parent
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commit
894f4b4668
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repos:
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- repo: https://github.com/ambv/black
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rev: stable
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hooks:
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- id: black
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args: ["--check"]
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@ -0,0 +1,433 @@
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<!doctype html>
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<html lang="en">
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<head>
|
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.benchmark API documentation</title>
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|
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<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
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</head>
|
||||
<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>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<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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|
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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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|
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|
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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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|
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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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|
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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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|
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def raw_results(self):
|
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return self.results
|
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|
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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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|
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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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|
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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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|
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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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|
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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>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class BenchmarkRunner:
|
||||
def __init__(self, solvers):
|
||||
assert isinstance(solvers, dict)
|
||||
for solver in solvers.values():
|
||||
assert isinstance(solver, LearningSolver)
|
||||
self.solvers = solvers
|
||||
self.results = None
|
||||
|
||||
def solve(self, instances, tee=False):
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
for i in tqdm(range(len((instances)))):
|
||||
results = solver.solve(deepcopy(instances[i]), tee=tee)
|
||||
self._push_result(
|
||||
results,
|
||||
solver=solver,
|
||||
solver_name=solver_name,
|
||||
instance=i,
|
||||
)
|
||||
|
||||
def parallel_solve(
|
||||
self,
|
||||
instances,
|
||||
n_jobs=1,
|
||||
n_trials=1,
|
||||
index_offset=0,
|
||||
):
|
||||
self._silence_miplearn_logger()
|
||||
trials = instances * n_trials
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
results = solver.parallel_solve(
|
||||
trials,
|
||||
n_jobs=n_jobs,
|
||||
label="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()
|
||||
|
||||
def raw_results(self):
|
||||
return self.results
|
||||
|
||||
def save_results(self, filename):
|
||||
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
||||
self.results.to_csv(filename)
|
||||
|
||||
def load_results(self, filename):
|
||||
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])
|
||||
|
||||
def load_state(self, filename):
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
solver.load_state(filename)
|
||||
|
||||
def fit(self, training_instances):
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
solver.fit(training_instances)
|
||||
|
||||
@staticmethod
|
||||
def _compute_gap(ub, lb):
|
||||
if lb is None or ub is None or lb * ub < 0:
|
||||
# solver did not find a solution and/or bound, use maximum gap possible
|
||||
return 1.0
|
||||
elif abs(ub - lb) < 1e-6:
|
||||
# avoid division by zero when ub = lb = 0
|
||||
return 0.0
|
||||
else:
|
||||
# divide by max(abs(ub),abs(lb)) to ensure gap <= 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=[
|
||||
"Solver",
|
||||
"Instance",
|
||||
]
|
||||
)
|
||||
result["Solver"] = solver_name
|
||||
result["Instance"] = instance
|
||||
result["Gap"] = self._compute_gap(
|
||||
ub=result["Upper bound"],
|
||||
lb=result["Lower bound"],
|
||||
)
|
||||
result["Mode"] = solver.mode
|
||||
self.results = self.results.append(pd.DataFrame([result]))
|
||||
|
||||
def _silence_miplearn_logger(self):
|
||||
miplearn_logger = logging.getLogger("miplearn")
|
||||
self.prev_log_level = miplearn_logger.getEffectiveLevel()
|
||||
miplearn_logger.setLevel(logging.WARNING)
|
||||
|
||||
def _restore_miplearn_logger(self):
|
||||
miplearn_logger = logging.getLogger("miplearn")
|
||||
miplearn_logger.setLevel(self.prev_log_level)</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.benchmark.BenchmarkRunner.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
solver.fit(training_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.benchmark.BenchmarkRunner.load_results"><code class="name flex">
|
||||
<span>def <span class="ident">load_results</span></span>(<span>self, filename)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load_results(self, filename):
|
||||
self.results = pd.concat([self.results, pd.read_csv(filename, index_col=0)])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.benchmark.BenchmarkRunner.load_state"><code class="name flex">
|
||||
<span>def <span class="ident">load_state</span></span>(<span>self, filename)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load_state(self, filename):
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
solver.load_state(filename)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.benchmark.BenchmarkRunner.parallel_solve"><code class="name flex">
|
||||
<span>def <span class="ident">parallel_solve</span></span>(<span>self, instances, n_jobs=1, n_trials=1, index_offset=0)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def parallel_solve(
|
||||
self,
|
||||
instances,
|
||||
n_jobs=1,
|
||||
n_trials=1,
|
||||
index_offset=0,
|
||||
):
|
||||
self._silence_miplearn_logger()
|
||||
trials = instances * n_trials
|
||||
for (solver_name, solver) in self.solvers.items():
|
||||
results = solver.parallel_solve(
|
||||
trials,
|
||||
n_jobs=n_jobs,
|
||||
label="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>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.benchmark.BenchmarkRunner" href="#miplearn.benchmark.BenchmarkRunner">BenchmarkRunner</a></code></h4>
|
||||
<ul class="two-column">
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.fit" href="#miplearn.benchmark.BenchmarkRunner.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_results" href="#miplearn.benchmark.BenchmarkRunner.load_results">load_results</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.load_state" href="#miplearn.benchmark.BenchmarkRunner.load_state">load_state</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.parallel_solve" href="#miplearn.benchmark.BenchmarkRunner.parallel_solve">parallel_solve</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.raw_results" href="#miplearn.benchmark.BenchmarkRunner.raw_results">raw_results</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.save_results" href="#miplearn.benchmark.BenchmarkRunner.save_results">save_results</a></code></li>
|
||||
<li><code><a title="miplearn.benchmark.BenchmarkRunner.solve" href="#miplearn.benchmark.BenchmarkRunner.solve">solve</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
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|
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.adaptive</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict
|
||||
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
from sklearn.pipeline import make_pipeline
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.classifiers.evaluator import ClassifierEvaluator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class AdaptiveClassifier(Classifier):
|
||||
"""
|
||||
A meta-classifier which dynamically selects what actual classifier to use
|
||||
based on its cross-validation score on a particular training data set.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
candidates: Dict[str, Any] = None,
|
||||
evaluator: ClassifierEvaluator = ClassifierEvaluator(),
|
||||
) -> None:
|
||||
"""
|
||||
Initializes the meta-classifier.
|
||||
"""
|
||||
if candidates is None:
|
||||
candidates = {
|
||||
"knn(100)": {
|
||||
"classifier": KNeighborsClassifier(n_neighbors=100),
|
||||
"min samples": 100,
|
||||
},
|
||||
"logistic": {
|
||||
"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
|
||||
"min samples": 30,
|
||||
},
|
||||
"counting": {
|
||||
"classifier": CountingClassifier(),
|
||||
"min samples": 0,
|
||||
},
|
||||
}
|
||||
self.candidates = candidates
|
||||
self.evaluator = evaluator
|
||||
self.classifier = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
best_name, best_clf, best_score = None, None, -float("inf")
|
||||
n_samples = x_train.shape[0]
|
||||
for (name, clf_dict) in self.candidates.items():
|
||||
if n_samples < clf_dict["min samples"]:
|
||||
continue
|
||||
clf = deepcopy(clf_dict["classifier"])
|
||||
clf.fit(x_train, y_train)
|
||||
score = self.evaluator.evaluate(clf, x_train, y_train)
|
||||
if score > best_score:
|
||||
best_name, best_clf, best_score = name, clf, score
|
||||
logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
|
||||
self.classifier = best_clf
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier"><code class="flex name class">
|
||||
<span>class <span class="ident">AdaptiveClassifier</span></span>
|
||||
<span>(</span><span>candidates=None, evaluator=<miplearn.classifiers.evaluator.ClassifierEvaluator object>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A meta-classifier which dynamically selects what actual classifier to use
|
||||
based on its cross-validation score on a particular training data set.</p>
|
||||
<p>Initializes the meta-classifier.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class AdaptiveClassifier(Classifier):
|
||||
"""
|
||||
A meta-classifier which dynamically selects what actual classifier to use
|
||||
based on its cross-validation score on a particular training data set.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
candidates: Dict[str, Any] = None,
|
||||
evaluator: ClassifierEvaluator = ClassifierEvaluator(),
|
||||
) -> None:
|
||||
"""
|
||||
Initializes the meta-classifier.
|
||||
"""
|
||||
if candidates is None:
|
||||
candidates = {
|
||||
"knn(100)": {
|
||||
"classifier": KNeighborsClassifier(n_neighbors=100),
|
||||
"min samples": 100,
|
||||
},
|
||||
"logistic": {
|
||||
"classifier": make_pipeline(StandardScaler(), LogisticRegression()),
|
||||
"min samples": 30,
|
||||
},
|
||||
"counting": {
|
||||
"classifier": CountingClassifier(),
|
||||
"min samples": 0,
|
||||
},
|
||||
}
|
||||
self.candidates = candidates
|
||||
self.evaluator = evaluator
|
||||
self.classifier = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
best_name, best_clf, best_score = None, None, -float("inf")
|
||||
n_samples = x_train.shape[0]
|
||||
for (name, clf_dict) in self.candidates.items():
|
||||
if n_samples < clf_dict["min samples"]:
|
||||
continue
|
||||
clf = deepcopy(clf_dict["classifier"])
|
||||
clf.fit(x_train, y_train)
|
||||
score = self.evaluator.evaluate(clf, x_train, y_train)
|
||||
if score > best_score:
|
||||
best_name, best_clf, best_score = name, clf, score
|
||||
logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
|
||||
self.classifier = best_clf
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, x_train, y_train):
|
||||
best_name, best_clf, best_score = None, None, -float("inf")
|
||||
n_samples = x_train.shape[0]
|
||||
for (name, clf_dict) in self.candidates.items():
|
||||
if n_samples < clf_dict["min samples"]:
|
||||
continue
|
||||
clf = deepcopy(clf_dict["classifier"])
|
||||
clf.fit(x_train, y_train)
|
||||
score = self.evaluator.evaluate(clf, x_train, y_train)
|
||||
if score > best_score:
|
||||
best_name, best_clf, best_score = name, clf, score
|
||||
logger.debug("Best classifier: %s (score=%.3f)" % (best_name, best_score))
|
||||
self.classifier = best_clf</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba"><code class="name flex">
|
||||
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.fit" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba" href="#miplearn.classifiers.adaptive.AdaptiveClassifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.counting</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
|
||||
class CountingClassifier(Classifier):
|
||||
"""
|
||||
A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.mean = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
self.mean = np.mean(y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
|
||||
|
||||
def __repr__(self):
|
||||
return "CountingClassifier(mean=%s)" % self.mean</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.counting.CountingClassifier"><code class="flex name class">
|
||||
<span>class <span class="ident">CountingClassifier</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CountingClassifier(Classifier):
|
||||
"""
|
||||
A classifier that generates constant predictions, based only on the
|
||||
frequency of the training labels. For example, if y_train is [1.0, 0.0, 0.0]
|
||||
this classifier always returns [0.66 0.33] for any x_test. It essentially
|
||||
counts how many times each label appeared, hence the name.
|
||||
"""
|
||||
|
||||
def __init__(self) -> None:
|
||||
self.mean = None
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
self.mean = np.mean(y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])
|
||||
|
||||
def __repr__(self):
|
||||
return "CountingClassifier(mean=%s)" % self.mean</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.counting.CountingClassifier.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, x_train, y_train):
|
||||
self.mean = np.mean(y_train)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.counting.CountingClassifier.predict_proba"><code class="name flex">
|
||||
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict_proba(self, x_test):
|
||||
return np.array([[1 - self.mean, self.mean] for _ in range(x_test.shape[0])])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.counting.CountingClassifier" href="#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.fit" href="#miplearn.classifiers.counting.CountingClassifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.counting.CountingClassifier.predict_proba" href="#miplearn.classifiers.counting.CountingClassifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.cv</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from sklearn.dummy import DummyClassifier
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import cross_val_score
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CrossValidatedClassifier(Classifier):
|
||||
"""
|
||||
A meta-classifier that, upon training, evaluates the performance of another
|
||||
classifier on the training data set using k-fold cross validation, then
|
||||
either adopts the other classifier it if the cv-score is high enough, or
|
||||
returns a constant label for every x_test otherwise.
|
||||
|
||||
The threshold is specified in comparison to a dummy classifier trained
|
||||
on the same dataset. For example, a threshold of 0.0 indicates that any
|
||||
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
||||
indicates that only classifier with a perfect cross-validation score are
|
||||
acceptable. Other numbers are a linear interpolation of these two extremes.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=LogisticRegression(),
|
||||
threshold=0.75,
|
||||
constant=0.0,
|
||||
cv=5,
|
||||
scoring="accuracy",
|
||||
):
|
||||
self.classifier = None
|
||||
self.classifier_prototype = classifier
|
||||
self.constant = constant
|
||||
self.threshold = threshold
|
||||
self.cv = cv
|
||||
self.scoring = scoring
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
# Calculate dummy score and absolute score threshold
|
||||
y_train_avg = np.average(y_train)
|
||||
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
||||
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
||||
|
||||
# Calculate cross validation score and decide which classifier to use
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
cv_score = float(
|
||||
np.mean(
|
||||
cross_val_score(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
cv=self.cv,
|
||||
scoring=self.scoring,
|
||||
)
|
||||
)
|
||||
)
|
||||
if cv_score >= absolute_threshold:
|
||||
logger.debug(
|
||||
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = clf
|
||||
else:
|
||||
logger.debug(
|
||||
"cv_score is below threshold (%.2f < %.2f); discarding"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = DummyClassifier(
|
||||
strategy="constant",
|
||||
constant=self.constant,
|
||||
)
|
||||
|
||||
# Train chosen classifier
|
||||
self.classifier.fit(x_train, y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier"><code class="flex name class">
|
||||
<span>class <span class="ident">CrossValidatedClassifier</span></span>
|
||||
<span>(</span><span>classifier=LogisticRegression(), threshold=0.75, constant=0.0, cv=5, scoring='accuracy')</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A meta-classifier that, upon training, evaluates the performance of another
|
||||
classifier on the training data set using k-fold cross validation, then
|
||||
either adopts the other classifier it if the cv-score is high enough, or
|
||||
returns a constant label for every x_test otherwise.</p>
|
||||
<p>The threshold is specified in comparison to a dummy classifier trained
|
||||
on the same dataset. For example, a threshold of 0.0 indicates that any
|
||||
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
||||
indicates that only classifier with a perfect cross-validation score are
|
||||
acceptable. Other numbers are a linear interpolation of these two extremes.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CrossValidatedClassifier(Classifier):
|
||||
"""
|
||||
A meta-classifier that, upon training, evaluates the performance of another
|
||||
classifier on the training data set using k-fold cross validation, then
|
||||
either adopts the other classifier it if the cv-score is high enough, or
|
||||
returns a constant label for every x_test otherwise.
|
||||
|
||||
The threshold is specified in comparison to a dummy classifier trained
|
||||
on the same dataset. For example, a threshold of 0.0 indicates that any
|
||||
classifier as good as the dummy predictor is acceptable. A threshold of 1.0
|
||||
indicates that only classifier with a perfect cross-validation score are
|
||||
acceptable. Other numbers are a linear interpolation of these two extremes.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=LogisticRegression(),
|
||||
threshold=0.75,
|
||||
constant=0.0,
|
||||
cv=5,
|
||||
scoring="accuracy",
|
||||
):
|
||||
self.classifier = None
|
||||
self.classifier_prototype = classifier
|
||||
self.constant = constant
|
||||
self.threshold = threshold
|
||||
self.cv = cv
|
||||
self.scoring = scoring
|
||||
|
||||
def fit(self, x_train, y_train):
|
||||
# Calculate dummy score and absolute score threshold
|
||||
y_train_avg = np.average(y_train)
|
||||
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
||||
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
||||
|
||||
# Calculate cross validation score and decide which classifier to use
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
cv_score = float(
|
||||
np.mean(
|
||||
cross_val_score(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
cv=self.cv,
|
||||
scoring=self.scoring,
|
||||
)
|
||||
)
|
||||
)
|
||||
if cv_score >= absolute_threshold:
|
||||
logger.debug(
|
||||
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = clf
|
||||
else:
|
||||
logger.debug(
|
||||
"cv_score is below threshold (%.2f < %.2f); discarding"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = DummyClassifier(
|
||||
strategy="constant",
|
||||
constant=self.constant,
|
||||
)
|
||||
|
||||
# Train chosen classifier
|
||||
self.classifier.fit(x_train, y_train)
|
||||
|
||||
def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.Classifier" href="index.html#miplearn.classifiers.Classifier">Classifier</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, x_train, y_train):
|
||||
# Calculate dummy score and absolute score threshold
|
||||
y_train_avg = np.average(y_train)
|
||||
dummy_score = max(y_train_avg, 1 - y_train_avg)
|
||||
absolute_threshold = 1.0 * self.threshold + dummy_score * (1 - self.threshold)
|
||||
|
||||
# Calculate cross validation score and decide which classifier to use
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
cv_score = float(
|
||||
np.mean(
|
||||
cross_val_score(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
cv=self.cv,
|
||||
scoring=self.scoring,
|
||||
)
|
||||
)
|
||||
)
|
||||
if cv_score >= absolute_threshold:
|
||||
logger.debug(
|
||||
"cv_score is above threshold (%.2f >= %.2f); keeping"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = clf
|
||||
else:
|
||||
logger.debug(
|
||||
"cv_score is below threshold (%.2f < %.2f); discarding"
|
||||
% (cv_score, absolute_threshold)
|
||||
)
|
||||
self.classifier = DummyClassifier(
|
||||
strategy="constant",
|
||||
constant=self.constant,
|
||||
)
|
||||
|
||||
# Train chosen classifier
|
||||
self.classifier.fit(x_train, y_train)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba"><code class="name flex">
|
||||
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict_proba(self, x_test):
|
||||
return self.classifier.predict_proba(x_test)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.fit" href="#miplearn.classifiers.cv.CrossValidatedClassifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba" href="#miplearn.classifiers.cv.CrossValidatedClassifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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<body>
|
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<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.evaluator</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from sklearn.metrics import roc_auc_score
|
||||
|
||||
|
||||
class ClassifierEvaluator:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.evaluator.ClassifierEvaluator"><code class="flex name class">
|
||||
<span>class <span class="ident">ClassifierEvaluator</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ClassifierEvaluator:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
|
||||
def evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, clf, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, clf, x_train, y_train):
|
||||
# FIXME: use cross-validation
|
||||
proba = clf.predict_proba(x_train)
|
||||
return roc_auc_score(y_train, proba[:, 1])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.evaluator.ClassifierEvaluator" href="#miplearn.classifiers.evaluator.ClassifierEvaluator">ClassifierEvaluator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate" href="#miplearn.classifiers.evaluator.ClassifierEvaluator.evaluate">evaluate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Classifier(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass
|
||||
|
||||
def predict(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)
|
||||
|
||||
|
||||
class Regressor(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Classifier"><code class="flex name class">
|
||||
<span>class <span class="ident">Classifier</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Classifier(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass
|
||||
|
||||
def predict(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.counting.CountingClassifier" href="counting.html#miplearn.classifiers.counting.CountingClassifier">CountingClassifier</a></li>
|
||||
<li><a title="miplearn.classifiers.adaptive.AdaptiveClassifier" href="adaptive.html#miplearn.classifiers.adaptive.AdaptiveClassifier">AdaptiveClassifier</a></li>
|
||||
<li><a title="miplearn.classifiers.cv.CrossValidatedClassifier" href="cv.html#miplearn.classifiers.cv.CrossValidatedClassifier">CrossValidatedClassifier</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Classifier.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Classifier.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, x_test)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, x_test):
|
||||
proba = self.predict_proba(x_test)
|
||||
assert isinstance(proba, np.ndarray)
|
||||
assert proba.shape == (x_test.shape[0], 2)
|
||||
return (proba[:, 1] > 0.5).astype(float)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Classifier.predict_proba"><code class="name flex">
|
||||
<span>def <span class="ident">predict_proba</span></span>(<span>self, x_test)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def predict_proba(self, x_test):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Regressor"><code class="flex name class">
|
||||
<span>class <span class="ident">Regressor</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Regressor(ABC):
|
||||
@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.Regressor.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def fit(self, x_train, y_train):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.Regressor.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def predict(self):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.adaptive" href="adaptive.html">miplearn.classifiers.adaptive</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.counting" href="counting.html">miplearn.classifiers.counting</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.cv" href="cv.html">miplearn.classifiers.cv</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.evaluator" href="evaluator.html">miplearn.classifiers.evaluator</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="tests/index.html">miplearn.classifiers.tests</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.threshold" href="threshold.html">miplearn.classifiers.threshold</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.Classifier" href="#miplearn.classifiers.Classifier">Classifier</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.Classifier.fit" href="#miplearn.classifiers.Classifier.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Classifier.predict" href="#miplearn.classifiers.Classifier.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Classifier.predict_proba" href="#miplearn.classifiers.Classifier.predict_proba">predict_proba</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.Regressor" href="#miplearn.classifiers.Regressor">Regressor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.Regressor.fit" href="#miplearn.classifiers.Regressor.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.classifiers.Regressor.predict" href="#miplearn.classifiers.Regressor.predict">predict</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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|
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</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_counting():
|
||||
clf = CountingClassifier()
|
||||
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||||
expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
|
||||
actual_proba = clf.predict_proba(np.zeros((2, 25)))
|
||||
assert norm(actual_proba - expected_proba) < E</code></pre>
|
||||
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|
||||
</section>
|
||||
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|
||||
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|
||||
<section>
|
||||
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|
||||
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|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_counting.test_counting"><code class="name flex">
|
||||
<span>def <span class="ident">test_counting</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
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||||
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|
||||
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|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_counting():
|
||||
clf = CountingClassifier()
|
||||
clf.fit(np.zeros((8, 25)), [0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0])
|
||||
expected_proba = np.array([[0.375, 0.625], [0.375, 0.625]])
|
||||
actual_proba = clf.predict_proba(np.zeros((2, 25)))
|
||||
assert norm(actual_proba - expected_proba) < E</code></pre>
|
||||
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|
||||
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||||
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||||
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||||
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||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_counting.test_counting" href="#miplearn.classifiers.tests.test_counting.test_counting">test_counting</a></code></li>
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|
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<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_cv</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.svm import SVC
|
||||
|
||||
from miplearn.classifiers.cv import CrossValidatedClassifier
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_cv():
|
||||
# Training set: label is true if point is inside a 2D circle
|
||||
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
|
||||
x_train = StandardScaler().fit_transform(x_train)
|
||||
n_samples = x_train.shape[0]
|
||||
|
||||
y_train = np.array(
|
||||
[
|
||||
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
|
||||
for x1 in range(-10, 11)
|
||||
for x2 in range(-10, 11)
|
||||
]
|
||||
)
|
||||
|
||||
# Support vector machines with linear kernels do not perform well on this
|
||||
# data set, so predictor should return the given constant.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, random_state=42),
|
||||
threshold=0.90,
|
||||
constant=0.0,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
|
||||
|
||||
# Support vector machines with quadratic kernels perform almost perfectly
|
||||
# on this data set, so predictor should return their prediction.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
|
||||
threshold=0.90,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
print(y_train - clf.predict(x_train))
|
||||
assert norm(y_train - clf.predict(x_train)) < E</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_cv.test_cv"><code class="name flex">
|
||||
<span>def <span class="ident">test_cv</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_cv():
|
||||
# Training set: label is true if point is inside a 2D circle
|
||||
x_train = np.array([[x1, x2] for x1 in range(-10, 11) for x2 in range(-10, 11)])
|
||||
x_train = StandardScaler().fit_transform(x_train)
|
||||
n_samples = x_train.shape[0]
|
||||
|
||||
y_train = np.array(
|
||||
[
|
||||
1.0 if x1 * x1 + x2 * x2 <= 100 else 0.0
|
||||
for x1 in range(-10, 11)
|
||||
for x2 in range(-10, 11)
|
||||
]
|
||||
)
|
||||
|
||||
# Support vector machines with linear kernels do not perform well on this
|
||||
# data set, so predictor should return the given constant.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, random_state=42),
|
||||
threshold=0.90,
|
||||
constant=0.0,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
assert norm(np.zeros(n_samples) - clf.predict(x_train)) < E
|
||||
|
||||
# Support vector machines with quadratic kernels perform almost perfectly
|
||||
# on this data set, so predictor should return their prediction.
|
||||
clf = CrossValidatedClassifier(
|
||||
classifier=SVC(probability=True, kernel="poly", degree=2, random_state=42),
|
||||
threshold=0.90,
|
||||
cv=30,
|
||||
)
|
||||
clf.fit(x_train, y_train)
|
||||
print(y_train - clf.predict(x_train))
|
||||
assert norm(y_train - clf.predict(x_train)) < E</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_cv.test_cv" href="#miplearn.classifiers.tests.test_cv.test_cv">test_cv</a></code></li>
|
||||
</ul>
|
||||
</li>
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||||
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|
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<body>
|
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<main>
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<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_evaluator</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
|
||||
from miplearn.classifiers.evaluator import ClassifierEvaluator
|
||||
|
||||
|
||||
def test_evaluator():
|
||||
clf_a = KNeighborsClassifier(n_neighbors=1)
|
||||
clf_b = KNeighborsClassifier(n_neighbors=2)
|
||||
x_train = np.array([[0, 0], [1, 0]])
|
||||
y_train = np.array([0, 1])
|
||||
clf_a.fit(x_train, y_train)
|
||||
clf_b.fit(x_train, y_train)
|
||||
ev = ClassifierEvaluator()
|
||||
assert ev.evaluate(clf_a, x_train, y_train) == 1.0
|
||||
assert ev.evaluate(clf_b, x_train, y_train) == 0.5</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_evaluator.test_evaluator"><code class="name flex">
|
||||
<span>def <span class="ident">test_evaluator</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_evaluator():
|
||||
clf_a = KNeighborsClassifier(n_neighbors=1)
|
||||
clf_b = KNeighborsClassifier(n_neighbors=2)
|
||||
x_train = np.array([[0, 0], [1, 0]])
|
||||
y_train = np.array([0, 1])
|
||||
clf_a.fit(x_train, y_train)
|
||||
clf_b.fit(x_train, y_train)
|
||||
ev = ClassifierEvaluator()
|
||||
assert ev.evaluate(clf_a, x_train, y_train) == 1.0
|
||||
assert ev.evaluate(clf_b, x_train, y_train) == 0.5</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_evaluator.test_evaluator" href="#miplearn.classifiers.tests.test_evaluator.test_evaluator">test_evaluator</a></code></li>
|
||||
</ul>
|
||||
</li>
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||||
</ul>
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||||
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
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</head>
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||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.tests.test_threshold</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.threshold import MinPrecisionThreshold
|
||||
|
||||
|
||||
def test_threshold_dynamic():
|
||||
clf = Mock(spec=Classifier)
|
||||
clf.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.10, 0.90],
|
||||
[0.10, 0.90],
|
||||
[0.20, 0.80],
|
||||
[0.30, 0.70],
|
||||
]
|
||||
)
|
||||
)
|
||||
x_train = np.array([0, 1, 2, 3])
|
||||
y_train = np.array([1, 1, 0, 0])
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=1.0)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.90
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.65)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.80
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.50)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.00)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic"><code class="name flex">
|
||||
<span>def <span class="ident">test_threshold_dynamic</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_threshold_dynamic():
|
||||
clf = Mock(spec=Classifier)
|
||||
clf.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.10, 0.90],
|
||||
[0.10, 0.90],
|
||||
[0.20, 0.80],
|
||||
[0.30, 0.70],
|
||||
]
|
||||
)
|
||||
)
|
||||
x_train = np.array([0, 1, 2, 3])
|
||||
y_train = np.array([1, 1, 0, 0])
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=1.0)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.90
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.65)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.80
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.50)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70
|
||||
|
||||
threshold = MinPrecisionThreshold(min_precision=0.00)
|
||||
assert threshold.find(clf, x_train, y_train) == 0.70</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers.tests" href="index.html">miplearn.classifiers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.tests.test_threshold.test_threshold_dynamic" href="#miplearn.classifiers.tests.test_threshold.test_threshold_dynamic">test_threshold_dynamic</a></code></li>
|
||||
</ul>
|
||||
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||||
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||||
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.classifiers.threshold</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from abc import abstractmethod, ABC
|
||||
|
||||
import numpy as np
|
||||
from sklearn.metrics._ranking import _binary_clf_curve
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
|
||||
|
||||
class DynamicThreshold(ABC):
|
||||
@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass
|
||||
|
||||
|
||||
class MinPrecisionThreshold(DynamicThreshold):
|
||||
"""
|
||||
The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).
|
||||
"""
|
||||
|
||||
def __init__(self, min_precision: float) -> None:
|
||||
self.min_precision = min_precision
|
||||
|
||||
def find(self, clf, x_train, y_train):
|
||||
proba = clf.predict_proba(x_train)
|
||||
|
||||
assert isinstance(proba, np.ndarray), "classifier should return numpy array"
|
||||
assert proba.shape == (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
), "classifier should return (%d,%d)-shaped array, not %s" % (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
str(proba.shape),
|
||||
)
|
||||
|
||||
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
|
||||
precision = tps / (tps + fps)
|
||||
|
||||
for k in reversed(range(len(precision))):
|
||||
if precision[k] >= self.min_precision:
|
||||
return thresholds[k]
|
||||
return 2.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.threshold.DynamicThreshold"><code class="flex name class">
|
||||
<span>class <span class="ident">DynamicThreshold</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class DynamicThreshold(ABC):
|
||||
@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.classifiers.threshold.DynamicThreshold.find"><code class="name flex">
|
||||
<span>def <span class="ident">find</span></span>(<span>self, clf, x_train, y_train)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Given a trained binary classifier <code>clf</code> and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def find(
|
||||
self,
|
||||
clf: Classifier,
|
||||
x_train: np.ndarray,
|
||||
y_train: np.ndarray,
|
||||
) -> float:
|
||||
"""
|
||||
Given a trained binary classifier `clf` and a training data set,
|
||||
returns the numerical threshold (float) satisfying some criterea.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.classifiers.threshold.MinPrecisionThreshold"><code class="flex name class">
|
||||
<span>class <span class="ident">MinPrecisionThreshold</span></span>
|
||||
<span>(</span><span>min_precision)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MinPrecisionThreshold(DynamicThreshold):
|
||||
"""
|
||||
The smallest possible threshold satisfying a minimum acceptable true
|
||||
positive rate (also known as precision).
|
||||
"""
|
||||
|
||||
def __init__(self, min_precision: float) -> None:
|
||||
self.min_precision = min_precision
|
||||
|
||||
def find(self, clf, x_train, y_train):
|
||||
proba = clf.predict_proba(x_train)
|
||||
|
||||
assert isinstance(proba, np.ndarray), "classifier should return numpy array"
|
||||
assert proba.shape == (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
), "classifier should return (%d,%d)-shaped array, not %s" % (
|
||||
x_train.shape[0],
|
||||
2,
|
||||
str(proba.shape),
|
||||
)
|
||||
|
||||
fps, tps, thresholds = _binary_clf_curve(y_train, proba[:, 1])
|
||||
precision = tps / (tps + fps)
|
||||
|
||||
for k in reversed(range(len(precision))):
|
||||
if precision[k] >= self.min_precision:
|
||||
return thresholds[k]
|
||||
return 2.0</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.classifiers" href="index.html">miplearn.classifiers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.threshold.DynamicThreshold" href="#miplearn.classifiers.threshold.DynamicThreshold">DynamicThreshold</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.classifiers.threshold.DynamicThreshold.find" href="#miplearn.classifiers.threshold.DynamicThreshold.find">find</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.classifiers.threshold.MinPrecisionThreshold" href="#miplearn.classifiers.threshold.MinPrecisionThreshold">MinPrecisionThreshold</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.component</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, List, Union, TYPE_CHECKING
|
||||
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.types import MIPSolveStats, TrainingSample
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
class Component(ABC):
|
||||
"""
|
||||
A Component is an object which adds functionality to a LearningSolver.
|
||||
|
||||
For better code maintainability, LearningSolver simply delegates most of its
|
||||
functionality to Components. Each Component is responsible for exactly one ML
|
||||
strategy.
|
||||
"""
|
||||
|
||||
def before_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver before the problem is solved.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver
|
||||
The solver calling this method.
|
||||
instance
|
||||
The instance being solved.
|
||||
model
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return
|
||||
|
||||
@abstractmethod
|
||||
def after_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
stats: MIPSolveStats,
|
||||
training_data: TrainingSample,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver after the problem is solved to optimality.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
stats: dict
|
||||
A dictionary containing statistics about the solution process, such as
|
||||
number of nodes explored and running time. Components are free to add
|
||||
their own statistics here. For example, PrimalSolutionComponent adds
|
||||
statistics regarding the number of predicted variables. All statistics in
|
||||
this dictionary are exported to the benchmark CSV file.
|
||||
training_data: dict
|
||||
A dictionary containing data that may be useful for training machine
|
||||
learning models and accelerating the solution process. Components are
|
||||
free to add their own training data here. For example,
|
||||
PrimalSolutionComponent adds the current primal solution. The data must
|
||||
be pickable.
|
||||
"""
|
||||
pass
|
||||
|
||||
def fit(
|
||||
self,
|
||||
training_instances: Union[List[str], List[Instance]],
|
||||
) -> None:
|
||||
return
|
||||
|
||||
def iteration_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> bool:
|
||||
"""
|
||||
Method called by LearningSolver at the end of each iteration.
|
||||
|
||||
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
|
||||
giving them a chance to modify the problem and resolve it before the solution
|
||||
process ends. For example, the lazy constraint component uses `iteration_cb`
|
||||
to check that all lazy constraints are satisfied.
|
||||
|
||||
If `iteration_cb` returns False for all components, the solution process
|
||||
ends. If it retunrs True for any component, the MIP is solved again.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return False
|
||||
|
||||
def lazy_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
return</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.component.Component"><code class="flex name class">
|
||||
<span>class <span class="ident">Component</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
|
||||
<p>For better code maintainability, LearningSolver simply delegates most of its
|
||||
functionality to Components. Each Component is responsible for exactly one ML
|
||||
strategy.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Component(ABC):
|
||||
"""
|
||||
A Component is an object which adds functionality to a LearningSolver.
|
||||
|
||||
For better code maintainability, LearningSolver simply delegates most of its
|
||||
functionality to Components. Each Component is responsible for exactly one ML
|
||||
strategy.
|
||||
"""
|
||||
|
||||
def before_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver before the problem is solved.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver
|
||||
The solver calling this method.
|
||||
instance
|
||||
The instance being solved.
|
||||
model
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return
|
||||
|
||||
@abstractmethod
|
||||
def after_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
stats: MIPSolveStats,
|
||||
training_data: TrainingSample,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver after the problem is solved to optimality.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
stats: dict
|
||||
A dictionary containing statistics about the solution process, such as
|
||||
number of nodes explored and running time. Components are free to add
|
||||
their own statistics here. For example, PrimalSolutionComponent adds
|
||||
statistics regarding the number of predicted variables. All statistics in
|
||||
this dictionary are exported to the benchmark CSV file.
|
||||
training_data: dict
|
||||
A dictionary containing data that may be useful for training machine
|
||||
learning models and accelerating the solution process. Components are
|
||||
free to add their own training data here. For example,
|
||||
PrimalSolutionComponent adds the current primal solution. The data must
|
||||
be pickable.
|
||||
"""
|
||||
pass
|
||||
|
||||
def fit(
|
||||
self,
|
||||
training_instances: Union[List[str], List[Instance]],
|
||||
) -> None:
|
||||
return
|
||||
|
||||
def iteration_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> bool:
|
||||
"""
|
||||
Method called by LearningSolver at the end of each iteration.
|
||||
|
||||
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
|
||||
giving them a chance to modify the problem and resolve it before the solution
|
||||
process ends. For example, the lazy constraint component uses `iteration_cb`
|
||||
to check that all lazy constraints are satisfied.
|
||||
|
||||
If `iteration_cb` returns False for all components, the solution process
|
||||
ends. If it retunrs True for any component, the MIP is solved again.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return False
|
||||
|
||||
def lazy_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
return</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.cuts.UserCutsComponent" href="cuts.html#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></li>
|
||||
<li><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent" href="lazy_dynamic.html#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent">DynamicLazyConstraintsComponent</a></li>
|
||||
<li><a title="miplearn.components.objective.ObjectiveValueComponent" href="objective.html#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></li>
|
||||
<li><a title="miplearn.components.primal.PrimalSolutionComponent" href="primal.html#miplearn.components.primal.PrimalSolutionComponent">PrimalSolutionComponent</a></li>
|
||||
<li><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="lazy_static.html#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></li>
|
||||
<li><a title="miplearn.components.composite.CompositeComponent" href="composite.html#miplearn.components.composite.CompositeComponent">CompositeComponent</a></li>
|
||||
<li><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="steps/drop_redundant.html#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></li>
|
||||
<li><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="steps/convert_tight.html#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></li>
|
||||
<li><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="steps/relax_integrality.html#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></li>
|
||||
<li><a title="miplearn.components.relaxation.RelaxationComponent" href="relaxation.html#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.component.Component.after_solve"><code class="name flex">
|
||||
<span>def <span class="ident">after_solve</span></span>(<span>self, solver, instance, model, stats, training_data)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Method called by LearningSolver after the problem is solved to optimality.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>solver</code></strong> : <code>LearningSolver</code></dt>
|
||||
<dd>The solver calling this method.</dd>
|
||||
<dt><strong><code>instance</code></strong> : <code>Instance</code></dt>
|
||||
<dd>The instance being solved.</dd>
|
||||
<dt><strong><code>model</code></strong> : <code>Any</code></dt>
|
||||
<dd>The concrete optimization model being solved.</dd>
|
||||
<dt><strong><code>stats</code></strong> : <code>dict</code></dt>
|
||||
<dd>A dictionary containing statistics about the solution process, such as
|
||||
number of nodes explored and running time. Components are free to add
|
||||
their own statistics here. For example, PrimalSolutionComponent adds
|
||||
statistics regarding the number of predicted variables. All statistics in
|
||||
this dictionary are exported to the benchmark CSV file.</dd>
|
||||
<dt><strong><code>training_data</code></strong> : <code>dict</code></dt>
|
||||
<dd>A dictionary containing data that may be useful for training machine
|
||||
learning models and accelerating the solution process. Components are
|
||||
free to add their own training data here. For example,
|
||||
PrimalSolutionComponent adds the current primal solution. The data must
|
||||
be pickable.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def after_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
stats: MIPSolveStats,
|
||||
training_data: TrainingSample,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver after the problem is solved to optimality.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
stats: dict
|
||||
A dictionary containing statistics about the solution process, such as
|
||||
number of nodes explored and running time. Components are free to add
|
||||
their own statistics here. For example, PrimalSolutionComponent adds
|
||||
statistics regarding the number of predicted variables. All statistics in
|
||||
this dictionary are exported to the benchmark CSV file.
|
||||
training_data: dict
|
||||
A dictionary containing data that may be useful for training machine
|
||||
learning models and accelerating the solution process. Components are
|
||||
free to add their own training data here. For example,
|
||||
PrimalSolutionComponent adds the current primal solution. The data must
|
||||
be pickable.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.component.Component.before_solve"><code class="name flex">
|
||||
<span>def <span class="ident">before_solve</span></span>(<span>self, solver, instance, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Method called by LearningSolver before the problem is solved.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>solver</code></strong></dt>
|
||||
<dd>The solver calling this method.</dd>
|
||||
<dt><strong><code>instance</code></strong></dt>
|
||||
<dd>The instance being solved.</dd>
|
||||
<dt><strong><code>model</code></strong></dt>
|
||||
<dd>The concrete optimization model being solved.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def before_solve(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
"""
|
||||
Method called by LearningSolver before the problem is solved.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver
|
||||
The solver calling this method.
|
||||
instance
|
||||
The instance being solved.
|
||||
model
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.component.Component.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(
|
||||
self,
|
||||
training_instances: Union[List[str], List[Instance]],
|
||||
) -> None:
|
||||
return</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.component.Component.iteration_cb"><code class="name flex">
|
||||
<span>def <span class="ident">iteration_cb</span></span>(<span>self, solver, instance, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Method called by LearningSolver at the end of each iteration.</p>
|
||||
<p>After solving the MIP, LearningSolver calls <code>iteration_cb</code> of each component,
|
||||
giving them a chance to modify the problem and resolve it before the solution
|
||||
process ends. For example, the lazy constraint component uses <code>iteration_cb</code>
|
||||
to check that all lazy constraints are satisfied.</p>
|
||||
<p>If <code>iteration_cb</code> returns False for all components, the solution process
|
||||
ends. If it retunrs True for any component, the MIP is solved again.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>solver</code></strong> : <code>LearningSolver</code></dt>
|
||||
<dd>The solver calling this method.</dd>
|
||||
<dt><strong><code>instance</code></strong> : <code>Instance</code></dt>
|
||||
<dd>The instance being solved.</dd>
|
||||
<dt><strong><code>model</code></strong> : <code>Any</code></dt>
|
||||
<dd>The concrete optimization model being solved.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def iteration_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> bool:
|
||||
"""
|
||||
Method called by LearningSolver at the end of each iteration.
|
||||
|
||||
After solving the MIP, LearningSolver calls `iteration_cb` of each component,
|
||||
giving them a chance to modify the problem and resolve it before the solution
|
||||
process ends. For example, the lazy constraint component uses `iteration_cb`
|
||||
to check that all lazy constraints are satisfied.
|
||||
|
||||
If `iteration_cb` returns False for all components, the solution process
|
||||
ends. If it retunrs True for any component, the MIP is solved again.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
solver: LearningSolver
|
||||
The solver calling this method.
|
||||
instance: Instance
|
||||
The instance being solved.
|
||||
model: Any
|
||||
The concrete optimization model being solved.
|
||||
"""
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.component.Component.lazy_cb"><code class="name flex">
|
||||
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def lazy_cb(
|
||||
self,
|
||||
solver: "LearningSolver",
|
||||
instance: Instance,
|
||||
model: Any,
|
||||
) -> None:
|
||||
return</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.component.Component" href="#miplearn.components.component.Component">Component</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.fit" href="#miplearn.components.component.Component.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.lazy_cb" href="#miplearn.components.component.Component.lazy_cb">lazy_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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|
||||
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|
||||
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|
||||
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|
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<title>miplearn.components.composite API documentation</title>
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|
||||
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|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.composite</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from miplearn.components.component import Component
|
||||
|
||||
|
||||
class CompositeComponent(Component):
|
||||
"""
|
||||
A Component which redirects each method call to one or more subcomponents.
|
||||
|
||||
Useful for breaking down complex components into smaller classes. See
|
||||
RelaxationComponent for a concrete example.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
children : list[Component]
|
||||
Subcomponents that compose this component.
|
||||
"""
|
||||
|
||||
def __init__(self, children):
|
||||
self.children = children
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
for child in self.children:
|
||||
child.before_solve(solver, instance, model)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
for child in self.children:
|
||||
child.after_solve(solver, instance, model, stats, training_data)
|
||||
|
||||
def fit(self, training_instances):
|
||||
for child in self.children:
|
||||
child.fit(training_instances)
|
||||
|
||||
def lazy_cb(self, solver, instance, model):
|
||||
for child in self.children:
|
||||
child.lazy_cb(solver, instance, model)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
should_repeat = False
|
||||
for child in self.children:
|
||||
if child.iteration_cb(solver, instance, model):
|
||||
should_repeat = True
|
||||
return should_repeat</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.composite.CompositeComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">CompositeComponent</span></span>
|
||||
<span>(</span><span>children)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A Component which redirects each method call to one or more subcomponents.</p>
|
||||
<p>Useful for breaking down complex components into smaller classes. See
|
||||
RelaxationComponent for a concrete example.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>children</code></strong> : <code>list</code>[<code>Component</code>]</dt>
|
||||
<dd>Subcomponents that compose this component.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CompositeComponent(Component):
|
||||
"""
|
||||
A Component which redirects each method call to one or more subcomponents.
|
||||
|
||||
Useful for breaking down complex components into smaller classes. See
|
||||
RelaxationComponent for a concrete example.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
children : list[Component]
|
||||
Subcomponents that compose this component.
|
||||
"""
|
||||
|
||||
def __init__(self, children):
|
||||
self.children = children
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
for child in self.children:
|
||||
child.before_solve(solver, instance, model)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
for child in self.children:
|
||||
child.after_solve(solver, instance, model, stats, training_data)
|
||||
|
||||
def fit(self, training_instances):
|
||||
for child in self.children:
|
||||
child.fit(training_instances)
|
||||
|
||||
def lazy_cb(self, solver, instance, model):
|
||||
for child in self.children:
|
||||
child.lazy_cb(solver, instance, model)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
should_repeat = False
|
||||
for child in self.children:
|
||||
if child.iteration_cb(solver, instance, model):
|
||||
should_repeat = True
|
||||
return should_repeat</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.composite.CompositeComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
for child in self.children:
|
||||
child.fit(training_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.composite.CompositeComponent.lazy_cb"><code class="name flex">
|
||||
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def lazy_cb(self, solver, instance, model):
|
||||
for child in self.children:
|
||||
child.lazy_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.composite.CompositeComponent" href="#miplearn.components.composite.CompositeComponent">CompositeComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.composite.CompositeComponent.fit" href="#miplearn.components.composite.CompositeComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.composite.CompositeComponent.lazy_cb" href="#miplearn.components.composite.CompositeComponent.lazy_cb">lazy_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.cuts</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict
|
||||
|
||||
import numpy as np
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.extractors import InstanceFeaturesExtractor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class UserCutsComponent(Component):
|
||||
"""
|
||||
A component that predicts which user cuts to enforce.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = CountingClassifier(),
|
||||
threshold: float = 0.05,
|
||||
):
|
||||
self.threshold: float = threshold
|
||||
self.classifier_prototype: Classifier = classifier
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
instance.found_violated_user_cuts = []
|
||||
logger.info("Predicting violated user cuts...")
