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Update 0.2 docs

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Alinson S. Xavier
2021-01-22 07:25:10 -06:00
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0.2/api/miplearn/benchmark.html
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.benchmark API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -30,40 +30,71 @@
import logging
import os
from copy import deepcopy
from typing import Dict, Union, List
import pandas as pd
from tqdm.auto import tqdm
from miplearn.instance import Instance
from miplearn.solvers.learning import LearningSolver
from miplearn.types import LearningSolveStats
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
&#34;&#34;&#34;
Utility class that simplifies the task of comparing the performance of different
solvers.
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,
)
Example
-------
```python
benchmark = BenchmarkRunner({
&#34;Baseline&#34;: LearningSolver(...),
&#34;Strategy A&#34;: LearningSolver(...),
&#34;Strategy B&#34;: LearningSolver(...),
&#34;Strategy C&#34;: LearningSolver(...),
})
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=5)
benchmark.save_results(&#34;result.csv&#34;)
```
Parameters
----------
solvers: Dict[str, LearningSolver]
Dictionary containing the solvers to compare. Solvers may have different
arguments and components. The key should be the name of the solver. It
appears in the exported tables of results.
&#34;&#34;&#34;
def __init__(self, solvers: Dict[str, LearningSolver]) -&gt; None:
self.solvers: Dict[str, LearningSolver] = solvers
self.results = pd.DataFrame(
columns=[
&#34;Solver&#34;,
&#34;Instance&#34;,
]
)
def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
instances: Union[List[str], List[Instance]],
n_jobs: int = 1,
n_trials: int = 3,
) -&gt; None:
&#34;&#34;&#34;
Solves the given instances in parallel and collect benchmark statistics.
Parameters
----------
instances: Union[List[str], List[Instance]]
List of instances to solve. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
n_jobs: int
List of instances to solve in parallel at a time.
n_trials: int
How many times each instance should be solved.
&#34;&#34;&#34;
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
@@ -74,69 +105,45 @@ class BenchmarkRunner:
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,
)
idx = i % len(instances)
results[i][&#34;Solver&#34;] = solver_name
results[i][&#34;Instance&#34;] = idx
self.results = self.results.append(pd.DataFrame([results[i]]))
self._restore_miplearn_logger()
def raw_results(self):
return self.results
def write_csv(self, filename: str) -&gt; None:
&#34;&#34;&#34;
Writes the collected results to a CSV file.
def save_results(self, filename):
Parameters
----------
filename: str
The name of the file.
&#34;&#34;&#34;
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 fit(self, instances: Union[List[str], List[Instance]]) -&gt; None:
&#34;&#34;&#34;
Trains all solvers with the provided training instances.
def load_state(self, filename):
Parameters
----------
instances: Union[List[str], List[Instance]]
List of training instances. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
&#34;&#34;&#34;
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)
solver.fit(instances)
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):
def _silence_miplearn_logger(self) -&gt; None:
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING)
def _restore_miplearn_logger(self):
def _restore_miplearn_logger(self) -&gt; None:
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
miplearn_logger.setLevel(self.prev_log_level)</code></pre>
</details>
@@ -155,37 +162,86 @@ class BenchmarkRunner:
<span>(</span><span>solvers)</span>
</code></dt>
<dd>
<section class="desc"></section>
<section class="desc"><p>Utility class that simplifies the task of comparing the performance of different
solvers.</p>
<h2 id="example">Example</h2>
<pre><code class="language-python">benchmark = BenchmarkRunner({
&quot;Baseline&quot;: LearningSolver(...),
&quot;Strategy A&quot;: LearningSolver(...),
&quot;Strategy B&quot;: LearningSolver(...),
&quot;Strategy C&quot;: LearningSolver(...),
})
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=5)
benchmark.save_results(&quot;result.csv&quot;)
</code></pre>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>solvers</code></strong> :&ensp;<code>Dict</code>[<code>str</code>, <code>LearningSolver</code>]</dt>
<dd>Dictionary containing the solvers to compare. Solvers may have different
arguments and components. The key should be the name of the solver. It
appears in the exported tables of results.</dd>
</dl></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
&#34;&#34;&#34;
Utility class that simplifies the task of comparing the performance of different
solvers.
