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Update 0.2 docs

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docs
Alinson S. Xavier 5 years ago
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commit 144523a5c0
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6
0.2/404.html
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@ -82,12 +82,6 @@
<li >
<a href="/benchmark/">Benchmark</a>
</li>
<li > <li >
<a href="/problems/">Problems</a> <a href="/problems/">Problems</a>
</li> </li>

6
0.2/about/index.html
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@ -82,12 +82,6 @@
<li >
<a href="../benchmark/">Benchmark</a>
</li>
<li > <li >
<a href="../problems/">Problems</a> <a href="../problems/">Problems</a>
</li> </li>

491
0.2/api/miplearn/benchmark.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.benchmark API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -30,40 +30,71 @@
import logging import logging
import os import os
from copy import deepcopy from typing import Dict, Union, List
import pandas as pd import pandas as pd
from tqdm.auto import tqdm
from miplearn.instance import Instance
from miplearn.solvers.learning import LearningSolver from miplearn.solvers.learning import LearningSolver
from miplearn.types import LearningSolveStats
class BenchmarkRunner: class BenchmarkRunner:
def __init__(self, solvers): &#34;&#34;&#34;
assert isinstance(solvers, dict) Utility class that simplifies the task of comparing the performance of different
for solver in solvers.values(): solvers.
assert isinstance(solver, LearningSolver)
self.solvers = solvers Example
self.results = None -------
```python
def solve(self, instances, tee=False): benchmark = BenchmarkRunner({
for (solver_name, solver) in self.solvers.items(): &#34;Baseline&#34;: LearningSolver(...),
for i in tqdm(range(len((instances)))): &#34;Strategy A&#34;: LearningSolver(...),
results = solver.solve(deepcopy(instances[i]), tee=tee) &#34;Strategy B&#34;: LearningSolver(...),
self._push_result( &#34;Strategy C&#34;: LearningSolver(...),
results, })
solver=solver, benchmark.fit(train_instances)
solver_name=solver_name, benchmark.parallel_solve(test_instances, n_jobs=5)
instance=i, 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( def parallel_solve(
self, self,
instances, instances: Union[List[str], List[Instance]],
n_jobs=1, n_jobs: int = 1,
n_trials=1, n_trials: int = 3,
index_offset=0, ) -&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() self._silence_miplearn_logger()
trials = instances * n_trials trials = instances * n_trials
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
@ -74,69 +105,45 @@ class BenchmarkRunner:
discard_outputs=True, discard_outputs=True,
) )
for i in range(len(trials)): for i in range(len(trials)):
idx = (i % len(instances)) + index_offset idx = i % len(instances)
self._push_result( results[i][&#34;Solver&#34;] = solver_name
results[i], results[i][&#34;Instance&#34;] = idx
solver=solver, self.results = self.results.append(pd.DataFrame([results[i]]))
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger() self._restore_miplearn_logger()
def raw_results(self): def write_csv(self, filename: str) -&gt; None:
return self.results &#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) os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename) self.results.to_csv(filename)
def load_results(self, filename): def fit(self, instances: Union[List[str], List[Instance]]) -&gt; None:
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)]) &#34;&#34;&#34;
Trains all solvers with the provided training instances.
def load_state(self, filename): Parameters
for (solver_name, solver) in self.solvers.items(): ----------
solver.load_state(filename) 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.
def fit(self, training_instances): &#34;&#34;&#34;
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances) solver.fit(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;) miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel() self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING) 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 = logging.getLogger(&#34;miplearn&#34;)
miplearn_logger.setLevel(self.prev_log_level)</code></pre> miplearn_logger.setLevel(self.prev_log_level)</code></pre>
</details> </details>
@ -155,37 +162,86 @@ class BenchmarkRunner:
<span>(</span><span>solvers)</span> <span>(</span><span>solvers)</span>
</code></dt> </code></dt>
<dd> <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"> <details class="source">
<summary> <summary>
<span>Expand source code</span> <span>Expand source code</span>
</summary> </summary>
<pre><code class="python">class BenchmarkRunner: <pre><code class="python">class BenchmarkRunner:
def __init__(self, solvers): &#34;&#34;&#34;
assert isinstance(solvers, dict) Utility class that simplifies the task of comparing the performance of different
for solver in solvers.values(): solvers.
assert isinstance(solver, LearningSolver)
self.solvers = solvers Example
self.results = None -------
```python
def solve(self, instances, tee=False): benchmark = BenchmarkRunner({
for (solver_name, solver) in self.solvers.items(): &#34;Baseline&#34;: LearningSolver(...),
for i in tqdm(range(len((instances)))): &#34;Strategy A&#34;: LearningSolver(...),
results = solver.solve(deepcopy(instances[i]), tee=tee) &#34;Strategy B&#34;: LearningSolver(...),
self._push_result( &#34;Strategy C&#34;: LearningSolver(...),
results, })
solver=solver, benchmark.fit(train_instances)
solver_name=solver_name, benchmark.parallel_solve(test_instances, n_jobs=5)
instance=i, 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( def parallel_solve(
self, self,
instances, instances: Union[List[str], List[Instance]],
n_jobs=1, n_jobs: int = 1,
n_trials=1, n_trials: int = 3,
index_offset=0, ) -&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() self._silence_miplearn_logger()
trials = instances * n_trials trials = instances * n_trials
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
@ -196,131 +252,122 @@ class BenchmarkRunner:
discard_outputs=True, discard_outputs=True,
) )
for i in range(len(trials)): for i in range(len(trials)):
idx = (i % len(instances)) + index_offset idx = i % len(instances)
self._push_result( results[i][&#34;Solver&#34;] = solver_name
results[i], results[i][&#34;Instance&#34;] = idx
solver=solver, self.results = self.results.append(pd.DataFrame([results[i]]))
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger() self._restore_miplearn_logger()
def raw_results(self): def write_csv(self, filename: str) -&gt; None:
return self.results &#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) os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename) self.results.to_csv(filename)
def load_results(self, filename): def fit(self, instances: Union[List[str], List[Instance]]) -&gt; None:
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)]) &#34;&#34;&#34;
Trains all solvers with the provided training instances.
def load_state(self, filename): Parameters
for (solver_name, solver) in self.solvers.items(): ----------
solver.load_state(filename) 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.