|
||||
violations = self.predict(instance)
|
||||
logger.info("Enforcing %d user cuts..." % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_user_cut(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
results,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(training_instances):
|
||||
if not hasattr(instance, "found_violated_user_cuts"):
|
||||
continue
|
||||
for v in instance.found_violated_user_cuts:
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (user cuts)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)
|
||||
|
||||
def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations
|
||||
|
||||
def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_user_cuts)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_user_cuts)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.cuts.UserCutsComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">UserCutsComponent</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A component that predicts which user cuts to enforce.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class UserCutsComponent(Component):
|
||||
"""
|
||||
A component that predicts which user cuts to enforce.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = CountingClassifier(),
|
||||
threshold: float = 0.05,
|
||||
):
|
||||
self.threshold: float = threshold
|
||||
self.classifier_prototype: Classifier = classifier
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
instance.found_violated_user_cuts = []
|
||||
logger.info("Predicting violated user cuts...")
|
||||
violations = self.predict(instance)
|
||||
logger.info("Enforcing %d user cuts..." % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_user_cut(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
results,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(training_instances):
|
||||
if not hasattr(instance, "found_violated_user_cuts"):
|
||||
continue
|
||||
for v in instance.found_violated_user_cuts:
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (user cuts)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)
|
||||
|
||||
def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations
|
||||
|
||||
def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_user_cuts)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_user_cuts)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.cuts.UserCutsComponent.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_user_cuts)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_user_cuts)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.cuts.UserCutsComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(training_instances):
|
||||
if not hasattr(instance, "found_violated_user_cuts"):
|
||||
continue
|
||||
for v in instance.found_violated_user_cuts:
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (user cuts)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.cuts.UserCutsComponent.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.cuts.UserCutsComponent" href="#miplearn.components.cuts.UserCutsComponent">UserCutsComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.cuts.UserCutsComponent.evaluate" href="#miplearn.components.cuts.UserCutsComponent.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.cuts.UserCutsComponent.fit" href="#miplearn.components.cuts.UserCutsComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.cuts.UserCutsComponent.predict" href="#miplearn.components.cuts.UserCutsComponent.predict">predict</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
||||
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
|
||||
def classifier_evaluation_dict(tp, tn, fp, fn):
|
||||
p = tp + fn
|
||||
n = fp + tn
|
||||
d = {
|
||||
"Predicted positive": fp + tp,
|
||||
"Predicted negative": fn + tn,
|
||||
"Condition positive": p,
|
||||
"Condition negative": n,
|
||||
"True positive": tp,
|
||||
"True negative": tn,
|
||||
"False positive": fp,
|
||||
"False negative": fn,
|
||||
"Accuracy": (tp + tn) / (p + n),
|
||||
"F1 score": (2 * tp) / (2 * tp + fp + fn),
|
||||
}
|
||||
|
||||
if p > 0:
|
||||
d["Recall"] = tp / p
|
||||
else:
|
||||
d["Recall"] = 1.0
|
||||
|
||||
if tp + fp > 0:
|
||||
d["Precision"] = tp / (tp + fp)
|
||||
else:
|
||||
d["Precision"] = 1.0
|
||||
|
||||
t = (p + n) / 100.0
|
||||
d["Predicted positive (%)"] = d["Predicted positive"] / t
|
||||
d["Predicted negative (%)"] = d["Predicted negative"] / t
|
||||
d["Condition positive (%)"] = d["Condition positive"] / t
|
||||
d["Condition negative (%)"] = d["Condition negative"] / t
|
||||
d["True positive (%)"] = d["True positive"] / t
|
||||
d["True negative (%)"] = d["True negative"] / t
|
||||
d["False positive (%)"] = d["False positive"] / t
|
||||
d["False negative (%)"] = d["False negative"] / t
|
||||
return d</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.components.component" href="component.html">miplearn.components.component</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.composite" href="composite.html">miplearn.components.composite</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.cuts" href="cuts.html">miplearn.components.cuts</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.lazy_dynamic" href="lazy_dynamic.html">miplearn.components.lazy_dynamic</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.lazy_static" href="lazy_static.html">miplearn.components.lazy_static</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.objective" href="objective.html">miplearn.components.objective</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.primal" href="primal.html">miplearn.components.primal</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.relaxation" href="relaxation.html">miplearn.components.relaxation</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps" href="steps/index.html">miplearn.components.steps</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.tests" href="tests/index.html">miplearn.components.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.classifier_evaluation_dict"><code class="name flex">
|
||||
<span>def <span class="ident">classifier_evaluation_dict</span></span>(<span>tp, tn, fp, fn)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def classifier_evaluation_dict(tp, tn, fp, fn):
|
||||
p = tp + fn
|
||||
n = fp + tn
|
||||
d = {
|
||||
"Predicted positive": fp + tp,
|
||||
"Predicted negative": fn + tn,
|
||||
"Condition positive": p,
|
||||
"Condition negative": n,
|
||||
"True positive": tp,
|
||||
"True negative": tn,
|
||||
"False positive": fp,
|
||||
"False negative": fn,
|
||||
"Accuracy": (tp + tn) / (p + n),
|
||||
"F1 score": (2 * tp) / (2 * tp + fp + fn),
|
||||
}
|
||||
|
||||
if p > 0:
|
||||
d["Recall"] = tp / p
|
||||
else:
|
||||
d["Recall"] = 1.0
|
||||
|
||||
if tp + fp > 0:
|
||||
d["Precision"] = tp / (tp + fp)
|
||||
else:
|
||||
d["Precision"] = 1.0
|
||||
|
||||
t = (p + n) / 100.0
|
||||
d["Predicted positive (%)"] = d["Predicted positive"] / t
|
||||
d["Predicted negative (%)"] = d["Predicted negative"] / t
|
||||
d["Condition positive (%)"] = d["Condition positive"] / t
|
||||
d["Condition negative (%)"] = d["Condition negative"] / t
|
||||
d["True positive (%)"] = d["True positive"] / t
|
||||
d["True negative (%)"] = d["True negative"] / t
|
||||
d["False positive (%)"] = d["False positive"] / t
|
||||
d["False negative (%)"] = d["False negative"] / t
|
||||
return d</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.component" href="component.html">miplearn.components.component</a></code></li>
|
||||
<li><code><a title="miplearn.components.composite" href="composite.html">miplearn.components.composite</a></code></li>
|
||||
<li><code><a title="miplearn.components.cuts" href="cuts.html">miplearn.components.cuts</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_dynamic" href="lazy_dynamic.html">miplearn.components.lazy_dynamic</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static" href="lazy_static.html">miplearn.components.lazy_static</a></code></li>
|
||||
<li><code><a title="miplearn.components.objective" href="objective.html">miplearn.components.objective</a></code></li>
|
||||
<li><code><a title="miplearn.components.primal" href="primal.html">miplearn.components.primal</a></code></li>
|
||||
<li><code><a title="miplearn.components.relaxation" href="relaxation.html">miplearn.components.relaxation</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps" href="steps/index.html">miplearn.components.steps</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests" href="tests/index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.classifier_evaluation_dict" href="#miplearn.components.classifier_evaluation_dict">classifier_evaluation_dict</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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||||
</nav>
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||||
</main>
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|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.lazy_dynamic</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from typing import Any, Dict
|
||||
|
||||
import numpy as np
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.extractors import InstanceFeaturesExtractor, InstanceIterator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DynamicLazyConstraintsComponent(Component):
|
||||
"""
|
||||
A component that predicts which lazy constraints to enforce.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = CountingClassifier(),
|
||||
threshold: float = 0.05,
|
||||
):
|
||||
self.threshold: float = threshold
|
||||
self.classifier_prototype: Classifier = classifier
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
instance.found_violated_lazy_constraints = []
|
||||
logger.info("Predicting violated lazy constraints...")
|
||||
violations = self.predict(instance)
|
||||
logger.info("Enforcing %d lazy constraints..." % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_lazy_constraint(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
logger.debug("Finding violated (dynamic) lazy constraints...")
|
||||
violations = instance.find_violated_lazy_constraints(model)
|
||||
if len(violations) == 0:
|
||||
return False
|
||||
instance.found_violated_lazy_constraints += violations
|
||||
logger.debug(" %d violations found" % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_lazy_constraint(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
return True
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
|
||||
for v in instance.found_violated_lazy_constraints:
|
||||
if isinstance(v, list):
|
||||
v = tuple(v)
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)
|
||||
|
||||
def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations
|
||||
|
||||
def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_lazy_constraints)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_lazy_constraints)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">DynamicLazyConstraintsComponent</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A component that predicts which lazy constraints to enforce.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class DynamicLazyConstraintsComponent(Component):
|
||||
"""
|
||||
A component that predicts which lazy constraints to enforce.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = CountingClassifier(),
|
||||
threshold: float = 0.05,
|
||||
):
|
||||
self.threshold: float = threshold
|
||||
self.classifier_prototype: Classifier = classifier
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
instance.found_violated_lazy_constraints = []
|
||||
logger.info("Predicting violated lazy constraints...")
|
||||
violations = self.predict(instance)
|
||||
logger.info("Enforcing %d lazy constraints..." % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_lazy_constraint(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
logger.debug("Finding violated (dynamic) lazy constraints...")
|
||||
violations = instance.find_violated_lazy_constraints(model)
|
||||
if len(violations) == 0:
|
||||
return False
|
||||
instance.found_violated_lazy_constraints += violations
|
||||
logger.debug(" %d violations found" % len(violations))
|
||||
for v in violations:
|
||||
cut = instance.build_lazy_constraint(model, v)
|
||||
solver.internal_solver.add_constraint(cut)
|
||||
return True
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
|
||||
for v in instance.found_violated_lazy_constraints:
|
||||
if isinstance(v, list):
|
||||
v = tuple(v)
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)
|
||||
|
||||
def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations
|
||||
|
||||
def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_lazy_constraints)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_lazy_constraints)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instances):
|
||||
results = {}
|
||||
all_violations = set()
|
||||
for instance in instances:
|
||||
all_violations |= set(instance.found_violated_lazy_constraints)
|
||||
for idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
instance = instances[idx]
|
||||
condition_positive = set(instance.found_violated_lazy_constraints)
|
||||
condition_negative = all_violations - condition_positive
|
||||
pred_positive = set(self.predict(instance)) & all_violations
|
||||
pred_negative = all_violations - pred_positive
|
||||
tp = len(pred_positive & condition_positive)
|
||||
tn = len(pred_negative & condition_negative)
|
||||
fp = len(pred_positive & condition_negative)
|
||||
fn = len(pred_negative & condition_positive)
|
||||
results[idx] = classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
return results</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Fitting...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
|
||||
self.classifiers = {}
|
||||
violation_to_instance_idx = {}
|
||||
for (idx, instance) in enumerate(InstanceIterator(training_instances)):
|
||||
for v in instance.found_violated_lazy_constraints:
|
||||
if isinstance(v, list):
|
||||
v = tuple(v)
|
||||
if v not in self.classifiers:
|
||||
self.classifiers[v] = deepcopy(self.classifier_prototype)
|
||||
violation_to_instance_idx[v] = []
|
||||
violation_to_instance_idx[v] += [idx]
|
||||
|
||||
for (v, classifier) in tqdm(
|
||||
self.classifiers.items(),
|
||||
desc="Fit (lazy)",
|
||||
disable=not sys.stdout.isatty(),
|
||||
):
|
||||
logger.debug("Training: %s" % (str(v)))
|
||||
label = np.zeros(len(training_instances))
|
||||
label[violation_to_instance_idx[v]] = 1.0
|
||||
classifier.fit(features, label)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, instance):
|
||||
violations = []
|
||||
features = InstanceFeaturesExtractor().extract([instance])
|
||||
for (v, classifier) in self.classifiers.items():
|
||||
proba = classifier.predict_proba(features)
|
||||
if proba[0][1] > self.threshold:
|
||||
violations += [v]
|
||||
return violations</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent">DynamicLazyConstraintsComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict" href="#miplearn.components.lazy_dynamic.DynamicLazyConstraintsComponent.predict">predict</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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<body>
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||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.lazy_static</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components.component import Component
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class LazyConstraint:
|
||||
def __init__(self, cid, obj):
|
||||
self.cid = cid
|
||||
self.obj = obj
|
||||
|
||||
|
||||
class StaticLazyConstraintsComponent(Component):
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.05,
|
||||
use_two_phase_gap=True,
|
||||
large_gap=1e-2,
|
||||
violation_tolerance=-0.5,
|
||||
):
|
||||
self.threshold = threshold
|
||||
self.classifier_prototype = classifier
|
||||
self.classifiers = {}
|
||||
self.pool = []
|
||||
self.original_gap = None
|
||||
self.large_gap = large_gap
|
||||
self.is_gap_large = False
|
||||
self.use_two_phase_gap = use_two_phase_gap
|
||||
self.violation_tolerance = violation_tolerance
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
self.pool = []
|
||||
if not solver.use_lazy_cb and self.use_two_phase_gap:
|
||||
logger.info("Increasing gap tolerance to %f", self.large_gap)
|
||||
self.original_gap = solver.gap_tolerance
|
||||
self.is_gap_large = True
|
||||
solver.internal_solver.set_gap_tolerance(self.large_gap)
|
||||
|
||||
instance.found_violated_lazy_constraints = []
|
||||
if instance.has_static_lazy_constraints():
|
||||
self._extract_and_predict_static(solver, instance)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if solver.use_lazy_cb:
|
||||
return False
|
||||
else:
|
||||
should_repeat = self._check_and_add(instance, solver)
|
||||
if should_repeat:
|
||||
return True
|
||||
else:
|
||||
if self.is_gap_large:
|
||||
logger.info("Restoring gap tolerance to %f", self.original_gap)
|
||||
solver.internal_solver.set_gap_tolerance(self.original_gap)
|
||||
self.is_gap_large = False
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def lazy_cb(self, solver, instance, model):
|
||||
self._check_and_add(instance, solver)
|
||||
|
||||
def _check_and_add(self, instance, solver):
|
||||
logger.debug("Finding violated lazy constraints...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj, tol=self.violation_tolerance
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
instance.found_violated_lazy_constraints += [c.cid]
|
||||
if len(constraints_to_add) > 0:
|
||||
logger.info(
|
||||
"%8d lazy constraints added %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def fit(self, training_instances):
|
||||
training_instances = [
|
||||
t
|
||||
for t in training_instances
|
||||
if hasattr(t, "found_violated_lazy_constraints")
|
||||
]
|
||||
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(
|
||||
x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
|
||||
):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def predict(self, instance):
|
||||
pass
|
||||
|
||||
def evaluate(self, instances):
|
||||
pass
|
||||
|
||||
def _extract_and_predict_static(self, solver, instance):
|
||||
x = {}
|
||||
constraints = {}
|
||||
logger.info("Extracting lazy constraints...")
|
||||
for cid in solver.internal_solver.get_constraint_ids():
|
||||
if instance.is_constraint_lazy(cid):
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
constraints[category] += [c]
|
||||
self.pool.append(c)
|
||||
logger.info("%8d lazy constraints extracted" % len(self.pool))
|
||||
logger.info("Predicting required lazy constraints...")
|
||||
n_added = 0
|
||||
for (category, x_values) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
if isinstance(x_values[0], np.ndarray):
|
||||
x[category] = np.array(x_values)
|
||||
proba = self.classifiers[category].predict_proba(x[category])
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] > self.threshold:
|
||||
n_added += 1
|
||||
c = constraints[category][i]
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
instance.found_violated_lazy_constraints += [c.cid]
|
||||
logger.info(
|
||||
"%8d lazy constraints added %8d in the pool"
|
||||
% (
|
||||
n_added,
|
||||
len(self.pool),
|
||||
)
|
||||
)
|
||||
|
||||
def _collect_constraints(self, train_instances):
|
||||
constraints = {}
|
||||
for instance in train_instances:
|
||||
for cid in instance.found_violated_lazy_constraints:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category not in constraints:
|
||||
constraints[category] = set()
|
||||
constraints[category].add(cid)
|
||||
for (category, cids) in constraints.items():
|
||||
constraints[category] = sorted(list(cids))
|
||||
return constraints
|
||||
|
||||
def x(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
result[category].append(instance.get_constraint_features(cid))
|
||||
return result
|
||||
|
||||
def y(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
if cid in instance.found_violated_lazy_constraints:
|
||||
result[category].append([0, 1])
|
||||
else:
|
||||
result[category].append([1, 0])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.lazy_static.LazyConstraint"><code class="flex name class">
|
||||
<span>class <span class="ident">LazyConstraint</span></span>
|
||||
<span>(</span><span>cid, obj)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class LazyConstraint:
|
||||
def __init__(self, cid, obj):
|
||||
self.cid = cid
|
||||
self.obj = obj</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">StaticLazyConstraintsComponent</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.05, use_two_phase_gap=True, large_gap=0.01, violation_tolerance=-0.5)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A Component is an object which adds functionality to a LearningSolver.</p>
|
||||
<p>For better code maintainability, LearningSolver simply delegates most of its
|
||||
functionality to Components. Each Component is responsible for exactly one ML
|
||||
strategy.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class StaticLazyConstraintsComponent(Component):
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.05,
|
||||
use_two_phase_gap=True,
|
||||
large_gap=1e-2,
|
||||
violation_tolerance=-0.5,
|
||||
):
|
||||
self.threshold = threshold
|
||||
self.classifier_prototype = classifier
|
||||
self.classifiers = {}
|
||||
self.pool = []
|
||||
self.original_gap = None
|
||||
self.large_gap = large_gap
|
||||
self.is_gap_large = False
|
||||
self.use_two_phase_gap = use_two_phase_gap
|
||||
self.violation_tolerance = violation_tolerance
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
self.pool = []
|
||||
if not solver.use_lazy_cb and self.use_two_phase_gap:
|
||||
logger.info("Increasing gap tolerance to %f", self.large_gap)
|
||||
self.original_gap = solver.gap_tolerance
|
||||
self.is_gap_large = True
|
||||
solver.internal_solver.set_gap_tolerance(self.large_gap)
|
||||
|
||||
instance.found_violated_lazy_constraints = []
|
||||
if instance.has_static_lazy_constraints():
|
||||
self._extract_and_predict_static(solver, instance)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if solver.use_lazy_cb:
|
||||
return False
|
||||
else:
|
||||
should_repeat = self._check_and_add(instance, solver)
|
||||
if should_repeat:
|
||||
return True
|
||||
else:
|
||||
if self.is_gap_large:
|
||||
logger.info("Restoring gap tolerance to %f", self.original_gap)
|
||||
solver.internal_solver.set_gap_tolerance(self.original_gap)
|
||||
self.is_gap_large = False
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def lazy_cb(self, solver, instance, model):
|
||||
self._check_and_add(instance, solver)
|
||||
|
||||
def _check_and_add(self, instance, solver):
|
||||
logger.debug("Finding violated lazy constraints...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj, tol=self.violation_tolerance
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
instance.found_violated_lazy_constraints += [c.cid]
|
||||
if len(constraints_to_add) > 0:
|
||||
logger.info(
|
||||
"%8d lazy constraints added %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
return True
|
||||
else:
|
||||
return False
|
||||
|
||||
def fit(self, training_instances):
|
||||
training_instances = [
|
||||
t
|
||||
for t in training_instances
|
||||
if hasattr(t, "found_violated_lazy_constraints")
|
||||
]
|
||||
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(
|
||||
x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
|
||||
):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def predict(self, instance):
|
||||
pass
|
||||
|
||||
def evaluate(self, instances):
|
||||
pass
|
||||
|
||||
def _extract_and_predict_static(self, solver, instance):
|
||||
x = {}
|
||||
constraints = {}
|
||||
logger.info("Extracting lazy constraints...")
|
||||
for cid in solver.internal_solver.get_constraint_ids():
|
||||
if instance.is_constraint_lazy(cid):
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
constraints[category] += [c]
|
||||
self.pool.append(c)
|
||||
logger.info("%8d lazy constraints extracted" % len(self.pool))
|
||||
logger.info("Predicting required lazy constraints...")
|
||||
n_added = 0
|
||||
for (category, x_values) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
if isinstance(x_values[0], np.ndarray):
|
||||
x[category] = np.array(x_values)
|
||||
proba = self.classifiers[category].predict_proba(x[category])
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] > self.threshold:
|
||||
n_added += 1
|
||||
c = constraints[category][i]
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
instance.found_violated_lazy_constraints += [c.cid]
|
||||
logger.info(
|
||||
"%8d lazy constraints added %8d in the pool"
|
||||
% (
|
||||
n_added,
|
||||
len(self.pool),
|
||||
)
|
||||
)
|
||||
|
||||
def _collect_constraints(self, train_instances):
|
||||
constraints = {}
|
||||
for instance in train_instances:
|
||||
for cid in instance.found_violated_lazy_constraints:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category not in constraints:
|
||||
constraints[category] = set()
|
||||
constraints[category].add(cid)
|
||||
for (category, cids) in constraints.items():
|
||||
constraints[category] = sorted(list(cids))
|
||||
return constraints
|
||||
|
||||
def x(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
result[category].append(instance.get_constraint_features(cid))
|
||||
return result
|
||||
|
||||
def y(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
if cid in instance.found_violated_lazy_constraints:
|
||||
result[category].append([0, 1])
|
||||
else:
|
||||
result[category].append([1, 0])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instances):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
training_instances = [
|
||||
t
|
||||
for t in training_instances
|
||||
if hasattr(t, "found_violated_lazy_constraints")
|
||||
]
|
||||
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(
|
||||
x.keys(), desc="Fit (lazy)", disable=not sys.stdout.isatty()
|
||||
):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb"><code class="name flex">
|
||||
<span>def <span class="ident">lazy_cb</span></span>(<span>self, solver, instance, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def lazy_cb(self, solver, instance, model):
|
||||
self._check_and_add(instance, solver)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, instance):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, train_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
result[category].append(instance.get_constraint_features(cid))
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, train_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, train_instances):
|
||||
result = {}
|
||||
constraints = self._collect_constraints(train_instances)
|
||||
for (category, cids) in constraints.items():
|
||||
result[category] = []
|
||||
for instance in train_instances:
|
||||
for cid in cids:
|
||||
if cid in instance.found_violated_lazy_constraints:
|
||||
result[category].append([0, 1])
|
||||
else:
|
||||
result[category].append([1, 0])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.lazy_static.LazyConstraint" href="#miplearn.components.lazy_static.LazyConstraint">LazyConstraint</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent">StaticLazyConstraintsComponent</a></code></h4>
|
||||
<ul class="two-column">
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.lazy_cb">lazy_cb</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.x" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.lazy_static.StaticLazyConstraintsComponent.y" href="#miplearn.components.lazy_static.StaticLazyConstraintsComponent.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
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||||
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|
||||
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||||
<!doctype html>
|
||||
<html lang="en">
|
||||
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<title>miplearn.components.objective API documentation</title>
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.objective</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.metrics import (
|
||||
mean_squared_error,
|
||||
explained_variance_score,
|
||||
max_error,
|
||||
mean_absolute_error,
|
||||
r2_score,
|
||||
)
|
||||
|
||||
from miplearn.classifiers import Regressor
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.extractors import InstanceFeaturesExtractor, ObjectiveValueExtractor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ObjectiveValueComponent(Component):
|
||||
"""
|
||||
A Component which predicts the optimal objective value of the problem.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
regressor: Regressor = LinearRegression(),
|
||||
) -> None:
|
||||
self.ub_regressor = None
|
||||
self.lb_regressor = None
|
||||
self.regressor_prototype = regressor
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
if self.ub_regressor is not None:
|
||||
logger.info("Predicting optimal value...")
|
||||
lb, ub = self.predict([instance])[0]
|
||||
instance.predicted_ub = ub
|
||||
instance.predicted_lb = lb
|
||||
logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if self.ub_regressor is not None:
|
||||
stats["Predicted UB"] = instance.predicted_ub
|
||||
stats["Predicted LB"] = instance.predicted_lb
|
||||
else:
|
||||
stats["Predicted UB"] = None
|
||||
stats["Predicted LB"] = None
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting features...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
|
||||
lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
|
||||
assert ub.shape == (len(training_instances), 1)
|
||||
assert lb.shape == (len(training_instances), 1)
|
||||
self.ub_regressor = deepcopy(self.regressor_prototype)
|
||||
self.lb_regressor = deepcopy(self.regressor_prototype)
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.ub_regressor.fit(features, ub.ravel())
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.lb_regressor.fit(features, lb.ravel())
|
||||
|
||||
def predict(self, instances):
|
||||
features = InstanceFeaturesExtractor().extract(instances)
|
||||
lb = self.lb_regressor.predict(features)
|
||||
ub = self.ub_regressor.predict(features)
|
||||
assert lb.shape == (len(instances),)
|
||||
assert ub.shape == (len(instances),)
|
||||
return np.array([lb, ub]).T
|
||||
|
||||
def evaluate(self, instances):
|
||||
y_pred = self.predict(instances)
|
||||
y_true = np.array(
|
||||
[
|
||||
[
|
||||
inst.training_data[0]["Lower bound"],
|
||||
inst.training_data[0]["Upper bound"],
|
||||
]
|
||||
for inst in instances
|
||||
]
|
||||
)
|
||||
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
|
||||
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
|
||||
ev = {
|
||||
"Lower bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
|
||||
"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
|
||||
"Max error": max_error(y_true_lb, y_pred_lb),
|
||||
"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
"R2": r2_score(y_true_lb, y_pred_lb),
|
||||
"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
},
|
||||
"Upper bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
|
||||
"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
|
||||
"Max error": max_error(y_true_ub, y_pred_ub),
|
||||
"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
"R2": r2_score(y_true_ub, y_pred_ub),
|
||||
"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
},
|
||||
}
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.objective.ObjectiveValueComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">ObjectiveValueComponent</span></span>
|
||||
<span>(</span><span>regressor=LinearRegression())</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A Component which predicts the optimal objective value of the problem.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ObjectiveValueComponent(Component):
|
||||
"""
|
||||
A Component which predicts the optimal objective value of the problem.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
regressor: Regressor = LinearRegression(),
|
||||
) -> None:
|
||||
self.ub_regressor = None
|
||||
self.lb_regressor = None
|
||||
self.regressor_prototype = regressor
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
if self.ub_regressor is not None:
|
||||
logger.info("Predicting optimal value...")
|
||||
lb, ub = self.predict([instance])[0]
|
||||
instance.predicted_ub = ub
|
||||
instance.predicted_lb = lb
|
||||
logger.info("Predicted values: lb=%.2f, ub=%.2f" % (lb, ub))
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if self.ub_regressor is not None:
|
||||
stats["Predicted UB"] = instance.predicted_ub
|
||||
stats["Predicted LB"] = instance.predicted_lb
|
||||
else:
|
||||
stats["Predicted UB"] = None
|
||||
stats["Predicted LB"] = None
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting features...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
|
||||
lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
|
||||
assert ub.shape == (len(training_instances), 1)
|
||||
assert lb.shape == (len(training_instances), 1)
|
||||
self.ub_regressor = deepcopy(self.regressor_prototype)
|
||||
self.lb_regressor = deepcopy(self.regressor_prototype)
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.ub_regressor.fit(features, ub.ravel())
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.lb_regressor.fit(features, lb.ravel())
|
||||
|
||||
def predict(self, instances):
|
||||
features = InstanceFeaturesExtractor().extract(instances)
|
||||
lb = self.lb_regressor.predict(features)
|
||||
ub = self.ub_regressor.predict(features)
|
||||
assert lb.shape == (len(instances),)
|
||||
assert ub.shape == (len(instances),)
|
||||
return np.array([lb, ub]).T
|
||||
|
||||
def evaluate(self, instances):
|
||||
y_pred = self.predict(instances)
|
||||
y_true = np.array(
|
||||
[
|
||||
[
|
||||
inst.training_data[0]["Lower bound"],
|
||||
inst.training_data[0]["Upper bound"],
|
||||
]
|
||||
for inst in instances
|
||||
]
|
||||
)
|
||||
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
|
||||
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
|
||||
ev = {
|
||||
"Lower bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
|
||||
"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
|
||||
"Max error": max_error(y_true_lb, y_pred_lb),
|
||||
"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
"R2": r2_score(y_true_lb, y_pred_lb),
|
||||
"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
},
|
||||
"Upper bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
|
||||
"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
|
||||
"Max error": max_error(y_true_ub, y_pred_ub),
|
||||
"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
"R2": r2_score(y_true_ub, y_pred_ub),
|
||||
"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
},
|
||||
}
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.objective.ObjectiveValueComponent.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instances):
|
||||
y_pred = self.predict(instances)
|
||||
y_true = np.array(
|
||||
[
|
||||
[
|
||||
inst.training_data[0]["Lower bound"],
|
||||
inst.training_data[0]["Upper bound"],
|
||||
]
|
||||
for inst in instances
|
||||
]
|
||||
)
|
||||
y_true_lb, y_true_ub = y_true[:, 0], y_true[:, 1]
|
||||
y_pred_lb, y_pred_ub = y_pred[:, 1], y_pred[:, 1]
|
||||
ev = {
|
||||
"Lower bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_lb, y_pred_lb),
|
||||
"Explained variance": explained_variance_score(y_true_lb, y_pred_lb),
|
||||
"Max error": max_error(y_true_lb, y_pred_lb),
|
||||
"Mean absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
"R2": r2_score(y_true_lb, y_pred_lb),
|
||||
"Median absolute error": mean_absolute_error(y_true_lb, y_pred_lb),
|
||||
},
|
||||
"Upper bound": {
|
||||
"Mean squared error": mean_squared_error(y_true_ub, y_pred_ub),
|
||||
"Explained variance": explained_variance_score(y_true_ub, y_pred_ub),
|
||||
"Max error": max_error(y_true_ub, y_pred_ub),
|
||||
"Mean absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
"R2": r2_score(y_true_ub, y_pred_ub),
|
||||
"Median absolute error": mean_absolute_error(y_true_ub, y_pred_ub),
|
||||
},
|
||||
}
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.objective.ObjectiveValueComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Extracting features...")
|
||||
features = InstanceFeaturesExtractor().extract(training_instances)
|
||||
ub = ObjectiveValueExtractor(kind="upper bound").extract(training_instances)
|
||||
lb = ObjectiveValueExtractor(kind="lower bound").extract(training_instances)
|
||||
assert ub.shape == (len(training_instances), 1)
|
||||
assert lb.shape == (len(training_instances), 1)
|
||||
self.ub_regressor = deepcopy(self.regressor_prototype)
|
||||
self.lb_regressor = deepcopy(self.regressor_prototype)
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.ub_regressor.fit(features, ub.ravel())
|
||||
logger.debug("Fitting ub_regressor...")
|
||||
self.lb_regressor.fit(features, lb.ravel())</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.objective.ObjectiveValueComponent.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, instances):
|
||||
features = InstanceFeaturesExtractor().extract(instances)
|
||||
lb = self.lb_regressor.predict(features)
|
||||
ub = self.ub_regressor.predict(features)
|
||||
assert lb.shape == (len(instances),)
|
||||
assert ub.shape == (len(instances),)
|
||||
return np.array([lb, ub]).T</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.objective.ObjectiveValueComponent" href="#miplearn.components.objective.ObjectiveValueComponent">ObjectiveValueComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.evaluate" href="#miplearn.components.objective.ObjectiveValueComponent.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.fit" href="#miplearn.components.objective.ObjectiveValueComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.objective.ObjectiveValueComponent.predict" href="#miplearn.components.objective.ObjectiveValueComponent.predict">predict</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.primal</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
from typing import Union, Dict, Any
|
||||
|
||||
import numpy as np
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.classifiers.adaptive import AdaptiveClassifier
|
||||
from miplearn.classifiers.threshold import MinPrecisionThreshold, DynamicThreshold
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.extractors import VariableFeaturesExtractor, SolutionExtractor, Extractor
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class PrimalSolutionComponent(Component):
|
||||
"""
|
||||
A component that predicts primal solutions.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = AdaptiveClassifier(),
|
||||
mode: str = "exact",
|
||||
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
|
||||
) -> None:
|
||||
self.mode = mode
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
|
||||
self.threshold_prototype = threshold
|
||||
self.classifier_prototype = classifier
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
logger.info("Predicting primal solution...")
|
||||
solution = self.predict(instance)
|
||||
if self.mode == "heuristic":
|
||||
solver.internal_solver.fix(solution)
|
||||
else:
|
||||
solver.internal_solver.set_warm_start(solution)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def x(self, training_instances):
|
||||
return VariableFeaturesExtractor().extract(training_instances)
|
||||
|
||||
def y(self, training_instances):
|
||||
return SolutionExtractor().extract(training_instances)
|
||||
|
||||
def fit(self, training_instances, n_jobs=1):
|
||||
logger.debug("Extracting features...")
|
||||
features = VariableFeaturesExtractor().extract(training_instances)
|
||||
solutions = SolutionExtractor().extract(training_instances)
|
||||
|
||||
for category in tqdm(
|
||||
features.keys(),
|
||||
desc="Fit (primal)",
|
||||
):
|
||||
x_train = features[category]
|
||||
for label in [0, 1]:
|
||||
y_train = solutions[category][:, label].astype(int)
|
||||
|
||||
# If all samples are either positive or negative, make constant
|
||||
# predictions
|
||||
y_avg = np.average(y_train)
|
||||
if y_avg < 0.001 or y_avg >= 0.999:
|
||||
self.classifiers[category, label] = round(y_avg)
|
||||
self.thresholds[category, label] = 0.50
|
||||
continue
|
||||
|
||||
# Create a copy of classifier prototype and train it
|
||||
if isinstance(self.classifier_prototype, list):
|
||||
clf = deepcopy(self.classifier_prototype[label])
|
||||
else:
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
clf.fit(x_train, y_train)
|
||||
|
||||
# Find threshold (dynamic or static)
|
||||
if isinstance(self.threshold_prototype, DynamicThreshold):
|
||||
self.thresholds[category, label] = self.threshold_prototype.find(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
)
|
||||
else:
|
||||
self.thresholds[category, label] = deepcopy(
|
||||
self.threshold_prototype
|
||||
)
|
||||
|
||||
self.classifiers[category, label] = clf
|
||||
|
||||
def predict(self, instance):
|
||||
solution = {}
|
||||
x_test = VariableFeaturesExtractor().extract([instance])
|
||||
var_split = Extractor.split_variables(instance)
|
||||
for category in var_split.keys():
|
||||
n = len(var_split[category])
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if var not in solution.keys():
|
||||
solution[var] = {}
|
||||
solution[var][index] = None
|
||||
for label in [0, 1]:
|
||||
if (category, label) not in self.classifiers.keys():
|
||||
continue
|
||||
clf = self.classifiers[category, label]
|
||||
if isinstance(clf, float) or isinstance(clf, int):
|
||||
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
||||
else:
|
||||
ws = clf.predict_proba(x_test[category])
|
||||
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
||||
n,
|
||||
ws.shape,
|
||||
)
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if ws[i, 1] >= self.thresholds[category, label]:
|
||||
solution[var][index] = label
|
||||
return solution
|
||||
|
||||
def evaluate(self, instances):
|
||||
ev = {"Fix zero": {}, "Fix one": {}}
|
||||
for instance_idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (primal)",
|
||||
):
|
||||
instance = instances[instance_idx]
|
||||
solution_actual = instance.training_data[0]["Solution"]
|
||||
solution_pred = self.predict(instance)
|
||||
|
||||
vars_all, vars_one, vars_zero = set(), set(), set()
|
||||
pred_one_positive, pred_zero_positive = set(), set()
|
||||
for (varname, var_dict) in solution_actual.items():
|
||||
if varname not in solution_pred.keys():
|
||||
continue
|
||||
for (idx, value) in var_dict.items():
|
||||
vars_all.add((varname, idx))
|
||||
if value > 0.5:
|
||||
vars_one.add((varname, idx))
|
||||
else:
|
||||
vars_zero.add((varname, idx))
|
||||
if solution_pred[varname][idx] is not None:
|
||||
if solution_pred[varname][idx] > 0.5:
|
||||
pred_one_positive.add((varname, idx))
|
||||
else:
|
||||
pred_zero_positive.add((varname, idx))
|
||||
pred_one_negative = vars_all - pred_one_positive
|
||||
pred_zero_negative = vars_all - pred_zero_positive
|
||||
|
||||
tp_zero = len(pred_zero_positive & vars_zero)
|
||||
fp_zero = len(pred_zero_positive & vars_one)
|
||||
tn_zero = len(pred_zero_negative & vars_one)
|
||||
fn_zero = len(pred_zero_negative & vars_zero)
|
||||
|
||||
tp_one = len(pred_one_positive & vars_one)
|
||||
fp_one = len(pred_one_positive & vars_zero)
|
||||
tn_one = len(pred_one_negative & vars_zero)
|
||||
fn_one = len(pred_one_negative & vars_one)
|
||||
|
||||
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_zero, tn_zero, fp_zero, fn_zero
|
||||
)
|
||||
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_one, tn_one, fp_one, fn_one
|
||||
)
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">PrimalSolutionComponent</span></span>
|
||||
<span>(</span><span>classifier=<miplearn.classifiers.adaptive.AdaptiveClassifier object>, mode='exact', threshold=<miplearn.classifiers.threshold.MinPrecisionThreshold object>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A component that predicts primal solutions.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class PrimalSolutionComponent(Component):
|
||||
"""
|
||||
A component that predicts primal solutions.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier: Classifier = AdaptiveClassifier(),
|
||||
mode: str = "exact",
|
||||
threshold: Union[float, DynamicThreshold] = MinPrecisionThreshold(0.98),
|
||||
) -> None:
|
||||
self.mode = mode
|
||||
self.classifiers: Dict[Any, Classifier] = {}
|
||||
self.thresholds: Dict[Any, Union[float, DynamicThreshold]] = {}
|
||||
self.threshold_prototype = threshold
|
||||
self.classifier_prototype = classifier
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
logger.info("Predicting primal solution...")
|
||||
solution = self.predict(instance)
|
||||
if self.mode == "heuristic":
|
||||
solver.internal_solver.fix(solution)
|
||||
else:
|
||||
solver.internal_solver.set_warm_start(solution)
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
pass
|
||||
|
||||
def x(self, training_instances):
|
||||
return VariableFeaturesExtractor().extract(training_instances)
|
||||
|
||||
def y(self, training_instances):
|
||||
return SolutionExtractor().extract(training_instances)
|
||||
|
||||
def fit(self, training_instances, n_jobs=1):
|
||||
logger.debug("Extracting features...")
|
||||
features = VariableFeaturesExtractor().extract(training_instances)
|
||||
solutions = SolutionExtractor().extract(training_instances)
|
||||
|
||||
for category in tqdm(
|
||||
features.keys(),
|
||||
desc="Fit (primal)",
|
||||
):
|
||||
x_train = features[category]
|
||||
for label in [0, 1]:
|
||||
y_train = solutions[category][:, label].astype(int)
|
||||
|
||||
# If all samples are either positive or negative, make constant
|
||||
# predictions
|
||||
y_avg = np.average(y_train)
|
||||
if y_avg < 0.001 or y_avg >= 0.999:
|
||||
self.classifiers[category, label] = round(y_avg)
|
||||
self.thresholds[category, label] = 0.50
|
||||
continue
|
||||
|
||||
# Create a copy of classifier prototype and train it
|
||||
if isinstance(self.classifier_prototype, list):
|
||||
clf = deepcopy(self.classifier_prototype[label])
|
||||
else:
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
clf.fit(x_train, y_train)
|
||||
|
||||
# Find threshold (dynamic or static)
|
||||
if isinstance(self.threshold_prototype, DynamicThreshold):
|
||||
self.thresholds[category, label] = self.threshold_prototype.find(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
)
|
||||
else:
|
||||
self.thresholds[category, label] = deepcopy(
|
||||
self.threshold_prototype
|
||||
)
|
||||
|
||||
self.classifiers[category, label] = clf
|
||||
|
||||
def predict(self, instance):
|
||||
solution = {}
|
||||
x_test = VariableFeaturesExtractor().extract([instance])
|
||||
var_split = Extractor.split_variables(instance)
|
||||
for category in var_split.keys():
|
||||
n = len(var_split[category])
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if var not in solution.keys():
|
||||
solution[var] = {}
|
||||
solution[var][index] = None
|
||||
for label in [0, 1]:
|
||||
if (category, label) not in self.classifiers.keys():
|
||||
continue
|
||||
clf = self.classifiers[category, label]
|
||||
if isinstance(clf, float) or isinstance(clf, int):
|
||||
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
||||
else:
|
||||
ws = clf.predict_proba(x_test[category])
|
||||
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
||||
n,
|
||||
ws.shape,
|
||||
)
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if ws[i, 1] >= self.thresholds[category, label]:
|
||||
solution[var][index] = label
|
||||
return solution
|
||||
|
||||
def evaluate(self, instances):
|
||||
ev = {"Fix zero": {}, "Fix one": {}}
|
||||
for instance_idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (primal)",
|
||||
):
|
||||
instance = instances[instance_idx]
|
||||
solution_actual = instance.training_data[0]["Solution"]
|
||||
solution_pred = self.predict(instance)
|
||||
|
||||
vars_all, vars_one, vars_zero = set(), set(), set()
|
||||
pred_one_positive, pred_zero_positive = set(), set()
|
||||
for (varname, var_dict) in solution_actual.items():
|
||||
if varname not in solution_pred.keys():
|
||||
continue
|
||||
for (idx, value) in var_dict.items():
|
||||
vars_all.add((varname, idx))
|
||||
if value > 0.5:
|
||||
vars_one.add((varname, idx))
|
||||
else:
|
||||
vars_zero.add((varname, idx))
|
||||
if solution_pred[varname][idx] is not None:
|
||||
if solution_pred[varname][idx] > 0.5:
|
||||
pred_one_positive.add((varname, idx))
|
||||
else:
|
||||
pred_zero_positive.add((varname, idx))
|
||||
pred_one_negative = vars_all - pred_one_positive
|
||||
pred_zero_negative = vars_all - pred_zero_positive
|
||||
|
||||
tp_zero = len(pred_zero_positive & vars_zero)
|
||||
fp_zero = len(pred_zero_positive & vars_one)
|
||||
tn_zero = len(pred_zero_negative & vars_one)
|
||||
fn_zero = len(pred_zero_negative & vars_zero)
|
||||
|
||||
tp_one = len(pred_one_positive & vars_one)
|
||||
fp_one = len(pred_one_positive & vars_zero)
|
||||
tn_one = len(pred_one_negative & vars_zero)
|
||||
fn_one = len(pred_one_negative & vars_one)
|
||||
|
||||
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_zero, tn_zero, fp_zero, fn_zero
|
||||
)
|
||||
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_one, tn_one, fp_one, fn_one
|
||||
)
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instances):
|
||||
ev = {"Fix zero": {}, "Fix one": {}}
|
||||
for instance_idx in tqdm(
|
||||
range(len(instances)),
|
||||
desc="Evaluate (primal)",
|
||||
):
|
||||
instance = instances[instance_idx]
|
||||
solution_actual = instance.training_data[0]["Solution"]
|
||||
solution_pred = self.predict(instance)
|
||||
|
||||
vars_all, vars_one, vars_zero = set(), set(), set()
|
||||
pred_one_positive, pred_zero_positive = set(), set()
|
||||
for (varname, var_dict) in solution_actual.items():
|
||||
if varname not in solution_pred.keys():
|
||||
continue
|
||||
for (idx, value) in var_dict.items():
|
||||
vars_all.add((varname, idx))
|
||||
if value > 0.5:
|
||||
vars_one.add((varname, idx))
|
||||
else:
|
||||
vars_zero.add((varname, idx))
|
||||
if solution_pred[varname][idx] is not None:
|
||||
if solution_pred[varname][idx] > 0.5:
|
||||
pred_one_positive.add((varname, idx))
|
||||
else:
|
||||
pred_zero_positive.add((varname, idx))
|
||||
pred_one_negative = vars_all - pred_one_positive
|
||||
pred_zero_negative = vars_all - pred_zero_positive
|
||||
|
||||
tp_zero = len(pred_zero_positive & vars_zero)
|
||||
fp_zero = len(pred_zero_positive & vars_one)
|
||||
tn_zero = len(pred_zero_negative & vars_one)
|
||||
fn_zero = len(pred_zero_negative & vars_zero)
|
||||
|
||||
tp_one = len(pred_one_positive & vars_one)
|
||||
fp_one = len(pred_one_positive & vars_zero)
|
||||
tn_one = len(pred_one_negative & vars_zero)
|
||||
fn_one = len(pred_one_negative & vars_one)
|
||||
|
||||
ev["Fix zero"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_zero, tn_zero, fp_zero, fn_zero
|
||||
)
|
||||
ev["Fix one"][instance_idx] = classifier_evaluation_dict(
|
||||
tp_one, tn_one, fp_one, fn_one
|
||||
)
|
||||
return ev</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances, n_jobs=1)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances, n_jobs=1):
|
||||
logger.debug("Extracting features...")