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,
)
Example
-------
```python
benchmark = BenchmarkRunner({
&#34;Baseline&#34;: LearningSolver(...),
&#34;Strategy A&#34;: LearningSolver(...),
&#34;Strategy B&#34;: LearningSolver(...),
&#34;Strategy C&#34;: LearningSolver(...),
})
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=5)
benchmark.save_results(&#34;result.csv&#34;)
```
Parameters
----------
solvers: Dict[str, LearningSolver]
Dictionary containing the solvers to compare. Solvers may have different
arguments and components. The key should be the name of the solver. It
appears in the exported tables of results.
&#34;&#34;&#34;
def __init__(self, solvers: Dict[str, LearningSolver]) -&gt; None:
self.solvers: Dict[str, LearningSolver] = solvers
self.results = pd.DataFrame(
columns=[
&#34;Solver&#34;,
&#34;Instance&#34;,
]
)
def parallel_solve(
self,
instances,
n_jobs=1,
n_trials=1,
index_offset=0,
):
instances: Union[List[str], List[Instance]],
n_jobs: int = 1,
n_trials: int = 3,
) -&gt; None:
&#34;&#34;&#34;
Solves the given instances in parallel and collect benchmark statistics.
Parameters
----------
instances: Union[List[str], List[Instance]]
List of instances to solve. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
n_jobs: int
List of instances to solve in parallel at a time.
n_trials: int
How many times each instance should be solved.
&#34;&#34;&#34;
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
@@ -196,131 +252,122 @@ class BenchmarkRunner:
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,
)
idx = i % len(instances)
results[i][&#34;Solver&#34;] = solver_name
results[i][&#34;Instance&#34;] = idx
self.results = self.results.append(pd.DataFrame([results[i]]))
self._restore_miplearn_logger()
def raw_results(self):
return self.results
def write_csv(self, filename: str) -&gt; None:
&#34;&#34;&#34;
Writes the collected results to a CSV file.
def save_results(self, filename):
Parameters
----------
filename: str
The name of the file.
&#34;&#34;&#34;
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 fit(self, instances: Union[List[str], List[Instance]]) -&gt; None:
&#34;&#34;&#34;
Trains all solvers with the provided training instances.
def load_state(self, filename):
Parameters
----------
instances: Union[List[str], List[Instance]]
List of training instances. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
&#34;&#34;&#34;
for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)
solver.fit(instances)
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):
def _silence_miplearn_logger(self) -&gt; None:
miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING)
def _restore_miplearn_logger(self):
def _restore_miplearn_logger(self) -&gt; None:
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>
<span>def <span class="ident">fit</span></span>(<span>self, instances)</span>
</code></dt>
<dd>
<section class="desc"></section>
<section class="desc"><p>Trains all solvers with the provided training instances.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>instances</code></strong> :&ensp; <code>Union</code>[<code>List</code>[<code>str</code>], <code>List</code>[<code>Instance</code>]]</dt>
<dd>List of training instances. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.</dd>
</dl></section>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def fit(self, training_instances):
<pre><code class="python">def fit(self, instances: Union[List[str], List[Instance]]) -&gt; None:
&#34;&#34;&#34;
Trains all solvers with the provided training instances.
Parameters
----------
instances: Union[List[str], List[Instance]]
List of training instances. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
&#34;&#34;&#34;
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>
solver.fit(instances)</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>
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=1, n_trials=3)</span>
</code></dt>
<dd>
<section class="desc"></section>
<section class="desc"><p>Solves the given instances in parallel and collect benchmark statistics.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>instances</code></strong> :&ensp;<code>Union</code>[<code>List</code>[<code>str</code>], <code>List</code>[<code>Instance</code>]]</dt>
<dd>List of instances to solve. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.</dd>
<dt><strong><code>n_jobs</code></strong> :&ensp;<code>int</code></dt>
<dd>List of instances to solve in parallel at a time.</dd>
<dt><strong><code>n_trials</code></strong> :&ensp;<code>int</code></dt>
<dd>How many times each instance should be solved.</dd>
</dl></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,
):
instances: Union[List[str], List[Instance]],
n_jobs: int = 1,
n_trials: int = 3,
) -&gt; None:
&#34;&#34;&#34;
Solves the given instances in parallel and collect benchmark statistics.
Parameters
----------
instances: Union[List[str], List[Instance]]
List of instances to solve. This can either be a list of instances
already loaded in memory, or a list of filenames pointing to pickled (and
optionally gzipped) files.
n_jobs: int
List of instances to solve in parallel at a time.
n_trials: int
How many times each instance should be solved.
