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433 lines
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433 lines
19 KiB
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</head>
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<body>
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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 copy import deepcopy
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import pandas as pd
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from tqdm.auto import tqdm
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from miplearn.solvers.learning import LearningSolver
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class BenchmarkRunner:
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def __init__(self, solvers):
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assert isinstance(solvers, dict)
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for solver in solvers.values():
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assert isinstance(solver, LearningSolver)
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self.solvers = solvers
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self.results = None
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def solve(self, instances, tee=False):
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for (solver_name, solver) in self.solvers.items():
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for i in tqdm(range(len((instances)))):
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results = solver.solve(deepcopy(instances[i]), tee=tee)
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self._push_result(
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results,
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solver=solver,
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solver_name=solver_name,
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instance=i,
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)
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def parallel_solve(
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self,
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instances,
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n_jobs=1,
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n_trials=1,
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index_offset=0,
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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)) + index_offset
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self._push_result(
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results[i],
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solver=solver,
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solver_name=solver_name,
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instance=idx,
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)
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self._restore_miplearn_logger()
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def raw_results(self):
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return self.results
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def save_results(self, filename):
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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 load_results(self, filename):
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self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
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def load_state(self, filename):
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for (solver_name, solver) in self.solvers.items():
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solver.load_state(filename)
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def fit(self, training_instances):
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for (solver_name, solver) in self.solvers.items():
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solver.fit(training_instances)
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@staticmethod
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def _compute_gap(ub, lb):
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if lb is None or ub is None or lb * ub < 0:
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# solver did not find a solution and/or bound, use maximum gap possible
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return 1.0
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elif abs(ub - lb) < 1e-6:
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# avoid division by zero when ub = lb = 0
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return 0.0
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else:
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# divide by max(abs(ub),abs(lb)) to ensure gap <= 1
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return (ub - lb) / max(abs(ub), abs(lb))
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def _push_result(self, result, solver, solver_name, instance):
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if self.results is None:
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self.results = pd.DataFrame(
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# Show the following columns first in the CSV file
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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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result["Solver"] = solver_name
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result["Instance"] = instance
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result["Gap"] = self._compute_gap(
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ub=result["Upper bound"],
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lb=result["Lower bound"],
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)
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result["Mode"] = solver.mode
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self.results = self.results.append(pd.DataFrame([result]))
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def _silence_miplearn_logger(self):
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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):
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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"></section>
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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">class BenchmarkRunner:
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def __init__(self, solvers):
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assert isinstance(solvers, dict)
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for solver in solvers.values():
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assert isinstance(solver, LearningSolver)
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self.solvers = solvers
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self.results = None
|
|
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def solve(self, instances, tee=False):
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for (solver_name, solver) in self.solvers.items():
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for i in tqdm(range(len((instances)))):
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results = solver.solve(deepcopy(instances[i]), tee=tee)
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self._push_result(
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results,
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solver=solver,
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solver_name=solver_name,
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instance=i,
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)
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def parallel_solve(
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self,
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instances,
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n_jobs=1,
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n_trials=1,
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index_offset=0,
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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)) + index_offset
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self._push_result(
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results[i],
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solver=solver,
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solver_name=solver_name,
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instance=idx,
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)