def fit(self, training_instances): &#34;&#34;&#34;
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
solver.fit(training_instances) solver.fit(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;) miplearn_logger = logging.getLogger(&#34;miplearn&#34;)
self.prev_log_level = miplearn_logger.getEffectiveLevel() self.prev_log_level = miplearn_logger.getEffectiveLevel()
miplearn_logger.setLevel(logging.WARNING) 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 = logging.getLogger(&#34;miplearn&#34;)
miplearn_logger.setLevel(self.prev_log_level)</code></pre> miplearn_logger.setLevel(self.prev_log_level)</code></pre>
</details> </details>
<h3>Methods</h3> <h3>Methods</h3>
<dl> <dl>
<dt id="miplearn.benchmark.BenchmarkRunner.fit"><code class="name flex"> <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> </code></dt>
<dd> <dd>
<section class="desc"></section> <section class="desc"><p>Trains all solvers with the provided training instances.</p>
<details class="source"> <h2 id="parameters">Parameters</h2>
<summary> <dl>
<span>Expand source code</span> <dt><strong><code>instances</code></strong> :&ensp; <code>Union</code>[<code>List</code>[<code>str</code>], <code>List</code>[<code>Instance</code>]]</dt>
</summary> <dd>List of training instances. This can either be a list of instances
<pre><code class="python">def fit(self, training_instances): already loaded in memory, or a list of filenames pointing to pickled (and
for (solver_name, solver) in self.solvers.items(): optionally gzipped) files.</dd>
solver.fit(training_instances)</code></pre> </dl></section>
</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"> <details class="source">
<summary> <summary>
<span>Expand source code</span> <span>Expand source code</span>
</summary> </summary>
<pre><code class="python">def load_state(self, filename): <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(): for (solver_name, solver) in self.solvers.items():
solver.load_state(filename)</code></pre> solver.fit(instances)</code></pre>
</details> </details>
</dd> </dd>
<dt id="miplearn.benchmark.BenchmarkRunner.parallel_solve"><code class="name flex"> <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> </code></dt>
<dd> <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"> <details class="source">
<summary> <summary>
<span>Expand source code</span> <span>Expand source code</span>
</summary> </summary>
<pre><code class="python">def parallel_solve( <pre><code class="python">def parallel_solve(
self, self,
instances, instances: Union[List[str], List[Instance]],
n_jobs=1, n_jobs: int = 1,
n_trials=1, n_trials: int = 3,
index_offset=0, ) -&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() self._silence_miplearn_logger()
trials = instances * n_trials trials = instances * n_trials
for (solver_name, solver) in self.solvers.items(): for (solver_name, solver) in self.solvers.items():
@ -331,64 +378,40 @@ class BenchmarkRunner:
discard_outputs=True, discard_outputs=True,
) )
for i in range(len(trials)): for i in range(len(trials)):
idx = (i % len(instances)) + index_offset idx = i % len(instances)
self._push_result( results[i][&#34;Solver&#34;] = solver_name
results[i], results[i][&#34;Instance&#34;] = idx
solver=solver, self.results = self.results.append(pd.DataFrame([results[i]]))
solver_name=solver_name,
instance=idx,
)
self._restore_miplearn_logger()</code></pre> self._restore_miplearn_logger()</code></pre>
</details> </details>
</dd> </dd>
<dt id="miplearn.benchmark.BenchmarkRunner.raw_results"><code class="name flex"> <dt id="miplearn.benchmark.BenchmarkRunner.write_csv"><code class="name flex">
<span>def <span class="ident">raw_results</span></span>(<span>self)</span> <span>def <span class="ident">write_csv</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 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> </code></dt>
<dd> <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"> <details class="source">
<summary> <summary>
<span>Expand source code</span> <span>Expand source code</span>
</summary> </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) os.makedirs(os.path.dirname(filename), exist_ok=True)
self.results.to_csv(filename)</code></pre> self.results.to_csv(filename)</code></pre>
</details> </details>
</dd> </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> </dl>
</dd> </dd>
</dl> </dl>
@ -409,14 +432,10 @@ class BenchmarkRunner:
<ul> <ul>
<li> <li>
<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4> <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.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.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.write_csv" href="#miplearn.benchmark.BenchmarkRunner.write_csv">write_csv</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> </ul>
</li> </li>
</ul> </ul>
@ -425,7 +444,7 @@ class BenchmarkRunner:
</nav> </nav>
</main> </main>
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</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.classifiers.adaptive API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> <dl>
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier"><code class="flex name class"> <dt id="miplearn.classifiers.adaptive.AdaptiveClassifier"><code class="flex name class">
<span>class <span class="ident">AdaptiveClassifier</span></span> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>A meta-classifier which dynamically selects what actual classifier to use <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>
</nav> </nav>
</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.counting API documentation</title> <title>miplearn.classifiers.counting API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -159,7 +159,7 @@ counts how many times each label appeared, hence the name.</p></section>
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</footer> </footer>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.cv API documentation</title> <title>miplearn.classifiers.cv API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -308,7 +308,7 @@ acceptable. Other numbers are a linear interpolation of these two extremes.</p><
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</footer> </footer>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.evaluator API documentation</title> <title>miplearn.classifiers.evaluator API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -115,7 +115,7 @@ class ClassifierEvaluator:
</nav> </nav>
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</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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 API documentation</title> <title>miplearn.classifiers API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> <dl>
<dt id="miplearn.classifiers.Classifier"><code class="flex name class"> <dt id="miplearn.classifiers.Classifier"><code class="flex name class">
<span>class <span class="ident">Classifier</span></span> <span>class <span class="ident">Classifier</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <section class="desc"><p>Helper class that provides a standard way to create an ABC using
@ -127,8 +126,8 @@ inheritance.</p></section>
</ul> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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> <li><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="cv.html#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></li>
