[label])
else:
clf = deepcopy(self.classifier_prototype)
clf.fit(x_train, y_train)
# Find threshold (dynamic or static)
if isinstance(self.threshold_prototype, DynamicThreshold):
self.thresholds[category, label] = self.threshold_prototype.find(
clf,
x_train,
y_train,
)
else:
self.thresholds[category, label] = deepcopy(
self.threshold_prototype
)
self.classifiers[category, label] = clf</code></pre>
</details>
</dd>
<dt id="miplearn.components.primal.PrimalSolutionComponent.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, instance):
solution = {}
x_test = VariableFeaturesExtractor().extract([instance])
var_split = Extractor.split_variables(instance)
for category in var_split.keys():
n = len(var_split[category])
for (i, (var, index)) in enumerate(var_split[category]):
if var not in solution.keys():
solution[var] = {}
solution[var][index] = None
for label in [0, 1]:
if (category, label) not in self.classifiers.keys():
continue
clf = self.classifiers[category, label]
if isinstance(clf, float) or isinstance(clf, int):
ws = np.array([[1 - clf, clf] for _ in range(n)])
else:
ws = clf.predict_proba(x_test[category])
assert ws.shape == (n, 2), &#34;ws.shape should be (%d, 2) not %s&#34; % (
n,
ws.shape,
)
for (i, (var, index)) in enumerate(var_split[category]):
if ws[i, 1] &gt;= self.thresholds[category, label]:
solution[var][index] = label
return solution</code></pre>
</details>
</dd>
<dt id="miplearn.components.primal.PrimalSolutionComponent.x"><code class="name flex">
<span>def <span class="ident">x</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def x(self, training_instances):
return VariableFeaturesExtractor().extract(training_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.components.primal.PrimalSolutionComponent.y"><code class="name flex">
<span>def <span class="ident">y</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def y(self, training_instances):
return SolutionExtractor().extract(training_instances)</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.relaxation</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.
import logging
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components.component import Component
from miplearn.components.composite import CompositeComponent
from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
logger = logging.getLogger(__name__)
class RelaxationComponent(Component):
&#34;&#34;&#34;
A Component that tries to build a relaxation that is simultaneously strong and easy
to solve. Currently, this component is composed by three steps:
- RelaxIntegralityStep
- DropRedundantInequalitiesStep
- ConvertTightIneqsIntoEqsStep
Parameters
----------
redundant_classifier : Classifier, optional
Classifier used to predict if a constraint is likely redundant. One deep
copy of this classifier is made for each constraint category.
redundant_threshold : float, optional
If the probability that a constraint is redundant exceeds this threshold, the
constraint is dropped from the linear relaxation.
tight_classifier : Classifier, optional
Classifier used to predict if a constraint is likely to be tight. One deep
copy of this classifier is made for each constraint category.
tight_threshold : float, optional
If the probability that a constraint is tight exceeds this threshold, the
constraint is converted into an equality constraint.
slack_tolerance : float, optional
If a constraint has slack greater than this threshold, then the constraint is
considered loose. By default, this threshold equals a small positive number to
compensate for numerical issues.
check_feasibility : bool, optional
If true, after the problem is solved, the component verifies that all dropped
constraints are still satisfied, re-adds the violated ones and resolves the
problem. This loop continues until either no violations are found, or a maximum
number of iterations is reached.
violation_tolerance : float, optional
If `check_dropped` is true, a constraint is considered satisfied during the
check if its violation is smaller than this tolerance.
max_check_iterations : int
If `check_dropped` is true, set the maximum number of iterations in the lazy
constraint loop.
&#34;&#34;&#34;
def __init__(
self,
redundant_classifier=CountingClassifier(),
redundant_threshold=0.95,
tight_classifier=CountingClassifier(),
tight_threshold=0.95,
slack_tolerance=1e-5,
check_feasibility=False,
violation_tolerance=1e-5,
max_check_iterations=3,
):
self.steps = [
RelaxIntegralityStep(),
DropRedundantInequalitiesStep(
classifier=redundant_classifier,
threshold=redundant_threshold,
slack_tolerance=slack_tolerance,
violation_tolerance=violation_tolerance,
max_iterations=max_check_iterations,
check_feasibility=check_feasibility,
),
ConvertTightIneqsIntoEqsStep(
classifier=tight_classifier,
threshold=tight_threshold,
slack_tolerance=slack_tolerance,
),
]
self.composite = CompositeComponent(self.steps)
def before_solve(self, solver, instance, model):
self.composite.before_solve(solver, instance, model)
def after_solve(self, solver, instance, model, stats, training_data):
self.composite.after_solve(solver, instance, model, stats, training_data)
def fit(self, training_instances):
self.composite.fit(training_instances)
def iteration_cb(self, solver, instance, model):
return self.composite.iteration_cb(solver, instance, model)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.relaxation.RelaxationComponent"><code class="flex name class">
<span>class <span class="ident">RelaxationComponent</span></span>
<span>(</span><span>redundant_classifier=CountingClassifier(mean=None), redundant_threshold=0.95, tight_classifier=CountingClassifier(mean=None), tight_threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_check_iterations=3)</span>
</code></dt>
<dd>
<section class="desc"><p>A Component that tries to build a relaxation that is simultaneously strong and easy
to solve. Currently, this component is composed by three steps:</p>
<ul>
<li>RelaxIntegralityStep</li>
<li>DropRedundantInequalitiesStep</li>
<li>ConvertTightIneqsIntoEqsStep</li>
</ul>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>redundant_classifier</code></strong> :&ensp;<code>Classifier</code>, optional</dt>
<dd>Classifier used to predict if a constraint is likely redundant. One deep
copy of this classifier is made for each constraint category.</dd>
<dt><strong><code>redundant_threshold</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>If the probability that a constraint is redundant exceeds this threshold, the
constraint is dropped from the linear relaxation.</dd>
<dt><strong><code>tight_classifier</code></strong> :&ensp;<code>Classifier</code>, optional</dt>
<dd>Classifier used to predict if a constraint is likely to be tight. One deep
copy of this classifier is made for each constraint category.</dd>
<dt><strong><code>tight_threshold</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>If the probability that a constraint is tight exceeds this threshold, the
constraint is converted into an equality constraint.</dd>
<dt><strong><code>slack_tolerance</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>If a constraint has slack greater than this threshold, then the constraint is
considered loose. By default, this threshold equals a small positive number to
compensate for numerical issues.</dd>
<dt><strong><code>check_feasibility</code></strong> :&ensp;<code>bool</code>, optional</dt>
<dd>If true, after the problem is solved, the component verifies that all dropped
constraints are still satisfied, re-adds the violated ones and resolves the
problem. This loop continues until either no violations are found, or a maximum
number of iterations is reached.</dd>
<dt><strong><code>violation_tolerance</code></strong> :&ensp;<code>float</code>, optional</dt>
<dd>If <code>check_dropped</code> is true, a constraint is considered satisfied during the
check if its violation is smaller than this tolerance.</dd>
<dt><strong><code>max_check_iterations</code></strong> :&ensp;<code>int</code></dt>
<dd>If <code>check_dropped</code> is true, set the maximum number of iterations in the lazy
constraint loop.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class RelaxationComponent(Component):
&#34;&#34;&#34;
A Component that tries to build a relaxation that is simultaneously strong and easy
to solve. Currently, this component is composed by three steps:
- RelaxIntegralityStep
- DropRedundantInequalitiesStep
- ConvertTightIneqsIntoEqsStep
Parameters
----------
redundant_classifier : Classifier, optional
Classifier used to predict if a constraint is likely redundant. One deep
copy of this classifier is made for each constraint category.
redundant_threshold : float, optional
If the probability that a constraint is redundant exceeds this threshold, the
constraint is dropped from the linear relaxation.
tight_classifier : Classifier, optional
Classifier used to predict if a constraint is likely to be tight. One deep
copy of this classifier is made for each constraint category.
tight_threshold : float, optional
If the probability that a constraint is tight exceeds this threshold, the
constraint is converted into an equality constraint.
slack_tolerance : float, optional
If a constraint has slack greater than this threshold, then the constraint is
considered loose. By default, this threshold equals a small positive number to
compensate for numerical issues.
check_feasibility : bool, optional
If true, after the problem is solved, the component verifies that all dropped
constraints are still satisfied, re-adds the violated ones and resolves the
problem. This loop continues until either no violations are found, or a maximum
number of iterations is reached.
violation_tolerance : float, optional
If `check_dropped` is true, a constraint is considered satisfied during the
check if its violation is smaller than this tolerance.
max_check_iterations : int
If `check_dropped` is true, set the maximum number of iterations in the lazy
constraint loop.
&#34;&#34;&#34;
def __init__(
self,
redundant_classifier=CountingClassifier(),
redundant_threshold=0.95,
tight_classifier=CountingClassifier(),
tight_threshold=0.95,
slack_tolerance=1e-5,
check_feasibility=False,
violation_tolerance=1e-5,
max_check_iterations=3,
):
self.steps = [
RelaxIntegralityStep(),
DropRedundantInequalitiesStep(
classifier=redundant_classifier,
threshold=redundant_threshold,
slack_tolerance=slack_tolerance,
violation_tolerance=violation_tolerance,
max_iterations=max_check_iterations,
check_feasibility=check_feasibility,
),
ConvertTightIneqsIntoEqsStep(
classifier=tight_classifier,
threshold=tight_threshold,
slack_tolerance=slack_tolerance,
),
]
self.composite = CompositeComponent(self.steps)
def before_solve(self, solver, instance, model):
self.composite.before_solve(solver, instance, model)
def after_solve(self, solver, instance, model, stats, training_data):
self.composite.after_solve(solver, instance, model, stats, training_data)
def fit(self, training_instances):
self.composite.fit(training_instances)
def iteration_cb(self, solver, instance, model):
return self.composite.iteration_cb(solver, instance, model)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.relaxation.RelaxationComponent.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
self.composite.fit(training_instances)</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
</article>
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<ul>
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
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</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.relaxation.RelaxationComponent" href="#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.relaxation.RelaxationComponent.fit" href="#miplearn.components.relaxation.RelaxationComponent.fit">fit</a></code></li>
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<header>
<h1 class="title">Module <code>miplearn.components.steps.convert_tight</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.
import logging
import random
from copy import deepcopy
import numpy as np
from tqdm import tqdm
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
from miplearn.extractors import InstanceIterator
logger = logging.getLogger(__name__)
class ConvertTightIneqsIntoEqsStep(Component):
&#34;&#34;&#34;
Component that predicts which inequality constraints are likely to be binding in
the LP relaxation of the problem and converts them into equality constraints.
This component always makes sure that the conversion process does not affect the
feasibility of the problem. It can also, optionally, make sure that it does not affect
the optimality, but this may be expensive.
This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.
&#34;&#34;&#34;
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=0.0,
check_optimality=False,
):
self.classifiers = {}
self.classifier_prototype = classifier
self.threshold = threshold
self.slack_tolerance = slack_tolerance
self.check_optimality = check_optimality
self.converted = []
self.original_sense = {}
def before_solve(self, solver, instance, _):
logger.info(&#34;Predicting tight LP constraints...&#34;)
x, constraints = DropRedundantInequalitiesStep._x_test(
instance,
constraint_ids=solver.internal_solver.get_constraint_ids(),
)
y = self.predict(x)
self.n_converted = 0
self.n_restored = 0
self.n_kept = 0
self.n_infeasible_iterations = 0
self.n_suboptimal_iterations = 0
for category in y.keys():
for i in range(len(y[category])):
if y[category][i][0] == 1:
cid = constraints[category][i]
s = solver.internal_solver.get_constraint_sense(cid)
self.original_sense[cid] = s
solver.internal_solver.set_constraint_sense(cid, &#34;=&#34;)
self.converted += [cid]
self.n_converted += 1
else:
self.n_kept += 1
logger.info(f&#34;Converted {self.n_converted} inequalities&#34;)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if &#34;slacks&#34; not in training_data.keys():
training_data[&#34;slacks&#34;] = solver.internal_solver.get_inequality_slacks()
stats[&#34;ConvertTight: Kept&#34;] = self.n_kept
stats[&#34;ConvertTight: Converted&#34;] = self.n_converted
stats[&#34;ConvertTight: Restored&#34;] = self.n_restored
stats[&#34;ConvertTight: Inf iterations&#34;] = self.n_infeasible_iterations
stats[&#34;ConvertTight: Subopt iterations&#34;] = self.n_suboptimal_iterations
def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:conv_ineqs)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
def x(self, instances):
return DropRedundantInequalitiesStep._x_train(instances)
def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:conv_ineqs:y)&#34;,
disable=len(instances) &lt; 5,
):
for (cid, slack) in instance.training_data[0][&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if 0 &lt;= slack &lt;= self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)
def iteration_cb(self, solver, instance, model):
is_infeasible, is_suboptimal = False, False
restored = []
def check_pi(msense, csense, pi):
if csense == &#34;=&#34;:
return True
if msense == &#34;max&#34;:
if csense == &#34;&lt;&#34;:
return pi &gt;= 0
else:
return pi &lt;= 0
else:
if csense == &#34;&gt;&#34;:
return pi &gt;= 0
else:
return pi &lt;= 0
def restore(cid):
nonlocal restored
csense = self.original_sense[cid]
solver.internal_solver.set_constraint_sense(cid, csense)
restored += [cid]
if solver.internal_solver.is_infeasible():
for cid in self.converted:
pi = solver.internal_solver.get_dual(cid)
if abs(pi) &gt; 0:
is_infeasible = True
restore(cid)
elif self.check_optimality:
random.shuffle(self.converted)
n_restored = 0
for cid in self.converted:
if n_restored &gt;= 100:
break
pi = solver.internal_solver.get_dual(cid)
csense = self.original_sense[cid]
msense = solver.internal_solver.get_sense()
if not check_pi(msense, csense, pi):
is_suboptimal = True
restore(cid)
n_restored += 1
for cid in restored:
self.converted.remove(cid)
if len(restored) &gt; 0:
self.n_restored += len(restored)
if is_infeasible:
self.n_infeasible_iterations += 1
if is_suboptimal:
self.n_suboptimal_iterations += 1
logger.info(f&#34;Restored {len(restored)} inequalities&#34;)
return True
else:
return False</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep"><code class="flex name class">
<span>class <span class="ident">ConvertTightIneqsIntoEqsStep</span></span>
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=0.0, check_optimality=False)</span>
</code></dt>
<dd>
<section class="desc"><p>Component that predicts which inequality constraints are likely to be binding in
the LP relaxation of the problem and converts them into equality constraints.</p>
<p>This component always makes sure that the conversion process does not affect the
feasibility of the problem. It can also, optionally, make sure that it does not affect
the optimality, but this may be expensive.</p>
<p>This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ConvertTightIneqsIntoEqsStep(Component):
&#34;&#34;&#34;
Component that predicts which inequality constraints are likely to be binding in
the LP relaxation of the problem and converts them into equality constraints.
This component always makes sure that the conversion process does not affect the
feasibility of the problem. It can also, optionally, make sure that it does not affect
the optimality, but this may be expensive.
This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.
&#34;&#34;&#34;
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=0.0,
check_optimality=False,
):
self.classifiers = {}
self.classifier_prototype = classifier
self.threshold = threshold
self.slack_tolerance = slack_tolerance
self.check_optimality = check_optimality
self.converted = []
self.original_sense = {}
def before_solve(self, solver, instance, _):
logger.info(&#34;Predicting tight LP constraints...&#34;)
x, constraints = DropRedundantInequalitiesStep._x_test(
instance,
constraint_ids=solver.internal_solver.get_constraint_ids(),
)
y = self.predict(x)
self.n_converted = 0
self.n_restored = 0
self.n_kept = 0
self.n_infeasible_iterations = 0
self.n_suboptimal_iterations = 0
for category in y.keys():
for i in range(len(y[category])):
if y[category][i][0] == 1:
cid = constraints[category][i]
s = solver.internal_solver.get_constraint_sense(cid)
self.original_sense[cid] = s
solver.internal_solver.set_constraint_sense(cid, &#34;=&#34;)
self.converted += [cid]
self.n_converted += 1
else:
self.n_kept += 1
logger.info(f&#34;Converted {self.n_converted} inequalities&#34;)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if &#34;slacks&#34; not in training_data.keys():
training_data[&#34;slacks&#34;] = solver.internal_solver.get_inequality_slacks()
stats[&#34;ConvertTight: Kept&#34;] = self.n_kept
stats[&#34;ConvertTight: Converted&#34;] = self.n_converted
stats[&#34;ConvertTight: Restored&#34;] = self.n_restored
stats[&#34;ConvertTight: Inf iterations&#34;] = self.n_infeasible_iterations
stats[&#34;ConvertTight: Subopt iterations&#34;] = self.n_suboptimal_iterations
def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:conv_ineqs)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
def x(self, instances):
return DropRedundantInequalitiesStep._x_train(instances)
def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:conv_ineqs:y)&#34;,
disable=len(instances) &lt; 5,
):
for (cid, slack) in instance.training_data[0][&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if 0 &lt;= slack &lt;= self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)
def iteration_cb(self, solver, instance, model):
is_infeasible, is_suboptimal = False, False
restored = []
def check_pi(msense, csense, pi):
if csense == &#34;=&#34;:
return True
if msense == &#34;max&#34;:
if csense == &#34;&lt;&#34;:
return pi &gt;= 0
else:
return pi &lt;= 0
else:
if csense == &#34;&gt;&#34;:
return pi &gt;= 0
else:
return pi &lt;= 0
def restore(cid):
nonlocal restored
csense = self.original_sense[cid]
solver.internal_solver.set_constraint_sense(cid, csense)
restored += [cid]
if solver.internal_solver.is_infeasible():
for cid in self.converted:
pi = solver.internal_solver.get_dual(cid)
if abs(pi) &gt; 0:
is_infeasible = True
restore(cid)
elif self.check_optimality:
random.shuffle(self.converted)
n_restored = 0
for cid in self.converted:
if n_restored &gt;= 100:
break
pi = solver.internal_solver.get_dual(cid)
csense = self.original_sense[cid]
msense = solver.internal_solver.get_sense()
if not check_pi(msense, csense, pi):
is_suboptimal = True
restore(cid)
n_restored += 1
for cid in restored:
self.converted.remove(cid)
if len(restored) &gt; 0:
self.n_restored += len(restored)
if is_infeasible:
self.n_infeasible_iterations += 1
if is_suboptimal:
self.n_suboptimal_iterations += 1
logger.info(f&#34;Restored {len(restored)} inequalities&#34;)
return True
else:
return False</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:conv_ineqs)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x"><code class="name flex">
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def x(self, instances):
return DropRedundantInequalitiesStep._x_train(instances)</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y"><code class="name flex">
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:conv_ineqs:y)&#34;,
disable=len(instances) &lt; 5,
):
for (cid, slack) in instance.training_data[0][&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if 0 &lt;= slack &lt;= self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</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" href="index.html">miplearn.components.steps</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit">fit</a></code></li>
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict">predict</a></code></li>
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x">x</a></code></li>
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y">y</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.steps.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.
import logging
from copy import deepcopy
import numpy as np
from tqdm import tqdm
from miplearn.classifiers.counting import CountingClassifier
from miplearn.components import classifier_evaluation_dict
from miplearn.components.component import Component
from miplearn.components.lazy_static import LazyConstraint
from miplearn.extractors import InstanceIterator
logger = logging.getLogger(__name__)
class DropRedundantInequalitiesStep(Component):
&#34;&#34;&#34;
Component that predicts which inequalities are likely loose in the LP and removes
them. Optionally, double checks after the problem is solved that all dropped
inequalities were in fact redundant, and, if not, re-adds them to the problem.
This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.
&#34;&#34;&#34;
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=1e-5,
check_feasibility=False,
violation_tolerance=1e-5,
max_iterations=3,
):
self.classifiers = {}
self.classifier_prototype = classifier
self.threshold = threshold
self.slack_tolerance = slack_tolerance
self.pool = []
self.check_feasibility = check_feasibility
self.violation_tolerance = violation_tolerance
self.max_iterations = max_iterations
self.current_iteration = 0
def before_solve(self, solver, instance, _):
self.current_iteration = 0
logger.info(&#34;Predicting redundant LP constraints...&#34;)
x, constraints = self._x_test(
instance,
constraint_ids=solver.internal_solver.get_constraint_ids(),
)
y = self.predict(x)
self.total_dropped = 0
self.total_restored = 0
self.total_kept = 0
self.total_iterations = 0
for category in y.keys():
for i in range(len(y[category])):
if y[category][i][0] == 1:
cid = constraints[category][i]
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
self.pool += [c]
self.total_dropped += 1
else:
self.total_kept += 1
logger.info(f&#34;Extracted {self.total_dropped} predicted constraints&#34;)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if &#34;slacks&#34; not in training_data.keys():
training_data[&#34;slacks&#34;] = solver.internal_solver.get_inequality_slacks()
stats.update(
{
&#34;DropRedundant: Kept&#34;: self.total_kept,
&#34;DropRedundant: Dropped&#34;: self.total_dropped,
&#34;DropRedundant: Restored&#34;: self.total_restored,
&#34;DropRedundant: Iterations&#34;: self.total_iterations,
}
)
def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:drop_ineq)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
@staticmethod
def _x_test(instance, constraint_ids):
x = {}
constraints = {}
cids = constraint_ids
for cid in cids:
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in x:
x[category] = []
constraints[category] = []
x[category] += [instance.get_constraint_features(cid)]
constraints[category] += [cid]
for category in x.keys():
x[category] = np.array(x[category])
return x, constraints
@staticmethod
def _x_train(instances):
x = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:drop_ineq:x)&#34;,
disable=len(instances) &lt; 5,
):
for training_data in instance.training_data:
cids = training_data[&#34;slacks&#34;].keys()
for cid in cids:
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in x:
x[category] = []
x[category] += [instance.get_constraint_features(cid)]
for category in x.keys():
x[category] = np.array(x[category])
return x
def x(self, instances):
return self._x_train(instances)
def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:drop_ineq:y)&#34;,
disable=len(instances) &lt; 5,
):
for training_data in instance.training_data:
for (cid, slack) in training_data[&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if slack &gt; self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)
def iteration_cb(self, solver, instance, model):
if not self.check_feasibility:
return False
if self.current_iteration &gt;= self.max_iterations:
return False
self.current_iteration += 1
logger.debug(&#34;Checking that dropped constraints are satisfied...&#34;)
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(
c.obj,
self.violation_tolerance,
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
if len(constraints_to_add) &gt; 0:
self.total_restored += len(constraints_to_add)
logger.info(
&#34;%8d constraints %8d in the pool&#34;
% (len(constraints_to_add), len(self.pool))
)
self.total_iterations += 1
return True
else:
return False</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep"><code class="flex name class">
<span>class <span class="ident">DropRedundantInequalitiesStep</span></span>
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_iterations=3)</span>
</code></dt>
<dd>
<section class="desc"><p>Component that predicts which inequalities are likely loose in the LP and removes
them. Optionally, double checks after the problem is solved that all dropped
inequalities were in fact redundant, and, if not, re-adds them to the problem.</p>
<p>This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class DropRedundantInequalitiesStep(Component):
&#34;&#34;&#34;
Component that predicts which inequalities are likely loose in the LP and removes
them. Optionally, double checks after the problem is solved that all dropped
inequalities were in fact redundant, and, if not, re-adds them to the problem.
This component does not work on MIPs. All integrality constraints must be relaxed
before this component is used.
&#34;&#34;&#34;
def __init__(
self,
classifier=CountingClassifier(),
threshold=0.95,
slack_tolerance=1e-5,
check_feasibility=False,
violation_tolerance=1e-5,
max_iterations=3,
):
self.classifiers = {}
self.classifier_prototype = classifier
self.threshold = threshold
self.slack_tolerance = slack_tolerance
self.pool = []
self.check_feasibility = check_feasibility
self.violation_tolerance = violation_tolerance
self.max_iterations = max_iterations
self.current_iteration = 0
def before_solve(self, solver, instance, _):
self.current_iteration = 0
logger.info(&#34;Predicting redundant LP constraints...&#34;)
x, constraints = self._x_test(
instance,
constraint_ids=solver.internal_solver.get_constraint_ids(),
)
y = self.predict(x)
self.total_dropped = 0
self.total_restored = 0
self.total_kept = 0
self.total_iterations = 0
for category in y.keys():
for i in range(len(y[category])):
if y[category][i][0] == 1:
cid = constraints[category][i]
c = LazyConstraint(
cid=cid,
obj=solver.internal_solver.extract_constraint(cid),
)
self.pool += [c]
self.total_dropped += 1
else:
self.total_kept += 1
logger.info(f&#34;Extracted {self.total_dropped} predicted constraints&#34;)
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
if &#34;slacks&#34; not in training_data.keys():
training_data[&#34;slacks&#34;] = solver.internal_solver.get_inequality_slacks()
stats.update(
{
&#34;DropRedundant: Kept&#34;: self.total_kept,
&#34;DropRedundant: Dropped&#34;: self.total_dropped,
&#34;DropRedundant: Restored&#34;: self.total_restored,
&#34;DropRedundant: Iterations&#34;: self.total_iterations,
}
)
def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:drop_ineq)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])
@staticmethod
def _x_test(instance, constraint_ids):
x = {}
constraints = {}
cids = constraint_ids
for cid in cids:
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in x:
x[category] = []
constraints[category] = []
x[category] += [instance.get_constraint_features(cid)]
constraints[category] += [cid]
for category in x.keys():
x[category] = np.array(x[category])
return x, constraints
@staticmethod
def _x_train(instances):
x = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:drop_ineq:x)&#34;,
disable=len(instances) &lt; 5,
):
for training_data in instance.training_data:
cids = training_data[&#34;slacks&#34;].keys()
for cid in cids:
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in x:
x[category] = []
x[category] += [instance.get_constraint_features(cid)]
for category in x.keys():
x[category] = np.array(x[category])
return x
def x(self, instances):
return self._x_train(instances)
def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:drop_ineq:y)&#34;,
disable=len(instances) &lt; 5,
):
for training_data in instance.training_data:
for (cid, slack) in training_data[&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if slack &gt; self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y
def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)
def iteration_cb(self, solver, instance, model):
if not self.check_feasibility:
return False
if self.current_iteration &gt;= self.max_iterations:
return False
self.current_iteration += 1
logger.debug(&#34;Checking that dropped constraints are satisfied...&#34;)
constraints_to_add = []
for c in self.pool:
if not solver.internal_solver.is_constraint_satisfied(
c.obj,
self.violation_tolerance,
):
constraints_to_add.append(c)
for c in constraints_to_add:
self.pool.remove(c)
solver.internal_solver.add_constraint(c.obj)
if len(constraints_to_add) &gt; 0:
self.total_restored += len(constraints_to_add)
logger.info(
&#34;%8d constraints %8d in the pool&#34;
% (len(constraints_to_add), len(self.pool))
)
self.total_iterations += 1
return True
else:
return False</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate"><code class="name flex">
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def evaluate(self, instance):
x = self.x([instance])
y_true = self.y([instance])
y_pred = self.predict(x)
tp, tn, fp, fn = 0, 0, 0, 0
for category in y_true.keys():
for i in range(len(y_true[category])):
if y_pred[category][i][0] == 1:
if y_true[category][i][0] == 1:
tp += 1
else:
fp += 1
else:
if y_true[category][i][0] == 1:
fn += 1
else:
tn += 1
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit"><code class="name flex">
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
logger.debug(&#34;Extracting x and y...&#34;)
x = self.x(training_instances)
y = self.y(training_instances)
logger.debug(&#34;Fitting...&#34;)
for category in tqdm(x.keys(), desc=&#34;Fit (rlx:drop_ineq)&#34;):
if category not in self.classifiers:
self.classifiers[category] = deepcopy(self.classifier_prototype)
self.classifiers[category].fit(x[category], y[category])</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def predict(self, x):
y = {}
for (category, x_cat) in x.items():
if category not in self.classifiers:
continue
y[category] = []
x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)):
if proba[i][1] &gt;= self.threshold:
y[category] += [[1]]
else:
y[category] += [[0]]
return y</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x"><code class="name flex">
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def x(self, instances):
return self._x_train(instances)</code></pre>
</details>
</dd>
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y"><code class="name flex">
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def y(self, instances):
y = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (rlx:drop_ineq:y)&#34;,
disable=len(instances) &lt; 5,
):
for training_data in instance.training_data:
for (cid, slack) in training_data[&#34;slacks&#34;].items():
category = instance.get_constraint_category(cid)
if category is None:
continue
if category not in y:
y[category] = []
if slack &gt; self.slack_tolerance:
y[category] += [[1]]
else:
y[category] += [[0]]
return y</code></pre>
</details>
</dd>
</dl>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
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<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></code></h4>
<ul class="">
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate">evaluate</a></code></li>
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit">fit</a></code></li>
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict">predict</a></code></li>
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x">x</a></code></li>
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y">y</a></code></li>
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<h1 class="title">Module <code>miplearn.components.steps</code></h1>
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<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
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<ul>
<li><code><a title="miplearn.components" href="../index.html">miplearn.components</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></li>
<li><code><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></li>
<li><code><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></li>
<li><code><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></li>
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<header>
<h1 class="title">Module <code>miplearn.components.steps.relax_integrality</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.
import logging
from miplearn.components.component import Component
logger = logging.getLogger(__name__)
class RelaxIntegralityStep(Component):
&#34;&#34;&#34;
Component that relaxes all integrality constraints before the problem is solved.
&#34;&#34;&#34;
def before_solve(self, solver, instance, _):
logger.info(&#34;Relaxing integrality...&#34;)
solver.internal_solver.relax()
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
return</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.components.steps.relax_integrality.RelaxIntegralityStep"><code class="flex name class">
<span>class <span class="ident">RelaxIntegralityStep</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Component that relaxes all integrality constraints before the problem is solved.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class RelaxIntegralityStep(Component):
&#34;&#34;&#34;
Component that relaxes all integrality constraints before the problem is solved.