&#34;&#34;&#34;
self._silence_miplearn_logger()
trials = instances * n_trials
for (solver_name, solver) in self.solvers.items():
@@ -331,64 +378,40 @@ class BenchmarkRunner:
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,
)
idx = i % len(instances)
results[i][&#34;Solver&#34;] = solver_name
results[i][&#34;Instance&#34;] = idx
self.results = self.results.append(pd.DataFrame([results[i]]))
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>
<dt id="miplearn.benchmark.BenchmarkRunner.write_csv"><code class="name flex">
<span>def <span class="ident">write_csv</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<section class="desc"></section>
<section class="desc"><p>Writes the collected results to a CSV file.</p>
<h2 id="parameters">Parameters</h2>
<dl>
<dt><strong><code>filename</code></strong> :&ensp;<code>str</code></dt>
<dd>The name of the file.</dd>
</dl></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):
<pre><code class="python">def write_csv(self, filename: str) -&gt; None:
&#34;&#34;&#34;
Writes the collected results to a CSV file.
Parameters
----------
filename: str
The name of the file.
&#34;&#34;&#34;
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>
@@ -409,14 +432,10 @@ class BenchmarkRunner:
<ul>
<li>
<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4>
<ul class="two-column">
<ul class="">
<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>
<li><code><a title="miplearn.benchmark.BenchmarkRunner.write_csv" href="#miplearn.benchmark.BenchmarkRunner.write_csv">write_csv</a></code></li>
</ul>
</li>
</ul>
@@ -425,7 +444,7 @@ class BenchmarkRunner:
</nav>
</main>
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6
0.2/api/miplearn/classifiers/adaptive.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.classifiers.adaptive API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -106,7 +106,7 @@ class AdaptiveClassifier(Classifier):
<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>
<span>(</span><span>candidates=None, evaluator=&lt;miplearn.classifiers.evaluator.ClassifierEvaluator object&gt;)</span>
</code></dt>
<dd>
<section class="desc"><p>A meta-classifier which dynamically selects what actual classifier to use
@@ -241,7 +241,7 @@ based on its cross-validation score on a particular training data set.</p>
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@@ -3,7 +3,7 @@
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@@ -159,7 +159,7 @@ counts how many times each label appeared, hence the name.</p></section>
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@@ -3,7 +3,7 @@
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@@ -308,7 +308,7 @@ acceptable. Other numbers are a linear interpolation of these two extremes.</p><
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@@ -3,7 +3,7 @@
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@@ -115,7 +115,7 @@ class ClassifierEvaluator:
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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
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<meta name="generator" content="pdoc 0.7.0" />
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -97,7 +97,6 @@ class Regressor(ABC):
<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
@@ -127,8 +126,8 @@ inheritance.</p></section>
</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.counting.CountingClassifier" href="counting.html#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></li>
<li><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="cv.html#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></li>
</ul>
<h3>Methods</h3>
@@ -181,7 +180,6 @@ def predict_proba(self, x_test):
</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
@@ -282,7 +280,7 @@ def predict(self):
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@@ -3,7 +3,7 @@
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@@ -80,7 +80,7 @@
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@@ -3,7 +3,7 @@
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@@ -93,7 +93,7 @@ def test_counting():
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@@ -3,7 +3,7 @@
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@@ -153,7 +153,7 @@ def test_cv():
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@@ -3,7 +3,7 @@
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@@ -99,7 +99,7 @@ def test_evaluator():
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@@ -3,7 +3,7 @@
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@@ -133,7 +133,7 @@ def test_threshold_dynamic():
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -93,7 +93,6 @@ class MinPrecisionThreshold(DynamicThreshold):
<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
@@ -238,7 +237,7 @@ positive rate (also known as precision).</p></section>
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@@ -3,7 +3,7 @@
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<title>miplearn.components.component API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -32,7 +32,7 @@ from abc import ABC, abstractmethod
from typing import Any, List, Union, TYPE_CHECKING
from miplearn.instance import Instance
from miplearn.types import MIPSolveStats, TrainingSample
from miplearn.types import LearningSolveStats, TrainingSample
if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver
@@ -73,7 +73,7 @@ class Component(ABC):
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
stats: LearningSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
@@ -87,13 +87,13 @@ class Component(ABC):
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
stats: LearningSolveStats
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
training_data: TrainingSample
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,
@@ -156,7 +156,6 @@ class Component(ABC):
<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>
@@ -202,7 +201,7 @@ strategy.</p></section>
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
stats: LearningSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
@@ -216,13 +215,13 @@ strategy.</p></section>
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
stats: LearningSolveStats
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
training_data: TrainingSample
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,
@@ -279,16 +278,16 @@ strategy.</p></section>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.components.composite.CompositeComponent" href="composite.html#miplearn.components.composite.CompositeComponent">CompositeComponent</a></li>
<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.lazy_static.StaticLazyConstraintsComponent" href="lazy_static.html#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</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>
<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.drop_redundant.DropRedundantInequalitiesStep" href="steps/drop_redundant.html#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</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>
</ul>
<h3>Methods</h3>
<dl>
@@ -305,13 +304,13 @@ strategy.</p></section>
<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>
<dt><strong><code>stats</code></strong> :&ensp;<code>LearningSolveStats</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>
<dt><strong><code>training_data</code></strong> :&ensp;<code>TrainingSample</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,
@@ -328,7 +327,7 @@ def after_solve(
solver: &#34;LearningSolver&#34;,
instance: Instance,
model: Any,
stats: MIPSolveStats,
stats: LearningSolveStats,
training_data: TrainingSample,
) -&gt; None:
&#34;&#34;&#34;
@@ -342,13 +341,13 @@ def after_solve(
The instance being solved.
