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<main>
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<article id="content">
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<header>
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<h1 class="title">Module <code>miplearn.benchmark</code></h1>
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</header>
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<section id="section-intro">
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<details class="source">
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<summary>
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<span>Expand source code</span>
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</summary>
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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import logging
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import os
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from typing import Dict, Union, List
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import pandas as pd
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from miplearn.instance import Instance
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from miplearn.solvers.learning import LearningSolver
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from miplearn.types import LearningSolveStats
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class BenchmarkRunner:
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"""
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Utility class that simplifies the task of comparing the performance of different
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solvers.
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Example
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-------
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```python
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benchmark = BenchmarkRunner({
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"Baseline": LearningSolver(...),
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"Strategy A": LearningSolver(...),
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"Strategy B": LearningSolver(...),
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"Strategy C": LearningSolver(...),
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})
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benchmark.fit(train_instances)
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benchmark.parallel_solve(test_instances, n_jobs=5)
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benchmark.save_results("result.csv")
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```
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Parameters
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----------
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solvers: Dict[str, LearningSolver]
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Dictionary containing the solvers to compare. Solvers may have different
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arguments and components. The key should be the name of the solver. It
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appears in the exported tables of results.
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"""
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def __init__(self, solvers: Dict[str, LearningSolver]) -> None:
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self.solvers: Dict[str, LearningSolver] = solvers
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self.results = pd.DataFrame(
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columns=[
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"Solver",
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"Instance",
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]
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)
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def parallel_solve(
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self,
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instances: Union[List[str], List[Instance]],
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n_jobs: int = 1,
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n_trials: int = 3,
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) -> None:
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"""
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Solves the given instances in parallel and collect benchmark statistics.
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|
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Parameters
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----------
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instances: Union[List[str], List[Instance]]
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List of instances to solve. This can either be a list of instances
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already loaded in memory, or a list of filenames pointing to pickled (and
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optionally gzipped) files.
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n_jobs: int
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List of instances to solve in parallel at a time.
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n_trials: int
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How many times each instance should be solved.
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"""
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self._silence_miplearn_logger()
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trials = instances * n_trials
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for (solver_name, solver) in self.solvers.items():
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results = solver.parallel_solve(
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trials,
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n_jobs=n_jobs,
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label="Solve (%s)" % solver_name,
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discard_outputs=True,
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)
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for i in range(len(trials)):
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idx = i % len(instances)
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results[i]["Solver"] = solver_name
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results[i]["Instance"] = idx
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self.results = self.results.append(pd.DataFrame([results[i]]))
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self._restore_miplearn_logger()
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def write_csv(self, filename: str) -> None:
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"""
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Writes the collected results to a CSV file.
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Parameters
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----------
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filename: str
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The name of the file.
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"""
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os.makedirs(os.path.dirname(filename), exist_ok=True)
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self.results.to_csv(filename)
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def fit(self, instances: Union[List[str], List[Instance]]) -> None:
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"""
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Trains all solvers with the provided training instances.
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Parameters
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----------
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instances: Union[List[str], List[Instance]]
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List of training instances. This can either be a list of instances
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already loaded in memory, or a list of filenames pointing to pickled (and
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optionally gzipped) files.
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"""
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for (solver_name, solver) in self.solvers.items():
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solver.fit(instances)
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def _silence_miplearn_logger(self) -> None:
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miplearn_logger = logging.getLogger("miplearn")
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self.prev_log_level = miplearn_logger.getEffectiveLevel()
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miplearn_logger.setLevel(logging.WARNING)
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def _restore_miplearn_logger(self) -> None:
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miplearn_logger = logging.getLogger("miplearn")
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miplearn_logger.setLevel(self.prev_log_level)</code></pre>
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</details>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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</section>
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<section>
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<h2 class="section-title" id="header-classes">Classes</h2>
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<dl>
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<dt id="miplearn.benchmark.BenchmarkRunner"><code class="flex name class">
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<span>class <span class="ident">BenchmarkRunner</span></span>
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<span>(</span><span>solvers)</span>
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</code></dt>
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<dd>
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<section class="desc"><p>Utility class that simplifies the task of comparing the performance of different
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solvers.</p>
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<h2 id="example">Example</h2>
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<pre><code class="language-python">benchmark = BenchmarkRunner({
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"Baseline": LearningSolver(...),
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"Strategy A": LearningSolver(...),
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"Strategy B": LearningSolver(...),
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"Strategy C": LearningSolver(...),
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})
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benchmark.fit(train_instances)
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benchmark.parallel_solve(test_instances, n_jobs=5)
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benchmark.save_results("result.csv")
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</code></pre>
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<h2 id="parameters">Parameters</h2>
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<dl>
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<dt><strong><code>solvers</code></strong> : <code>Dict</code>[<code>str</code>, <code>LearningSolver</code>]</dt>
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<dd>Dictionary containing the solvers to compare. Solvers may have different
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arguments and components. The key should be the name of the solver. It
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appears in the exported tables of results.</dd>
|
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</dl></section>
|
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<details class="source">
|
|
<summary>
|
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<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">class BenchmarkRunner:
|
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"""
|
|
Utility class that simplifies the task of comparing the performance of different
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solvers.
|
|
|
|
Example
|
|
-------
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```python
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benchmark = BenchmarkRunner({
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"Baseline": LearningSolver(...),
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"Strategy A": LearningSolver(...),
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"Strategy B": LearningSolver(...),
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"Strategy C": LearningSolver(...),
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})
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benchmark.fit(train_instances)
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benchmark.parallel_solve(test_instances, n_jobs=5)
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benchmark.save_results("result.csv")
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```
|
|
|
|
Parameters
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----------
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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.