&#34;&#34;&#34;
def before_solve(self, solver, instance, _):
logger.info(&#34;Relaxing integrality...&#34;)
solver.internal_solver.relax()
def after_solve(
self,
solver,
instance,
model,
stats,
training_data,
):
return</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
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<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></code></h4>
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<h2 class="section-title" id="header-submodules">Sub-modules</h2>
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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>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.steps.tests.test_drop_redundant" href="test_drop_redundant.html">miplearn.components.steps.tests.test_drop_redundant</a></code></dt>
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></li>
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<h1 class="title">Module <code>miplearn.components.steps.tests.test_convert_tight</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">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[&#34;Upper bound&#34;]
# Should collect training data
assert instance.training_data[0][&#34;slacks&#34;][&#34;eq_capacity&#34;] == 0.0
# Fit and resolve
solver.fit([instance])
stats = solver.solve(instance)
# Objective value should be the same
assert stats[&#34;Upper bound&#34;] == original_upper_bound
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 0
class SampleInstance(Instance):
def to_model(self):
import gurobipy as grb
m = grb.Model(&#34;model&#34;)
x1 = m.addVar(name=&#34;x1&#34;)
x2 = m.addVar(name=&#34;x2&#34;)
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
m.addConstr(x1 &lt;= 2, name=&#34;c1&#34;)
m.addConstr(x2 &lt;= 2, name=&#34;c2&#34;)
m.addConstr(x1 + x2 &lt;= 3, name=&#34;c2&#34;)
return m
def test_convert_tight_infeasibility():
comp = ConvertTightIneqsIntoEqsStep()
comp.classifiers = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 1
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 0
def test_convert_tight_suboptimality():
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
comp.classifiers = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[1, 0]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 1
def test_convert_tight_optimal():
comp = ConvertTightIneqsIntoEqsStep()
comp.classifiers = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[1, 0]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 0</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_convert_tight.test_convert_tight_infeasibility"><code class="name flex">
<span>def <span class="ident">test_convert_tight_infeasibility</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_infeasibility():
comp = ConvertTightIneqsIntoEqsStep()
comp.classifiers = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 1
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 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 = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[1, 0]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 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 = {
&#34;c1&#34;: Mock(spec=Classifier),
&#34;c2&#34;: Mock(spec=Classifier),
&#34;c3&#34;: Mock(spec=Classifier),
}
comp.classifiers[&#34;c1&#34;].predict_proba = Mock(return_value=[[0, 1]])
comp.classifiers[&#34;c2&#34;].predict_proba = Mock(return_value=[[1, 0]])
comp.classifiers[&#34;c3&#34;].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[&#34;Upper bound&#34;] == 5.0
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 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[&#34;Upper bound&#34;]
# Should collect training data
assert instance.training_data[0][&#34;slacks&#34;][&#34;eq_capacity&#34;] == 0.0
# Fit and resolve
solver.fit([instance])
stats = solver.solve(instance)
# Objective value should be the same
assert stats[&#34;Upper bound&#34;] == original_upper_bound
assert stats[&#34;ConvertTight: Inf iterations&#34;] == 0
assert stats[&#34;ConvertTight: Subopt iterations&#34;] == 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(&#34;model&#34;)
x1 = m.addVar(name=&#34;x1&#34;)
x2 = m.addVar(name=&#34;x2&#34;)
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
m.addConstr(x1 &lt;= 2, name=&#34;c1&#34;)
m.addConstr(x2 &lt;= 2, name=&#34;c2&#34;)
m.addConstr(x1 + x2 &lt;= 3, name=&#34;c2&#34;)
return m</code></pre>
</details>
<h3>Ancestors</h3>
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<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>
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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>
<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>
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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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<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=[&#34;c1&#34;, &#34;c2&#34;, &#34;c3&#34;, &#34;c4&#34;])
internal.get_inequality_slacks = Mock(
side_effect=lambda: {
&#34;c1&#34;: 0.5,
&#34;c2&#34;: 0.0,
&#34;c3&#34;: 0.0,
&#34;c4&#34;: 1.4,
}
)
internal.extract_constraint = Mock(side_effect=lambda cid: &#34;&lt;%s&gt;&#34; % cid)
internal.is_constraint_satisfied = Mock(return_value=False)
instance = Mock(spec=Instance)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: np.array([1.0, 0.0]),
&#34;c3&#34;: np.array([0.5, 0.5]),
&#34;c4&#34;: np.array([1.0]),
}[cid]
)
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
classifiers = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
classifiers[&#34;type-a&#34;].predict_proba = Mock(
return_value=np.array(
[
[0.20, 0.80],
[0.05, 0.95],
]
)
)
classifiers[&#34;type-b&#34;].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(&#34;c1&#34;),
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask ML to predict whether constraint should be removed
type_a_actual = component.classifiers[&#34;type-a&#34;].predict_proba.call_args[0][0]
type_b_actual = component.classifiers[&#34;type-b&#34;].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(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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[&#34;slacks&#34;] == {
&#34;c1&#34;: 0.5,
&#34;c2&#34;: 0.0,
&#34;c3&#34;: 0.0,
&#34;c4&#34;: 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(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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(&#34;&lt;c3&gt;&#34;, 1e-3),
call(&#34;&lt;c4&gt;&#34;, 1e-3),
]
)
# Should add constraints back to LP relaxation
internal.add_constraint.assert_has_calls([call(&#34;&lt;c3&gt;&#34;), call(&#34;&lt;c4&gt;&#34;)])
# 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 = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;type-a&#34;].predict_proba = Mock(
return_value=[
np.array([0.20, 0.80]),
]
)
component.classifiers[&#34;type-b&#34;].predict_proba = Mock(
return_value=np.array(
[
[0.50, 0.50],
[0.05, 0.95],
]
)
)
# First mock instance
instances[0].training_data = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.05,
&#34;c3&#34;: 0.00,
&#34;c4&#34;: 30.0,
}
}
]
instances[0].get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
instances[0].get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: np.array([1.0, 0.0]),
&#34;c3&#34;: np.array([0.5, 0.5]),
&#34;c4&#34;: np.array([1.0]),
}[cid]
)
# Second mock instance
instances[1].training_data = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c3&#34;: 0.30,
&#34;c4&#34;: 0.00,
&#34;c5&#34;: 0.00,
}
}
]
instances[1].get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instances[1].get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c3&#34;: np.array([0.3, 0.4]),
&#34;c4&#34;: np.array([0.7]),
&#34;c5&#34;: np.array([0.8]),
}[cid]
)
expected_x = {
&#34;type-a&#34;: np.array(
[
[1.0, 0.0],
[0.5, 0.5],
[0.3, 0.4],
]
),
&#34;type-b&#34;: np.array(
[
[1.0],
[0.7],
[0.8],
]
),
}
expected_y = {
&#34;type-a&#34;: np.array([[0], [0], [1]]),
&#34;type-b&#34;: 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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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) == {&#34;type-a&#34;: [[1]], &#34;type-b&#34;: [[0], [1]]}
ev = component.evaluate(instances[1])
assert ev[&#34;True positive&#34;] == 1
assert ev[&#34;True negative&#34;] == 1
assert ev[&#34;False positive&#34;] == 1
assert ev[&#34;False negative&#34;] == 0
def test_x_multiple_solves():
instance = Mock(spec=Instance)
instance.training_data = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.05,
&#34;c3&#34;: 0.00,
&#34;c4&#34;: 30.0,
}
},
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.00,
&#34;c3&#34;: 1.00,
&#34;c4&#34;: 0.0,
}
},
]
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: np.array([1.0, 0.0]),
&#34;c3&#34;: np.array([0.5, 0.5]),
&#34;c4&#34;: np.array([1.0]),
}[cid]
)
expected_x = {
&#34;type-a&#34;: np.array(
[
[1.0, 0.0],
[0.5, 0.5],
[1.0, 0.0],
[0.5, 0.5],
]
),
&#34;type-b&#34;: np.array(
[
[1.0],
[1.0],
]
),
}
expected_y = {
&#34;type-a&#34;: np.array([[1], [0], [0], [1]]),
&#34;type-b&#34;: 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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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(&#34;c1&#34;),
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask ML to predict whether constraint should be removed
type_a_actual = component.classifiers[&#34;type-a&#34;].predict_proba.call_args[0][0]
type_b_actual = component.classifiers[&#34;type-b&#34;].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(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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[&#34;slacks&#34;] == {
&#34;c1&#34;: 0.5,
&#34;c2&#34;: 0.0,
&#34;c3&#34;: 0.0,
&#34;c4&#34;: 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(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# 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(&#34;&lt;c3&gt;&#34;, 1e-3),
call(&#34;&lt;c4&gt;&#34;, 1e-3),
]
)
# Should add constraints back to LP relaxation
internal.add_constraint.assert_has_calls([call(&#34;&lt;c3&gt;&#34;), call(&#34;&lt;c4&gt;&#34;)])
# 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 = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.05,
&#34;c3&#34;: 0.00,
&#34;c4&#34;: 30.0,
}
},
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.00,
&#34;c3&#34;: 1.00,
&#34;c4&#34;: 0.0,
}
},
]
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: np.array([1.0, 0.0]),
&#34;c3&#34;: np.array([0.5, 0.5]),
&#34;c4&#34;: np.array([1.0]),
}[cid]
)
expected_x = {
&#34;type-a&#34;: np.array(
[
[1.0, 0.0],
[0.5, 0.5],
[1.0, 0.0],
[0.5, 0.5],
]
),
&#34;type-b&#34;: np.array(
[
[1.0],
[1.0],
]
),
}
expected_y = {
&#34;type-a&#34;: np.array([[1], [0], [0], [1]]),
&#34;type-b&#34;: 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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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 = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;type-a&#34;].predict_proba = Mock(
return_value=[
np.array([0.20, 0.80]),
]
)
component.classifiers[&#34;type-b&#34;].predict_proba = Mock(
return_value=np.array(
[
[0.50, 0.50],
[0.05, 0.95],
]
)
)
# First mock instance
instances[0].training_data = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c2&#34;: 0.05,
&#34;c3&#34;: 0.00,
&#34;c4&#34;: 30.0,
}
}
]
instances[0].get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
instances[0].get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: np.array([1.0, 0.0]),
&#34;c3&#34;: np.array([0.5, 0.5]),
&#34;c4&#34;: np.array([1.0]),
}[cid]
)
# Second mock instance
instances[1].training_data = [
{
&#34;slacks&#34;: {
&#34;c1&#34;: 0.00,
&#34;c3&#34;: 0.30,
&#34;c4&#34;: 0.00,
&#34;c5&#34;: 0.00,
}
}
]
instances[1].get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: None,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instances[1].get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c3&#34;: np.array([0.3, 0.4]),
&#34;c4&#34;: np.array([0.7]),
&#34;c5&#34;: np.array([0.8]),
}[cid]
)
expected_x = {
&#34;type-a&#34;: np.array(
[
[1.0, 0.0],
[0.5, 0.5],
[0.3, 0.4],
]
),
&#34;type-b&#34;: np.array(
[
[1.0],
[0.7],
[0.8],
]
),
}
expected_y = {
&#34;type-a&#34;: np.array([[0], [0], [1]]),
&#34;type-b&#34;: 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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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 [&#34;type-a&#34;, &#34;type-b&#34;]:
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) == {&#34;type-a&#34;: [[1]], &#34;type-b&#34;: [[0], [1]]}
ev = component.evaluate(instances[1])
assert ev[&#34;True positive&#34;] == 1
assert ev[&#34;True negative&#34;] == 1
assert ev[&#34;False positive&#34;] == 1
assert ev[&#34;False negative&#34;] == 0</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
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<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>
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<h1 class="title">Module <code>miplearn.components.tests</code></h1>
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<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.</code></pre>
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<dl>
<dt><code class="name"><a title="miplearn.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.tests.test_lazy_dynamic" href="test_lazy_dynamic.html">miplearn.components.tests.test_lazy_dynamic</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.tests.test_lazy_static" href="test_lazy_static.html">miplearn.components.tests.test_lazy_static</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components.tests.test_objective" href="test_objective.html">miplearn.components.tests.test_objective</a></code></dt>
<dd>
<section class="desc"></section>
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<dt><code class="name"><a title="miplearn.components.tests.test_primal" href="test_primal.html">miplearn.components.tests.test_primal</a></code></dt>
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<li><code><a title="miplearn.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</a></code></li>
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</head>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.tests.test_composite</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
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)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.tests.test_composite.test_composite"><code class="name flex">
<span>def <span class="ident">test_composite</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_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)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
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<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.components.tests.test_composite.test_composite" href="#miplearn.components.tests.test_composite.test_composite">test_composite</a></code></li>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.tests.test_lazy_dynamic</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
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 = [&#34;a&#34;, &#34;b&#34;]
instances[1].found_violated_lazy_constraints = [&#34;b&#34;, &#34;c&#34;]
classifier = Mock(spec=Classifier)
component = DynamicLazyConstraintsComponent(classifier=classifier)
component.fit(instances)
# Should create one classifier for each violation
assert &#34;a&#34; in component.classifiers
assert &#34;b&#34; in component.classifiers
assert &#34;c&#34; 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[&#34;a&#34;].fit.call_args[0][0]
actual_x_train_b = component.classifiers[&#34;b&#34;].fit.call_args[0][0]
actual_x_train_c = component.classifiers[&#34;c&#34;].fit.call_args[0][0]
assert norm(expected_x_train_a - actual_x_train_a) &lt; E
assert norm(expected_x_train_b - actual_x_train_b) &lt; E
assert norm(expected_x_train_c - actual_x_train_c) &lt; 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[&#34;a&#34;].fit.call_args[0][1]
actual_y_train_b = component.classifiers[&#34;b&#34;].fit.call_args[0][1]
actual_y_train_c = component.classifiers[&#34;c&#34;].fit.call_args[0][1]
assert norm(expected_y_train_a - actual_y_train_a) &lt; E
assert norm(expected_y_train_b - actual_y_train_b) &lt; E
assert norm(expected_y_train_c - actual_y_train_c) &lt; E
def test_lazy_before():
instances, models = get_test_pyomo_instances()
instances[0].build_lazy_constraint = Mock(return_value=&#34;c1&#34;)
solver = LearningSolver()
solver.internal_solver = Mock(spec=InternalSolver)
component = DynamicLazyConstraintsComponent(threshold=0.10)
component.classifiers = {&#34;a&#34;: Mock(spec=Classifier), &#34;b&#34;: Mock(spec=Classifier)}
component.classifiers[&#34;a&#34;].predict_proba = Mock(return_value=[[0.95, 0.05]])
component.classifiers[&#34;b&#34;].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[&#34;a&#34;].predict_proba.call_args[0][0]
actual_x_test_b = component.classifiers[&#34;b&#34;].predict_proba.call_args[0][0]
assert norm(expected_x_test_a - actual_x_test_a) &lt; E
assert norm(expected_x_test_b - actual_x_test_b) &lt; 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], &#34;b&#34;)
# Should ask internal solver to add generated constraint
solver.internal_solver.add_constraint.assert_called_once_with(&#34;c1&#34;)
def test_lazy_evaluate():
instances, models = get_test_pyomo_instances()
component = DynamicLazyConstraintsComponent()
component.classifiers = {
&#34;a&#34;: Mock(spec=Classifier),
&#34;b&#34;: Mock(spec=Classifier),
&#34;c&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;a&#34;].predict_proba = Mock(return_value=[[1.0, 0.0]])
component.classifiers[&#34;b&#34;].predict_proba = Mock(return_value=[[0.0, 1.0]])
component.classifiers[&#34;c&#34;].predict_proba = Mock(return_value=[[0.0, 1.0]])
instances[0].found_violated_lazy_constraints = [&#34;a&#34;, &#34;b&#34;, &#34;c&#34;]
instances[1].found_violated_lazy_constraints = [&#34;b&#34;, &#34;d&#34;]
assert component.evaluate(instances) == {
0: {
&#34;Accuracy&#34;: 0.75,
&#34;F1 score&#34;: 0.8,
&#34;Precision&#34;: 1.0,
&#34;Recall&#34;: 2 / 3.0,
&#34;Predicted positive&#34;: 2,
&#34;Predicted negative&#34;: 2,
&#34;Condition positive&#34;: 3,
&#34;Condition negative&#34;: 1,
&#34;False negative&#34;: 1,
&#34;False positive&#34;: 0,
&#34;True negative&#34;: 1,
&#34;True positive&#34;: 2,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Condition positive (%)&#34;: 75.0,
&#34;Condition negative (%)&#34;: 25.0,
&#34;False negative (%)&#34;: 25.0,
&#34;False positive (%)&#34;: 0,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive (%)&#34;: 50.0,
},
1: {
&#34;Accuracy&#34;: 0.5,
&#34;F1 score&#34;: 0.5,
&#34;Precision&#34;: 0.5,
&#34;Recall&#34;: 0.5,
&#34;Predicted positive&#34;: 2,
&#34;Predicted negative&#34;: 2,
&#34;Condition positive&#34;: 2,
&#34;Condition negative&#34;: 2,
&#34;False negative&#34;: 1,
&#34;False positive&#34;: 1,
&#34;True negative&#34;: 1,
&#34;True positive&#34;: 1,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Condition positive (%)&#34;: 50.0,
&#34;Condition negative (%)&#34;: 50.0,
&#34;False negative (%)&#34;: 25.0,
&#34;False positive (%)&#34;: 25.0,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive (%)&#34;: 25.0,
},
}</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_before"><code class="name flex">
<span>def <span class="ident">test_lazy_before</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_lazy_before():
instances, models = get_test_pyomo_instances()
instances[0].build_lazy_constraint = Mock(return_value=&#34;c1&#34;)
solver = LearningSolver()
solver.internal_solver = Mock(spec=InternalSolver)
component = DynamicLazyConstraintsComponent(threshold=0.10)
component.classifiers = {&#34;a&#34;: Mock(spec=Classifier), &#34;b&#34;: Mock(spec=Classifier)}
component.classifiers[&#34;a&#34;].predict_proba = Mock(return_value=[[0.95, 0.05]])
component.classifiers[&#34;b&#34;].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[&#34;a&#34;].predict_proba.call_args[0][0]
actual_x_test_b = component.classifiers[&#34;b&#34;].predict_proba.call_args[0][0]
assert norm(expected_x_test_a - actual_x_test_a) &lt; E
assert norm(expected_x_test_b - actual_x_test_b) &lt; 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], &#34;b&#34;)
# Should ask internal solver to add generated constraint
solver.internal_solver.add_constraint.assert_called_once_with(&#34;c1&#34;)</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate"><code class="name flex">
<span>def <span class="ident">test_lazy_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_lazy_evaluate():
instances, models = get_test_pyomo_instances()
component = DynamicLazyConstraintsComponent()
component.classifiers = {
&#34;a&#34;: Mock(spec=Classifier),
&#34;b&#34;: Mock(spec=Classifier),
&#34;c&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;a&#34;].predict_proba = Mock(return_value=[[1.0, 0.0]])
component.classifiers[&#34;b&#34;].predict_proba = Mock(return_value=[[0.0, 1.0]])
component.classifiers[&#34;c&#34;].predict_proba = Mock(return_value=[[0.0, 1.0]])
instances[0].found_violated_lazy_constraints = [&#34;a&#34;, &#34;b&#34;, &#34;c&#34;]
instances[1].found_violated_lazy_constraints = [&#34;b&#34;, &#34;d&#34;]
assert component.evaluate(instances) == {
0: {
&#34;Accuracy&#34;: 0.75,
&#34;F1 score&#34;: 0.8,
&#34;Precision&#34;: 1.0,
&#34;Recall&#34;: 2 / 3.0,
&#34;Predicted positive&#34;: 2,
&#34;Predicted negative&#34;: 2,
&#34;Condition positive&#34;: 3,
&#34;Condition negative&#34;: 1,
&#34;False negative&#34;: 1,
&#34;False positive&#34;: 0,
&#34;True negative&#34;: 1,
&#34;True positive&#34;: 2,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Condition positive (%)&#34;: 75.0,
&#34;Condition negative (%)&#34;: 25.0,
&#34;False negative (%)&#34;: 25.0,
&#34;False positive (%)&#34;: 0,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive (%)&#34;: 50.0,
},
1: {
&#34;Accuracy&#34;: 0.5,
&#34;F1 score&#34;: 0.5,
&#34;Precision&#34;: 0.5,
&#34;Recall&#34;: 0.5,
&#34;Predicted positive&#34;: 2,
&#34;Predicted negative&#34;: 2,
&#34;Condition positive&#34;: 2,
&#34;Condition negative&#34;: 2,
&#34;False negative&#34;: 1,
&#34;False positive&#34;: 1,
&#34;True negative&#34;: 1,
&#34;True positive&#34;: 1,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Condition positive (%)&#34;: 50.0,
&#34;Condition negative (%)&#34;: 50.0,
&#34;False negative (%)&#34;: 25.0,
&#34;False positive (%)&#34;: 25.0,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive (%)&#34;: 25.0,
},
}</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit"><code class="name flex">
<span>def <span class="ident">test_lazy_fit</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_lazy_fit():
instances, models = get_test_pyomo_instances()
instances[0].found_violated_lazy_constraints = [&#34;a&#34;, &#34;b&#34;]
instances[1].found_violated_lazy_constraints = [&#34;b&#34;, &#34;c&#34;]
classifier = Mock(spec=Classifier)
component = DynamicLazyConstraintsComponent(classifier=classifier)
component.fit(instances)
# Should create one classifier for each violation
assert &#34;a&#34; in component.classifiers
assert &#34;b&#34; in component.classifiers
assert &#34;c&#34; 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[&#34;a&#34;].fit.call_args[0][0]
actual_x_train_b = component.classifiers[&#34;b&#34;].fit.call_args[0][0]
actual_x_train_c = component.classifiers[&#34;c&#34;].fit.call_args[0][0]
assert norm(expected_x_train_a - actual_x_train_a) &lt; E
assert norm(expected_x_train_b - actual_x_train_b) &lt; E
assert norm(expected_x_train_c - actual_x_train_c) &lt; 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[&#34;a&#34;].fit.call_args[0][1]
actual_y_train_b = component.classifiers[&#34;b&#34;].fit.call_args[0][1]
actual_y_train_c = component.classifiers[&#34;c&#34;].fit.call_args[0][1]
assert norm(expected_y_train_a - actual_y_train_a) &lt; E
assert norm(expected_y_train_b - actual_y_train_b) &lt; E
assert norm(expected_y_train_c - actual_y_train_c) &lt; E</code></pre>
</details>
</dd>
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<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_before" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_before">test_lazy_before</a></code></li>
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate">test_lazy_evaluate</a></code></li>
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_fit">test_lazy_fit</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.tests.test_lazy_static</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
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=[&#34;c1&#34;, &#34;c2&#34;, &#34;c3&#34;, &#34;c4&#34;])
internal.extract_constraint = Mock(side_effect=lambda cid: &#34;&lt;%s&gt;&#34; % 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: {
&#34;c1&#34;: False,
&#34;c2&#34;: True,
&#34;c3&#34;: True,
&#34;c4&#34;: True,
}[cid]
)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: [1.0, 0.0],
&#34;c3&#34;: [0.5, 0.5],
&#34;c4&#34;: [1.0],
}[cid]
)
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
component = StaticLazyConstraintsComponent(
threshold=0.90,
use_two_phase_gap=False,
violation_tolerance=1.0,
)
component.classifiers = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;type-a&#34;].predict_proba = Mock(
return_value=[
[0.20, 0.80],
[0.05, 0.95],
]
)
component.classifiers[&#34;type-b&#34;].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(&#34;c1&#34;),
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# For the lazy ones, should ask for features
instance.get_constraint_features.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should also ask for categories
assert instance.get_constraint_category.call_count == 3
instance.get_constraint_category.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask internal solver to remove constraints identified as lazy
assert internal.extract_constraint.call_count == 3
internal.extract_constraint.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask ML to predict whether each lazy constraint should be enforced
component.classifiers[&#34;type-a&#34;].predict_proba.assert_called_once_with(
[[1.0, 0.0], [0.5, 0.5]]
)
component.classifiers[&#34;type-b&#34;].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(&#34;&lt;c3&gt;&#34;),
call(&#34;&lt;c4&gt;&#34;),
]
)
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(&#34;&lt;c2&gt;&#34;, tol=1.0)
internal.is_constraint_satisfied.reset_mock()
internal.add_constraint.assert_called_once_with(&#34;&lt;c2&gt;&#34;)
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 == [&#34;c3&#34;, &#34;c4&#34;, &#34;c2&#34;]
def test_fit():
instance_1 = Mock(spec=Instance)
instance_1.found_violated_lazy_constraints = [&#34;c1&#34;, &#34;c2&#34;, &#34;c4&#34;, &#34;c5&#34;]
instance_1.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: &#34;type-a&#34;,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instance_1.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: [1, 1],
&#34;c2&#34;: [1, 2],
&#34;c3&#34;: [1, 3],
&#34;c4&#34;: [1, 4, 0],
&#34;c5&#34;: [1, 5, 0],
}[cid]
)
instance_2 = Mock(spec=Instance)
instance_2.found_violated_lazy_constraints = [&#34;c2&#34;, &#34;c3&#34;, &#34;c4&#34;]
instance_2.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: &#34;type-a&#34;,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instance_2.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: [2, 1],
&#34;c2&#34;: [2, 2],
&#34;c3&#34;: [2, 3],
&#34;c4&#34;: [2, 4, 0],
&#34;c5&#34;: [2, 5, 0],
}[cid]
)
instances = [instance_1, instance_2]
component = StaticLazyConstraintsComponent()
component.classifiers = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
expected_constraints = {
&#34;type-a&#34;: [&#34;c1&#34;, &#34;c2&#34;, &#34;c3&#34;],
&#34;type-b&#34;: [&#34;c4&#34;, &#34;c5&#34;],
}
expected_x = {
&#34;type-a&#34;: [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
&#34;type-b&#34;: [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
}
expected_y = {
&#34;type-a&#34;: [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
&#34;type-b&#34;: [[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[&#34;type-a&#34;].fit.assert_called_once_with(
expected_x[&#34;type-a&#34;],
expected_y[&#34;type-a&#34;],
)
component.classifiers[&#34;type-b&#34;].fit.assert_called_once_with(
expected_x[&#34;type-b&#34;],
expected_y[&#34;type-b&#34;],
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.tests.test_lazy_static.test_fit"><code class="name flex">
<span>def <span class="ident">test_fit</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_fit():
instance_1 = Mock(spec=Instance)
instance_1.found_violated_lazy_constraints = [&#34;c1&#34;, &#34;c2&#34;, &#34;c4&#34;, &#34;c5&#34;]
instance_1.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: &#34;type-a&#34;,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instance_1.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: [1, 1],
&#34;c2&#34;: [1, 2],
&#34;c3&#34;: [1, 3],
&#34;c4&#34;: [1, 4, 0],
&#34;c5&#34;: [1, 5, 0],
}[cid]
)
instance_2 = Mock(spec=Instance)
instance_2.found_violated_lazy_constraints = [&#34;c2&#34;, &#34;c3&#34;, &#34;c4&#34;]
instance_2.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: &#34;type-a&#34;,
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
&#34;c5&#34;: &#34;type-b&#34;,
}[cid]
)
instance_2.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c1&#34;: [2, 1],
&#34;c2&#34;: [2, 2],
&#34;c3&#34;: [2, 3],
&#34;c4&#34;: [2, 4, 0],
&#34;c5&#34;: [2, 5, 0],
}[cid]
)
instances = [instance_1, instance_2]
component = StaticLazyConstraintsComponent()
component.classifiers = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
expected_constraints = {
&#34;type-a&#34;: [&#34;c1&#34;, &#34;c2&#34;, &#34;c3&#34;],
&#34;type-b&#34;: [&#34;c4&#34;, &#34;c5&#34;],
}
expected_x = {
&#34;type-a&#34;: [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
&#34;type-b&#34;: [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
}
expected_y = {
&#34;type-a&#34;: [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
&#34;type-b&#34;: [[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[&#34;type-a&#34;].fit.assert_called_once_with(
expected_x[&#34;type-a&#34;],
expected_y[&#34;type-a&#34;],
)
component.classifiers[&#34;type-b&#34;].fit.assert_called_once_with(
expected_x[&#34;type-b&#34;],
expected_y[&#34;type-b&#34;],
)</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_lazy_static.test_usage_with_solver"><code class="name flex">
<span>def <span class="ident">test_usage_with_solver</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_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=[&#34;c1&#34;, &#34;c2&#34;, &#34;c3&#34;, &#34;c4&#34;])
internal.extract_constraint = Mock(side_effect=lambda cid: &#34;&lt;%s&gt;&#34; % 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: {
&#34;c1&#34;: False,
&#34;c2&#34;: True,
&#34;c3&#34;: True,
&#34;c4&#34;: True,
}[cid]
)
instance.get_constraint_features = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: [1.0, 0.0],
&#34;c3&#34;: [0.5, 0.5],
&#34;c4&#34;: [1.0],
}[cid]
)
instance.get_constraint_category = Mock(
side_effect=lambda cid: {
&#34;c2&#34;: &#34;type-a&#34;,
&#34;c3&#34;: &#34;type-a&#34;,
&#34;c4&#34;: &#34;type-b&#34;,
}[cid]
)
component = StaticLazyConstraintsComponent(
threshold=0.90,
use_two_phase_gap=False,
violation_tolerance=1.0,
)
component.classifiers = {
&#34;type-a&#34;: Mock(spec=Classifier),
&#34;type-b&#34;: Mock(spec=Classifier),
}
component.classifiers[&#34;type-a&#34;].predict_proba = Mock(
return_value=[
[0.20, 0.80],
[0.05, 0.95],
]
)
component.classifiers[&#34;type-b&#34;].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(&#34;c1&#34;),
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# For the lazy ones, should ask for features
instance.get_constraint_features.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should also ask for categories
assert instance.get_constraint_category.call_count == 3
instance.get_constraint_category.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask internal solver to remove constraints identified as lazy
assert internal.extract_constraint.call_count == 3
internal.extract_constraint.assert_has_calls(
[
call(&#34;c2&#34;),
call(&#34;c3&#34;),
call(&#34;c4&#34;),
]
)
# Should ask ML to predict whether each lazy constraint should be enforced
component.classifiers[&#34;type-a&#34;].predict_proba.assert_called_once_with(
[[1.0, 0.0], [0.5, 0.5]]
)
component.classifiers[&#34;type-b&#34;].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(&#34;&lt;c3&gt;&#34;),
call(&#34;&lt;c4&gt;&#34;),
]
)
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(&#34;&lt;c2&gt;&#34;, tol=1.0)
internal.is_constraint_satisfied.reset_mock()
internal.add_constraint.assert_called_once_with(&#34;&lt;c2&gt;&#34;)
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 == [&#34;c3&#34;, &#34;c4&#34;, &#34;c2&#34;]</code></pre>
</details>
</dd>
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<li><code><a title="miplearn.components.tests.test_lazy_static.test_fit" href="#miplearn.components.tests.test_lazy_static.test_fit">test_fit</a></code></li>
<li><code><a title="miplearn.components.tests.test_lazy_static.test_usage_with_solver" href="#miplearn.components.tests.test_lazy_static.test_usage_with_solver">test_usage_with_solver</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.tests.test_objective</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
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][&#34;Lower bound&#34;] == 1183.0