|
||||
features = VariableFeaturesExtractor().extract(training_instances)
|
||||
solutions = SolutionExtractor().extract(training_instances)
|
||||
|
||||
for category in tqdm(
|
||||
features.keys(),
|
||||
desc="Fit (primal)",
|
||||
):
|
||||
x_train = features[category]
|
||||
for label in [0, 1]:
|
||||
y_train = solutions[category][:, label].astype(int)
|
||||
|
||||
# If all samples are either positive or negative, make constant
|
||||
# predictions
|
||||
y_avg = np.average(y_train)
|
||||
if y_avg < 0.001 or y_avg >= 0.999:
|
||||
self.classifiers[category, label] = round(y_avg)
|
||||
self.thresholds[category, label] = 0.50
|
||||
continue
|
||||
|
||||
# Create a copy of classifier prototype and train it
|
||||
if isinstance(self.classifier_prototype, list):
|
||||
clf = deepcopy(self.classifier_prototype[label])
|
||||
else:
|
||||
clf = deepcopy(self.classifier_prototype)
|
||||
clf.fit(x_train, y_train)
|
||||
|
||||
# Find threshold (dynamic or static)
|
||||
if isinstance(self.threshold_prototype, DynamicThreshold):
|
||||
self.thresholds[category, label] = self.threshold_prototype.find(
|
||||
clf,
|
||||
x_train,
|
||||
y_train,
|
||||
)
|
||||
else:
|
||||
self.thresholds[category, label] = deepcopy(
|
||||
self.threshold_prototype
|
||||
)
|
||||
|
||||
self.classifiers[category, label] = clf</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, instance):
|
||||
solution = {}
|
||||
x_test = VariableFeaturesExtractor().extract([instance])
|
||||
var_split = Extractor.split_variables(instance)
|
||||
for category in var_split.keys():
|
||||
n = len(var_split[category])
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if var not in solution.keys():
|
||||
solution[var] = {}
|
||||
solution[var][index] = None
|
||||
for label in [0, 1]:
|
||||
if (category, label) not in self.classifiers.keys():
|
||||
continue
|
||||
clf = self.classifiers[category, label]
|
||||
if isinstance(clf, float) or isinstance(clf, int):
|
||||
ws = np.array([[1 - clf, clf] for _ in range(n)])
|
||||
else:
|
||||
ws = clf.predict_proba(x_test[category])
|
||||
assert ws.shape == (n, 2), "ws.shape should be (%d, 2) not %s" % (
|
||||
n,
|
||||
ws.shape,
|
||||
)
|
||||
for (i, (var, index)) in enumerate(var_split[category]):
|
||||
if ws[i, 1] >= self.thresholds[category, label]:
|
||||
solution[var][index] = label
|
||||
return solution</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, training_instances):
|
||||
return VariableFeaturesExtractor().extract(training_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.primal.PrimalSolutionComponent.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, training_instances):
|
||||
return SolutionExtractor().extract(training_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.primal.PrimalSolutionComponent" href="#miplearn.components.primal.PrimalSolutionComponent">PrimalSolutionComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.evaluate" href="#miplearn.components.primal.PrimalSolutionComponent.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.fit" href="#miplearn.components.primal.PrimalSolutionComponent.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.predict" href="#miplearn.components.primal.PrimalSolutionComponent.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.x" href="#miplearn.components.primal.PrimalSolutionComponent.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.primal.PrimalSolutionComponent.y" href="#miplearn.components.primal.PrimalSolutionComponent.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
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|
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<html lang="en">
|
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<title>miplearn.components.relaxation API documentation</title>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.relaxation</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.composite import CompositeComponent
|
||||
from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
|
||||
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
|
||||
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RelaxationComponent(Component):
|
||||
"""
|
||||
A Component that tries to build a relaxation that is simultaneously strong and easy
|
||||
to solve. Currently, this component is composed by three steps:
|
||||
|
||||
- RelaxIntegralityStep
|
||||
- DropRedundantInequalitiesStep
|
||||
- ConvertTightIneqsIntoEqsStep
|
||||
|
||||
Parameters
|
||||
----------
|
||||
redundant_classifier : Classifier, optional
|
||||
Classifier used to predict if a constraint is likely redundant. One deep
|
||||
copy of this classifier is made for each constraint category.
|
||||
redundant_threshold : float, optional
|
||||
If the probability that a constraint is redundant exceeds this threshold, the
|
||||
constraint is dropped from the linear relaxation.
|
||||
tight_classifier : Classifier, optional
|
||||
Classifier used to predict if a constraint is likely to be tight. One deep
|
||||
copy of this classifier is made for each constraint category.
|
||||
tight_threshold : float, optional
|
||||
If the probability that a constraint is tight exceeds this threshold, the
|
||||
constraint is converted into an equality constraint.
|
||||
slack_tolerance : float, optional
|
||||
If a constraint has slack greater than this threshold, then the constraint is
|
||||
considered loose. By default, this threshold equals a small positive number to
|
||||
compensate for numerical issues.
|
||||
check_feasibility : bool, optional
|
||||
If true, after the problem is solved, the component verifies that all dropped
|
||||
constraints are still satisfied, re-adds the violated ones and resolves the
|
||||
problem. This loop continues until either no violations are found, or a maximum
|
||||
number of iterations is reached.
|
||||
violation_tolerance : float, optional
|
||||
If `check_dropped` is true, a constraint is considered satisfied during the
|
||||
check if its violation is smaller than this tolerance.
|
||||
max_check_iterations : int
|
||||
If `check_dropped` is true, set the maximum number of iterations in the lazy
|
||||
constraint loop.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
redundant_classifier=CountingClassifier(),
|
||||
redundant_threshold=0.95,
|
||||
tight_classifier=CountingClassifier(),
|
||||
tight_threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_check_iterations=3,
|
||||
):
|
||||
self.steps = [
|
||||
RelaxIntegralityStep(),
|
||||
DropRedundantInequalitiesStep(
|
||||
classifier=redundant_classifier,
|
||||
threshold=redundant_threshold,
|
||||
slack_tolerance=slack_tolerance,
|
||||
violation_tolerance=violation_tolerance,
|
||||
max_iterations=max_check_iterations,
|
||||
check_feasibility=check_feasibility,
|
||||
),
|
||||
ConvertTightIneqsIntoEqsStep(
|
||||
classifier=tight_classifier,
|
||||
threshold=tight_threshold,
|
||||
slack_tolerance=slack_tolerance,
|
||||
),
|
||||
]
|
||||
self.composite = CompositeComponent(self.steps)
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
self.composite.before_solve(solver, instance, model)
|
||||
|
||||
def after_solve(self, solver, instance, model, stats, training_data):
|
||||
self.composite.after_solve(solver, instance, model, stats, training_data)
|
||||
|
||||
def fit(self, training_instances):
|
||||
self.composite.fit(training_instances)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
return self.composite.iteration_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.relaxation.RelaxationComponent"><code class="flex name class">
|
||||
<span>class <span class="ident">RelaxationComponent</span></span>
|
||||
<span>(</span><span>redundant_classifier=CountingClassifier(mean=None), redundant_threshold=0.95, tight_classifier=CountingClassifier(mean=None), tight_threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_check_iterations=3)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>A Component that tries to build a relaxation that is simultaneously strong and easy
|
||||
to solve. Currently, this component is composed by three steps:</p>
|
||||
<ul>
|
||||
<li>RelaxIntegralityStep</li>
|
||||
<li>DropRedundantInequalitiesStep</li>
|
||||
<li>ConvertTightIneqsIntoEqsStep</li>
|
||||
</ul>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>redundant_classifier</code></strong> : <code>Classifier</code>, optional</dt>
|
||||
<dd>Classifier used to predict if a constraint is likely redundant. One deep
|
||||
copy of this classifier is made for each constraint category.</dd>
|
||||
<dt><strong><code>redundant_threshold</code></strong> : <code>float</code>, optional</dt>
|
||||
<dd>If the probability that a constraint is redundant exceeds this threshold, the
|
||||
constraint is dropped from the linear relaxation.</dd>
|
||||
<dt><strong><code>tight_classifier</code></strong> : <code>Classifier</code>, optional</dt>
|
||||
<dd>Classifier used to predict if a constraint is likely to be tight. One deep
|
||||
copy of this classifier is made for each constraint category.</dd>
|
||||
<dt><strong><code>tight_threshold</code></strong> : <code>float</code>, optional</dt>
|
||||
<dd>If the probability that a constraint is tight exceeds this threshold, the
|
||||
constraint is converted into an equality constraint.</dd>
|
||||
<dt><strong><code>slack_tolerance</code></strong> : <code>float</code>, optional</dt>
|
||||
<dd>If a constraint has slack greater than this threshold, then the constraint is
|
||||
considered loose. By default, this threshold equals a small positive number to
|
||||
compensate for numerical issues.</dd>
|
||||
<dt><strong><code>check_feasibility</code></strong> : <code>bool</code>, optional</dt>
|
||||
<dd>If true, after the problem is solved, the component verifies that all dropped
|
||||
constraints are still satisfied, re-adds the violated ones and resolves the
|
||||
problem. This loop continues until either no violations are found, or a maximum
|
||||
number of iterations is reached.</dd>
|
||||
<dt><strong><code>violation_tolerance</code></strong> : <code>float</code>, optional</dt>
|
||||
<dd>If <code>check_dropped</code> is true, a constraint is considered satisfied during the
|
||||
check if its violation is smaller than this tolerance.</dd>
|
||||
<dt><strong><code>max_check_iterations</code></strong> : <code>int</code></dt>
|
||||
<dd>If <code>check_dropped</code> is true, set the maximum number of iterations in the lazy
|
||||
constraint loop.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class RelaxationComponent(Component):
|
||||
"""
|
||||
A Component that tries to build a relaxation that is simultaneously strong and easy
|
||||
to solve. Currently, this component is composed by three steps:
|
||||
|
||||
- RelaxIntegralityStep
|
||||
- DropRedundantInequalitiesStep
|
||||
- ConvertTightIneqsIntoEqsStep
|
||||
|
||||
Parameters
|
||||
----------
|
||||
redundant_classifier : Classifier, optional
|
||||
Classifier used to predict if a constraint is likely redundant. One deep
|
||||
copy of this classifier is made for each constraint category.
|
||||
redundant_threshold : float, optional
|
||||
If the probability that a constraint is redundant exceeds this threshold, the
|
||||
constraint is dropped from the linear relaxation.
|
||||
tight_classifier : Classifier, optional
|
||||
Classifier used to predict if a constraint is likely to be tight. One deep
|
||||
copy of this classifier is made for each constraint category.
|
||||
tight_threshold : float, optional
|
||||
If the probability that a constraint is tight exceeds this threshold, the
|
||||
constraint is converted into an equality constraint.
|
||||
slack_tolerance : float, optional
|
||||
If a constraint has slack greater than this threshold, then the constraint is
|
||||
considered loose. By default, this threshold equals a small positive number to
|
||||
compensate for numerical issues.
|
||||
check_feasibility : bool, optional
|
||||
If true, after the problem is solved, the component verifies that all dropped
|
||||
constraints are still satisfied, re-adds the violated ones and resolves the
|
||||
problem. This loop continues until either no violations are found, or a maximum
|
||||
number of iterations is reached.
|
||||
violation_tolerance : float, optional
|
||||
If `check_dropped` is true, a constraint is considered satisfied during the
|
||||
check if its violation is smaller than this tolerance.
|
||||
max_check_iterations : int
|
||||
If `check_dropped` is true, set the maximum number of iterations in the lazy
|
||||
constraint loop.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
redundant_classifier=CountingClassifier(),
|
||||
redundant_threshold=0.95,
|
||||
tight_classifier=CountingClassifier(),
|
||||
tight_threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_check_iterations=3,
|
||||
):
|
||||
self.steps = [
|
||||
RelaxIntegralityStep(),
|
||||
DropRedundantInequalitiesStep(
|
||||
classifier=redundant_classifier,
|
||||
threshold=redundant_threshold,
|
||||
slack_tolerance=slack_tolerance,
|
||||
violation_tolerance=violation_tolerance,
|
||||
max_iterations=max_check_iterations,
|
||||
check_feasibility=check_feasibility,
|
||||
),
|
||||
ConvertTightIneqsIntoEqsStep(
|
||||
classifier=tight_classifier,
|
||||
threshold=tight_threshold,
|
||||
slack_tolerance=slack_tolerance,
|
||||
),
|
||||
]
|
||||
self.composite = CompositeComponent(self.steps)
|
||||
|
||||
def before_solve(self, solver, instance, model):
|
||||
self.composite.before_solve(solver, instance, model)
|
||||
|
||||
def after_solve(self, solver, instance, model, stats, training_data):
|
||||
self.composite.after_solve(solver, instance, model, stats, training_data)
|
||||
|
||||
def fit(self, training_instances):
|
||||
self.composite.fit(training_instances)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
return self.composite.iteration_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.relaxation.RelaxationComponent.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
self.composite.fit(training_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.relaxation.RelaxationComponent" href="#miplearn.components.relaxation.RelaxationComponent">RelaxationComponent</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.relaxation.RelaxationComponent.fit" href="#miplearn.components.relaxation.RelaxationComponent.fit">fit</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
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|
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|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.convert_tight</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import random
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesStep
|
||||
from miplearn.extractors import InstanceIterator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class ConvertTightIneqsIntoEqsStep(Component):
|
||||
"""
|
||||
Component that predicts which inequality constraints are likely to be binding in
|
||||
the LP relaxation of the problem and converts them into equality constraints.
|
||||
|
||||
This component always makes sure that the conversion process does not affect the
|
||||
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
||||
the optimality, but this may be expensive.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=0.0,
|
||||
check_optimality=False,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.check_optimality = check_optimality
|
||||
self.converted = []
|
||||
self.original_sense = {}
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Predicting tight LP constraints...")
|
||||
x, constraints = DropRedundantInequalitiesStep._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.n_converted = 0
|
||||
self.n_restored = 0
|
||||
self.n_kept = 0
|
||||
self.n_infeasible_iterations = 0
|
||||
self.n_suboptimal_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
s = solver.internal_solver.get_constraint_sense(cid)
|
||||
self.original_sense[cid] = s
|
||||
solver.internal_solver.set_constraint_sense(cid, "=")
|
||||
self.converted += [cid]
|
||||
self.n_converted += 1
|
||||
else:
|
||||
self.n_kept += 1
|
||||
|
||||
logger.info(f"Converted {self.n_converted} inequalities")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats["ConvertTight: Kept"] = self.n_kept
|
||||
stats["ConvertTight: Converted"] = self.n_converted
|
||||
stats["ConvertTight: Restored"] = self.n_restored
|
||||
stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
|
||||
stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
is_infeasible, is_suboptimal = False, False
|
||||
restored = []
|
||||
|
||||
def check_pi(msense, csense, pi):
|
||||
if csense == "=":
|
||||
return True
|
||||
if msense == "max":
|
||||
if csense == "<":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
else:
|
||||
if csense == ">":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
|
||||
def restore(cid):
|
||||
nonlocal restored
|
||||
csense = self.original_sense[cid]
|
||||
solver.internal_solver.set_constraint_sense(cid, csense)
|
||||
restored += [cid]
|
||||
|
||||
if solver.internal_solver.is_infeasible():
|
||||
for cid in self.converted:
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
if abs(pi) > 0:
|
||||
is_infeasible = True
|
||||
restore(cid)
|
||||
elif self.check_optimality:
|
||||
random.shuffle(self.converted)
|
||||
n_restored = 0
|
||||
for cid in self.converted:
|
||||
if n_restored >= 100:
|
||||
break
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
csense = self.original_sense[cid]
|
||||
msense = solver.internal_solver.get_sense()
|
||||
if not check_pi(msense, csense, pi):
|
||||
is_suboptimal = True
|
||||
restore(cid)
|
||||
n_restored += 1
|
||||
|
||||
for cid in restored:
|
||||
self.converted.remove(cid)
|
||||
|
||||
if len(restored) > 0:
|
||||
self.n_restored += len(restored)
|
||||
if is_infeasible:
|
||||
self.n_infeasible_iterations += 1
|
||||
if is_suboptimal:
|
||||
self.n_suboptimal_iterations += 1
|
||||
logger.info(f"Restored {len(restored)} inequalities")
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep"><code class="flex name class">
|
||||
<span>class <span class="ident">ConvertTightIneqsIntoEqsStep</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=0.0, check_optimality=False)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that predicts which inequality constraints are likely to be binding in
|
||||
the LP relaxation of the problem and converts them into equality constraints.</p>
|
||||
<p>This component always makes sure that the conversion process does not affect the
|
||||
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
||||
the optimality, but this may be expensive.</p>
|
||||
<p>This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ConvertTightIneqsIntoEqsStep(Component):
|
||||
"""
|
||||
Component that predicts which inequality constraints are likely to be binding in
|
||||
the LP relaxation of the problem and converts them into equality constraints.
|
||||
|
||||
This component always makes sure that the conversion process does not affect the
|
||||
feasibility of the problem. It can also, optionally, make sure that it does not affect
|
||||
the optimality, but this may be expensive.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=0.0,
|
||||
check_optimality=False,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.check_optimality = check_optimality
|
||||
self.converted = []
|
||||
self.original_sense = {}
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Predicting tight LP constraints...")
|
||||
x, constraints = DropRedundantInequalitiesStep._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.n_converted = 0
|
||||
self.n_restored = 0
|
||||
self.n_kept = 0
|
||||
self.n_infeasible_iterations = 0
|
||||
self.n_suboptimal_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
s = solver.internal_solver.get_constraint_sense(cid)
|
||||
self.original_sense[cid] = s
|
||||
solver.internal_solver.set_constraint_sense(cid, "=")
|
||||
self.converted += [cid]
|
||||
self.n_converted += 1
|
||||
else:
|
||||
self.n_kept += 1
|
||||
|
||||
logger.info(f"Converted {self.n_converted} inequalities")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats["ConvertTight: Kept"] = self.n_kept
|
||||
stats["ConvertTight: Converted"] = self.n_converted
|
||||
stats["ConvertTight: Restored"] = self.n_restored
|
||||
stats["ConvertTight: Inf iterations"] = self.n_infeasible_iterations
|
||||
stats["ConvertTight: Subopt iterations"] = self.n_suboptimal_iterations
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
is_infeasible, is_suboptimal = False, False
|
||||
restored = []
|
||||
|
||||
def check_pi(msense, csense, pi):
|
||||
if csense == "=":
|
||||
return True
|
||||
if msense == "max":
|
||||
if csense == "<":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
else:
|
||||
if csense == ">":
|
||||
return pi >= 0
|
||||
else:
|
||||
return pi <= 0
|
||||
|
||||
def restore(cid):
|
||||
nonlocal restored
|
||||
csense = self.original_sense[cid]
|
||||
solver.internal_solver.set_constraint_sense(cid, csense)
|
||||
restored += [cid]
|
||||
|
||||
if solver.internal_solver.is_infeasible():
|
||||
for cid in self.converted:
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
if abs(pi) > 0:
|
||||
is_infeasible = True
|
||||
restore(cid)
|
||||
elif self.check_optimality:
|
||||
random.shuffle(self.converted)
|
||||
n_restored = 0
|
||||
for cid in self.converted:
|
||||
if n_restored >= 100:
|
||||
break
|
||||
pi = solver.internal_solver.get_dual(cid)
|
||||
csense = self.original_sense[cid]
|
||||
msense = solver.internal_solver.get_sense()
|
||||
if not check_pi(msense, csense, pi):
|
||||
is_suboptimal = True
|
||||
restore(cid)
|
||||
n_restored += 1
|
||||
|
||||
for cid in restored:
|
||||
self.converted.remove(cid)
|
||||
|
||||
if len(restored) > 0:
|
||||
self.n_restored += len(restored)
|
||||
if is_infeasible:
|
||||
self.n_infeasible_iterations += 1
|
||||
if is_suboptimal:
|
||||
self.n_suboptimal_iterations += 1
|
||||
logger.info(f"Restored {len(restored)} inequalities")
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:conv_ineqs)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, instances):
|
||||
return DropRedundantInequalitiesStep._x_train(instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:conv_ineqs:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for (cid, slack) in instance.training_data[0]["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if 0 <= slack <= self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep">ConvertTightIneqsIntoEqsStep</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y" href="#miplearn.components.steps.convert_tight.ConvertTightIneqsIntoEqsStep.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.drop_redundant</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
from miplearn.classifiers.counting import CountingClassifier
|
||||
from miplearn.components import classifier_evaluation_dict
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.lazy_static import LazyConstraint
|
||||
from miplearn.extractors import InstanceIterator
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DropRedundantInequalitiesStep(Component):
|
||||
"""
|
||||
Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_iterations=3,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.pool = []
|
||||
self.check_feasibility = check_feasibility
|
||||
self.violation_tolerance = violation_tolerance
|
||||
self.max_iterations = max_iterations
|
||||
self.current_iteration = 0
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
self.current_iteration = 0
|
||||
|
||||
logger.info("Predicting redundant LP constraints...")
|
||||
x, constraints = self._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.total_dropped = 0
|
||||
self.total_restored = 0
|
||||
self.total_kept = 0
|
||||
self.total_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
self.pool += [c]
|
||||
self.total_dropped += 1
|
||||
else:
|
||||
self.total_kept += 1
|
||||
logger.info(f"Extracted {self.total_dropped} predicted constraints")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats.update(
|
||||
{
|
||||
"DropRedundant: Kept": self.total_kept,
|
||||
"DropRedundant: Dropped": self.total_dropped,
|
||||
"DropRedundant: Restored": self.total_restored,
|
||||
"DropRedundant: Iterations": self.total_iterations,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
@staticmethod
|
||||
def _x_test(instance, constraint_ids):
|
||||
x = {}
|
||||
constraints = {}
|
||||
cids = constraint_ids
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
constraints[category] += [cid]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x, constraints
|
||||
|
||||
@staticmethod
|
||||
def _x_train(instances):
|
||||
x = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:x)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
cids = training_data["slacks"].keys()
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x
|
||||
|
||||
def x(self, instances):
|
||||
return self._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if not self.check_feasibility:
|
||||
return False
|
||||
if self.current_iteration >= self.max_iterations:
|
||||
return False
|
||||
self.current_iteration += 1
|
||||
logger.debug("Checking that dropped constraints are satisfied...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj,
|
||||
self.violation_tolerance,
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
if len(constraints_to_add) > 0:
|
||||
self.total_restored += len(constraints_to_add)
|
||||
logger.info(
|
||||
"%8d constraints %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
self.total_iterations += 1
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep"><code class="flex name class">
|
||||
<span>class <span class="ident">DropRedundantInequalitiesStep</span></span>
|
||||
<span>(</span><span>classifier=CountingClassifier(mean=None), threshold=0.95, slack_tolerance=1e-05, check_feasibility=False, violation_tolerance=1e-05, max_iterations=3)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.</p>
|
||||
<p>This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class DropRedundantInequalitiesStep(Component):
|
||||
"""
|
||||
Component that predicts which inequalities are likely loose in the LP and removes
|
||||
them. Optionally, double checks after the problem is solved that all dropped
|
||||
inequalities were in fact redundant, and, if not, re-adds them to the problem.
|
||||
|
||||
This component does not work on MIPs. All integrality constraints must be relaxed
|
||||
before this component is used.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
classifier=CountingClassifier(),
|
||||
threshold=0.95,
|
||||
slack_tolerance=1e-5,
|
||||
check_feasibility=False,
|
||||
violation_tolerance=1e-5,
|
||||
max_iterations=3,
|
||||
):
|
||||
self.classifiers = {}
|
||||
self.classifier_prototype = classifier
|
||||
self.threshold = threshold
|
||||
self.slack_tolerance = slack_tolerance
|
||||
self.pool = []
|
||||
self.check_feasibility = check_feasibility
|
||||
self.violation_tolerance = violation_tolerance
|
||||
self.max_iterations = max_iterations
|
||||
self.current_iteration = 0
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
self.current_iteration = 0
|
||||
|
||||
logger.info("Predicting redundant LP constraints...")
|
||||
x, constraints = self._x_test(
|
||||
instance,
|
||||
constraint_ids=solver.internal_solver.get_constraint_ids(),
|
||||
)
|
||||
y = self.predict(x)
|
||||
|
||||
self.total_dropped = 0
|
||||
self.total_restored = 0
|
||||
self.total_kept = 0
|
||||
self.total_iterations = 0
|
||||
for category in y.keys():
|
||||
for i in range(len(y[category])):
|
||||
if y[category][i][0] == 1:
|
||||
cid = constraints[category][i]
|
||||
c = LazyConstraint(
|
||||
cid=cid,
|
||||
obj=solver.internal_solver.extract_constraint(cid),
|
||||
)
|
||||
self.pool += [c]
|
||||
self.total_dropped += 1
|
||||
else:
|
||||
self.total_kept += 1
|
||||
logger.info(f"Extracted {self.total_dropped} predicted constraints")
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
if "slacks" not in training_data.keys():
|
||||
training_data["slacks"] = solver.internal_solver.get_inequality_slacks()
|
||||
stats.update(
|
||||
{
|
||||
"DropRedundant: Kept": self.total_kept,
|
||||
"DropRedundant: Dropped": self.total_dropped,
|
||||
"DropRedundant: Restored": self.total_restored,
|
||||
"DropRedundant: Iterations": self.total_iterations,
|
||||
}
|
||||
)
|
||||
|
||||
def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])
|
||||
|
||||
@staticmethod
|
||||
def _x_test(instance, constraint_ids):
|
||||
x = {}
|
||||
constraints = {}
|
||||
cids = constraint_ids
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
constraints[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
constraints[category] += [cid]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x, constraints
|
||||
|
||||
@staticmethod
|
||||
def _x_train(instances):
|
||||
x = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:x)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
cids = training_data["slacks"].keys()
|
||||
for cid in cids:
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in x:
|
||||
x[category] = []
|
||||
x[category] += [instance.get_constraint_features(cid)]
|
||||
for category in x.keys():
|
||||
x[category] = np.array(x[category])
|
||||
return x
|
||||
|
||||
def x(self, instances):
|
||||
return self._x_train(instances)
|
||||
|
||||
def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y
|
||||
|
||||
def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)
|
||||
|
||||
def iteration_cb(self, solver, instance, model):
|
||||
if not self.check_feasibility:
|
||||
return False
|
||||
if self.current_iteration >= self.max_iterations:
|
||||
return False
|
||||
self.current_iteration += 1
|
||||
logger.debug("Checking that dropped constraints are satisfied...")
|
||||
constraints_to_add = []
|
||||
for c in self.pool:
|
||||
if not solver.internal_solver.is_constraint_satisfied(
|
||||
c.obj,
|
||||
self.violation_tolerance,
|
||||
):
|
||||
constraints_to_add.append(c)
|
||||
for c in constraints_to_add:
|
||||
self.pool.remove(c)
|
||||
solver.internal_solver.add_constraint(c.obj)
|
||||
if len(constraints_to_add) > 0:
|
||||
self.total_restored += len(constraints_to_add)
|
||||
logger.info(
|
||||
"%8d constraints %8d in the pool"
|
||||
% (len(constraints_to_add), len(self.pool))
|
||||
)
|
||||
self.total_iterations += 1
|
||||
return True
|
||||
else:
|
||||
return False</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">evaluate</span></span>(<span>self, instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def evaluate(self, instance):
|
||||
x = self.x([instance])
|
||||
y_true = self.y([instance])
|
||||
y_pred = self.predict(x)
|
||||
tp, tn, fp, fn = 0, 0, 0, 0
|
||||
for category in y_true.keys():
|
||||
for i in range(len(y_true[category])):
|
||||
if y_pred[category][i][0] == 1:
|
||||
if y_true[category][i][0] == 1:
|
||||
tp += 1
|
||||
else:
|
||||
fp += 1
|
||||
else:
|
||||
if y_true[category][i][0] == 1:
|
||||
fn += 1
|
||||
else:
|
||||
tn += 1
|
||||
return classifier_evaluation_dict(tp, tn, fp, fn)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit"><code class="name flex">
|
||||
<span>def <span class="ident">fit</span></span>(<span>self, training_instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def fit(self, training_instances):
|
||||
logger.debug("Extracting x and y...")
|
||||
x = self.x(training_instances)
|
||||
y = self.y(training_instances)
|
||||
logger.debug("Fitting...")
|
||||
for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
|
||||
if category not in self.classifiers:
|
||||
self.classifiers[category] = deepcopy(self.classifier_prototype)
|
||||
self.classifiers[category].fit(x[category], y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict"><code class="name flex">
|
||||
<span>def <span class="ident">predict</span></span>(<span>self, x)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def predict(self, x):
|
||||
y = {}
|
||||
for (category, x_cat) in x.items():
|
||||
if category not in self.classifiers:
|
||||
continue
|
||||
y[category] = []
|
||||
x_cat = np.array(x_cat)
|
||||
proba = self.classifiers[category].predict_proba(x_cat)
|
||||
for i in range(len(proba)):
|
||||
if proba[i][1] >= self.threshold:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x"><code class="name flex">
|
||||
<span>def <span class="ident">x</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def x(self, instances):
|
||||
return self._x_train(instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y"><code class="name flex">
|
||||
<span>def <span class="ident">y</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def y(self, instances):
|
||||
y = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (rlx:drop_ineq:y)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
for training_data in instance.training_data:
|
||||
for (cid, slack) in training_data["slacks"].items():
|
||||
category = instance.get_constraint_category(cid)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in y:
|
||||
y[category] = []
|
||||
if slack > self.slack_tolerance:
|
||||
y[category] += [[1]]
|
||||
else:
|
||||
y[category] += [[0]]
|
||||
return y</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep">DropRedundantInequalitiesStep</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.evaluate">evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.fit">fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.predict">predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.x">x</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y" href="#miplearn.components.steps.drop_redundant.DropRedundantInequalitiesStep.y">y</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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<section>
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||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
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|
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||||
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|
||||
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|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components" href="../index.html">miplearn.components</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.convert_tight" href="convert_tight.html">miplearn.components.steps.convert_tight</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.drop_redundant" href="drop_redundant.html">miplearn.components.steps.drop_redundant</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.relax_integrality" href="relax_integrality.html">miplearn.components.steps.relax_integrality</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="tests/index.html">miplearn.components.steps.tests</a></code></li>
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|
||||
<h1 class="title">Module <code>miplearn.components.steps.relax_integrality</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.components.component import Component
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class RelaxIntegralityStep(Component):
|
||||
"""
|
||||
Component that relaxes all integrality constraints before the problem is solved.
|
||||
"""
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Relaxing integrality...")
|
||||
solver.internal_solver.relax()
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
return</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.relax_integrality.RelaxIntegralityStep"><code class="flex name class">
|
||||
<span>class <span class="ident">RelaxIntegralityStep</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Component that relaxes all integrality constraints before the problem is solved.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class RelaxIntegralityStep(Component):
|
||||
"""
|
||||
Component that relaxes all integrality constraints before the problem is solved.
|
||||
"""
|
||||
|
||||
def before_solve(self, solver, instance, _):
|
||||
logger.info("Relaxing integrality...")
|
||||
solver.internal_solver.relax()
|
||||
|
||||
def after_solve(
|
||||
self,
|
||||
solver,
|
||||
instance,
|
||||
model,
|
||||
stats,
|
||||
training_data,
|
||||
):
|
||||
return</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.components.component.Component" href="../component.html#miplearn.components.component.Component">Component</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.components.component.Component.after_solve" href="../component.html#miplearn.components.component.Component.after_solve">after_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.before_solve" href="../component.html#miplearn.components.component.Component.before_solve">before_solve</a></code></li>
|
||||
<li><code><a title="miplearn.components.component.Component.iteration_cb" href="../component.html#miplearn.components.component.Component.iteration_cb">iteration_cb</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps" href="index.html">miplearn.components.steps</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.components.steps.relax_integrality.RelaxIntegralityStep" href="#miplearn.components.steps.relax_integrality.RelaxIntegralityStep">RelaxIntegralityStep</a></code></h4>
|
||||
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|
||||
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|
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|
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<dt><code class="name"><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.steps.tests.test_drop_redundant" href="test_drop_redundant.html">miplearn.components.steps.tests.test_drop_redundant</a></code></dt>
|
||||
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|
||||
<section class="desc"></section>
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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<li><code><a title="miplearn.components.steps.tests.test_convert_tight" href="test_convert_tight.html">miplearn.components.steps.tests.test_convert_tight</a></code></li>
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<header>
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<h1 class="title">Module <code>miplearn.components.steps.tests.test_convert_tight</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">from unittest.mock import Mock
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
|
||||
from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.problems.knapsack import GurobiKnapsackInstance
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_convert_tight_usage():
|
||||
instance = GurobiKnapsackInstance(
|
||||
weights=[3.0, 5.0, 10.0],
|
||||
prices=[1.0, 1.0, 1.0],
|
||||
capacity=16.0,
|
||||
)
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
ConvertTightIneqsIntoEqsStep(),
|
||||
],
|
||||
)
|
||||
|
||||
# Solve original problem
|
||||
stats = solver.solve(instance)
|
||||
original_upper_bound = stats["Upper bound"]
|
||||
|
||||
# Should collect training data
|
||||
assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
|
||||
|
||||
# Fit and resolve
|
||||
solver.fit([instance])
|
||||
stats = solver.solve(instance)
|
||||
|
||||
# Objective value should be the same
|
||||
assert stats["Upper bound"] == original_upper_bound
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
class SampleInstance(Instance):
|
||||
def to_model(self):
|
||||
import gurobipy as grb
|
||||
|
||||
m = grb.Model("model")
|
||||
x1 = m.addVar(name="x1")
|
||||
x2 = m.addVar(name="x2")
|
||||
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
|
||||
m.addConstr(x1 <= 2, name="c1")
|
||||
m.addConstr(x2 <= 2, name="c2")
|
||||
m.addConstr(x1 + x2 <= 3, name="c2")
|
||||
return m
|
||||
|
||||
|
||||
def test_convert_tight_infeasibility():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 1
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0
|
||||
|
||||
|
||||
def test_convert_tight_suboptimality():
|
||||
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 1
|
||||
|
||||
|
||||
def test_convert_tight_optimal():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_infeasibility</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_infeasibility():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 1
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_optimal</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_optimal():
|
||||
comp = ConvertTightIneqsIntoEqsStep()
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_suboptimality</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_suboptimality():
|
||||
comp = ConvertTightIneqsIntoEqsStep(check_optimality=True)
|
||||
comp.classifiers = {
|
||||
"c1": Mock(spec=Classifier),
|
||||
"c2": Mock(spec=Classifier),
|
||||
"c3": Mock(spec=Classifier),
|
||||
}
|
||||
comp.classifiers["c1"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
comp.classifiers["c2"].predict_proba = Mock(return_value=[[1, 0]])
|
||||
comp.classifiers["c3"].predict_proba = Mock(return_value=[[0, 1]])
|
||||
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[comp],
|
||||
solve_lp_first=False,
|
||||
)
|
||||
instance = SampleInstance()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Upper bound"] == 5.0
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 1</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage"><code class="name flex">
|
||||
<span>def <span class="ident">test_convert_tight_usage</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_convert_tight_usage():
|
||||
instance = GurobiKnapsackInstance(
|
||||
weights=[3.0, 5.0, 10.0],
|
||||
prices=[1.0, 1.0, 1.0],
|
||||
capacity=16.0,
|
||||
)
|
||||
solver = LearningSolver(
|
||||
solver=GurobiSolver,
|
||||
components=[
|
||||
RelaxIntegralityStep(),
|
||||
ConvertTightIneqsIntoEqsStep(),
|
||||
],
|
||||
)
|
||||
|
||||
# Solve original problem
|
||||
stats = solver.solve(instance)
|
||||
original_upper_bound = stats["Upper bound"]
|
||||
|
||||
# Should collect training data
|
||||
assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
|
||||
|
||||
# Fit and resolve
|
||||
solver.fit([instance])
|
||||
stats = solver.solve(instance)
|
||||
|
||||
# Objective value should be the same
|
||||
assert stats["Upper bound"] == original_upper_bound
|
||||
assert stats["ConvertTight: Inf iterations"] == 0
|
||||
assert stats["ConvertTight: Subopt iterations"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_convert_tight.SampleInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">SampleInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class SampleInstance(Instance):
|
||||
def to_model(self):
|
||||
import gurobipy as grb
|
||||
|
||||
m = grb.Model("model")
|
||||
x1 = m.addVar(name="x1")
|
||||
x2 = m.addVar(name="x2")
|
||||
m.setObjective(x1 + 2 * x2, grb.GRB.MAXIMIZE)
|
||||
m.addConstr(x1 <= 2, name="c1")
|
||||
m.addConstr(x2 <= 2, name="c2")
|
||||
m.addConstr(x1 + x2 <= 3, name="c2")
|
||||
return m</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
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<h1>Index</h1>
|
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<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_infeasibility">test_convert_tight_infeasibility</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_optimal">test_convert_tight_optimal</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_suboptimality">test_convert_tight_suboptimality</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage" href="#miplearn.components.steps.tests.test_convert_tight.test_convert_tight_usage">test_convert_tight_usage</a></code></li>
|
||||
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|
||||
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|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
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|
||||
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|
||||
<h4><code><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></code></h4>
|
||||
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|
||||
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|
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<main>
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<header>
|
||||
<h1 class="title">Module <code>miplearn.components.steps.tests.test_drop_redundant</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.relaxation import DropRedundantInequalitiesStep
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def _setup():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.get_inequality_slacks = Mock(
|
||||
side_effect=lambda: {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
)
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
return solver, internal, instance, classifiers
|
||||
|
||||
|
||||
def test_drop_redundant():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep()
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should query list of constraints
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should query category and features for each constraint in the model
|
||||
assert instance.get_constraint_category.call_count == 4
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For constraint with non-null categories, should ask for features
|
||||
assert instance.get_constraint_features.call_count == 3
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
||||
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
||||
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
||||
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
||||
|
||||
# Should ask internal solver to remove constraints predicted as redundant
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
training_data = {}
|
||||
component.after_solve(solver, instance, None, {}, training_data)
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert training_data["slacks"] == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}
|
||||
|
||||
|
||||
def test_drop_redundant_with_check_feasibility():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep(
|
||||
check_feasibility=True,
|
||||
violation_tolerance=1e-3,
|
||||
)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
|
||||
# Should ask LearningSolver to repeat
|
||||
assert should_repeat
|
||||
|
||||
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
||||
internal.is_constraint_satisfied.assert_has_calls(
|
||||
[
|
||||
call("<c3>", 1e-3),
|
||||
call("<c4>", 1e-3),
|
||||
]
|
||||
)
|
||||
|
||||
# Should add constraints back to LP relaxation
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
|
||||
def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
np.array([0.20, 0.80]),
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[0].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[0].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c3": 0.30,
|
||||
"c4": 0.00,
|
||||
"c5": 0.00,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[1].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[1].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c3": np.array([0.3, 0.4]),
|
||||
"c4": np.array([0.7]),
|
||||
"c5": np.array([0.8]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[0.3, 0.4],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[0.7],
|
||||
[0.8],
|
||||
]
|
||||
),
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": np.array([[0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
actual_x = component.x(instances)
|
||||
actual_y = component.y(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
actual_x = component.classifiers[category].fit.call_args[0][0]
|
||||
actual_y = component.classifiers[category].fit.call_args[0][1]
|
||||
np.testing.assert_array_equal(actual_x, expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y, expected_y[category])
|
||||
|
||||
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
assert ev["True negative"] == 1
|
||||
assert ev["False positive"] == 1
|
||||
assert ev["False negative"] == 0
|
||||
|
||||
|
||||
def test_x_multiple_solves():
|
||||
instance = Mock(spec=Instance)
|
||||
instance.training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
},
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
},
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array([[1], [0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x = component.x([instance])
|
||||
actual_y = component.y([instance])
|
||||
print(actual_x)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_drop_redundant():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep()
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should query list of constraints
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should query category and features for each constraint in the model
|
||||
assert instance.get_constraint_category.call_count == 4
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For constraint with non-null categories, should ask for features
|
||||
assert instance.get_constraint_features.call_count == 3
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether constraint should be removed
|
||||
type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
|
||||
type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
|
||||
np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
|
||||
np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
|
||||
|
||||
# Should ask internal solver to remove constraints predicted as redundant
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls after_solve
|
||||
training_data = {}
|
||||
component.after_solve(solver, instance, None, {}, training_data)
|
||||
|
||||
# Should query slack for all inequalities
|
||||
internal.get_inequality_slacks.assert_called_once()
|
||||
|
||||
# Should store constraint slacks in instance object
|
||||
assert training_data["slacks"] == {
|
||||
"c1": 0.5,
|
||||
"c2": 0.0,
|
||||
"c3": 0.0,
|
||||
"c4": 1.4,
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility"><code class="name flex">
|
||||
<span>def <span class="ident">test_drop_redundant_with_check_feasibility</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_drop_redundant_with_check_feasibility():
|
||||
solver, internal, instance, classifiers = _setup()
|
||||
|
||||
component = DropRedundantInequalitiesStep(
|
||||
check_feasibility=True,
|
||||
violation_tolerance=1e-3,
|
||||
)
|
||||
component.classifiers = classifiers
|
||||
|
||||
# LearningSolver call before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Assert constraints are extracted
|
||||
assert internal.extract_constraint.call_count == 2
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls iteration_cb (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
|
||||
# Should ask LearningSolver to repeat
|
||||
assert should_repeat
|
||||
|
||||
# Should ask solver if removed constraints are satisfied (mock always returns false)
|
||||
internal.is_constraint_satisfied.assert_has_calls(
|
||||
[
|
||||
call("<c3>", 1e-3),
|
||||
call("<c4>", 1e-3),
|
||||
]
|
||||
)
|
||||
|
||||
# Should add constraints back to LP relaxation
|
||||
internal.add_constraint.assert_has_calls([call("<c3>"), call("<c4>")])
|
||||
|
||||
# LearningSolver calls iteration_cb (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_multiple_solves</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_x_multiple_solves():
|
||||
instance = Mock(spec=Instance)
|
||||
instance.training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
},
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.00,
|
||||
"c3": 1.00,
|
||||
"c4": 0.0,
|
||||
}
|
||||
},
|
||||
]
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[1.0],
|
||||
]
|
||||
),
|
||||
}
|
||||
|
||||
expected_y = {
|
||||
"type-a": np.array([[1], [0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
component = DropRedundantInequalitiesStep()
|
||||
actual_x = component.x([instance])
|
||||
actual_y = component.y([instance])
|
||||
print(actual_x)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_x_y_fit_predict_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_x_y_fit_predict_evaluate():
|
||||
instances = [Mock(spec=Instance), Mock(spec=Instance)]
|
||||
component = DropRedundantInequalitiesStep(slack_tolerance=0.05, threshold=0.80)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
np.array([0.20, 0.80]),
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.50, 0.50],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
)
|
||||
|
||||
# First mock instance
|
||||
instances[0].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c2": 0.05,
|
||||
"c3": 0.00,
|
||||
"c4": 30.0,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[0].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[0].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": np.array([1.0, 0.0]),
|
||||
"c3": np.array([0.5, 0.5]),
|
||||
"c4": np.array([1.0]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
# Second mock instance
|
||||
instances[1].training_data = [
|
||||
{
|
||||
"slacks": {
|
||||
"c1": 0.00,
|
||||
"c3": 0.30,
|
||||
"c4": 0.00,
|
||||
"c5": 0.00,
|
||||
}
|
||||
}
|
||||
]
|
||||
instances[1].get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": None,
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instances[1].get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c3": np.array([0.3, 0.4]),
|
||||
"c4": np.array([0.7]),
|
||||
"c5": np.array([0.8]),
|
||||
}[cid]
|
||||
)
|
||||
|
||||
expected_x = {
|
||||
"type-a": np.array(
|
||||
[
|
||||
[1.0, 0.0],
|
||||
[0.5, 0.5],
|
||||
[0.3, 0.4],
|
||||
]
|
||||
),
|
||||
"type-b": np.array(
|
||||
[
|
||||
[1.0],
|
||||
[0.7],
|
||||
[0.8],
|
||||
]
|
||||
),
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": np.array([[0], [0], [1]]),
|
||||
"type-b": np.array([[1], [0], [0]]),
|
||||
}
|
||||
|
||||
# Should build X and Y matrices correctly
|
||||
actual_x = component.x(instances)
|
||||
actual_y = component.y(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
np.testing.assert_array_equal(actual_x[category], expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y[category], expected_y[category])
|
||||
|
||||
# Should pass along X and Y matrices to classifiers
|
||||
component.fit(instances)
|
||||
for category in ["type-a", "type-b"]:
|
||||
actual_x = component.classifiers[category].fit.call_args[0][0]
|
||||
actual_y = component.classifiers[category].fit.call_args[0][1]
|
||||
np.testing.assert_array_equal(actual_x, expected_x[category])
|
||||
np.testing.assert_array_equal(actual_y, expected_y[category])
|
||||
|
||||
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}
|
||||
|
||||
ev = component.evaluate(instances[1])
|
||||
assert ev["True positive"] == 1
|
||||
assert ev["True negative"] == 1
|
||||
assert ev["False positive"] == 1
|
||||
assert ev["False negative"] == 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.steps.tests" href="index.html">miplearn.components.steps.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant">test_drop_redundant</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility" href="#miplearn.components.steps.tests.test_drop_redundant.test_drop_redundant_with_check_feasibility">test_drop_redundant_with_check_feasibility</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_multiple_solves">test_x_multiple_solves</a></code></li>
|
||||
<li><code><a title="miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate" href="#miplearn.components.steps.tests.test_drop_redundant.test_x_y_fit_predict_evaluate">test_x_y_fit_predict_evaluate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
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|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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|
||||
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|
||||
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|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.</code></pre>
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||||
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||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.components.tests.test_lazy_dynamic" href="test_lazy_dynamic.html">miplearn.components.tests.test_lazy_dynamic</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
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|
||||
<dt><code class="name"><a title="miplearn.components.tests.test_lazy_static" href="test_lazy_static.html">miplearn.components.tests.test_lazy_static</a></code></dt>
|
||||
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|
||||
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|
||||
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|
||||
<dt><code class="name"><a title="miplearn.components.tests.test_objective" href="test_objective.html">miplearn.components.tests.test_objective</a></code></dt>
|
||||
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|
||||
<section class="desc"></section>
|
||||
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|
||||
<dt><code class="name"><a title="miplearn.components.tests.test_primal" href="test_primal.html">miplearn.components.tests.test_primal</a></code></dt>
|
||||
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
<li><code><a title="miplearn.components.tests.test_composite" href="test_composite.html">miplearn.components.tests.test_composite</a></code></li>
|
||||
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|
||||
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|
||||
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|
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<li><code><a title="miplearn.components.tests.test_primal" href="test_primal.html">miplearn.components.tests.test_primal</a></code></li>
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|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_composite</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn.components.component import Component
|
||||
from miplearn.components.composite import CompositeComponent
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_composite():
|
||||
solver, instance, model = (
|
||||
Mock(spec=LearningSolver),
|
||||
Mock(spec=Instance),
|
||||
Mock(),
|
||||
)
|
||||
|
||||
c1 = Mock(spec=Component)
|
||||
c2 = Mock(spec=Component)
|
||||
cc = CompositeComponent([c1, c2])
|
||||
|
||||
# Should broadcast before_solve
|
||||
cc.before_solve(solver, instance, model)
|
||||
c1.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
c2.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast after_solve
|
||||
cc.after_solve(solver, instance, model, {}, {})
|
||||
c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
|
||||
# Should broadcast fit
|
||||
cc.fit([1, 2, 3])
|
||||
c1.fit.assert_has_calls([call([1, 2, 3])])
|
||||
c2.fit.assert_has_calls([call([1, 2, 3])])
|
||||
|
||||
# Should broadcast lazy_cb
|
||||
cc.lazy_cb(solver, instance, model)
|
||||
c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast iteration_cb
|
||||
cc.iteration_cb(solver, instance, model)
|
||||
c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# If at least one child component returns true, iteration_cb should return True
|
||||
c1.iteration_cb = Mock(return_value=True)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert cc.iteration_cb(solver, instance, model)
|
||||
|
||||
# If all children return False, iteration_cb should return False
|
||||
c1.iteration_cb = Mock(return_value=False)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert not cc.iteration_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_composite.test_composite"><code class="name flex">
|
||||
<span>def <span class="ident">test_composite</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_composite():
|
||||
solver, instance, model = (
|
||||
Mock(spec=LearningSolver),
|
||||
Mock(spec=Instance),
|
||||
Mock(),
|
||||
)
|
||||
|
||||
c1 = Mock(spec=Component)
|
||||
c2 = Mock(spec=Component)
|
||||
cc = CompositeComponent([c1, c2])
|
||||
|
||||
# Should broadcast before_solve
|
||||
cc.before_solve(solver, instance, model)
|
||||
c1.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
c2.before_solve.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast after_solve
|
||||
cc.after_solve(solver, instance, model, {}, {})
|
||||
c1.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
c2.after_solve.assert_has_calls([call(solver, instance, model, {}, {})])
|
||||
|
||||
# Should broadcast fit
|
||||
cc.fit([1, 2, 3])
|
||||
c1.fit.assert_has_calls([call([1, 2, 3])])
|
||||
c2.fit.assert_has_calls([call([1, 2, 3])])
|
||||
|
||||
# Should broadcast lazy_cb
|
||||
cc.lazy_cb(solver, instance, model)
|
||||
c1.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.lazy_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# Should broadcast iteration_cb
|
||||
cc.iteration_cb(solver, instance, model)
|
||||
c1.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
c2.iteration_cb.assert_has_calls([call(solver, instance, model)])
|
||||
|
||||
# If at least one child component returns true, iteration_cb should return True
|
||||
c1.iteration_cb = Mock(return_value=True)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert cc.iteration_cb(solver, instance, model)
|
||||
|
||||
# If all children return False, iteration_cb should return False
|
||||
c1.iteration_cb = Mock(return_value=False)
|
||||
c2.iteration_cb = Mock(return_value=False)
|
||||
assert not cc.iteration_cb(solver, instance, model)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
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<header>
|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_lazy_dynamic</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_dynamic import DynamicLazyConstraintsComponent
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
E = 0.1
|
||||
|
||||
|
||||
def test_lazy_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "c"]
|
||||
classifier = Mock(spec=Classifier)
|
||||
component = DynamicLazyConstraintsComponent(classifier=classifier)
|
||||
|
||||
component.fit(instances)
|
||||
|
||||
# Should create one classifier for each violation
|
||||
assert "a" in component.classifiers
|
||||
assert "b" in component.classifiers
|
||||
assert "c" in component.classifiers
|
||||
|
||||
# Should provide correct x_train to each classifier
|
||||
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
|
||||
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
|
||||
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
|
||||
assert norm(expected_x_train_a - actual_x_train_a) < E
|
||||
assert norm(expected_x_train_b - actual_x_train_b) < E
|
||||
assert norm(expected_x_train_c - actual_x_train_c) < E
|
||||
|
||||
# Should provide correct y_train to each classifier
|
||||
expected_y_train_a = np.array([1.0, 0.0])
|
||||
expected_y_train_b = np.array([1.0, 1.0])
|
||||
expected_y_train_c = np.array([0.0, 1.0])
|
||||
actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
|
||||
actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
|
||||
actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
|
||||
assert norm(expected_y_train_a - actual_y_train_a) < E
|
||||
assert norm(expected_y_train_b - actual_y_train_b) < E
|
||||
assert norm(expected_y_train_c - actual_y_train_c) < E
|
||||
|
||||
|
||||
def test_lazy_before():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].build_lazy_constraint = Mock(return_value="c1")
|
||||
solver = LearningSolver()
|
||||
solver.internal_solver = Mock(spec=InternalSolver)
|
||||
component = DynamicLazyConstraintsComponent(threshold=0.10)
|
||||
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
|
||||
|
||||
component.before_solve(solver, instances[0], models[0])
|
||||
|
||||
# Should ask classifier likelihood of each constraint being violated
|
||||
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
|
||||
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
|
||||
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
|
||||
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
|
||||
assert norm(expected_x_test_a - actual_x_test_a) < E
|
||||
assert norm(expected_x_test_b - actual_x_test_b) < E
|
||||
|
||||
# Should ask instance to generate cut for constraints whose likelihood
|
||||
# of being violated exceeds the threshold
|
||||
instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
|
||||
|
||||
# Should ask internal solver to add generated constraint
|
||||
solver.internal_solver.add_constraint.assert_called_once_with("c1")
|
||||
|
||||
|
||||
def test_lazy_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
component = DynamicLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"a": Mock(spec=Classifier),
|
||||
"b": Mock(spec=Classifier),
|
||||
"c": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "d"]
|
||||
assert component.evaluate(instances) == {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"F1 score": 0.8,
|
||||
"Precision": 1.0,
|
||||
"Recall": 2 / 3.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 3,
|
||||
"Condition negative": 1,
|
||||
"False negative": 1,
|
||||
"False positive": 0,
|
||||
"True negative": 1,
|
||||
"True positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 75.0,
|
||||
"Condition negative (%)": 25.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 50.0,
|
||||
},
|
||||
1: {
|
||||
"Accuracy": 0.5,
|
||||
"F1 score": 0.5,
|
||||
"Precision": 0.5,
|
||||
"Recall": 0.5,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 2,
|
||||
"Condition negative": 2,
|
||||
"False negative": 1,
|
||||
"False positive": 1,
|
||||
"True negative": 1,
|
||||
"True positive": 1,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 50.0,
|
||||
"Condition negative (%)": 50.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 25.0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 25.0,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_before"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_before</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_lazy_before():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].build_lazy_constraint = Mock(return_value="c1")
|
||||
solver = LearningSolver()
|
||||
solver.internal_solver = Mock(spec=InternalSolver)
|
||||
component = DynamicLazyConstraintsComponent(threshold=0.10)
|
||||
component.classifiers = {"a": Mock(spec=Classifier), "b": Mock(spec=Classifier)}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[0.95, 0.05]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.02, 0.80]])
|
||||
|
||||
component.before_solve(solver, instances[0], models[0])
|
||||
|
||||
# Should ask classifier likelihood of each constraint being violated
|
||||
expected_x_test_a = np.array([[67.0, 21.75, 1287.92]])
|
||||
expected_x_test_b = np.array([[67.0, 21.75, 1287.92]])
|
||||
actual_x_test_a = component.classifiers["a"].predict_proba.call_args[0][0]
|
||||
actual_x_test_b = component.classifiers["b"].predict_proba.call_args[0][0]
|
||||
assert norm(expected_x_test_a - actual_x_test_a) < E
|
||||
assert norm(expected_x_test_b - actual_x_test_b) < E
|
||||
|
||||
# Should ask instance to generate cut for constraints whose likelihood
|
||||
# of being violated exceeds the threshold
|
||||
instances[0].build_lazy_constraint.assert_called_once_with(models[0], "b")
|
||||
|
||||
# Should ask internal solver to add generated constraint
|
||||
solver.internal_solver.add_constraint.assert_called_once_with("c1")</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_lazy_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
component = DynamicLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"a": Mock(spec=Classifier),
|
||||
"b": Mock(spec=Classifier),
|
||||
"c": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["a"].predict_proba = Mock(return_value=[[1.0, 0.0]])
|
||||
component.classifiers["b"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
component.classifiers["c"].predict_proba = Mock(return_value=[[0.0, 1.0]])
|
||||
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b", "c"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "d"]
|
||||
assert component.evaluate(instances) == {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"F1 score": 0.8,
|
||||
"Precision": 1.0,
|
||||
"Recall": 2 / 3.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 3,
|
||||
"Condition negative": 1,
|
||||
"False negative": 1,
|
||||
"False positive": 0,
|
||||
"True negative": 1,
|
||||
"True positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 75.0,
|
||||
"Condition negative (%)": 25.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 50.0,
|
||||
},
|
||||
1: {
|
||||
"Accuracy": 0.5,
|
||||
"F1 score": 0.5,
|
||||
"Precision": 0.5,
|
||||
"Recall": 0.5,
|
||||
"Predicted positive": 2,
|
||||
"Predicted negative": 2,
|
||||
"Condition positive": 2,
|
||||
"Condition negative": 2,
|
||||
"False negative": 1,
|
||||
"False positive": 1,
|
||||
"True negative": 1,
|
||||
"True positive": 1,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Condition positive (%)": 50.0,
|
||||
"Condition negative (%)": 50.0,
|
||||
"False negative (%)": 25.0,
|
||||
"False positive (%)": 25.0,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive (%)": 25.0,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_fit</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_lazy_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
instances[0].found_violated_lazy_constraints = ["a", "b"]
|
||||
instances[1].found_violated_lazy_constraints = ["b", "c"]
|
||||
classifier = Mock(spec=Classifier)
|
||||
component = DynamicLazyConstraintsComponent(classifier=classifier)
|
||||
|
||||
component.fit(instances)
|
||||
|
||||
# Should create one classifier for each violation
|
||||
assert "a" in component.classifiers
|
||||
assert "b" in component.classifiers
|
||||
assert "c" in component.classifiers
|
||||
|
||||
# Should provide correct x_train to each classifier
|
||||
expected_x_train_a = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_b = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
expected_x_train_c = np.array([[67.0, 21.75, 1287.92], [70.0, 23.75, 1199.83]])
|
||||
actual_x_train_a = component.classifiers["a"].fit.call_args[0][0]
|
||||
actual_x_train_b = component.classifiers["b"].fit.call_args[0][0]
|
||||
actual_x_train_c = component.classifiers["c"].fit.call_args[0][0]
|
||||
assert norm(expected_x_train_a - actual_x_train_a) < E
|
||||
assert norm(expected_x_train_b - actual_x_train_b) < E
|
||||
assert norm(expected_x_train_c - actual_x_train_c) < E
|
||||
|
||||
# Should provide correct y_train to each classifier
|
||||
expected_y_train_a = np.array([1.0, 0.0])
|
||||
expected_y_train_b = np.array([1.0, 1.0])
|
||||
expected_y_train_c = np.array([0.0, 1.0])
|
||||
actual_y_train_a = component.classifiers["a"].fit.call_args[0][1]
|
||||
actual_y_train_b = component.classifiers["b"].fit.call_args[0][1]
|
||||
actual_y_train_c = component.classifiers["c"].fit.call_args[0][1]
|
||||
assert norm(expected_y_train_a - actual_y_train_a) < E
|
||||
assert norm(expected_y_train_b - actual_y_train_b) < E
|
||||
assert norm(expected_y_train_c - actual_y_train_c) < E</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_before" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_before">test_lazy_before</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_evaluate">test_lazy_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_dynamic.test_lazy_fit" href="#miplearn.components.tests.test_lazy_dynamic.test_lazy_fit">test_lazy_fit</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
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|
||||
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|
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</head>
|
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_lazy_static</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock, call
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.lazy_static import StaticLazyConstraintsComponent
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_usage_with_solver():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
solver.use_lazy_cb = False
|
||||
solver.gap_tolerance = 1e-4
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.has_static_lazy_constraints = Mock(return_value=True)
|
||||
instance.is_constraint_lazy = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": False,
|
||||
"c2": True,
|
||||
"c3": True,
|
||||
"c4": True,
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
component = StaticLazyConstraintsComponent(
|
||||
threshold=0.90,
|
||||
use_two_phase_gap=False,
|
||||
violation_tolerance=1.0,
|
||||
)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should ask if instance has static lazy constraints
|
||||
instance.has_static_lazy_constraints.assert_called_once()
|
||||
|
||||
# Should ask internal solver for a list of constraints in the model
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should ask if each constraint in the model is lazy
|
||||
instance.is_constraint_lazy.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For the lazy ones, should ask for features
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should also ask for categories
|
||||
assert instance.get_constraint_category.call_count == 3
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask internal solver to remove constraints identified as lazy
|
||||
assert internal.extract_constraint.call_count == 3
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether each lazy constraint should be enforced