model: Any
The concrete optimization model being solved.
stats: dict
stats: LearningSolveStats
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
training_data: TrainingSample
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,
@@ -518,7 +517,7 @@ ends. If it retunrs True for any component, the MIP is solved again.</p>
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@@ -3,7 +3,7 @@
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@@ -226,7 +226,7 @@ RelaxationComponent for a concrete example.</p>
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@@ -3,7 +3,7 @@
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@@ -378,7 +378,7 @@ class UserCutsComponent(Component):
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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.components API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -203,7 +203,7 @@ def classifier_evaluation_dict(tp, tn, fp, fn):
</nav>
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.lazy_dynamic API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -402,7 +402,7 @@ class DynamicLazyConstraintsComponent(Component):
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<head>
<meta charset="utf-8">
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<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.lazy_static API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -616,7 +616,7 @@ strategy.</p></section>
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.objective API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -384,7 +384,7 @@ class ObjectiveValueComponent(Component):
</nav>
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<script>hljs.initHighlightingOnLoad()</script>

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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.primal API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -214,7 +214,7 @@ class PrimalSolutionComponent(Component):
<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>
<span>(</span><span>classifier=&lt;miplearn.classifiers.adaptive.AdaptiveClassifier object&gt;, mode='exact', threshold=&lt;miplearn.classifiers.threshold.MinPrecisionThreshold object&gt;)</span>
</code></dt>
<dd>
<section class="desc"><p>A component that predicts primal solutions.</p></section>
@@ -602,7 +602,7 @@ class PrimalSolutionComponent(Component):
</nav>
</main>
<footer id="footer">
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<script>hljs.initHighlightingOnLoad()</script>

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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
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<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.relaxation API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -318,7 +318,7 @@ constraint loop.</dd>
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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
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<title>miplearn.components.steps.convert_tight API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -627,7 +627,7 @@ before this component is used.</p></section>
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<script>hljs.initHighlightingOnLoad()</script>

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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
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<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.steps.drop_redundant API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -655,7 +655,7 @@ before this component is used.</p></section>
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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.steps API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -72,7 +72,7 @@
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</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<script>hljs.initHighlightingOnLoad()</script>

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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.steps.relax_integrality API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -66,7 +66,6 @@ class RelaxIntegralityStep(Component):
<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>
@@ -134,7 +133,7 @@ class RelaxIntegralityStep(Component):
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@@ -3,7 +3,7 @@
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<meta charset="utf-8">
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<title>miplearn.components.steps.tests API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -62,7 +62,7 @@
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
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<title>miplearn.components.steps.tests.test_convert_tight API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -377,7 +377,7 @@ features, which can be provided as inputs to machine learning models.</p></secti
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.components.steps.tests.test_drop_redundant API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -756,7 +756,7 @@ def test_x_multiple_solves():
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
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<title>miplearn.components.tests API documentation</title>
<meta name="description" content="" />
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@@ -85,7 +85,7 @@
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.tests.test_composite API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -171,7 +171,7 @@ def test_composite():
</nav>
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.tests.test_lazy_dynamic API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -357,7 +357,7 @@ def test_lazy_evaluate():
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.components.tests.test_lazy_static API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -530,7 +530,7 @@ def test_fit():
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.components.tests.test_objective API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -166,7 +166,7 @@ def test_obj_evaluate():
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.components.tests.test_primal API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -298,7 +298,7 @@ def test_primal_parallel_fit():
</nav>
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.extractors API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -196,7 +196,6 @@ class ObjectiveValueExtractor(Extractor):
<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
@@ -230,10 +229,10 @@ inheritance.</p></section>
</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>
<li><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></li>
<li><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></li>
</ul>
<h3>Static methods</h3>
<dl>
@@ -282,7 +281,6 @@ def extract(self, instances):
</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
@@ -541,7 +539,6 @@ inheritance.</p></section>
</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