assert instances[0].training_data[0][&#34;Upper bound&#34;] == 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 == {
&#34;Lower bound&#34;: {
&#34;Explained variance&#34;: 0.0,
&#34;Max error&#34;: 183.0,
&#34;Mean absolute error&#34;: 126.5,
&#34;Mean squared error&#34;: 19194.5,
&#34;Median absolute error&#34;: 126.5,
&#34;R2&#34;: -5.012843605607331,
},
&#34;Upper bound&#34;: {
&#34;Explained variance&#34;: 0.0,
&#34;Max error&#34;: 183.0,
&#34;Mean absolute error&#34;: 126.5,
&#34;Mean squared error&#34;: 19194.5,
&#34;Median absolute error&#34;: 126.5,
&#34;R2&#34;: -5.012843605607331,
},
}</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.tests.test_objective.test_obj_evaluate"><code class="name flex">
<span>def <span class="ident">test_obj_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_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 == {
&#34;Lower bound&#34;: {
&#34;Explained variance&#34;: 0.0,
&#34;Max error&#34;: 183.0,
&#34;Mean absolute error&#34;: 126.5,
&#34;Mean squared error&#34;: 19194.5,
&#34;Median absolute error&#34;: 126.5,
&#34;R2&#34;: -5.012843605607331,
},
&#34;Upper bound&#34;: {
&#34;Explained variance&#34;: 0.0,
&#34;Max error&#34;: 183.0,
&#34;Mean absolute error&#34;: 126.5,
&#34;Mean squared error&#34;: 19194.5,
&#34;Median absolute error&#34;: 126.5,
&#34;R2&#34;: -5.012843605607331,
},
}</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_objective.test_usage"><code class="name flex">
<span>def <span class="ident">test_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_usage():
instances, models = get_test_pyomo_instances()
comp = ObjectiveValueComponent()
comp.fit(instances)
assert instances[0].training_data[0][&#34;Lower bound&#34;] == 1183.0
assert instances[0].training_data[0][&#34;Upper bound&#34;] == 1183.0
assert np.round(comp.predict(instances), 2).tolist() == [
[1183.0, 1183.0],
[1070.0, 1070.0],
]</code></pre>
</details>
</dd>
</dl>
</section>
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</section>
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<ul class="">
<li><code><a title="miplearn.components.tests.test_objective.test_obj_evaluate" href="#miplearn.components.tests.test_objective.test_obj_evaluate">test_obj_evaluate</a></code></li>
<li><code><a title="miplearn.components.tests.test_objective.test_usage" href="#miplearn.components.tests.test_objective.test_usage">test_usage</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.components.tests.test_primal</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
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 &#34;x&#34; in solution
assert 0 in solution[&#34;x&#34;]
assert 1 in solution[&#34;x&#34;]
assert 2 in solution[&#34;x&#34;]
assert 3 in solution[&#34;x&#34;]
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]) == {&#34;x&#34;: {0: 0, 1: 0, 2: 1, 3: None}}
assert instances[0].training_data[0][&#34;Solution&#34;] == {&#34;x&#34;: {0: 1, 1: 0, 2: 1, 3: 1}}
ev = comp.evaluate(instances[:1])
assert ev == {
&#34;Fix one&#34;: {
0: {
&#34;Accuracy&#34;: 0.5,
&#34;Condition negative&#34;: 1,
&#34;Condition negative (%)&#34;: 25.0,
&#34;Condition positive&#34;: 3,
&#34;Condition positive (%)&#34;: 75.0,
&#34;F1 score&#34;: 0.5,
&#34;False negative&#34;: 2,
&#34;False negative (%)&#34;: 50.0,
&#34;False positive&#34;: 0,
&#34;False positive (%)&#34;: 0.0,
&#34;Precision&#34;: 1.0,
&#34;Predicted negative&#34;: 3,
&#34;Predicted negative (%)&#34;: 75.0,
&#34;Predicted positive&#34;: 1,
&#34;Predicted positive (%)&#34;: 25.0,
&#34;Recall&#34;: 0.3333333333333333,
&#34;True negative&#34;: 1,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive&#34;: 1,
&#34;True positive (%)&#34;: 25.0,
}
},
&#34;Fix zero&#34;: {
0: {
&#34;Accuracy&#34;: 0.75,
&#34;Condition negative&#34;: 3,
&#34;Condition negative (%)&#34;: 75.0,
&#34;Condition positive&#34;: 1,
&#34;Condition positive (%)&#34;: 25.0,
&#34;F1 score&#34;: 0.6666666666666666,
&#34;False negative&#34;: 0,
&#34;False negative (%)&#34;: 0.0,
&#34;False positive&#34;: 1,
&#34;False positive (%)&#34;: 25.0,
&#34;Precision&#34;: 0.5,
&#34;Predicted negative&#34;: 2,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Predicted positive&#34;: 2,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Recall&#34;: 1.0,
&#34;True negative&#34;: 2,
&#34;True negative (%)&#34;: 50.0,
&#34;True positive&#34;: 1,
&#34;True positive (%)&#34;: 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</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.components.tests.test_primal.test_evaluate"><code class="name flex">
<span>def <span class="ident">test_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_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]) == {&#34;x&#34;: {0: 0, 1: 0, 2: 1, 3: None}}
assert instances[0].training_data[0][&#34;Solution&#34;] == {&#34;x&#34;: {0: 1, 1: 0, 2: 1, 3: 1}}
ev = comp.evaluate(instances[:1])
assert ev == {
&#34;Fix one&#34;: {
0: {
&#34;Accuracy&#34;: 0.5,
&#34;Condition negative&#34;: 1,
&#34;Condition negative (%)&#34;: 25.0,
&#34;Condition positive&#34;: 3,
&#34;Condition positive (%)&#34;: 75.0,
&#34;F1 score&#34;: 0.5,
&#34;False negative&#34;: 2,
&#34;False negative (%)&#34;: 50.0,
&#34;False positive&#34;: 0,
&#34;False positive (%)&#34;: 0.0,
&#34;Precision&#34;: 1.0,
&#34;Predicted negative&#34;: 3,
&#34;Predicted negative (%)&#34;: 75.0,
&#34;Predicted positive&#34;: 1,
&#34;Predicted positive (%)&#34;: 25.0,
&#34;Recall&#34;: 0.3333333333333333,
&#34;True negative&#34;: 1,
&#34;True negative (%)&#34;: 25.0,
&#34;True positive&#34;: 1,
&#34;True positive (%)&#34;: 25.0,
}
},
&#34;Fix zero&#34;: {
0: {
&#34;Accuracy&#34;: 0.75,
&#34;Condition negative&#34;: 3,
&#34;Condition negative (%)&#34;: 75.0,
&#34;Condition positive&#34;: 1,
&#34;Condition positive (%)&#34;: 25.0,
&#34;F1 score&#34;: 0.6666666666666666,
&#34;False negative&#34;: 0,
&#34;False negative (%)&#34;: 0.0,
&#34;False positive&#34;: 1,
&#34;False positive (%)&#34;: 25.0,
&#34;Precision&#34;: 0.5,
&#34;Predicted negative&#34;: 2,
&#34;Predicted negative (%)&#34;: 50.0,
&#34;Predicted positive&#34;: 2,
&#34;Predicted positive (%)&#34;: 50.0,
&#34;Recall&#34;: 1.0,
&#34;True negative&#34;: 2,
&#34;True negative (%)&#34;: 50.0,
&#34;True positive&#34;: 1,
&#34;True positive (%)&#34;: 25.0,
}
},
}</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_primal.test_predict"><code class="name flex">
<span>def <span class="ident">test_predict</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_predict():
instances, models = get_test_pyomo_instances()
comp = PrimalSolutionComponent()
comp.fit(instances)
solution = comp.predict(instances[0])
assert &#34;x&#34; in solution
assert 0 in solution[&#34;x&#34;]
assert 1 in solution[&#34;x&#34;]
assert 2 in solution[&#34;x&#34;]
assert 3 in solution[&#34;x&#34;]</code></pre>
</details>
</dd>
<dt id="miplearn.components.tests.test_primal.test_primal_parallel_fit"><code class="name flex">
<span>def <span class="ident">test_primal_parallel_fit</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_primal_parallel_fit():
instances, models = get_test_pyomo_instances()
comp = PrimalSolutionComponent()
comp.fit(instances, n_jobs=2)
assert len(comp.classifiers) == 2</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.tests" href="index.html">miplearn.components.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.components.tests.test_primal.test_evaluate" href="#miplearn.components.tests.test_primal.test_evaluate">test_evaluate</a></code></li>
<li><code><a title="miplearn.components.tests.test_primal.test_predict" href="#miplearn.components.tests.test_primal.test_predict">test_predict</a></code></li>
<li><code><a title="miplearn.components.tests.test_primal.test_primal_parallel_fit" href="#miplearn.components.tests.test_primal.test_primal_parallel_fit">test_primal_parallel_fit</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.extractors</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.
import gzip
import logging
import pickle
from abc import ABC, abstractmethod
import numpy as np
from tqdm.auto import tqdm
logger = logging.getLogger(__name__)
class InstanceIterator:
def __init__(self, instances):
self.instances = instances
self.current = 0
def __iter__(self):
return self
def __next__(self):
if self.current &gt;= len(self.instances):
raise StopIteration
result = self.instances[self.current]
self.current += 1
if isinstance(result, str):
logger.debug(&#34;Read: %s&#34; % result)
try:
if result.endswith(&#34;.gz&#34;):
with gzip.GzipFile(result, &#34;rb&#34;) as file:
result = pickle.load(file)
else:
with open(result, &#34;rb&#34;) as file:
result = pickle.load(file)
except pickle.UnpicklingError:
raise Exception(f&#34;Invalid instance file: {result}&#34;)
return result
class Extractor(ABC):
@abstractmethod
def extract(self, instances):
pass
@staticmethod
def split_variables(instance):
result = {}
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for var_name in lp_solution:
for index in lp_solution[var_name]:
category = instance.get_variable_category(var_name, index)
if category is None:
continue
if category not in result:
result[category] = []
result[category] += [(var_name, index)]
return result
class VariableFeaturesExtractor(Extractor):
def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (vars)&#34;,
disable=len(instances) &lt; 5,
):
instance_features = instance.get_instance_features()
var_split = self.split_variables(instance)
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
result[category] += [
instance_features.tolist()
+ instance.get_variable_features(var_name, index).tolist()
+ [lp_solution[var_name][index]]
]
for category in result:
result[category] = np.array(result[category])
return result
class SolutionExtractor(Extractor):
def __init__(self, relaxation=False):
self.relaxation = relaxation
def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (solution)&#34;,
disable=len(instances) &lt; 5,
):
var_split = self.split_variables(instance)
if self.relaxation:
solution = instance.training_data[0][&#34;LP solution&#34;]
else:
solution = instance.training_data[0][&#34;Solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
v = solution[var_name][index]
if v is None:
result[category] += [[0, 0]]
else:
result[category] += [[1 - v, v]]
for category in result:
result[category] = np.array(result[category])
return result
class InstanceFeaturesExtractor(Extractor):
def extract(self, instances):
return np.vstack(
[
np.hstack(
[
instance.get_instance_features(),
instance.training_data[0][&#34;LP value&#34;],
]
)
for instance in InstanceIterator(instances)
]
)
class ObjectiveValueExtractor(Extractor):
def __init__(self, kind=&#34;lp&#34;):
assert kind in [&#34;lower bound&#34;, &#34;upper bound&#34;, &#34;lp&#34;]
self.kind = kind
def extract(self, instances):
if self.kind == &#34;lower bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Lower bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;upper bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Upper bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;lp&#34;:
return np.array(
[
[instance.training_data[0][&#34;LP value&#34;]]
for instance in InstanceIterator(instances)
]
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.extractors.Extractor"><code class="flex name class">
<span>class <span class="ident">Extractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Extractor(ABC):
@abstractmethod
def extract(self, instances):
pass
@staticmethod
def split_variables(instance):
result = {}
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for var_name in lp_solution:
for index in lp_solution[var_name]:
category = instance.get_variable_category(var_name, index)
if category is None:
continue
if category not in result:
result[category] = []
result[category] += [(var_name, index)]
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></li>
<li><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></li>
<li><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></li>
<li><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></li>
</ul>
<h3>Static methods</h3>
<dl>
<dt id="miplearn.extractors.Extractor.split_variables"><code class="name flex">
<span>def <span class="ident">split_variables</span></span>(<span>instance)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@staticmethod
def split_variables(instance):
result = {}
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for var_name in lp_solution:
for index in lp_solution[var_name]:
category = instance.get_variable_category(var_name, index)
if category is None:
continue
if category not in result:
result[category] = []
result[category] += [(var_name, index)]
return result</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="miplearn.extractors.Extractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def extract(self, instances):
pass</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.extractors.InstanceFeaturesExtractor"><code class="flex name class">
<span>class <span class="ident">InstanceFeaturesExtractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class InstanceFeaturesExtractor(Extractor):
def extract(self, instances):
return np.vstack(
[
np.hstack(
[
instance.get_instance_features(),
instance.training_data[0][&#34;LP value&#34;],
]
)
for instance in InstanceIterator(instances)
]
)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.extractors.InstanceFeaturesExtractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract(self, instances):
return np.vstack(
[
np.hstack(
[
instance.get_instance_features(),
instance.training_data[0][&#34;LP value&#34;],
]
)
for instance in InstanceIterator(instances)
]
)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.extractors.InstanceIterator"><code class="flex name class">
<span>class <span class="ident">InstanceIterator</span></span>
<span>(</span><span>instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class InstanceIterator:
def __init__(self, instances):
self.instances = instances
self.current = 0
def __iter__(self):
return self
def __next__(self):
if self.current &gt;= len(self.instances):
raise StopIteration
result = self.instances[self.current]
self.current += 1
if isinstance(result, str):
logger.debug(&#34;Read: %s&#34; % result)
try:
if result.endswith(&#34;.gz&#34;):
with gzip.GzipFile(result, &#34;rb&#34;) as file:
result = pickle.load(file)
else:
with open(result, &#34;rb&#34;) as file:
result = pickle.load(file)
except pickle.UnpicklingError:
raise Exception(f&#34;Invalid instance file: {result}&#34;)
return result</code></pre>
</details>
</dd>
<dt id="miplearn.extractors.ObjectiveValueExtractor"><code class="flex name class">
<span>class <span class="ident">ObjectiveValueExtractor</span></span>
<span>(</span><span>kind='lp')</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ObjectiveValueExtractor(Extractor):
def __init__(self, kind=&#34;lp&#34;):
assert kind in [&#34;lower bound&#34;, &#34;upper bound&#34;, &#34;lp&#34;]
self.kind = kind
def extract(self, instances):
if self.kind == &#34;lower bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Lower bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;upper bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Upper bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;lp&#34;:
return np.array(
[
[instance.training_data[0][&#34;LP value&#34;]]
for instance in InstanceIterator(instances)
]
)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.extractors.ObjectiveValueExtractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract(self, instances):
if self.kind == &#34;lower bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Lower bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;upper bound&#34;:
return np.array(
[
[instance.training_data[0][&#34;Upper bound&#34;]]
for instance in InstanceIterator(instances)
]
)
if self.kind == &#34;lp&#34;:
return np.array(
[
[instance.training_data[0][&#34;LP value&#34;]]
for instance in InstanceIterator(instances)
]
)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.extractors.SolutionExtractor"><code class="flex name class">
<span>class <span class="ident">SolutionExtractor</span></span>
<span>(</span><span>relaxation=False)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class SolutionExtractor(Extractor):
def __init__(self, relaxation=False):
self.relaxation = relaxation
def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (solution)&#34;,
disable=len(instances) &lt; 5,
):
var_split = self.split_variables(instance)
if self.relaxation:
solution = instance.training_data[0][&#34;LP solution&#34;]
else:
solution = instance.training_data[0][&#34;Solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
v = solution[var_name][index]
if v is None:
result[category] += [[0, 0]]
else:
result[category] += [[1 - v, v]]
for category in result:
result[category] = np.array(result[category])
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.extractors.SolutionExtractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (solution)&#34;,
disable=len(instances) &lt; 5,
):
var_split = self.split_variables(instance)
if self.relaxation:
solution = instance.training_data[0][&#34;LP solution&#34;]
else:
solution = instance.training_data[0][&#34;Solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
v = solution[var_name][index]
if v is None:
result[category] += [[0, 0]]
else:
result[category] += [[1 - v, v]]
for category in result:
result[category] = np.array(result[category])
return result</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.extractors.VariableFeaturesExtractor"><code class="flex name class">
<span>class <span class="ident">VariableFeaturesExtractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
inheritance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class VariableFeaturesExtractor(Extractor):
def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (vars)&#34;,
disable=len(instances) &lt; 5,
):
instance_features = instance.get_instance_features()
var_split = self.split_variables(instance)
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
result[category] += [
instance_features.tolist()
+ instance.get_variable_features(var_name, index).tolist()
+ [lp_solution[var_name][index]]
]
for category in result:
result[category] = np.array(result[category])
return result</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.extractors.VariableFeaturesExtractor.extract"><code class="name flex">
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract(self, instances):
result = {}
for instance in tqdm(
InstanceIterator(instances),
desc=&#34;Extract (vars)&#34;,
disable=len(instances) &lt; 5,
):
instance_features = instance.get_instance_features()
var_split = self.split_variables(instance)
lp_solution = instance.training_data[0][&#34;LP solution&#34;]
for (category, var_index_pairs) in var_split.items():
if category not in result:
result[category] = []
for (var_name, index) in var_index_pairs:
result[category] += [
instance_features.tolist()
+ instance.get_variable_features(var_name, index).tolist()
+ [lp_solution[var_name][index]]
]
for category in result:
result[category] = np.array(result[category])
return result</code></pre>
</details>
</dd>
</dl>
</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" href="index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.extractors.Extractor.extract" href="#miplearn.extractors.Extractor.extract">extract</a></code></li>
<li><code><a title="miplearn.extractors.Extractor.split_variables" href="#miplearn.extractors.Extractor.split_variables">split_variables</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.extractors.InstanceFeaturesExtractor.extract" href="#miplearn.extractors.InstanceFeaturesExtractor.extract">extract</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.extractors.InstanceIterator" href="#miplearn.extractors.InstanceIterator">InstanceIterator</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.extractors.ObjectiveValueExtractor.extract" href="#miplearn.extractors.ObjectiveValueExtractor.extract">extract</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.extractors.SolutionExtractor.extract" href="#miplearn.extractors.SolutionExtractor.extract">extract</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></code></h4>
<ul class="">
<li><code><a title="miplearn.extractors.VariableFeaturesExtractor.extract" href="#miplearn.extractors.VariableFeaturesExtractor.extract">extract</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn</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 .benchmark import BenchmarkRunner
from .classifiers import Classifier, Regressor
from .classifiers.adaptive import AdaptiveClassifier
from .classifiers.threshold import MinPrecisionThreshold
from .components.component import Component
from .components.cuts import UserCutsComponent
from .components.lazy_dynamic import DynamicLazyConstraintsComponent
from .components.lazy_static import StaticLazyConstraintsComponent
from .components.objective import ObjectiveValueComponent
from .components.primal import PrimalSolutionComponent
from .components.relaxation import RelaxationComponent
from .components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
from .components.steps.drop_redundant import DropRedundantInequalitiesStep
from .components.steps.relax_integrality import RelaxIntegralityStep
from .extractors import (
SolutionExtractor,
InstanceFeaturesExtractor,
ObjectiveValueExtractor,
VariableFeaturesExtractor,
)
from .instance import Instance
from .log import setup_logger
from .solvers.gurobi import GurobiSolver
from .solvers.internal import InternalSolver
from .solvers.learning import LearningSolver
from .solvers.pyomo.base import BasePyomoSolver
from .solvers.pyomo.cplex import CplexPyomoSolver
from .solvers.pyomo.gurobi import GurobiPyomoSolver</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.benchmark" href="benchmark.html">miplearn.benchmark</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.classifiers" href="classifiers/index.html">miplearn.classifiers</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.components" href="components/index.html">miplearn.components</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.extractors" href="extractors.html">miplearn.extractors</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.instance" href="instance.html">miplearn.instance</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.log" href="log.html">miplearn.log</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.problems" href="problems/index.html">miplearn.problems</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers" href="solvers/index.html">miplearn.solvers</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.tests" href="tests/index.html">miplearn.tests</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.types" href="types.html">miplearn.types</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
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</section>
<section>
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<li><h3><a href="#header-submodules">Sub-modules</a></h3>
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<li><code><a title="miplearn.benchmark" href="benchmark.html">miplearn.benchmark</a></code></li>
<li><code><a title="miplearn.classifiers" href="classifiers/index.html">miplearn.classifiers</a></code></li>
<li><code><a title="miplearn.components" href="components/index.html">miplearn.components</a></code></li>
<li><code><a title="miplearn.extractors" href="extractors.html">miplearn.extractors</a></code></li>
<li><code><a title="miplearn.instance" href="instance.html">miplearn.instance</a></code></li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.instance</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.
import gzip
import json
from abc import ABC, abstractmethod
from typing import Any, List
import numpy as np
from miplearn.types import TrainingSample
class Instance(ABC):
&#34;&#34;&#34;
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.
&#34;&#34;&#34;
def __init__(self):
self.training_data: List[TrainingSample] = []
@abstractmethod
def to_model(self) -&gt; Any:
&#34;&#34;&#34;
Returns the optimization model corresponding to this instance.
&#34;&#34;&#34;
pass
def get_instance_features(self):
&#34;&#34;&#34;
Returns a 1-dimensional Numpy array of (numerical) features describing the
entire instance.
The array is used by LearningSolver to determine how similar two instances
are. It may also be used to predict, in combination with variable-specific
features, the values of binary decision variables in the problem.
There is not necessarily a one-to-one correspondence between models and
instance features: the features may encode only part of the data necessary to
generate the complete model. Features may also be statistics computed from
the original data. For example, in the knapsack problem, an implementation
may decide to provide as instance features only the average weights, average
prices, number of items and the size of the knapsack.
The returned array MUST have the same length for all relevant instances of
the problem. If two instances map into arrays of different lengths,
they cannot be solved by the same LearningSolver object.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)
def get_variable_features(self, var, index):
&#34;&#34;&#34;
Returns a 1-dimensional array of (numerical) features describing a particular
decision variable.
The argument `var` is a pyomo.core.Var object, which represents a collection
of decision variables. The argument `index` specifies which variable in the
collection is the relevant one.
In combination with instance features, variable features are used by
LearningSolver to predict, among other things, the optimal value of each
decision variable before the optimization takes place. In the knapsack
problem, for example, an implementation could provide as variable features
the weight and the price of a specific item.
Like instance features, the arrays returned by this method MUST have the same
length for all variables within the same category, for all relevant instances
of the problem.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)
def get_variable_category(self, var, index):
&#34;&#34;&#34;
Returns the category (a string, an integer or any hashable type) for each
decision variable.
If two variables have the same category, LearningSolver will use the same
internal ML model to predict the values of both variables. If the returned
category is None, ML models will ignore the variable.
By default, returns &#34;default&#34;.
&#34;&#34;&#34;
return &#34;default&#34;
def get_constraint_features(self, cid):
return np.zeros(1)
def get_constraint_category(self, cid):
return cid
def has_static_lazy_constraints(self):
return False
def has_dynamic_lazy_constraints(self):
return False
def is_constraint_lazy(self, cid):
return False
def find_violated_lazy_constraints(self, model):
&#34;&#34;&#34;
Returns lazy constraint violations found for the current solution.
After solving a model, LearningSolver will ask the instance to identify which
lazy constraints are violated by the current solution. For each identified
violation, LearningSolver will then call the build_lazy_constraint, add the
generated Pyomo constraint to the model, then resolve the problem. The
process repeats until no further lazy constraint violations are found.
Each &#34;violation&#34; is simply a string, a tuple or any other hashable type which
allows the instance to identify unambiguously which lazy constraint should be
generated. In the Traveling Salesman Problem, for example, a subtour
violation could be a frozen set containing the cities in the subtour.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
return []
def build_lazy_constraint(self, model, violation):
&#34;&#34;&#34;
Returns a Pyomo constraint which fixes a given violation.
This method is typically called immediately after
find_violated_lazy_constraints. The violation object provided to this method
is exactly the same object returned earlier by
find_violated_lazy_constraints. After some training, LearningSolver may
decide to proactively build some lazy constraints at the beginning of the
optimization process, before a solution is even available. In this case,
build_lazy_constraints will be called without a corresponding call to
find_violated_lazy_constraints.
The implementation should not directly add the constraint to the model. The
constraint will be added by LearningSolver after the method returns.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
pass
def find_violated_user_cuts(self, model):
return []
def build_user_cut(self, model, violation):
pass
def load(self, filename):
with gzip.GzipFile(filename, &#34;r&#34;) as f:
data = json.loads(f.read().decode(&#34;utf-8&#34;))
self.__dict__ = data
def dump(self, filename):
data = json.dumps(self.__dict__, indent=2).encode(&#34;utf-8&#34;)
with gzip.GzipFile(filename, &#34;w&#34;) as f:
f.write(data)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.instance.Instance"><code class="flex name class">
<span>class <span class="ident">Instance</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 Instance(ABC):
&#34;&#34;&#34;
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.
&#34;&#34;&#34;
def __init__(self):
self.training_data: List[TrainingSample] = []
@abstractmethod
def to_model(self) -&gt; Any:
&#34;&#34;&#34;
Returns the optimization model corresponding to this instance.
&#34;&#34;&#34;
pass
def get_instance_features(self):
&#34;&#34;&#34;
Returns a 1-dimensional Numpy array of (numerical) features describing the
entire instance.
The array is used by LearningSolver to determine how similar two instances
are. It may also be used to predict, in combination with variable-specific
features, the values of binary decision variables in the problem.
There is not necessarily a one-to-one correspondence between models and
instance features: the features may encode only part of the data necessary to
generate the complete model. Features may also be statistics computed from
the original data. For example, in the knapsack problem, an implementation
may decide to provide as instance features only the average weights, average
prices, number of items and the size of the knapsack.
The returned array MUST have the same length for all relevant instances of
the problem. If two instances map into arrays of different lengths,
they cannot be solved by the same LearningSolver object.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)
def get_variable_features(self, var, index):
&#34;&#34;&#34;
Returns a 1-dimensional array of (numerical) features describing a particular
decision variable.
The argument `var` is a pyomo.core.Var object, which represents a collection
of decision variables. The argument `index` specifies which variable in the
collection is the relevant one.
In combination with instance features, variable features are used by
LearningSolver to predict, among other things, the optimal value of each
decision variable before the optimization takes place. In the knapsack
problem, for example, an implementation could provide as variable features
the weight and the price of a specific item.
Like instance features, the arrays returned by this method MUST have the same
length for all variables within the same category, for all relevant instances
of the problem.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)
def get_variable_category(self, var, index):
&#34;&#34;&#34;
Returns the category (a string, an integer or any hashable type) for each
decision variable.
If two variables have the same category, LearningSolver will use the same
internal ML model to predict the values of both variables. If the returned
category is None, ML models will ignore the variable.
By default, returns &#34;default&#34;.
&#34;&#34;&#34;
return &#34;default&#34;
def get_constraint_features(self, cid):
return np.zeros(1)
def get_constraint_category(self, cid):
return cid
def has_static_lazy_constraints(self):
return False
def has_dynamic_lazy_constraints(self):
return False
def is_constraint_lazy(self, cid):
return False
def find_violated_lazy_constraints(self, model):
&#34;&#34;&#34;
Returns lazy constraint violations found for the current solution.
After solving a model, LearningSolver will ask the instance to identify which
lazy constraints are violated by the current solution. For each identified
violation, LearningSolver will then call the build_lazy_constraint, add the
generated Pyomo constraint to the model, then resolve the problem. The
process repeats until no further lazy constraint violations are found.
Each &#34;violation&#34; is simply a string, a tuple or any other hashable type which
allows the instance to identify unambiguously which lazy constraint should be
generated. In the Traveling Salesman Problem, for example, a subtour
violation could be a frozen set containing the cities in the subtour.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
return []
def build_lazy_constraint(self, model, violation):
&#34;&#34;&#34;
Returns a Pyomo constraint which fixes a given violation.
This method is typically called immediately after
find_violated_lazy_constraints. The violation object provided to this method
is exactly the same object returned earlier by
find_violated_lazy_constraints. After some training, LearningSolver may
decide to proactively build some lazy constraints at the beginning of the
optimization process, before a solution is even available. In this case,
build_lazy_constraints will be called without a corresponding call to
find_violated_lazy_constraints.