</ul> </ul>
<h3>Methods</h3> <h3>Methods</h3>
@ -181,7 +180,6 @@ def predict_proba(self, x_test):
</dd> </dd>
<dt id="miplearn.classifiers.Regressor"><code class="flex name class"> <dt id="miplearn.classifiers.Regressor"><code class="flex name class">
<span>class <span class="ident">Regressor</span></span> <span>class <span class="ident">Regressor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <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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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.tests API documentation</title> <title>miplearn.classifiers.tests API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -80,7 +80,7 @@
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</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.tests.test_counting API documentation</title> <title>miplearn.classifiers.tests.test_counting API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -93,7 +93,7 @@ def test_counting():
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.classifiers.tests.test_cv API documentation</title> <title>miplearn.classifiers.tests.test_cv API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -153,7 +153,7 @@ def test_cv():
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.classifiers.tests.test_evaluator API documentation</title> <title>miplearn.classifiers.tests.test_evaluator API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -99,7 +99,7 @@ def test_evaluator():
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.classifiers.tests.test_threshold API documentation</title> <title>miplearn.classifiers.tests.test_threshold API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -133,7 +133,7 @@ def test_threshold_dynamic():
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.classifiers.threshold API documentation</title> <title>miplearn.classifiers.threshold API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> <dl>
<dt id="miplearn.classifiers.threshold.DynamicThreshold"><code class="flex name class"> <dt id="miplearn.classifiers.threshold.DynamicThreshold"><code class="flex name class">
<span>class <span class="ident">DynamicThreshold</span></span> <span>class <span class="ident">DynamicThreshold</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <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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<title>miplearn.components.component API documentation</title> <title>miplearn.components.component API documentation</title>
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@ -32,7 +32,7 @@ from abc import ABC, abstractmethod
from typing import Any, List, Union, TYPE_CHECKING from typing import Any, List, Union, TYPE_CHECKING
from miplearn.instance import Instance from miplearn.instance import Instance
from miplearn.types import MIPSolveStats, TrainingSample from miplearn.types import LearningSolveStats, TrainingSample
if TYPE_CHECKING: if TYPE_CHECKING:
from miplearn.solvers.learning import LearningSolver from miplearn.solvers.learning import LearningSolver
@ -73,7 +73,7 @@ class Component(ABC):
solver: &#34;LearningSolver&#34;, solver: &#34;LearningSolver&#34;,
instance: Instance, instance: Instance,
model: Any, model: Any,
stats: MIPSolveStats, stats: LearningSolveStats,
training_data: TrainingSample, training_data: TrainingSample,
) -&gt; None: ) -&gt; None:
&#34;&#34;&#34; &#34;&#34;&#34;
@ -87,13 +87,13 @@ class Component(ABC):
The instance being solved. The instance being solved.
model: Any model: Any
The concrete optimization model being solved. The concrete optimization model being solved.
stats: dict stats: LearningSolveStats
A dictionary containing statistics about the solution process, such as A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file. 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 A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are learning models and accelerating the solution process. Components are
free to add their own training data here. For example, free to add their own training data here. For example,
@ -156,7 +156,6 @@ class Component(ABC):
<dl> <dl>
<dt id="miplearn.components.component.Component"><code class="flex name class"> <dt id="miplearn.components.component.Component"><code class="flex name class">
<span>class <span class="ident">Component</span></span> <span>class <span class="ident">Component</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p> <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;, solver: &#34;LearningSolver&#34;,
instance: Instance, instance: Instance,
model: Any, model: Any,
stats: MIPSolveStats, stats: LearningSolveStats,
training_data: TrainingSample, training_data: TrainingSample,
) -&gt; None: ) -&gt; None:
&#34;&#34;&#34; &#34;&#34;&#34;
@ -216,13 +215,13 @@ strategy.</p></section>
The instance being solved. The instance being solved.
model: Any model: Any
The concrete optimization model being solved. The concrete optimization model being solved.
stats: dict stats: LearningSolveStats
A dictionary containing statistics about the solution process, such as A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file. 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 A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are learning models and accelerating the solution process. Components are
free to add their own training data here. For example, free to add their own training data here. For example,
@ -279,16 +278,16 @@ strategy.</p></section>
</ul> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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_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.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.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.relaxation.RelaxationComponent" href="relaxation.html#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</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.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> <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> </ul>
<h3>Methods</h3> <h3>Methods</h3>
<dl> <dl>
@ -305,13 +304,13 @@ strategy.</p></section>
<dd>The instance being solved.</dd> <dd>The instance being solved.</dd>
<dt><strong><code>model</code></strong> :&ensp;<code>Any</code></dt> <dt><strong><code>model</code></strong> :&ensp;<code>Any</code></dt>
<dd>The concrete optimization model being solved.</dd> <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 <dd>A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file.</dd> 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 <dd>A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are learning models and accelerating the solution process. Components are
free to add their own training data here. For example, free to add their own training data here. For example,
@ -328,7 +327,7 @@ def after_solve(
solver: &#34;LearningSolver&#34;, solver: &#34;LearningSolver&#34;,
instance: Instance, instance: Instance,
model: Any, model: Any,
stats: MIPSolveStats, stats: LearningSolveStats,
training_data: TrainingSample, training_data: TrainingSample,
) -&gt; None: ) -&gt; None:
&#34;&#34;&#34; &#34;&#34;&#34;
@ -342,13 +341,13 @@ def after_solve(
The instance being solved. The instance being solved.
model: Any model: Any
The concrete optimization model being solved. The concrete optimization model being solved.