@@ -672,7 +669,7 @@ inheritance.</p></section>
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@@ -3,7 +3,7 @@
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@@ -134,7 +134,7 @@ from .solvers.pyomo.gurobi import GurobiPyomoSolver</code></pre>
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@@ -3,7 +3,7 @@
<head>
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@@ -373,13 +373,13 @@ features, which can be provided as inputs to machine learning models.</p></secti
</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.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>
<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.knapsack.MultiKnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</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>
<li><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></li>
<li><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></li>
</ul>
<h3>Methods</h3>
<dl>
@@ -767,7 +767,7 @@ def to_model(self) -&gt; Any:
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@@ -3,7 +3,7 @@
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@@ -286,7 +286,7 @@ it is formatted using formatException() and appended to the message.</p></sectio
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@@ -3,7 +3,7 @@
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@@ -80,7 +80,7 @@
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -515,7 +515,7 @@ instead of Pyomo, used for testing.</p></section>
</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>
<span>(</span><span>n=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, m=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, w=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, K=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, u=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, alpha=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, fix_w=False, w_jitter=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, round=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Initialize the problem generator.</p>
@@ -873,7 +873,7 @@ same size and items don't shuffle around.</p></section>
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -208,7 +208,7 @@ class MaxWeightStableSetInstance(Instance):
</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>
<span>(</span><span>w=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, n=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, p=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, fix_graph=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Random instance generator for the Maximum-Weight Stable Set Problem.</p>
@@ -426,7 +426,7 @@ a subset of vertices, no two of which are adjacent.</p>
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@@ -3,7 +3,7 @@
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@@ -75,7 +75,7 @@
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -107,7 +107,7 @@ def test_knapsack_generator():
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -183,7 +183,7 @@ def test_stab_generator_random_graph():
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -234,7 +234,7 @@ def test_subtour():
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -264,7 +264,7 @@ class TravelingSalesmanInstance(Instance):
</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>
<span>(</span><span>x=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, y=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, n=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, gamma=&lt;scipy.stats._distn_infrastructure.rv_frozen object&gt;, fix_cities=True, round=True)</span>
</code></dt>
<dd>
<section class="desc"><p>Random generator for the Traveling Salesman Problem.</p>
@@ -579,7 +579,7 @@ one of Karp's 21 NP-complete problems.</p></section>
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@@ -3,7 +3,7 @@
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@@ -920,7 +920,7 @@ LP relaxation of that node.</dd>
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@@ -3,7 +3,7 @@
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -115,7 +115,7 @@ class _RedirectOutput:
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@@ -3,7 +3,7 @@
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<title>miplearn.solvers.internal API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -303,7 +303,6 @@ class InternalSolver(ABC):
<dl>
<dt id="miplearn.solvers.internal.InternalSolver"><code class="flex name class">
<span>class <span class="ident">InternalSolver</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt>
<dd>
<section class="desc"><p>Abstract class representing the MIP solver used internally by LearningSolver.</p></section>
@@ -561,8 +560,8 @@ class InternalSolver(ABC):
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="pyomo/base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
<li><a title="miplearn.solvers.gurobi.GurobiSolver" href="gurobi.html#miplearn.solvers.gurobi.GurobiSolver">GurobiSolver</a></li>
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="pyomo/base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
</ul>
<h3>Methods</h3>
<dl>
@@ -1134,7 +1133,7 @@ def solve_lp(
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@@ -3,7 +3,7 @@
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<title>miplearn.solvers.learning API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -46,7 +46,7 @@ from miplearn.instance import Instance
from miplearn.solvers import _RedirectOutput
from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
from miplearn.types import MIPSolveStats, TrainingSample
from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats
logger = logging.getLogger(__name__)
@@ -153,7 +153,7 @@ class LearningSolver:
output_filename: Optional[str] = None,
discard_output: bool = False,
tee: bool = False,
) -&gt; MIPSolveStats:
) -&gt; LearningSolveStats:
# Load instance from file, if necessary
filename = None
@@ -229,15 +229,24 @@ class LearningSolver:
# Solve MILP
logger.info(&#34;Solving MILP...&#34;)
stats = self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
stats = cast(
LearningSolveStats,
self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
),
)
if &#34;LP value&#34; in training_sample.keys():
stats[&#34;LP value&#34;] = training_sample[&#34;LP value&#34;]
stats[&#34;Solver&#34;] = &#34;default&#34;
stats[&#34;Gap&#34;] = self._compute_gap(
ub=stats[&#34;Upper bound&#34;],
lb=stats[&#34;Lower bound&#34;],
)
stats[&#34;Mode&#34;] = self.mode
# Read MIP solution and bounds
# Add some information to training_sample
training_sample[&#34;Lower bound&#34;] = stats[&#34;Lower bound&#34;]
training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;]
training_sample[&#34;MIP log&#34;] = stats[&#34;Log&#34;]
@@ -268,7 +277,7 @@ class LearningSolver:
output_filename: Optional[str] = None,
discard_output: bool = False,
tee: bool = False,
) -&gt; MIPSolveStats:
) -&gt; LearningSolveStats:
&#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process.