The implementation should not directly add the constraint to the model. The
constraint will be added by LearningSolver after the method returns.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
pass
def find_violated_user_cuts(self, model):
return []
def build_user_cut(self, model, violation):
pass
def load(self, filename):
with gzip.GzipFile(filename, &#34;r&#34;) as f:
data = json.loads(f.read().decode(&#34;utf-8&#34;))
self.__dict__ = data
def dump(self, filename):
data = json.dumps(self.__dict__, indent=2).encode(&#34;utf-8&#34;)
with gzip.GzipFile(filename, &#34;w&#34;) as f:
f.write(data)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></li>
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
<li><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></li>
<li><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></li>
<li><a title="miplearn.problems.stab.MaxWeightStableSetInstance" href="problems/stab.html#miplearn.problems.stab.MaxWeightStableSetInstance">MaxWeightStableSetInstance</a></li>
<li><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="problems/tsp.html#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></li>
<li><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="components/steps/tests/test_convert_tight.html#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.instance.Instance.build_lazy_constraint"><code class="name flex">
<span>def <span class="ident">build_lazy_constraint</span></span>(<span>self, model, violation)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns a Pyomo constraint which fixes a given violation.</p>
<p>This method is typically called immediately after
find_violated_lazy_constraints. The violation object provided to this method
is exactly the same object returned earlier by
find_violated_lazy_constraints. After some training, LearningSolver may
decide to proactively build some lazy constraints at the beginning of the
optimization process, before a solution is even available. In this case,
build_lazy_constraints will be called without a corresponding call to
find_violated_lazy_constraints.</p>
<p>The implementation should not directly add the constraint to the model. The
constraint will be added by LearningSolver after the method returns.</p>
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def build_lazy_constraint(self, model, violation):
&#34;&#34;&#34;
Returns a Pyomo constraint which fixes a given violation.
This method is typically called immediately after
find_violated_lazy_constraints. The violation object provided to this method
is exactly the same object returned earlier by
find_violated_lazy_constraints. After some training, LearningSolver may
decide to proactively build some lazy constraints at the beginning of the
optimization process, before a solution is even available. In this case,
build_lazy_constraints will be called without a corresponding call to
find_violated_lazy_constraints.
The implementation should not directly add the constraint to the model. The
constraint will be added by LearningSolver after the method returns.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
pass</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.build_user_cut"><code class="name flex">
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def build_user_cut(self, model, violation):
pass</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.dump"><code class="name flex">
<span>def <span class="ident">dump</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def dump(self, filename):
data = json.dumps(self.__dict__, indent=2).encode(&#34;utf-8&#34;)
with gzip.GzipFile(filename, &#34;w&#34;) as f:
f.write(data)</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.find_violated_lazy_constraints"><code class="name flex">
<span>def <span class="ident">find_violated_lazy_constraints</span></span>(<span>self, model)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns lazy constraint violations found for the current solution.</p>
<p>After solving a model, LearningSolver will ask the instance to identify which
lazy constraints are violated by the current solution. For each identified
violation, LearningSolver will then call the build_lazy_constraint, add the
generated Pyomo constraint to the model, then resolve the problem. The
process repeats until no further lazy constraint violations are found.</p>
<p>Each "violation" is simply a string, a tuple or any other hashable type which
allows the instance to identify unambiguously which lazy constraint should be
generated. In the Traveling Salesman Problem, for example, a subtour
violation could be a frozen set containing the cities in the subtour.</p>
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_violated_lazy_constraints(self, model):
&#34;&#34;&#34;
Returns lazy constraint violations found for the current solution.
After solving a model, LearningSolver will ask the instance to identify which
lazy constraints are violated by the current solution. For each identified
violation, LearningSolver will then call the build_lazy_constraint, add the
generated Pyomo constraint to the model, then resolve the problem. The
process repeats until no further lazy constraint violations are found.
Each &#34;violation&#34; is simply a string, a tuple or any other hashable type which
allows the instance to identify unambiguously which lazy constraint should be
generated. In the Traveling Salesman Problem, for example, a subtour
violation could be a frozen set containing the cities in the subtour.
For a concrete example, see TravelingSalesmanInstance.
&#34;&#34;&#34;
return []</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.find_violated_user_cuts"><code class="name flex">
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_violated_user_cuts(self, model):
return []</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.get_constraint_category"><code class="name flex">
<span>def <span class="ident">get_constraint_category</span></span>(<span>self, cid)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_constraint_category(self, cid):
return cid</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.get_constraint_features"><code class="name flex">
<span>def <span class="ident">get_constraint_features</span></span>(<span>self, cid)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_constraint_features(self, cid):
return np.zeros(1)</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.get_instance_features"><code class="name flex">
<span>def <span class="ident">get_instance_features</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns a 1-dimensional Numpy array of (numerical) features describing the
entire instance.</p>
<p>The array is used by LearningSolver to determine how similar two instances
are. It may also be used to predict, in combination with variable-specific
features, the values of binary decision variables in the problem.</p>
<p>There is not necessarily a one-to-one correspondence between models and
instance features: the features may encode only part of the data necessary to
generate the complete model. Features may also be statistics computed from
the original data. For example, in the knapsack problem, an implementation
may decide to provide as instance features only the average weights, average
prices, number of items and the size of the knapsack.</p>
<p>The returned array MUST have the same length for all relevant instances of
the problem. If two instances map into arrays of different lengths,
they cannot be solved by the same LearningSolver object.</p>
<p>By default, returns [0].</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_instance_features(self):
&#34;&#34;&#34;
Returns a 1-dimensional Numpy array of (numerical) features describing the
entire instance.
The array is used by LearningSolver to determine how similar two instances
are. It may also be used to predict, in combination with variable-specific
features, the values of binary decision variables in the problem.
There is not necessarily a one-to-one correspondence between models and
instance features: the features may encode only part of the data necessary to
generate the complete model. Features may also be statistics computed from
the original data. For example, in the knapsack problem, an implementation
may decide to provide as instance features only the average weights, average
prices, number of items and the size of the knapsack.
The returned array MUST have the same length for all relevant instances of
the problem. If two instances map into arrays of different lengths,
they cannot be solved by the same LearningSolver object.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.get_variable_category"><code class="name flex">
<span>def <span class="ident">get_variable_category</span></span>(<span>self, var, index)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns the category (a string, an integer or any hashable type) for each
decision variable.</p>
<p>If two variables have the same category, LearningSolver will use the same
internal ML model to predict the values of both variables. If the returned
category is None, ML models will ignore the variable.</p>
<p>By default, returns "default".</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_variable_category(self, var, index):
&#34;&#34;&#34;
Returns the category (a string, an integer or any hashable type) for each
decision variable.
If two variables have the same category, LearningSolver will use the same
internal ML model to predict the values of both variables. If the returned
category is None, ML models will ignore the variable.
By default, returns &#34;default&#34;.
&#34;&#34;&#34;
return &#34;default&#34;</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.get_variable_features"><code class="name flex">
<span>def <span class="ident">get_variable_features</span></span>(<span>self, var, index)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns a 1-dimensional array of (numerical) features describing a particular
decision variable.</p>
<p>The argument <code>var</code> is a pyomo.core.Var object, which represents a collection
of decision variables. The argument <code>index</code> specifies which variable in the
collection is the relevant one.</p>
<p>In combination with instance features, variable features are used by
LearningSolver to predict, among other things, the optimal value of each
decision variable before the optimization takes place. In the knapsack
problem, for example, an implementation could provide as variable features
the weight and the price of a specific item.</p>
<p>Like instance features, the arrays returned by this method MUST have the same
length for all variables within the same category, for all relevant instances
of the problem.</p>
<p>By default, returns [0].</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_variable_features(self, var, index):
&#34;&#34;&#34;
Returns a 1-dimensional array of (numerical) features describing a particular
decision variable.
The argument `var` is a pyomo.core.Var object, which represents a collection
of decision variables. The argument `index` specifies which variable in the
collection is the relevant one.
In combination with instance features, variable features are used by
LearningSolver to predict, among other things, the optimal value of each
decision variable before the optimization takes place. In the knapsack
problem, for example, an implementation could provide as variable features
the weight and the price of a specific item.
Like instance features, the arrays returned by this method MUST have the same
length for all variables within the same category, for all relevant instances
of the problem.
By default, returns [0].
&#34;&#34;&#34;
return np.zeros(1)</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.has_dynamic_lazy_constraints"><code class="name flex">
<span>def <span class="ident">has_dynamic_lazy_constraints</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def has_dynamic_lazy_constraints(self):
return False</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.has_static_lazy_constraints"><code class="name flex">
<span>def <span class="ident">has_static_lazy_constraints</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def has_static_lazy_constraints(self):
return False</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.is_constraint_lazy"><code class="name flex">
<span>def <span class="ident">is_constraint_lazy</span></span>(<span>self, cid)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def is_constraint_lazy(self, cid):
return False</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.load"><code class="name flex">
<span>def <span class="ident">load</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load(self, filename):
with gzip.GzipFile(filename, &#34;r&#34;) as f:
data = json.loads(f.read().decode(&#34;utf-8&#34;))
self.__dict__ = data</code></pre>
</details>
</dd>
<dt id="miplearn.instance.Instance.to_model"><code class="name flex">
<span>def <span class="ident">to_model</span></span>(<span>self)</span>
</code></dt>
<dd>
<section class="desc"><p>Returns the optimization model corresponding to this instance.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def to_model(self) -&gt; Any:
&#34;&#34;&#34;
Returns the optimization model corresponding to this instance.
&#34;&#34;&#34;
pass</code></pre>
</details>
</dd>
</dl>
</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" href="index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.instance.Instance" href="#miplearn.instance.Instance">Instance</a></code></h4>
<ul class="">
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
<li><code><a title="miplearn.instance.Instance.build_user_cut" href="#miplearn.instance.Instance.build_user_cut">build_user_cut</a></code></li>
<li><code><a title="miplearn.instance.Instance.dump" href="#miplearn.instance.Instance.dump">dump</a></code></li>
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
<li><code><a title="miplearn.instance.Instance.find_violated_user_cuts" href="#miplearn.instance.Instance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
<li><code><a title="miplearn.instance.Instance.get_constraint_category" href="#miplearn.instance.Instance.get_constraint_category">get_constraint_category</a></code></li>
<li><code><a title="miplearn.instance.Instance.get_constraint_features" href="#miplearn.instance.Instance.get_constraint_features">get_constraint_features</a></code></li>
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
<li><code><a title="miplearn.instance.Instance.has_dynamic_lazy_constraints" href="#miplearn.instance.Instance.has_dynamic_lazy_constraints">has_dynamic_lazy_constraints</a></code></li>
<li><code><a title="miplearn.instance.Instance.has_static_lazy_constraints" href="#miplearn.instance.Instance.has_static_lazy_constraints">has_static_lazy_constraints</a></code></li>
<li><code><a title="miplearn.instance.Instance.is_constraint_lazy" href="#miplearn.instance.Instance.is_constraint_lazy">is_constraint_lazy</a></code></li>
<li><code><a title="miplearn.instance.Instance.load" href="#miplearn.instance.Instance.load">load</a></code></li>
<li><code><a title="miplearn.instance.Instance.to_model" href="#miplearn.instance.Instance.to_model">to_model</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.log</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.
import logging
import sys
import time
class TimeFormatter(logging.Formatter):
def __init__(self, start_time, log_colors):
super().__init__()
self.start_time = start_time
self.log_colors = log_colors
def format(self, record):
if record.levelno &gt;= logging.ERROR:
color = self.log_colors[&#34;red&#34;]
elif record.levelno &gt;= logging.WARNING:
color = self.log_colors[&#34;yellow&#34;]
else:
color = self.log_colors[&#34;green&#34;]
return &#34;%s[%12.3f]%s %s&#34; % (
color,
record.created - self.start_time,
self.log_colors[&#34;reset&#34;],
record.getMessage(),
)
def setup_logger(start_time=None, force_color=False):
if start_time is None:
start_time = time.time()
if sys.stdout.isatty() or force_color:
log_colors = {
&#34;green&#34;: &#34;\033[92m&#34;,
&#34;yellow&#34;: &#34;\033[93m&#34;,
&#34;red&#34;: &#34;\033[91m&#34;,
&#34;reset&#34;: &#34;\033[0m&#34;,
}
else:
log_colors = {
&#34;green&#34;: &#34;&#34;,
&#34;yellow&#34;: &#34;&#34;,
&#34;red&#34;: &#34;&#34;,
&#34;reset&#34;: &#34;&#34;,
}
handler = logging.StreamHandler()
handler.setFormatter(TimeFormatter(start_time, log_colors))
logging.getLogger().addHandler(handler)
logging.getLogger(&#34;miplearn&#34;).setLevel(logging.INFO)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.log.setup_logger"><code class="name flex">
<span>def <span class="ident">setup_logger</span></span>(<span>start_time=None, force_color=False)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def setup_logger(start_time=None, force_color=False):
if start_time is None:
start_time = time.time()
if sys.stdout.isatty() or force_color:
log_colors = {
&#34;green&#34;: &#34;\033[92m&#34;,
&#34;yellow&#34;: &#34;\033[93m&#34;,
&#34;red&#34;: &#34;\033[91m&#34;,
&#34;reset&#34;: &#34;\033[0m&#34;,
}
else:
log_colors = {
&#34;green&#34;: &#34;&#34;,
&#34;yellow&#34;: &#34;&#34;,
&#34;red&#34;: &#34;&#34;,
&#34;reset&#34;: &#34;&#34;,
}
handler = logging.StreamHandler()
handler.setFormatter(TimeFormatter(start_time, log_colors))
logging.getLogger().addHandler(handler)
logging.getLogger(&#34;miplearn&#34;).setLevel(logging.INFO)</code></pre>
</details>
</dd>
</dl>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.log.TimeFormatter"><code class="flex name class">
<span>class <span class="ident">TimeFormatter</span></span>
<span>(</span><span>start_time, log_colors)</span>
</code></dt>
<dd>
<section class="desc"><p>Formatter instances are used to convert a LogRecord to text.</p>
<p>Formatters need to know how a LogRecord is constructed. They are
responsible for converting a LogRecord to (usually) a string which can
be interpreted by either a human or an external system. The base Formatter
allows a formatting string to be specified. If none is supplied, the
the style-dependent default value, "%(message)s", "{message}", or
"${message}", is used.</p>
<p>The Formatter can be initialized with a format string which makes use of
knowledge of the LogRecord attributes - e.g. the default value mentioned
above makes use of the fact that the user's message and arguments are pre-
formatted into a LogRecord's message attribute. Currently, the useful
attributes in a LogRecord are described by:</p>
<p>%(name)s
Name of the logger (logging channel)
%(levelno)s
Numeric logging level for the message (DEBUG, INFO,
WARNING, ERROR, CRITICAL)
%(levelname)s
Text logging level for the message ("DEBUG", "INFO",
"WARNING", "ERROR", "CRITICAL")
%(pathname)s
Full pathname of the source file where the logging
call was issued (if available)
%(filename)s
Filename portion of pathname
%(module)s
Module (name portion of filename)
%(lineno)d
Source line number where the logging call was issued
(if available)
%(funcName)s
Function name
%(created)f
Time when the LogRecord was created (time.time()
return value)
%(asctime)s
Textual time when the LogRecord was created
%(msecs)d
Millisecond portion of the creation time
%(relativeCreated)d Time in milliseconds when the LogRecord was created,
relative to the time the logging module was loaded
(typically at application startup time)
%(thread)d
Thread ID (if available)
%(threadName)s
Thread name (if available)
%(process)d
Process ID (if available)
%(message)s
The result of record.getMessage(), computed just as
the record is emitted</p>
<p>Initialize the formatter with specified format strings.</p>
<p>Initialize the formatter either with the specified format string, or a
default as described above. Allow for specialized date formatting with
the optional datefmt argument. If datefmt is omitted, you get an
ISO8601-like (or RFC 3339-like) format.</p>
<p>Use a style parameter of '%', '{' or '$' to specify that you want to
use one of %-formatting, :meth:<code>str.format</code> (<code>{}</code>) formatting or
:class:<code>string.Template</code> formatting in your format string.</p>
<div class="admonition versionchanged">
<p class="admonition-title">Changed in version:&ensp;3.2</p>
<p>Added the <code>style</code> parameter.</p>
</div></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TimeFormatter(logging.Formatter):
def __init__(self, start_time, log_colors):
super().__init__()
self.start_time = start_time
self.log_colors = log_colors
def format(self, record):
if record.levelno &gt;= logging.ERROR:
color = self.log_colors[&#34;red&#34;]
elif record.levelno &gt;= logging.WARNING:
color = self.log_colors[&#34;yellow&#34;]
else:
color = self.log_colors[&#34;green&#34;]
return &#34;%s[%12.3f]%s %s&#34; % (
color,
record.created - self.start_time,
self.log_colors[&#34;reset&#34;],
record.getMessage(),
)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>logging.Formatter</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="miplearn.log.TimeFormatter.format"><code class="name flex">
<span>def <span class="ident">format</span></span>(<span>self, record)</span>
</code></dt>
<dd>
<section class="desc"><p>Format the specified record as text.</p>
<p>The record's attribute dictionary is used as the operand to a
string formatting operation which yields the returned string.
Before formatting the dictionary, a couple of preparatory steps
are carried out. The message attribute of the record is computed
using LogRecord.getMessage(). If the formatting string uses the
time (as determined by a call to usesTime(), formatTime() is
called to format the event time. If there is exception information,
it is formatted using formatException() and appended to the message.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def format(self, record):
if record.levelno &gt;= logging.ERROR:
color = self.log_colors[&#34;red&#34;]
elif record.levelno &gt;= logging.WARNING:
color = self.log_colors[&#34;yellow&#34;]
else:
color = self.log_colors[&#34;green&#34;]
return &#34;%s[%12.3f]%s %s&#34; % (
color,
record.created - self.start_time,
self.log_colors[&#34;reset&#34;],
record.getMessage(),
)</code></pre>
</details>
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</ul>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<li>
<h4><code><a title="miplearn.log.TimeFormatter" href="#miplearn.log.TimeFormatter">TimeFormatter</a></code></h4>
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<header>
<h1 class="title">Module <code>miplearn.problems</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.</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.problems.tsp" href="tsp.html">miplearn.problems.tsp</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
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<section>
</section>
<section>
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</section>
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<ul>
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></li>
<li><code><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></li>
<li><code><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></li>
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<h1 class="title">Module <code>miplearn.problems.knapsack</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.
import numpy as np
import pyomo.environ as pe
from scipy.stats import uniform, randint
from scipy.stats.distributions import rv_frozen
from miplearn.instance import Instance
class ChallengeA:
&#34;&#34;&#34;
- 250 variables, 10 constraints, fixed weights
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
- K = 500, u ~ U(0., 1.)
- alpha = 0.25
&#34;&#34;&#34;
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.gen = MultiKnapsackGenerator(
n=randint(low=250, high=251),
m=randint(low=10, high=11),
w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=True,
w_jitter=uniform(loc=0.95, scale=0.1),
)
np.random.seed(seed + 1)
self.training_instances = self.gen.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.gen.generate(n_test_instances)
class MultiKnapsackInstance(Instance):
&#34;&#34;&#34;Representation of the Multidimensional 0-1 Knapsack Problem.
Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
This implementation assigns a different category for each decision variable, and therefore
trains one ML model per variable. It is only suitable when training and test instances have
same size and items don&#39;t shuffle around.
&#34;&#34;&#34;
def __init__(self, prices, capacities, weights):
super().__init__()
assert isinstance(prices, np.ndarray)
assert isinstance(capacities, np.ndarray)
assert isinstance(weights, np.ndarray)
assert len(weights.shape) == 2
self.m, self.n = weights.shape
assert prices.shape == (self.n,)
assert capacities.shape == (self.m,)
self.prices = prices
self.capacities = capacities
self.weights = weights
def to_model(self):
model = pe.ConcreteModel()
model.x = pe.Var(range(self.n), domain=pe.Binary)
model.OBJ = pe.Objective(
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
sense=pe.maximize,
)
model.eq_capacity = pe.ConstraintList()
for i in range(self.m):
model.eq_capacity.add(
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
&lt;= self.capacities[i]
)
return model
def get_instance_features(self):
return np.hstack(
[
np.mean(self.prices),
self.capacities,
]
)
def get_variable_features(self, var, index):
return np.hstack(
[
self.prices[index],
self.weights[:, index],
]
)
# def get_variable_category(self, var, index):
# return index
class MultiKnapsackGenerator:
def __init__(
self,
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=500),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
):
&#34;&#34;&#34;Initialize the problem generator.
Instances have a random number of items (or variables) and a random number of knapsacks
(or constraints), as specified by the provided probability distributions `n` and `m`,
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
the provided distribution `w`. The capacity of knapsack `j` is set to:
alpha_j * sum(w[i,j] for i in range(n)),
where `alpha_j`, the tightness ratio, is sampled from the provided probability
distribution `alpha`. To make the instances more challenging, the costs of the items
are linearly correlated to their average weights. More specifically, the weight of each
item `i` is set to:
sum(w[i,j]/m for j in range(m)) + K * u_i,
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
from the provided probability distributions. Note that `K` is only sample once for the
entire instance.
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
also implies that n and m are kept fixed. Although the prices and capacities are derived
from w[i,j], as long as u and K are not constants, the generated instances will still not
be completely identical.
If a probability distribution w_jitter is provided, then item weights will be set to
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
fix_w=True, this argument may be used to generate instances where the weight of each item
is roughly the same, but not exactly identical, across all instances. The prices of the
items and the capacities of the knapsacks will be calculated as above, but using these
perturbed weights instead.
By default, all generated prices, weights and capacities are rounded to the nearest integer
number. If `round=False` is provided, this rounding will be disabled.
Parameters
----------
n: rv_discrete
Probability distribution for the number of items (or variables)
m: rv_discrete
Probability distribution for the number of knapsacks (or constraints)
w: rv_continuous
Probability distribution for the item weights
K: rv_continuous
Probability distribution for the profit correlation coefficient
u: rv_continuous
Probability distribution for the profit multiplier
alpha: rv_continuous
Probability distribution for the tightness ratio
fix_w: boolean
If true, weights are kept the same (minus the noise from w_jitter) in all instances
w_jitter: rv_continuous
Probability distribution for random noise added to the weights
round: boolean
If true, all prices, weights and capacities are rounded to the nearest integer
&#34;&#34;&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(m, rv_frozen), &#34;m should be a SciPy probability distribution&#34;
assert isinstance(w, rv_frozen), &#34;w should be a SciPy probability distribution&#34;
assert isinstance(K, rv_frozen), &#34;K should be a SciPy probability distribution&#34;
assert isinstance(u, rv_frozen), &#34;u should be a SciPy probability distribution&#34;
assert isinstance(
alpha, rv_frozen
), &#34;alpha should be a SciPy probability distribution&#34;
assert isinstance(fix_w, bool), &#34;fix_w should be boolean&#34;
assert isinstance(
w_jitter, rv_frozen
), &#34;w_jitter should be a SciPy probability distribution&#34;
self.n = n
self.m = m
self.w = w
self.K = K
self.u = u
self.alpha = alpha
self.w_jitter = w_jitter
self.round = round
if fix_w:
self.fix_n = self.n.rvs()
self.fix_m = self.m.rvs()
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
self.fix_u = self.u.rvs(self.fix_n)
self.fix_K = self.K.rvs()
else:
self.fix_n = None
self.fix_m = None
self.fix_w = None
self.fix_u = None
self.fix_K = None
def generate(self, n_samples):
def _sample():
if self.fix_w is not None:
n = self.fix_n
m = self.fix_m
w = self.fix_w
u = self.fix_u
K = self.fix_K
else:
n = self.n.rvs()
m = self.m.rvs()
w = np.array([self.w.rvs(n) for _ in range(m)])
u = self.u.rvs(n)
K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m)
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
if self.round:
p = p.round()
b = b.round()
w = w.round()
return MultiKnapsackInstance(p, b, w)
return [_sample() for _ in range(n_samples)]
class KnapsackInstance(Instance):
&#34;&#34;&#34;
Simpler (one-dimensional) Knapsack Problem, used for testing.
&#34;&#34;&#34;
def __init__(self, weights, prices, capacity):
super().__init__()
self.weights = weights
self.prices = prices
self.capacity = capacity
def to_model(self):
model = pe.ConcreteModel()
items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
)
model.eq_capacity = pe.Constraint(
expr=sum(model.x[v] * self.weights[v] for v in items) &lt;= self.capacity
)
return model
def get_instance_features(self):
return np.array(
[
self.capacity,
np.average(self.weights),
]
)
def get_variable_features(self, var, index):
return np.array(
[
self.weights[index],
self.prices[index],
]
)
class GurobiKnapsackInstance(KnapsackInstance):
&#34;&#34;&#34;
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
instead of Pyomo, used for testing.
&#34;&#34;&#34;
def __init__(self, weights, prices, capacity):
super().__init__(weights, prices, capacity)
def to_model(self):
import gurobipy as gp
from gurobipy import GRB
model = gp.Model(&#34;Knapsack&#34;)
n = len(self.weights)
x = model.addVars(n, vtype=GRB.BINARY, name=&#34;x&#34;)
model.addConstr(
gp.quicksum(x[i] * self.weights[i] for i in range(n)) &lt;= self.capacity,
&#34;eq_capacity&#34;,
)
model.setObjective(
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
)
return model</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.problems.knapsack.ChallengeA"><code class="flex name class">
<span>class <span class="ident">ChallengeA</span></span>
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
</code></dt>
<dd>
<section class="desc"><ul>
<li>250 variables, 10 constraints, fixed weights</li>
<li>w ~ U(0, 1000), jitter ~ U(0.95, 1.05)</li>
<li>K = 500, u ~ U(0., 1.)</li>
<li>alpha = 0.25</li>
</ul></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ChallengeA:
&#34;&#34;&#34;
- 250 variables, 10 constraints, fixed weights
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
- K = 500, u ~ U(0., 1.)
- alpha = 0.25
&#34;&#34;&#34;
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.gen = MultiKnapsackGenerator(
n=randint(low=250, high=251),
m=randint(low=10, high=11),
w=uniform(loc=0.0, scale=1000.0),
K=uniform(loc=500.0, scale=0.0),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=True,
w_jitter=uniform(loc=0.95, scale=0.1),
)
np.random.seed(seed + 1)
self.training_instances = self.gen.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.gen.generate(n_test_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.problems.knapsack.GurobiKnapsackInstance"><code class="flex name class">
<span>class <span class="ident">GurobiKnapsackInstance</span></span>
<span>(</span><span>weights, prices, capacity)</span>
</code></dt>
<dd>
<section class="desc"><p>Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
instead of Pyomo, used for testing.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GurobiKnapsackInstance(KnapsackInstance):
&#34;&#34;&#34;
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
instead of Pyomo, used for testing.
&#34;&#34;&#34;
def __init__(self, weights, prices, capacity):
super().__init__(weights, prices, capacity)
def to_model(self):
import gurobipy as gp
from gurobipy import GRB
model = gp.Model(&#34;Knapsack&#34;)
n = len(self.weights)
x = model.addVars(n, vtype=GRB.BINARY, name=&#34;x&#34;)
model.addConstr(
gp.quicksum(x[i] * self.weights[i] for i in range(n)) &lt;= self.capacity,
&#34;eq_capacity&#34;,
)
model.setObjective(
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
)
return model</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
<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.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.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.problems.knapsack.KnapsackInstance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
</ul>
</li>
</ul>
</dd>
<dt id="miplearn.problems.knapsack.KnapsackInstance"><code class="flex name class">
<span>class <span class="ident">KnapsackInstance</span></span>
<span>(</span><span>weights, prices, capacity)</span>
</code></dt>
<dd>
<section class="desc"><p>Simpler (one-dimensional) Knapsack Problem, used for testing.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class KnapsackInstance(Instance):
&#34;&#34;&#34;
Simpler (one-dimensional) Knapsack Problem, used for testing.
&#34;&#34;&#34;
def __init__(self, weights, prices, capacity):
super().__init__()
self.weights = weights
self.prices = prices
self.capacity = capacity
def to_model(self):
model = pe.ConcreteModel()
items = range(len(self.weights))
model.x = pe.Var(items, domain=pe.Binary)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
)
model.eq_capacity = pe.Constraint(
expr=sum(model.x[v] * self.weights[v] for v in items) &lt;= self.capacity
)
return model
def get_instance_features(self):
return np.array(
[
self.capacity,
np.average(self.weights),
]
)
def get_variable_features(self, var, index):
return np.array(
[
self.weights[index],
self.prices[index],
]
)</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>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></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>
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator"><code class="flex name class">
<span>class <span class="ident">MultiKnapsackGenerator</span></span>
<span>(</span><span>n=<scipy.stats._distn_infrastructure.rv_frozen object>, m=<scipy.stats._distn_infrastructure.rv_frozen object>, w=<scipy.stats._distn_infrastructure.rv_frozen object>, K=<scipy.stats._distn_infrastructure.rv_frozen object>, u=<scipy.stats._distn_infrastructure.rv_frozen object>, alpha=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_w=False, w_jitter=<scipy.stats._distn_infrastructure.rv_frozen object>, round=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Initialize the problem generator.</p>
<p>Instances have a random number of items (or variables) and a random number of knapsacks
(or constraints), as specified by the provided probability distributions <code>n</code> and <code>m</code>,
respectively. The weight of each item <code>i</code> on knapsack <code>j</code> is sampled independently from
the provided distribution <code>w</code>. The capacity of knapsack <code>j</code> is set to:</p>
<pre><code>alpha_j * sum(w[i,j] for i in range(n)),
</code></pre>
<p>where <code>alpha_j</code>, the tightness ratio, is sampled from the provided probability
distribution <code>alpha</code>. To make the instances more challenging, the costs of the items
are linearly correlated to their average weights. More specifically, the weight of each
item <code>i</code> is set to:</p>
<pre><code>sum(w[i,j]/m for j in range(m)) + K * u_i,
</code></pre>
<p>where <code>K</code>, the correlation coefficient, and <code>u_i</code>, the correlation multiplier, are sampled
from the provided probability distributions. Note that <code>K</code> is only sample once for the
entire instance.</p>
<p>If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
also implies that n and m are kept fixed. Although the prices and capacities are derived
from w[i,j], as long as u and K are not constants, the generated instances will still not
be completely identical.</p>
<p>If a probability distribution w_jitter is provided, then item weights will be set to
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
fix_w=True, this argument may be used to generate instances where the weight of each item
is roughly the same, but not exactly identical, across all instances. The prices of the
items and the capacities of the knapsacks will be calculated as above, but using these
perturbed weights instead.</p>
<p>By default, all generated prices, weights and capacities are rounded to the nearest integer
number. If <code>round=False</code> is provided, this rounding will be disabled.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>n</code></strong> :&ensp;<code>rv_discrete</code></dt>
<dd>Probability distribution for the number of items (or variables)</dd>
<dt><strong><code>m</code></strong> :&ensp;<code>rv_discrete</code></dt>
<dd>Probability distribution for the number of knapsacks (or constraints)</dd>
<dt><strong><code>w</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the item weights</dd>
<dt><strong><code>K</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the profit correlation coefficient</dd>
<dt><strong><code>u</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the profit multiplier</dd>
<dt><strong><code>alpha</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the tightness ratio</dd>
<dt><strong><code>fix_w</code></strong> :&ensp;<code>boolean</code></dt>
<dd>If true, weights are kept the same (minus the noise from w_jitter) in all instances</dd>
<dt><strong><code>w_jitter</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for random noise added to the weights</dd>
<dt><strong><code>round</code></strong> :&ensp;<code>boolean</code></dt>
<dd>If true, all prices, weights and capacities are rounded to the nearest integer</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MultiKnapsackGenerator:
def __init__(
self,
n=randint(low=100, high=101),
m=randint(low=30, high=31),
w=randint(low=0, high=1000),
K=randint(low=500, high=500),
u=uniform(loc=0.0, scale=1.0),
alpha=uniform(loc=0.25, scale=0.0),
fix_w=False,
w_jitter=uniform(loc=1.0, scale=0.0),
round=True,
):
&#34;&#34;&#34;Initialize the problem generator.