stats: dict stats: LearningSolveStats
A dictionary containing statistics about the solution process, such as A dictionary containing statistics about the solution process, such as
number of nodes explored and running time. Components are free to add number of nodes explored and running time. Components are free to add
their own statistics here. For example, PrimalSolutionComponent adds their own statistics here. For example, PrimalSolutionComponent adds
statistics regarding the number of predicted variables. All statistics in statistics regarding the number of predicted variables. All statistics in
this dictionary are exported to the benchmark CSV file. 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 A dictionary containing data that may be useful for training machine
learning models and accelerating the solution process. Components are learning models and accelerating the solution process. Components are
free to add their own training data here. For example, 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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<title>miplearn.components.composite API documentation</title> <title>miplearn.components.composite API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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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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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.cuts API documentation</title> <title>miplearn.components.cuts API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -378,7 +378,7 @@ class UserCutsComponent(Component):
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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 API documentation</title> <title>miplearn.components API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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):
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.components.lazy_dynamic API documentation</title> <title>miplearn.components.lazy_dynamic API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -402,7 +402,7 @@ class DynamicLazyConstraintsComponent(Component):
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@ -3,7 +3,7 @@
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@ -616,7 +616,7 @@ strategy.</p></section>
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.components.objective API documentation</title> <title>miplearn.components.objective API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -384,7 +384,7 @@ class ObjectiveValueComponent(Component):
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@ -3,7 +3,7 @@
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@ -214,7 +214,7 @@ class PrimalSolutionComponent(Component):
<dl> <dl>
<dt id="miplearn.components.primal.PrimalSolutionComponent"><code class="flex name class"> <dt id="miplearn.components.primal.PrimalSolutionComponent"><code class="flex name class">
<span>class <span class="ident">PrimalSolutionComponent</span></span> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>A component that predicts primal solutions.</p></section> <section class="desc"><p>A component that predicts primal solutions.</p></section>
@ -602,7 +602,7 @@ class PrimalSolutionComponent(Component):
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@ -3,7 +3,7 @@
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@ -318,7 +318,7 @@ constraint loop.</dd>
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<title>miplearn.components.steps.drop_redundant API documentation</title> <title>miplearn.components.steps.drop_redundant API documentation</title>
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@ -655,7 +655,7 @@ before this component is used.</p></section>
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<meta charset="utf-8"> <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 API documentation</title> <title>miplearn.components.steps API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.components.steps.relax_integrality API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> <dl>
<dt id="miplearn.components.steps.relax_integrality.RelaxIntegralityStep"><code class="flex name class"> <dt id="miplearn.components.steps.relax_integrality.RelaxIntegralityStep"><code class="flex name class">
<span>class <span class="ident">RelaxIntegralityStep</span></span> <span>class <span class="ident">RelaxIntegralityStep</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Component that relaxes all integrality constraints before the problem is solved.</p></section> <section class="desc"><p>Component that relaxes all integrality constraints before the problem is solved.</p></section>
@ -134,7 +133,7 @@ class RelaxIntegralityStep(Component):
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.tests API documentation</title> <title>miplearn.components.steps.tests API documentation</title>
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@ -62,7 +62,7 @@
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.components.steps.tests.test_convert_tight API documentation</title> <title>miplearn.components.steps.tests.test_convert_tight API documentation</title>
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@ -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> <head>
<meta charset="utf-8"> <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.tests.test_drop_redundant API documentation</title> <title>miplearn.components.steps.tests.test_drop_redundant API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -756,7 +756,7 @@ def test_x_multiple_solves():
</nav> </nav>
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.components.tests.test_composite API documentation</title> <title>miplearn.components.tests.test_composite API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.components.tests.test_lazy_dynamic API documentation</title> <title>miplearn.components.tests.test_lazy_dynamic API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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():
</nav> </nav>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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_static API documentation</title> <title>miplearn.components.tests.test_lazy_static API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -530,7 +530,7 @@ def test_fit():
</nav> </nav>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.components.tests.test_objective API documentation</title> <title>miplearn.components.tests.test_objective API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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():
</nav> </nav>
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.components.tests.test_primal API documentation</title> <title>miplearn.components.tests.test_primal API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.extractors API documentation</title> <title>miplearn.extractors API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> <dl>
<dt id="miplearn.extractors.Extractor"><code class="flex name class"> <dt id="miplearn.extractors.Extractor"><code class="flex name class">
<span>class <span class="ident">Extractor</span></span> <span>class <span class="ident">Extractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <section class="desc"><p>Helper class that provides a standard way to create an ABC using
@ -230,10 +229,10 @@ inheritance.</p></section>
</ul> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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.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> </ul>
<h3>Static methods</h3> <h3>Static methods</h3>
<dl> <dl>
@ -282,7 +281,6 @@ def extract(self, instances):
</dd> </dd>
<dt id="miplearn.extractors.InstanceFeaturesExtractor"><code class="flex name class"> <dt id="miplearn.extractors.InstanceFeaturesExtractor"><code class="flex name class">
<span>class <span class="ident">InstanceFeaturesExtractor</span></span> <span>class <span class="ident">InstanceFeaturesExtractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <section class="desc"><p>Helper class that provides a standard way to create an ABC using
@ -541,7 +539,6 @@ inheritance.</p></section>
</dd> </dd>
<dt id="miplearn.extractors.VariableFeaturesExtractor"><code class="flex name class"> <dt id="miplearn.extractors.VariableFeaturesExtractor"><code class="flex name class">
<span>class <span class="ident">VariableFeaturesExtractor</span></span> <span>class <span class="ident">VariableFeaturesExtractor</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Helper class that provides a standard way to create an ABC using <section class="desc"><p>Helper class that provides a standard way to create an ABC using
@ -672,7 +669,7 @@ inheritance.</p></section>
</nav> </nav>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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 API documentation</title> <title>miplearn API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -134,7 +134,7 @@ from .solvers.pyomo.gurobi import GurobiPyomoSolver</code></pre>
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</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.instance API documentation</title> <title>miplearn.instance API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -373,13 +373,13 @@ features, which can be provided as inputs to machine learning models.</p></secti
</ul> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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.problems.knapsack.MultiKnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</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.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.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> </ul>
<h3>Methods</h3> <h3>Methods</h3>
<dl> <dl>
@ -767,7 +767,7 @@ def to_model(self) -&gt; Any:
</nav> </nav>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.log API documentation</title> <title>miplearn.log API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -286,7 +286,7 @@ it is formatted using formatException() and appended to the message.</p></sectio
</nav> </nav>
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems API documentation</title> <title>miplearn.problems API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -80,7 +80,7 @@
</nav> </nav>