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with(
|
||||
[[1.0, 0.0], [0.5, 0.5]]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
|
||||
|
||||
# For the ones that should be enforced, should ask solver to re-add them
|
||||
# to the formulation. The remaining ones should remain in the pool.
|
||||
assert internal.add_constraint.call_count == 2
|
||||
internal.add_constraint.assert_has_calls(
|
||||
[
|
||||
call("<c3>"),
|
||||
call("<c4>"),
|
||||
]
|
||||
)
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert should_repeat
|
||||
|
||||
# Should ask internal solver to verify if constraints in the pool are
|
||||
# satisfied and add the ones that are not
|
||||
internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
|
||||
internal.is_constraint_satisfied.reset_mock()
|
||||
internal.add_constraint.assert_called_once_with("<c2>")
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
# The lazy constraint pool should be empty by now, so no calls should be made
|
||||
internal.is_constraint_satisfied.assert_not_called()
|
||||
internal.add_constraint.assert_not_called()
|
||||
|
||||
# Should update instance object
|
||||
assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]
|
||||
|
||||
|
||||
def test_fit():
|
||||
instance_1 = Mock(spec=Instance)
|
||||
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
|
||||
instance_1.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_1.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [1, 1],
|
||||
"c2": [1, 2],
|
||||
"c3": [1, 3],
|
||||
"c4": [1, 4, 0],
|
||||
"c5": [1, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instance_2 = Mock(spec=Instance)
|
||||
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
|
||||
instance_2.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_2.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [2, 1],
|
||||
"c2": [2, 2],
|
||||
"c3": [2, 3],
|
||||
"c4": [2, 4, 0],
|
||||
"c5": [2, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instances = [instance_1, instance_2]
|
||||
component = StaticLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
|
||||
expected_constraints = {
|
||||
"type-a": ["c1", "c2", "c3"],
|
||||
"type-b": ["c4", "c5"],
|
||||
}
|
||||
expected_x = {
|
||||
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
|
||||
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
|
||||
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
|
||||
}
|
||||
assert component._collect_constraints(instances) == expected_constraints
|
||||
assert component.x(instances) == expected_x
|
||||
assert component.y(instances) == expected_y
|
||||
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_once_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_once_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_lazy_static.test_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_fit</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_fit():
|
||||
instance_1 = Mock(spec=Instance)
|
||||
instance_1.found_violated_lazy_constraints = ["c1", "c2", "c4", "c5"]
|
||||
instance_1.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_1.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [1, 1],
|
||||
"c2": [1, 2],
|
||||
"c3": [1, 3],
|
||||
"c4": [1, 4, 0],
|
||||
"c5": [1, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instance_2 = Mock(spec=Instance)
|
||||
instance_2.found_violated_lazy_constraints = ["c2", "c3", "c4"]
|
||||
instance_2.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": "type-a",
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
"c5": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
instance_2.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": [2, 1],
|
||||
"c2": [2, 2],
|
||||
"c3": [2, 3],
|
||||
"c4": [2, 4, 0],
|
||||
"c5": [2, 5, 0],
|
||||
}[cid]
|
||||
)
|
||||
|
||||
instances = [instance_1, instance_2]
|
||||
component = StaticLazyConstraintsComponent()
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
|
||||
expected_constraints = {
|
||||
"type-a": ["c1", "c2", "c3"],
|
||||
"type-b": ["c4", "c5"],
|
||||
}
|
||||
expected_x = {
|
||||
"type-a": [[1, 1], [1, 2], [1, 3], [2, 1], [2, 2], [2, 3]],
|
||||
"type-b": [[1, 4, 0], [1, 5, 0], [2, 4, 0], [2, 5, 0]],
|
||||
}
|
||||
expected_y = {
|
||||
"type-a": [[0, 1], [0, 1], [1, 0], [1, 0], [0, 1], [0, 1]],
|
||||
"type-b": [[0, 1], [0, 1], [0, 1], [1, 0]],
|
||||
}
|
||||
assert component._collect_constraints(instances) == expected_constraints
|
||||
assert component.x(instances) == expected_x
|
||||
assert component.y(instances) == expected_y
|
||||
|
||||
component.fit(instances)
|
||||
component.classifiers["type-a"].fit.assert_called_once_with(
|
||||
expected_x["type-a"],
|
||||
expected_y["type-a"],
|
||||
)
|
||||
component.classifiers["type-b"].fit.assert_called_once_with(
|
||||
expected_x["type-b"],
|
||||
expected_y["type-b"],
|
||||
)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_lazy_static.test_usage_with_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_usage_with_solver</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_usage_with_solver():
|
||||
solver = Mock(spec=LearningSolver)
|
||||
solver.use_lazy_cb = False
|
||||
solver.gap_tolerance = 1e-4
|
||||
|
||||
internal = solver.internal_solver = Mock(spec=InternalSolver)
|
||||
internal.get_constraint_ids = Mock(return_value=["c1", "c2", "c3", "c4"])
|
||||
internal.extract_constraint = Mock(side_effect=lambda cid: "<%s>" % cid)
|
||||
internal.is_constraint_satisfied = Mock(return_value=False)
|
||||
|
||||
instance = Mock(spec=Instance)
|
||||
instance.has_static_lazy_constraints = Mock(return_value=True)
|
||||
instance.is_constraint_lazy = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c1": False,
|
||||
"c2": True,
|
||||
"c3": True,
|
||||
"c4": True,
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_features = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": [1.0, 0.0],
|
||||
"c3": [0.5, 0.5],
|
||||
"c4": [1.0],
|
||||
}[cid]
|
||||
)
|
||||
instance.get_constraint_category = Mock(
|
||||
side_effect=lambda cid: {
|
||||
"c2": "type-a",
|
||||
"c3": "type-a",
|
||||
"c4": "type-b",
|
||||
}[cid]
|
||||
)
|
||||
|
||||
component = StaticLazyConstraintsComponent(
|
||||
threshold=0.90,
|
||||
use_two_phase_gap=False,
|
||||
violation_tolerance=1.0,
|
||||
)
|
||||
component.classifiers = {
|
||||
"type-a": Mock(spec=Classifier),
|
||||
"type-b": Mock(spec=Classifier),
|
||||
}
|
||||
component.classifiers["type-a"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.20, 0.80],
|
||||
[0.05, 0.95],
|
||||
]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba = Mock(
|
||||
return_value=[
|
||||
[0.02, 0.98],
|
||||
]
|
||||
)
|
||||
|
||||
# LearningSolver calls before_solve
|
||||
component.before_solve(solver, instance, None)
|
||||
|
||||
# Should ask if instance has static lazy constraints
|
||||
instance.has_static_lazy_constraints.assert_called_once()
|
||||
|
||||
# Should ask internal solver for a list of constraints in the model
|
||||
internal.get_constraint_ids.assert_called_once()
|
||||
|
||||
# Should ask if each constraint in the model is lazy
|
||||
instance.is_constraint_lazy.assert_has_calls(
|
||||
[
|
||||
call("c1"),
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# For the lazy ones, should ask for features
|
||||
instance.get_constraint_features.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should also ask for categories
|
||||
assert instance.get_constraint_category.call_count == 3
|
||||
instance.get_constraint_category.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask internal solver to remove constraints identified as lazy
|
||||
assert internal.extract_constraint.call_count == 3
|
||||
internal.extract_constraint.assert_has_calls(
|
||||
[
|
||||
call("c2"),
|
||||
call("c3"),
|
||||
call("c4"),
|
||||
]
|
||||
)
|
||||
|
||||
# Should ask ML to predict whether each lazy constraint should be enforced
|
||||
component.classifiers["type-a"].predict_proba.assert_called_once_with(
|
||||
[[1.0, 0.0], [0.5, 0.5]]
|
||||
)
|
||||
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]])
|
||||
|
||||
# For the ones that should be enforced, should ask solver to re-add them
|
||||
# to the formulation. The remaining ones should remain in the pool.
|
||||
assert internal.add_constraint.call_count == 2
|
||||
internal.add_constraint.assert_has_calls(
|
||||
[
|
||||
call("<c3>"),
|
||||
call("<c4>"),
|
||||
]
|
||||
)
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (first time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert should_repeat
|
||||
|
||||
# Should ask internal solver to verify if constraints in the pool are
|
||||
# satisfied and add the ones that are not
|
||||
internal.is_constraint_satisfied.assert_called_once_with("<c2>", tol=1.0)
|
||||
internal.is_constraint_satisfied.reset_mock()
|
||||
internal.add_constraint.assert_called_once_with("<c2>")
|
||||
internal.add_constraint.reset_mock()
|
||||
|
||||
# LearningSolver calls after_iteration (second time)
|
||||
should_repeat = component.iteration_cb(solver, instance, None)
|
||||
assert not should_repeat
|
||||
|
||||
# The lazy constraint pool should be empty by now, so no calls should be made
|
||||
internal.is_constraint_satisfied.assert_not_called()
|
||||
internal.add_constraint.assert_not_called()
|
||||
|
||||
# Should update instance object
|
||||
assert instance.found_violated_lazy_constraints == ["c3", "c4", "c2"]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_static.test_fit" href="#miplearn.components.tests.test_lazy_static.test_fit">test_fit</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_lazy_static.test_usage_with_solver" href="#miplearn.components.tests.test_lazy_static.test_usage_with_solver">test_usage_with_solver</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
<html lang="en">
|
||||
<head>
|
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
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<title>miplearn.components.tests.test_objective API documentation</title>
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|
||||
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|
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_objective</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Regressor
|
||||
from miplearn.components.objective import ObjectiveValueComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_usage():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(instances)
|
||||
assert instances[0].training_data[0]["Lower bound"] == 1183.0
|
||||
assert instances[0].training_data[0]["Upper bound"] == 1183.0
|
||||
assert np.round(comp.predict(instances), 2).tolist() == [
|
||||
[1183.0, 1183.0],
|
||||
[1070.0, 1070.0],
|
||||
]
|
||||
|
||||
|
||||
def test_obj_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
reg = Mock(spec=Regressor)
|
||||
reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
|
||||
comp = ObjectiveValueComponent(regressor=reg)
|
||||
comp.fit(instances)
|
||||
ev = comp.evaluate(instances)
|
||||
assert ev == {
|
||||
"Lower bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
"Upper bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_objective.test_obj_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_obj_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_obj_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
reg = Mock(spec=Regressor)
|
||||
reg.predict = Mock(return_value=np.array([1000.0, 1000.0]))
|
||||
comp = ObjectiveValueComponent(regressor=reg)
|
||||
comp.fit(instances)
|
||||
ev = comp.evaluate(instances)
|
||||
assert ev == {
|
||||
"Lower bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
"Upper bound": {
|
||||
"Explained variance": 0.0,
|
||||
"Max error": 183.0,
|
||||
"Mean absolute error": 126.5,
|
||||
"Mean squared error": 19194.5,
|
||||
"Median absolute error": 126.5,
|
||||
"R2": -5.012843605607331,
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_objective.test_usage"><code class="name flex">
|
||||
<span>def <span class="ident">test_usage</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_usage():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(instances)
|
||||
assert instances[0].training_data[0]["Lower bound"] == 1183.0
|
||||
assert instances[0].training_data[0]["Upper bound"] == 1183.0
|
||||
assert np.round(comp.predict(instances), 2).tolist() == [
|
||||
[1183.0, 1183.0],
|
||||
[1070.0, 1070.0],
|
||||
]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
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|
||||
<section>
|
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</section>
|
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|
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<ul id="index">
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<li><h3>Super-module</h3>
|
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<ul>
|
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<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_objective.test_obj_evaluate" href="#miplearn.components.tests.test_objective.test_obj_evaluate">test_obj_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_objective.test_usage" href="#miplearn.components.tests.test_objective.test_usage">test_usage</a></code></li>
|
||||
</ul>
|
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</head>
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<body>
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||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.components.tests.test_primal</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from unittest.mock import Mock
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.classifiers import Classifier
|
||||
from miplearn.components.primal import PrimalSolutionComponent
|
||||
from miplearn.tests import get_test_pyomo_instances
|
||||
|
||||
|
||||
def test_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances)
|
||||
solution = comp.predict(instances[0])
|
||||
assert "x" in solution
|
||||
assert 0 in solution["x"]
|
||||
assert 1 in solution["x"]
|
||||
assert 2 in solution["x"]
|
||||
assert 3 in solution["x"]
|
||||
|
||||
|
||||
def test_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
clf_zero = Mock(spec=Classifier)
|
||||
clf_zero.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.0, 1.0], # x[0]
|
||||
[0.0, 1.0], # x[1]
|
||||
[1.0, 0.0], # x[2]
|
||||
[1.0, 0.0], # x[3]
|
||||
]
|
||||
)
|
||||
)
|
||||
clf_one = Mock(spec=Classifier)
|
||||
clf_one.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[1.0, 0.0], # x[0] instances[0]
|
||||
[1.0, 0.0], # x[1] instances[0]
|
||||
[0.0, 1.0], # x[2] instances[0]
|
||||
[1.0, 0.0], # x[3] instances[0]
|
||||
]
|
||||
)
|
||||
)
|
||||
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
|
||||
comp.fit(instances[:1])
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
|
||||
ev = comp.evaluate(instances[:1])
|
||||
assert ev == {
|
||||
"Fix one": {
|
||||
0: {
|
||||
"Accuracy": 0.5,
|
||||
"Condition negative": 1,
|
||||
"Condition negative (%)": 25.0,
|
||||
"Condition positive": 3,
|
||||
"Condition positive (%)": 75.0,
|
||||
"F1 score": 0.5,
|
||||
"False negative": 2,
|
||||
"False negative (%)": 50.0,
|
||||
"False positive": 0,
|
||||
"False positive (%)": 0.0,
|
||||
"Precision": 1.0,
|
||||
"Predicted negative": 3,
|
||||
"Predicted negative (%)": 75.0,
|
||||
"Predicted positive": 1,
|
||||
"Predicted positive (%)": 25.0,
|
||||
"Recall": 0.3333333333333333,
|
||||
"True negative": 1,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
"Fix zero": {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"Condition negative": 3,
|
||||
"Condition negative (%)": 75.0,
|
||||
"Condition positive": 1,
|
||||
"Condition positive (%)": 25.0,
|
||||
"F1 score": 0.6666666666666666,
|
||||
"False negative": 0,
|
||||
"False negative (%)": 0.0,
|
||||
"False positive": 1,
|
||||
"False positive (%)": 25.0,
|
||||
"Precision": 0.5,
|
||||
"Predicted negative": 2,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Recall": 1.0,
|
||||
"True negative": 2,
|
||||
"True negative (%)": 50.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def test_primal_parallel_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances, n_jobs=2)
|
||||
assert len(comp.classifiers) == 2</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.components.tests.test_primal.test_evaluate"><code class="name flex">
|
||||
<span>def <span class="ident">test_evaluate</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_evaluate():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
clf_zero = Mock(spec=Classifier)
|
||||
clf_zero.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[0.0, 1.0], # x[0]
|
||||
[0.0, 1.0], # x[1]
|
||||
[1.0, 0.0], # x[2]
|
||||
[1.0, 0.0], # x[3]
|
||||
]
|
||||
)
|
||||
)
|
||||
clf_one = Mock(spec=Classifier)
|
||||
clf_one.predict_proba = Mock(
|
||||
return_value=np.array(
|
||||
[
|
||||
[1.0, 0.0], # x[0] instances[0]
|
||||
[1.0, 0.0], # x[1] instances[0]
|
||||
[0.0, 1.0], # x[2] instances[0]
|
||||
[1.0, 0.0], # x[3] instances[0]
|
||||
]
|
||||
)
|
||||
)
|
||||
comp = PrimalSolutionComponent(classifier=[clf_zero, clf_one], threshold=0.50)
|
||||
comp.fit(instances[:1])
|
||||
assert comp.predict(instances[0]) == {"x": {0: 0, 1: 0, 2: 1, 3: None}}
|
||||
assert instances[0].training_data[0]["Solution"] == {"x": {0: 1, 1: 0, 2: 1, 3: 1}}
|
||||
ev = comp.evaluate(instances[:1])
|
||||
assert ev == {
|
||||
"Fix one": {
|
||||
0: {
|
||||
"Accuracy": 0.5,
|
||||
"Condition negative": 1,
|
||||
"Condition negative (%)": 25.0,
|
||||
"Condition positive": 3,
|
||||
"Condition positive (%)": 75.0,
|
||||
"F1 score": 0.5,
|
||||
"False negative": 2,
|
||||
"False negative (%)": 50.0,
|
||||
"False positive": 0,
|
||||
"False positive (%)": 0.0,
|
||||
"Precision": 1.0,
|
||||
"Predicted negative": 3,
|
||||
"Predicted negative (%)": 75.0,
|
||||
"Predicted positive": 1,
|
||||
"Predicted positive (%)": 25.0,
|
||||
"Recall": 0.3333333333333333,
|
||||
"True negative": 1,
|
||||
"True negative (%)": 25.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
"Fix zero": {
|
||||
0: {
|
||||
"Accuracy": 0.75,
|
||||
"Condition negative": 3,
|
||||
"Condition negative (%)": 75.0,
|
||||
"Condition positive": 1,
|
||||
"Condition positive (%)": 25.0,
|
||||
"F1 score": 0.6666666666666666,
|
||||
"False negative": 0,
|
||||
"False negative (%)": 0.0,
|
||||
"False positive": 1,
|
||||
"False positive (%)": 25.0,
|
||||
"Precision": 0.5,
|
||||
"Predicted negative": 2,
|
||||
"Predicted negative (%)": 50.0,
|
||||
"Predicted positive": 2,
|
||||
"Predicted positive (%)": 50.0,
|
||||
"Recall": 1.0,
|
||||
"True negative": 2,
|
||||
"True negative (%)": 50.0,
|
||||
"True positive": 1,
|
||||
"True positive (%)": 25.0,
|
||||
}
|
||||
},
|
||||
}</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_primal.test_predict"><code class="name flex">
|
||||
<span>def <span class="ident">test_predict</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_predict():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances)
|
||||
solution = comp.predict(instances[0])
|
||||
assert "x" in solution
|
||||
assert 0 in solution["x"]
|
||||
assert 1 in solution["x"]
|
||||
assert 2 in solution["x"]
|
||||
assert 3 in solution["x"]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.components.tests.test_primal.test_primal_parallel_fit"><code class="name flex">
|
||||
<span>def <span class="ident">test_primal_parallel_fit</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_primal_parallel_fit():
|
||||
instances, models = get_test_pyomo_instances()
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(instances, n_jobs=2)
|
||||
assert len(comp.classifiers) == 2</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.components.tests" href="index.html">miplearn.components.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_evaluate" href="#miplearn.components.tests.test_primal.test_evaluate">test_evaluate</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_predict" href="#miplearn.components.tests.test_primal.test_predict">test_predict</a></code></li>
|
||||
<li><code><a title="miplearn.components.tests.test_primal.test_primal_parallel_fit" href="#miplearn.components.tests.test_primal.test_primal_parallel_fit">test_primal_parallel_fit</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<script>hljs.initHighlightingOnLoad()</script>
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|
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||||
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|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.extractors</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import gzip
|
||||
import logging
|
||||
import pickle
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
import numpy as np
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class InstanceIterator:
|
||||
def __init__(self, instances):
|
||||
self.instances = instances
|
||||
self.current = 0
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.current >= len(self.instances):
|
||||
raise StopIteration
|
||||
result = self.instances[self.current]
|
||||
self.current += 1
|
||||
if isinstance(result, str):
|
||||
logger.debug("Read: %s" % result)
|
||||
try:
|
||||
if result.endswith(".gz"):
|
||||
with gzip.GzipFile(result, "rb") as file:
|
||||
result = pickle.load(file)
|
||||
else:
|
||||
with open(result, "rb") as file:
|
||||
result = pickle.load(file)
|
||||
except pickle.UnpicklingError:
|
||||
raise Exception(f"Invalid instance file: {result}")
|
||||
return result
|
||||
|
||||
|
||||
class Extractor(ABC):
|
||||
@abstractmethod
|
||||
def extract(self, instances):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def split_variables(instance):
|
||||
result = {}
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for var_name in lp_solution:
|
||||
for index in lp_solution[var_name]:
|
||||
category = instance.get_variable_category(var_name, index)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
result[category] += [(var_name, index)]
|
||||
return result
|
||||
|
||||
|
||||
class VariableFeaturesExtractor(Extractor):
|
||||
def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (vars)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
instance_features = instance.get_instance_features()
|
||||
var_split = self.split_variables(instance)
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
result[category] += [
|
||||
instance_features.tolist()
|
||||
+ instance.get_variable_features(var_name, index).tolist()
|
||||
+ [lp_solution[var_name][index]]
|
||||
]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result
|
||||
|
||||
|
||||
class SolutionExtractor(Extractor):
|
||||
def __init__(self, relaxation=False):
|
||||
self.relaxation = relaxation
|
||||
|
||||
def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (solution)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
var_split = self.split_variables(instance)
|
||||
if self.relaxation:
|
||||
solution = instance.training_data[0]["LP solution"]
|
||||
else:
|
||||
solution = instance.training_data[0]["Solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
v = solution[var_name][index]
|
||||
if v is None:
|
||||
result[category] += [[0, 0]]
|
||||
else:
|
||||
result[category] += [[1 - v, v]]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result
|
||||
|
||||
|
||||
class InstanceFeaturesExtractor(Extractor):
|
||||
def extract(self, instances):
|
||||
return np.vstack(
|
||||
[
|
||||
np.hstack(
|
||||
[
|
||||
instance.get_instance_features(),
|
||||
instance.training_data[0]["LP value"],
|
||||
]
|
||||
)
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
class ObjectiveValueExtractor(Extractor):
|
||||
def __init__(self, kind="lp"):
|
||||
assert kind in ["lower bound", "upper bound", "lp"]
|
||||
self.kind = kind
|
||||
|
||||
def extract(self, instances):
|
||||
if self.kind == "lower bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Lower bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "upper bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Upper bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "lp":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["LP value"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.Extractor"><code class="flex name class">
|
||||
<span>class <span class="ident">Extractor</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Extractor(ABC):
|
||||
@abstractmethod
|
||||
def extract(self, instances):
|
||||
pass
|
||||
|
||||
@staticmethod
|
||||
def split_variables(instance):
|
||||
result = {}
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for var_name in lp_solution:
|
||||
for index in lp_solution[var_name]:
|
||||
category = instance.get_variable_category(var_name, index)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
result[category] += [(var_name, index)]
|
||||
return result</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></li>
|
||||
<li><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></li>
|
||||
<li><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></li>
|
||||
<li><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></li>
|
||||
</ul>
|
||||
<h3>Static methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.Extractor.split_variables"><code class="name flex">
|
||||
<span>def <span class="ident">split_variables</span></span>(<span>instance)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@staticmethod
|
||||
def split_variables(instance):
|
||||
result = {}
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for var_name in lp_solution:
|
||||
for index in lp_solution[var_name]:
|
||||
category = instance.get_variable_category(var_name, index)
|
||||
if category is None:
|
||||
continue
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
result[category] += [(var_name, index)]
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.Extractor.extract"><code class="name flex">
|
||||
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def extract(self, instances):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.extractors.InstanceFeaturesExtractor"><code class="flex name class">
|
||||
<span>class <span class="ident">InstanceFeaturesExtractor</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InstanceFeaturesExtractor(Extractor):
|
||||
def extract(self, instances):
|
||||
return np.vstack(
|
||||
[
|
||||
np.hstack(
|
||||
[
|
||||
instance.get_instance_features(),
|
||||
instance.training_data[0]["LP value"],
|
||||
]
|
||||
)
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.InstanceFeaturesExtractor.extract"><code class="name flex">
|
||||
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def extract(self, instances):
|
||||
return np.vstack(
|
||||
[
|
||||
np.hstack(
|
||||
[
|
||||
instance.get_instance_features(),
|
||||
instance.training_data[0]["LP value"],
|
||||
]
|
||||
)
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.extractors.InstanceIterator"><code class="flex name class">
|
||||
<span>class <span class="ident">InstanceIterator</span></span>
|
||||
<span>(</span><span>instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InstanceIterator:
|
||||
def __init__(self, instances):
|
||||
self.instances = instances
|
||||
self.current = 0
|
||||
|
||||
def __iter__(self):
|
||||
return self
|
||||
|
||||
def __next__(self):
|
||||
if self.current >= len(self.instances):
|
||||
raise StopIteration
|
||||
result = self.instances[self.current]
|
||||
self.current += 1
|
||||
if isinstance(result, str):
|
||||
logger.debug("Read: %s" % result)
|
||||
try:
|
||||
if result.endswith(".gz"):
|
||||
with gzip.GzipFile(result, "rb") as file:
|
||||
result = pickle.load(file)
|
||||
else:
|
||||
with open(result, "rb") as file:
|
||||
result = pickle.load(file)
|
||||
except pickle.UnpicklingError:
|
||||
raise Exception(f"Invalid instance file: {result}")
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.extractors.ObjectiveValueExtractor"><code class="flex name class">
|
||||
<span>class <span class="ident">ObjectiveValueExtractor</span></span>
|
||||
<span>(</span><span>kind='lp')</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ObjectiveValueExtractor(Extractor):
|
||||
def __init__(self, kind="lp"):
|
||||
assert kind in ["lower bound", "upper bound", "lp"]
|
||||
self.kind = kind
|
||||
|
||||
def extract(self, instances):
|
||||
if self.kind == "lower bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Lower bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "upper bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Upper bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "lp":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["LP value"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.ObjectiveValueExtractor.extract"><code class="name flex">
|
||||
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def extract(self, instances):
|
||||
if self.kind == "lower bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Lower bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "upper bound":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["Upper bound"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)
|
||||
if self.kind == "lp":
|
||||
return np.array(
|
||||
[
|
||||
[instance.training_data[0]["LP value"]]
|
||||
for instance in InstanceIterator(instances)
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.extractors.SolutionExtractor"><code class="flex name class">
|
||||
<span>class <span class="ident">SolutionExtractor</span></span>
|
||||
<span>(</span><span>relaxation=False)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class SolutionExtractor(Extractor):
|
||||
def __init__(self, relaxation=False):
|
||||
self.relaxation = relaxation
|
||||
|
||||
def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (solution)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
var_split = self.split_variables(instance)
|
||||
if self.relaxation:
|
||||
solution = instance.training_data[0]["LP solution"]
|
||||
else:
|
||||
solution = instance.training_data[0]["Solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
v = solution[var_name][index]
|
||||
if v is None:
|
||||
result[category] += [[0, 0]]
|
||||
else:
|
||||
result[category] += [[1 - v, v]]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.SolutionExtractor.extract"><code class="name flex">
|
||||
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (solution)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
var_split = self.split_variables(instance)
|
||||
if self.relaxation:
|
||||
solution = instance.training_data[0]["LP solution"]
|
||||
else:
|
||||
solution = instance.training_data[0]["Solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
v = solution[var_name][index]
|
||||
if v is None:
|
||||
result[category] += [[0, 0]]
|
||||
else:
|
||||
result[category] += [[1 - v, v]]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.extractors.VariableFeaturesExtractor"><code class="flex name class">
|
||||
<span>class <span class="ident">VariableFeaturesExtractor</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Helper class that provides a standard way to create an ABC using
|
||||
inheritance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class VariableFeaturesExtractor(Extractor):
|
||||
def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (vars)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
instance_features = instance.get_instance_features()
|
||||
var_split = self.split_variables(instance)
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
result[category] += [
|
||||
instance_features.tolist()
|
||||
+ instance.get_variable_features(var_name, index).tolist()
|
||||
+ [lp_solution[var_name][index]]
|
||||
]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.extractors.VariableFeaturesExtractor.extract"><code class="name flex">
|
||||
<span>def <span class="ident">extract</span></span>(<span>self, instances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def extract(self, instances):
|
||||
result = {}
|
||||
for instance in tqdm(
|
||||
InstanceIterator(instances),
|
||||
desc="Extract (vars)",
|
||||
disable=len(instances) < 5,
|
||||
):
|
||||
instance_features = instance.get_instance_features()
|
||||
var_split = self.split_variables(instance)
|
||||
lp_solution = instance.training_data[0]["LP solution"]
|
||||
for (category, var_index_pairs) in var_split.items():
|
||||
if category not in result:
|
||||
result[category] = []
|
||||
for (var_name, index) in var_index_pairs:
|
||||
result[category] += [
|
||||
instance_features.tolist()
|
||||
+ instance.get_variable_features(var_name, index).tolist()
|
||||
+ [lp_solution[var_name][index]]
|
||||
]
|
||||
for category in result:
|
||||
result[category] = np.array(result[category])
|
||||
return result</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.Extractor" href="#miplearn.extractors.Extractor">Extractor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.extractors.Extractor.extract" href="#miplearn.extractors.Extractor.extract">extract</a></code></li>
|
||||
<li><code><a title="miplearn.extractors.Extractor.split_variables" href="#miplearn.extractors.Extractor.split_variables">split_variables</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.InstanceFeaturesExtractor" href="#miplearn.extractors.InstanceFeaturesExtractor">InstanceFeaturesExtractor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.extractors.InstanceFeaturesExtractor.extract" href="#miplearn.extractors.InstanceFeaturesExtractor.extract">extract</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.InstanceIterator" href="#miplearn.extractors.InstanceIterator">InstanceIterator</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.ObjectiveValueExtractor" href="#miplearn.extractors.ObjectiveValueExtractor">ObjectiveValueExtractor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.extractors.ObjectiveValueExtractor.extract" href="#miplearn.extractors.ObjectiveValueExtractor.extract">extract</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.SolutionExtractor" href="#miplearn.extractors.SolutionExtractor">SolutionExtractor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.extractors.SolutionExtractor.extract" href="#miplearn.extractors.SolutionExtractor.extract">extract</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.extractors.VariableFeaturesExtractor" href="#miplearn.extractors.VariableFeaturesExtractor">VariableFeaturesExtractor</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.extractors.VariableFeaturesExtractor.extract" href="#miplearn.extractors.VariableFeaturesExtractor.extract">extract</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
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||||
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from .benchmark import BenchmarkRunner
|
||||
from .classifiers import Classifier, Regressor
|
||||
from .classifiers.adaptive import AdaptiveClassifier
|
||||
from .classifiers.threshold import MinPrecisionThreshold
|
||||
from .components.component import Component
|
||||
from .components.cuts import UserCutsComponent
|
||||
from .components.lazy_dynamic import DynamicLazyConstraintsComponent
|
||||
from .components.lazy_static import StaticLazyConstraintsComponent
|
||||
from .components.objective import ObjectiveValueComponent
|
||||
from .components.primal import PrimalSolutionComponent
|
||||
from .components.relaxation import RelaxationComponent
|
||||
from .components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
|
||||
from .components.steps.drop_redundant import DropRedundantInequalitiesStep
|
||||
from .components.steps.relax_integrality import RelaxIntegralityStep
|
||||
from .extractors import (
|
||||
SolutionExtractor,
|
||||
InstanceFeaturesExtractor,
|
||||
ObjectiveValueExtractor,
|
||||
VariableFeaturesExtractor,
|
||||
)
|
||||
from .instance import Instance
|
||||
from .log import setup_logger
|
||||
from .solvers.gurobi import GurobiSolver
|
||||
from .solvers.internal import InternalSolver
|
||||
from .solvers.learning import LearningSolver
|
||||
from .solvers.pyomo.base import BasePyomoSolver
|
||||
from .solvers.pyomo.cplex import CplexPyomoSolver
|
||||
from .solvers.pyomo.gurobi import GurobiPyomoSolver</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.benchmark" href="benchmark.html">miplearn.benchmark</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.classifiers" href="classifiers/index.html">miplearn.classifiers</a></code></dt>
|
||||
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<header>
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||||
<h1 class="title">Module <code>miplearn.instance</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import gzip
|
||||
import json
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Any, List
|
||||
|
||||
import numpy as np
|
||||
|
||||
from miplearn.types import TrainingSample
|
||||
|
||||
|
||||
class Instance(ABC):
|
||||
"""
|
||||
Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.
|
||||
|
||||
In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.training_data: List[TrainingSample] = []
|
||||
|
||||
@abstractmethod
|
||||
def to_model(self) -> Any:
|
||||
"""
|
||||
Returns the optimization model corresponding to this instance.