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|
"""
|
|
|
|
def __init__(self, solvers: Dict[str, LearningSolver]) -> None:
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|
self.solvers: Dict[str, LearningSolver] = solvers
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|
self.results = pd.DataFrame(
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|
columns=[
|
|
"Solver",
|
|
"Instance",
|
|
]
|
|
)
|
|
|
|
def parallel_solve(
|
|
self,
|
|
instances: Union[List[str], List[Instance]],
|
|
n_jobs: int = 1,
|
|
n_trials: int = 3,
|
|
) -> None:
|
|
"""
|
|
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.
|
|
"""
|
|
self._silence_miplearn_logger()
|
|
trials = instances * n_trials
|
|
for (solver_name, solver) in self.solvers.items():
|
|
results = solver.parallel_solve(
|
|
trials,
|
|
n_jobs=n_jobs,
|
|
label="Solve (%s)" % solver_name,
|
|
discard_outputs=True,
|
|
)
|
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for i in range(len(trials)):
|
|
idx = i % len(instances)
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results[i]["Solver"] = solver_name
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|
results[i]["Instance"] = idx
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|
self.results = self.results.append(pd.DataFrame([results[i]]))
|
|
self._restore_miplearn_logger()
|
|
|
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def write_csv(self, filename: str) -> None:
|
|
"""
|
|
Writes the collected results to a CSV file.
|
|
|
|
Parameters
|
|
----------
|
|
filename: str
|
|
The name of the file.
|
|
"""
|
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os.makedirs(os.path.dirname(filename), exist_ok=True)
|
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self.results.to_csv(filename)
|
|
|
|
def fit(self, instances: Union[List[str], List[Instance]]) -> None:
|
|
"""
|
|
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.
|
|
|
|
"""
|
|
for (solver_name, solver) in self.solvers.items():
|
|
solver.fit(instances)
|
|
|
|
def _silence_miplearn_logger(self) -> None:
|
|
miplearn_logger = logging.getLogger("miplearn")
|
|
self.prev_log_level = miplearn_logger.getEffectiveLevel()
|
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miplearn_logger.setLevel(logging.WARNING)
|
|
|
|
def _restore_miplearn_logger(self) -> None:
|
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miplearn_logger = logging.getLogger("miplearn")
|
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miplearn_logger.setLevel(self.prev_log_level)</code></pre>
|
|
</details>
|
|
<h3>Methods</h3>
|
|
<dl>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, instances)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Trains all solvers with the provided training instances.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>instances</code></strong> :  <code>Union</code>[<code>List</code>[<code>str</code>], <code>List</code>[<code>Instance</code>]]</dt>
|
|
<dd>List of training instances. This can either be a list of instances
|
|
already loaded in memory, or a list of filenames pointing to pickled (and
|
|
optionally gzipped) files.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, instances: Union[List[str], List[Instance]]) -> None:
|
|
"""
|
|
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.
|
|
|
|
"""
|
|
for (solver_name, solver) in self.solvers.items():
|
|
solver.fit(instances)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.parallel_solve"><code class="name flex">
|
|
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=1, n_trials=3)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<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> : <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> : <code>int</code></dt>
|
|
<dd>List of instances to solve in parallel at a time.</dd>
|
|
<dt><strong><code>n_trials</code></strong> : <code>int</code></dt>
|
|
<dd>How many times each instance should be solved.</dd>
|
|
</dl></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def parallel_solve(
|
|
self,
|
|
instances: Union[List[str], List[Instance]],
|
|
n_jobs: int = 1,
|
|
n_trials: int = 3,
|
|
) -> None:
|
|
"""
|
|
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.
|
|
"""
|
|
self._silence_miplearn_logger()
|
|
trials = instances * n_trials
|
|
for (solver_name, solver) in self.solvers.items():
|
|
results = solver.parallel_solve(
|
|
trials,
|
|
n_jobs=n_jobs,
|
|
label="Solve (%s)" % solver_name,
|
|
discard_outputs=True,
|
|
)
|
|
for i in range(len(trials)):
|
|
idx = i % len(instances)
|
|
results[i]["Solver"] = solver_name
|
|
results[i]["Instance"] = idx
|
|
self.results = self.results.append(pd.DataFrame([results[i]]))
|
|
self._restore_miplearn_logger()</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.write_csv"><code class="name flex">
|
|
<span>def <span class="ident">write_csv</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"><p>Writes the collected results to a CSV file.</p>
|
|
<h2 id="parameters">Parameters</h2>
|
|
<dl>
|
|
<dt><strong><code>filename</code></strong> : <code>str</code></dt>
|
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<dd>The name of the file.</dd>
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</dl></section>
|
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<details class="source">
|
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<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def write_csv(self, filename: str) -> None:
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|
"""
|
|
Writes the collected results to a CSV file.
|
|
|
|
Parameters
|
|
----------
|
|
filename: str
|
|
The name of the file.
|
|
"""
|
|
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
|
self.results.to_csv(filename)</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
</dl>
|
|
</section>
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|
</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
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<ul></ul>
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</div>
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<ul id="index">
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|
<li><h3>Super-module</h3>
|
|
<ul>
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|
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
|
</ul>
|
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</li>
|
|
<li><h3><a href="#header-classes">Classes</a></h3>
|
|
<ul>
|
|
<li>
|
|
<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4>
|
|
<ul class="">
|
|
<li><code><a title="miplearn.benchmark.BenchmarkRunner.fit" href="#miplearn.benchmark.BenchmarkRunner.fit">fit</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.write_csv" href="#miplearn.benchmark.BenchmarkRunner.write_csv">write_csv</a></code></li>
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</ul>
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</li>
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</ul>
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