Instances have a random number of items (or variables) and a random number of knapsacks
(or constraints), as specified by the provided probability distributions `n` and `m`,
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
the provided distribution `w`. The capacity of knapsack `j` is set to:
alpha_j * sum(w[i,j] for i in range(n)),
where `alpha_j`, the tightness ratio, is sampled from the provided probability
distribution `alpha`. To make the instances more challenging, the costs of the items
are linearly correlated to their average weights. More specifically, the weight of each
item `i` is set to:
sum(w[i,j]/m for j in range(m)) + K * u_i,
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
from the provided probability distributions. Note that `K` is only sample once for the
entire instance.
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
also implies that n and m are kept fixed. Although the prices and capacities are derived
from w[i,j], as long as u and K are not constants, the generated instances will still not
be completely identical.
If a probability distribution w_jitter is provided, then item weights will be set to
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
fix_w=True, this argument may be used to generate instances where the weight of each item
is roughly the same, but not exactly identical, across all instances. The prices of the
items and the capacities of the knapsacks will be calculated as above, but using these
perturbed weights instead.
By default, all generated prices, weights and capacities are rounded to the nearest integer
number. If `round=False` is provided, this rounding will be disabled.
Parameters
----------
n: rv_discrete
Probability distribution for the number of items (or variables)
m: rv_discrete
Probability distribution for the number of knapsacks (or constraints)
w: rv_continuous
Probability distribution for the item weights
K: rv_continuous
Probability distribution for the profit correlation coefficient
u: rv_continuous
Probability distribution for the profit multiplier
alpha: rv_continuous
Probability distribution for the tightness ratio
fix_w: boolean
If true, weights are kept the same (minus the noise from w_jitter) in all instances
w_jitter: rv_continuous
Probability distribution for random noise added to the weights
round: boolean
If true, all prices, weights and capacities are rounded to the nearest integer
&#34;&#34;&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(m, rv_frozen), &#34;m should be a SciPy probability distribution&#34;
assert isinstance(w, rv_frozen), &#34;w should be a SciPy probability distribution&#34;
assert isinstance(K, rv_frozen), &#34;K should be a SciPy probability distribution&#34;
assert isinstance(u, rv_frozen), &#34;u should be a SciPy probability distribution&#34;
assert isinstance(
alpha, rv_frozen
), &#34;alpha should be a SciPy probability distribution&#34;
assert isinstance(fix_w, bool), &#34;fix_w should be boolean&#34;
assert isinstance(
w_jitter, rv_frozen
), &#34;w_jitter should be a SciPy probability distribution&#34;
self.n = n
self.m = m
self.w = w
self.K = K
self.u = u
self.alpha = alpha
self.w_jitter = w_jitter
self.round = round
if fix_w:
self.fix_n = self.n.rvs()
self.fix_m = self.m.rvs()
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
self.fix_u = self.u.rvs(self.fix_n)
self.fix_K = self.K.rvs()
else:
self.fix_n = None
self.fix_m = None
self.fix_w = None
self.fix_u = None
self.fix_K = None
def generate(self, n_samples):
def _sample():
if self.fix_w is not None:
n = self.fix_n
m = self.fix_m
w = self.fix_w
u = self.fix_u
K = self.fix_K
else:
n = self.n.rvs()
m = self.m.rvs()
w = np.array([self.w.rvs(n) for _ in range(m)])
u = self.u.rvs(n)
K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m)
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
if self.round:
p = p.round()
b = b.round()
w = w.round()
return MultiKnapsackInstance(p, b, w)
return [_sample() for _ in range(n_samples)]</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator.generate"><code class="name flex">
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def generate(self, n_samples):
def _sample():
if self.fix_w is not None:
n = self.fix_n
m = self.fix_m
w = self.fix_w
u = self.fix_u
K = self.fix_K
else:
n = self.n.rvs()
m = self.m.rvs()
w = np.array([self.w.rvs(n) for _ in range(m)])
u = self.u.rvs(n)
K = self.K.rvs()
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
alpha = self.alpha.rvs(m)
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
if self.round:
p = p.round()
b = b.round()
w = w.round()
return MultiKnapsackInstance(p, b, w)
return [_sample() for _ in range(n_samples)]</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.problems.knapsack.MultiKnapsackInstance"><code class="flex name class">
<span>class <span class="ident">MultiKnapsackInstance</span></span>
<span>(</span><span>prices, capacities, weights)</span>
</code></dt>
<dd>
<section class="desc"><p>Representation of the Multidimensional 0-1 Knapsack Problem.</p>
<p>Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].</p>
<p>This implementation assigns a different category for each decision variable, and therefore
trains one ML model per variable. It is only suitable when training and test instances have
same size and items don't shuffle around.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MultiKnapsackInstance(Instance):
&#34;&#34;&#34;Representation of the Multidimensional 0-1 Knapsack Problem.
Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
This implementation assigns a different category for each decision variable, and therefore
trains one ML model per variable. It is only suitable when training and test instances have
same size and items don&#39;t shuffle around.
&#34;&#34;&#34;
def __init__(self, prices, capacities, weights):
super().__init__()
assert isinstance(prices, np.ndarray)
assert isinstance(capacities, np.ndarray)
assert isinstance(weights, np.ndarray)
assert len(weights.shape) == 2
self.m, self.n = weights.shape
assert prices.shape == (self.n,)
assert capacities.shape == (self.m,)
self.prices = prices
self.capacities = capacities
self.weights = weights
def to_model(self):
model = pe.ConcreteModel()
model.x = pe.Var(range(self.n), domain=pe.Binary)
model.OBJ = pe.Objective(
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
sense=pe.maximize,
)
model.eq_capacity = pe.ConstraintList()
for i in range(self.m):
model.eq_capacity.add(
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
&lt;= self.capacities[i]
)
return model
def get_instance_features(self):
return np.hstack(
[
np.mean(self.prices),
self.capacities,
]
)
def get_variable_features(self, var, index):
return np.hstack(
[
self.prices[index],
self.weights[:, index],
]
)</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.problems" href="index.html">miplearn.problems</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.problems.knapsack.ChallengeA" href="#miplearn.problems.knapsack.ChallengeA">ChallengeA</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator" href="#miplearn.problems.knapsack.MultiKnapsackGenerator">MultiKnapsackGenerator</a></code></h4>
<ul class="">
<li><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator.generate" href="#miplearn.problems.knapsack.MultiKnapsackGenerator.generate">generate</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></code></h4>
</li>
</ul>
</li>
</ul>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.problems.stab</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.
import networkx as nx
import numpy as np
import pyomo.environ as pe
from scipy.stats import uniform, randint
from scipy.stats.distributions import rv_frozen
from miplearn.instance import Instance
class ChallengeA:
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = MaxWeightStableSetGenerator(
w=uniform(loc=100.0, scale=50.0),
n=randint(low=200, high=201),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.generator.generate(n_test_instances)
class MaxWeightStableSetGenerator:
&#34;&#34;&#34;Random instance generator for the Maximum-Weight Stable Set Problem.
The generator has two modes of operation. When `fix_graph=True` is provided, one random
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
from user-provided probability distributions `n` and `p`. To generate each instance, the
generator independently samples each $w_v$ from the user-provided probability distribution `w`.
When `fix_graph=False`, a new random graph is generated for each instance; the remaining
parameters are sampled in the same way.
&#34;&#34;&#34;
def __init__(
self,
w=uniform(loc=10.0, scale=1.0),
n=randint(low=250, high=251),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
):
&#34;&#34;&#34;Initialize the problem generator.
Parameters
----------
w: rv_continuous
Probability distribution for vertex weights.
n: rv_discrete
Probability distribution for parameter $n$ in Erdős-Rényi model.
p: rv_continuous
Probability distribution for parameter $p$ in Erdős-Rényi model.
&#34;&#34;&#34;
assert isinstance(w, rv_frozen), &#34;w should be a SciPy probability distribution&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(p, rv_frozen), &#34;p should be a SciPy probability distribution&#34;
self.w = w
self.n = n
self.p = p
self.fix_graph = fix_graph
self.graph = None
if fix_graph:
self.graph = self._generate_graph()
def generate(self, n_samples):
def _sample():
if self.graph is not None:
graph = self.graph
else:
graph = self._generate_graph()
weights = self.w.rvs(graph.number_of_nodes())
return MaxWeightStableSetInstance(graph, weights)
return [_sample() for _ in range(n_samples)]
def _generate_graph(self):
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
class MaxWeightStableSetInstance(Instance):
&#34;&#34;&#34;An instance of the Maximum-Weight Stable Set Problem.
Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
a subset of vertices, no two of which are adjacent.
This is one of Karp&#39;s 21 NP-complete problems.
&#34;&#34;&#34;
def __init__(self, graph, weights):
super().__init__()
self.graph = graph
self.weights = weights
def to_model(self):
nodes = list(self.graph.nodes)
model = pe.ConcreteModel()
model.x = pe.Var(nodes, domain=pe.Binary)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
)
model.clique_eqs = pe.ConstraintList()
for clique in nx.find_cliques(self.graph):
model.clique_eqs.add(sum(model.x[i] for i in clique) &lt;= 1)
return model
def get_instance_features(self):
return np.ones(0)
def get_variable_features(self, var, index):
neighbor_weights = [0] * 15
neighbor_degrees = [100] * 15
for n in self.graph.neighbors(index):
neighbor_weights += [self.weights[n] / self.weights[index]]
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
neighbor_weights.sort(reverse=True)
neighbor_degrees.sort()
features = []
features += neighbor_weights[:5]
features += neighbor_degrees[:5]
features += [self.graph.degree(index)]
return np.array(features)
def get_variable_category(self, var, index):
return &#34;default&#34;</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.problems.stab.ChallengeA"><code class="flex name class">
<span>class <span class="ident">ChallengeA</span></span>
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ChallengeA:
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = MaxWeightStableSetGenerator(
w=uniform(loc=100.0, scale=50.0),
n=randint(low=200, high=201),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.generator.generate(n_test_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator"><code class="flex name class">
<span>class <span class="ident">MaxWeightStableSetGenerator</span></span>
<span>(</span><span>w=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, p=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_graph=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Random instance generator for the Maximum-Weight Stable Set Problem.</p>
<p>The generator has two modes of operation. When <code>fix_graph=True</code> is provided, one random
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the
generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>.</p>
<p>When <code>fix_graph=False</code>, a new random graph is generated for each instance; the remaining
parameters are sampled in the same way.</p>
<p>Initialize the problem generator.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>w</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for vertex weights.</dd>
<dt><strong><code>n</code></strong> :&ensp;<code>rv_discrete</code></dt>
<dd>Probability distribution for parameter $n$ in Erdős-Rényi model.</dd>
<dt><strong><code>p</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for parameter $p$ in Erdős-Rényi model.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MaxWeightStableSetGenerator:
&#34;&#34;&#34;Random instance generator for the Maximum-Weight Stable Set Problem.
The generator has two modes of operation. When `fix_graph=True` is provided, one random
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
from user-provided probability distributions `n` and `p`. To generate each instance, the
generator independently samples each $w_v$ from the user-provided probability distribution `w`.
When `fix_graph=False`, a new random graph is generated for each instance; the remaining
parameters are sampled in the same way.
&#34;&#34;&#34;
def __init__(
self,
w=uniform(loc=10.0, scale=1.0),
n=randint(low=250, high=251),
p=uniform(loc=0.05, scale=0.0),
fix_graph=True,
):
&#34;&#34;&#34;Initialize the problem generator.
Parameters
----------
w: rv_continuous
Probability distribution for vertex weights.
n: rv_discrete
Probability distribution for parameter $n$ in Erdős-Rényi model.
p: rv_continuous
Probability distribution for parameter $p$ in Erdős-Rényi model.
&#34;&#34;&#34;
assert isinstance(w, rv_frozen), &#34;w should be a SciPy probability distribution&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(p, rv_frozen), &#34;p should be a SciPy probability distribution&#34;
self.w = w
self.n = n
self.p = p
self.fix_graph = fix_graph
self.graph = None
if fix_graph:
self.graph = self._generate_graph()
def generate(self, n_samples):
def _sample():
if self.graph is not None:
graph = self.graph
else:
graph = self._generate_graph()
weights = self.w.rvs(graph.number_of_nodes())
return MaxWeightStableSetInstance(graph, weights)
return [_sample() for _ in range(n_samples)]
def _generate_graph(self):
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator.generate"><code class="name flex">
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def generate(self, n_samples):
def _sample():
if self.graph is not None:
graph = self.graph
else:
graph = self._generate_graph()
weights = self.w.rvs(graph.number_of_nodes())
return MaxWeightStableSetInstance(graph, weights)
return [_sample() for _ in range(n_samples)]</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.problems.stab.MaxWeightStableSetInstance"><code class="flex name class">
<span>class <span class="ident">MaxWeightStableSetInstance</span></span>
<span>(</span><span>graph, weights)</span>
</code></dt>
<dd>
<section class="desc"><p>An instance of the Maximum-Weight Stable Set Problem.</p>
<p>Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
a subset of vertices, no two of which are adjacent.</p>
<p>This is one of Karp's 21 NP-complete problems.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class MaxWeightStableSetInstance(Instance):
&#34;&#34;&#34;An instance of the Maximum-Weight Stable Set Problem.
Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
a subset of vertices, no two of which are adjacent.
This is one of Karp&#39;s 21 NP-complete problems.
&#34;&#34;&#34;
def __init__(self, graph, weights):
super().__init__()
self.graph = graph
self.weights = weights
def to_model(self):
nodes = list(self.graph.nodes)
model = pe.ConcreteModel()
model.x = pe.Var(nodes, domain=pe.Binary)
model.OBJ = pe.Objective(
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
)
model.clique_eqs = pe.ConstraintList()
for clique in nx.find_cliques(self.graph):
model.clique_eqs.add(sum(model.x[i] for i in clique) &lt;= 1)
return model
def get_instance_features(self):
return np.ones(0)
def get_variable_features(self, var, index):
neighbor_weights = [0] * 15
neighbor_degrees = [100] * 15
for n in self.graph.neighbors(index):
neighbor_weights += [self.weights[n] / self.weights[index]]
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
neighbor_weights.sort(reverse=True)
neighbor_degrees.sort()
features = []
features += neighbor_weights[:5]
features += neighbor_degrees[:5]
features += [self.graph.degree(index)]
return np.array(features)
def get_variable_category(self, var, index):
return &#34;default&#34;</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>
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<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.</code></pre>
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<dt><code class="name"><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></dt>
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<section class="desc"></section>
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<dd>
<section class="desc"></section>
</dd>
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<section class="desc"></section>
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<h1 class="title">Module <code>miplearn.problems.tests.test_knapsack</code></h1>
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<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.
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</code></pre>
</details>
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<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_knapsack.test_knapsack_generator"><code class="name flex">
<span>def <span class="ident">test_knapsack_generator</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_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</code></pre>
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<ul class="">
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</head>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.problems.tests.test_stab</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.
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[&#34;Lower bound&#34;] == 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</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_stab.test_stab"><code class="name flex">
<span>def <span class="ident">test_stab</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_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[&#34;Lower bound&#34;] == 2.0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph"><code class="name flex">
<span>def <span class="ident">test_stab_generator_fixed_graph</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_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</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_random_graph"><code class="name flex">
<span>def <span class="ident">test_stab_generator_random_graph</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_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</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.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.problems.tests.test_stab.test_stab" href="#miplearn.problems.tests.test_stab.test_stab">test_stab</a></code></li>
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph">test_stab_generator_fixed_graph</a></code></li>
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_random_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_random_graph">test_stab_generator_random_graph</a></code></li>
</ul>
</li>
</ul>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.problems.tests.test_tsp</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.
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) &lt; 1e-6
d = [instance.distances[0, 1] for instance in instances]
assert np.std(d) &gt; 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][&#34;Solution&#34;][&#34;x&#34;]
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[&#34;Lower bound&#34;] == 4.0
assert stats[&#34;Upper bound&#34;] == 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, &#34;found_violated_lazy_constraints&#34;)
assert hasattr(instance, &#34;found_violated_user_cuts&#34;)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
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)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.problems.tests.test_tsp.test_generator"><code class="name flex">
<span>def <span class="ident">test_generator</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_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) &lt; 1e-6
d = [instance.distances[0, 1] for instance in instances]
assert np.std(d) &gt; 0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_tsp.test_instance"><code class="name flex">
<span>def <span class="ident">test_instance</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_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][&#34;Solution&#34;][&#34;x&#34;]
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[&#34;Lower bound&#34;] == 4.0
assert stats[&#34;Upper bound&#34;] == 4.0</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tests.test_tsp.test_subtour"><code class="name flex">
<span>def <span class="ident">test_subtour</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_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, &#34;found_violated_lazy_constraints&#34;)
assert hasattr(instance, &#34;found_violated_user_cuts&#34;)
x = instance.training_data[0][&#34;Solution&#34;][&#34;x&#34;]
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)</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.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.problems.tests.test_tsp.test_generator" href="#miplearn.problems.tests.test_tsp.test_generator">test_generator</a></code></li>
<li><code><a title="miplearn.problems.tests.test_tsp.test_instance" href="#miplearn.problems.tests.test_tsp.test_instance">test_instance</a></code></li>
<li><code><a title="miplearn.problems.tests.test_tsp.test_subtour" href="#miplearn.problems.tests.test_tsp.test_subtour">test_subtour</a></code></li>
</ul>
</li>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.problems.tsp</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.
import networkx as nx
import numpy as np
import pyomo.environ as pe
from scipy.spatial.distance import pdist, squareform
from scipy.stats import uniform, randint
from scipy.stats.distributions import rv_frozen
from miplearn.instance import Instance
class ChallengeA:
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.generator.generate(n_test_instances)
class TravelingSalesmanGenerator:
&#34;&#34;&#34;Random generator for the Traveling Salesman Problem.&#34;&#34;&#34;
def __init__(
self,
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=1.0, scale=0.0),
fix_cities=True,
round=True,
):
&#34;&#34;&#34;Initializes the problem generator.
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
sampled independently from the provided probability distributions `n`, `x` and `y`. For each
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
where gamma is sampled from the provided probability distribution `gamma`.
If fix_cities=True, the list of cities is kept the same for all generated instances. The
gamma values, and therefore also the distances, are still different.
By default, all distances d[i,j] are rounded to the nearest integer. If `round=False`
is provided, this rounding will be disabled.
Arguments
---------
x: rv_continuous
Probability distribution for the x-coordinate of each city.
y: rv_continuous
Probability distribution for the y-coordinate of each city.
n: rv_discrete
Probability distribution for the number of cities.
fix_cities: bool
If False, cities will be resampled for every generated instance. Otherwise, list of
cities will be computed once, during the constructor.
round: bool
If True, distances are rounded to the nearest integer.
&#34;&#34;&#34;
assert isinstance(x, rv_frozen), &#34;x should be a SciPy probability distribution&#34;
assert isinstance(y, rv_frozen), &#34;y should be a SciPy probability distribution&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(
gamma,
rv_frozen,
), &#34;gamma should be a SciPy probability distribution&#34;
self.x = x
self.y = y
self.n = n
self.gamma = gamma
self.round = round
if fix_cities:
self.fixed_n, self.fixed_cities = self._generate_cities()
else:
self.fixed_n = None
self.fixed_cities = None
def generate(self, n_samples):
def _sample():
if self.fixed_cities is not None:
n, cities = self.fixed_n, self.fixed_cities
else:
n, cities = self._generate_cities()
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
distances = np.tril(distances) + np.triu(distances.T, 1)
if self.round:
distances = distances.round()
return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)]
def _generate_cities(self):
n = self.n.rvs()
cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
return n, cities
class TravelingSalesmanInstance(Instance):
&#34;&#34;&#34;An instance ot the Traveling Salesman Problem.
Given a list of cities and the distance between each pair of cities, the problem asks for the
shortest route starting at the first city, visiting each other city exactly once, then
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
one of Karp&#39;s 21 NP-complete problems.
&#34;&#34;&#34;
def __init__(self, n_cities, distances):
assert isinstance(distances, np.ndarray)
assert distances.shape == (n_cities, n_cities)
self.n_cities = n_cities
self.distances = distances
def to_model(self):
model = pe.ConcreteModel()
model.edges = edges = [
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
]
model.x = pe.Var(edges, domain=pe.Binary)
model.obj = pe.Objective(
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
sense=pe.minimize,
)
model.eq_degree = pe.ConstraintList()
model.eq_subtour = pe.ConstraintList()
for i in range(self.n_cities):
model.eq_degree.add(
sum(
model.x[min(i, j), max(i, j)]
for j in range(self.n_cities)
if i != j
)
== 2
)
return model
def get_instance_features(self):
return np.array([1])
def get_variable_features(self, var_name, index):
return np.array([1])
def get_variable_category(self, var_name, index):
return index
def find_violated_lazy_constraints(self, model):
selected_edges = [e for e in model.edges if model.x[e].value &gt; 0.5]
graph = nx.Graph()
graph.add_edges_from(selected_edges)
components = [frozenset(c) for c in list(nx.connected_components(graph))]
violations = []
for c in components:
if len(c) &lt; self.n_cities:
violations += [c]
return violations
def build_lazy_constraint(self, model, component):
cut_edges = [
e
for e in model.edges
if (e[0] in component and e[1] not in component)
or (e[0] not in component and e[1] in component)
]
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) &gt;= 2)
def find_violated_user_cuts(self, model):
return self.find_violated_lazy_constraints(model)
def build_user_cut(self, model, violation):
return self.build_lazy_constraint(model, violation)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.problems.tsp.ChallengeA"><code class="flex name class">
<span>class <span class="ident">ChallengeA</span></span>
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class ChallengeA:
def __init__(
self,
seed=42,
n_training_instances=500,
n_test_instances=50,
):
np.random.seed(seed)
self.generator = TravelingSalesmanGenerator(
x=uniform(loc=0.0, scale=1000.0),
y=uniform(loc=0.0, scale=1000.0),
n=randint(low=350, high=351),
gamma=uniform(loc=0.95, scale=0.1),
fix_cities=True,
round=True,
)
np.random.seed(seed + 1)
self.training_instances = self.generator.generate(n_training_instances)
np.random.seed(seed + 2)
self.test_instances = self.generator.generate(n_test_instances)</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator"><code class="flex name class">
<span>class <span class="ident">TravelingSalesmanGenerator</span></span>
<span>(</span><span>x=<scipy.stats._distn_infrastructure.rv_frozen object>, y=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, gamma=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_cities=True, round=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Random generator for the Traveling Salesman Problem.</p>
<p>Initializes the problem generator.</p>
<p>Initially, the generator creates n cities (x_1,y_1),&hellip;,(x_n,y_n) where n, x_i and y_i are
sampled independently from the provided probability distributions <code>n</code>, <code>x</code> and <code>y</code>. For each
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:</p>
<pre><code>d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
</code></pre>
<p>where gamma is sampled from the provided probability distribution <code>gamma</code>.</p>
<p>If fix_cities=True, the list of cities is kept the same for all generated instances. The
gamma values, and therefore also the distances, are still different.</p>
<p>By default, all distances d[i,j] are rounded to the nearest integer.
If <code>round=False</code>
is provided, this rounding will be disabled.</p>
<h2 id="arguments">Arguments</h2>
<dl>
<dt><strong><code>x</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the x-coordinate of each city.</dd>
<dt><strong><code>y</code></strong> :&ensp;<code>rv_continuous</code></dt>
<dd>Probability distribution for the y-coordinate of each city.</dd>
<dt><strong><code>n</code></strong> :&ensp;<code>rv_discrete</code></dt>
<dd>Probability distribution for the number of cities.</dd>
<dt><strong><code>fix_cities</code></strong> :&ensp;<code>bool</code></dt>
<dd>If False, cities will be resampled for every generated instance. Otherwise, list of
cities will be computed once, during the constructor.</dd>
<dt><strong><code>round</code></strong> :&ensp;<code>bool</code></dt>
<dd>If True, distances are rounded to the nearest integer.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TravelingSalesmanGenerator:
&#34;&#34;&#34;Random generator for the Traveling Salesman Problem.&#34;&#34;&#34;
def __init__(
self,
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=1.0, scale=0.0),
fix_cities=True,
round=True,
):
&#34;&#34;&#34;Initializes the problem generator.
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
sampled independently from the provided probability distributions `n`, `x` and `y`. For each
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
where gamma is sampled from the provided probability distribution `gamma`.
If fix_cities=True, the list of cities is kept the same for all generated instances. The
gamma values, and therefore also the distances, are still different.
By default, all distances d[i,j] are rounded to the nearest integer. If `round=False`
is provided, this rounding will be disabled.
Arguments
---------
x: rv_continuous
Probability distribution for the x-coordinate of each city.
y: rv_continuous
Probability distribution for the y-coordinate of each city.
n: rv_discrete
Probability distribution for the number of cities.
fix_cities: bool
If False, cities will be resampled for every generated instance. Otherwise, list of
cities will be computed once, during the constructor.
round: bool
If True, distances are rounded to the nearest integer.
&#34;&#34;&#34;
assert isinstance(x, rv_frozen), &#34;x should be a SciPy probability distribution&#34;
assert isinstance(y, rv_frozen), &#34;y should be a SciPy probability distribution&#34;
assert isinstance(n, rv_frozen), &#34;n should be a SciPy probability distribution&#34;
assert isinstance(
gamma,
rv_frozen,
), &#34;gamma should be a SciPy probability distribution&#34;
self.x = x
self.y = y
self.n = n
self.gamma = gamma
self.round = round
if fix_cities:
self.fixed_n, self.fixed_cities = self._generate_cities()
else:
self.fixed_n = None
self.fixed_cities = None
def generate(self, n_samples):
def _sample():
if self.fixed_cities is not None:
n, cities = self.fixed_n, self.fixed_cities
else:
n, cities = self._generate_cities()
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
distances = np.tril(distances) + np.triu(distances.T, 1)
if self.round:
distances = distances.round()
return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)]
def _generate_cities(self):
n = self.n.rvs()
cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
return n, cities</code></pre>
</details>
<h3>Methods</h3>
<dl>
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator.generate"><code class="name flex">
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def generate(self, n_samples):
def _sample():
if self.fixed_cities is not None:
n, cities = self.fixed_n, self.fixed_cities
else:
n, cities = self._generate_cities()
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
distances = np.tril(distances) + np.triu(distances.T, 1)
if self.round:
distances = distances.round()
return TravelingSalesmanInstance(n, distances)
return [_sample() for _ in range(n_samples)]</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance"><code class="flex name class">
<span>class <span class="ident">TravelingSalesmanInstance</span></span>
<span>(</span><span>n_cities, distances)</span>
</code></dt>
<dd>
<section class="desc"><p>An instance ot the Traveling Salesman Problem.</p>
<p>Given a list of cities and the distance between each pair of cities, the problem asks for the
shortest route starting at the first city, visiting each other city exactly once, then
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
one of Karp's 21 NP-complete problems.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class TravelingSalesmanInstance(Instance):
&#34;&#34;&#34;An instance ot the Traveling Salesman Problem.
Given a list of cities and the distance between each pair of cities, the problem asks for the
shortest route starting at the first city, visiting each other city exactly once, then
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
one of Karp&#39;s 21 NP-complete problems.