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_instance_features(self):
|
||||
"""
|
||||
Returns a 1-dimensional Numpy array of (numerical) features describing the
|
||||
entire instance.
|
||||
|
||||
The array is used by LearningSolver to determine how similar two instances
|
||||
are. It may also be used to predict, in combination with variable-specific
|
||||
features, the values of binary decision variables in the problem.
|
||||
|
||||
There is not necessarily a one-to-one correspondence between models and
|
||||
instance features: the features may encode only part of the data necessary to
|
||||
generate the complete model. Features may also be statistics computed from
|
||||
the original data. For example, in the knapsack problem, an implementation
|
||||
may decide to provide as instance features only the average weights, average
|
||||
prices, number of items and the size of the knapsack.
|
||||
|
||||
The returned array MUST have the same length for all relevant instances of
|
||||
the problem. If two instances map into arrays of different lengths,
|
||||
they cannot be solved by the same LearningSolver object.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
"""
|
||||
Returns a 1-dimensional array of (numerical) features describing a particular
|
||||
decision variable.
|
||||
|
||||
The argument `var` is a pyomo.core.Var object, which represents a collection
|
||||
of decision variables. The argument `index` specifies which variable in the
|
||||
collection is the relevant one.
|
||||
|
||||
In combination with instance features, variable features are used by
|
||||
LearningSolver to predict, among other things, the optimal value of each
|
||||
decision variable before the optimization takes place. In the knapsack
|
||||
problem, for example, an implementation could provide as variable features
|
||||
the weight and the price of a specific item.
|
||||
|
||||
Like instance features, the arrays returned by this method MUST have the same
|
||||
length for all variables within the same category, for all relevant instances
|
||||
of the problem.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
"""
|
||||
Returns the category (a string, an integer or any hashable type) for each
|
||||
decision variable.
|
||||
|
||||
If two variables have the same category, LearningSolver will use the same
|
||||
internal ML model to predict the values of both variables. If the returned
|
||||
category is None, ML models will ignore the variable.
|
||||
|
||||
By default, returns "default".
|
||||
"""
|
||||
return "default"
|
||||
|
||||
def get_constraint_features(self, cid):
|
||||
return np.zeros(1)
|
||||
|
||||
def get_constraint_category(self, cid):
|
||||
return cid
|
||||
|
||||
def has_static_lazy_constraints(self):
|
||||
return False
|
||||
|
||||
def has_dynamic_lazy_constraints(self):
|
||||
return False
|
||||
|
||||
def is_constraint_lazy(self, cid):
|
||||
return False
|
||||
|
||||
def find_violated_lazy_constraints(self, model):
|
||||
"""
|
||||
Returns lazy constraint violations found for the current solution.
|
||||
|
||||
After solving a model, LearningSolver will ask the instance to identify which
|
||||
lazy constraints are violated by the current solution. For each identified
|
||||
violation, LearningSolver will then call the build_lazy_constraint, add the
|
||||
generated Pyomo constraint to the model, then resolve the problem. The
|
||||
process repeats until no further lazy constraint violations are found.
|
||||
|
||||
Each "violation" is simply a string, a tuple or any other hashable type which
|
||||
allows the instance to identify unambiguously which lazy constraint should be
|
||||
generated. In the Traveling Salesman Problem, for example, a subtour
|
||||
violation could be a frozen set containing the cities in the subtour.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
return []
|
||||
|
||||
def build_lazy_constraint(self, model, violation):
|
||||
"""
|
||||
Returns a Pyomo constraint which fixes a given violation.
|
||||
|
||||
This method is typically called immediately after
|
||||
find_violated_lazy_constraints. The violation object provided to this method
|
||||
is exactly the same object returned earlier by
|
||||
find_violated_lazy_constraints. After some training, LearningSolver may
|
||||
decide to proactively build some lazy constraints at the beginning of the
|
||||
optimization process, before a solution is even available. In this case,
|
||||
build_lazy_constraints will be called without a corresponding call to
|
||||
find_violated_lazy_constraints.
|
||||
|
||||
The implementation should not directly add the constraint to the model. The
|
||||
constraint will be added by LearningSolver after the method returns.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
pass
|
||||
|
||||
def find_violated_user_cuts(self, model):
|
||||
return []
|
||||
|
||||
def build_user_cut(self, model, violation):
|
||||
pass
|
||||
|
||||
def load(self, filename):
|
||||
with gzip.GzipFile(filename, "r") as f:
|
||||
data = json.loads(f.read().decode("utf-8"))
|
||||
self.__dict__ = data
|
||||
|
||||
def dump(self, filename):
|
||||
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
|
||||
with gzip.GzipFile(filename, "w") as f:
|
||||
f.write(data)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.instance.Instance"><code class="flex name class">
|
||||
<span>class <span class="ident">Instance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Instance(ABC):
|
||||
"""
|
||||
Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.
|
||||
|
||||
In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
self.training_data: List[TrainingSample] = []
|
||||
|
||||
@abstractmethod
|
||||
def to_model(self) -> Any:
|
||||
"""
|
||||
Returns the optimization model corresponding to this instance.
|
||||
"""
|
||||
pass
|
||||
|
||||
def get_instance_features(self):
|
||||
"""
|
||||
Returns a 1-dimensional Numpy array of (numerical) features describing the
|
||||
entire instance.
|
||||
|
||||
The array is used by LearningSolver to determine how similar two instances
|
||||
are. It may also be used to predict, in combination with variable-specific
|
||||
features, the values of binary decision variables in the problem.
|
||||
|
||||
There is not necessarily a one-to-one correspondence between models and
|
||||
instance features: the features may encode only part of the data necessary to
|
||||
generate the complete model. Features may also be statistics computed from
|
||||
the original data. For example, in the knapsack problem, an implementation
|
||||
may decide to provide as instance features only the average weights, average
|
||||
prices, number of items and the size of the knapsack.
|
||||
|
||||
The returned array MUST have the same length for all relevant instances of
|
||||
the problem. If two instances map into arrays of different lengths,
|
||||
they cannot be solved by the same LearningSolver object.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
"""
|
||||
Returns a 1-dimensional array of (numerical) features describing a particular
|
||||
decision variable.
|
||||
|
||||
The argument `var` is a pyomo.core.Var object, which represents a collection
|
||||
of decision variables. The argument `index` specifies which variable in the
|
||||
collection is the relevant one.
|
||||
|
||||
In combination with instance features, variable features are used by
|
||||
LearningSolver to predict, among other things, the optimal value of each
|
||||
decision variable before the optimization takes place. In the knapsack
|
||||
problem, for example, an implementation could provide as variable features
|
||||
the weight and the price of a specific item.
|
||||
|
||||
Like instance features, the arrays returned by this method MUST have the same
|
||||
length for all variables within the same category, for all relevant instances
|
||||
of the problem.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
"""
|
||||
Returns the category (a string, an integer or any hashable type) for each
|
||||
decision variable.
|
||||
|
||||
If two variables have the same category, LearningSolver will use the same
|
||||
internal ML model to predict the values of both variables. If the returned
|
||||
category is None, ML models will ignore the variable.
|
||||
|
||||
By default, returns "default".
|
||||
"""
|
||||
return "default"
|
||||
|
||||
def get_constraint_features(self, cid):
|
||||
return np.zeros(1)
|
||||
|
||||
def get_constraint_category(self, cid):
|
||||
return cid
|
||||
|
||||
def has_static_lazy_constraints(self):
|
||||
return False
|
||||
|
||||
def has_dynamic_lazy_constraints(self):
|
||||
return False
|
||||
|
||||
def is_constraint_lazy(self, cid):
|
||||
return False
|
||||
|
||||
def find_violated_lazy_constraints(self, model):
|
||||
"""
|
||||
Returns lazy constraint violations found for the current solution.
|
||||
|
||||
After solving a model, LearningSolver will ask the instance to identify which
|
||||
lazy constraints are violated by the current solution. For each identified
|
||||
violation, LearningSolver will then call the build_lazy_constraint, add the
|
||||
generated Pyomo constraint to the model, then resolve the problem. The
|
||||
process repeats until no further lazy constraint violations are found.
|
||||
|
||||
Each "violation" is simply a string, a tuple or any other hashable type which
|
||||
allows the instance to identify unambiguously which lazy constraint should be
|
||||
generated. In the Traveling Salesman Problem, for example, a subtour
|
||||
violation could be a frozen set containing the cities in the subtour.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
return []
|
||||
|
||||
def build_lazy_constraint(self, model, violation):
|
||||
"""
|
||||
Returns a Pyomo constraint which fixes a given violation.
|
||||
|
||||
This method is typically called immediately after
|
||||
find_violated_lazy_constraints. The violation object provided to this method
|
||||
is exactly the same object returned earlier by
|
||||
find_violated_lazy_constraints. After some training, LearningSolver may
|
||||
decide to proactively build some lazy constraints at the beginning of the
|
||||
optimization process, before a solution is even available. In this case,
|
||||
build_lazy_constraints will be called without a corresponding call to
|
||||
find_violated_lazy_constraints.
|
||||
|
||||
The implementation should not directly add the constraint to the model. The
|
||||
constraint will be added by LearningSolver after the method returns.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
pass
|
||||
|
||||
def find_violated_user_cuts(self, model):
|
||||
return []
|
||||
|
||||
def build_user_cut(self, model, violation):
|
||||
pass
|
||||
|
||||
def load(self, filename):
|
||||
with gzip.GzipFile(filename, "r") as f:
|
||||
data = json.loads(f.read().decode("utf-8"))
|
||||
self.__dict__ = data
|
||||
|
||||
def dump(self, filename):
|
||||
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
|
||||
with gzip.GzipFile(filename, "w") as f:
|
||||
f.write(data)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></li>
|
||||
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="problems/knapsack.html#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
|
||||
<li><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></li>
|
||||
<li><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="solvers/tests/index.html#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></li>
|
||||
<li><a title="miplearn.problems.stab.MaxWeightStableSetInstance" href="problems/stab.html#miplearn.problems.stab.MaxWeightStableSetInstance">MaxWeightStableSetInstance</a></li>
|
||||
<li><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="problems/tsp.html#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></li>
|
||||
<li><a title="miplearn.components.steps.tests.test_convert_tight.SampleInstance" href="components/steps/tests/test_convert_tight.html#miplearn.components.steps.tests.test_convert_tight.SampleInstance">SampleInstance</a></li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.instance.Instance.build_lazy_constraint"><code class="name flex">
|
||||
<span>def <span class="ident">build_lazy_constraint</span></span>(<span>self, model, violation)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns a Pyomo constraint which fixes a given violation.</p>
|
||||
<p>This method is typically called immediately after
|
||||
find_violated_lazy_constraints. The violation object provided to this method
|
||||
is exactly the same object returned earlier by
|
||||
find_violated_lazy_constraints. After some training, LearningSolver may
|
||||
decide to proactively build some lazy constraints at the beginning of the
|
||||
optimization process, before a solution is even available. In this case,
|
||||
build_lazy_constraints will be called without a corresponding call to
|
||||
find_violated_lazy_constraints.</p>
|
||||
<p>The implementation should not directly add the constraint to the model. The
|
||||
constraint will be added by LearningSolver after the method returns.</p>
|
||||
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def build_lazy_constraint(self, model, violation):
|
||||
"""
|
||||
Returns a Pyomo constraint which fixes a given violation.
|
||||
|
||||
This method is typically called immediately after
|
||||
find_violated_lazy_constraints. The violation object provided to this method
|
||||
is exactly the same object returned earlier by
|
||||
find_violated_lazy_constraints. After some training, LearningSolver may
|
||||
decide to proactively build some lazy constraints at the beginning of the
|
||||
optimization process, before a solution is even available. In this case,
|
||||
build_lazy_constraints will be called without a corresponding call to
|
||||
find_violated_lazy_constraints.
|
||||
|
||||
The implementation should not directly add the constraint to the model. The
|
||||
constraint will be added by LearningSolver after the method returns.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.build_user_cut"><code class="name flex">
|
||||
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def build_user_cut(self, model, violation):
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.dump"><code class="name flex">
|
||||
<span>def <span class="ident">dump</span></span>(<span>self, filename)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def dump(self, filename):
|
||||
data = json.dumps(self.__dict__, indent=2).encode("utf-8")
|
||||
with gzip.GzipFile(filename, "w") as f:
|
||||
f.write(data)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.find_violated_lazy_constraints"><code class="name flex">
|
||||
<span>def <span class="ident">find_violated_lazy_constraints</span></span>(<span>self, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns lazy constraint violations found for the current solution.</p>
|
||||
<p>After solving a model, LearningSolver will ask the instance to identify which
|
||||
lazy constraints are violated by the current solution. For each identified
|
||||
violation, LearningSolver will then call the build_lazy_constraint, add the
|
||||
generated Pyomo constraint to the model, then resolve the problem. The
|
||||
process repeats until no further lazy constraint violations are found.</p>
|
||||
<p>Each "violation" is simply a string, a tuple or any other hashable type which
|
||||
allows the instance to identify unambiguously which lazy constraint should be
|
||||
generated. In the Traveling Salesman Problem, for example, a subtour
|
||||
violation could be a frozen set containing the cities in the subtour.</p>
|
||||
<p>For a concrete example, see TravelingSalesmanInstance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def find_violated_lazy_constraints(self, model):
|
||||
"""
|
||||
Returns lazy constraint violations found for the current solution.
|
||||
|
||||
After solving a model, LearningSolver will ask the instance to identify which
|
||||
lazy constraints are violated by the current solution. For each identified
|
||||
violation, LearningSolver will then call the build_lazy_constraint, add the
|
||||
generated Pyomo constraint to the model, then resolve the problem. The
|
||||
process repeats until no further lazy constraint violations are found.
|
||||
|
||||
Each "violation" is simply a string, a tuple or any other hashable type which
|
||||
allows the instance to identify unambiguously which lazy constraint should be
|
||||
generated. In the Traveling Salesman Problem, for example, a subtour
|
||||
violation could be a frozen set containing the cities in the subtour.
|
||||
|
||||
For a concrete example, see TravelingSalesmanInstance.
|
||||
"""
|
||||
return []</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.find_violated_user_cuts"><code class="name flex">
|
||||
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def find_violated_user_cuts(self, model):
|
||||
return []</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.get_constraint_category"><code class="name flex">
|
||||
<span>def <span class="ident">get_constraint_category</span></span>(<span>self, cid)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_constraint_category(self, cid):
|
||||
return cid</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.get_constraint_features"><code class="name flex">
|
||||
<span>def <span class="ident">get_constraint_features</span></span>(<span>self, cid)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_constraint_features(self, cid):
|
||||
return np.zeros(1)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.get_instance_features"><code class="name flex">
|
||||
<span>def <span class="ident">get_instance_features</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns a 1-dimensional Numpy array of (numerical) features describing the
|
||||
entire instance.</p>
|
||||
<p>The array is used by LearningSolver to determine how similar two instances
|
||||
are. It may also be used to predict, in combination with variable-specific
|
||||
features, the values of binary decision variables in the problem.</p>
|
||||
<p>There is not necessarily a one-to-one correspondence between models and
|
||||
instance features: the features may encode only part of the data necessary to
|
||||
generate the complete model. Features may also be statistics computed from
|
||||
the original data. For example, in the knapsack problem, an implementation
|
||||
may decide to provide as instance features only the average weights, average
|
||||
prices, number of items and the size of the knapsack.</p>
|
||||
<p>The returned array MUST have the same length for all relevant instances of
|
||||
the problem. If two instances map into arrays of different lengths,
|
||||
they cannot be solved by the same LearningSolver object.</p>
|
||||
<p>By default, returns [0].</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_instance_features(self):
|
||||
"""
|
||||
Returns a 1-dimensional Numpy array of (numerical) features describing the
|
||||
entire instance.
|
||||
|
||||
The array is used by LearningSolver to determine how similar two instances
|
||||
are. It may also be used to predict, in combination with variable-specific
|
||||
features, the values of binary decision variables in the problem.
|
||||
|
||||
There is not necessarily a one-to-one correspondence between models and
|
||||
instance features: the features may encode only part of the data necessary to
|
||||
generate the complete model. Features may also be statistics computed from
|
||||
the original data. For example, in the knapsack problem, an implementation
|
||||
may decide to provide as instance features only the average weights, average
|
||||
prices, number of items and the size of the knapsack.
|
||||
|
||||
The returned array MUST have the same length for all relevant instances of
|
||||
the problem. If two instances map into arrays of different lengths,
|
||||
they cannot be solved by the same LearningSolver object.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.get_variable_category"><code class="name flex">
|
||||
<span>def <span class="ident">get_variable_category</span></span>(<span>self, var, index)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the category (a string, an integer or any hashable type) for each
|
||||
decision variable.</p>
|
||||
<p>If two variables have the same category, LearningSolver will use the same
|
||||
internal ML model to predict the values of both variables. If the returned
|
||||
category is None, ML models will ignore the variable.</p>
|
||||
<p>By default, returns "default".</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_variable_category(self, var, index):
|
||||
"""
|
||||
Returns the category (a string, an integer or any hashable type) for each
|
||||
decision variable.
|
||||
|
||||
If two variables have the same category, LearningSolver will use the same
|
||||
internal ML model to predict the values of both variables. If the returned
|
||||
category is None, ML models will ignore the variable.
|
||||
|
||||
By default, returns "default".
|
||||
"""
|
||||
return "default"</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.get_variable_features"><code class="name flex">
|
||||
<span>def <span class="ident">get_variable_features</span></span>(<span>self, var, index)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns a 1-dimensional array of (numerical) features describing a particular
|
||||
decision variable.</p>
|
||||
<p>The argument <code>var</code> is a pyomo.core.Var object, which represents a collection
|
||||
of decision variables. The argument <code>index</code> specifies which variable in the
|
||||
collection is the relevant one.</p>
|
||||
<p>In combination with instance features, variable features are used by
|
||||
LearningSolver to predict, among other things, the optimal value of each
|
||||
decision variable before the optimization takes place. In the knapsack
|
||||
problem, for example, an implementation could provide as variable features
|
||||
the weight and the price of a specific item.</p>
|
||||
<p>Like instance features, the arrays returned by this method MUST have the same
|
||||
length for all variables within the same category, for all relevant instances
|
||||
of the problem.</p>
|
||||
<p>By default, returns [0].</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_variable_features(self, var, index):
|
||||
"""
|
||||
Returns a 1-dimensional array of (numerical) features describing a particular
|
||||
decision variable.
|
||||
|
||||
The argument `var` is a pyomo.core.Var object, which represents a collection
|
||||
of decision variables. The argument `index` specifies which variable in the
|
||||
collection is the relevant one.
|
||||
|
||||
In combination with instance features, variable features are used by
|
||||
LearningSolver to predict, among other things, the optimal value of each
|
||||
decision variable before the optimization takes place. In the knapsack
|
||||
problem, for example, an implementation could provide as variable features
|
||||
the weight and the price of a specific item.
|
||||
|
||||
Like instance features, the arrays returned by this method MUST have the same
|
||||
length for all variables within the same category, for all relevant instances
|
||||
of the problem.
|
||||
|
||||
By default, returns [0].
|
||||
"""
|
||||
return np.zeros(1)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.has_dynamic_lazy_constraints"><code class="name flex">
|
||||
<span>def <span class="ident">has_dynamic_lazy_constraints</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def has_dynamic_lazy_constraints(self):
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.has_static_lazy_constraints"><code class="name flex">
|
||||
<span>def <span class="ident">has_static_lazy_constraints</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def has_static_lazy_constraints(self):
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.is_constraint_lazy"><code class="name flex">
|
||||
<span>def <span class="ident">is_constraint_lazy</span></span>(<span>self, cid)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def is_constraint_lazy(self, cid):
|
||||
return False</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.load"><code class="name flex">
|
||||
<span>def <span class="ident">load</span></span>(<span>self, filename)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def load(self, filename):
|
||||
with gzip.GzipFile(filename, "r") as f:
|
||||
data = json.loads(f.read().decode("utf-8"))
|
||||
self.__dict__ = data</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.instance.Instance.to_model"><code class="name flex">
|
||||
<span>def <span class="ident">to_model</span></span>(<span>self)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Returns the optimization model corresponding to this instance.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">@abstractmethod
|
||||
def to_model(self) -> Any:
|
||||
"""
|
||||
Returns the optimization model corresponding to this instance.
|
||||
"""
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.instance.Instance" href="#miplearn.instance.Instance">Instance</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.build_user_cut" href="#miplearn.instance.Instance.build_user_cut">build_user_cut</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.dump" href="#miplearn.instance.Instance.dump">dump</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_user_cuts" href="#miplearn.instance.Instance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_constraint_category" href="#miplearn.instance.Instance.get_constraint_category">get_constraint_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_constraint_features" href="#miplearn.instance.Instance.get_constraint_features">get_constraint_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.has_dynamic_lazy_constraints" href="#miplearn.instance.Instance.has_dynamic_lazy_constraints">has_dynamic_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.has_static_lazy_constraints" href="#miplearn.instance.Instance.has_static_lazy_constraints">has_static_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.is_constraint_lazy" href="#miplearn.instance.Instance.is_constraint_lazy">is_constraint_lazy</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.load" href="#miplearn.instance.Instance.load">load</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
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</main>
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.log</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import sys
|
||||
import time
|
||||
|
||||
|
||||
class TimeFormatter(logging.Formatter):
|
||||
def __init__(self, start_time, log_colors):
|
||||
super().__init__()
|
||||
self.start_time = start_time
|
||||
self.log_colors = log_colors
|
||||
|
||||
def format(self, record):
|
||||
if record.levelno >= logging.ERROR:
|
||||
color = self.log_colors["red"]
|
||||
elif record.levelno >= logging.WARNING:
|
||||
color = self.log_colors["yellow"]
|
||||
else:
|
||||
color = self.log_colors["green"]
|
||||
return "%s[%12.3f]%s %s" % (
|
||||
color,
|
||||
record.created - self.start_time,
|
||||
self.log_colors["reset"],
|
||||
record.getMessage(),
|
||||
)
|
||||
|
||||
|
||||
def setup_logger(start_time=None, force_color=False):
|
||||
if start_time is None:
|
||||
start_time = time.time()
|
||||
if sys.stdout.isatty() or force_color:
|
||||
log_colors = {
|
||||
"green": "\033[92m",
|
||||
"yellow": "\033[93m",
|
||||
"red": "\033[91m",
|
||||
"reset": "\033[0m",
|
||||
}
|
||||
else:
|
||||
log_colors = {
|
||||
"green": "",
|
||||
"yellow": "",
|
||||
"red": "",
|
||||
"reset": "",
|
||||
}
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(TimeFormatter(start_time, log_colors))
|
||||
logging.getLogger().addHandler(handler)
|
||||
logging.getLogger("miplearn").setLevel(logging.INFO)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.log.setup_logger"><code class="name flex">
|
||||
<span>def <span class="ident">setup_logger</span></span>(<span>start_time=None, force_color=False)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def setup_logger(start_time=None, force_color=False):
|
||||
if start_time is None:
|
||||
start_time = time.time()
|
||||
if sys.stdout.isatty() or force_color:
|
||||
log_colors = {
|
||||
"green": "\033[92m",
|
||||
"yellow": "\033[93m",
|
||||
"red": "\033[91m",
|
||||
"reset": "\033[0m",
|
||||
}
|
||||
else:
|
||||
log_colors = {
|
||||
"green": "",
|
||||
"yellow": "",
|
||||
"red": "",
|
||||
"reset": "",
|
||||
}
|
||||
handler = logging.StreamHandler()
|
||||
handler.setFormatter(TimeFormatter(start_time, log_colors))
|
||||
logging.getLogger().addHandler(handler)
|
||||
logging.getLogger("miplearn").setLevel(logging.INFO)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.log.TimeFormatter"><code class="flex name class">
|
||||
<span>class <span class="ident">TimeFormatter</span></span>
|
||||
<span>(</span><span>start_time, log_colors)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Formatter instances are used to convert a LogRecord to text.</p>
|
||||
<p>Formatters need to know how a LogRecord is constructed. They are
|
||||
responsible for converting a LogRecord to (usually) a string which can
|
||||
be interpreted by either a human or an external system. The base Formatter
|
||||
allows a formatting string to be specified. If none is supplied, the
|
||||
the style-dependent default value, "%(message)s", "{message}", or
|
||||
"${message}", is used.</p>
|
||||
<p>The Formatter can be initialized with a format string which makes use of
|
||||
knowledge of the LogRecord attributes - e.g. the default value mentioned
|
||||
above makes use of the fact that the user's message and arguments are pre-
|
||||
formatted into a LogRecord's message attribute. Currently, the useful
|
||||
attributes in a LogRecord are described by:</p>
|
||||
<p>%(name)s
|
||||
Name of the logger (logging channel)
|
||||
%(levelno)s
|
||||
Numeric logging level for the message (DEBUG, INFO,
|
||||
WARNING, ERROR, CRITICAL)
|
||||
%(levelname)s
|
||||
Text logging level for the message ("DEBUG", "INFO",
|
||||
"WARNING", "ERROR", "CRITICAL")
|
||||
%(pathname)s
|
||||
Full pathname of the source file where the logging
|
||||
call was issued (if available)
|
||||
%(filename)s
|
||||
Filename portion of pathname
|
||||
%(module)s
|
||||
Module (name portion of filename)
|
||||
%(lineno)d
|
||||
Source line number where the logging call was issued
|
||||
(if available)
|
||||
%(funcName)s
|
||||
Function name
|
||||
%(created)f
|
||||
Time when the LogRecord was created (time.time()
|
||||
return value)
|
||||
%(asctime)s
|
||||
Textual time when the LogRecord was created
|
||||
%(msecs)d
|
||||
Millisecond portion of the creation time
|
||||
%(relativeCreated)d Time in milliseconds when the LogRecord was created,
|
||||
relative to the time the logging module was loaded
|
||||
(typically at application startup time)
|
||||
%(thread)d
|
||||
Thread ID (if available)
|
||||
%(threadName)s
|
||||
Thread name (if available)
|
||||
%(process)d
|
||||
Process ID (if available)
|
||||
%(message)s
|
||||
The result of record.getMessage(), computed just as
|
||||
the record is emitted</p>
|
||||
<p>Initialize the formatter with specified format strings.</p>
|
||||
<p>Initialize the formatter either with the specified format string, or a
|
||||
default as described above. Allow for specialized date formatting with
|
||||
the optional datefmt argument. If datefmt is omitted, you get an
|
||||
ISO8601-like (or RFC 3339-like) format.</p>
|
||||
<p>Use a style parameter of '%', '{' or '$' to specify that you want to
|
||||
use one of %-formatting, :meth:<code>str.format</code> (<code>{}</code>) formatting or
|
||||
:class:<code>string.Template</code> formatting in your format string.</p>
|
||||
<div class="admonition versionchanged">
|
||||
<p class="admonition-title">Changed in version: 3.2</p>
|
||||
<p>Added the <code>style</code> parameter.</p>
|
||||
</div></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class TimeFormatter(logging.Formatter):
|
||||
def __init__(self, start_time, log_colors):
|
||||
super().__init__()
|
||||
self.start_time = start_time
|
||||
self.log_colors = log_colors
|
||||
|
||||
def format(self, record):
|
||||
if record.levelno >= logging.ERROR:
|
||||
color = self.log_colors["red"]
|
||||
elif record.levelno >= logging.WARNING:
|
||||
color = self.log_colors["yellow"]
|
||||
else:
|
||||
color = self.log_colors["green"]
|
||||
return "%s[%12.3f]%s %s" % (
|
||||
color,
|
||||
record.created - self.start_time,
|
||||
self.log_colors["reset"],
|
||||
record.getMessage(),
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>logging.Formatter</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.log.TimeFormatter.format"><code class="name flex">
|
||||
<span>def <span class="ident">format</span></span>(<span>self, record)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Format the specified record as text.</p>
|
||||
<p>The record's attribute dictionary is used as the operand to a
|
||||
string formatting operation which yields the returned string.
|
||||
Before formatting the dictionary, a couple of preparatory steps
|
||||
are carried out. The message attribute of the record is computed
|
||||
using LogRecord.getMessage(). If the formatting string uses the
|
||||
time (as determined by a call to usesTime(), formatTime() is
|
||||
called to format the event time. If there is exception information,
|
||||
it is formatted using formatException() and appended to the message.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def format(self, record):
|
||||
if record.levelno >= logging.ERROR:
|
||||
color = self.log_colors["red"]
|
||||
elif record.levelno >= logging.WARNING:
|
||||
color = self.log_colors["yellow"]
|
||||
else:
|
||||
color = self.log_colors["green"]
|
||||
return "%s[%12.3f]%s %s" % (
|
||||
color,
|
||||
record.created - self.start_time,
|
||||
self.log_colors["reset"],
|
||||
record.getMessage(),
|
||||
)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.log.setup_logger" href="#miplearn.log.setup_logger">setup_logger</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
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||||
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|
||||
<h4><code><a title="miplearn.log.TimeFormatter" href="#miplearn.log.TimeFormatter">TimeFormatter</a></code></h4>
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<ul class="">
|
||||
<li><code><a title="miplearn.log.TimeFormatter.format" href="#miplearn.log.TimeFormatter.format">format</a></code></li>
|
||||
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<main>
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<article id="content">
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<header>
|
||||
<h1 class="title">Module <code>miplearn.problems</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tsp" href="tsp.html">miplearn.problems.tsp</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
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<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.knapsack" href="knapsack.html">miplearn.problems.knapsack</a></code></li>
|
||||
<li><code><a title="miplearn.problems.stab" href="stab.html">miplearn.problems.stab</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests" href="tests/index.html">miplearn.problems.tests</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tsp" href="tsp.html">miplearn.problems.tsp</a></code></li>
|
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|
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<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.knapsack</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
import pyomo.environ as pe
|
||||
from scipy.stats import uniform, randint
|
||||
from scipy.stats.distributions import rv_frozen
|
||||
|
||||
from miplearn.instance import Instance
|
||||
|
||||
|
||||
class ChallengeA:
|
||||
"""
|
||||
- 250 variables, 10 constraints, fixed weights
|
||||
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
|
||||
- K = 500, u ~ U(0., 1.)
|
||||
- alpha = 0.25
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.gen = MultiKnapsackGenerator(
|
||||
n=randint(low=250, high=251),
|
||||
m=randint(low=10, high=11),
|
||||
w=uniform(loc=0.0, scale=1000.0),
|
||||
K=uniform(loc=500.0, scale=0.0),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=True,
|
||||
w_jitter=uniform(loc=0.95, scale=0.1),
|
||||
)
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.gen.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.gen.generate(n_test_instances)
|
||||
|
||||
|
||||
class MultiKnapsackInstance(Instance):
|
||||
"""Representation of the Multidimensional 0-1 Knapsack Problem.
|
||||
|
||||
Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
|
||||
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
||||
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
|
||||
|
||||
This implementation assigns a different category for each decision variable, and therefore
|
||||
trains one ML model per variable. It is only suitable when training and test instances have
|
||||
same size and items don't shuffle around.
|
||||
"""
|
||||
|
||||
def __init__(self, prices, capacities, weights):
|
||||
super().__init__()
|
||||
assert isinstance(prices, np.ndarray)
|
||||
assert isinstance(capacities, np.ndarray)
|
||||
assert isinstance(weights, np.ndarray)
|
||||
assert len(weights.shape) == 2
|
||||
self.m, self.n = weights.shape
|
||||
assert prices.shape == (self.n,)
|
||||
assert capacities.shape == (self.m,)
|
||||
self.prices = prices
|
||||
self.capacities = capacities
|
||||
self.weights = weights
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(range(self.n), domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
|
||||
sense=pe.maximize,
|
||||
)
|
||||
model.eq_capacity = pe.ConstraintList()
|
||||
for i in range(self.m):
|
||||
model.eq_capacity.add(
|
||||
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
|
||||
<= self.capacities[i]
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.hstack(
|
||||
[
|
||||
np.mean(self.prices),
|
||||
self.capacities,
|
||||
]
|
||||
)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
return np.hstack(
|
||||
[
|
||||
self.prices[index],
|
||||
self.weights[:, index],
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
# def get_variable_category(self, var, index):
|
||||
# return index
|
||||
|
||||
|
||||
class MultiKnapsackGenerator:
|
||||
def __init__(
|
||||
self,
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=500),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=False,
|
||||
w_jitter=uniform(loc=1.0, scale=0.0),
|
||||
round=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions `n` and `m`,
|
||||
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
|
||||
the provided distribution `w`. The capacity of knapsack `j` is set to:
|
||||
|
||||
alpha_j * sum(w[i,j] for i in range(n)),
|
||||
|
||||
where `alpha_j`, the tightness ratio, is sampled from the provided probability
|
||||
distribution `alpha`. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item `i` is set to:
|
||||
|
||||
sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
|
||||
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that `K` is only sample once for the
|
||||
entire instance.
|
||||
|
||||
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.
|
||||
|
||||
If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.
|
||||
|
||||
By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If `round=False` is provided, this rounding will be disabled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of items (or variables)
|
||||
m: rv_discrete
|
||||
Probability distribution for the number of knapsacks (or constraints)
|
||||
w: rv_continuous
|
||||
Probability distribution for the item weights
|
||||
K: rv_continuous
|
||||
Probability distribution for the profit correlation coefficient
|
||||
u: rv_continuous
|
||||
Probability distribution for the profit multiplier
|
||||
alpha: rv_continuous
|
||||
Probability distribution for the tightness ratio
|
||||
fix_w: boolean
|
||||
If true, weights are kept the same (minus the noise from w_jitter) in all instances
|
||||
w_jitter: rv_continuous
|
||||
Probability distribution for random noise added to the weights
|
||||
round: boolean
|
||||
If true, all prices, weights and capacities are rounded to the nearest integer
|
||||
"""
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
|
||||
assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
alpha, rv_frozen
|
||||
), "alpha should be a SciPy probability distribution"
|
||||
assert isinstance(fix_w, bool), "fix_w should be boolean"
|
||||
assert isinstance(
|
||||
w_jitter, rv_frozen
|
||||
), "w_jitter should be a SciPy probability distribution"
|
||||
|
||||
self.n = n
|
||||
self.m = m
|
||||
self.w = w
|
||||
self.K = K
|
||||
self.u = u
|
||||
self.alpha = alpha
|
||||
self.w_jitter = w_jitter
|
||||
self.round = round
|
||||
|
||||
if fix_w:
|
||||
self.fix_n = self.n.rvs()
|
||||
self.fix_m = self.m.rvs()
|
||||
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
|
||||
self.fix_u = self.u.rvs(self.fix_n)
|
||||
self.fix_K = self.K.rvs()
|
||||
else:
|
||||
self.fix_n = None
|
||||
self.fix_m = None
|
||||
self.fix_w = None
|
||||
self.fix_u = None
|
||||
self.fix_K = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
|
||||
class KnapsackInstance(Instance):
|
||||
"""
|
||||
Simpler (one-dimensional) Knapsack Problem, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__()
|
||||
self.weights = weights
|
||||
self.prices = prices
|
||||
self.capacity = capacity
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
items = range(len(self.weights))
|
||||
model.x = pe.Var(items, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
|
||||
)
|
||||
model.eq_capacity = pe.Constraint(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
|
||||
)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.array(
|
||||
[
|
||||
self.capacity,
|
||||
np.average(self.weights),
|
||||
]
|
||||
)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
return np.array(
|
||||
[
|
||||
self.weights[index],
|
||||
self.prices[index],
|
||||
]
|
||||
)
|
||||
|
||||
|
||||
class GurobiKnapsackInstance(KnapsackInstance):
|
||||
"""
|
||||
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__(weights, prices, capacity)
|
||||
|
||||
def to_model(self):
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model("Knapsack")
|
||||
n = len(self.weights)
|
||||
x = model.addVars(n, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(
|
||||
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
|
||||
"eq_capacity",
|
||||
)
|
||||
model.setObjective(
|
||||
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
|
||||
)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.knapsack.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><ul>
|
||||
<li>250 variables, 10 constraints, fixed weights</li>
|
||||
<li>w ~ U(0, 1000), jitter ~ U(0.95, 1.05)</li>
|
||||
<li>K = 500, u ~ U(0., 1.)</li>
|
||||
<li>alpha = 0.25</li>
|
||||
</ul></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ChallengeA:
|
||||
"""
|
||||
- 250 variables, 10 constraints, fixed weights
|
||||
- w ~ U(0, 1000), jitter ~ U(0.95, 1.05)
|
||||
- K = 500, u ~ U(0., 1.)
|
||||
- alpha = 0.25
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.gen = MultiKnapsackGenerator(
|
||||
n=randint(low=250, high=251),
|
||||
m=randint(low=10, high=11),
|
||||
w=uniform(loc=0.0, scale=1000.0),
|
||||
K=uniform(loc=500.0, scale=0.0),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=True,
|
||||
w_jitter=uniform(loc=0.95, scale=0.1),
|
||||
)
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.gen.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.gen.generate(n_test_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.GurobiKnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">GurobiKnapsackInstance</span></span>
|
||||
<span>(</span><span>weights, prices, capacity)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class GurobiKnapsackInstance(KnapsackInstance):
|
||||
"""
|
||||
Simpler (one-dimensional) knapsack instance, implemented directly in Gurobi
|
||||
instead of Pyomo, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__(weights, prices, capacity)
|
||||
|
||||
def to_model(self):
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model("Knapsack")
|
||||
n = len(self.weights)
|
||||
x = model.addVars(n, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(
|
||||
gp.quicksum(x[i] * self.weights[i] for i in range(n)) <= self.capacity,
|
||||
"eq_capacity",
|
||||
)
|
||||
model.setObjective(
|
||||
gp.quicksum(x[i] * self.prices[i] for i in range(n)), GRB.MAXIMIZE
|
||||
)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></li>
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.problems.knapsack.KnapsackInstance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.KnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">KnapsackInstance</span></span>
|
||||
<span>(</span><span>weights, prices, capacity)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Simpler (one-dimensional) Knapsack Problem, used for testing.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class KnapsackInstance(Instance):
|
||||
"""
|
||||
Simpler (one-dimensional) Knapsack Problem, used for testing.
|
||||
"""
|
||||
|
||||
def __init__(self, weights, prices, capacity):
|
||||
super().__init__()
|
||||
self.weights = weights
|
||||
self.prices = prices
|
||||
self.capacity = capacity
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
items = range(len(self.weights))
|
||||
model.x = pe.Var(items, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.prices[v] for v in items), sense=pe.maximize
|
||||
)
|
||||
model.eq_capacity = pe.Constraint(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in items) <= self.capacity
|
||||
)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.array(
|
||||
[
|
||||
self.capacity,
|
||||
np.average(self.weights),
|
||||
]
|
||||
)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
return np.array(
|
||||
[
|
||||
self.weights[index],
|
||||
self.prices[index],
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">MultiKnapsackGenerator</span></span>
|
||||
<span>(</span><span>n=<scipy.stats._distn_infrastructure.rv_frozen object>, m=<scipy.stats._distn_infrastructure.rv_frozen object>, w=<scipy.stats._distn_infrastructure.rv_frozen object>, K=<scipy.stats._distn_infrastructure.rv_frozen object>, u=<scipy.stats._distn_infrastructure.rv_frozen object>, alpha=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_w=False, w_jitter=<scipy.stats._distn_infrastructure.rv_frozen object>, round=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Initialize the problem generator.</p>
|
||||
<p>Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions <code>n</code> and <code>m</code>,
|
||||
respectively. The weight of each item <code>i</code> on knapsack <code>j</code> is sampled independently from
|
||||
the provided distribution <code>w</code>. The capacity of knapsack <code>j</code> is set to:</p>
|
||||
<pre><code>alpha_j * sum(w[i,j] for i in range(n)),
|
||||
</code></pre>
|
||||
<p>where <code>alpha_j</code>, the tightness ratio, is sampled from the provided probability
|
||||
distribution <code>alpha</code>. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item <code>i</code> is set to:</p>
|
||||
<pre><code>sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
</code></pre>
|
||||
<p>where <code>K</code>, the correlation coefficient, and <code>u_i</code>, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that <code>K</code> is only sample once for the
|
||||
entire instance.</p>
|
||||
<p>If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.</p>
|
||||
<p>If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.</p>
|
||||
<p>By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If <code>round=False</code> is provided, this rounding will be disabled.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of items (or variables)</dd>
|
||||
<dt><strong><code>m</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of knapsacks (or constraints)</dd>
|
||||
<dt><strong><code>w</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the item weights</dd>
|
||||
<dt><strong><code>K</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the profit correlation coefficient</dd>
|
||||
<dt><strong><code>u</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the profit multiplier</dd>
|
||||
<dt><strong><code>alpha</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the tightness ratio</dd>
|
||||
<dt><strong><code>fix_w</code></strong> : <code>boolean</code></dt>
|
||||
<dd>If true, weights are kept the same (minus the noise from w_jitter) in all instances</dd>
|
||||
<dt><strong><code>w_jitter</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for random noise added to the weights</dd>
|
||||
<dt><strong><code>round</code></strong> : <code>boolean</code></dt>
|
||||
<dd>If true, all prices, weights and capacities are rounded to the nearest integer</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MultiKnapsackGenerator:
|
||||
def __init__(
|
||||
self,
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=500),
|
||||
u=uniform(loc=0.0, scale=1.0),
|
||||
alpha=uniform(loc=0.25, scale=0.0),
|
||||
fix_w=False,
|
||||
w_jitter=uniform(loc=1.0, scale=0.0),
|
||||
round=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Instances have a random number of items (or variables) and a random number of knapsacks
|
||||
(or constraints), as specified by the provided probability distributions `n` and `m`,
|
||||
respectively. The weight of each item `i` on knapsack `j` is sampled independently from
|
||||
the provided distribution `w`. The capacity of knapsack `j` is set to:
|
||||
|
||||
alpha_j * sum(w[i,j] for i in range(n)),
|
||||
|
||||
where `alpha_j`, the tightness ratio, is sampled from the provided probability
|
||||
distribution `alpha`. To make the instances more challenging, the costs of the items
|
||||
are linearly correlated to their average weights. More specifically, the weight of each
|
||||
item `i` is set to:
|
||||
|
||||
sum(w[i,j]/m for j in range(m)) + K * u_i,
|
||||
|
||||
where `K`, the correlation coefficient, and `u_i`, the correlation multiplier, are sampled
|
||||
from the provided probability distributions. Note that `K` is only sample once for the
|
||||
entire instance.
|
||||
|
||||
If fix_w=True is provided, then w[i,j] are kept the same in all generated instances. This
|
||||
also implies that n and m are kept fixed. Although the prices and capacities are derived
|
||||
from w[i,j], as long as u and K are not constants, the generated instances will still not
|
||||
be completely identical.
|
||||
|
||||
If a probability distribution w_jitter is provided, then item weights will be set to
|
||||
w[i,j] * gamma[i,j] where gamma[i,j] is sampled from w_jitter. When combined with
|
||||
fix_w=True, this argument may be used to generate instances where the weight of each item
|
||||
is roughly the same, but not exactly identical, across all instances. The prices of the
|
||||
items and the capacities of the knapsacks will be calculated as above, but using these
|
||||
perturbed weights instead.
|
||||
|
||||
By default, all generated prices, weights and capacities are rounded to the nearest integer
|
||||
number. If `round=False` is provided, this rounding will be disabled.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of items (or variables)
|
||||
m: rv_discrete
|
||||
Probability distribution for the number of knapsacks (or constraints)
|
||||
w: rv_continuous
|
||||
Probability distribution for the item weights
|
||||
K: rv_continuous
|
||||
Probability distribution for the profit correlation coefficient
|
||||
u: rv_continuous
|
||||
Probability distribution for the profit multiplier
|
||||
alpha: rv_continuous
|
||||
Probability distribution for the tightness ratio
|
||||
fix_w: boolean
|
||||
If true, weights are kept the same (minus the noise from w_jitter) in all instances
|
||||
w_jitter: rv_continuous
|
||||
Probability distribution for random noise added to the weights
|
||||
round: boolean
|
||||
If true, all prices, weights and capacities are rounded to the nearest integer
|
||||
"""
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(m, rv_frozen), "m should be a SciPy probability distribution"
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(K, rv_frozen), "K should be a SciPy probability distribution"
|
||||
assert isinstance(u, rv_frozen), "u should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
alpha, rv_frozen
|
||||
), "alpha should be a SciPy probability distribution"
|
||||
assert isinstance(fix_w, bool), "fix_w should be boolean"
|
||||
assert isinstance(
|
||||
w_jitter, rv_frozen
|
||||
), "w_jitter should be a SciPy probability distribution"
|
||||
|
||||
self.n = n
|
||||
self.m = m
|
||||
self.w = w
|
||||
self.K = K
|
||||
self.u = u
|
||||
self.alpha = alpha
|
||||
self.w_jitter = w_jitter
|
||||
self.round = round
|
||||
|
||||
if fix_w:
|
||||
self.fix_n = self.n.rvs()
|
||||
self.fix_m = self.m.rvs()
|
||||
self.fix_w = np.array([self.w.rvs(self.fix_n) for _ in range(self.fix_m)])
|
||||
self.fix_u = self.u.rvs(self.fix_n)
|
||||
self.fix_K = self.K.rvs()
|
||||
else:
|
||||
self.fix_n = None
|
||||
self.fix_m = None
|
||||
self.fix_w = None
|
||||
self.fix_u = None
|
||||
self.fix_K = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fix_w is not None:
|
||||
n = self.fix_n
|
||||
m = self.fix_m
|
||||
w = self.fix_w
|
||||
u = self.fix_u
|
||||
K = self.fix_K
|
||||
else:
|
||||
n = self.n.rvs()
|
||||
m = self.m.rvs()
|
||||
w = np.array([self.w.rvs(n) for _ in range(m)])
|
||||
u = self.u.rvs(n)
|
||||
K = self.K.rvs()
|
||||
w = w * np.array([self.w_jitter.rvs(n) for _ in range(m)])
|
||||
alpha = self.alpha.rvs(m)
|
||||
p = np.array([w[:, j].sum() / m + K * u[j] for j in range(n)])
|
||||
b = np.array([w[i, :].sum() * alpha[i] for i in range(m)])
|
||||
if self.round:
|
||||
p = p.round()
|
||||
b = b.round()
|
||||
w = w.round()
|
||||
return MultiKnapsackInstance(p, b, w)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.knapsack.MultiKnapsackInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">MultiKnapsackInstance</span></span>
|
||||
<span>(</span><span>prices, capacities, weights)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Representation of the Multidimensional 0-1 Knapsack Problem.</p>
|
||||
<p>Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
|
||||
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
||||
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].</p>
|
||||
<p>This implementation assigns a different category for each decision variable, and therefore
|
||||
trains one ML model per variable. It is only suitable when training and test instances have
|
||||
same size and items don't shuffle around.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MultiKnapsackInstance(Instance):
|
||||
"""Representation of the Multidimensional 0-1 Knapsack Problem.
|
||||
|
||||
Given a set of n items and m knapsacks, the problem is to find a subset of items S maximizing
|
||||
sum(prices[i] for i in S). If selected, each item i occupies weights[i,j] units of space in
|
||||
each knapsack j. Furthermore, each knapsack j has limited storage space, given by capacities[j].
|
||||
|
||||
This implementation assigns a different category for each decision variable, and therefore
|
||||
trains one ML model per variable. It is only suitable when training and test instances have
|
||||
same size and items don't shuffle around.