@@ -301,7 +310,7 @@ class LearningSolver:
Returns
-------
MIPSolveStats
LearningSolveStats
A dictionary of solver statistics containing at least the following
keys: &#34;Lower bound&#34;, &#34;Upper bound&#34;, &#34;Wallclock time&#34;, &#34;Nodes&#34;,
&#34;Sense&#34;, &#34;Log&#34;, &#34;Warm start value&#34; and &#34;LP value&#34;.
@@ -337,7 +346,7 @@ class LearningSolver:
label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]:
) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34;
Solves multiple instances in parallel.
@@ -364,7 +373,7 @@ class LearningSolver:
Returns
-------
List[MIPSolveStats]
List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]`
@@ -409,7 +418,19 @@ class LearningSolver:
def __getstate__(self) -&gt; Dict:
self.internal_solver = None
return self.__dict__</code></pre>
return self.__dict__
@staticmethod
def _compute_gap(ub: Optional[float], lb: Optional[float]) -&gt; Optional[float]:
if lb is None or ub is None or lb * ub &lt; 0:
# solver did not find a solution and/or bound
return None
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))</code></pre>
</details>
</section>
<section>
@@ -531,7 +552,7 @@ the theoretical performance of perfect ML models.</dd>
output_filename: Optional[str] = None,
discard_output: bool = False,
tee: bool = False,
) -&gt; MIPSolveStats:
) -&gt; LearningSolveStats:
# Load instance from file, if necessary
filename = None
@@ -607,15 +628,24 @@ the theoretical performance of perfect ML models.</dd>
# Solve MILP
logger.info(&#34;Solving MILP...&#34;)
stats = self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
stats = cast(
LearningSolveStats,
self.internal_solver.solve(
tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
),
)
if &#34;LP value&#34; in training_sample.keys():
stats[&#34;LP value&#34;] = training_sample[&#34;LP value&#34;]
stats[&#34;Solver&#34;] = &#34;default&#34;
stats[&#34;Gap&#34;] = self._compute_gap(
ub=stats[&#34;Upper bound&#34;],
lb=stats[&#34;Lower bound&#34;],
)
stats[&#34;Mode&#34;] = self.mode
# Read MIP solution and bounds
# Add some information to training_sample
training_sample[&#34;Lower bound&#34;] = stats[&#34;Lower bound&#34;]
training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;]
training_sample[&#34;MIP log&#34;] = stats[&#34;Log&#34;]
@@ -646,7 +676,7 @@ the theoretical performance of perfect ML models.</dd>
output_filename: Optional[str] = None,
discard_output: bool = False,
tee: bool = False,
) -&gt; MIPSolveStats:
) -&gt; LearningSolveStats:
&#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process.
@@ -679,7 +709,7 @@ the theoretical performance of perfect ML models.</dd>
Returns
-------
MIPSolveStats
LearningSolveStats
A dictionary of solver statistics containing at least the following
keys: &#34;Lower bound&#34;, &#34;Upper bound&#34;, &#34;Wallclock time&#34;, &#34;Nodes&#34;,
&#34;Sense&#34;, &#34;Log&#34;, &#34;Warm start value&#34; and &#34;LP value&#34;.
@@ -715,7 +745,7 @@ the theoretical performance of perfect ML models.</dd>
label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]:
) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34;
Solves multiple instances in parallel.