</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems.knapsack API documentation</title> <title>miplearn.problems.knapsack API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </dd>
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator"><code class="flex name class"> <dt id="miplearn.problems.knapsack.MultiKnapsackGenerator"><code class="flex name class">
<span>class <span class="ident">MultiKnapsackGenerator</span></span> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>Initialize the problem generator.</p> <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 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems.stab API documentation</title> <title>miplearn.problems.stab API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </dd>
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator"><code class="flex name class"> <dt id="miplearn.problems.stab.MaxWeightStableSetGenerator"><code class="flex name class">
<span>class <span class="ident">MaxWeightStableSetGenerator</span></span> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>Random instance generator for the Maximum-Weight Stable Set Problem.</p> <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 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems.tests API documentation</title> <title>miplearn.problems.tests API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -75,7 +75,7 @@
</nav> </nav>
</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems.tests.test_knapsack API documentation</title> <title>miplearn.problems.tests.test_knapsack API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.problems.tests.test_stab API documentation</title> <title>miplearn.problems.tests.test_stab API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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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<title>miplearn.problems.tests.test_tsp API documentation</title> <title>miplearn.problems.tests.test_tsp API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -234,7 +234,7 @@ def test_subtour():
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<meta name="description" content="" /> <meta name="description" content="" />
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@ -264,7 +264,7 @@ class TravelingSalesmanInstance(Instance):
</dd> </dd>
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator"><code class="flex name class"> <dt id="miplearn.problems.tsp.TravelingSalesmanGenerator"><code class="flex name class">
<span>class <span class="ident">TravelingSalesmanGenerator</span></span> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>Random generator for the Traveling Salesman Problem.</p> <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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<title>miplearn.solvers.gurobi API documentation</title> <title>miplearn.solvers.gurobi API documentation</title>
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@ -920,7 +920,7 @@ LP relaxation of that node.</dd>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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 API documentation</title> <title>miplearn.solvers API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -115,7 +115,7 @@ class _RedirectOutput:
</nav> </nav>
</main> </main>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.solvers.internal API documentation</title> <title>miplearn.solvers.internal API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
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@ -303,7 +303,6 @@ class InternalSolver(ABC):
<dl> <dl>
<dt id="miplearn.solvers.internal.InternalSolver"><code class="flex name class"> <dt id="miplearn.solvers.internal.InternalSolver"><code class="flex name class">
<span>class <span class="ident">InternalSolver</span></span> <span>class <span class="ident">InternalSolver</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"><p>Abstract class representing the MIP solver used internally by LearningSolver.</p></section> <section class="desc"><p>Abstract class representing the MIP solver used internally by LearningSolver.</p></section>
@ -561,8 +560,8 @@ class InternalSolver(ABC):
</ul> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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> </ul>
<h3>Methods</h3> <h3>Methods</h3>
<dl> <dl>
@ -1134,7 +1133,7 @@ def solve_lp(
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@ -3,7 +3,7 @@
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<meta charset="utf-8"> <meta charset="utf-8">
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<title>miplearn.solvers.learning API documentation</title> <title>miplearn.solvers.learning API documentation</title>
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@ -46,7 +46,7 @@ from miplearn.instance import Instance
from miplearn.solvers import _RedirectOutput from miplearn.solvers import _RedirectOutput
from miplearn.solvers.internal import InternalSolver from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
from miplearn.types import MIPSolveStats, TrainingSample from miplearn.types import MIPSolveStats, TrainingSample, LearningSolveStats
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -153,7 +153,7 @@ class LearningSolver:
output_filename: Optional[str] = None, output_filename: Optional[str] = None,
discard_output: bool = False, discard_output: bool = False,
tee: bool = False, tee: bool = False,
) -&gt; MIPSolveStats: ) -&gt; LearningSolveStats:
# Load instance from file, if necessary # Load instance from file, if necessary
filename = None filename = None
@ -229,15 +229,24 @@ class LearningSolver:
# Solve MILP # Solve MILP
logger.info(&#34;Solving MILP...&#34;) logger.info(&#34;Solving MILP...&#34;)
stats = self.internal_solver.solve( stats = cast(
tee=tee, LearningSolveStats,
iteration_cb=iteration_cb_wrapper, self.internal_solver.solve(
lazy_cb=lazy_cb, tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
),
) )
if &#34;LP value&#34; in training_sample.keys(): if &#34;LP value&#34; in training_sample.keys():
stats[&#34;LP value&#34;] = training_sample[&#34;LP value&#34;] 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;Lower bound&#34;] = stats[&#34;Lower bound&#34;]
training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;] training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;]
training_sample[&#34;MIP log&#34;] = stats[&#34;Log&#34;] training_sample[&#34;MIP log&#34;] = stats[&#34;Log&#34;]
@ -268,7 +277,7 @@ class LearningSolver:
output_filename: Optional[str] = None, output_filename: Optional[str] = None,
discard_output: bool = False, discard_output: bool = False,
tee: bool = False, tee: bool = False,
) -&gt; MIPSolveStats: ) -&gt; LearningSolveStats:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process. available, they will be used to accelerate the solution process.
@ -301,7 +310,7 @@ class LearningSolver:
Returns Returns
------- -------
MIPSolveStats LearningSolveStats
A dictionary of solver statistics containing at least the following 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;, 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;. &#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;, label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None, output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False, discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]: ) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves multiple instances in parallel. Solves multiple instances in parallel.
@ -364,7 +373,7 @@ class LearningSolver:
Returns Returns
------- -------
List[MIPSolveStats] List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance. List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]` `[solver.solve(p) for p in instances]`
@ -409,7 +418,19 @@ class LearningSolver:
def __getstate__(self) -&gt; Dict: def __getstate__(self) -&gt; Dict:
self.internal_solver = None 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> </details>
</section> </section>
<section> <section>
@ -531,7 +552,7 @@ the theoretical performance of perfect ML models.</dd>
output_filename: Optional[str] = None, output_filename: Optional[str] = None,
discard_output: bool = False, discard_output: bool = False,
tee: bool = False, tee: bool = False,
) -&gt; MIPSolveStats: ) -&gt; LearningSolveStats:
# Load instance from file, if necessary # Load instance from file, if necessary
filename = None filename = None
@ -607,15 +628,24 @@ the theoretical performance of perfect ML models.</dd>
# Solve MILP # Solve MILP
logger.info(&#34;Solving MILP...&#34;) logger.info(&#34;Solving MILP...&#34;)
stats = self.internal_solver.solve( stats = cast(
tee=tee, LearningSolveStats,
iteration_cb=iteration_cb_wrapper, self.internal_solver.solve(
lazy_cb=lazy_cb, tee=tee,
iteration_cb=iteration_cb_wrapper,
lazy_cb=lazy_cb,
),
) )
if &#34;LP value&#34; in training_sample.keys(): if &#34;LP value&#34; in training_sample.keys():
stats[&#34;LP value&#34;] = training_sample[&#34;LP value&#34;] 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;Lower bound&#34;] = stats[&#34;Lower bound&#34;]
training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;] training_sample[&#34;Upper bound&#34;] = stats[&#34;Upper bound&#34;]
training_sample[&#34;MIP log&#34;] = stats[&#34;Log&#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, output_filename: Optional[str] = None,
discard_output: bool = False, discard_output: bool = False,
tee: bool = False, tee: bool = False,
) -&gt; MIPSolveStats: ) -&gt; LearningSolveStats:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process. available, they will be used to accelerate the solution process.
@ -679,7 +709,7 @@ the theoretical performance of perfect ML models.</dd>
Returns Returns
------- -------
MIPSolveStats LearningSolveStats
A dictionary of solver statistics containing at least the following 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;, 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;. &#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;, label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None, output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False, discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]: ) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves multiple instances in parallel. Solves multiple instances in parallel.