|
||||
"""
|
||||
|
||||
def __init__(self, prices, capacities, weights):
|
||||
super().__init__()
|
||||
assert isinstance(prices, np.ndarray)
|
||||
assert isinstance(capacities, np.ndarray)
|
||||
assert isinstance(weights, np.ndarray)
|
||||
assert len(weights.shape) == 2
|
||||
self.m, self.n = weights.shape
|
||||
assert prices.shape == (self.n,)
|
||||
assert capacities.shape == (self.m,)
|
||||
self.prices = prices
|
||||
self.capacities = capacities
|
||||
self.weights = weights
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(range(self.n), domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
rule=lambda model: sum(model.x[j] * self.prices[j] for j in range(self.n)),
|
||||
sense=pe.maximize,
|
||||
)
|
||||
model.eq_capacity = pe.ConstraintList()
|
||||
for i in range(self.m):
|
||||
model.eq_capacity.add(
|
||||
sum(model.x[j] * self.weights[i, j] for j in range(self.n))
|
||||
<= self.capacities[i]
|
||||
)
|
||||
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.hstack(
|
||||
[
|
||||
np.mean(self.prices),
|
||||
self.capacities,
|
||||
]
|
||||
)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
return np.hstack(
|
||||
[
|
||||
self.prices[index],
|
||||
self.weights[:, index],
|
||||
]
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems" href="index.html">miplearn.problems</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.ChallengeA" href="#miplearn.problems.knapsack.ChallengeA">ChallengeA</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.GurobiKnapsackInstance" href="#miplearn.problems.knapsack.GurobiKnapsackInstance">GurobiKnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.KnapsackInstance" href="#miplearn.problems.knapsack.KnapsackInstance">KnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator" href="#miplearn.problems.knapsack.MultiKnapsackGenerator">MultiKnapsackGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.knapsack.MultiKnapsackGenerator.generate" href="#miplearn.problems.knapsack.MultiKnapsackGenerator.generate">generate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.knapsack.MultiKnapsackInstance" href="#miplearn.problems.knapsack.MultiKnapsackInstance">MultiKnapsackInstance</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.stab</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import networkx as nx
|
||||
import numpy as np
|
||||
import pyomo.environ as pe
|
||||
from scipy.stats import uniform, randint
|
||||
from scipy.stats.distributions import rv_frozen
|
||||
|
||||
from miplearn.instance import Instance
|
||||
|
||||
|
||||
class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=100.0, scale=50.0),
|
||||
n=randint(low=200, high=201),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.generator.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.generator.generate(n_test_instances)
|
||||
|
||||
|
||||
class MaxWeightStableSetGenerator:
|
||||
"""Random instance generator for the Maximum-Weight Stable Set Problem.
|
||||
|
||||
The generator has two modes of operation. When `fix_graph=True` is provided, one random
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions `n` and `p`. To generate each instance, the
|
||||
generator independently samples each $w_v$ from the user-provided probability distribution `w`.
|
||||
|
||||
When `fix_graph=False`, a new random graph is generated for each instance; the remaining
|
||||
parameters are sampled in the same way.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
w=uniform(loc=10.0, scale=1.0),
|
||||
n=randint(low=250, high=251),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
w: rv_continuous
|
||||
Probability distribution for vertex weights.
|
||||
n: rv_discrete
|
||||
Probability distribution for parameter $n$ in Erdős-Rényi model.
|
||||
p: rv_continuous
|
||||
Probability distribution for parameter $p$ in Erdős-Rényi model.
|
||||
"""
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
|
||||
self.w = w
|
||||
self.n = n
|
||||
self.p = p
|
||||
self.fix_graph = fix_graph
|
||||
self.graph = None
|
||||
if fix_graph:
|
||||
self.graph = self._generate_graph()
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_graph(self):
|
||||
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())
|
||||
|
||||
|
||||
class MaxWeightStableSetInstance(Instance):
|
||||
"""An instance of the Maximum-Weight Stable Set Problem.
|
||||
|
||||
Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
|
||||
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
|
||||
a subset of vertices, no two of which are adjacent.
|
||||
|
||||
This is one of Karp's 21 NP-complete problems.
|
||||
"""
|
||||
|
||||
def __init__(self, graph, weights):
|
||||
super().__init__()
|
||||
self.graph = graph
|
||||
self.weights = weights
|
||||
|
||||
def to_model(self):
|
||||
nodes = list(self.graph.nodes)
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(nodes, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
|
||||
)
|
||||
model.clique_eqs = pe.ConstraintList()
|
||||
for clique in nx.find_cliques(self.graph):
|
||||
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.ones(0)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
neighbor_weights = [0] * 15
|
||||
neighbor_degrees = [100] * 15
|
||||
for n in self.graph.neighbors(index):
|
||||
neighbor_weights += [self.weights[n] / self.weights[index]]
|
||||
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
|
||||
neighbor_weights.sort(reverse=True)
|
||||
neighbor_degrees.sort()
|
||||
features = []
|
||||
features += neighbor_weights[:5]
|
||||
features += neighbor_degrees[:5]
|
||||
features += [self.graph.degree(index)]
|
||||
return np.array(features)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
return "default"</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.stab.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=100.0, scale=50.0),
|
||||
n=randint(low=200, high=201),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.generator.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.generator.generate(n_test_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">MaxWeightStableSetGenerator</span></span>
|
||||
<span>(</span><span>w=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, p=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_graph=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Random instance generator for the Maximum-Weight Stable Set Problem.</p>
|
||||
<p>The generator has two modes of operation. When <code>fix_graph=True</code> is provided, one random
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions <code>n</code> and <code>p</code>. To generate each instance, the
|
||||
generator independently samples each $w_v$ from the user-provided probability distribution <code>w</code>.</p>
|
||||
<p>When <code>fix_graph=False</code>, a new random graph is generated for each instance; the remaining
|
||||
parameters are sampled in the same way.</p>
|
||||
<p>Initialize the problem generator.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>w</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for vertex weights.</dd>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for parameter $n$ in Erdős-Rényi model.</dd>
|
||||
<dt><strong><code>p</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for parameter $p$ in Erdős-Rényi model.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MaxWeightStableSetGenerator:
|
||||
"""Random instance generator for the Maximum-Weight Stable Set Problem.
|
||||
|
||||
The generator has two modes of operation. When `fix_graph=True` is provided, one random
|
||||
Erdős-Rényi graph $G_{n,p}$ is generated in the constructor, where $n$ and $p$ are sampled
|
||||
from user-provided probability distributions `n` and `p`. To generate each instance, the
|
||||
generator independently samples each $w_v$ from the user-provided probability distribution `w`.
|
||||
|
||||
When `fix_graph=False`, a new random graph is generated for each instance; the remaining
|
||||
parameters are sampled in the same way.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
w=uniform(loc=10.0, scale=1.0),
|
||||
n=randint(low=250, high=251),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
):
|
||||
"""Initialize the problem generator.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
w: rv_continuous
|
||||
Probability distribution for vertex weights.
|
||||
n: rv_discrete
|
||||
Probability distribution for parameter $n$ in Erdős-Rényi model.
|
||||
p: rv_continuous
|
||||
Probability distribution for parameter $p$ in Erdős-Rényi model.
|
||||
"""
|
||||
assert isinstance(w, rv_frozen), "w should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(p, rv_frozen), "p should be a SciPy probability distribution"
|
||||
self.w = w
|
||||
self.n = n
|
||||
self.p = p
|
||||
self.fix_graph = fix_graph
|
||||
self.graph = None
|
||||
if fix_graph:
|
||||
self.graph = self._generate_graph()
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_graph(self):
|
||||
return nx.generators.random_graphs.binomial_graph(self.n.rvs(), self.p.rvs())</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.graph is not None:
|
||||
graph = self.graph
|
||||
else:
|
||||
graph = self._generate_graph()
|
||||
weights = self.w.rvs(graph.number_of_nodes())
|
||||
return MaxWeightStableSetInstance(graph, weights)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.stab.MaxWeightStableSetInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">MaxWeightStableSetInstance</span></span>
|
||||
<span>(</span><span>graph, weights)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An instance of the Maximum-Weight Stable Set Problem.</p>
|
||||
<p>Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
|
||||
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
|
||||
a subset of vertices, no two of which are adjacent.</p>
|
||||
<p>This is one of Karp's 21 NP-complete problems.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class MaxWeightStableSetInstance(Instance):
|
||||
"""An instance of the Maximum-Weight Stable Set Problem.
|
||||
|
||||
Given a graph G=(V,E) and a weight w_v for each vertex v, the problem asks for a stable
|
||||
set S of G maximizing sum(w_v for v in S). A stable set (also called independent set) is
|
||||
a subset of vertices, no two of which are adjacent.
|
||||
|
||||
This is one of Karp's 21 NP-complete problems.
|
||||
"""
|
||||
|
||||
def __init__(self, graph, weights):
|
||||
super().__init__()
|
||||
self.graph = graph
|
||||
self.weights = weights
|
||||
|
||||
def to_model(self):
|
||||
nodes = list(self.graph.nodes)
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var(nodes, domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(
|
||||
expr=sum(model.x[v] * self.weights[v] for v in nodes), sense=pe.maximize
|
||||
)
|
||||
model.clique_eqs = pe.ConstraintList()
|
||||
for clique in nx.find_cliques(self.graph):
|
||||
model.clique_eqs.add(sum(model.x[i] for i in clique) <= 1)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.ones(0)
|
||||
|
||||
def get_variable_features(self, var, index):
|
||||
neighbor_weights = [0] * 15
|
||||
neighbor_degrees = [100] * 15
|
||||
for n in self.graph.neighbors(index):
|
||||
neighbor_weights += [self.weights[n] / self.weights[index]]
|
||||
neighbor_degrees += [self.graph.degree(n) / self.graph.degree(index)]
|
||||
neighbor_weights.sort(reverse=True)
|
||||
neighbor_degrees.sort()
|
||||
features = []
|
||||
features += neighbor_weights[:5]
|
||||
features += neighbor_degrees[:5]
|
||||
features += [self.graph.degree(index)]
|
||||
return np.array(features)
|
||||
|
||||
def get_variable_category(self, var, index):
|
||||
return "default"</code></pre>
|
||||
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|
||||
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|
||||
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|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
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<h4><code><a title="miplearn.problems.stab.ChallengeA" href="#miplearn.problems.stab.ChallengeA">ChallengeA</a></code></h4>
|
||||
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|
||||
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|
||||
<h4><code><a title="miplearn.problems.stab.MaxWeightStableSetGenerator" href="#miplearn.problems.stab.MaxWeightStableSetGenerator">MaxWeightStableSetGenerator</a></code></h4>
|
||||
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|
||||
<li><code><a title="miplearn.problems.stab.MaxWeightStableSetGenerator.generate" href="#miplearn.problems.stab.MaxWeightStableSetGenerator.generate">generate</a></code></li>
|
||||
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|
||||
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|
||||
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|
||||
<h4><code><a title="miplearn.problems.stab.MaxWeightStableSetInstance" href="#miplearn.problems.stab.MaxWeightStableSetInstance">MaxWeightStableSetInstance</a></code></h4>
|
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|
||||
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|
||||
<span>Expand source code</span>
|
||||
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|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.</code></pre>
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||||
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||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_knapsack" href="test_knapsack.html">miplearn.problems.tests.test_knapsack</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_stab" href="test_stab.html">miplearn.problems.tests.test_stab</a></code></dt>
|
||||
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|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.problems.tests.test_tsp" href="test_tsp.html">miplearn.problems.tests.test_tsp</a></code></dt>
|
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|
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||||
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|
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|
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|
||||
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||||
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<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_knapsack</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.knapsack import MultiKnapsackGenerator
|
||||
|
||||
|
||||
def test_knapsack_generator():
|
||||
gen = MultiKnapsackGenerator(
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=501),
|
||||
u=uniform(loc=1.0, scale=1.0),
|
||||
alpha=uniform(loc=0.50, scale=0.0),
|
||||
)
|
||||
instances = gen.generate(100)
|
||||
w_sum = sum(instance.weights for instance in instances) / len(instances)
|
||||
b_sum = sum(instance.capacities for instance in instances) / len(instances)
|
||||
assert round(np.mean(w_sum), -1) == 500.0
|
||||
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_knapsack.test_knapsack_generator"><code class="name flex">
|
||||
<span>def <span class="ident">test_knapsack_generator</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_knapsack_generator():
|
||||
gen = MultiKnapsackGenerator(
|
||||
n=randint(low=100, high=101),
|
||||
m=randint(low=30, high=31),
|
||||
w=randint(low=0, high=1000),
|
||||
K=randint(low=500, high=501),
|
||||
u=uniform(loc=1.0, scale=1.0),
|
||||
alpha=uniform(loc=0.50, scale=0.0),
|
||||
)
|
||||
instances = gen.generate(100)
|
||||
w_sum = sum(instance.weights for instance in instances) / len(instances)
|
||||
b_sum = sum(instance.capacities for instance in instances) / len(instances)
|
||||
assert round(np.mean(w_sum), -1) == 500.0
|
||||
assert round(np.mean(b_sum), -3) == 25000.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
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||||
<nav id="sidebar">
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||||
<h1>Index</h1>
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<li><code><a title="miplearn.problems.tests.test_knapsack.test_knapsack_generator" href="#miplearn.problems.tests.test_knapsack.test_knapsack_generator">test_knapsack_generator</a></code></li>
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<article id="content">
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<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_stab</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import networkx as nx
|
||||
import numpy as np
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.stab import MaxWeightStableSetInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_stab():
|
||||
graph = nx.cycle_graph(5)
|
||||
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
instance = MaxWeightStableSetInstance(graph, weights)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Lower bound"] == 2.0
|
||||
|
||||
|
||||
def test_stab_generator_fixed_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=10, high=11),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
weights = np.array([instance.weights for instance in instances])
|
||||
weights_avg_actual = np.round(np.average(weights, axis=0))
|
||||
weights_avg_expected = [55.0] * 10
|
||||
assert list(weights_avg_actual) == weights_avg_expected
|
||||
|
||||
|
||||
def test_stab_generator_random_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=30, high=41),
|
||||
p=uniform(loc=0.5, scale=0.0),
|
||||
fix_graph=False,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
|
||||
n_edges = [instance.graph.number_of_edges() for instance in instances]
|
||||
assert np.round(np.mean(n_nodes)) == 35.0
|
||||
assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab():
|
||||
graph = nx.cycle_graph(5)
|
||||
weights = [1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
instance = MaxWeightStableSetInstance(graph, weights)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
assert stats["Lower bound"] == 2.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab_generator_fixed_graph</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab_generator_fixed_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=10, high=11),
|
||||
p=uniform(loc=0.05, scale=0.0),
|
||||
fix_graph=True,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
weights = np.array([instance.weights for instance in instances])
|
||||
weights_avg_actual = np.round(np.average(weights, axis=0))
|
||||
weights_avg_expected = [55.0] * 10
|
||||
assert list(weights_avg_actual) == weights_avg_expected</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_stab.test_stab_generator_random_graph"><code class="name flex">
|
||||
<span>def <span class="ident">test_stab_generator_random_graph</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_stab_generator_random_graph():
|
||||
np.random.seed(42)
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
|
||||
gen = MaxWeightStableSetGenerator(
|
||||
w=uniform(loc=50.0, scale=10.0),
|
||||
n=randint(low=30, high=41),
|
||||
p=uniform(loc=0.5, scale=0.0),
|
||||
fix_graph=False,
|
||||
)
|
||||
instances = gen.generate(1_000)
|
||||
n_nodes = [instance.graph.number_of_nodes() for instance in instances]
|
||||
n_edges = [instance.graph.number_of_edges() for instance in instances]
|
||||
assert np.round(np.mean(n_nodes)) == 35.0
|
||||
assert np.round(np.mean(n_edges), -1) == 300.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
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<h1>Index</h1>
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<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab" href="#miplearn.problems.tests.test_stab.test_stab">test_stab</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_fixed_graph">test_stab_generator_fixed_graph</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_stab.test_stab_generator_random_graph" href="#miplearn.problems.tests.test_stab.test_stab_generator_random_graph">test_stab_generator_random_graph</a></code></li>
|
||||
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|
||||
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<body>
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<main>
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<article id="content">
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<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tests.test_tsp</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import numpy as np
|
||||
from numpy.linalg import norm
|
||||
from scipy.spatial.distance import pdist, squareform
|
||||
from scipy.stats import uniform, randint
|
||||
|
||||
from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_generator():
|
||||
instances = TravelingSalesmanGenerator(
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=100, high=101),
|
||||
gamma=uniform(loc=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
).generate(100)
|
||||
assert len(instances) == 100
|
||||
assert instances[0].n_cities == 100
|
||||
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
|
||||
d = [instance.distances[0, 1] for instance in instances]
|
||||
assert np.std(d) > 0
|
||||
|
||||
|
||||
def test_instance():
|
||||
n_cities = 4
|
||||
distances = np.array(
|
||||
[
|
||||
[0.0, 1.0, 2.0, 1.0],
|
||||
[1.0, 0.0, 1.0, 2.0],
|
||||
[2.0, 1.0, 0.0, 1.0],
|
||||
[1.0, 2.0, 1.0, 0.0],
|
||||
]
|
||||
)
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 2] == 0.0
|
||||
assert x[0, 3] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[1, 3] == 0.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert stats["Lower bound"] == 4.0
|
||||
assert stats["Upper bound"] == 4.0
|
||||
|
||||
|
||||
def test_subtour():
|
||||
n_cities = 6
|
||||
cities = np.array(
|
||||
[
|
||||
[0.0, 0.0],
|
||||
[1.0, 0.0],
|
||||
[2.0, 0.0],
|
||||
[3.0, 0.0],
|
||||
[0.0, 1.0],
|
||||
[3.0, 1.0],
|
||||
]
|
||||
)
|
||||
distances = squareform(pdist(cities))
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
solver.solve(instance)
|
||||
assert hasattr(instance, "found_violated_lazy_constraints")
|
||||
assert hasattr(instance, "found_violated_user_cuts")
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 4] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert x[3, 5] == 1.0
|
||||
assert x[4, 5] == 1.0
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_generator"><code class="name flex">
|
||||
<span>def <span class="ident">test_generator</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_generator():
|
||||
instances = TravelingSalesmanGenerator(
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=100, high=101),
|
||||
gamma=uniform(loc=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
).generate(100)
|
||||
assert len(instances) == 100
|
||||
assert instances[0].n_cities == 100
|
||||
assert norm(instances[0].distances - instances[0].distances.T) < 1e-6
|
||||
d = [instance.distances[0, 1] for instance in instances]
|
||||
assert np.std(d) > 0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_instance"><code class="name flex">
|
||||
<span>def <span class="ident">test_instance</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_instance():
|
||||
n_cities = 4
|
||||
distances = np.array(
|
||||
[
|
||||
[0.0, 1.0, 2.0, 1.0],
|
||||
[1.0, 0.0, 1.0, 2.0],
|
||||
[2.0, 1.0, 0.0, 1.0],
|
||||
[1.0, 2.0, 1.0, 0.0],
|
||||
]
|
||||
)
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
stats = solver.solve(instance)
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 2] == 0.0
|
||||
assert x[0, 3] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[1, 3] == 0.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert stats["Lower bound"] == 4.0
|
||||
assert stats["Upper bound"] == 4.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tests.test_tsp.test_subtour"><code class="name flex">
|
||||
<span>def <span class="ident">test_subtour</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_subtour():
|
||||
n_cities = 6
|
||||
cities = np.array(
|
||||
[
|
||||
[0.0, 0.0],
|
||||
[1.0, 0.0],
|
||||
[2.0, 0.0],
|
||||
[3.0, 0.0],
|
||||
[0.0, 1.0],
|
||||
[3.0, 1.0],
|
||||
]
|
||||
)
|
||||
distances = squareform(pdist(cities))
|
||||
instance = TravelingSalesmanInstance(n_cities, distances)
|
||||
solver = LearningSolver()
|
||||
solver.solve(instance)
|
||||
assert hasattr(instance, "found_violated_lazy_constraints")
|
||||
assert hasattr(instance, "found_violated_user_cuts")
|
||||
x = instance.training_data[0]["Solution"]["x"]
|
||||
assert x[0, 1] == 1.0
|
||||
assert x[0, 4] == 1.0
|
||||
assert x[1, 2] == 1.0
|
||||
assert x[2, 3] == 1.0
|
||||
assert x[3, 5] == 1.0
|
||||
assert x[4, 5] == 1.0
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems.tests" href="index.html">miplearn.problems.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_generator" href="#miplearn.problems.tests.test_tsp.test_generator">test_generator</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_instance" href="#miplearn.problems.tests.test_tsp.test_instance">test_instance</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tests.test_tsp.test_subtour" href="#miplearn.problems.tests.test_tsp.test_subtour">test_subtour</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.problems.tsp</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import networkx as nx
|
||||
import numpy as np
|
||||
import pyomo.environ as pe
|
||||
from scipy.spatial.distance import pdist, squareform
|
||||
from scipy.stats import uniform, randint
|
||||
from scipy.stats.distributions import rv_frozen
|
||||
|
||||
from miplearn.instance import Instance
|
||||
|
||||
|
||||
class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = TravelingSalesmanGenerator(
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=350, high=351),
|
||||
gamma=uniform(loc=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
round=True,
|
||||
)
|
||||
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.generator.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.generator.generate(n_test_instances)
|
||||
|
||||
|
||||
class TravelingSalesmanGenerator:
|
||||
"""Random generator for the Traveling Salesman Problem."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=100, high=101),
|
||||
gamma=uniform(loc=1.0, scale=0.0),
|
||||
fix_cities=True,
|
||||
round=True,
|
||||
):
|
||||
"""Initializes the problem generator.
|
||||
|
||||
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
|
||||
sampled independently from the provided probability distributions `n`, `x` and `y`. For each
|
||||
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
|
||||
|
||||
d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
|
||||
|
||||
where gamma is sampled from the provided probability distribution `gamma`.
|
||||
|
||||
If fix_cities=True, the list of cities is kept the same for all generated instances. The
|
||||
gamma values, and therefore also the distances, are still different.
|
||||
|
||||
By default, all distances d[i,j] are rounded to the nearest integer. If `round=False`
|
||||
is provided, this rounding will be disabled.
|
||||
|
||||
Arguments
|
||||
---------
|
||||
x: rv_continuous
|
||||
Probability distribution for the x-coordinate of each city.
|
||||
y: rv_continuous
|
||||
Probability distribution for the y-coordinate of each city.
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of cities.
|
||||
fix_cities: bool
|
||||
If False, cities will be resampled for every generated instance. Otherwise, list of
|
||||
cities will be computed once, during the constructor.
|
||||
round: bool
|
||||
If True, distances are rounded to the nearest integer.
|
||||
"""
|
||||
assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
|
||||
assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
gamma,
|
||||
rv_frozen,
|
||||
), "gamma should be a SciPy probability distribution"
|
||||
self.x = x
|
||||
self.y = y
|
||||
self.n = n
|
||||
self.gamma = gamma
|
||||
self.round = round
|
||||
|
||||
if fix_cities:
|
||||
self.fixed_n, self.fixed_cities = self._generate_cities()
|
||||
else:
|
||||
self.fixed_n = None
|
||||
self.fixed_cities = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fixed_cities is not None:
|
||||
n, cities = self.fixed_n, self.fixed_cities
|
||||
else:
|
||||
n, cities = self._generate_cities()
|
||||
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
|
||||
distances = np.tril(distances) + np.triu(distances.T, 1)
|
||||
if self.round:
|
||||
distances = distances.round()
|
||||
return TravelingSalesmanInstance(n, distances)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_cities(self):
|
||||
n = self.n.rvs()
|
||||
cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
|
||||
return n, cities
|
||||
|
||||
|
||||
class TravelingSalesmanInstance(Instance):
|
||||
"""An instance ot the Traveling Salesman Problem.
|
||||
|
||||
Given a list of cities and the distance between each pair of cities, the problem asks for the
|
||||
shortest route starting at the first city, visiting each other city exactly once, then
|
||||
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
|
||||
one of Karp's 21 NP-complete problems.
|
||||
"""
|
||||
|
||||
def __init__(self, n_cities, distances):
|
||||
assert isinstance(distances, np.ndarray)
|
||||
assert distances.shape == (n_cities, n_cities)
|
||||
self.n_cities = n_cities
|
||||
self.distances = distances
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
model.edges = edges = [
|
||||
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
|
||||
]
|
||||
model.x = pe.Var(edges, domain=pe.Binary)
|
||||
model.obj = pe.Objective(
|
||||
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
|
||||
sense=pe.minimize,
|
||||
)
|
||||
model.eq_degree = pe.ConstraintList()
|
||||
model.eq_subtour = pe.ConstraintList()
|
||||
for i in range(self.n_cities):
|
||||
model.eq_degree.add(
|
||||
sum(
|
||||
model.x[min(i, j), max(i, j)]
|
||||
for j in range(self.n_cities)
|
||||
if i != j
|
||||
)
|
||||
== 2
|
||||
)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.array([1])
|
||||
|
||||
def get_variable_features(self, var_name, index):
|
||||
return np.array([1])
|
||||
|
||||
def get_variable_category(self, var_name, index):
|
||||
return index
|
||||
|
||||
def find_violated_lazy_constraints(self, model):
|
||||
selected_edges = [e for e in model.edges if model.x[e].value > 0.5]
|
||||
graph = nx.Graph()
|
||||
graph.add_edges_from(selected_edges)
|
||||
components = [frozenset(c) for c in list(nx.connected_components(graph))]
|
||||
violations = []
|
||||
for c in components:
|
||||
if len(c) < self.n_cities:
|
||||
violations += [c]
|
||||
return violations
|
||||
|
||||
def build_lazy_constraint(self, model, component):
|
||||
cut_edges = [
|
||||
e
|
||||
for e in model.edges
|
||||
if (e[0] in component and e[1] not in component)
|
||||
or (e[0] not in component and e[1] in component)
|
||||
]
|
||||
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
|
||||
|
||||
def find_violated_user_cuts(self, model):
|
||||
return self.find_violated_lazy_constraints(model)
|
||||
|
||||
def build_user_cut(self, model, violation):
|
||||
return self.build_lazy_constraint(model, violation)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.ChallengeA"><code class="flex name class">
|
||||
<span>class <span class="ident">ChallengeA</span></span>
|
||||
<span>(</span><span>seed=42, n_training_instances=500, n_test_instances=50)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class ChallengeA:
|
||||
def __init__(
|
||||
self,
|
||||
seed=42,
|
||||
n_training_instances=500,
|
||||
n_test_instances=50,
|
||||
):
|
||||
|
||||
np.random.seed(seed)
|
||||
self.generator = TravelingSalesmanGenerator(
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=350, high=351),
|
||||
gamma=uniform(loc=0.95, scale=0.1),
|
||||
fix_cities=True,
|
||||
round=True,
|
||||
)
|
||||
|
||||
np.random.seed(seed + 1)
|
||||
self.training_instances = self.generator.generate(n_training_instances)
|
||||
|
||||
np.random.seed(seed + 2)
|
||||
self.test_instances = self.generator.generate(n_test_instances)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator"><code class="flex name class">
|
||||
<span>class <span class="ident">TravelingSalesmanGenerator</span></span>
|
||||
<span>(</span><span>x=<scipy.stats._distn_infrastructure.rv_frozen object>, y=<scipy.stats._distn_infrastructure.rv_frozen object>, n=<scipy.stats._distn_infrastructure.rv_frozen object>, gamma=<scipy.stats._distn_infrastructure.rv_frozen object>, fix_cities=True, round=True)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Random generator for the Traveling Salesman Problem.</p>
|
||||
<p>Initializes the problem generator.</p>
|
||||
<p>Initially, the generator creates n cities (x_1,y_1),…,(x_n,y_n) where n, x_i and y_i are
|
||||
sampled independently from the provided probability distributions <code>n</code>, <code>x</code> and <code>y</code>. For each
|
||||
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:</p>
|
||||
<pre><code>d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
|
||||
</code></pre>
|
||||
<p>where gamma is sampled from the provided probability distribution <code>gamma</code>.</p>
|
||||
<p>If fix_cities=True, the list of cities is kept the same for all generated instances. The
|
||||
gamma values, and therefore also the distances, are still different.</p>
|
||||
<p>By default, all distances d[i,j] are rounded to the nearest integer.
|
||||
If <code>round=False</code>
|
||||
is provided, this rounding will be disabled.</p>
|
||||
<h2 id="arguments">Arguments</h2>
|
||||
<dl>
|
||||
<dt><strong><code>x</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the x-coordinate of each city.</dd>
|
||||
<dt><strong><code>y</code></strong> : <code>rv_continuous</code></dt>
|
||||
<dd>Probability distribution for the y-coordinate of each city.</dd>
|
||||
<dt><strong><code>n</code></strong> : <code>rv_discrete</code></dt>
|
||||
<dd>Probability distribution for the number of cities.</dd>
|
||||
<dt><strong><code>fix_cities</code></strong> : <code>bool</code></dt>
|
||||
<dd>If False, cities will be resampled for every generated instance. Otherwise, list of
|
||||
cities will be computed once, during the constructor.</dd>
|
||||
<dt><strong><code>round</code></strong> : <code>bool</code></dt>
|
||||
<dd>If True, distances are rounded to the nearest integer.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class TravelingSalesmanGenerator:
|
||||
"""Random generator for the Traveling Salesman Problem."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
x=uniform(loc=0.0, scale=1000.0),
|
||||
y=uniform(loc=0.0, scale=1000.0),
|
||||
n=randint(low=100, high=101),
|
||||
gamma=uniform(loc=1.0, scale=0.0),
|
||||
fix_cities=True,
|
||||
round=True,
|
||||
):
|
||||
"""Initializes the problem generator.
|
||||
|
||||
Initially, the generator creates n cities (x_1,y_1),...,(x_n,y_n) where n, x_i and y_i are
|
||||
sampled independently from the provided probability distributions `n`, `x` and `y`. For each
|
||||
(unordered) pair of cities (i,j), the distance d[i,j] between them is set to:
|
||||
|
||||
d[i,j] = gamma[i,j] \sqrt{(x_i - x_j)^2 + (y_i - y_j)^2}
|
||||
|
||||
where gamma is sampled from the provided probability distribution `gamma`.
|
||||
|
||||
If fix_cities=True, the list of cities is kept the same for all generated instances. The
|
||||
gamma values, and therefore also the distances, are still different.
|
||||
|
||||
By default, all distances d[i,j] are rounded to the nearest integer. If `round=False`
|
||||
is provided, this rounding will be disabled.
|
||||
|
||||
Arguments
|
||||
---------
|
||||
x: rv_continuous
|
||||
Probability distribution for the x-coordinate of each city.
|
||||
y: rv_continuous
|
||||
Probability distribution for the y-coordinate of each city.
|
||||
n: rv_discrete
|
||||
Probability distribution for the number of cities.
|
||||
fix_cities: bool
|
||||
If False, cities will be resampled for every generated instance. Otherwise, list of
|
||||
cities will be computed once, during the constructor.
|
||||
round: bool
|
||||
If True, distances are rounded to the nearest integer.
|
||||
"""
|
||||
assert isinstance(x, rv_frozen), "x should be a SciPy probability distribution"
|
||||
assert isinstance(y, rv_frozen), "y should be a SciPy probability distribution"
|
||||
assert isinstance(n, rv_frozen), "n should be a SciPy probability distribution"
|
||||
assert isinstance(
|
||||
gamma,
|
||||
rv_frozen,
|
||||
), "gamma should be a SciPy probability distribution"
|
||||
self.x = x
|
||||
self.y = y
|
||||
self.n = n
|
||||
self.gamma = gamma
|
||||
self.round = round
|
||||
|
||||
if fix_cities:
|
||||
self.fixed_n, self.fixed_cities = self._generate_cities()
|
||||
else:
|
||||
self.fixed_n = None
|
||||
self.fixed_cities = None
|
||||
|
||||
def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fixed_cities is not None:
|
||||
n, cities = self.fixed_n, self.fixed_cities
|
||||
else:
|
||||
n, cities = self._generate_cities()
|
||||
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
|
||||
distances = np.tril(distances) + np.triu(distances.T, 1)
|
||||
if self.round:
|
||||
distances = distances.round()
|
||||
return TravelingSalesmanInstance(n, distances)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]
|
||||
|
||||
def _generate_cities(self):
|
||||
n = self.n.rvs()
|
||||
cities = np.array([(self.x.rvs(), self.y.rvs()) for _ in range(n)])
|
||||
return n, cities</code></pre>
|
||||
</details>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanGenerator.generate"><code class="name flex">
|
||||
<span>def <span class="ident">generate</span></span>(<span>self, n_samples)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def generate(self, n_samples):
|
||||
def _sample():
|
||||
if self.fixed_cities is not None:
|
||||
n, cities = self.fixed_n, self.fixed_cities
|
||||
else:
|
||||
n, cities = self._generate_cities()
|
||||
distances = squareform(pdist(cities)) * self.gamma.rvs(size=(n, n))
|
||||
distances = np.tril(distances) + np.triu(distances.T, 1)
|
||||
if self.round:
|
||||
distances = distances.round()
|
||||
return TravelingSalesmanInstance(n, distances)
|
||||
|
||||
return [_sample() for _ in range(n_samples)]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">TravelingSalesmanInstance</span></span>
|
||||
<span>(</span><span>n_cities, distances)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An instance ot the Traveling Salesman Problem.</p>
|
||||
<p>Given a list of cities and the distance between each pair of cities, the problem asks for the
|
||||
shortest route starting at the first city, visiting each other city exactly once, then
|
||||
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
|
||||
one of Karp's 21 NP-complete problems.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class TravelingSalesmanInstance(Instance):
|
||||
"""An instance ot the Traveling Salesman Problem.
|
||||
|
||||
Given a list of cities and the distance between each pair of cities, the problem asks for the
|
||||
shortest route starting at the first city, visiting each other city exactly once, then
|
||||
returning to the first city. This problem is a generalization of the Hamiltonian path problem,
|
||||
one of Karp's 21 NP-complete problems.
|
||||
"""
|
||||
|
||||
def __init__(self, n_cities, distances):
|
||||
assert isinstance(distances, np.ndarray)
|
||||
assert distances.shape == (n_cities, n_cities)
|
||||
self.n_cities = n_cities
|
||||
self.distances = distances
|
||||
|
||||
def to_model(self):
|
||||
model = pe.ConcreteModel()
|
||||
model.edges = edges = [
|
||||
(i, j) for i in range(self.n_cities) for j in range(i + 1, self.n_cities)
|
||||
]
|
||||
model.x = pe.Var(edges, domain=pe.Binary)
|
||||
model.obj = pe.Objective(
|
||||
expr=sum(model.x[i, j] * self.distances[i, j] for (i, j) in edges),
|
||||
sense=pe.minimize,
|
||||
)
|
||||
model.eq_degree = pe.ConstraintList()
|
||||
model.eq_subtour = pe.ConstraintList()
|
||||
for i in range(self.n_cities):
|
||||
model.eq_degree.add(
|
||||
sum(
|
||||
model.x[min(i, j), max(i, j)]
|
||||
for j in range(self.n_cities)
|
||||
if i != j
|
||||
)
|
||||
== 2
|
||||
)
|
||||
return model
|
||||
|
||||
def get_instance_features(self):
|
||||
return np.array([1])
|
||||
|
||||
def get_variable_features(self, var_name, index):
|
||||
return np.array([1])
|
||||
|
||||
def get_variable_category(self, var_name, index):
|
||||
return index
|
||||
|
||||
def find_violated_lazy_constraints(self, model):
|
||||
selected_edges = [e for e in model.edges if model.x[e].value > 0.5]
|
||||
graph = nx.Graph()
|
||||
graph.add_edges_from(selected_edges)
|
||||
components = [frozenset(c) for c in list(nx.connected_components(graph))]
|
||||
violations = []
|
||||
for c in components:
|
||||
if len(c) < self.n_cities:
|
||||
violations += [c]
|
||||
return violations
|
||||
|
||||
def build_lazy_constraint(self, model, component):
|
||||
cut_edges = [
|
||||
e
|
||||
for e in model.edges
|
||||
if (e[0] in component and e[1] not in component)
|
||||
or (e[0] not in component and e[1] in component)
|
||||
]
|
||||
return model.eq_subtour.add(sum(model.x[e] for e in cut_edges) >= 2)
|
||||
|
||||
def find_violated_user_cuts(self, model):
|
||||
return self.find_violated_lazy_constraints(model)
|
||||
|
||||
def build_user_cut(self, model, violation):
|
||||
return self.build_lazy_constraint(model, violation)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Methods</h3>
|
||||
<dl>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut"><code class="name flex">
|
||||
<span>def <span class="ident">build_user_cut</span></span>(<span>self, model, violation)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def build_user_cut(self, model, violation):
|
||||
return self.build_lazy_constraint(model, violation)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts"><code class="name flex">
|
||||
<span>def <span class="ident">find_violated_user_cuts</span></span>(<span>self, model)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def find_violated_user_cuts(self, model):
|
||||
return self.find_violated_lazy_constraints(model)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.problems" href="index.html">miplearn.problems</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.ChallengeA" href="#miplearn.problems.tsp.ChallengeA">ChallengeA</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator" href="#miplearn.problems.tsp.TravelingSalesmanGenerator">TravelingSalesmanGenerator</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanGenerator.generate" href="#miplearn.problems.tsp.TravelingSalesmanGenerator.generate">generate</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance" href="#miplearn.problems.tsp.TravelingSalesmanInstance">TravelingSalesmanInstance</a></code></h4>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut" href="#miplearn.problems.tsp.TravelingSalesmanInstance.build_user_cut">build_user_cut</a></code></li>
|
||||
<li><code><a title="miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts" href="#miplearn.problems.tsp.TravelingSalesmanInstance.find_violated_user_cuts">find_violated_user_cuts</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
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</head>
|
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<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.gurobi</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
import logging
|
||||
import re
|
||||
import sys
|
||||
from io import StringIO
|
||||
from random import randint
|
||||
from typing import List, Any, Dict, Optional
|
||||
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers import _RedirectOutput
|
||||
from miplearn.solvers.internal import (
|
||||
InternalSolver,
|
||||
LPSolveStats,
|
||||
IterationCallback,
|
||||
LazyCallback,
|
||||
MIPSolveStats,
|
||||
)
|
||||
from miplearn.types import VarIndex, SolverParams, Solution
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GurobiSolver(InternalSolver):
|
||||
"""
|
||||
An InternalSolver backed by Gurobi's Python API (without Pyomo).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: Optional[SolverParams]
|
||||
Parameters to pass to Gurobi. For example, `params={"MIPGap": 1e-3}`
|
||||
sets the gap tolerance to 1e-3.
|
||||
lazy_cb_frequency: int
|
||||
If 1, calls lazy constraint callbacks whenever an integer solution
|
||||
is found. If 2, calls it also at every node, after solving the
|
||||
LP relaxation of that node.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: Optional[SolverParams] = None,
|
||||
lazy_cb_frequency: int = 1,
|
||||
) -> None:
|
||||
import gurobipy
|
||||
|
||||
if params is None:
|
||||
params = {}
|
||||
params["InfUnbdInfo"] = True
|
||||
|
||||
self.gp = gurobipy
|
||||
self.instance: Optional[Instance] = None
|
||||
self.model: Optional["gurobipy.Model"] = None
|
||||
self.params: SolverParams = params
|
||||
self._all_vars: Dict = {}
|
||||
self._bin_vars: Optional[Dict[str, Dict[VarIndex, "gurobipy.Var"]]] = None
|
||||
self.cb_where: Optional[int] = None
|
||||
|
||||
assert lazy_cb_frequency in [1, 2]
|
||||
if lazy_cb_frequency == 1:
|
||||
self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
|
||||
else:
|
||||
self.lazy_cb_where = [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]
|
||||
|
||||
def set_instance(
|
||||
self,
|
||||
instance: Instance,
|
||||
model: Any = None,
|
||||
) -> None:
|
||||
self._raise_if_callback()
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
assert isinstance(model, self.gp.Model)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self.model.update()
|
||||
self._update_vars()
|
||||
|
||||
def _raise_if_callback(self) -> None:
|
||||
if self.cb_where is not None:
|
||||
raise Exception("method cannot be called from a callback")
|
||||
|
||||
def _update_vars(self) -> None:
|
||||
assert self.model is not None
|
||||
self._all_vars = {}
|
||||
self._bin_vars = {}
|
||||
idx: VarIndex
|
||||
for var in self.model.getVars():
|
||||
m = re.search(r"([^[]*)\[(.*)]", var.varName)
|
||||
if m is None:
|
||||
name = var.varName
|
||||
idx = [0]
|
||||
else:
|
||||
name = m.group(1)
|
||||
parts = m.group(2).split(",")
|
||||
idx = [int(k) if k.isdecimal else k for k in parts]
|
||||
if len(idx) == 1:
|
||||
idx = idx[0]
|
||||
if name not in self._all_vars:
|
||||
self._all_vars[name] = {}
|
||||
self._all_vars[name][idx] = var
|
||||
if var.vtype != "C":
|
||||
if name not in self._bin_vars:
|
||||
self._bin_vars[name] = {}
|
||||
self._bin_vars[name][idx] = var
|
||||
|
||||
def _apply_params(self, streams: List[Any]) -> None:
|
||||
assert self.model is not None
|
||||
with _RedirectOutput(streams):
|
||||
for (name, value) in self.params.items():
|
||||
self.model.setParam(name, value)
|
||||
if "seed" not in [k.lower() for k in self.params.keys()]:
|
||||
self.model.setParam("Seed", randint(0, 1_000_000))
|
||||
|
||||
def solve_lp(
|
||||
self,
|
||||
tee: bool = False,
|
||||
) -> LPSolveStats:
|
||||
self._raise_if_callback()
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
self._apply_params(streams)
|
||||
assert self.model is not None
|
||||
assert self._bin_vars is not None
|
||||
for (varname, vardict) in self._bin_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.vtype = self.gp.GRB.CONTINUOUS
|
||||
var.lb = 0.0
|
||||
var.ub = 1.0
|
||||
with _RedirectOutput(streams):
|
||||
self.model.optimize()
|
||||
for (varname, vardict) in self._bin_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.vtype = self.gp.GRB.BINARY
|
||||
log = streams[0].getvalue()
|
||||
opt_value = None
|
||||
if not self.is_infeasible():
|
||||
opt_value = self.model.objVal
|
||||
return {
|
||||
"Optimal value": opt_value,
|
||||
"Log": log,
|
||||
}
|
||||
|
||||
def solve(
|
||||
self,
|
||||
tee: bool = False,
|
||||
iteration_cb: IterationCallback = None,
|
||||
lazy_cb: LazyCallback = None,
|
||||
) -> MIPSolveStats:
|
||||
self._raise_if_callback()
|
||||
assert self.model is not None
|
||||
|
||||
def cb_wrapper(cb_model, cb_where):
|
||||
try:
|
||||
self.cb_where = cb_where
|
||||
if cb_where in self.lazy_cb_where:
|
||||
lazy_cb(self, self.model)
|
||||
except:
|
||||
logger.exception("callback error")
|
||||
finally:
|
||||
self.cb_where = None
|
||||
|
||||
if lazy_cb:
|
||||
self.params["LazyConstraints"] = 1
|
||||
total_wallclock_time = 0
|
||||
total_nodes = 0
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
self._apply_params(streams)
|
||||
if iteration_cb is None:
|
||||
iteration_cb = lambda: False
|
||||
while True:
|
||||
with _RedirectOutput(streams):
|
||||
if lazy_cb is None:
|
||||
self.model.optimize()
|
||||
else:
|
||||
self.model.optimize(cb_wrapper)
|
||||
total_wallclock_time += self.model.runtime
|
||||
total_nodes += int(self.model.nodeCount)
|
||||
should_repeat = iteration_cb()
|
||||
if not should_repeat:
|
||||
break
|
||||
log = streams[0].getvalue()
|
||||
ub, lb = None, None
|
||||
sense = "min" if self.model.modelSense == 1 else "max"
|
||||
if self.model.solCount > 0:
|
||||
if self.model.modelSense == 1:
|
||||
lb = self.model.objBound
|
||||
ub = self.model.objVal
|
||||
else:
|
||||
lb = self.model.objVal
|
||||
ub = self.model.objBound
|
||||
ws_value = self._extract_warm_start_value(log)
|
||||
stats: MIPSolveStats = {
|
||||
"Lower bound": lb,
|
||||
"Upper bound": ub,
|
||||
"Wallclock time": total_wallclock_time,
|
||||
"Nodes": total_nodes,
|
||||
"Sense": sense,
|
||||
"Log": log,
|
||||
"Warm start value": ws_value,
|
||||
"LP value": None,
|
||||
}
|
||||
return stats
|
||||
|
||||
def get_solution(self) -> Optional[Solution]:
|
||||
self._raise_if_callback()
|
||||
assert self.model is not None
|
||||
if self.model.solCount == 0:
|
||||
return None
|
||||
solution: Solution = {}
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
solution[varname] = {}
|
||||
for (idx, var) in vardict.items():
|
||||
solution[varname][idx] = var.x
|
||||
return solution
|
||||
|
||||
def set_warm_start(self, solution: Solution) -> None:
|
||||
self._raise_if_callback()
|
||||
self._clear_warm_start()
|
||||
count_fixed, count_total = 0, 0
|
||||
for (varname, vardict) in solution.items():
|
||||
for (idx, value) in vardict.items():
|
||||
count_total += 1
|
||||
if value is not None:
|
||||
count_fixed += 1
|
||||
self._all_vars[varname][idx].start = value
|
||||
logger.info(
|
||||
"Setting start values for %d variables (out of %d)"
|
||||
% (count_fixed, count_total)
|
||||
)
|
||||
|
||||
def get_sense(self) -> str:
|
||||
assert self.model is not None
|
||||
if self.model.modelSense == 1:
|
||||
return "min"
|
||||
else:
|
||||
return "max"
|
||||
|
||||
def get_value(
|
||||
self,
|
||||
var_name: str,
|
||||
index: VarIndex,
|
||||
) -> Optional[float]:
|
||||
var = self._all_vars[var_name][index]
|
||||
return self._get_value(var)
|
||||
|
||||
def is_infeasible(self) -> bool:
|
||||
assert self.model is not None
|
||||
return self.model.status in [self.gp.GRB.INFEASIBLE, self.gp.GRB.INF_OR_UNBD]
|
||||
|
||||
def get_dual(self, cid: str) -> float:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
if self.is_infeasible():
|
||||
return c.farkasDual
|
||||
else:
|
||||
return c.pi
|
||||
|
||||
def _get_value(self, var: Any) -> Optional[float]:
|
||||
assert self.model is not None
|
||||
if self.cb_where == self.gp.GRB.Callback.MIPSOL:
|
||||
return self.model.cbGetSolution(var)
|
||||
elif self.cb_where == self.gp.GRB.Callback.MIPNODE:
|
||||
return self.model.cbGetNodeRel(var)
|
||||
elif self.cb_where is None:
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
else:
|
||||
return var.x
|
||||
else:
|
||||
raise Exception(
|
||||
"get_value cannot be called from cb_where=%s" % self.cb_where
|
||||
)
|
||||
|
||||
def get_empty_solution(self) -> Solution:
|
||||
self._raise_if_callback()
|
||||
solution: Solution = {}
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
solution[varname] = {}
|
||||
for (idx, var) in vardict.items():
|
||||
solution[varname][idx] = None
|
||||
return solution
|
||||
|
||||
def add_constraint(
|
||||
self,
|
||||
constraint: Any,
|
||||
name: str = "",
|
||||
) -> None:
|
||||
assert self.model is not None
|
||||
if type(constraint) is tuple:
|
||||
lhs, sense, rhs, name = constraint
|
||||
if self.cb_where in [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]:
|
||||
self.model.cbLazy(lhs, sense, rhs)
|
||||
else:
|
||||
self.model.addConstr(lhs, sense, rhs, name)
|
||||
else:
|
||||
if self.cb_where in [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]:
|
||||
self.model.cbLazy(constraint)
|
||||
else:
|
||||
self.model.addConstr(constraint, name=name)
|
||||
|
||||
def _clear_warm_start(self) -> None:
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.start = self.gp.GRB.UNDEFINED
|
||||
|
||||
def fix(self, solution: Solution) -> None:
|
||||
self._raise_if_callback()
|
||||
for (varname, vardict) in solution.items():
|
||||
for (idx, value) in vardict.items():
|
||||
if value is None:
|
||||
continue
|
||||
var = self._all_vars[varname][idx]
|
||||
var.vtype = self.gp.GRB.CONTINUOUS
|
||||
var.lb = value
|
||||
var.ub = value
|
||||
|
||||
def get_constraint_ids(self):
|
||||
self._raise_if_callback()
|
||||
self.model.update()
|
||||
return [c.ConstrName for c in self.model.getConstrs()]
|
||||
|
||||
def extract_constraint(self, cid):
|
||||
self._raise_if_callback()
|
||||
constr = self.model.getConstrByName(cid)
|
||||
cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
|
||||
self.model.remove(constr)
|
||||
return cobj
|
||||
|
||||
def is_constraint_satisfied(self, cobj, tol=1e-5):
|
||||
lhs, sense, rhs, name = cobj
|
||||
if self.cb_where is not None:
|
||||
lhs_value = lhs.getConstant()
|
||||
for i in range(lhs.size()):
|
||||
var = lhs.getVar(i)
|
||||
coeff = lhs.getCoeff(i)
|
||||
lhs_value += self._get_value(var) * coeff
|
||||
else:
|
||||
lhs_value = lhs.getValue()
|
||||
if sense == "<":
|
||||
return lhs_value <= rhs + tol
|
||||
elif sense == ">":
|
||||
return lhs_value >= rhs - tol
|
||||
elif sense == "=":
|
||||
return abs(rhs - lhs_value) < abs(tol)
|
||||
else:
|
||||
raise Exception("Unknown sense: %s" % sense)
|
||||
|
||||
def get_inequality_slacks(self) -> Dict[str, float]:
|
||||
assert self.model is not None
|
||||
ineqs = [c for c in self.model.getConstrs() if c.sense != "="]
|
||||
return {c.ConstrName: c.Slack for c in ineqs}
|
||||
|
||||
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
c.Sense = sense
|
||||
|
||||
def get_constraint_sense(self, cid: str) -> str:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
return c.Sense
|
||||
|
||||
def relax(self) -> None:
|
||||
assert self.model is not None
|
||||
self.model = self.model.relax()
|
||||
self._update_vars()
|
||||
|
||||
def _extract_warm_start_value(self, log: str) -> Optional[float]:
|
||||
ws = self.__extract(log, "MIP start with objective ([0-9.e+-]*)")
|
||||
if ws is None:
|
||||
return None
|
||||
return float(ws)
|
||||
|
||||
@staticmethod
|
||||
def __extract(
|
||||
log: str,
|
||||
regexp: str,
|
||||
default: Optional[str] = None,
|
||||
) -> Optional[str]:
|
||||
value = default
|
||||
for line in log.splitlines():
|
||||
matches = re.findall(regexp, line)
|
||||
if len(matches) == 0:
|
||||
continue
|
||||
value = matches[0]
|
||||
return value
|
||||
|
||||
def __getstate__(self):
|
||||
return {
|
||||
"params": self.params,
|
||||
"lazy_cb_where": self.lazy_cb_where,
|
||||
}
|
||||
|
||||
def __setstate__(self, state):
|
||||
|
||||
self.params = state["params"]
|
||||
self.lazy_cb_where = state["lazy_cb_where"]
|
||||
self.instance = None
|
||||
self.model = None
|
||||
self._all_vars = None
|
||||
self._bin_vars = None
|
||||
self.cb_where = None</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.gurobi.GurobiSolver"><code class="flex name class">
|
||||
<span>class <span class="ident">GurobiSolver</span></span>
|
||||
<span>(</span><span>params=None, lazy_cb_frequency=1)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An InternalSolver backed by Gurobi's Python API (without Pyomo).</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>params</code></strong> : <code>Optional</code>[<code>SolverParams</code>]</dt>
|
||||
<dd>Parameters to pass to Gurobi. For example, <code>params={"MIPGap": 1e-3}</code>
|
||||
sets the gap tolerance to 1e-3.</dd>
|
||||
<dt><strong><code>lazy_cb_frequency</code></strong> : <code>int</code></dt>
|
||||
<dd>If 1, calls lazy constraint callbacks whenever an integer solution
|
||||
is found. If 2, calls it also at every node, after solving the
|
||||
LP relaxation of that node.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class GurobiSolver(InternalSolver):
|
||||
"""
|
||||
An InternalSolver backed by Gurobi's Python API (without Pyomo).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: Optional[SolverParams]
|
||||
Parameters to pass to Gurobi. For example, `params={"MIPGap": 1e-3}`
|
||||
sets the gap tolerance to 1e-3.