@@ -742,7 +772,7 @@ the theoretical performance of perfect ML models.</dd>
Returns
-------
List[MIPSolveStats]
List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]`
@@ -787,7 +817,19 @@ the theoretical performance of perfect ML models.</dd>
def __getstate__(self) -&gt; Dict:
self.internal_solver = None
return self.__dict__</code></pre>
return self.__dict__
@staticmethod
def _compute_gap(ub: Optional[float], lb: Optional[float]) -&gt; Optional[float]:
if lb is None or ub is None or lb * ub &lt; 0:
# solver did not find a solution and/or bound
return None
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))</code></pre>
</details>
<h3>Methods</h3>
<dl>
@@ -808,7 +850,7 @@ the theoretical performance of perfect ML models.</dd>
</details>
</dd>
<dt id="miplearn.solvers.learning.LearningSolver.parallel_solve"><code class="name flex">
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=4, label=&#39;Solve&#39;, output_filenames=None, discard_outputs=False)</span>
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=4, label='Solve', output_filenames=None, discard_outputs=False)</span>
</code></dt>
<dd>
<section class="desc"><p>Solves multiple instances in parallel.</p>
@@ -834,7 +876,7 @@ them instead. Useful during benchmarking.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>List</code>[<code>MIPSolveStats</code>]</dt>
<dt><code>List</code>[<code>LearningSolveStats</code>]</dt>
<dd>List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling
<code>[solver.solve(p) for p in instances]</code></dd>
@@ -850,7 +892,7 @@ The list is the same you would obtain by calling
label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]:
) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34;
Solves multiple instances in parallel.
@@ -877,7 +919,7 @@ The list is the same you would obtain by calling
Returns
-------
List[MIPSolveStats]
List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]`
@@ -933,7 +975,7 @@ them. Useful during benchmarking.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>MIPSolveStats</code></dt>
<dt><code>LearningSolveStats</code></dt>
<dd>
<p>A dictionary of solver statistics containing at least the following
keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
@@ -955,7 +997,7 @@ details.</p>
output_filename: Optional[str] = None,
discard_output: bool = False,
tee: bool = False,
) -&gt; MIPSolveStats:
) -&gt; LearningSolveStats:
&#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process.
@@ -988,7 +1030,7 @@ details.</p>
Returns
-------
MIPSolveStats
LearningSolveStats
A dictionary of solver statistics containing at least the following
keys: &#34;Lower bound&#34;, &#34;Upper bound&#34;, &#34;Wallclock time&#34;, &#34;Nodes&#34;,
&#34;Sense&#34;, &#34;Log&#34;, &#34;Warm start value&#34; and &#34;LP value&#34;.
@@ -1050,7 +1092,7 @@ details.</p>
</nav>
</main>
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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6
0.2/api/miplearn/solvers/pyomo/base.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.pyomo.base API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -665,8 +665,8 @@ class BasePyomoSolver(InternalSolver):
</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.gurobi.GurobiPyomoSolver" href="gurobi.html#miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver">GurobiPyomoSolver</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>
@@ -722,7 +722,7 @@ class BasePyomoSolver(InternalSolver):
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/cplex.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.pyomo.cplex API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -185,7 +185,7 @@ class CplexPyomoSolver(BasePyomoSolver):
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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</footer>
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4
0.2/api/miplearn/solvers/pyomo/gurobi.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.pyomo.gurobi API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -213,7 +213,7 @@ class GurobiPyomoSolver(BasePyomoSolver):
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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</footer>
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<script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/index.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.pyomo API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -80,7 +80,7 @@
</nav>
</main>
<footer id="footer">
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</footer>
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<script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/xpress.html
View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.pyomo.xpress API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -166,7 +166,7 @@ class XpressPyomoSolver(BasePyomoSolver):
</nav>
</main>
<footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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View File

@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
<meta name="generator" content="pdoc 0.7.0" />
<meta name="generator" content="pdoc 0.7.5" />
<title>miplearn.solvers.tests API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -242,7 +242,7 @@ features, which can be provided as inputs to machine learning models.</p></secti
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4
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@@ -3,7 +3,7 @@
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<title>miplearn.solvers.tests.test_internal_solver API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -535,7 +535,7 @@ def test_iteration_cb():