@ -742,7 +772,7 @@ the theoretical performance of perfect ML models.</dd>
Returns Returns
------- -------
List[MIPSolveStats] List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance. List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]` `[solver.solve(p) for p in instances]`
@ -787,7 +817,19 @@ the theoretical performance of perfect ML models.</dd>
def __getstate__(self) -&gt; Dict: def __getstate__(self) -&gt; Dict:
self.internal_solver = None 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> </details>
<h3>Methods</h3> <h3>Methods</h3>
<dl> <dl>
@ -808,7 +850,7 @@ the theoretical performance of perfect ML models.</dd>
</details> </details>
</dd> </dd>
<dt id="miplearn.solvers.learning.LearningSolver.parallel_solve"><code class="name flex"> <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> </code></dt>
<dd> <dd>
<section class="desc"><p>Solves multiple instances in parallel.</p> <section class="desc"><p>Solves multiple instances in parallel.</p>
@ -834,7 +876,7 @@ them instead. Useful during benchmarking.</dd>
</dl> </dl>
<h2 id="returns">Returns</h2> <h2 id="returns">Returns</h2>
<dl> <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. <dd>List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling The list is the same you would obtain by calling
<code>[solver.solve(p) for p in instances]</code></dd> <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;, label: str = &#34;Solve&#34;,
output_filenames: Optional[List[str]] = None, output_filenames: Optional[List[str]] = None,
discard_outputs: bool = False, discard_outputs: bool = False,
) -&gt; List[MIPSolveStats]: ) -&gt; List[LearningSolveStats]:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves multiple instances in parallel. Solves multiple instances in parallel.
@ -877,7 +919,7 @@ The list is the same you would obtain by calling
Returns Returns
------- -------
List[MIPSolveStats] List[LearningSolveStats]
List of solver statistics, with one entry for each provided instance. List of solver statistics, with one entry for each provided instance.
The list is the same you would obtain by calling The list is the same you would obtain by calling
`[solver.solve(p) for p in instances]` `[solver.solve(p) for p in instances]`
@ -933,7 +975,7 @@ them. Useful during benchmarking.</dd>
</dl> </dl>
<h2 id="returns">Returns</h2> <h2 id="returns">Returns</h2>
<dl> <dl>
<dt><code>MIPSolveStats</code></dt> <dt><code>LearningSolveStats</code></dt>
<dd> <dd>
<p>A dictionary of solver statistics containing at least the following <p>A dictionary of solver statistics containing at least the following
keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes", keys: "Lower bound", "Upper bound", "Wallclock time", "Nodes",
@ -955,7 +997,7 @@ details.</p>
output_filename: Optional[str] = None, output_filename: Optional[str] = None,
discard_output: bool = False, discard_output: bool = False,
tee: bool = False, tee: bool = False,
) -&gt; MIPSolveStats: ) -&gt; LearningSolveStats:
&#34;&#34;&#34; &#34;&#34;&#34;
Solves the given instance. If trained machine-learning models are Solves the given instance. If trained machine-learning models are
available, they will be used to accelerate the solution process. available, they will be used to accelerate the solution process.
@ -988,7 +1030,7 @@ details.</p>
Returns Returns
------- -------
MIPSolveStats LearningSolveStats
A dictionary of solver statistics containing at least the following 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;, 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;. &#34;Sense&#34;, &#34;Log&#34;, &#34;Warm start value&#34; and &#34;LP value&#34;.
@ -1050,7 +1092,7 @@ details.</p>
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

6
0.2/api/miplearn/solvers/pyomo/base.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.pyomo.base API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </ul>
<h3>Subclasses</h3> <h3>Subclasses</h3>
<ul class="hlist"> <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.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> <li><a title="miplearn.solvers.pyomo.xpress.XpressPyomoSolver" href="xpress.html#miplearn.solvers.pyomo.xpress.XpressPyomoSolver">XpressPyomoSolver</a></li>
</ul> </ul>
<h3>Inherited members</h3> <h3>Inherited members</h3>
@ -722,7 +722,7 @@ class BasePyomoSolver(InternalSolver):
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/cplex.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.pyomo.cplex API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.pyomo.gurobi API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/index.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.pyomo API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -80,7 +80,7 @@
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
0.2/api/miplearn/solvers/pyomo/xpress.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.pyomo.xpress API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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> <title>miplearn.solvers.tests API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
<script>hljs.initHighlightingOnLoad()</script> <script>hljs.initHighlightingOnLoad()</script>

4
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.test_internal_solver API documentation</title> <title>miplearn.solvers.tests.test_internal_solver API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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4
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.test_lazy_cb API documentation</title> <title>miplearn.solvers.tests.test_lazy_cb API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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36
0.2/api/miplearn/solvers/tests/test_learning_solver.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.test_learning_solver API documentation</title> <title>miplearn.solvers.tests.test_learning_solver API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <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, simulate_perfect=True,
) )
stats = solver.solve(tmp.name) 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> </details>
</section> </section>
<section> <section>
@ -165,6 +175,25 @@ def test_simulate_perfect():
<section> <section>
<h2 class="section-title" id="header-functions">Functions</h2> <h2 class="section-title" id="header-functions">Functions</h2>
<dl> <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"> <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> <span>def <span class="ident">test_learning_solver</span></span>(<span>)</span>
</code></dt> </code></dt>
@ -346,6 +375,7 @@ def test_simulate_perfect():
</li> </li>
<li><h3><a href="#header-functions">Functions</a></h3> <li><h3><a href="#header-functions">Functions</a></h3>
<ul class=""> <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_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_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_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():
</nav> </nav>
</main> </main>
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</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.tests API documentation</title> <title>miplearn.tests API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -130,7 +130,7 @@ def get_test_pyomo_instances():