&#34;&#34;&#34;
def __init__(self, n_cities, distances):
assert isinstance(distances, np.ndarray)
assert distances.shape == (n_cities, n_cities)
self.n_cities = n_cities
self.distances = distances
def to_model(self):
model = pe.ConcreteModel()
model.edges = edges = [
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
]
model.x = pe.Var(edges, domain=pe.Binary)
model.obj = pe.Objective(
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
sense=pe.minimize,
)
model.eq_degree = pe.ConstraintList()
model.eq_subtour = pe.ConstraintList()
for i in range(self.n_cities):
model.eq_degree.add(
sum(
model.x[min(i, j), max(i, j)]
for j in range(self.n_cities)
if i != j
)
== 2
)
return model
def get_instance_features(self):
return np.array([1])
def get_variable_features(self, var_name, index):
return np.array([1])
def get_variable_category(self, var_name, index):
return index
def find_violated_lazy_constraints(self, model):
selected_edges = [e for e in model.edges if model.x[e].value &gt; 0.5]
graph = nx.Graph()
graph.add_edges_from(selected_edges)
components = [frozenset(c) for c in list(nx.connected_components(graph))]
violations = []
for c in components:
if len(c) &lt; self.n_cities:
violations += [c]
return violations
def build_lazy_constraint(self, model, component):
cut_edges = [
e
for e in model.edges
if (e[0] in component and e[1] not in component)
or (e[0] not in component and e[1] in component)
]
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) &gt;= 2)
def find_violated_user_cuts(self, model):
return self.find_violated_lazy_constraints(model)
def build_user_cut(self, model, violation):
return self.build_lazy_constraint(model, violation)</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>Methods</h3>
<dl>
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut"><code class="name flex">
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def build_user_cut(self, model, violation):
return self.build_lazy_constraint(model, violation)</code></pre>
</details>
</dd>
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts"><code class="name flex">
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def find_violated_user_cuts(self, model):
return self.find_violated_lazy_constraints(model)</code></pre>
</details>
</dd>
</dl>
<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.problems" href="index.html">miplearn.problems</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.problems.tsp.ChallengeA" href="#miplearn.problems.tsp.ChallengeA">ChallengeA</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator" href="#miplearn.problems.tsp.TravelingSalesmanGenerator">TravelingSalesmanGenerator</a></code></h4>
<ul class="">
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator.generate" href="#miplearn.problems.tsp.TravelingSalesmanGenerator.generate">generate</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></code></h4>
<ul class="">
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut" href="#miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut">build_user_cut</a></code></li>
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts" href="#miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
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<header>
<h1 class="title">Module <code>miplearn.solvers.gurobi</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.
import logging
import re
import sys
from io import StringIO
from random import randint
from typing import List, Any, Dict, Optional
from miplearn.instance import Instance
from miplearn.solvers import _RedirectOutput
from miplearn.solvers.internal import (
InternalSolver,
LPSolveStats,
IterationCallback,
LazyCallback,
MIPSolveStats,
)
from miplearn.types import VarIndex, SolverParams, Solution
logger = logging.getLogger(__name__)
class GurobiSolver(InternalSolver):
&#34;&#34;&#34;
An InternalSolver backed by Gurobi&#39;s Python API (without Pyomo).
Parameters
----------
params: Optional[SolverParams]
Parameters to pass to Gurobi. For example, `params={&#34;MIPGap&#34;: 1e-3}`
sets the gap tolerance to 1e-3.
lazy_cb_frequency: int
If 1, calls lazy constraint callbacks whenever an integer solution
is found. If 2, calls it also at every node, after solving the
LP relaxation of that node.
&#34;&#34;&#34;
def __init__(
self,
params: Optional[SolverParams] = None,
lazy_cb_frequency: int = 1,
) -&gt; None:
import gurobipy
if params is None:
params = {}
params[&#34;InfUnbdInfo&#34;] = True
self.gp = gurobipy
self.instance: Optional[Instance] = None
self.model: Optional[&#34;gurobipy.Model&#34;] = None
self.params: SolverParams = params
self._all_vars: Dict = {}
self._bin_vars: Optional[Dict[str, Dict[VarIndex, &#34;gurobipy.Var&#34;]]] = None
self.cb_where: Optional[int] = None
assert lazy_cb_frequency in [1, 2]
if lazy_cb_frequency == 1:
self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
else:
self.lazy_cb_where = [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]
def set_instance(
self,
instance: Instance,
model: Any = None,
) -&gt; None:
self._raise_if_callback()
if model is None:
model = instance.to_model()
assert isinstance(model, self.gp.Model)
self.instance = instance
self.model = model
self.model.update()
self._update_vars()
def _raise_if_callback(self) -&gt; None:
if self.cb_where is not None:
raise Exception(&#34;method cannot be called from a callback&#34;)
def _update_vars(self) -&gt; None:
assert self.model is not None
self._all_vars = {}
self._bin_vars = {}
idx: VarIndex
for var in self.model.getVars():
m = re.search(r&#34;([^[]*)\[(.*)]&#34;, var.varName)
if m is None:
name = var.varName
idx = [0]
else:
name = m.group(1)
parts = m.group(2).split(&#34;,&#34;)
idx = [int(k) if k.isdecimal else k for k in parts]
if len(idx) == 1:
idx = idx[0]
if name not in self._all_vars:
self._all_vars[name] = {}
self._all_vars[name][idx] = var
if var.vtype != &#34;C&#34;:
if name not in self._bin_vars:
self._bin_vars[name] = {}
self._bin_vars[name][idx] = var
def _apply_params(self, streams: List[Any]) -&gt; None:
assert self.model is not None
with _RedirectOutput(streams):
for (name, value) in self.params.items():
self.model.setParam(name, value)
if &#34;seed&#34; not in [k.lower() for k in self.params.keys()]:
self.model.setParam(&#34;Seed&#34;, randint(0, 1_000_000))
def solve_lp(
self,
tee: bool = False,
) -&gt; LPSolveStats:
self._raise_if_callback()
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
self._apply_params(streams)
assert self.model is not None
assert self._bin_vars is not None
for (varname, vardict) in self._bin_vars.items():
for (idx, var) in vardict.items():
var.vtype = self.gp.GRB.CONTINUOUS
var.lb = 0.0
var.ub = 1.0
with _RedirectOutput(streams):
self.model.optimize()
for (varname, vardict) in self._bin_vars.items():
for (idx, var) in vardict.items():
var.vtype = self.gp.GRB.BINARY
log = streams[0].getvalue()
opt_value = None
if not self.is_infeasible():
opt_value = self.model.objVal
return {
&#34;Optimal value&#34;: opt_value,
&#34;Log&#34;: log,
}
def solve(
self,
tee: bool = False,
iteration_cb: IterationCallback = None,
lazy_cb: LazyCallback = None,
) -&gt; MIPSolveStats:
self._raise_if_callback()
assert self.model is not None
def cb_wrapper(cb_model, cb_where):
try:
self.cb_where = cb_where
if cb_where in self.lazy_cb_where:
lazy_cb(self, self.model)
except:
logger.exception(&#34;callback error&#34;)
finally:
self.cb_where = None
if lazy_cb:
self.params[&#34;LazyConstraints&#34;] = 1
total_wallclock_time = 0
total_nodes = 0
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
self._apply_params(streams)
if iteration_cb is None:
iteration_cb = lambda: False
while True:
with _RedirectOutput(streams):
if lazy_cb is None:
self.model.optimize()
else:
self.model.optimize(cb_wrapper)
total_wallclock_time += self.model.runtime
total_nodes += int(self.model.nodeCount)
should_repeat = iteration_cb()
if not should_repeat:
break
log = streams[0].getvalue()
ub, lb = None, None
sense = &#34;min&#34; if self.model.modelSense == 1 else &#34;max&#34;
if self.model.solCount &gt; 0:
if self.model.modelSense == 1:
lb = self.model.objBound
ub = self.model.objVal
else:
lb = self.model.objVal
ub = self.model.objBound
ws_value = self._extract_warm_start_value(log)
stats: MIPSolveStats = {
&#34;Lower bound&#34;: lb,
&#34;Upper bound&#34;: ub,
&#34;Wallclock time&#34;: total_wallclock_time,
&#34;Nodes&#34;: total_nodes,
&#34;Sense&#34;: sense,
&#34;Log&#34;: log,
&#34;Warm start value&#34;: ws_value,
&#34;LP value&#34;: None,
}
return stats
def get_solution(self) -&gt; Optional[Solution]:
self._raise_if_callback()
assert self.model is not None
if self.model.solCount == 0:
return None
solution: Solution = {}
for (varname, vardict) in self._all_vars.items():
solution[varname] = {}
for (idx, var) in vardict.items():
solution[varname][idx] = var.x
return solution
def set_warm_start(self, solution: Solution) -&gt; None:
self._raise_if_callback()
self._clear_warm_start()
count_fixed, count_total = 0, 0
for (varname, vardict) in solution.items():
for (idx, value) in vardict.items():
count_total += 1
if value is not None:
count_fixed += 1
self._all_vars[varname][idx].start = value
logger.info(
&#34;Setting start values for %d variables (out of %d)&#34;
% (count_fixed, count_total)
)
def get_sense(self) -&gt; str:
assert self.model is not None
if self.model.modelSense == 1:
return &#34;min&#34;
else:
return &#34;max&#34;
def get_value(
self,
var_name: str,
index: VarIndex,
) -&gt; Optional[float]:
var = self._all_vars[var_name][index]
return self._get_value(var)
def is_infeasible(self) -&gt; bool:
assert self.model is not None
return self.model.status in [self.gp.GRB.INFEASIBLE, self.gp.GRB.INF_OR_UNBD]
def get_dual(self, cid: str) -&gt; float:
assert self.model is not None
c = self.model.getConstrByName(cid)
if self.is_infeasible():
return c.farkasDual
else:
return c.pi
def _get_value(self, var: Any) -&gt; Optional[float]:
assert self.model is not None
if self.cb_where == self.gp.GRB.Callback.MIPSOL:
return self.model.cbGetSolution(var)
elif self.cb_where == self.gp.GRB.Callback.MIPNODE:
return self.model.cbGetNodeRel(var)
elif self.cb_where is None:
if self.is_infeasible():
return None
else:
return var.x
else:
raise Exception(
&#34;get_value cannot be called from cb_where=%s&#34; % self.cb_where
)
def get_empty_solution(self) -&gt; Solution:
self._raise_if_callback()
solution: Solution = {}
for (varname, vardict) in self._all_vars.items():
solution[varname] = {}
for (idx, var) in vardict.items():
solution[varname][idx] = None
return solution
def add_constraint(
self,
constraint: Any,
name: str = &#34;&#34;,
) -&gt; None:
assert self.model is not None
if type(constraint) is tuple:
lhs, sense, rhs, name = constraint
if self.cb_where in [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]:
self.model.cbLazy(lhs, sense, rhs)
else:
self.model.addConstr(lhs, sense, rhs, name)
else:
if self.cb_where in [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]:
self.model.cbLazy(constraint)
else:
self.model.addConstr(constraint, name=name)
def _clear_warm_start(self) -&gt; None:
for (varname, vardict) in self._all_vars.items():
for (idx, var) in vardict.items():
var.start = self.gp.GRB.UNDEFINED
def fix(self, solution: Solution) -&gt; None:
self._raise_if_callback()
for (varname, vardict) in solution.items():
for (idx, value) in vardict.items():
if value is None:
continue
var = self._all_vars[varname][idx]
var.vtype = self.gp.GRB.CONTINUOUS
var.lb = value
var.ub = value
def get_constraint_ids(self):
self._raise_if_callback()
self.model.update()
return [c.ConstrName for c in self.model.getConstrs()]
def extract_constraint(self, cid):
self._raise_if_callback()
constr = self.model.getConstrByName(cid)
cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
self.model.remove(constr)
return cobj
def is_constraint_satisfied(self, cobj, tol=1e-5):
lhs, sense, rhs, name = cobj
if self.cb_where is not None:
lhs_value = lhs.getConstant()
for i in range(lhs.size()):
var = lhs.getVar(i)
coeff = lhs.getCoeff(i)
lhs_value += self._get_value(var) * coeff
else:
lhs_value = lhs.getValue()
if sense == &#34;&lt;&#34;:
return lhs_value &lt;= rhs + tol
elif sense == &#34;&gt;&#34;:
return lhs_value &gt;= rhs - tol
elif sense == &#34;=&#34;:
return abs(rhs - lhs_value) &lt; abs(tol)
else:
raise Exception(&#34;Unknown sense: %s&#34; % sense)
def get_inequality_slacks(self) -&gt; Dict[str, float]:
assert self.model is not None
ineqs = [c for c in self.model.getConstrs() if c.sense != &#34;=&#34;]
return {c.ConstrName: c.Slack for c in ineqs}
def set_constraint_sense(self, cid: str, sense: str) -&gt; None:
assert self.model is not None
c = self.model.getConstrByName(cid)
c.Sense = sense
def get_constraint_sense(self, cid: str) -&gt; str:
assert self.model is not None
c = self.model.getConstrByName(cid)
return c.Sense
def relax(self) -&gt; None:
assert self.model is not None
self.model = self.model.relax()
self._update_vars()
def _extract_warm_start_value(self, log: str) -&gt; Optional[float]:
ws = self.__extract(log, &#34;MIP start with objective ([0-9.e+-]*)&#34;)
if ws is None:
return None
return float(ws)
@staticmethod
def __extract(
log: str,
regexp: str,
default: Optional[str] = None,
) -&gt; Optional[str]:
value = default
for line in log.splitlines():
matches = re.findall(regexp, line)
if len(matches) == 0:
continue
value = matches[0]
return value
def __getstate__(self):
return {
&#34;params&#34;: self.params,
&#34;lazy_cb_where&#34;: self.lazy_cb_where,
}
def __setstate__(self, state):
self.params = state[&#34;params&#34;]
self.lazy_cb_where = state[&#34;lazy_cb_where&#34;]
self.instance = None
self.model = None
self._all_vars = None
self._bin_vars = None
self.cb_where = None</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.gurobi.GurobiSolver"><code class="flex name class">
<span>class <span class="ident">GurobiSolver</span></span>
<span>(</span><span>params=None, lazy_cb_frequency=1)</span>
</code></dt>
<dd>
<section class="desc"><p>An InternalSolver backed by Gurobi's Python API (without Pyomo).</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>params</code></strong> :&ensp;<code>Optional</code>[<code>SolverParams</code>]</dt>
<dd>Parameters to pass to Gurobi. For example, <code>params={"MIPGap": 1e-3}</code>
sets the gap tolerance to 1e-3.</dd>
<dt><strong><code>lazy_cb_frequency</code></strong> :&ensp;<code>int</code></dt>
<dd>If 1, calls lazy constraint callbacks whenever an integer solution
is found. If 2, calls it also at every node, after solving the
LP relaxation of that node.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GurobiSolver(InternalSolver):
&#34;&#34;&#34;
An InternalSolver backed by Gurobi&#39;s Python API (without Pyomo).
Parameters
----------
params: Optional[SolverParams]
Parameters to pass to Gurobi. For example, `params={&#34;MIPGap&#34;: 1e-3}`
sets the gap tolerance to 1e-3.
lazy_cb_frequency: int
If 1, calls lazy constraint callbacks whenever an integer solution
is found. If 2, calls it also at every node, after solving the
LP relaxation of that node.
&#34;&#34;&#34;
def __init__(
self,
params: Optional[SolverParams] = None,
lazy_cb_frequency: int = 1,
) -&gt; None:
import gurobipy
if params is None:
params = {}
params[&#34;InfUnbdInfo&#34;] = True
self.gp = gurobipy
self.instance: Optional[Instance] = None
self.model: Optional[&#34;gurobipy.Model&#34;] = None
self.params: SolverParams = params
self._all_vars: Dict = {}
self._bin_vars: Optional[Dict[str, Dict[VarIndex, &#34;gurobipy.Var&#34;]]] = None
self.cb_where: Optional[int] = None
assert lazy_cb_frequency in [1, 2]
if lazy_cb_frequency == 1:
self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
else:
self.lazy_cb_where = [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]
def set_instance(
self,
instance: Instance,
model: Any = None,
) -&gt; None:
self._raise_if_callback()
if model is None:
model = instance.to_model()
assert isinstance(model, self.gp.Model)
self.instance = instance
self.model = model
self.model.update()
self._update_vars()
def _raise_if_callback(self) -&gt; None:
if self.cb_where is not None:
raise Exception(&#34;method cannot be called from a callback&#34;)
def _update_vars(self) -&gt; None:
assert self.model is not None
self._all_vars = {}
self._bin_vars = {}
idx: VarIndex
for var in self.model.getVars():
m = re.search(r&#34;([^[]*)\[(.*)]&#34;, var.varName)
if m is None:
name = var.varName
idx = [0]
else:
name = m.group(1)
parts = m.group(2).split(&#34;,&#34;)
idx = [int(k) if k.isdecimal else k for k in parts]
if len(idx) == 1:
idx = idx[0]
if name not in self._all_vars:
self._all_vars[name] = {}
self._all_vars[name][idx] = var
if var.vtype != &#34;C&#34;:
if name not in self._bin_vars:
self._bin_vars[name] = {}
self._bin_vars[name][idx] = var
def _apply_params(self, streams: List[Any]) -&gt; None:
assert self.model is not None
with _RedirectOutput(streams):
for (name, value) in self.params.items():
self.model.setParam(name, value)
if &#34;seed&#34; not in [k.lower() for k in self.params.keys()]:
self.model.setParam(&#34;Seed&#34;, randint(0, 1_000_000))
def solve_lp(
self,
tee: bool = False,
) -&gt; LPSolveStats:
self._raise_if_callback()
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
self._apply_params(streams)
assert self.model is not None
assert self._bin_vars is not None
for (varname, vardict) in self._bin_vars.items():
for (idx, var) in vardict.items():
var.vtype = self.gp.GRB.CONTINUOUS
var.lb = 0.0
var.ub = 1.0
with _RedirectOutput(streams):
self.model.optimize()
for (varname, vardict) in self._bin_vars.items():
for (idx, var) in vardict.items():
var.vtype = self.gp.GRB.BINARY
log = streams[0].getvalue()
opt_value = None
if not self.is_infeasible():
opt_value = self.model.objVal
return {
&#34;Optimal value&#34;: opt_value,
&#34;Log&#34;: log,
}
def solve(
self,
tee: bool = False,
iteration_cb: IterationCallback = None,
lazy_cb: LazyCallback = None,
) -&gt; MIPSolveStats:
self._raise_if_callback()
assert self.model is not None
def cb_wrapper(cb_model, cb_where):
try:
self.cb_where = cb_where
if cb_where in self.lazy_cb_where:
lazy_cb(self, self.model)
except:
logger.exception(&#34;callback error&#34;)
finally:
self.cb_where = None
if lazy_cb:
self.params[&#34;LazyConstraints&#34;] = 1
total_wallclock_time = 0
total_nodes = 0
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
self._apply_params(streams)
if iteration_cb is None:
iteration_cb = lambda: False
while True:
with _RedirectOutput(streams):
if lazy_cb is None:
self.model.optimize()
else:
self.model.optimize(cb_wrapper)
total_wallclock_time += self.model.runtime
total_nodes += int(self.model.nodeCount)
should_repeat = iteration_cb()
if not should_repeat:
break
log = streams[0].getvalue()
ub, lb = None, None
sense = &#34;min&#34; if self.model.modelSense == 1 else &#34;max&#34;
if self.model.solCount &gt; 0:
if self.model.modelSense == 1:
lb = self.model.objBound
ub = self.model.objVal
else:
lb = self.model.objVal
ub = self.model.objBound
ws_value = self._extract_warm_start_value(log)
stats: MIPSolveStats = {
&#34;Lower bound&#34;: lb,
&#34;Upper bound&#34;: ub,
&#34;Wallclock time&#34;: total_wallclock_time,
&#34;Nodes&#34;: total_nodes,
&#34;Sense&#34;: sense,
&#34;Log&#34;: log,
&#34;Warm start value&#34;: ws_value,
&#34;LP value&#34;: None,
}
return stats
def get_solution(self) -&gt; Optional[Solution]:
self._raise_if_callback()
assert self.model is not None
if self.model.solCount == 0:
return None
solution: Solution = {}
for (varname, vardict) in self._all_vars.items():
solution[varname] = {}
for (idx, var) in vardict.items():
solution[varname][idx] = var.x
return solution
def set_warm_start(self, solution: Solution) -&gt; None:
self._raise_if_callback()
self._clear_warm_start()
count_fixed, count_total = 0, 0
for (varname, vardict) in solution.items():
for (idx, value) in vardict.items():
count_total += 1
if value is not None:
count_fixed += 1
self._all_vars[varname][idx].start = value
logger.info(
&#34;Setting start values for %d variables (out of %d)&#34;
% (count_fixed, count_total)
)
def get_sense(self) -&gt; str:
assert self.model is not None
if self.model.modelSense == 1:
return &#34;min&#34;
else:
return &#34;max&#34;
def get_value(
self,
var_name: str,
index: VarIndex,
) -&gt; Optional[float]:
var = self._all_vars[var_name][index]
return self._get_value(var)
def is_infeasible(self) -&gt; bool:
assert self.model is not None
return self.model.status in [self.gp.GRB.INFEASIBLE, self.gp.GRB.INF_OR_UNBD]
def get_dual(self, cid: str) -&gt; float:
assert self.model is not None
c = self.model.getConstrByName(cid)
if self.is_infeasible():
return c.farkasDual
else:
return c.pi
def _get_value(self, var: Any) -&gt; Optional[float]:
assert self.model is not None
if self.cb_where == self.gp.GRB.Callback.MIPSOL:
return self.model.cbGetSolution(var)
elif self.cb_where == self.gp.GRB.Callback.MIPNODE:
return self.model.cbGetNodeRel(var)
elif self.cb_where is None:
if self.is_infeasible():
return None
else:
return var.x
else:
raise Exception(
&#34;get_value cannot be called from cb_where=%s&#34; % self.cb_where
)
def get_empty_solution(self) -&gt; Solution:
self._raise_if_callback()
solution: Solution = {}
for (varname, vardict) in self._all_vars.items():
solution[varname] = {}
for (idx, var) in vardict.items():
solution[varname][idx] = None
return solution
def add_constraint(
self,
constraint: Any,
name: str = &#34;&#34;,
) -&gt; None:
assert self.model is not None
if type(constraint) is tuple:
lhs, sense, rhs, name = constraint
if self.cb_where in [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]:
self.model.cbLazy(lhs, sense, rhs)
else:
self.model.addConstr(lhs, sense, rhs, name)
else:
if self.cb_where in [
self.gp.GRB.Callback.MIPSOL,
self.gp.GRB.Callback.MIPNODE,
]:
self.model.cbLazy(constraint)
else:
self.model.addConstr(constraint, name=name)
def _clear_warm_start(self) -&gt; None:
for (varname, vardict) in self._all_vars.items():
for (idx, var) in vardict.items():
var.start = self.gp.GRB.UNDEFINED
def fix(self, solution: Solution) -&gt; None:
self._raise_if_callback()
for (varname, vardict) in solution.items():
for (idx, value) in vardict.items():
if value is None:
continue
var = self._all_vars[varname][idx]
var.vtype = self.gp.GRB.CONTINUOUS
var.lb = value
var.ub = value
def get_constraint_ids(self):
self._raise_if_callback()
self.model.update()
return [c.ConstrName for c in self.model.getConstrs()]
def extract_constraint(self, cid):
self._raise_if_callback()
constr = self.model.getConstrByName(cid)
cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
self.model.remove(constr)
return cobj
def is_constraint_satisfied(self, cobj, tol=1e-5):
lhs, sense, rhs, name = cobj
if self.cb_where is not None:
lhs_value = lhs.getConstant()
for i in range(lhs.size()):
var = lhs.getVar(i)
coeff = lhs.getCoeff(i)
lhs_value += self._get_value(var) * coeff
else:
lhs_value = lhs.getValue()
if sense == &#34;&lt;&#34;:
return lhs_value &lt;= rhs + tol
elif sense == &#34;&gt;&#34;:
return lhs_value &gt;= rhs - tol
elif sense == &#34;=&#34;:
return abs(rhs - lhs_value) &lt; abs(tol)
else:
raise Exception(&#34;Unknown sense: %s&#34; % sense)
def get_inequality_slacks(self) -&gt; Dict[str, float]:
assert self.model is not None
ineqs = [c for c in self.model.getConstrs() if c.sense != &#34;=&#34;]
return {c.ConstrName: c.Slack for c in ineqs}
def set_constraint_sense(self, cid: str, sense: str) -&gt; None:
assert self.model is not None
c = self.model.getConstrByName(cid)
c.Sense = sense
def get_constraint_sense(self, cid: str) -&gt; str:
assert self.model is not None
c = self.model.getConstrByName(cid)
return c.Sense
def relax(self) -&gt; None:
assert self.model is not None
self.model = self.model.relax()
self._update_vars()
def _extract_warm_start_value(self, log: str) -&gt; Optional[float]:
ws = self.__extract(log, &#34;MIP start with objective ([0-9.e+-]*)&#34;)
if ws is None:
return None
return float(ws)
@staticmethod
def __extract(
log: str,
regexp: str,
default: Optional[str] = None,
) -&gt; Optional[str]:
value = default
for line in log.splitlines():
matches = re.findall(regexp, line)
if len(matches) == 0:
continue
value = matches[0]
return value
def __getstate__(self):
return {
&#34;params&#34;: self.params,
&#34;lazy_cb_where&#34;: self.lazy_cb_where,
}
def __setstate__(self, state):
self.params = state[&#34;params&#34;]
self.lazy_cb_where = state[&#34;lazy_cb_where&#34;]
self.instance = None
self.model = None
self._all_vars = None
self._bin_vars = None
self.cb_where = None</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.internal.InternalSolver" href="internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.solvers.internal.InternalSolver" href="internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.solvers.internal.InternalSolver.add_constraint" href="internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.extract_constraint" href="internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.fix" href="internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_ids" href="internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_dual" href="internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_empty_solution" href="internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_inequality_slacks" href="internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_solution" href="internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_value" href="internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied" href="internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_infeasible" href="internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.relax" href="internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_branching_priorities" href="internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_constraint_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_instance" href="internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_warm_start" href="internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve" href="internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve_lp" href="internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
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<header>
<h1 class="title">Module <code>miplearn.solvers</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.
import logging
import sys
from typing import Any, List
logger = logging.getLogger(__name__)
class _RedirectOutput:
def __init__(self, streams: List[Any]):
self.streams = streams
def write(self, data: Any) -&gt; None:
for stream in self.streams:
stream.write(data)
def flush(self) -&gt; None:
for stream in self.streams:
stream.flush()
def __enter__(self):
self._original_stdout = sys.stdout
self._original_stderr = sys.stderr
sys.stdout = self
sys.stderr = self
return self
def __exit__(self, _type, _value, _traceback):
sys.stdout = self._original_stdout
sys.stderr = self._original_stderr</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.solvers.gurobi" href="gurobi.html">miplearn.solvers.gurobi</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
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<dt><code class="name"><a title="miplearn.solvers.pyomo" href="pyomo/index.html">miplearn.solvers.pyomo</a></code></dt>
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<dt><code class="name"><a title="miplearn.solvers.tests" href="tests/index.html">miplearn.solvers.tests</a></code></dt>
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<header>
<h1 class="title">Module <code>miplearn.solvers.pyomo.base</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.
import logging
import re
import sys
from io import StringIO
from typing import Any, List, Dict, Optional
import pyomo
from pyomo import environ as pe
from pyomo.core import Var, Constraint
from pyomo.opt import TerminationCondition
from pyomo.opt.base.solvers import SolverFactory
from miplearn.instance import Instance
from miplearn.solvers import _RedirectOutput
from miplearn.solvers.internal import (
InternalSolver,
LPSolveStats,
IterationCallback,
LazyCallback,
MIPSolveStats,
)
from miplearn.types import VarIndex, SolverParams, Solution
logger = logging.getLogger(__name__)
class BasePyomoSolver(InternalSolver):
&#34;&#34;&#34;
Base class for all Pyomo solvers.