</nav>
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4
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
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<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.solvers.tests.test_lazy_cb API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -111,7 +111,7 @@ def test_lazy_cb():
</nav>
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36
0.2/api/miplearn/solvers/tests/test_learning_solver.html
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@@ -3,7 +3,7 @@
<head>
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<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.solvers.tests.test_learning_solver API documentation</title>
<meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -155,7 +155,17 @@ def test_simulate_perfect():
simulate_perfect=True,
)
stats = solver.solve(tmp.name)
assert stats[&#34;Lower bound&#34;] == stats[&#34;Predicted LB&#34;]</code></pre>
assert stats[&#34;Lower bound&#34;] == stats[&#34;Predicted LB&#34;]
def test_gap():
assert LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
assert LearningSolver._compute_gap(ub=None, lb=None) is None</code></pre>
</details>
</section>
<section>
@@ -165,6 +175,25 @@ def test_simulate_perfect():
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="miplearn.solvers.tests.test_learning_solver.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 LearningSolver._compute_gap(ub=0.0, lb=0.0) == 0.0
assert LearningSolver._compute_gap(ub=1.0, lb=0.5) == 0.5
assert LearningSolver._compute_gap(ub=1.0, lb=1.0) == 0.0
assert LearningSolver._compute_gap(ub=1.0, lb=-1.0) is None
assert LearningSolver._compute_gap(ub=1.0, lb=None) is None
assert LearningSolver._compute_gap(ub=None, lb=1.0) is None
assert LearningSolver._compute_gap(ub=None, lb=None) is None</code></pre>
</details>
</dd>
<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>
@@ -346,6 +375,7 @@ def test_simulate_perfect():
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_gap" href="#miplearn.solvers.tests.test_learning_solver.test_gap">test_gap</a></code></li>
<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>
@@ -357,7 +387,7 @@ def test_simulate_perfect():
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@@ -3,7 +3,7 @@
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@@ -130,7 +130,7 @@ def get_test_pyomo_instances():
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54
0.2/api/miplearn/tests/test_benchmark.html
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@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8">
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<title>miplearn.tests.test_benchmark API documentation</title>
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@@ -55,24 +55,10 @@ def test_benchmark():
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)
assert benchmark.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>
benchmark.write_csv(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)</code></pre>
</details>
</section>
<section>
@@ -109,33 +95,10 @@ def test_gap():
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)
assert benchmark.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>
benchmark.write_csv(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)</code></pre>
</details>
</dd>
</dl>
@@ -157,14 +120,13 @@ def test_gap():
<li><h3><a href="#header-functions">Functions</a></h3>
<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>
</ul>
</li>
</ul>
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4
0.2/api/miplearn/tests/test_extractors.html
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@@ -3,7 +3,7 @@
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@@ -193,7 +193,7 @@ def test_variable_features_extractor():
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48
0.2/api/miplearn/types.html
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@@ -3,7 +3,7 @@
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<title>miplearn.types API documentation</title>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@@ -73,6 +73,25 @@ MIPSolveStats = TypedDict(
},
)
LearningSolveStats = TypedDict(
&#34;LearningSolveStats&#34;,
{
&#34;Gap&#34;: Optional[float],
&#34;Instance&#34;: Union[str, int],
&#34;LP value&#34;: Optional[float],
&#34;Log&#34;: str,
&#34;Lower bound&#34;: Optional[float],
&#34;Mode&#34;: str,
&#34;Nodes&#34;: Optional[int],
&#34;Sense&#34;: str,
&#34;Solver&#34;: str,
&#34;Upper bound&#34;: Optional[float],
&#34;Wallclock time&#34;: float,
&#34;Warm start value&#34;: Optional[float],
},
total=False,
)
IterationCallback = Callable[[], bool]
LazyCallback = Callable[[Any, Any], None]
@@ -97,7 +116,6 @@ class Constraint:
<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>
@@ -130,6 +148,27 @@ dict(one=1, two=2)</p></section>
<li>builtins.dict</li>
</ul>
</dd>
<dt id="miplearn.types.LearningSolveStats"><code class="flex name class">
<span>class <span class="ident">LearningSolveStats</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>
@@ -195,6 +234,9 @@ dict(one=1, two=2)</p></section>
<h4><code><a title="miplearn.types.LPSolveStats" href="#miplearn.types.LPSolveStats">LPSolveStats</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.types.LearningSolveStats" href="#miplearn.types.LearningSolveStats">LearningSolveStats</a></code></h4>
</li>
<li>
<h4><code><a title="miplearn.types.MIPSolveStats" href="#miplearn.types.MIPSolveStats">MIPSolveStats</a></code></h4>
</li>
<li>
@@ -206,7 +248,7 @@ dict(one=1, two=2)</p></section>
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