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self._restore_miplearn_logger()
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def raw_results(self):
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return self.results
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def save_results(self, filename):
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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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|
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def load_results(self, filename):
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self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
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def load_state(self, filename):
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for (solver_name, solver) in self.solvers.items():
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solver.load_state(filename)
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|
|
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def fit(self, training_instances):
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|
for (solver_name, solver) in self.solvers.items():
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solver.fit(training_instances)
|
|
|
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@staticmethod
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def _compute_gap(ub, lb):
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|
if lb is None or ub is None or lb * ub < 0:
|
|
# solver did not find a solution and/or bound, use maximum gap possible
|
|
return 1.0
|
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elif abs(ub - lb) < 1e-6:
|
|
# avoid division by zero when ub = lb = 0
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|
return 0.0
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|
else:
|
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# divide by max(abs(ub),abs(lb)) to ensure gap <= 1
|
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return (ub - lb) / max(abs(ub), abs(lb))
|
|
|
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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
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|
columns=[
|
|
"Solver",
|
|
"Instance",
|
|
]
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)
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result["Solver"] = solver_name
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|
result["Instance"] = instance
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|
result["Gap"] = self._compute_gap(
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ub=result["Upper bound"],
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lb=result["Lower bound"],
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)
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result["Mode"] = solver.mode
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self.results = self.results.append(pd.DataFrame([result]))
|
|
|
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def _silence_miplearn_logger(self):
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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):
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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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<h3>Methods</h3>
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<dl>
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<dt id="miplearn.benchmark.BenchmarkRunner.fit"><code class="name flex">
|
|
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
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|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def fit(self, training_instances):
|
|
for (solver_name, solver) in self.solvers.items():
|
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solver.fit(training_instances)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.load_results"><code class="name flex">
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|
<span>def <span class="ident">load_results</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def load_results(self, filename):
|
|
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.load_state"><code class="name flex">
|
|
<span>def <span class="ident">load_state</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def load_state(self, filename):
|
|
for (solver_name, solver) in self.solvers.items():
|
|
solver.load_state(filename)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.parallel_solve"><code class="name flex">
|
|
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=1, n_trials=1, index_offset=0)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def parallel_solve(
|
|
self,
|
|
instances,
|
|
n_jobs=1,
|
|
n_trials=1,
|
|
index_offset=0,
|
|
):
|
|
self._silence_miplearn_logger()
|
|
trials = instances * n_trials
|
|
for (solver_name, solver) in self.solvers.items():
|
|
results = solver.parallel_solve(
|
|
trials,
|
|
n_jobs=n_jobs,
|
|
label="Solve (%s)" % solver_name,
|
|
discard_outputs=True,
|
|
)
|
|
for i in range(len(trials)):
|
|
idx = (i % len(instances)) + index_offset
|
|
self._push_result(
|
|
results[i],
|
|
solver=solver,
|
|
solver_name=solver_name,
|
|
instance=idx,
|
|
)
|
|
self._restore_miplearn_logger()</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.raw_results"><code class="name flex">
|
|
<span>def <span class="ident">raw_results</span></span>(<span>self)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def raw_results(self):
|
|
return self.results</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.save_results"><code class="name flex">
|
|
<span>def <span class="ident">save_results</span></span>(<span>self, filename)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def save_results(self, filename):
|
|
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
|
self.results.to_csv(filename)</code></pre>
|
|
</details>
|
|
</dd>
|
|
<dt id="miplearn.benchmark.BenchmarkRunner.solve"><code class="name flex">
|
|
<span>def <span class="ident">solve</span></span>(<span>self, instances, tee=False)</span>
|
|
</code></dt>
|
|
<dd>
|
|
<section class="desc"></section>
|
|
<details class="source">
|
|
<summary>
|
|
<span>Expand source code</span>
|
|
</summary>
|
|
<pre><code class="python">def solve(self, instances, tee=False):
|
|
for (solver_name, solver) in self.solvers.items():
|
|
for i in tqdm(range(len((instances)))):
|
|
results = solver.solve(deepcopy(instances[i]), tee=tee)
|
|
self._push_result(
|
|
results,
|
|
solver=solver,
|
|
solver_name=solver_name,
|
|
instance=i,
|
|
)</code></pre>
|
|
</details>
|
|
</dd>
|
|
</dl>
|
|
</dd>
|
|
</dl>
|
|
</section>
|
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</article>
|
|
<nav id="sidebar">
|
|
<h1>Index</h1>
|
|
<div class="toc">
|
|
<ul></ul>
|
|
</div>
|
|
<ul id="index">
|
|
<li><h3>Super-module</h3>
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<ul>
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<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
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</ul>
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</li>
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<li><h3><a href="#header-classes">Classes</a></h3>
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<ul>
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<li>
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<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4>
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<ul class="two-column">
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.fit" href="#miplearn.benchmark.BenchmarkRunner.fit">fit</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_results" href="#miplearn.benchmark.BenchmarkRunner.load_results">load_results</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_state" href="#miplearn.benchmark.BenchmarkRunner.load_state">load_state</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.parallel_solve" href="#miplearn.benchmark.BenchmarkRunner.parallel_solve">parallel_solve</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.raw_results" href="#miplearn.benchmark.BenchmarkRunner.raw_results">raw_results</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.save_results" href="#miplearn.benchmark.BenchmarkRunner.save_results">save_results</a></code></li>
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<li><code><a title="miplearn.benchmark.BenchmarkRunner.solve" href="#miplearn.benchmark.BenchmarkRunner.solve">solve</a></code></li>
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</ul>
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</li>
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</ul>
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