&#34;&#34;&#34;
def __init__(
self,
solver_factory: SolverFactory,
params: SolverParams,
) -&gt; None:
self.instance: Optional[Instance] = None
self.model: Optional[pe.ConcreteModel] = None
self._all_vars: List[pe.Var] = []
self._bin_vars: List[pe.Var] = []
self._is_warm_start_available: bool = False
self._pyomo_solver: SolverFactory = solver_factory
self._obj_sense: str = &#34;min&#34;
self._varname_to_var: Dict[str, pe.Var] = {}
self._cname_to_constr: Dict[str, pe.Constraint] = {}
self._termination_condition: str = &#34;&#34;
for (key, value) in params.items():
self._pyomo_solver.options[key] = value
def solve_lp(
self,
tee: bool = False,
) -&gt; LPSolveStats:
self.relax()
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
with _RedirectOutput(streams):
results = self._pyomo_solver.solve(tee=True)
self._restore_integrality()
opt_value = None
if not self.is_infeasible():
opt_value = results[&#34;Problem&#34;][0][&#34;Lower bound&#34;]
return {
&#34;Optimal value&#34;: opt_value,
&#34;Log&#34;: streams[0].getvalue(),
}
def _restore_integrality(self) -&gt; None:
for var in self._bin_vars:
var.domain = pyomo.core.base.set_types.Binary
self._pyomo_solver.update_var(var)
def solve(
self,
tee: bool = False,
iteration_cb: IterationCallback = None,
lazy_cb: LazyCallback = None,
) -&gt; MIPSolveStats:
if lazy_cb is not None:
raise Exception(&#34;lazy callback not supported&#34;)
total_wallclock_time = 0
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
if iteration_cb is None:
iteration_cb = lambda: False
while True:
logger.debug(&#34;Solving MIP...&#34;)
with _RedirectOutput(streams):
results = self._pyomo_solver.solve(
tee=True,
warmstart=self._is_warm_start_available,
)
total_wallclock_time += results[&#34;Solver&#34;][0][&#34;Wallclock time&#34;]
should_repeat = iteration_cb()
if not should_repeat:
break
log = streams[0].getvalue()
node_count = self._extract_node_count(log)
ws_value = self._extract_warm_start_value(log)
self._termination_condition = results[&#34;Solver&#34;][0][&#34;Termination condition&#34;]
lb, ub = None, None
if not self.is_infeasible():
lb = results[&#34;Problem&#34;][0][&#34;Lower bound&#34;]
ub = results[&#34;Problem&#34;][0][&#34;Upper bound&#34;]
stats: MIPSolveStats = {
&#34;Lower bound&#34;: lb,
&#34;Upper bound&#34;: ub,
&#34;Wallclock time&#34;: total_wallclock_time,
&#34;Sense&#34;: self._obj_sense,
&#34;Log&#34;: log,
&#34;Nodes&#34;: node_count,
&#34;Warm start value&#34;: ws_value,
&#34;LP value&#34;: None,
}
return stats
def get_solution(self) -&gt; Optional[Solution]:
assert self.model is not None
if self.is_infeasible():
return None
solution: Solution = {}
for var in self.model.component_objects(Var):
solution[str(var)] = {}
for index in var:
if var[index].fixed:
continue
solution[str(var)][index] = var[index].value
return solution
def set_warm_start(self, solution: Solution) -&gt; None:
self._clear_warm_start()
count_total, count_fixed = 0, 0
for var_name in solution:
var = self._varname_to_var[var_name]
for index in solution[var_name]:
count_total += 1
var[index].value = solution[var_name][index]
if solution[var_name][index] is not None:
count_fixed += 1
if count_fixed &gt; 0:
self._is_warm_start_available = True
logger.info(
&#34;Setting start values for %d variables (out of %d)&#34;
% (count_fixed, count_total)
)
def set_instance(
self,
instance: Instance,
model: Any = None,
) -&gt; None:
if model is None:
model = instance.to_model()
assert isinstance(model, pe.ConcreteModel)
self.instance = instance
self.model = model
self._pyomo_solver.set_instance(model)
self._update_obj()
self._update_vars()
self._update_constrs()
def get_value(self, var_name: str, index: VarIndex) -&gt; Optional[float]:
if self.is_infeasible():
return None
else:
var = self._varname_to_var[var_name]
return var[index].value
def get_empty_solution(self) -&gt; Solution:
assert self.model is not None
solution: Solution = {}
for var in self.model.component_objects(Var):
svar = str(var)
solution[svar] = {}
for index in var:
if var[index].fixed:
continue
solution[svar][index] = None
return solution
def _clear_warm_start(self) -&gt; None:
for var in self._all_vars:
if not var.fixed:
var.value = None
self._is_warm_start_available = False
def _update_obj(self) -&gt; None:
self._obj_sense = &#34;max&#34;
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
self._obj_sense = &#34;min&#34;
def _update_vars(self) -&gt; None:
assert self.model is not None
self._all_vars = []
self._bin_vars = []
self._varname_to_var = {}
for var in self.model.component_objects(Var):
self._varname_to_var[var.name] = var
for idx in var:
self._all_vars += [var[idx]]
if var[idx].domain == pyomo.core.base.set_types.Binary:
self._bin_vars += [var[idx]]
def _update_constrs(self) -&gt; None:
assert self.model is not None
self._cname_to_constr = {}
for constr in self.model.component_objects(Constraint):
self._cname_to_constr[constr.name] = constr
def fix(self, solution):
count_total, count_fixed = 0, 0
for varname in solution:
for index in solution[varname]:
var = self._varname_to_var[varname]
count_total += 1
if solution[varname][index] is None:
continue
count_fixed += 1
var[index].fix(solution[varname][index])
self._pyomo_solver.update_var(var[index])
logger.info(
&#34;Fixing values for %d variables (out of %d)&#34;
% (
count_fixed,
count_total,
)
)
def add_constraint(self, constraint):
self._pyomo_solver.add_constraint(constraint)
self._update_constrs()
@staticmethod
def __extract(
log: str,
regexp: Optional[str],
default: Optional[str] = None,
) -&gt; Optional[str]:
if regexp is None:
return default
value = default
for line in log.splitlines():
matches = re.findall(regexp, line)
if len(matches) == 0:
continue
value = matches[0]
return value
def _extract_warm_start_value(self, log: str) -&gt; Optional[float]:
value = self.__extract(log, self._get_warm_start_regexp())
if value is None:
return None
return float(value)
def _extract_node_count(self, log: str) -&gt; Optional[int]:
value = self.__extract(log, self._get_node_count_regexp())
if value is None:
return None
return int(value)
def get_constraint_ids(self):
return list(self._cname_to_constr.keys())
def _get_warm_start_regexp(self) -&gt; Optional[str]:
return None
def _get_node_count_regexp(self) -&gt; Optional[str]:
return None
def relax(self) -&gt; None:
for var in self._bin_vars:
lb, ub = var.bounds
var.setlb(lb)
var.setub(ub)
var.domain = pyomo.core.base.set_types.Reals
self._pyomo_solver.update_var(var)
def get_inequality_slacks(self) -&gt; Dict[str, float]:
result: Dict[str, float] = {}
for (cname, cobj) in self._cname_to_constr.items():
if cobj.equality:
continue
result[cname] = cobj.slack()
return result
def get_constraint_sense(self, cid: str) -&gt; str:
cobj = self._cname_to_constr[cid]
has_ub = cobj.has_ub()
has_lb = cobj.has_lb()
assert (not has_lb) or (not has_ub), &#34;range constraints not supported&#34;
if has_lb:
return &#34;&gt;&#34;
elif has_ub:
return &#34;&lt;&#34;
else:
return &#34;=&#34;
def set_constraint_sense(self, cid: str, sense: str) -&gt; None:
raise Exception(&#34;Not implemented&#34;)
def extract_constraint(self, cid: str) -&gt; Constraint:
raise Exception(&#34;Not implemented&#34;)
def is_constraint_satisfied(self, cobj: Constraint) -&gt; bool:
raise Exception(&#34;Not implemented&#34;)
def is_infeasible(self) -&gt; bool:
return self._termination_condition == TerminationCondition.infeasible
def get_dual(self, cid):
raise Exception(&#34;Not implemented&#34;)
def get_sense(self) -&gt; str:
return self._obj_sense</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.pyomo.base.BasePyomoSolver"><code class="flex name class">
<span>class <span class="ident">BasePyomoSolver</span></span>
<span>(</span><span>solver_factory, params)</span>
</code></dt>
<dd>
<section class="desc"><p>Base class for all Pyomo solvers.</p></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class BasePyomoSolver(InternalSolver):
&#34;&#34;&#34;
Base class for all Pyomo solvers.
&#34;&#34;&#34;
def __init__(
self,
solver_factory: SolverFactory,
params: SolverParams,
) -&gt; None:
self.instance: Optional[Instance] = None
self.model: Optional[pe.ConcreteModel] = None
self._all_vars: List[pe.Var] = []
self._bin_vars: List[pe.Var] = []
self._is_warm_start_available: bool = False
self._pyomo_solver: SolverFactory = solver_factory
self._obj_sense: str = &#34;min&#34;
self._varname_to_var: Dict[str, pe.Var] = {}
self._cname_to_constr: Dict[str, pe.Constraint] = {}
self._termination_condition: str = &#34;&#34;
for (key, value) in params.items():
self._pyomo_solver.options[key] = value
def solve_lp(
self,
tee: bool = False,
) -&gt; LPSolveStats:
self.relax()
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
with _RedirectOutput(streams):
results = self._pyomo_solver.solve(tee=True)
self._restore_integrality()
opt_value = None
if not self.is_infeasible():
opt_value = results[&#34;Problem&#34;][0][&#34;Lower bound&#34;]
return {
&#34;Optimal value&#34;: opt_value,
&#34;Log&#34;: streams[0].getvalue(),
}
def _restore_integrality(self) -&gt; None:
for var in self._bin_vars:
var.domain = pyomo.core.base.set_types.Binary
self._pyomo_solver.update_var(var)
def solve(
self,
tee: bool = False,
iteration_cb: IterationCallback = None,
lazy_cb: LazyCallback = None,
) -&gt; MIPSolveStats:
if lazy_cb is not None:
raise Exception(&#34;lazy callback not supported&#34;)
total_wallclock_time = 0
streams: List[Any] = [StringIO()]
if tee:
streams += [sys.stdout]
if iteration_cb is None:
iteration_cb = lambda: False
while True:
logger.debug(&#34;Solving MIP...&#34;)
with _RedirectOutput(streams):
results = self._pyomo_solver.solve(
tee=True,
warmstart=self._is_warm_start_available,
)
total_wallclock_time += results[&#34;Solver&#34;][0][&#34;Wallclock time&#34;]
should_repeat = iteration_cb()
if not should_repeat:
break
log = streams[0].getvalue()
node_count = self._extract_node_count(log)
ws_value = self._extract_warm_start_value(log)
self._termination_condition = results[&#34;Solver&#34;][0][&#34;Termination condition&#34;]
lb, ub = None, None
if not self.is_infeasible():
lb = results[&#34;Problem&#34;][0][&#34;Lower bound&#34;]
ub = results[&#34;Problem&#34;][0][&#34;Upper bound&#34;]
stats: MIPSolveStats = {
&#34;Lower bound&#34;: lb,
&#34;Upper bound&#34;: ub,
&#34;Wallclock time&#34;: total_wallclock_time,
&#34;Sense&#34;: self._obj_sense,
&#34;Log&#34;: log,
&#34;Nodes&#34;: node_count,
&#34;Warm start value&#34;: ws_value,
&#34;LP value&#34;: None,
}
return stats
def get_solution(self) -&gt; Optional[Solution]:
assert self.model is not None
if self.is_infeasible():
return None
solution: Solution = {}
for var in self.model.component_objects(Var):
solution[str(var)] = {}
for index in var:
if var[index].fixed:
continue
solution[str(var)][index] = var[index].value
return solution
def set_warm_start(self, solution: Solution) -&gt; None:
self._clear_warm_start()
count_total, count_fixed = 0, 0
for var_name in solution:
var = self._varname_to_var[var_name]
for index in solution[var_name]:
count_total += 1
var[index].value = solution[var_name][index]
if solution[var_name][index] is not None:
count_fixed += 1
if count_fixed &gt; 0:
self._is_warm_start_available = True
logger.info(
&#34;Setting start values for %d variables (out of %d)&#34;
% (count_fixed, count_total)
)
def set_instance(
self,
instance: Instance,
model: Any = None,
) -&gt; None:
if model is None:
model = instance.to_model()
assert isinstance(model, pe.ConcreteModel)
self.instance = instance
self.model = model
self._pyomo_solver.set_instance(model)
self._update_obj()
self._update_vars()
self._update_constrs()
def get_value(self, var_name: str, index: VarIndex) -&gt; Optional[float]:
if self.is_infeasible():
return None
else:
var = self._varname_to_var[var_name]
return var[index].value
def get_empty_solution(self) -&gt; Solution:
assert self.model is not None
solution: Solution = {}
for var in self.model.component_objects(Var):
svar = str(var)
solution[svar] = {}
for index in var:
if var[index].fixed:
continue
solution[svar][index] = None
return solution
def _clear_warm_start(self) -&gt; None:
for var in self._all_vars:
if not var.fixed:
var.value = None
self._is_warm_start_available = False
def _update_obj(self) -&gt; None:
self._obj_sense = &#34;max&#34;
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
self._obj_sense = &#34;min&#34;
def _update_vars(self) -&gt; None:
assert self.model is not None
self._all_vars = []
self._bin_vars = []
self._varname_to_var = {}
for var in self.model.component_objects(Var):
self._varname_to_var[var.name] = var
for idx in var:
self._all_vars += [var[idx]]
if var[idx].domain == pyomo.core.base.set_types.Binary:
self._bin_vars += [var[idx]]
def _update_constrs(self) -&gt; None:
assert self.model is not None
self._cname_to_constr = {}
for constr in self.model.component_objects(Constraint):
self._cname_to_constr[constr.name] = constr
def fix(self, solution):
count_total, count_fixed = 0, 0
for varname in solution:
for index in solution[varname]:
var = self._varname_to_var[varname]
count_total += 1
if solution[varname][index] is None:
continue
count_fixed += 1
var[index].fix(solution[varname][index])
self._pyomo_solver.update_var(var[index])
logger.info(
&#34;Fixing values for %d variables (out of %d)&#34;
% (
count_fixed,
count_total,
)
)
def add_constraint(self, constraint):
self._pyomo_solver.add_constraint(constraint)
self._update_constrs()
@staticmethod
def __extract(
log: str,
regexp: Optional[str],
default: Optional[str] = None,
) -&gt; Optional[str]:
if regexp is None:
return default
value = default
for line in log.splitlines():
matches = re.findall(regexp, line)
if len(matches) == 0:
continue
value = matches[0]
return value
def _extract_warm_start_value(self, log: str) -&gt; Optional[float]:
value = self.__extract(log, self._get_warm_start_regexp())
if value is None:
return None
return float(value)
def _extract_node_count(self, log: str) -&gt; Optional[int]:
value = self.__extract(log, self._get_node_count_regexp())
if value is None:
return None
return int(value)
def get_constraint_ids(self):
return list(self._cname_to_constr.keys())
def _get_warm_start_regexp(self) -&gt; Optional[str]:
return None
def _get_node_count_regexp(self) -&gt; Optional[str]:
return None
def relax(self) -&gt; None:
for var in self._bin_vars:
lb, ub = var.bounds
var.setlb(lb)
var.setub(ub)
var.domain = pyomo.core.base.set_types.Reals
self._pyomo_solver.update_var(var)
def get_inequality_slacks(self) -&gt; Dict[str, float]:
result: Dict[str, float] = {}
for (cname, cobj) in self._cname_to_constr.items():
if cobj.equality:
continue
result[cname] = cobj.slack()
return result
def get_constraint_sense(self, cid: str) -&gt; str:
cobj = self._cname_to_constr[cid]
has_ub = cobj.has_ub()
has_lb = cobj.has_lb()
assert (not has_lb) or (not has_ub), &#34;range constraints not supported&#34;
if has_lb:
return &#34;&gt;&#34;
elif has_ub:
return &#34;&lt;&#34;
else:
return &#34;=&#34;
def set_constraint_sense(self, cid: str, sense: str) -&gt; None:
raise Exception(&#34;Not implemented&#34;)
def extract_constraint(self, cid: str) -&gt; Constraint:
raise Exception(&#34;Not implemented&#34;)
def is_constraint_satisfied(self, cobj: Constraint) -&gt; bool:
raise Exception(&#34;Not implemented&#34;)
def is_infeasible(self) -&gt; bool:
return self._termination_condition == TerminationCondition.infeasible
def get_dual(self, cid):
raise Exception(&#34;Not implemented&#34;)
def get_sense(self) -&gt; str:
return self._obj_sense</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver" href="gurobi.html#miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver">GurobiPyomoSolver</a></li>
<li><a title="miplearn.solvers.pyomo.cplex.CplexPyomoSolver" href="cplex.html#miplearn.solvers.pyomo.cplex.CplexPyomoSolver">CplexPyomoSolver</a></li>
<li><a title="miplearn.solvers.pyomo.xpress.XpressPyomoSolver" href="xpress.html#miplearn.solvers.pyomo.xpress.XpressPyomoSolver">XpressPyomoSolver</a></li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.solvers.internal.InternalSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
</ul>
</li>
</ul>
</dd>
</dl>
</section>
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<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<li>
<h4><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></code></h4>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.solvers.pyomo.cplex</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 typing import Optional
from pyomo import environ as pe
from scipy.stats import randint
from miplearn.solvers.pyomo.base import BasePyomoSolver
from miplearn.types import SolverParams
class CplexPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses CPLEX and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;mip_display&#34;: 5} to increase the log verbosity.
&#34;&#34;&#34;
def __init__(
self,
params: Optional[SolverParams] = None,
) -&gt; None:
if params is None:
params = {}
if &#34;randomseed&#34; not in params.keys():
params[&#34;randomseed&#34;] = randint(low=0, high=1000).rvs()
if &#34;mip_display&#34; not in params.keys():
params[&#34;mip_display&#34;] = 4
super().__init__(
solver_factory=pe.SolverFactory(&#34;cplex_persistent&#34;),
params=params,
)
def _get_warm_start_regexp(self):
return &#34;MIP start .* with objective ([0-9.e+-]*)\\.&#34;
def _get_node_count_regexp(self):
return &#34;^[ *] *([0-9]+)&#34;</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.pyomo.cplex.CplexPyomoSolver"><code class="flex name class">
<span>class <span class="ident">CplexPyomoSolver</span></span>
<span>(</span><span>params=None)</span>
</code></dt>
<dd>
<section class="desc"><p>An InternalSolver that uses CPLEX and the Pyomo modeling language.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>params</code></strong> :&ensp;<code>dict</code></dt>
<dd>Dictionary of options to pass to the Pyomo solver. For example,
{"mip_display": 5} to increase the log verbosity.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class CplexPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses CPLEX and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;mip_display&#34;: 5} to increase the log verbosity.
&#34;&#34;&#34;
def __init__(
self,
params: Optional[SolverParams] = None,
) -&gt; None:
if params is None:
params = {}
if &#34;randomseed&#34; not in params.keys():
params[&#34;randomseed&#34;] = randint(low=0, high=1000).rvs()
if &#34;mip_display&#34; not in params.keys():
params[&#34;mip_display&#34;] = 4
super().__init__(
solver_factory=pe.SolverFactory(&#34;cplex_persistent&#34;),
params=params,
)
def _get_warm_start_regexp(self):
return &#34;MIP start .* with objective ([0-9.e+-]*)\\.&#34;
def _get_node_count_regexp(self):
return &#34;^[ *] *([0-9]+)&#34;</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
</ul>
</li>
</ul>
</dd>
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<li><h3>Super-module</h3>
<ul>
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
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<h4><code><a title="miplearn.solvers.pyomo.cplex.CplexPyomoSolver" href="#miplearn.solvers.pyomo.cplex.CplexPyomoSolver">CplexPyomoSolver</a></code></h4>
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<header>
<h1 class="title">Module <code>miplearn.solvers.pyomo.gurobi</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.
import logging
from typing import Optional
from pyomo import environ as pe
from scipy.stats import randint
from miplearn.solvers.pyomo.base import BasePyomoSolver
from miplearn.types import SolverParams, BranchPriorities
logger = logging.getLogger(__name__)
class GurobiPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses Gurobi and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;Threads&#34;: 4} to set the number of threads.
&#34;&#34;&#34;
def __init__(
self,
params: SolverParams = None,
) -&gt; None:
if params is None:
params = {}
if &#34;seed&#34; not in params.keys():
params[&#34;seed&#34;] = randint(low=0, high=1000).rvs()
super().__init__(
solver_factory=pe.SolverFactory(&#34;gurobi_persistent&#34;),
params=params,
)
def _extract_node_count(self, log: str) -&gt; int:
return max(1, int(self._pyomo_solver._solver_model.getAttr(&#34;NodeCount&#34;)))
def _get_warm_start_regexp(self) -&gt; str:
return &#34;MIP start with objective ([0-9.e+-]*)&#34;
def _get_node_count_regexp(self) -&gt; Optional[str]:
return None
def set_branching_priorities(self, priorities: BranchPriorities) -&gt; None:
from gurobipy import GRB
for varname in priorities.keys():
var = self._varname_to_var[varname]
for (index, priority) in priorities[varname].items():
if priority is None:
continue
gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver"><code class="flex name class">
<span>class <span class="ident">GurobiPyomoSolver</span></span>
<span>(</span><span>params=None)</span>
</code></dt>
<dd>
<section class="desc"><p>An InternalSolver that uses Gurobi and the Pyomo modeling language.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>params</code></strong> :&ensp;<code>dict</code></dt>
<dd>Dictionary of options to pass to the Pyomo solver. For example,
{"Threads": 4} to set the number of threads.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class GurobiPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses Gurobi and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;Threads&#34;: 4} to set the number of threads.
&#34;&#34;&#34;
def __init__(
self,
params: SolverParams = None,
) -&gt; None:
if params is None:
params = {}
if &#34;seed&#34; not in params.keys():
params[&#34;seed&#34;] = randint(low=0, high=1000).rvs()
super().__init__(
solver_factory=pe.SolverFactory(&#34;gurobi_persistent&#34;),
params=params,
)
def _extract_node_count(self, log: str) -&gt; int:
return max(1, int(self._pyomo_solver._solver_model.getAttr(&#34;NodeCount&#34;)))
def _get_warm_start_regexp(self) -&gt; str:
return &#34;MIP start with objective ([0-9.e+-]*)&#34;
def _get_node_count_regexp(self) -&gt; Optional[str]:
return None
def set_branching_priorities(self, priorities: BranchPriorities) -&gt; None:
from gurobipy import GRB
for varname in priorities.keys():
var = self._varname_to_var[varname]
for (index, priority) in priorities[varname].items():
if priority is None:
continue
gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
</ul>
</li>
</ul>
</dd>
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<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
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<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver" href="#miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver">GurobiPyomoSolver</a></code></h4>
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<article id="content">
<header>
<h1 class="title">Module <code>miplearn.solvers.pyomo</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.</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.solvers.pyomo.base" href="base.html">miplearn.solvers.pyomo.base</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers.pyomo.cplex" href="cplex.html">miplearn.solvers.pyomo.cplex</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers.pyomo.gurobi" href="gurobi.html">miplearn.solvers.pyomo.gurobi</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers.pyomo.xpress" href="xpress.html">miplearn.solvers.pyomo.xpress</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
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<section>
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</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
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<header>
<h1 class="title">Module <code>miplearn.solvers.pyomo.xpress</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.
import logging
from pyomo import environ as pe
from scipy.stats import randint
from miplearn.solvers.pyomo.base import BasePyomoSolver
from miplearn.types import SolverParams
logger = logging.getLogger(__name__)
class XpressPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses XPRESS and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;Threads&#34;: 4} to set the number of threads.
&#34;&#34;&#34;
def __init__(self, params: SolverParams = None) -&gt; None:
if params is None:
params = {}
if &#34;randomseed&#34; not in params.keys():
params[&#34;randomseed&#34;] = randint(low=0, high=1000).rvs()
super().__init__(
solver_factory=pe.SolverFactory(&#34;xpress_persistent&#34;),
params=params,
)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.pyomo.xpress.XpressPyomoSolver"><code class="flex name class">
<span>class <span class="ident">XpressPyomoSolver</span></span>
<span>(</span><span>params=None)</span>
</code></dt>
<dd>
<section class="desc"><p>An InternalSolver that uses XPRESS and the Pyomo modeling language.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>params</code></strong> :&ensp;<code>dict</code></dt>
<dd>Dictionary of options to pass to the Pyomo solver. For example,
{"Threads": 4} to set the number of threads.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class XpressPyomoSolver(BasePyomoSolver):
&#34;&#34;&#34;
An InternalSolver that uses XPRESS and the Pyomo modeling language.
Parameters
----------
params: dict
Dictionary of options to pass to the Pyomo solver. For example,
{&#34;Threads&#34;: 4} to set the number of threads.
&#34;&#34;&#34;
def __init__(self, params: SolverParams = None) -&gt; None:
if params is None:
params = {}
if &#34;randomseed&#34; not in params.keys():
params[&#34;randomseed&#34;] = randint(low=0, high=1000).rvs()
super().__init__(
solver_factory=pe.SolverFactory(&#34;xpress_persistent&#34;),
params=params,
)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
<li>abc.ABC</li>
</ul>
<h3>Inherited members</h3>
<ul class="hlist">
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
<ul class="hlist">
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
</ul>
</li>
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<li><h3>Super-module</h3>
<ul>
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.solvers.pyomo.xpress.XpressPyomoSolver" href="#miplearn.solvers.pyomo.xpress.XpressPyomoSolver">XpressPyomoSolver</a></code></h4>
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<header>
<h1 class="title">Module <code>miplearn.solvers.tests</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 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) -&gt; 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] &gt;= 2)
return model
class InfeasibleGurobiInstance(Instance):
def to_model(self) -&gt; Any:
import gurobipy as gp
from gurobipy import GRB
model = gp.Model()
x = model.addVars(1, vtype=GRB.BINARY, name=&#34;x&#34;)
model.addConstr(x[0] &gt;= 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() -&gt; List[Callable[[], InternalSolver]]:
return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.solvers.tests.InfeasibleGurobiInstance"><code class="flex name class">
<span>class <span class="ident">InfeasibleGurobiInstance</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 InfeasibleGurobiInstance(Instance):
def to_model(self) -&gt; Any:
import gurobipy as gp
from gurobipy import GRB
model = gp.Model()
x = model.addVars(1, vtype=GRB.BINARY, name=&#34;x&#34;)
model.addConstr(x[0] &gt;= 2)
model.setObjective(x[0])
return model</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>
<dt id="miplearn.solvers.tests.InfeasiblePyomoInstance"><code class="flex name class">
<span>class <span class="ident">InfeasiblePyomoInstance</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 InfeasiblePyomoInstance(Instance):
def to_model(self) -&gt; 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] &gt;= 2)
return model</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>
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<li><code><a title="miplearn.solvers" href="../index.html">miplearn.solvers</a></code></li>
</ul>
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<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></li>
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<h4><code><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></code></h4>
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<header>
<h1 class="title">Module <code>miplearn.solvers.tests.test_internal_solver</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.
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(&#34;Hello world&#34;)
assert sys.stdout == original_stdout
assert io.getvalue() == &#34;Hello world\n&#34;
def test_internal_solver_warm_starts():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % solver_class)
instance = _get_knapsack_instance(solver_class)
model = instance.to_model()
solver = solver_class()
solver.set_instance(instance, model)
solver.set_warm_start(
{
&#34;x&#34;: {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
if stats[&#34;Warm start value&#34;] is not None:
assert stats[&#34;Warm start value&#34;] == 725.0
else:
warn(f&#34;{solver_class.__name__} should set warm start value&#34;)
solver.set_warm_start(
{
&#34;x&#34;: {
0: 1.0,
1: 1.0,
2: 1.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
assert stats[&#34;Warm start value&#34;] is None
solver.fix(
{
&#34;x&#34;: {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
assert stats[&#34;Lower bound&#34;] == 725.0
assert stats[&#34;Upper bound&#34;] == 725.0
def test_internal_solver():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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[&#34;Optimal value&#34;], 3) == 1287.923
assert len(stats[&#34;Log&#34;]) &gt; 100
solution = solver.get_solution()
assert round(solution[&#34;x&#34;][0], 3) == 1.000
assert round(solution[&#34;x&#34;][1], 3) == 0.923
assert round(solution[&#34;x&#34;][2], 3) == 1.000
assert round(solution[&#34;x&#34;][3], 3) == 0.000
stats = solver.solve(tee=True)
assert not solver.is_infeasible()
assert len(stats[&#34;Log&#34;]) &gt; 100
assert stats[&#34;Lower bound&#34;] == 1183.0
assert stats[&#34;Upper bound&#34;] == 1183.0
assert stats[&#34;Sense&#34;] == &#34;max&#34;
assert isinstance(stats[&#34;Wallclock time&#34;], float)
solution = solver.get_solution()
assert solution[&#34;x&#34;][0] == 1.0
assert solution[&#34;x&#34;][1] == 0.0
assert solution[&#34;x&#34;][2] == 1.0
assert solution[&#34;x&#34;][3] == 1.0
# Add a brand new constraint
if isinstance(solver, BasePyomoSolver):
model.cut = pe.Constraint(expr=model.x[0] &lt;= 0.0, name=&#34;cut&#34;)
solver.add_constraint(model.cut)
elif isinstance(solver, GurobiSolver):
x = model.getVarByName(&#34;x[0]&#34;)
solver.add_constraint(x &lt;= 0.0, name=&#34;cut&#34;)
else:
raise Exception(&#34;Illegal state&#34;)
# New constraint should affect solution and should be listed in
# constraint ids
assert solver.get_constraint_ids() == [&#34;eq_capacity&#34;, &#34;cut&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 1030.0
assert solver.get_sense() == &#34;max&#34;
assert solver.get_constraint_sense(&#34;cut&#34;) == &#34;&lt;&#34;
assert solver.get_constraint_sense(&#34;eq_capacity&#34;) == &#34;&lt;&#34;
# Verify slacks
assert solver.get_inequality_slacks() == {
&#34;cut&#34;: 0.0,
&#34;eq_capacity&#34;: 3.0,
}
if isinstance(solver, GurobiSolver):
# Extract the new constraint
cobj = solver.extract_constraint(&#34;cut&#34;)
# New constraint should no longer affect solution and should no longer
# be listed in constraint ids
assert solver.get_constraint_ids() == [&#34;eq_capacity&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 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() == [&#34;eq_capacity&#34;, &#34;cut&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 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(&#34;cut&#34;, &#34;=&#34;)
stats = solver.solve()
assert round(stats[&#34;Lower bound&#34;]) == 1030.0
assert round(solver.get_dual(&#34;eq_capacity&#34;)) == 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[&#34;Lower bound&#34;]) == 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[&#34;Upper bound&#34;] is None
assert stats[&#34;Lower bound&#34;] is None
stats = solver.solve_lp()
assert solver.get_solution() is None
assert stats[&#34;Optimal value&#34;] is None
assert solver.get_value(&#34;x&#34;, 0) is None
def test_iteration_cb():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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 &lt; 5
solver.solve(iteration_cb=custom_iteration_cb)
assert count == 5</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance"><code class="name flex">
<span>def <span class="ident">test_infeasible_instance</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_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[&#34;Upper bound&#34;] is None
assert stats[&#34;Lower bound&#34;] is None
stats = solver.solve_lp()
assert solver.get_solution() is None
assert stats[&#34;Optimal value&#34;] is None
assert solver.get_value(&#34;x&#34;, 0) is None</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver"><code class="name flex">
<span>def <span class="ident">test_internal_solver</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_internal_solver():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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[&#34;Optimal value&#34;], 3) == 1287.923
assert len(stats[&#34;Log&#34;]) &gt; 100
solution = solver.get_solution()
assert round(solution[&#34;x&#34;][0], 3) == 1.000
assert round(solution[&#34;x&#34;][1], 3) == 0.923
assert round(solution[&#34;x&#34;][2], 3) == 1.000
assert round(solution[&#34;x&#34;][3], 3) == 0.000
stats = solver.solve(tee=True)
assert not solver.is_infeasible()
assert len(stats[&#34;Log&#34;]) &gt; 100
assert stats[&#34;Lower bound&#34;] == 1183.0
assert stats[&#34;Upper bound&#34;] == 1183.0
assert stats[&#34;Sense&#34;] == &#34;max&#34;
assert isinstance(stats[&#34;Wallclock time&#34;], float)
solution = solver.get_solution()
assert solution[&#34;x&#34;][0] == 1.0
assert solution[&#34;x&#34;][1] == 0.0
assert solution[&#34;x&#34;][2] == 1.0
assert solution[&#34;x&#34;][3] == 1.0
# Add a brand new constraint
if isinstance(solver, BasePyomoSolver):
model.cut = pe.Constraint(expr=model.x[0] &lt;= 0.0, name=&#34;cut&#34;)
solver.add_constraint(model.cut)
elif isinstance(solver, GurobiSolver):
x = model.getVarByName(&#34;x[0]&#34;)
solver.add_constraint(x &lt;= 0.0, name=&#34;cut&#34;)
else:
raise Exception(&#34;Illegal state&#34;)
# New constraint should affect solution and should be listed in
# constraint ids
assert solver.get_constraint_ids() == [&#34;eq_capacity&#34;, &#34;cut&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 1030.0
assert solver.get_sense() == &#34;max&#34;
assert solver.get_constraint_sense(&#34;cut&#34;) == &#34;&lt;&#34;
assert solver.get_constraint_sense(&#34;eq_capacity&#34;) == &#34;&lt;&#34;
# Verify slacks
assert solver.get_inequality_slacks() == {
&#34;cut&#34;: 0.0,
&#34;eq_capacity&#34;: 3.0,
}
if isinstance(solver, GurobiSolver):
# Extract the new constraint
cobj = solver.extract_constraint(&#34;cut&#34;)
# New constraint should no longer affect solution and should no longer
# be listed in constraint ids
assert solver.get_constraint_ids() == [&#34;eq_capacity&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 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() == [&#34;eq_capacity&#34;, &#34;cut&#34;]
stats = solver.solve()
assert stats[&#34;Lower bound&#34;] == 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(&#34;cut&#34;, &#34;=&#34;)
stats = solver.solve()
assert round(stats[&#34;Lower bound&#34;]) == 1030.0
assert round(solver.get_dual(&#34;eq_capacity&#34;)) == 0.0</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts"><code class="name flex">
<span>def <span class="ident">test_internal_solver_warm_starts</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_internal_solver_warm_starts():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % solver_class)
instance = _get_knapsack_instance(solver_class)
model = instance.to_model()
solver = solver_class()
solver.set_instance(instance, model)
solver.set_warm_start(
{
&#34;x&#34;: {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
if stats[&#34;Warm start value&#34;] is not None:
assert stats[&#34;Warm start value&#34;] == 725.0
else:
warn(f&#34;{solver_class.__name__} should set warm start value&#34;)
solver.set_warm_start(
{
&#34;x&#34;: {
0: 1.0,
1: 1.0,
2: 1.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
assert stats[&#34;Warm start value&#34;] is None
solver.fix(
{
&#34;x&#34;: {
0: 1.0,
1: 0.0,
2: 0.0,
3: 1.0,
}
}
)
stats = solver.solve(tee=True)
assert stats[&#34;Lower bound&#34;] == 725.0
assert stats[&#34;Upper bound&#34;] == 725.0</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_internal_solver.test_iteration_cb"><code class="name flex">
<span>def <span class="ident">test_iteration_cb</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_iteration_cb():
for solver_class in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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 &lt; 5
solver.solve(iteration_cb=custom_iteration_cb)
assert count == 5</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_internal_solver.test_redirect_output"><code class="name flex">
<span>def <span class="ident">test_redirect_output</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_redirect_output():
import sys
original_stdout = sys.stdout
io = StringIO()
with _RedirectOutput([io]):
print(&#34;Hello world&#34;)
assert sys.stdout == original_stdout
assert io.getvalue() == &#34;Hello world\n&#34;</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_internal_solver.test_relax"><code class="name flex">
<span>def <span class="ident">test_relax</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_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[&#34;Lower bound&#34;]) == 1288.0</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
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<ul>
<li><code><a title="miplearn.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance" href="#miplearn.solvers.tests.test_internal_solver.test_infeasible_instance">test_infeasible_instance</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver">test_internal_solver</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts">test_internal_solver_warm_starts</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_iteration_cb" href="#miplearn.solvers.tests.test_internal_solver.test_iteration_cb">test_iteration_cb</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_redirect_output" href="#miplearn.solvers.tests.test_internal_solver.test_redirect_output">test_redirect_output</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_relax" href="#miplearn.solvers.tests.test_internal_solver.test_relax">test_relax</a></code></li>
</ul>
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<h1 class="title">Module <code>miplearn.solvers.tests.test_lazy_cb</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.