</nav> </nav>
</main> </main>
<footer id="footer"> <footer id="footer">
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p> <p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.5</a>.</p>
</footer> </footer>
<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
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54
0.2/api/miplearn/tests/test_benchmark.html
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@ -3,7 +3,7 @@
<head> <head>
<meta charset="utf-8"> <meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" /> <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.tests.test_benchmark API documentation</title> <title>miplearn.tests.test_benchmark API documentation</title>
<meta name="description" content="" /> <meta name="description" content="" />
<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'> <link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
@ -55,24 +55,10 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances) benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2) 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;) benchmark.write_csv(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;) assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)</code></pre>
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> </details>
</section> </section>
<section> <section>
@ -109,33 +95,10 @@ def test_gap():
benchmark = BenchmarkRunner(test_solvers) benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances) benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2) 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;) benchmark.write_csv(&#34;/tmp/benchmark.csv&#34;)
assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;) assert os.path.isfile(&#34;/tmp/benchmark.csv&#34;)</code></pre>
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>
</details> </details>
</dd> </dd>
</dl> </dl>
@ -157,14 +120,13 @@ def test_gap():
<li><h3><a href="#header-functions">Functions</a></h3> <li><h3><a href="#header-functions">Functions</a></h3>
<ul class=""> <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_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> </ul>
</li> </li>
</ul> </ul>
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<title>miplearn.tests.test_extractors API documentation</title> <title>miplearn.tests.test_extractors API documentation</title>
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@ -193,7 +193,7 @@ def test_variable_features_extractor():
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<meta charset="utf-8"> <meta charset="utf-8">
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@ -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] IterationCallback = Callable[[], bool]
LazyCallback = Callable[[Any, Any], None] LazyCallback = Callable[[Any, Any], None]
@ -97,7 +116,6 @@ class Constraint:
<dl> <dl>
<dt id="miplearn.types.Constraint"><code class="flex name class"> <dt id="miplearn.types.Constraint"><code class="flex name class">
<span>class <span class="ident">Constraint</span></span> <span>class <span class="ident">Constraint</span></span>
<span>(</span><span>*args, **kwargs)</span>
</code></dt> </code></dt>
<dd> <dd>
<section class="desc"></section> <section class="desc"></section>
@ -130,6 +148,27 @@ dict(one=1, two=2)</p></section>
<li>builtins.dict</li> <li>builtins.dict</li>
</ul> </ul>
</dd> </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"> <dt id="miplearn.types.MIPSolveStats"><code class="flex name class">
<span>class <span class="ident">MIPSolveStats</span></span> <span>class <span class="ident">MIPSolveStats</span></span>
<span>(</span><span>*args, **kwargs)</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> <h4><code><a title="miplearn.types.LPSolveStats" href="#miplearn.types.LPSolveStats">LPSolveStats</a></code></h4>
</li> </li>
<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> <h4><code><a title="miplearn.types.MIPSolveStats" href="#miplearn.types.MIPSolveStats">MIPSolveStats</a></code></h4>
</li> </li>
<li> <li>
@ -206,7 +248,7 @@ dict(one=1, two=2)</p></section>
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<li class="first-level active"><a href="#benchmarks-utilities">Benchmarks Utilities</a></li>
<li class="second-level"><a href="#using-benchmarkrunner">Using BenchmarkRunner</a></li>
<li class="second-level"><a href="#saving-and-loading-benchmark-results">Saving and loading benchmark results</a></li>
</ul>
</div></div>
<div class="col-md-9" role="main">
<h1 id="benchmarks-utilities">Benchmarks Utilities</h1>
<h3 id="using-benchmarkrunner">Using <code>BenchmarkRunner</code></h3>
<p>MIPLearn provides the utility class <code>BenchmarkRunner</code>, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:</p>
<pre><code class="language-python">from miplearn import BenchmarkRunner, LearningSolver
# Create train and test instances
train_instances = [...]
test_instances = [...]
# Training phase...
training_solver = LearningSolver(...)
training_solver.parallel_solve(train_instances, n_jobs=10)
# Test phase...
test_solvers = {
&quot;Baseline&quot;: LearningSolver(...), # each solver may have different parameters
&quot;Strategy A&quot;: LearningSolver(...),
&quot;Strategy B&quot;: LearningSolver(...),
&quot;Strategy C&quot;: LearningSolver(...),
}
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2)
print(benchmark.raw_results())
</code></pre>
<p>The method <code>fit</code> trains the ML models for each individual solver. The method <code>parallel_solve</code> solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, <code>raw_results</code> produces a table of results (Pandas DataFrame) with the following columns:</p>
<ul>
<li><strong>Solver,</strong> the name of the solver.</li>
<li><strong>Instance,</strong> the sequence number identifying the instance.</li>
<li><strong>Wallclock Time,</strong> the wallclock running time (in seconds) spent by the solver;</li>
<li><strong>Lower Bound,</strong> the best lower bound obtained by the solver;</li>
<li><strong>Upper Bound,</strong> the best upper bound obtained by the solver;</li>
<li><strong>Gap,</strong> the relative MIP integrality gap at the end of the optimization;</li>
<li><strong>Nodes,</strong> the number of explored branch-and-bound nodes.</li>
</ul>
<p>In addition to the above, there is also a "Relative" version of most columns, where the raw number is compared to the solver which provided the best performance. The <em>Relative Wallclock Time</em> for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.</p>
<h3 id="saving-and-loading-benchmark-results">Saving and loading benchmark results</h3>
<p>When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. <code>BenchmarkRunner</code> provides the methods <code>save_results</code> and <code>load_results</code>, which can be used to avoid this repetition, as the next example shows:</p>
<pre><code class="language-python"># Benchmark baseline solvers and save results to a file.
benchmark = BenchmarkRunner(baseline_solvers)
benchmark.parallel_solve(test_instances)
benchmark.save_results(&quot;baseline_results.csv&quot;)