|
||||
lazy_cb_frequency: int
|
||||
If 1, calls lazy constraint callbacks whenever an integer solution
|
||||
is found. If 2, calls it also at every node, after solving the
|
||||
LP relaxation of that node.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: Optional[SolverParams] = None,
|
||||
lazy_cb_frequency: int = 1,
|
||||
) -> None:
|
||||
import gurobipy
|
||||
|
||||
if params is None:
|
||||
params = {}
|
||||
params["InfUnbdInfo"] = True
|
||||
|
||||
self.gp = gurobipy
|
||||
self.instance: Optional[Instance] = None
|
||||
self.model: Optional["gurobipy.Model"] = None
|
||||
self.params: SolverParams = params
|
||||
self._all_vars: Dict = {}
|
||||
self._bin_vars: Optional[Dict[str, Dict[VarIndex, "gurobipy.Var"]]] = None
|
||||
self.cb_where: Optional[int] = None
|
||||
|
||||
assert lazy_cb_frequency in [1, 2]
|
||||
if lazy_cb_frequency == 1:
|
||||
self.lazy_cb_where = [self.gp.GRB.Callback.MIPSOL]
|
||||
else:
|
||||
self.lazy_cb_where = [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]
|
||||
|
||||
def set_instance(
|
||||
self,
|
||||
instance: Instance,
|
||||
model: Any = None,
|
||||
) -> None:
|
||||
self._raise_if_callback()
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
assert isinstance(model, self.gp.Model)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self.model.update()
|
||||
self._update_vars()
|
||||
|
||||
def _raise_if_callback(self) -> None:
|
||||
if self.cb_where is not None:
|
||||
raise Exception("method cannot be called from a callback")
|
||||
|
||||
def _update_vars(self) -> None:
|
||||
assert self.model is not None
|
||||
self._all_vars = {}
|
||||
self._bin_vars = {}
|
||||
idx: VarIndex
|
||||
for var in self.model.getVars():
|
||||
m = re.search(r"([^[]*)\[(.*)]", var.varName)
|
||||
if m is None:
|
||||
name = var.varName
|
||||
idx = [0]
|
||||
else:
|
||||
name = m.group(1)
|
||||
parts = m.group(2).split(",")
|
||||
idx = [int(k) if k.isdecimal else k for k in parts]
|
||||
if len(idx) == 1:
|
||||
idx = idx[0]
|
||||
if name not in self._all_vars:
|
||||
self._all_vars[name] = {}
|
||||
self._all_vars[name][idx] = var
|
||||
if var.vtype != "C":
|
||||
if name not in self._bin_vars:
|
||||
self._bin_vars[name] = {}
|
||||
self._bin_vars[name][idx] = var
|
||||
|
||||
def _apply_params(self, streams: List[Any]) -> None:
|
||||
assert self.model is not None
|
||||
with _RedirectOutput(streams):
|
||||
for (name, value) in self.params.items():
|
||||
self.model.setParam(name, value)
|
||||
if "seed" not in [k.lower() for k in self.params.keys()]:
|
||||
self.model.setParam("Seed", randint(0, 1_000_000))
|
||||
|
||||
def solve_lp(
|
||||
self,
|
||||
tee: bool = False,
|
||||
) -> LPSolveStats:
|
||||
self._raise_if_callback()
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
self._apply_params(streams)
|
||||
assert self.model is not None
|
||||
assert self._bin_vars is not None
|
||||
for (varname, vardict) in self._bin_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.vtype = self.gp.GRB.CONTINUOUS
|
||||
var.lb = 0.0
|
||||
var.ub = 1.0
|
||||
with _RedirectOutput(streams):
|
||||
self.model.optimize()
|
||||
for (varname, vardict) in self._bin_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.vtype = self.gp.GRB.BINARY
|
||||
log = streams[0].getvalue()
|
||||
opt_value = None
|
||||
if not self.is_infeasible():
|
||||
opt_value = self.model.objVal
|
||||
return {
|
||||
"Optimal value": opt_value,
|
||||
"Log": log,
|
||||
}
|
||||
|
||||
def solve(
|
||||
self,
|
||||
tee: bool = False,
|
||||
iteration_cb: IterationCallback = None,
|
||||
lazy_cb: LazyCallback = None,
|
||||
) -> MIPSolveStats:
|
||||
self._raise_if_callback()
|
||||
assert self.model is not None
|
||||
|
||||
def cb_wrapper(cb_model, cb_where):
|
||||
try:
|
||||
self.cb_where = cb_where
|
||||
if cb_where in self.lazy_cb_where:
|
||||
lazy_cb(self, self.model)
|
||||
except:
|
||||
logger.exception("callback error")
|
||||
finally:
|
||||
self.cb_where = None
|
||||
|
||||
if lazy_cb:
|
||||
self.params["LazyConstraints"] = 1
|
||||
total_wallclock_time = 0
|
||||
total_nodes = 0
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
self._apply_params(streams)
|
||||
if iteration_cb is None:
|
||||
iteration_cb = lambda: False
|
||||
while True:
|
||||
with _RedirectOutput(streams):
|
||||
if lazy_cb is None:
|
||||
self.model.optimize()
|
||||
else:
|
||||
self.model.optimize(cb_wrapper)
|
||||
total_wallclock_time += self.model.runtime
|
||||
total_nodes += int(self.model.nodeCount)
|
||||
should_repeat = iteration_cb()
|
||||
if not should_repeat:
|
||||
break
|
||||
log = streams[0].getvalue()
|
||||
ub, lb = None, None
|
||||
sense = "min" if self.model.modelSense == 1 else "max"
|
||||
if self.model.solCount > 0:
|
||||
if self.model.modelSense == 1:
|
||||
lb = self.model.objBound
|
||||
ub = self.model.objVal
|
||||
else:
|
||||
lb = self.model.objVal
|
||||
ub = self.model.objBound
|
||||
ws_value = self._extract_warm_start_value(log)
|
||||
stats: MIPSolveStats = {
|
||||
"Lower bound": lb,
|
||||
"Upper bound": ub,
|
||||
"Wallclock time": total_wallclock_time,
|
||||
"Nodes": total_nodes,
|
||||
"Sense": sense,
|
||||
"Log": log,
|
||||
"Warm start value": ws_value,
|
||||
"LP value": None,
|
||||
}
|
||||
return stats
|
||||
|
||||
def get_solution(self) -> Optional[Solution]:
|
||||
self._raise_if_callback()
|
||||
assert self.model is not None
|
||||
if self.model.solCount == 0:
|
||||
return None
|
||||
solution: Solution = {}
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
solution[varname] = {}
|
||||
for (idx, var) in vardict.items():
|
||||
solution[varname][idx] = var.x
|
||||
return solution
|
||||
|
||||
def set_warm_start(self, solution: Solution) -> None:
|
||||
self._raise_if_callback()
|
||||
self._clear_warm_start()
|
||||
count_fixed, count_total = 0, 0
|
||||
for (varname, vardict) in solution.items():
|
||||
for (idx, value) in vardict.items():
|
||||
count_total += 1
|
||||
if value is not None:
|
||||
count_fixed += 1
|
||||
self._all_vars[varname][idx].start = value
|
||||
logger.info(
|
||||
"Setting start values for %d variables (out of %d)"
|
||||
% (count_fixed, count_total)
|
||||
)
|
||||
|
||||
def get_sense(self) -> str:
|
||||
assert self.model is not None
|
||||
if self.model.modelSense == 1:
|
||||
return "min"
|
||||
else:
|
||||
return "max"
|
||||
|
||||
def get_value(
|
||||
self,
|
||||
var_name: str,
|
||||
index: VarIndex,
|
||||
) -> Optional[float]:
|
||||
var = self._all_vars[var_name][index]
|
||||
return self._get_value(var)
|
||||
|
||||
def is_infeasible(self) -> bool:
|
||||
assert self.model is not None
|
||||
return self.model.status in [self.gp.GRB.INFEASIBLE, self.gp.GRB.INF_OR_UNBD]
|
||||
|
||||
def get_dual(self, cid: str) -> float:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
if self.is_infeasible():
|
||||
return c.farkasDual
|
||||
else:
|
||||
return c.pi
|
||||
|
||||
def _get_value(self, var: Any) -> Optional[float]:
|
||||
assert self.model is not None
|
||||
if self.cb_where == self.gp.GRB.Callback.MIPSOL:
|
||||
return self.model.cbGetSolution(var)
|
||||
elif self.cb_where == self.gp.GRB.Callback.MIPNODE:
|
||||
return self.model.cbGetNodeRel(var)
|
||||
elif self.cb_where is None:
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
else:
|
||||
return var.x
|
||||
else:
|
||||
raise Exception(
|
||||
"get_value cannot be called from cb_where=%s" % self.cb_where
|
||||
)
|
||||
|
||||
def get_empty_solution(self) -> Solution:
|
||||
self._raise_if_callback()
|
||||
solution: Solution = {}
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
solution[varname] = {}
|
||||
for (idx, var) in vardict.items():
|
||||
solution[varname][idx] = None
|
||||
return solution
|
||||
|
||||
def add_constraint(
|
||||
self,
|
||||
constraint: Any,
|
||||
name: str = "",
|
||||
) -> None:
|
||||
assert self.model is not None
|
||||
if type(constraint) is tuple:
|
||||
lhs, sense, rhs, name = constraint
|
||||
if self.cb_where in [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]:
|
||||
self.model.cbLazy(lhs, sense, rhs)
|
||||
else:
|
||||
self.model.addConstr(lhs, sense, rhs, name)
|
||||
else:
|
||||
if self.cb_where in [
|
||||
self.gp.GRB.Callback.MIPSOL,
|
||||
self.gp.GRB.Callback.MIPNODE,
|
||||
]:
|
||||
self.model.cbLazy(constraint)
|
||||
else:
|
||||
self.model.addConstr(constraint, name=name)
|
||||
|
||||
def _clear_warm_start(self) -> None:
|
||||
for (varname, vardict) in self._all_vars.items():
|
||||
for (idx, var) in vardict.items():
|
||||
var.start = self.gp.GRB.UNDEFINED
|
||||
|
||||
def fix(self, solution: Solution) -> None:
|
||||
self._raise_if_callback()
|
||||
for (varname, vardict) in solution.items():
|
||||
for (idx, value) in vardict.items():
|
||||
if value is None:
|
||||
continue
|
||||
var = self._all_vars[varname][idx]
|
||||
var.vtype = self.gp.GRB.CONTINUOUS
|
||||
var.lb = value
|
||||
var.ub = value
|
||||
|
||||
def get_constraint_ids(self):
|
||||
self._raise_if_callback()
|
||||
self.model.update()
|
||||
return [c.ConstrName for c in self.model.getConstrs()]
|
||||
|
||||
def extract_constraint(self, cid):
|
||||
self._raise_if_callback()
|
||||
constr = self.model.getConstrByName(cid)
|
||||
cobj = (self.model.getRow(constr), constr.sense, constr.RHS, constr.ConstrName)
|
||||
self.model.remove(constr)
|
||||
return cobj
|
||||
|
||||
def is_constraint_satisfied(self, cobj, tol=1e-5):
|
||||
lhs, sense, rhs, name = cobj
|
||||
if self.cb_where is not None:
|
||||
lhs_value = lhs.getConstant()
|
||||
for i in range(lhs.size()):
|
||||
var = lhs.getVar(i)
|
||||
coeff = lhs.getCoeff(i)
|
||||
lhs_value += self._get_value(var) * coeff
|
||||
else:
|
||||
lhs_value = lhs.getValue()
|
||||
if sense == "<":
|
||||
return lhs_value <= rhs + tol
|
||||
elif sense == ">":
|
||||
return lhs_value >= rhs - tol
|
||||
elif sense == "=":
|
||||
return abs(rhs - lhs_value) < abs(tol)
|
||||
else:
|
||||
raise Exception("Unknown sense: %s" % sense)
|
||||
|
||||
def get_inequality_slacks(self) -> Dict[str, float]:
|
||||
assert self.model is not None
|
||||
ineqs = [c for c in self.model.getConstrs() if c.sense != "="]
|
||||
return {c.ConstrName: c.Slack for c in ineqs}
|
||||
|
||||
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
c.Sense = sense
|
||||
|
||||
def get_constraint_sense(self, cid: str) -> str:
|
||||
assert self.model is not None
|
||||
c = self.model.getConstrByName(cid)
|
||||
return c.Sense
|
||||
|
||||
def relax(self) -> None:
|
||||
assert self.model is not None
|
||||
self.model = self.model.relax()
|
||||
self._update_vars()
|
||||
|
||||
def _extract_warm_start_value(self, log: str) -> Optional[float]:
|
||||
ws = self.__extract(log, "MIP start with objective ([0-9.e+-]*)")
|
||||
if ws is None:
|
||||
return None
|
||||
return float(ws)
|
||||
|
||||
@staticmethod
|
||||
def __extract(
|
||||
log: str,
|
||||
regexp: str,
|
||||
default: Optional[str] = None,
|
||||
) -> Optional[str]:
|
||||
value = default
|
||||
for line in log.splitlines():
|
||||
matches = re.findall(regexp, line)
|
||||
if len(matches) == 0:
|
||||
continue
|
||||
value = matches[0]
|
||||
return value
|
||||
|
||||
def __getstate__(self):
|
||||
return {
|
||||
"params": self.params,
|
||||
"lazy_cb_where": self.lazy_cb_where,
|
||||
}
|
||||
|
||||
def __setstate__(self, state):
|
||||
|
||||
self.params = state["params"]
|
||||
self.lazy_cb_where = state["lazy_cb_where"]
|
||||
self.instance = None
|
||||
self.model = None
|
||||
self._all_vars = None
|
||||
self._bin_vars = None
|
||||
self.cb_where = None</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.internal.InternalSolver" href="internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.solvers.internal.InternalSolver" href="internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.add_constraint" href="internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.extract_constraint" href="internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.fix" href="internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_ids" href="internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_dual" href="internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_empty_solution" href="internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_inequality_slacks" href="internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_solution" href="internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_value" href="internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied" href="internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_infeasible" href="internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.relax" href="internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_branching_priorities" href="internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_constraint_sense" href="internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_instance" href="internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_warm_start" href="internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve" href="internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve_lp" href="internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
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|
||||
<nav id="sidebar">
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers" href="index.html">miplearn.solvers</a></code></li>
|
||||
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|
||||
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|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
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|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.gurobi.GurobiSolver" href="#miplearn.solvers.gurobi.GurobiSolver">GurobiSolver</a></code></h4>
|
||||
</li>
|
||||
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|
||||
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|
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|
||||
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import sys
|
||||
from typing import Any, List
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class _RedirectOutput:
|
||||
def __init__(self, streams: List[Any]):
|
||||
self.streams = streams
|
||||
|
||||
def write(self, data: Any) -> None:
|
||||
for stream in self.streams:
|
||||
stream.write(data)
|
||||
|
||||
def flush(self) -> None:
|
||||
for stream in self.streams:
|
||||
stream.flush()
|
||||
|
||||
def __enter__(self):
|
||||
self._original_stdout = sys.stdout
|
||||
self._original_stderr = sys.stderr
|
||||
sys.stdout = self
|
||||
sys.stderr = self
|
||||
return self
|
||||
|
||||
def __exit__(self, _type, _value, _traceback):
|
||||
sys.stdout = self._original_stdout
|
||||
sys.stderr = self._original_stderr</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.solvers.gurobi" href="gurobi.html">miplearn.solvers.gurobi</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.internal" href="internal.html">miplearn.solvers.internal</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.learning" href="learning.html">miplearn.solvers.learning</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.pyomo" href="pyomo/index.html">miplearn.solvers.pyomo</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.tests" href="tests/index.html">miplearn.solvers.tests</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.gurobi" href="gurobi.html">miplearn.solvers.gurobi</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal" href="internal.html">miplearn.solvers.internal</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.learning" href="learning.html">miplearn.solvers.learning</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo" href="pyomo/index.html">miplearn.solvers.pyomo</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests" href="tests/index.html">miplearn.solvers.tests</a></code></li>
|
||||
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|
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|
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|
||||
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|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.pyomo.base</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import re
|
||||
import sys
|
||||
from io import StringIO
|
||||
from typing import Any, List, Dict, Optional
|
||||
|
||||
import pyomo
|
||||
from pyomo import environ as pe
|
||||
from pyomo.core import Var, Constraint
|
||||
from pyomo.opt import TerminationCondition
|
||||
from pyomo.opt.base.solvers import SolverFactory
|
||||
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.solvers import _RedirectOutput
|
||||
from miplearn.solvers.internal import (
|
||||
InternalSolver,
|
||||
LPSolveStats,
|
||||
IterationCallback,
|
||||
LazyCallback,
|
||||
MIPSolveStats,
|
||||
)
|
||||
from miplearn.types import VarIndex, SolverParams, Solution
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class BasePyomoSolver(InternalSolver):
|
||||
"""
|
||||
Base class for all Pyomo solvers.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
solver_factory: SolverFactory,
|
||||
params: SolverParams,
|
||||
) -> None:
|
||||
self.instance: Optional[Instance] = None
|
||||
self.model: Optional[pe.ConcreteModel] = None
|
||||
self._all_vars: List[pe.Var] = []
|
||||
self._bin_vars: List[pe.Var] = []
|
||||
self._is_warm_start_available: bool = False
|
||||
self._pyomo_solver: SolverFactory = solver_factory
|
||||
self._obj_sense: str = "min"
|
||||
self._varname_to_var: Dict[str, pe.Var] = {}
|
||||
self._cname_to_constr: Dict[str, pe.Constraint] = {}
|
||||
self._termination_condition: str = ""
|
||||
|
||||
for (key, value) in params.items():
|
||||
self._pyomo_solver.options[key] = value
|
||||
|
||||
def solve_lp(
|
||||
self,
|
||||
tee: bool = False,
|
||||
) -> LPSolveStats:
|
||||
self.relax()
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
with _RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(tee=True)
|
||||
self._restore_integrality()
|
||||
opt_value = None
|
||||
if not self.is_infeasible():
|
||||
opt_value = results["Problem"][0]["Lower bound"]
|
||||
return {
|
||||
"Optimal value": opt_value,
|
||||
"Log": streams[0].getvalue(),
|
||||
}
|
||||
|
||||
def _restore_integrality(self) -> None:
|
||||
for var in self._bin_vars:
|
||||
var.domain = pyomo.core.base.set_types.Binary
|
||||
self._pyomo_solver.update_var(var)
|
||||
|
||||
def solve(
|
||||
self,
|
||||
tee: bool = False,
|
||||
iteration_cb: IterationCallback = None,
|
||||
lazy_cb: LazyCallback = None,
|
||||
) -> MIPSolveStats:
|
||||
if lazy_cb is not None:
|
||||
raise Exception("lazy callback not supported")
|
||||
total_wallclock_time = 0
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
if iteration_cb is None:
|
||||
iteration_cb = lambda: False
|
||||
while True:
|
||||
logger.debug("Solving MIP...")
|
||||
with _RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(
|
||||
tee=True,
|
||||
warmstart=self._is_warm_start_available,
|
||||
)
|
||||
total_wallclock_time += results["Solver"][0]["Wallclock time"]
|
||||
should_repeat = iteration_cb()
|
||||
if not should_repeat:
|
||||
break
|
||||
log = streams[0].getvalue()
|
||||
node_count = self._extract_node_count(log)
|
||||
ws_value = self._extract_warm_start_value(log)
|
||||
self._termination_condition = results["Solver"][0]["Termination condition"]
|
||||
lb, ub = None, None
|
||||
if not self.is_infeasible():
|
||||
lb = results["Problem"][0]["Lower bound"]
|
||||
ub = results["Problem"][0]["Upper bound"]
|
||||
stats: MIPSolveStats = {
|
||||
"Lower bound": lb,
|
||||
"Upper bound": ub,
|
||||
"Wallclock time": total_wallclock_time,
|
||||
"Sense": self._obj_sense,
|
||||
"Log": log,
|
||||
"Nodes": node_count,
|
||||
"Warm start value": ws_value,
|
||||
"LP value": None,
|
||||
}
|
||||
return stats
|
||||
|
||||
def get_solution(self) -> Optional[Solution]:
|
||||
assert self.model is not None
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
solution: Solution = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
solution[str(var)] = {}
|
||||
for index in var:
|
||||
if var[index].fixed:
|
||||
continue
|
||||
solution[str(var)][index] = var[index].value
|
||||
return solution
|
||||
|
||||
def set_warm_start(self, solution: Solution) -> None:
|
||||
self._clear_warm_start()
|
||||
count_total, count_fixed = 0, 0
|
||||
for var_name in solution:
|
||||
var = self._varname_to_var[var_name]
|
||||
for index in solution[var_name]:
|
||||
count_total += 1
|
||||
var[index].value = solution[var_name][index]
|
||||
if solution[var_name][index] is not None:
|
||||
count_fixed += 1
|
||||
if count_fixed > 0:
|
||||
self._is_warm_start_available = True
|
||||
logger.info(
|
||||
"Setting start values for %d variables (out of %d)"
|
||||
% (count_fixed, count_total)
|
||||
)
|
||||
|
||||
def set_instance(
|
||||
self,
|
||||
instance: Instance,
|
||||
model: Any = None,
|
||||
) -> None:
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
assert isinstance(model, pe.ConcreteModel)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self._pyomo_solver.set_instance(model)
|
||||
self._update_obj()
|
||||
self._update_vars()
|
||||
self._update_constrs()
|
||||
|
||||
def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
else:
|
||||
var = self._varname_to_var[var_name]
|
||||
return var[index].value
|
||||
|
||||
def get_empty_solution(self) -> Solution:
|
||||
assert self.model is not None
|
||||
solution: Solution = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
svar = str(var)
|
||||
solution[svar] = {}
|
||||
for index in var:
|
||||
if var[index].fixed:
|
||||
continue
|
||||
solution[svar][index] = None
|
||||
return solution
|
||||
|
||||
def _clear_warm_start(self) -> None:
|
||||
for var in self._all_vars:
|
||||
if not var.fixed:
|
||||
var.value = None
|
||||
self._is_warm_start_available = False
|
||||
|
||||
def _update_obj(self) -> None:
|
||||
self._obj_sense = "max"
|
||||
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
|
||||
self._obj_sense = "min"
|
||||
|
||||
def _update_vars(self) -> None:
|
||||
assert self.model is not None
|
||||
self._all_vars = []
|
||||
self._bin_vars = []
|
||||
self._varname_to_var = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
self._varname_to_var[var.name] = var
|
||||
for idx in var:
|
||||
self._all_vars += [var[idx]]
|
||||
if var[idx].domain == pyomo.core.base.set_types.Binary:
|
||||
self._bin_vars += [var[idx]]
|
||||
|
||||
def _update_constrs(self) -> None:
|
||||
assert self.model is not None
|
||||
self._cname_to_constr = {}
|
||||
for constr in self.model.component_objects(Constraint):
|
||||
self._cname_to_constr[constr.name] = constr
|
||||
|
||||
def fix(self, solution):
|
||||
count_total, count_fixed = 0, 0
|
||||
for varname in solution:
|
||||
for index in solution[varname]:
|
||||
var = self._varname_to_var[varname]
|
||||
count_total += 1
|
||||
if solution[varname][index] is None:
|
||||
continue
|
||||
count_fixed += 1
|
||||
var[index].fix(solution[varname][index])
|
||||
self._pyomo_solver.update_var(var[index])
|
||||
logger.info(
|
||||
"Fixing values for %d variables (out of %d)"
|
||||
% (
|
||||
count_fixed,
|
||||
count_total,
|
||||
)
|
||||
)
|
||||
|
||||
def add_constraint(self, constraint):
|
||||
self._pyomo_solver.add_constraint(constraint)
|
||||
self._update_constrs()
|
||||
|
||||
@staticmethod
|
||||
def __extract(
|
||||
log: str,
|
||||
regexp: Optional[str],
|
||||
default: Optional[str] = None,
|
||||
) -> Optional[str]:
|
||||
if regexp is None:
|
||||
return default
|
||||
value = default
|
||||
for line in log.splitlines():
|
||||
matches = re.findall(regexp, line)
|
||||
if len(matches) == 0:
|
||||
continue
|
||||
value = matches[0]
|
||||
return value
|
||||
|
||||
def _extract_warm_start_value(self, log: str) -> Optional[float]:
|
||||
value = self.__extract(log, self._get_warm_start_regexp())
|
||||
if value is None:
|
||||
return None
|
||||
return float(value)
|
||||
|
||||
def _extract_node_count(self, log: str) -> Optional[int]:
|
||||
value = self.__extract(log, self._get_node_count_regexp())
|
||||
if value is None:
|
||||
return None
|
||||
return int(value)
|
||||
|
||||
def get_constraint_ids(self):
|
||||
return list(self._cname_to_constr.keys())
|
||||
|
||||
def _get_warm_start_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def _get_node_count_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def relax(self) -> None:
|
||||
for var in self._bin_vars:
|
||||
lb, ub = var.bounds
|
||||
var.setlb(lb)
|
||||
var.setub(ub)
|
||||
var.domain = pyomo.core.base.set_types.Reals
|
||||
self._pyomo_solver.update_var(var)
|
||||
|
||||
def get_inequality_slacks(self) -> Dict[str, float]:
|
||||
result: Dict[str, float] = {}
|
||||
for (cname, cobj) in self._cname_to_constr.items():
|
||||
if cobj.equality:
|
||||
continue
|
||||
result[cname] = cobj.slack()
|
||||
return result
|
||||
|
||||
def get_constraint_sense(self, cid: str) -> str:
|
||||
cobj = self._cname_to_constr[cid]
|
||||
has_ub = cobj.has_ub()
|
||||
has_lb = cobj.has_lb()
|
||||
assert (not has_lb) or (not has_ub), "range constraints not supported"
|
||||
if has_lb:
|
||||
return ">"
|
||||
elif has_ub:
|
||||
return "<"
|
||||
else:
|
||||
return "="
|
||||
|
||||
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def extract_constraint(self, cid: str) -> Constraint:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def is_constraint_satisfied(self, cobj: Constraint) -> bool:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def is_infeasible(self) -> bool:
|
||||
return self._termination_condition == TerminationCondition.infeasible
|
||||
|
||||
def get_dual(self, cid):
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def get_sense(self) -> str:
|
||||
return self._obj_sense</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.pyomo.base.BasePyomoSolver"><code class="flex name class">
|
||||
<span>class <span class="ident">BasePyomoSolver</span></span>
|
||||
<span>(</span><span>solver_factory, params)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Base class for all Pyomo solvers.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class BasePyomoSolver(InternalSolver):
|
||||
"""
|
||||
Base class for all Pyomo solvers.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
solver_factory: SolverFactory,
|
||||
params: SolverParams,
|
||||
) -> None:
|
||||
self.instance: Optional[Instance] = None
|
||||
self.model: Optional[pe.ConcreteModel] = None
|
||||
self._all_vars: List[pe.Var] = []
|
||||
self._bin_vars: List[pe.Var] = []
|
||||
self._is_warm_start_available: bool = False
|
||||
self._pyomo_solver: SolverFactory = solver_factory
|
||||
self._obj_sense: str = "min"
|
||||
self._varname_to_var: Dict[str, pe.Var] = {}
|
||||
self._cname_to_constr: Dict[str, pe.Constraint] = {}
|
||||
self._termination_condition: str = ""
|
||||
|
||||
for (key, value) in params.items():
|
||||
self._pyomo_solver.options[key] = value
|
||||
|
||||
def solve_lp(
|
||||
self,
|
||||
tee: bool = False,
|
||||
) -> LPSolveStats:
|
||||
self.relax()
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
with _RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(tee=True)
|
||||
self._restore_integrality()
|
||||
opt_value = None
|
||||
if not self.is_infeasible():
|
||||
opt_value = results["Problem"][0]["Lower bound"]
|
||||
return {
|
||||
"Optimal value": opt_value,
|
||||
"Log": streams[0].getvalue(),
|
||||
}
|
||||
|
||||
def _restore_integrality(self) -> None:
|
||||
for var in self._bin_vars:
|
||||
var.domain = pyomo.core.base.set_types.Binary
|
||||
self._pyomo_solver.update_var(var)
|
||||
|
||||
def solve(
|
||||
self,
|
||||
tee: bool = False,
|
||||
iteration_cb: IterationCallback = None,
|
||||
lazy_cb: LazyCallback = None,
|
||||
) -> MIPSolveStats:
|
||||
if lazy_cb is not None:
|
||||
raise Exception("lazy callback not supported")
|
||||
total_wallclock_time = 0
|
||||
streams: List[Any] = [StringIO()]
|
||||
if tee:
|
||||
streams += [sys.stdout]
|
||||
if iteration_cb is None:
|
||||
iteration_cb = lambda: False
|
||||
while True:
|
||||
logger.debug("Solving MIP...")
|
||||
with _RedirectOutput(streams):
|
||||
results = self._pyomo_solver.solve(
|
||||
tee=True,
|
||||
warmstart=self._is_warm_start_available,
|
||||
)
|
||||
total_wallclock_time += results["Solver"][0]["Wallclock time"]
|
||||
should_repeat = iteration_cb()
|
||||
if not should_repeat:
|
||||
break
|
||||
log = streams[0].getvalue()
|
||||
node_count = self._extract_node_count(log)
|
||||
ws_value = self._extract_warm_start_value(log)
|
||||
self._termination_condition = results["Solver"][0]["Termination condition"]
|
||||
lb, ub = None, None
|
||||
if not self.is_infeasible():
|
||||
lb = results["Problem"][0]["Lower bound"]
|
||||
ub = results["Problem"][0]["Upper bound"]
|
||||
stats: MIPSolveStats = {
|
||||
"Lower bound": lb,
|
||||
"Upper bound": ub,
|
||||
"Wallclock time": total_wallclock_time,
|
||||
"Sense": self._obj_sense,
|
||||
"Log": log,
|
||||
"Nodes": node_count,
|
||||
"Warm start value": ws_value,
|
||||
"LP value": None,
|
||||
}
|
||||
return stats
|
||||
|
||||
def get_solution(self) -> Optional[Solution]:
|
||||
assert self.model is not None
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
solution: Solution = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
solution[str(var)] = {}
|
||||
for index in var:
|
||||
if var[index].fixed:
|
||||
continue
|
||||
solution[str(var)][index] = var[index].value
|
||||
return solution
|
||||
|
||||
def set_warm_start(self, solution: Solution) -> None:
|
||||
self._clear_warm_start()
|
||||
count_total, count_fixed = 0, 0
|
||||
for var_name in solution:
|
||||
var = self._varname_to_var[var_name]
|
||||
for index in solution[var_name]:
|
||||
count_total += 1
|
||||
var[index].value = solution[var_name][index]
|
||||
if solution[var_name][index] is not None:
|
||||
count_fixed += 1
|
||||
if count_fixed > 0:
|
||||
self._is_warm_start_available = True
|
||||
logger.info(
|
||||
"Setting start values for %d variables (out of %d)"
|
||||
% (count_fixed, count_total)
|
||||
)
|
||||
|
||||
def set_instance(
|
||||
self,
|
||||
instance: Instance,
|
||||
model: Any = None,
|
||||
) -> None:
|
||||
if model is None:
|
||||
model = instance.to_model()
|
||||
assert isinstance(model, pe.ConcreteModel)
|
||||
self.instance = instance
|
||||
self.model = model
|
||||
self._pyomo_solver.set_instance(model)
|
||||
self._update_obj()
|
||||
self._update_vars()
|
||||
self._update_constrs()
|
||||
|
||||
def get_value(self, var_name: str, index: VarIndex) -> Optional[float]:
|
||||
if self.is_infeasible():
|
||||
return None
|
||||
else:
|
||||
var = self._varname_to_var[var_name]
|
||||
return var[index].value
|
||||
|
||||
def get_empty_solution(self) -> Solution:
|
||||
assert self.model is not None
|
||||
solution: Solution = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
svar = str(var)
|
||||
solution[svar] = {}
|
||||
for index in var:
|
||||
if var[index].fixed:
|
||||
continue
|
||||
solution[svar][index] = None
|
||||
return solution
|
||||
|
||||
def _clear_warm_start(self) -> None:
|
||||
for var in self._all_vars:
|
||||
if not var.fixed:
|
||||
var.value = None
|
||||
self._is_warm_start_available = False
|
||||
|
||||
def _update_obj(self) -> None:
|
||||
self._obj_sense = "max"
|
||||
if self._pyomo_solver._objective.sense == pyomo.core.kernel.objective.minimize:
|
||||
self._obj_sense = "min"
|
||||
|
||||
def _update_vars(self) -> None:
|
||||
assert self.model is not None
|
||||
self._all_vars = []
|
||||
self._bin_vars = []
|
||||
self._varname_to_var = {}
|
||||
for var in self.model.component_objects(Var):
|
||||
self._varname_to_var[var.name] = var
|
||||
for idx in var:
|
||||
self._all_vars += [var[idx]]
|
||||
if var[idx].domain == pyomo.core.base.set_types.Binary:
|
||||
self._bin_vars += [var[idx]]
|
||||
|
||||
def _update_constrs(self) -> None:
|
||||
assert self.model is not None
|
||||
self._cname_to_constr = {}
|
||||
for constr in self.model.component_objects(Constraint):
|
||||
self._cname_to_constr[constr.name] = constr
|
||||
|
||||
def fix(self, solution):
|
||||
count_total, count_fixed = 0, 0
|
||||
for varname in solution:
|
||||
for index in solution[varname]:
|
||||
var = self._varname_to_var[varname]
|
||||
count_total += 1
|
||||
if solution[varname][index] is None:
|
||||
continue
|
||||
count_fixed += 1
|
||||
var[index].fix(solution[varname][index])
|
||||
self._pyomo_solver.update_var(var[index])
|
||||
logger.info(
|
||||
"Fixing values for %d variables (out of %d)"
|
||||
% (
|
||||
count_fixed,
|
||||
count_total,
|
||||
)
|
||||
)
|
||||
|
||||
def add_constraint(self, constraint):
|
||||
self._pyomo_solver.add_constraint(constraint)
|
||||
self._update_constrs()
|
||||
|
||||
@staticmethod
|
||||
def __extract(
|
||||
log: str,
|
||||
regexp: Optional[str],
|
||||
default: Optional[str] = None,
|
||||
) -> Optional[str]:
|
||||
if regexp is None:
|
||||
return default
|
||||
value = default
|
||||
for line in log.splitlines():
|
||||
matches = re.findall(regexp, line)
|
||||
if len(matches) == 0:
|
||||
continue
|
||||
value = matches[0]
|
||||
return value
|
||||
|
||||
def _extract_warm_start_value(self, log: str) -> Optional[float]:
|
||||
value = self.__extract(log, self._get_warm_start_regexp())
|
||||
if value is None:
|
||||
return None
|
||||
return float(value)
|
||||
|
||||
def _extract_node_count(self, log: str) -> Optional[int]:
|
||||
value = self.__extract(log, self._get_node_count_regexp())
|
||||
if value is None:
|
||||
return None
|
||||
return int(value)
|
||||
|
||||
def get_constraint_ids(self):
|
||||
return list(self._cname_to_constr.keys())
|
||||
|
||||
def _get_warm_start_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def _get_node_count_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def relax(self) -> None:
|
||||
for var in self._bin_vars:
|
||||
lb, ub = var.bounds
|
||||
var.setlb(lb)
|
||||
var.setub(ub)
|
||||
var.domain = pyomo.core.base.set_types.Reals
|
||||
self._pyomo_solver.update_var(var)
|
||||
|
||||
def get_inequality_slacks(self) -> Dict[str, float]:
|
||||
result: Dict[str, float] = {}
|
||||
for (cname, cobj) in self._cname_to_constr.items():
|
||||
if cobj.equality:
|
||||
continue
|
||||
result[cname] = cobj.slack()
|
||||
return result
|
||||
|
||||
def get_constraint_sense(self, cid: str) -> str:
|
||||
cobj = self._cname_to_constr[cid]
|
||||
has_ub = cobj.has_ub()
|
||||
has_lb = cobj.has_lb()
|
||||
assert (not has_lb) or (not has_ub), "range constraints not supported"
|
||||
if has_lb:
|
||||
return ">"
|
||||
elif has_ub:
|
||||
return "<"
|
||||
else:
|
||||
return "="
|
||||
|
||||
def set_constraint_sense(self, cid: str, sense: str) -> None:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def extract_constraint(self, cid: str) -> Constraint:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def is_constraint_satisfied(self, cobj: Constraint) -> bool:
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def is_infeasible(self) -> bool:
|
||||
return self._termination_condition == TerminationCondition.infeasible
|
||||
|
||||
def get_dual(self, cid):
|
||||
raise Exception("Not implemented")
|
||||
|
||||
def get_sense(self) -> str:
|
||||
return self._obj_sense</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Subclasses</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver" href="gurobi.html#miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver">GurobiPyomoSolver</a></li>
|
||||
<li><a title="miplearn.solvers.pyomo.cplex.CplexPyomoSolver" href="cplex.html#miplearn.solvers.pyomo.cplex.CplexPyomoSolver">CplexPyomoSolver</a></li>
|
||||
<li><a title="miplearn.solvers.pyomo.xpress.XpressPyomoSolver" href="xpress.html#miplearn.solvers.pyomo.xpress.XpressPyomoSolver">XpressPyomoSolver</a></li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.internal.InternalSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
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||||
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|
||||
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|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.pyomo.cplex</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
from typing import Optional
|
||||
|
||||
from pyomo import environ as pe
|
||||
from scipy.stats import randint
|
||||
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.types import SolverParams
|
||||
|
||||
|
||||
class CplexPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses CPLEX and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"mip_display": 5} to increase the log verbosity.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: Optional[SolverParams] = None,
|
||||
) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "randomseed" not in params.keys():
|
||||
params["randomseed"] = randint(low=0, high=1000).rvs()
|
||||
if "mip_display" not in params.keys():
|
||||
params["mip_display"] = 4
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("cplex_persistent"),
|
||||
params=params,
|
||||
)
|
||||
|
||||
def _get_warm_start_regexp(self):
|
||||
return "MIP start .* with objective ([0-9.e+-]*)\\."
|
||||
|
||||
def _get_node_count_regexp(self):
|
||||
return "^[ *] *([0-9]+)"</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.pyomo.cplex.CplexPyomoSolver"><code class="flex name class">
|
||||
<span>class <span class="ident">CplexPyomoSolver</span></span>
|
||||
<span>(</span><span>params=None)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An InternalSolver that uses CPLEX and the Pyomo modeling language.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>params</code></strong> : <code>dict</code></dt>
|
||||
<dd>Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"mip_display": 5} to increase the log verbosity.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class CplexPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses CPLEX and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"mip_display": 5} to increase the log verbosity.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: Optional[SolverParams] = None,
|
||||
) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "randomseed" not in params.keys():
|
||||
params["randomseed"] = randint(low=0, high=1000).rvs()
|
||||
if "mip_display" not in params.keys():
|
||||
params["mip_display"] = 4
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("cplex_persistent"),
|
||||
params=params,
|
||||
)
|
||||
|
||||
def _get_warm_start_regexp(self):
|
||||
return "MIP start .* with objective ([0-9.e+-]*)\\."