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(&#34;x[0] = %.f&#34; % cb_solver.get_value(&#34;x&#34;, 0))
cobj = (cb_model.getVarByName(&#34;x[0]&#34;) * 1.0, &#34;&lt;&#34;, 0.0, &#34;cut&#34;)
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[&#34;x&#34;][0] == 0.0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb"><code class="name flex">
<span>def <span class="ident">test_lazy_cb</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_lazy_cb():
solver = GurobiSolver()
instance = _get_knapsack_instance(solver)
model = instance.to_model()
def lazy_cb(cb_solver, cb_model):
logger.info(&#34;x[0] = %.f&#34; % cb_solver.get_value(&#34;x&#34;, 0))
cobj = (cb_model.getVarByName(&#34;x[0]&#34;) * 1.0, &#34;&lt;&#34;, 0.0, &#34;cut&#34;)
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[&#34;x&#34;][0] == 0.0</code></pre>
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<ul>
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</ul>
</li>
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<ul class="">
<li><code><a title="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb" href="#miplearn.solvers.tests.test_lazy_cb.test_lazy_cb">test_lazy_cb</a></code></li>
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<h1 class="title">Module <code>miplearn.solvers.tests.test_learning_solver</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.
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 [&#34;exact&#34;, &#34;heuristic&#34;]:
for internal_solver in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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[&#34;Solution&#34;][&#34;x&#34;][0] == 1.0
assert data[&#34;Solution&#34;][&#34;x&#34;][1] == 0.0
assert data[&#34;Solution&#34;][&#34;x&#34;][2] == 1.0
assert data[&#34;Solution&#34;][&#34;x&#34;][3] == 1.0
assert data[&#34;Lower bound&#34;] == 1183.0
assert data[&#34;Upper bound&#34;] == 1183.0
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][0], 3) == 1.000
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][1], 3) == 0.923
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][2], 3) == 1.000
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][3], 3) == 0.000
assert round(data[&#34;LP value&#34;], 3) == 1287.923
assert len(data[&#34;MIP log&#34;]) &gt; 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(&#34;Solver: %s&#34; % 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[&#34;Solution&#34;][&#34;x&#34;].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=&#34;.pkl&#34;, 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], &#34;rb&#34;) as file:
instance = pickle.load(file)
assert len(instance.training_data) &gt; 0
# Test: parallel_solve
solver.parallel_solve(filenames)
for filename in filenames:
with open(filename, &#34;rb&#34;) as file:
instance = pickle.load(file)
assert len(instance.training_data) &gt; 0
# Test: solve (with specified output)
output = [f + &#34;.out&#34; 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=&#34;.pkl&#34;, 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[&#34;Lower bound&#34;] == stats[&#34;Predicted LB&#34;]</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.solvers.tests.test_learning_solver.test_learning_solver"><code class="name flex">
<span>def <span class="ident">test_learning_solver</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_learning_solver():
for mode in [&#34;exact&#34;, &#34;heuristic&#34;]:
for internal_solver in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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[&#34;Solution&#34;][&#34;x&#34;][0] == 1.0
assert data[&#34;Solution&#34;][&#34;x&#34;][1] == 0.0
assert data[&#34;Solution&#34;][&#34;x&#34;][2] == 1.0
assert data[&#34;Solution&#34;][&#34;x&#34;][3] == 1.0
assert data[&#34;Lower bound&#34;] == 1183.0
assert data[&#34;Upper bound&#34;] == 1183.0
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][0], 3) == 1.000
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][1], 3) == 0.923
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][2], 3) == 1.000
assert round(data[&#34;LP solution&#34;][&#34;x&#34;][3], 3) == 0.000
assert round(data[&#34;LP value&#34;], 3) == 1287.923
assert len(data[&#34;MIP log&#34;]) &gt; 100
solver.fit([instance])
solver.solve(instance)
# Assert solver is picklable
with tempfile.TemporaryFile() as file:
pickle.dump(solver, file)</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_learning_solver.test_parallel_solve"><code class="name flex">
<span>def <span class="ident">test_parallel_solve</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_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[&#34;Solution&#34;][&#34;x&#34;].keys()) == 4</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect"><code class="name flex">
<span>def <span class="ident">test_simulate_perfect</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_simulate_perfect():
internal_solver = GurobiSolver
instance = _get_knapsack_instance(internal_solver)
with tempfile.NamedTemporaryFile(suffix=&#34;.pkl&#34;, 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[&#34;Lower bound&#34;] == stats[&#34;Predicted LB&#34;]</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk"><code class="name flex">
<span>def <span class="ident">test_solve_fit_from_disk</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_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=&#34;.pkl&#34;, 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], &#34;rb&#34;) as file:
instance = pickle.load(file)
assert len(instance.training_data) &gt; 0
# Test: parallel_solve
solver.parallel_solve(filenames)
for filename in filenames:
with open(filename, &#34;rb&#34;) as file:
instance = pickle.load(file)
assert len(instance.training_data) &gt; 0
# Test: solve (with specified output)
output = [f + &#34;.out&#34; 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)</code></pre>
</details>
</dd>
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp"><code class="name flex">
<span>def <span class="ident">test_solve_without_lp</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_solve_without_lp():
for internal_solver in _get_internal_solvers():
logger.info(&#34;Solver: %s&#34; % 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)</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.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_learning_solver" href="#miplearn.solvers.tests.test_learning_solver.test_learning_solver">test_learning_solver</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_parallel_solve" href="#miplearn.solvers.tests.test_learning_solver.test_parallel_solve">test_parallel_solve</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect" href="#miplearn.solvers.tests.test_learning_solver.test_simulate_perfect">test_simulate_perfect</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk" href="#miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk">test_solve_fit_from_disk</a></code></li>
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp" href="#miplearn.solvers.tests.test_learning_solver.test_solve_without_lp">test_solve_without_lp</a></code></li>
</ul>
</li>
</ul>
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<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.tests</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 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</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="miplearn.tests.test_benchmark" href="test_benchmark.html">miplearn.tests.test_benchmark</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
<dt><code class="name"><a title="miplearn.tests.test_extractors" href="test_extractors.html">miplearn.tests.test_extractors</a></code></dt>
<dd>
<section class="desc"></section>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.tests.get_test_pyomo_instances"><code class="name flex">
<span>def <span class="ident">get_test_pyomo_instances</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 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</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" href="../index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
<ul>
<li><code><a title="miplearn.tests.test_benchmark" href="test_benchmark.html">miplearn.tests.test_benchmark</a></code></li>
<li><code><a title="miplearn.tests.test_extractors" href="test_extractors.html">miplearn.tests.test_extractors</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.tests.get_test_pyomo_instances" href="#miplearn.tests.get_test_pyomo_instances">get_test_pyomo_instances</a></code></li>
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</li>
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<header>
<h1 class="title">Module <code>miplearn.tests.test_benchmark</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.
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 = {
&#34;Strategy A&#34;: LearningSolver(),
&#34;Strategy B&#34;: LearningSolver(),
}
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12, 14)
benchmark.save_results(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results(&#34;/tmp/benchmark.csv&#34;)
assert benchmark.raw_results().values.shape == (12, 14)
def test_gap():
assert BenchmarkRunner._compute_gap(ub=0.0, lb=0.0) == 0.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=0.5) == 0.5
assert BenchmarkRunner._compute_gap(ub=1.0, lb=1.0) == 0.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=-1.0) == 1.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=None) == 1.0
assert BenchmarkRunner._compute_gap(ub=None, lb=1.0) == 1.0
assert BenchmarkRunner._compute_gap(ub=None, lb=None) == 1.0</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.tests.test_benchmark.test_benchmark"><code class="name flex">
<span>def <span class="ident">test_benchmark</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_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 = {
&#34;Strategy A&#34;: LearningSolver(),
&#34;Strategy B&#34;: LearningSolver(),
}
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12, 14)
benchmark.save_results(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results(&#34;/tmp/benchmark.csv&#34;)
assert benchmark.raw_results().values.shape == (12, 14)</code></pre>
</details>
</dd>
<dt id="miplearn.tests.test_benchmark.test_gap"><code class="name flex">
<span>def <span class="ident">test_gap</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_gap():
assert BenchmarkRunner._compute_gap(ub=0.0, lb=0.0) == 0.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=0.5) == 0.5
assert BenchmarkRunner._compute_gap(ub=1.0, lb=1.0) == 0.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=-1.0) == 1.0
assert BenchmarkRunner._compute_gap(ub=1.0, lb=None) == 1.0
assert BenchmarkRunner._compute_gap(ub=None, lb=1.0) == 1.0
assert BenchmarkRunner._compute_gap(ub=None, lb=None) == 1.0</code></pre>
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<ul class="">
<li><code><a title="miplearn.tests.test_benchmark.test_benchmark" href="#miplearn.tests.test_benchmark.test_benchmark">test_benchmark</a></code></li>
<li><code><a title="miplearn.tests.test_benchmark.test_gap" href="#miplearn.tests.test_benchmark.test_gap">test_gap</a></code></li>
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<header>
<h1 class="title">Module <code>miplearn.tests.test_extractors</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.
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 &#34;default&#34; in features.keys()
assert isinstance(features[&#34;default&#34;], np.ndarray)
assert features[&#34;default&#34;].shape == (6, 2)
assert features[&#34;default&#34;].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 &#34;default&#34; in features
assert features[&#34;default&#34;].shape == (6, 5)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.tests.test_extractors.test_instance_features_extractor"><code class="name flex">
<span>def <span class="ident">test_instance_features_extractor</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_instance_features_extractor():
instances, models = _get_instances()
features = InstanceFeaturesExtractor().extract(instances)
assert features.shape == (2, 3)</code></pre>
</details>
</dd>
<dt id="miplearn.tests.test_extractors.test_solution_extractor"><code class="name flex">
<span>def <span class="ident">test_solution_extractor</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_solution_extractor():
instances, models = _get_instances()
features = SolutionExtractor().extract(instances)
assert isinstance(features, dict)
assert &#34;default&#34; in features.keys()
assert isinstance(features[&#34;default&#34;], np.ndarray)
assert features[&#34;default&#34;].shape == (6, 2)
assert features[&#34;default&#34;].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,
]</code></pre>
</details>
</dd>
<dt id="miplearn.tests.test_extractors.test_variable_features_extractor"><code class="name flex">
<span>def <span class="ident">test_variable_features_extractor</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_variable_features_extractor():
instances, models = _get_instances()
features = VariableFeaturesExtractor().extract(instances)
assert isinstance(features, dict)
assert &#34;default&#34; in features
assert features[&#34;default&#34;].shape == (6, 5)</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.tests" href="index.html">miplearn.tests</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.tests.test_extractors.test_instance_features_extractor" href="#miplearn.tests.test_extractors.test_instance_features_extractor">test_instance_features_extractor</a></code></li>
<li><code><a title="miplearn.tests.test_extractors.test_solution_extractor" href="#miplearn.tests.test_extractors.test_solution_extractor">test_solution_extractor</a></code></li>
<li><code><a title="miplearn.tests.test_extractors.test_variable_features_extractor" href="#miplearn.tests.test_extractors.test_variable_features_extractor">test_variable_features_extractor</a></code></li>
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</head>
<body>
<main>
<article id="content">
<header>
<h1 class="title">Module <code>miplearn.types</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 typing import Optional, Dict, Callable, Any, Union, List
from mypy_extensions import TypedDict
VarIndex = Union[str, int, List[Union[str, int]]]
Solution = Dict[str, Dict[VarIndex, Optional[float]]]
TrainingSample = TypedDict(
&#34;TrainingSample&#34;,
{
&#34;LP log&#34;: str,
&#34;LP solution&#34;: Optional[Solution],
&#34;LP value&#34;: Optional[float],
&#34;Lower bound&#34;: Optional[float],
&#34;MIP log&#34;: str,
&#34;Solution&#34;: Optional[Solution],
&#34;Upper bound&#34;: Optional[float],
&#34;slacks&#34;: Dict,
},
total=False,
)
LPSolveStats = TypedDict(
&#34;LPSolveStats&#34;,
{
&#34;Optimal value&#34;: Optional[float],
&#34;Log&#34;: str,
},
)
MIPSolveStats = TypedDict(
&#34;MIPSolveStats&#34;,
{
&#34;Lower bound&#34;: Optional[float],
&#34;Upper bound&#34;: Optional[float],
&#34;Wallclock time&#34;: float,
&#34;Nodes&#34;: Optional[int],
&#34;Sense&#34;: str,
&#34;Log&#34;: str,
&#34;Warm start value&#34;: Optional[float],
&#34;LP value&#34;: Optional[float],
},
)
IterationCallback = Callable[[], bool]
LazyCallback = Callable[[Any, Any], None]
SolverParams = Dict[str, Any]
BranchPriorities = Solution
class Constraint:
pass</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="miplearn.types.Constraint"><code class="flex name class">
<span>class <span class="ident">Constraint</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Constraint:
pass</code></pre>
</details>
</dd>
<dt id="miplearn.types.LPSolveStats"><code class="flex name class">
<span>class <span class="ident">LPSolveStats</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>dict() -&gt; new empty dictionary
dict(mapping) -&gt; new dictionary initialized from a mapping object's
(key, value) pairs
dict(iterable) -&gt; new dictionary initialized as if via:
d = {}
for k, v in iterable:
d[k] = v
dict(**kwargs) -&gt; new dictionary initialized with the name=value pairs
in the keyword argument list.
For example:
dict(one=1, two=2)</p></section>
<h3>Ancestors</h3>
<ul class="hlist">
<li>builtins.dict</li>
</ul>
</dd>
<dt id="miplearn.types.MIPSolveStats"><code class="flex name class">
<span>class <span class="ident">MIPSolveStats</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>dict() -&gt; new empty dictionary
dict(mapping) -&gt; new dictionary initialized from a mapping object's
(key, value) pairs
dict(iterable) -&gt; new dictionary initialized as if via:
d = {}
for k, v in iterable:
d[k] = v
dict(**kwargs) -&gt; new dictionary initialized with the name=value pairs
in the keyword argument list.
For example:
dict(one=1, two=2)</p></section>
<h3>Ancestors</h3>
<ul class="hlist">
<li>builtins.dict</li>
</ul>
</dd>
<dt id="miplearn.types.TrainingSample"><code class="flex name class">
<span>class <span class="ident">TrainingSample</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>dict() -&gt; new empty dictionary
dict(mapping) -&gt; new dictionary initialized from a mapping object's
(key, value) pairs
dict(iterable) -&gt; new dictionary initialized as if via:
d = {}
for k, v in iterable:
d[k] = v
dict(**kwargs) -&gt; new dictionary initialized with the name=value pairs
in the keyword argument list.
For example:
dict(one=1, two=2)</p></section>
<h3>Ancestors</h3>
<ul class="hlist">
<li>builtins.dict</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" href="index.html">miplearn</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="miplearn.types.Constraint" href="#miplearn.types.Constraint">Constraint</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.types.LPSolveStats" href="#miplearn.types.LPSolveStats">LPSolveStats</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.types.MIPSolveStats" href="#miplearn.types.MIPSolveStats">MIPSolveStats</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.types.TrainingSample" href="#miplearn.types.TrainingSample">TrainingSample</a></code></h4>
</li>
</ul>
</li>
</ul>
</nav>
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</li>
<li >
<a href="../api/miplearn/index.html">API</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">

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</li>
<li >
<a href="../api/miplearn/index.html">API</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
@@ -153,11 +159,28 @@
<h1 id="customization">Customization</h1>
<h2 id="customizing-solver-parameters">Customizing solver parameters</h2>
<h3 id="selecting-the-internal-mip-solver">Selecting the internal MIP solver</h3>
<p>By default, <code>LearningSolver</code> uses <a href="https://www.gurobi.com/">Gurobi</a> as its internal MIP solver. Another supported solver is <a href="https://www.ibm.com/products/ilog-cplex-optimization-studio">IBM ILOG CPLEX</a>. To switch between solvers, use the <code>solver</code> constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.</p>
<pre><code class="language-python">from miplearn import LearningSolver
solver = LearningSolver(solver=&quot;cplex&quot;,
time_limit=300,
gap_tolerance=1e-3)
<p>By default, <code>LearningSolver</code> uses <a href="https://www.gurobi.com/">Gurobi</a> as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Supported solvers and modeling languages include:</p>
<ul>
<li><code>GurobiPyomoSolver</code>: Gurobi with Pyomo (default).</li>
<li><code>CplexPyomoSolver</code>: <a href="https://www.ibm.com/products/ilog-cplex-optimization-studio">IBM ILOG CPLEX</a> with Pyomo.</li>
<li><code>XpressPyomoSolver</code>: <a href="https://www.fico.com/en/products/fico-xpress-solver">FICO XPRESS Solver</a> with Pyomo.</li>
<li><code>GurobiSolver</code>: Gurobi without any modeling language.</li>
</ul>
<p>To switch between solvers, provide the desired class using the <code>solver</code> argument:</p>
<pre><code class="language-python">from miplearn import LearningSolver, CplexPyomoSolver
solver = LearningSolver(solver=CplexPyomoSolver)
</code></pre>
<p>To configure a particular solver, use the <code>params</code> constructor argument, as shown below.</p>
<pre><code class="language-python">from miplearn import LearningSolver, GurobiPyomoSolver
solver = LearningSolver(
solver=lambda: GurobiPyomoSolver(
params={
&quot;TimeLimit&quot;: 900,
&quot;MIPGap&quot;: 1e-3,
&quot;NodeLimit&quot;: 1000,
}
),
)
</code></pre>
<h2 id="customizing-solver-components">Customizing solver components</h2>
<p><code>LearningSolver</code> is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:</p>
@@ -181,13 +204,6 @@ solver2 = LearningSolver(components=[
PrimalSolutionComponent(...),
])
</code></pre>
<p>It is also possible to add components to an existing solver using the <code>solver.add</code> method, as shown below. If the solver already holds another component of that type, the new component will replace the previous one.</p>
<pre><code class="language-python"># Create solver with default components
solver = LearningSolver()
# Replace the default LazyConstraintComponent by one with custom parameters
solver.add(LazyConstraintComponent(...))
</code></pre>
<h3 id="adjusting-component-aggressiveness">Adjusting component aggressiveness</h3>
<p>The aggressiveness of classification components (such as <code>PrimalSolutionComponent</code> and <code>LazyConstraintComponent</code>) can
be adjusted through the <code>threshold</code> constructor argument. Internally, these components ask the ML models how confident

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</li>
<li >
<a href="api/miplearn/index.html">API</a>
</li>
</ul>
<ul class="nav navbar-nav navbar-right">
@@ -148,8 +154,10 @@
<div class="col-md-9" role="main">
<h1 id="miplearn">MIPLearn</h1>
<p><strong>MIPLearn</strong> is an extensible framework for <strong>Learning-Enhanced Mixed-Integer Optimization</strong>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.</p>
<p>The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <a href="problems/">benchmark results</a> and <a href="about/#references">references</a> for more details).</p>
<p><strong>MIPLearn</strong> is an extensible framework for solving discrete optimization problems using a combination of Mixed-Integer Linear Programming (MIP) and Machine Learning (ML). The framework uses ML methods to automatically identify patterns in previously solved instances of the problem, then uses these patterns to accelerate the performance of conventional state-of-the-art MIP solvers (such as CPLEX, Gurobi or XPRESS).</p>
<p>Unlike pure ML methods, MIPLearn is not only able to find high-quality solutions to discrete optimization problems, but it can also prove the optimality and feasibility of these solutions.
Unlike conventional MIP solvers, MIPLearn can take full advantage of very specific observations that happen to be true in a particular family of instances (such as the observation that a particular constraint is typically redundant, or that a particular variable typically assumes a certain value). </p>
<p>For certain classes of problems, this approach has been shown to provide significant performance benefits (see <a href="problems/">benchmarks</a> and <a href="about/">references</a>).</p>
<h3 id="features">Features</h3>
<ul>
<li>
@@ -285,5 +293,5 @@
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</li>
</ul>
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</li>
</ul>
<ul class="nav navbar-nav navbar-right">
@@ -149,11 +155,12 @@
<li class="third-level"><a href="#42-adding-lazy-constraints-through-callbacks">4.2 Adding lazy constraints through callbacks</a></li>
<li class="second-level"><a href="#5-obtaining-heuristic-solutions">5. Obtaining heuristic solutions</a></li>
<li class="second-level"><a href="#6-saving-and-loading-solver-state">6. Saving and loading solver state</a></li>
<li class="second-level"><a href="#6-scaling-up">6. Scaling Up</a></li>
<li class="second-level"><a href="#7-solving-training-instances-in-parallel">7. Solving training instances in parallel</a></li>
<li class="second-level"><a href="#8-current-limitations">8. Current Limitations</a></li>
<li class="third-level"><a href="#61-saving-and-loading-solver-state">6.1 Saving and loading solver state</a></li>
<li class="third-level"><a href="#62-solving-instances-in-parallel">6.2 Solving instances in parallel</a></li>
<li class="third-level"><a href="#63-solving-instances-from-the-disk">6.3 Solving instances from the disk</a></li>
<li class="second-level"><a href="#7-current-limitations">7. Current Limitations</a></li>
</ul>
</div></div>
@@ -242,7 +249,8 @@ for instance in test_instances:
<p class="admonition-title">Danger</p>
<p>The <code>heuristic</code> mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.</p>
</div>
<h2 id="6-saving-and-loading-solver-state">6. Saving and loading solver state</h2>
<h2 id="6-scaling-up">6. Scaling Up</h2>
<h3 id="61-saving-and-loading-solver-state">6.1 Saving and loading solver state</h3>
<p>After solving a large number of training instances, it may be desirable to save the current state of <code>LearningSolver</code> to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the standard <code>pickle</code> module, as the following example illustrates:</p>
<pre><code class="language-python">from miplearn import LearningSolver
import pickle
@@ -257,20 +265,22 @@ for instance in training_instances:
solver.fit(training_instances)
# Save trained solver to disk
pickle.dump(solver, open(&quot;solver.pickle&quot;, &quot;wb&quot;))
with open(&quot;solver.pickle&quot;, &quot;wb&quot;) as file:
pickle.dump(solver, file)
# Application restarts...
# Load trained solver from disk
solver = pickle.load(open(&quot;solver.pickle&quot;, &quot;rb&quot;))
with open(&quot;solver.pickle&quot;, &quot;rb&quot;) as file:
solver = pickle.load(file)
# Solve additional instances
test_instances = [...]
for instance in test_instances:
solver.solve(instance)
</code></pre>
<h2 id="7-solving-training-instances-in-parallel">7. Solving training instances in parallel</h2>
<p>In many situations, training and test instances can be solved in parallel to accelerate the training process. <code>LearningSolver</code> provides the method <code>parallel_solve(instances)</code> to easily achieve this:</p>
<h3 id="62-solving-instances-in-parallel">6.2 Solving instances in parallel</h3>
<p>In many situations, instances can be solved in parallel to accelerate the training process. <code>LearningSolver</code> provides the method <code>parallel_solve(instances)</code> to easily achieve this:</p>
<pre><code class="language-python">from miplearn import LearningSolver
training_instances = [...]
@@ -282,9 +292,48 @@ solver.fit(training_instances)
test_instances = [...]
solver.parallel_solve(test_instances)
</code></pre>
<h2 id="8-current-limitations">8. Current Limitations</h2>
<h3 id="63-solving-instances-from-the-disk">6.3 Solving instances from the disk</h3>
<p>In all examples above, we have assumed that instances are available as Python objects, stored in memory. When problem instances are very large, or when there is a large number of problem instances, this approach may require an excessive amount of memory. To reduce memory requirements, MIPLearn can also operate on instances that are stored on disk. More precisely, the methods <code>fit</code>, <code>solve</code> and <code>parallel_solve</code> in <code>LearningSolver</code> can operate on filenames (or lists of filenames) instead of instance objects, as the next example illustrates.
Instance files must be pickled instance objects. The method <code>solve</code> loads at most one instance to memory at a time, while <code>parallel_solve</code> loads at most <code>n_jobs</code> instances.</p>
<pre><code class="language-python">from miplearn import LearningSolver
# Construct and pickle 600 problem instances
for i in range(600):
instance = CustomInstance([...])
with open(&quot;instance_%03d.pkl&quot; % i, &quot;w&quot;) as file:
pickle.dump(instance, obj)
# Split instances into training and test
test_instances = [&quot;instance_%03d.pkl&quot; % i for i in range(500)]
train_instances = [&quot;instance_%03d.pkl&quot; % i for i in range(500, 600)]
# Create solver
solver = LearningSolver([...])
# Solve training instances
solver.parallel_solve(train_instances, n_jobs=4)
# Train ML models
solver.fit(train_instances)
# Solve test instances
solver.parallel_solve(test_instances, n_jobs=4)
</code></pre>
<p>By default, <code>solve</code> and <code>parallel_solve</code> modify files in place. That is, after the instances are loaded from disk and solved, MIPLearn writes them back to the disk, overwriting the original files. To write to an alternative file instead, the argument <code>output</code> may be used. In <code>solve</code>, this argument should be a single filename. In <code>parallel_solve</code>, it should be a list, containing exactly as many filenames as instances. If <code>output</code> is <code>None</code>, the modifications are simply discarded. This can be useful, for example, during benchmarks.</p>
<pre><code class="language-python"># Solve a single instance file and store the output to another file
solver.solve(&quot;knapsack_1.orig.pkl&quot;, output=&quot;knapsack_1.solved.pkl&quot;)
# Solve a list of instance files
instances = [&quot;knapsack_%03d.orig.pkl&quot; % i for i in range(100)]
output = [&quot;knapsack_%03d.solved.pkl&quot; % i for i in range(100)]
solver.parallel_solve(instances, output=output)
# Solve instances and discard solutions and training data
solver.parallel_solve(instances, output=None)
</code></pre>
<h2 id="7-current-limitations">7. Current Limitations</h2>
<ul>
<li>Only binary and continuous decision variables are currently supported.</li>
<li>Only binary and continuous decision variables are currently supported. General integer variables are not currently supported by all solver components.</li>
</ul></div>