# Benchmark remaining solvers, loading baseline results from file.
benchmark = BenchmarkRunner(alternative_solvers)
benchmark.load_results(&quot;baseline_results.csv&quot;)
benchmark.fit(training_instances)
benchmark.parallel_solve(test_instances)
</code></pre></div>
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@ -82,12 +82,6 @@
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@ -176,8 +170,7 @@ Unlike conventional MIP solvers, MIPLearn can take full advantage of very specif
<h3 id="documentation">Documentation</h3> <h3 id="documentation">Documentation</h3>
<ul> <ul>
<li><a href="usage/">Installation and typical usage</a></li> <li><a href="usage/">Installation and typical usage</a></li>
<li><a href="benchmark/">Benchmark utilities</a></li> <li><a href="problems/">Benchmark problems and results</a></li>
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<li><a href="customization/">Customizing the solver</a></li> <li><a href="customization/">Customizing the solver</a></li>
<li><a href="about/">License, authors, references and acknowledgments</a></li> <li><a href="about/">License, authors, references and acknowledgments</a></li>
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@ -293,5 +286,5 @@ Unlike conventional MIP solvers, MIPLearn can take full advantage of very specif
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</li>
<li > <li >
<a href="../problems/">Problems</a> <a href="../problems/">Problems</a>
</li> </li>
@ -125,7 +119,7 @@
</a> </a>
</li> </li>
<li > <li >
<a rel="next" href="../benchmark/"> <a rel="next" href="../problems/">
Next <i class="fas fa-arrow-right"></i> Next <i class="fas fa-arrow-right"></i>
</a> </a>
</li> </li>
@ -160,7 +154,9 @@
<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="#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="#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="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> <li class="second-level"><a href="#7-running-benchmarks">7. Running benchmarks</a></li>
<li class="second-level"><a href="#8-current-limitations">8. Current Limitations</a></li>
</ul> </ul>
</div></div> </div></div>
@ -168,9 +164,9 @@
<h1 id="usage">Usage</h1> <h1 id="usage">Usage</h1>
<h2 id="1-installation">1. Installation</h2> <h2 id="1-installation">1. Installation</h2>
<p>In these docs, we describe the Python/Pyomo version of the package, although a <a href="https://github.com/ANL-CEEESA/MIPLearn.jl">Julia/JuMP version</a> is also available. A mixed-integer solver is also required and its Python bindings must be properly installed. Supported solvers are currently CPLEX and Gurobi.</p> <p>In these docs, we describe the Python/Pyomo version of the package, although a <a href="https://github.com/ANL-CEEESA/MIPLearn.jl">Julia/JuMP version</a> is also available. A mixed-integer solver is also required and its Python bindings must be properly installed. Supported solvers are currently CPLEX, Gurobi and XPRESS.</p>
<p>To install MIPLearn, run: </p> <p>To install MIPLearn, run: </p>
<pre><code class="language-bash">pip3 install miplearn <pre><code class="language-bash">pip3 install --upgrade miplearn==0.2.*
</code></pre> </code></pre>
<p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported <p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported
as follows:</p> as follows:</p>
@ -244,7 +240,7 @@ for instance in test_instances:
</div> </div>
<h2 id="5-obtaining-heuristic-solutions">5. Obtaining heuristic solutions</h2> <h2 id="5-obtaining-heuristic-solutions">5. Obtaining heuristic solutions</h2>
<p>By default, <code>LearningSolver</code> uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts.</p> <p>By default, <code>LearningSolver</code> uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts.</p>
<p>For more significant performance benefits, <code>LearningSolver</code> can also be configured to place additional trust in the Machine Learning predictors, by using the <code>mode="heuristic"</code> constructor argument. When operating in this mode, if a ML model is statistically shown (through <em>stratified k-fold cross validation</em>) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see <a href="../about/#references">references</a> and <a href="../benchmark/">benchmark results</a>).</p> <p>For more significant performance benefits, <code>LearningSolver</code> can also be configured to place additional trust in the Machine Learning predictors, by using the <code>mode="heuristic"</code> constructor argument. When operating in this mode, if a ML model is statistically shown (through <em>stratified k-fold cross validation</em>) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see <a href="../about/#references">references</a> and <a href="../problems/">benchmark results</a>).</p>
<div class="admonition danger"> <div class="admonition danger">
<p class="admonition-title">Danger</p> <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> <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>
@ -295,11 +291,12 @@ solver.parallel_solve(test_instances)
<h3 id="63-solving-instances-from-the-disk">6.3 Solving instances from the disk</h3> <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. <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> 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 <pre><code class="language-python">import pickle
from miplearn import LearningSolver
# Construct and pickle 600 problem instances # Construct and pickle 600 problem instances
for i in range(600): for i in range(600):
instance = CustomInstance([...]) instance = MyProblemInstance([...])
with open(&quot;instance_%03d.pkl&quot; % i, &quot;w&quot;) as file: with open(&quot;instance_%03d.pkl&quot; % i, &quot;w&quot;) as file:
pickle.dump(instance, obj) pickle.dump(instance, obj)
@ -319,21 +316,45 @@ solver.fit(train_instances)
# Solve test instances # Solve test instances
solver.parallel_solve(test_instances, n_jobs=4) solver.parallel_solve(test_instances, n_jobs=4)
</code></pre> </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> <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, use the arguments <code>output_filename</code> (in <code>solve</code>) and <code>output_filenames</code> (in <code>parallel_solve</code>). To discard the modifications instead, use <code>discard_outputs=True</code>. 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 <pre><code class="language-python"># Solve a single instance file and write the output to another file
solver.solve(&quot;knapsack_1.orig.pkl&quot;, output=&quot;knapsack_1.solved.pkl&quot;) solver.solve(&quot;knapsack_1.orig.pkl&quot;, output_filename=&quot;knapsack_1.solved.pkl&quot;)
# Solve a list of instance files # Solve a list of instance files
instances = [&quot;knapsack_%03d.orig.pkl&quot; % i for i in range(100)] 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)] output = [&quot;knapsack_%03d.solved.pkl&quot; % i for i in range(100)]
solver.parallel_solve(instances, output=output) solver.parallel_solve(instances, output_filenames=output)
# Solve instances and discard solutions and training data # Solve instances and discard solutions and training data
solver.parallel_solve(instances, output=None) solver.parallel_solve(instances, discard_outputs=True)
</code></pre>
<h2 id="7-running-benchmarks">7. Running benchmarks</h2>
<p>MIPLearn provides the utility class <code>BenchmarkRunner</code>, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:</p>
<pre><code class="language-python">from miplearn import BenchmarkRunner, LearningSolver
# Create train and test instances
train_instances = [...]
test_instances = [...]
# Training phase...
training_solver = LearningSolver(...)
training_solver.parallel_solve(train_instances, n_jobs=10)
# Test phase...
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.write_csv(&quot;results.csv&quot;)
</code></pre> </code></pre>
<h2 id="7-current-limitations">7. Current Limitations</h2> <p>The method <code>fit</code> trains the ML models for each individual solver. The method <code>parallel_solve</code> solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, <code>write_csv</code> produces a table of results. The columns in the CSV file depend on the components added to the solver.</p>
<h2 id="8-current-limitations">8. Current Limitations</h2>
<ul> <ul>
<li>Only binary and continuous decision variables are currently supported. General integer variables are not currently supported by all solver components.</li> <li>Only binary and continuous decision variables are currently supported. General integer variables are not currently supported by some solver components.</li>
</ul></div> </ul></div>

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