|
||||
|
||||
def _get_node_count_regexp(self):
|
||||
return "^[ *] *([0-9]+)"</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
|
||||
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.pyomo.cplex.CplexPyomoSolver" href="#miplearn.solvers.pyomo.cplex.CplexPyomoSolver">CplexPyomoSolver</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.pyomo.gurobi</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
from pyomo import environ as pe
|
||||
from scipy.stats import randint
|
||||
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.types import SolverParams, BranchPriorities
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class GurobiPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses Gurobi and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: SolverParams = None,
|
||||
) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "seed" not in params.keys():
|
||||
params["seed"] = randint(low=0, high=1000).rvs()
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("gurobi_persistent"),
|
||||
params=params,
|
||||
)
|
||||
|
||||
def _extract_node_count(self, log: str) -> int:
|
||||
return max(1, int(self._pyomo_solver._solver_model.getAttr("NodeCount")))
|
||||
|
||||
def _get_warm_start_regexp(self) -> str:
|
||||
return "MIP start with objective ([0-9.e+-]*)"
|
||||
|
||||
def _get_node_count_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def set_branching_priorities(self, priorities: BranchPriorities) -> None:
|
||||
from gurobipy import GRB
|
||||
|
||||
for varname in priorities.keys():
|
||||
var = self._varname_to_var[varname]
|
||||
for (index, priority) in priorities[varname].items():
|
||||
if priority is None:
|
||||
continue
|
||||
gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
|
||||
gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver"><code class="flex name class">
|
||||
<span>class <span class="ident">GurobiPyomoSolver</span></span>
|
||||
<span>(</span><span>params=None)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An InternalSolver that uses Gurobi and the Pyomo modeling language.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>params</code></strong> : <code>dict</code></dt>
|
||||
<dd>Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class GurobiPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses Gurobi and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
params: SolverParams = None,
|
||||
) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "seed" not in params.keys():
|
||||
params["seed"] = randint(low=0, high=1000).rvs()
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("gurobi_persistent"),
|
||||
params=params,
|
||||
)
|
||||
|
||||
def _extract_node_count(self, log: str) -> int:
|
||||
return max(1, int(self._pyomo_solver._solver_model.getAttr("NodeCount")))
|
||||
|
||||
def _get_warm_start_regexp(self) -> str:
|
||||
return "MIP start with objective ([0-9.e+-]*)"
|
||||
|
||||
def _get_node_count_regexp(self) -> Optional[str]:
|
||||
return None
|
||||
|
||||
def set_branching_priorities(self, priorities: BranchPriorities) -> None:
|
||||
from gurobipy import GRB
|
||||
|
||||
for varname in priorities.keys():
|
||||
var = self._varname_to_var[varname]
|
||||
for (index, priority) in priorities[varname].items():
|
||||
if priority is None:
|
||||
continue
|
||||
gvar = self._pyomo_solver._pyomo_var_to_solver_var_map[var[index]]
|
||||
gvar.setAttr(GRB.Attr.BranchPriority, int(round(priority)))</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
|
||||
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
|
||||
</ul>
|
||||
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|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver" href="#miplearn.solvers.pyomo.gurobi.GurobiPyomoSolver">GurobiPyomoSolver</a></code></h4>
|
||||
</li>
|
||||
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|
||||
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|
||||
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<main>
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||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.pyomo</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.solvers.pyomo.base" href="base.html">miplearn.solvers.pyomo.base</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.pyomo.cplex" href="cplex.html">miplearn.solvers.pyomo.cplex</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.pyomo.gurobi" href="gurobi.html">miplearn.solvers.pyomo.gurobi</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.pyomo.xpress" href="xpress.html">miplearn.solvers.pyomo.xpress</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
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|
||||
<section>
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers" href="../index.html">miplearn.solvers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base" href="base.html">miplearn.solvers.pyomo.base</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.cplex" href="cplex.html">miplearn.solvers.pyomo.cplex</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.gurobi" href="gurobi.html">miplearn.solvers.pyomo.gurobi</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.xpress" href="xpress.html">miplearn.solvers.pyomo.xpress</a></code></li>
|
||||
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
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|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.pyomo.xpress</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from pyomo import environ as pe
|
||||
from scipy.stats import randint
|
||||
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.types import SolverParams
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class XpressPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses XPRESS and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.
|
||||
"""
|
||||
|
||||
def __init__(self, params: SolverParams = None) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "randomseed" not in params.keys():
|
||||
params["randomseed"] = randint(low=0, high=1000).rvs()
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("xpress_persistent"),
|
||||
params=params,
|
||||
)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.pyomo.xpress.XpressPyomoSolver"><code class="flex name class">
|
||||
<span>class <span class="ident">XpressPyomoSolver</span></span>
|
||||
<span>(</span><span>params=None)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>An InternalSolver that uses XPRESS and the Pyomo modeling language.</p>
|
||||
<h2 id="parameters">Parameters</h2>
|
||||
<dl>
|
||||
<dt><strong><code>params</code></strong> : <code>dict</code></dt>
|
||||
<dd>Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.</dd>
|
||||
</dl></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class XpressPyomoSolver(BasePyomoSolver):
|
||||
"""
|
||||
An InternalSolver that uses XPRESS and the Pyomo modeling language.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
params: dict
|
||||
Dictionary of options to pass to the Pyomo solver. For example,
|
||||
{"Threads": 4} to set the number of threads.
|
||||
"""
|
||||
|
||||
def __init__(self, params: SolverParams = None) -> None:
|
||||
if params is None:
|
||||
params = {}
|
||||
if "randomseed" not in params.keys():
|
||||
params["randomseed"] = randint(low=0, high=1000).rvs()
|
||||
super().__init__(
|
||||
solver_factory=pe.SolverFactory("xpress_persistent"),
|
||||
params=params,
|
||||
)</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></li>
|
||||
<li><a title="miplearn.solvers.internal.InternalSolver" href="../internal.html#miplearn.solvers.internal.InternalSolver">InternalSolver</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.solvers.pyomo.base.BasePyomoSolver" href="base.html#miplearn.solvers.pyomo.base.BasePyomoSolver">BasePyomoSolver</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.add_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.add_constraint">add_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.extract_constraint" href="../internal.html#miplearn.solvers.internal.InternalSolver.extract_constraint">extract_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.fix" href="../internal.html#miplearn.solvers.internal.InternalSolver.fix">fix</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_ids" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_ids">get_constraint_ids</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_constraint_sense">get_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_dual" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_dual">get_dual</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_empty_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_empty_solution">get_empty_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_inequality_slacks" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_inequality_slacks">get_inequality_slacks</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_sense">get_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_solution" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_solution">get_solution</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.get_value" href="../internal.html#miplearn.solvers.internal.InternalSolver.get_value">get_value</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_constraint_satisfied" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_constraint_satisfied">is_constraint_satisfied</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.is_infeasible" href="../internal.html#miplearn.solvers.internal.InternalSolver.is_infeasible">is_infeasible</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.relax" href="../internal.html#miplearn.solvers.internal.InternalSolver.relax">relax</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_branching_priorities" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_branching_priorities">set_branching_priorities</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_constraint_sense" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_constraint_sense">set_constraint_sense</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_instance" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_instance">set_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.set_warm_start" href="../internal.html#miplearn.solvers.internal.InternalSolver.set_warm_start">set_warm_start</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve">solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.pyomo.base.BasePyomoSolver.solve_lp" href="../internal.html#miplearn.solvers.internal.InternalSolver.solve_lp">solve_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.pyomo" href="index.html">miplearn.solvers.pyomo</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.pyomo.xpress.XpressPyomoSolver" href="#miplearn.solvers.pyomo.xpress.XpressPyomoSolver">XpressPyomoSolver</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
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|
||||
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|
||||
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<main>
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<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from inspect import isclass
|
||||
from typing import List, Callable, Any
|
||||
|
||||
from pyomo import environ as pe
|
||||
|
||||
from miplearn.instance import Instance
|
||||
from miplearn.problems.knapsack import KnapsackInstance, GurobiKnapsackInstance
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.internal import InternalSolver
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
|
||||
from miplearn.solvers.pyomo.xpress import XpressPyomoSolver
|
||||
|
||||
|
||||
class InfeasiblePyomoInstance(Instance):
|
||||
def to_model(self) -> pe.ConcreteModel:
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var([0], domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
|
||||
model.eq = pe.Constraint(expr=model.x[0] >= 2)
|
||||
return model
|
||||
|
||||
|
||||
class InfeasibleGurobiInstance(Instance):
|
||||
def to_model(self) -> Any:
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model()
|
||||
x = model.addVars(1, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(x[0] >= 2)
|
||||
model.setObjective(x[0])
|
||||
return model
|
||||
|
||||
|
||||
def _is_subclass_or_instance(obj, parent_class):
|
||||
return isinstance(obj, parent_class) or (
|
||||
isclass(obj) and issubclass(obj, parent_class)
|
||||
)
|
||||
|
||||
|
||||
def _get_knapsack_instance(solver):
|
||||
if _is_subclass_or_instance(solver, BasePyomoSolver):
|
||||
return KnapsackInstance(
|
||||
weights=[23.0, 26.0, 20.0, 18.0],
|
||||
prices=[505.0, 352.0, 458.0, 220.0],
|
||||
capacity=67.0,
|
||||
)
|
||||
if _is_subclass_or_instance(solver, GurobiSolver):
|
||||
return GurobiKnapsackInstance(
|
||||
weights=[23.0, 26.0, 20.0, 18.0],
|
||||
prices=[505.0, 352.0, 458.0, 220.0],
|
||||
capacity=67.0,
|
||||
)
|
||||
assert False
|
||||
|
||||
|
||||
def _get_infeasible_instance(solver):
|
||||
if _is_subclass_or_instance(solver, BasePyomoSolver):
|
||||
return InfeasiblePyomoInstance()
|
||||
if _is_subclass_or_instance(solver, GurobiSolver):
|
||||
return InfeasibleGurobiInstance()
|
||||
|
||||
|
||||
def _get_internal_solvers() -> List[Callable[[], InternalSolver]]:
|
||||
return [GurobiPyomoSolver, GurobiSolver, XpressPyomoSolver]</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.InfeasibleGurobiInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">InfeasibleGurobiInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InfeasibleGurobiInstance(Instance):
|
||||
def to_model(self) -> Any:
|
||||
import gurobipy as gp
|
||||
from gurobipy import GRB
|
||||
|
||||
model = gp.Model()
|
||||
x = model.addVars(1, vtype=GRB.BINARY, name="x")
|
||||
model.addConstr(x[0] >= 2)
|
||||
model.setObjective(x[0])
|
||||
return model</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.InfeasiblePyomoInstance"><code class="flex name class">
|
||||
<span>class <span class="ident">InfeasiblePyomoInstance</span></span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>Abstract class holding all the data necessary to generate a concrete model of the
|
||||
problem.</p>
|
||||
<p>In the knapsack problem, for example, this class could hold the number of items,
|
||||
their weights and costs, as well as the size of the knapsack. Objects
|
||||
implementing this class are able to convert themselves into a concrete
|
||||
optimization model, which can be optimized by a solver, or into arrays of
|
||||
features, which can be provided as inputs to machine learning models.</p></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class InfeasiblePyomoInstance(Instance):
|
||||
def to_model(self) -> pe.ConcreteModel:
|
||||
model = pe.ConcreteModel()
|
||||
model.x = pe.Var([0], domain=pe.Binary)
|
||||
model.OBJ = pe.Objective(expr=model.x[0], sense=pe.maximize)
|
||||
model.eq = pe.Constraint(expr=model.x[0] >= 2)
|
||||
return model</code></pre>
|
||||
</details>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></li>
|
||||
<li>abc.ABC</li>
|
||||
</ul>
|
||||
<h3>Inherited members</h3>
|
||||
<ul class="hlist">
|
||||
<li><code><b><a title="miplearn.instance.Instance" href="../../instance.html#miplearn.instance.Instance">Instance</a></b></code>:
|
||||
<ul class="hlist">
|
||||
<li><code><a title="miplearn.instance.Instance.build_lazy_constraint" href="../../instance.html#miplearn.instance.Instance.build_lazy_constraint">build_lazy_constraint</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.find_violated_lazy_constraints" href="../../instance.html#miplearn.instance.Instance.find_violated_lazy_constraints">find_violated_lazy_constraints</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_instance_features" href="../../instance.html#miplearn.instance.Instance.get_instance_features">get_instance_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_category" href="../../instance.html#miplearn.instance.Instance.get_variable_category">get_variable_category</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.get_variable_features" href="../../instance.html#miplearn.instance.Instance.get_variable_features">get_variable_features</a></code></li>
|
||||
<li><code><a title="miplearn.instance.Instance.to_model" href="../../instance.html#miplearn.instance.Instance.to_model">to_model</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers" href="../index.html">miplearn.solvers</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver" href="test_internal_solver.html">miplearn.solvers.tests.test_internal_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_lazy_cb" href="test_lazy_cb.html">miplearn.solvers.tests.test_lazy_cb</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver" href="test_learning_solver.html">miplearn.solvers.tests.test_learning_solver</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.tests.InfeasibleGurobiInstance" href="#miplearn.solvers.tests.InfeasibleGurobiInstance">InfeasibleGurobiInstance</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.solvers.tests.InfeasiblePyomoInstance" href="#miplearn.solvers.tests.InfeasiblePyomoInstance">InfeasiblePyomoInstance</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
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</ul>
|
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|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
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<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_internal_solver</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
from io import StringIO
|
||||
from warnings import warn
|
||||
|
||||
import pyomo.environ as pe
|
||||
|
||||
from miplearn.solvers import _RedirectOutput
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.pyomo.base import BasePyomoSolver
|
||||
from miplearn.solvers.tests import (
|
||||
_get_knapsack_instance,
|
||||
_get_internal_solvers,
|
||||
_get_infeasible_instance,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_redirect_output():
|
||||
import sys
|
||||
|
||||
original_stdout = sys.stdout
|
||||
io = StringIO()
|
||||
with _RedirectOutput([io]):
|
||||
print("Hello world")
|
||||
assert sys.stdout == original_stdout
|
||||
assert io.getvalue() == "Hello world\n"
|
||||
|
||||
|
||||
def test_internal_solver_warm_starts():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
if stats["Warm start value"] is not None:
|
||||
assert stats["Warm start value"] == 725.0
|
||||
else:
|
||||
warn(f"{solver_class.__name__} should set warm start value")
|
||||
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 1.0,
|
||||
2: 1.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Warm start value"] is None
|
||||
|
||||
solver.fix(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Lower bound"] == 725.0
|
||||
assert stats["Upper bound"] == 725.0
|
||||
|
||||
|
||||
def test_internal_solver():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert not solver.is_infeasible()
|
||||
assert round(stats["Optimal value"], 3) == 1287.923
|
||||
assert len(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert round(solution["x"][0], 3) == 1.000
|
||||
assert round(solution["x"][1], 3) == 0.923
|
||||
assert round(solution["x"][2], 3) == 1.000
|
||||
assert round(solution["x"][3], 3) == 0.000
|
||||
|
||||
stats = solver.solve(tee=True)
|
||||
assert not solver.is_infeasible()
|
||||
assert len(stats["Log"]) > 100
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
assert stats["Upper bound"] == 1183.0
|
||||
assert stats["Sense"] == "max"
|
||||
assert isinstance(stats["Wallclock time"], float)
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 1.0
|
||||
assert solution["x"][1] == 0.0
|
||||
assert solution["x"][2] == 1.0
|
||||
assert solution["x"][3] == 1.0
|
||||
|
||||
# Add a brand new constraint
|
||||
if isinstance(solver, BasePyomoSolver):
|
||||
model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
|
||||
solver.add_constraint(model.cut)
|
||||
elif isinstance(solver, GurobiSolver):
|
||||
x = model.getVarByName("x[0]")
|
||||
solver.add_constraint(x <= 0.0, name="cut")
|
||||
else:
|
||||
raise Exception("Illegal state")
|
||||
|
||||
# New constraint should affect solution and should be listed in
|
||||
# constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
assert solver.get_sense() == "max"
|
||||
assert solver.get_constraint_sense("cut") == "<"
|
||||
assert solver.get_constraint_sense("eq_capacity") == "<"
|
||||
|
||||
# Verify slacks
|
||||
assert solver.get_inequality_slacks() == {
|
||||
"cut": 0.0,
|
||||
"eq_capacity": 3.0,
|
||||
}
|
||||
|
||||
if isinstance(solver, GurobiSolver):
|
||||
# Extract the new constraint
|
||||
cobj = solver.extract_constraint("cut")
|
||||
|
||||
# New constraint should no longer affect solution and should no longer
|
||||
# be listed in constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
|
||||
# New constraint should not be satisfied by current solution
|
||||
assert not solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Re-add constraint
|
||||
solver.add_constraint(cobj)
|
||||
|
||||
# Constraint should affect solution again
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
# New constraint should now be satisfied
|
||||
assert solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Relax problem and make cut into an equality constraint
|
||||
solver.relax()
|
||||
solver.set_constraint_sense("cut", "=")
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1030.0
|
||||
assert round(solver.get_dual("eq_capacity")) == 0.0
|
||||
|
||||
|
||||
def test_relax():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
solver.relax()
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1288.0
|
||||
|
||||
|
||||
def test_infeasible_instance():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_infeasible_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
stats = solver.solve()
|
||||
|
||||
assert solver.is_infeasible()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Upper bound"] is None
|
||||
assert stats["Lower bound"] is None
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Optimal value"] is None
|
||||
assert solver.get_value("x", 0) is None
|
||||
|
||||
|
||||
def test_iteration_cb():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
count = 0
|
||||
|
||||
def custom_iteration_cb():
|
||||
nonlocal count
|
||||
count += 1
|
||||
return count < 5
|
||||
|
||||
solver.solve(iteration_cb=custom_iteration_cb)
|
||||
assert count == 5</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance"><code class="name flex">
|
||||
<span>def <span class="ident">test_infeasible_instance</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_infeasible_instance():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_infeasible_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
stats = solver.solve()
|
||||
|
||||
assert solver.is_infeasible()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Upper bound"] is None
|
||||
assert stats["Lower bound"] is None
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert solver.get_solution() is None
|
||||
assert stats["Optimal value"] is None
|
||||
assert solver.get_value("x", 0) is None</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_internal_solver</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_internal_solver():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
|
||||
stats = solver.solve_lp()
|
||||
assert not solver.is_infeasible()
|
||||
assert round(stats["Optimal value"], 3) == 1287.923
|
||||
assert len(stats["Log"]) > 100
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert round(solution["x"][0], 3) == 1.000
|
||||
assert round(solution["x"][1], 3) == 0.923
|
||||
assert round(solution["x"][2], 3) == 1.000
|
||||
assert round(solution["x"][3], 3) == 0.000
|
||||
|
||||
stats = solver.solve(tee=True)
|
||||
assert not solver.is_infeasible()
|
||||
assert len(stats["Log"]) > 100
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
assert stats["Upper bound"] == 1183.0
|
||||
assert stats["Sense"] == "max"
|
||||
assert isinstance(stats["Wallclock time"], float)
|
||||
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 1.0
|
||||
assert solution["x"][1] == 0.0
|
||||
assert solution["x"][2] == 1.0
|
||||
assert solution["x"][3] == 1.0
|
||||
|
||||
# Add a brand new constraint
|
||||
if isinstance(solver, BasePyomoSolver):
|
||||
model.cut = pe.Constraint(expr=model.x[0] <= 0.0, name="cut")
|
||||
solver.add_constraint(model.cut)
|
||||
elif isinstance(solver, GurobiSolver):
|
||||
x = model.getVarByName("x[0]")
|
||||
solver.add_constraint(x <= 0.0, name="cut")
|
||||
else:
|
||||
raise Exception("Illegal state")
|
||||
|
||||
# New constraint should affect solution and should be listed in
|
||||
# constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
assert solver.get_sense() == "max"
|
||||
assert solver.get_constraint_sense("cut") == "<"
|
||||
assert solver.get_constraint_sense("eq_capacity") == "<"
|
||||
|
||||
# Verify slacks
|
||||
assert solver.get_inequality_slacks() == {
|
||||
"cut": 0.0,
|
||||
"eq_capacity": 3.0,
|
||||
}
|
||||
|
||||
if isinstance(solver, GurobiSolver):
|
||||
# Extract the new constraint
|
||||
cobj = solver.extract_constraint("cut")
|
||||
|
||||
# New constraint should no longer affect solution and should no longer
|
||||
# be listed in constraint ids
|
||||
assert solver.get_constraint_ids() == ["eq_capacity"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1183.0
|
||||
|
||||
# New constraint should not be satisfied by current solution
|
||||
assert not solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Re-add constraint
|
||||
solver.add_constraint(cobj)
|
||||
|
||||
# Constraint should affect solution again
|
||||
assert solver.get_constraint_ids() == ["eq_capacity", "cut"]
|
||||
stats = solver.solve()
|
||||
assert stats["Lower bound"] == 1030.0
|
||||
|
||||
# New constraint should now be satisfied
|
||||
assert solver.is_constraint_satisfied(cobj)
|
||||
|
||||
# Relax problem and make cut into an equality constraint
|
||||
solver.relax()
|
||||
solver.set_constraint_sense("cut", "=")
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1030.0
|
||||
assert round(solver.get_dual("eq_capacity")) == 0.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts"><code class="name flex">
|
||||
<span>def <span class="ident">test_internal_solver_warm_starts</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_internal_solver_warm_starts():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
model = instance.to_model()
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance, model)
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
if stats["Warm start value"] is not None:
|
||||
assert stats["Warm start value"] == 725.0
|
||||
else:
|
||||
warn(f"{solver_class.__name__} should set warm start value")
|
||||
|
||||
solver.set_warm_start(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 1.0,
|
||||
2: 1.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Warm start value"] is None
|
||||
|
||||
solver.fix(
|
||||
{
|
||||
"x": {
|
||||
0: 1.0,
|
||||
1: 0.0,
|
||||
2: 0.0,
|
||||
3: 1.0,
|
||||
}
|
||||
}
|
||||
)
|
||||
stats = solver.solve(tee=True)
|
||||
assert stats["Lower bound"] == 725.0
|
||||
assert stats["Upper bound"] == 725.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_iteration_cb"><code class="name flex">
|
||||
<span>def <span class="ident">test_iteration_cb</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_iteration_cb():
|
||||
for solver_class in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % solver_class)
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
count = 0
|
||||
|
||||
def custom_iteration_cb():
|
||||
nonlocal count
|
||||
count += 1
|
||||
return count < 5
|
||||
|
||||
solver.solve(iteration_cb=custom_iteration_cb)
|
||||
assert count == 5</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_redirect_output"><code class="name flex">
|
||||
<span>def <span class="ident">test_redirect_output</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_redirect_output():
|
||||
import sys
|
||||
|
||||
original_stdout = sys.stdout
|
||||
io = StringIO()
|
||||
with _RedirectOutput([io]):
|
||||
print("Hello world")
|
||||
assert sys.stdout == original_stdout
|
||||
assert io.getvalue() == "Hello world\n"</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_internal_solver.test_relax"><code class="name flex">
|
||||
<span>def <span class="ident">test_relax</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_relax():
|
||||
for solver_class in _get_internal_solvers():
|
||||
instance = _get_knapsack_instance(solver_class)
|
||||
solver = solver_class()
|
||||
solver.set_instance(instance)
|
||||
solver.relax()
|
||||
stats = solver.solve()
|
||||
assert round(stats["Lower bound"]) == 1288.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_infeasible_instance" href="#miplearn.solvers.tests.test_internal_solver.test_infeasible_instance">test_infeasible_instance</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver">test_internal_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts" href="#miplearn.solvers.tests.test_internal_solver.test_internal_solver_warm_starts">test_internal_solver_warm_starts</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_iteration_cb" href="#miplearn.solvers.tests.test_internal_solver.test_iteration_cb">test_iteration_cb</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_redirect_output" href="#miplearn.solvers.tests.test_internal_solver.test_redirect_output">test_redirect_output</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_internal_solver.test_relax" href="#miplearn.solvers.tests.test_internal_solver.test_relax">test_relax</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
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</html>
|
||||
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|
||||
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|
||||
<html lang="en">
|
||||
<head>
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
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||||
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|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_lazy_cb</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.tests import _get_knapsack_instance
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_lazy_cb():
|
||||
solver = GurobiSolver()
|
||||
instance = _get_knapsack_instance(solver)
|
||||
model = instance.to_model()
|
||||
|
||||
def lazy_cb(cb_solver, cb_model):
|
||||
logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
|
||||
cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
|
||||
if not cb_solver.is_constraint_satisfied(cobj):
|
||||
cb_solver.add_constraint(cobj)
|
||||
|
||||
solver.set_instance(instance, model)
|
||||
solver.solve(lazy_cb=lazy_cb)
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 0.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb"><code class="name flex">
|
||||
<span>def <span class="ident">test_lazy_cb</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_lazy_cb():
|
||||
solver = GurobiSolver()
|
||||
instance = _get_knapsack_instance(solver)
|
||||
model = instance.to_model()
|
||||
|
||||
def lazy_cb(cb_solver, cb_model):
|
||||
logger.info("x[0] = %.f" % cb_solver.get_value("x", 0))
|
||||
cobj = (cb_model.getVarByName("x[0]") * 1.0, "<", 0.0, "cut")
|
||||
if not cb_solver.is_constraint_satisfied(cobj):
|
||||
cb_solver.add_constraint(cobj)
|
||||
|
||||
solver.set_instance(instance, model)
|
||||
solver.solve(lazy_cb=lazy_cb)
|
||||
solution = solver.get_solution()
|
||||
assert solution["x"][0] == 0.0</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
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<nav id="sidebar">
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<h1>Index</h1>
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<div class="toc">
|
||||
<ul></ul>
|
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</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_lazy_cb.test_lazy_cb" href="#miplearn.solvers.tests.test_lazy_cb.test_lazy_cb">test_lazy_cb</a></code></li>
|
||||
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|
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</head>
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<body>
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<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.solvers.tests.test_learning_solver</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import logging
|
||||
import pickle
|
||||
import tempfile
|
||||
import os
|
||||
|
||||
from miplearn.solvers.gurobi import GurobiSolver
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
from miplearn.solvers.tests import _get_knapsack_instance, _get_internal_solvers
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def test_learning_solver():
|
||||
for mode in ["exact", "heuristic"]:
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
solver.solve(instance)
|
||||
data = instance.training_data[0]
|
||||
assert data["Solution"]["x"][0] == 1.0
|
||||
assert data["Solution"]["x"][1] == 0.0
|
||||
assert data["Solution"]["x"][2] == 1.0
|
||||
assert data["Solution"]["x"][3] == 1.0
|
||||
assert data["Lower bound"] == 1183.0
|
||||
assert data["Upper bound"] == 1183.0
|
||||
assert round(data["LP solution"]["x"][0], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][1], 3) == 0.923
|
||||
assert round(data["LP solution"]["x"][2], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][3], 3) == 0.000
|
||||
assert round(data["LP value"], 3) == 1287.923
|
||||
assert len(data["MIP log"]) > 100
|
||||
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
# Assert solver is picklable
|
||||
with tempfile.TemporaryFile() as file:
|
||||
pickle.dump(solver, file)
|
||||
|
||||
|
||||
def test_solve_without_lp():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
solve_lp_first=False,
|
||||
)
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
|
||||
def test_parallel_solve():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
results = solver.parallel_solve(instances, n_jobs=3)
|
||||
assert len(results) == 10
|
||||
for instance in instances:
|
||||
data = instance.training_data[0]
|
||||
assert len(data["Solution"]["x"].keys()) == 4
|
||||
|
||||
|
||||
def test_solve_fit_from_disk():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
# Create instances and pickle them
|
||||
filenames = []
|
||||
for k in range(3):
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
|
||||
filenames += [file.name]
|
||||
pickle.dump(instance, file)
|
||||
|
||||
# Test: solve
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
solver.solve(filenames[0])
|
||||
with open(filenames[0], "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: parallel_solve
|
||||
solver.parallel_solve(filenames)
|
||||
for filename in filenames:
|
||||
with open(filename, "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: solve (with specified output)
|
||||
output = [f + ".out" for f in filenames]
|
||||
solver.solve(
|
||||
filenames[0],
|
||||
output_filename=output[0],
|
||||
)
|
||||
assert os.path.isfile(output[0])
|
||||
|
||||
# Test: parallel_solve (with specified output)
|
||||
solver.parallel_solve(
|
||||
filenames,
|
||||
output_filenames=output,
|
||||
)
|
||||
for filename in output:
|
||||
assert os.path.isfile(filename)
|
||||
|
||||
# Delete temporary files
|
||||
for filename in filenames:
|
||||
os.remove(filename)
|
||||
for filename in output:
|
||||
os.remove(filename)
|
||||
|
||||
|
||||
def test_simulate_perfect():
|
||||
internal_solver = GurobiSolver
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
|
||||
pickle.dump(instance, tmp)
|
||||
tmp.flush()
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
simulate_perfect=True,
|
||||
)
|
||||
stats = solver.solve(tmp.name)
|
||||
assert stats["Lower bound"] == stats["Predicted LB"]</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_learning_solver"><code class="name flex">
|
||||
<span>def <span class="ident">test_learning_solver</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_learning_solver():
|
||||
for mode in ["exact", "heuristic"]:
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
mode=mode,
|
||||
)
|
||||
|
||||
solver.solve(instance)
|
||||
data = instance.training_data[0]
|
||||
assert data["Solution"]["x"][0] == 1.0
|
||||
assert data["Solution"]["x"][1] == 0.0
|
||||
assert data["Solution"]["x"][2] == 1.0
|
||||
assert data["Solution"]["x"][3] == 1.0
|
||||
assert data["Lower bound"] == 1183.0
|
||||
assert data["Upper bound"] == 1183.0
|
||||
assert round(data["LP solution"]["x"][0], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][1], 3) == 0.923
|
||||
assert round(data["LP solution"]["x"][2], 3) == 1.000
|
||||
assert round(data["LP solution"]["x"][3], 3) == 0.000
|
||||
assert round(data["LP value"], 3) == 1287.923
|
||||
assert len(data["MIP log"]) > 100
|
||||
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)
|
||||
|
||||
# Assert solver is picklable
|
||||
with tempfile.TemporaryFile() as file:
|
||||
pickle.dump(solver, file)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_parallel_solve"><code class="name flex">
|
||||
<span>def <span class="ident">test_parallel_solve</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_parallel_solve():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
instances = [_get_knapsack_instance(internal_solver) for _ in range(10)]
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
results = solver.parallel_solve(instances, n_jobs=3)
|
||||
assert len(results) == 10
|
||||
for instance in instances:
|
||||
data = instance.training_data[0]
|
||||
assert len(data["Solution"]["x"].keys()) == 4</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect"><code class="name flex">
|
||||
<span>def <span class="ident">test_simulate_perfect</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_simulate_perfect():
|
||||
internal_solver = GurobiSolver
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as tmp:
|
||||
pickle.dump(instance, tmp)
|
||||
tmp.flush()
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
simulate_perfect=True,
|
||||
)
|
||||
stats = solver.solve(tmp.name)
|
||||
assert stats["Lower bound"] == stats["Predicted LB"]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk"><code class="name flex">
|
||||
<span>def <span class="ident">test_solve_fit_from_disk</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_solve_fit_from_disk():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
# Create instances and pickle them
|
||||
filenames = []
|
||||
for k in range(3):
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
with tempfile.NamedTemporaryFile(suffix=".pkl", delete=False) as file:
|
||||
filenames += [file.name]
|
||||
pickle.dump(instance, file)
|
||||
|
||||
# Test: solve
|
||||
solver = LearningSolver(solver=internal_solver)
|
||||
solver.solve(filenames[0])
|
||||
with open(filenames[0], "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: parallel_solve
|
||||
solver.parallel_solve(filenames)
|
||||
for filename in filenames:
|
||||
with open(filename, "rb") as file:
|
||||
instance = pickle.load(file)
|
||||
assert len(instance.training_data) > 0
|
||||
|
||||
# Test: solve (with specified output)
|
||||
output = [f + ".out" for f in filenames]
|
||||
solver.solve(
|
||||
filenames[0],
|
||||
output_filename=output[0],
|
||||
)
|
||||
assert os.path.isfile(output[0])
|
||||
|
||||
# Test: parallel_solve (with specified output)
|
||||
solver.parallel_solve(
|
||||
filenames,
|
||||
output_filenames=output,
|
||||
)
|
||||
for filename in output:
|
||||
assert os.path.isfile(filename)
|
||||
|
||||
# Delete temporary files
|
||||
for filename in filenames:
|
||||
os.remove(filename)
|
||||
for filename in output:
|
||||
os.remove(filename)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp"><code class="name flex">
|
||||
<span>def <span class="ident">test_solve_without_lp</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_solve_without_lp():
|
||||
for internal_solver in _get_internal_solvers():
|
||||
logger.info("Solver: %s" % internal_solver)
|
||||
instance = _get_knapsack_instance(internal_solver)
|
||||
solver = LearningSolver(
|
||||
solver=internal_solver,
|
||||
solve_lp_first=False,
|
||||
)
|
||||
solver.solve(instance)
|
||||
solver.fit([instance])
|
||||
solver.solve(instance)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.solvers.tests" href="index.html">miplearn.solvers.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_learning_solver" href="#miplearn.solvers.tests.test_learning_solver.test_learning_solver">test_learning_solver</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_parallel_solve" href="#miplearn.solvers.tests.test_learning_solver.test_parallel_solve">test_parallel_solve</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_simulate_perfect" href="#miplearn.solvers.tests.test_learning_solver.test_simulate_perfect">test_simulate_perfect</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk" href="#miplearn.solvers.tests.test_learning_solver.test_solve_fit_from_disk">test_solve_fit_from_disk</a></code></li>
|
||||
<li><code><a title="miplearn.solvers.tests.test_learning_solver.test_solve_without_lp" href="#miplearn.solvers.tests.test_learning_solver.test_solve_without_lp">test_solve_without_lp</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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|
||||
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|
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|
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</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.tests</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from miplearn.problems.knapsack import KnapsackInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def get_test_pyomo_instances():
|
||||
instances = [
|
||||
KnapsackInstance(
|
||||
weights=[23.0, 26.0, 20.0, 18.0],
|
||||
prices=[505.0, 352.0, 458.0, 220.0],
|
||||
capacity=67.0,
|
||||
),
|
||||
KnapsackInstance(
|
||||
weights=[25.0, 30.0, 22.0, 18.0],
|
||||
prices=[500.0, 365.0, 420.0, 150.0],
|
||||
capacity=70.0,
|
||||
),
|
||||
]
|
||||
models = [instance.to_model() for instance in instances]
|
||||
solver = LearningSolver()
|
||||
for i in range(len(instances)):
|
||||
solver.solve(instances[i], models[i])
|
||||
return instances, models</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
|
||||
<dl>
|
||||
<dt><code class="name"><a title="miplearn.tests.test_benchmark" href="test_benchmark.html">miplearn.tests.test_benchmark</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
<dt><code class="name"><a title="miplearn.tests.test_extractors" href="test_extractors.html">miplearn.tests.test_extractors</a></code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.tests.get_test_pyomo_instances"><code class="name flex">
|
||||
<span>def <span class="ident">get_test_pyomo_instances</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def get_test_pyomo_instances():
|
||||
instances = [
|
||||
KnapsackInstance(
|
||||
weights=[23.0, 26.0, 20.0, 18.0],
|
||||
prices=[505.0, 352.0, 458.0, 220.0],
|
||||
capacity=67.0,
|
||||
),
|
||||
KnapsackInstance(
|
||||
weights=[25.0, 30.0, 22.0, 18.0],
|
||||
prices=[500.0, 365.0, 420.0, 150.0],
|
||||
capacity=70.0,
|
||||
),
|
||||
]
|
||||
models = [instance.to_model() for instance in instances]
|
||||
solver = LearningSolver()
|
||||
for i in range(len(instances)):
|
||||
solver.solve(instances[i], models[i])
|
||||
return instances, models</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="../index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-submodules">Sub-modules</a></h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.tests.test_benchmark" href="test_benchmark.html">miplearn.tests.test_benchmark</a></code></li>
|
||||
<li><code><a title="miplearn.tests.test_extractors" href="test_extractors.html">miplearn.tests.test_extractors</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.tests.get_test_pyomo_instances" href="#miplearn.tests.get_test_pyomo_instances">get_test_pyomo_instances</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
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||||
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|
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<head>
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|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.tests.test_benchmark</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
import os.path
|
||||
|
||||
from miplearn.benchmark import BenchmarkRunner
|
||||
from miplearn.problems.stab import MaxWeightStableSetGenerator
|
||||
from scipy.stats import randint
|
||||
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def test_benchmark():
|
||||
# Generate training and test instances
|
||||
generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
|
||||
train_instances = generator.generate(5)
|
||||
test_instances = generator.generate(3)
|
||||
|
||||
# Training phase...
|
||||
training_solver = LearningSolver()
|
||||
training_solver.parallel_solve(train_instances, n_jobs=10)
|
||||
|
||||
# Test phase...
|
||||
test_solvers = {
|
||||
"Strategy A": LearningSolver(),
|
||||
"Strategy B": LearningSolver(),
|
||||
}
|
||||
benchmark = BenchmarkRunner(test_solvers)
|
||||
benchmark.fit(train_instances)
|
||||
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
|
||||
assert benchmark.raw_results().values.shape == (12, 14)
|
||||
|
||||
benchmark.save_results("/tmp/benchmark.csv")
|
||||
assert os.path.isfile("/tmp/benchmark.csv")
|
||||
|
||||
benchmark = BenchmarkRunner(test_solvers)
|
||||
benchmark.load_results("/tmp/benchmark.csv")
|
||||
assert benchmark.raw_results().values.shape == (12, 14)
|
||||
|
||||
|
||||
def test_gap():
|
||||
assert BenchmarkRunner._compute_gap(ub=0.0, lb=0.0) == 0.0
|
||||
assert BenchmarkRunner._compute_gap(ub=1.0, lb=0.5) == 0.5
|
||||
assert BenchmarkRunner._compute_gap(ub=1.0, lb=1.0) == 0.0
|
||||
assert BenchmarkRunner._compute_gap(ub=1.0, lb=-1.0) == 1.0
|
||||
assert BenchmarkRunner._compute_gap(ub=1.0, lb=None) == 1.0
|
||||
assert BenchmarkRunner._compute_gap(ub=None, lb=1.0) == 1.0
|
||||
assert BenchmarkRunner._compute_gap(ub=None, lb=None) == 1.0</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.tests.test_benchmark.test_benchmark"><code class="name flex">
|
||||
<span>def <span class="ident">test_benchmark</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_benchmark():
|
||||
# Generate training and test instances
|
||||
generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
|
||||
train_instances = generator.generate(5)
|
||||
test_instances = generator.generate(3)
|
||||
|
||||
# Training phase...
|
||||
training_solver = LearningSolver()
|
||||
training_solver.parallel_solve(train_instances, n_jobs=10)
|
||||
|
||||
# Test phase...
|
||||
test_solvers = {
|
||||
"Strategy A": LearningSolver(),
|
||||
"Strategy B": LearningSolver(),
|
||||
}
|
||||
benchmark = BenchmarkRunner(test_solvers)
|
||||
benchmark.fit(train_instances)
|
||||
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
|
||||
assert benchmark.raw_results().values.shape == (12, 14)
|
||||
|
||||
benchmark.save_results("/tmp/benchmark.csv")
|
||||
assert os.path.isfile("/tmp/benchmark.csv")
|
||||
|
||||
benchmark = BenchmarkRunner(test_solvers)
|
||||
benchmark.load_results("/tmp/benchmark.csv")
|
||||
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>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.tests" href="index.html">miplearn.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.tests.test_benchmark.test_benchmark" href="#miplearn.tests.test_benchmark.test_benchmark">test_benchmark</a></code></li>
|
||||
<li><code><a title="miplearn.tests.test_benchmark.test_gap" href="#miplearn.tests.test_benchmark.test_gap">test_gap</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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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>
|
||||
<h1 class="title">Module <code>miplearn.tests.test_extractors</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
import numpy as np
|
||||
|
||||
from miplearn.extractors import (
|
||||
SolutionExtractor,
|
||||
InstanceFeaturesExtractor,
|
||||
VariableFeaturesExtractor,
|
||||
)
|
||||
from miplearn.problems.knapsack import KnapsackInstance
|
||||
from miplearn.solvers.learning import LearningSolver
|
||||
|
||||
|
||||
def _get_instances():
|
||||
instances = [
|
||||
KnapsackInstance(
|
||||
weights=[1.0, 2.0, 3.0],
|
||||
prices=[10.0, 20.0, 30.0],
|
||||
capacity=2.5,
|
||||
),
|
||||
KnapsackInstance(
|
||||
weights=[3.0, 4.0, 5.0],
|
||||
prices=[20.0, 30.0, 40.0],
|
||||
capacity=4.5,
|
||||
),
|
||||
]
|
||||
models = [instance.to_model() for instance in instances]
|
||||
solver = LearningSolver()
|
||||
for (i, instance) in enumerate(instances):
|
||||
solver.solve(instances[i], models[i])
|
||||
return instances, models
|
||||
|
||||
|
||||
def test_solution_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = SolutionExtractor().extract(instances)
|
||||
assert isinstance(features, dict)
|
||||
assert "default" in features.keys()
|
||||
assert isinstance(features["default"], np.ndarray)
|
||||
assert features["default"].shape == (6, 2)
|
||||
assert features["default"].ravel().tolist() == [
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
]
|
||||
|
||||
|
||||
def test_instance_features_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = InstanceFeaturesExtractor().extract(instances)
|
||||
assert features.shape == (2, 3)
|
||||
|
||||
|
||||
def test_variable_features_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = VariableFeaturesExtractor().extract(instances)
|
||||
assert isinstance(features, dict)
|
||||
assert "default" in features
|
||||
assert features["default"].shape == (6, 5)</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-functions">Functions</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.tests.test_extractors.test_instance_features_extractor"><code class="name flex">
|
||||
<span>def <span class="ident">test_instance_features_extractor</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_instance_features_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = InstanceFeaturesExtractor().extract(instances)
|
||||
assert features.shape == (2, 3)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.tests.test_extractors.test_solution_extractor"><code class="name flex">
|
||||
<span>def <span class="ident">test_solution_extractor</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_solution_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = SolutionExtractor().extract(instances)
|
||||
assert isinstance(features, dict)
|
||||
assert "default" in features.keys()
|
||||
assert isinstance(features["default"], np.ndarray)
|
||||
assert features["default"].shape == (6, 2)
|
||||
assert features["default"].ravel().tolist() == [
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
1.0,
|
||||
0.0,
|
||||
0.0,
|
||||
1.0,
|
||||
1.0,
|
||||
0.0,
|
||||
]</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.tests.test_extractors.test_variable_features_extractor"><code class="name flex">
|
||||
<span>def <span class="ident">test_variable_features_extractor</span></span>(<span>)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">def test_variable_features_extractor():
|
||||
instances, models = _get_instances()
|
||||
features = VariableFeaturesExtractor().extract(instances)
|
||||
assert isinstance(features, dict)
|
||||
assert "default" in features
|
||||
assert features["default"].shape == (6, 5)</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn.tests" href="index.html">miplearn.tests</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-functions">Functions</a></h3>
|
||||
<ul class="">
|
||||
<li><code><a title="miplearn.tests.test_extractors.test_instance_features_extractor" href="#miplearn.tests.test_extractors.test_instance_features_extractor">test_instance_features_extractor</a></code></li>
|
||||
<li><code><a title="miplearn.tests.test_extractors.test_solution_extractor" href="#miplearn.tests.test_extractors.test_solution_extractor">test_solution_extractor</a></code></li>
|
||||
<li><code><a title="miplearn.tests.test_extractors.test_variable_features_extractor" href="#miplearn.tests.test_extractors.test_variable_features_extractor">test_variable_features_extractor</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
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</main>
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<footer id="footer">
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<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
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</footer>
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
</body>
|
||||
</html>
|
||||
@ -0,0 +1,214 @@
|
||||
<!doctype html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1" />
|
||||
<meta name="generator" content="pdoc 0.7.0" />
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<title>miplearn.types API documentation</title>
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<meta name="description" content="" />
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<link href='https://cdnjs.cloudflare.com/ajax/libs/normalize/8.0.0/normalize.min.css' rel='stylesheet'>
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<link href='https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/8.0.0/sanitize.min.css' rel='stylesheet'>
|
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<link href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/styles/github.min.css" rel="stylesheet">
|
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<style>.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:30px;overflow:hidden}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:1em 0 .50em 0}h3{font-size:1.4em;margin:25px 0 10px 0}h4{margin:0;font-size:105%}a{color:#058;text-decoration:none;transition:color .3s ease-in-out}a:hover{color:#e82}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900}pre code{background:#f8f8f8;font-size:.8em;line-height:1.4em}code{background:#f2f2f1;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{background:#f8f8f8;border:0;border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0;padding:1ex}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{font-weight:bold}#index h4 + ul{margin-bottom:.6em}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-weight:bold;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}.admonition{padding:.1em .5em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
|
||||
<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.item .name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul{padding-left:1.5em}.toc > ul > li{margin-top:.5em}}</style>
|
||||
<style media="print">@media print{#sidebar h1{page-break-before:always}.source{display:none}}@media print{*{background:transparent !important;color:#000 !important;box-shadow:none !important;text-shadow:none !important}a[href]:after{content:" (" attr(href) ")";font-size:90%}a[href][title]:after{content:none}abbr[title]:after{content:" (" attr(title) ")"}.ir a:after,a[href^="javascript:"]:after,a[href^="#"]:after{content:""}pre,blockquote{border:1px solid #999;page-break-inside:avoid}thead{display:table-header-group}tr,img{page-break-inside:avoid}img{max-width:100% !important}@page{margin:0.5cm}p,h2,h3{orphans:3;widows:3}h1,h2,h3,h4,h5,h6{page-break-after:avoid}}</style>
|
||||
</head>
|
||||
<body>
|
||||
<main>
|
||||
<article id="content">
|
||||
<header>
|
||||
<h1 class="title">Module <code>miplearn.types</code></h1>
|
||||
</header>
|
||||
<section id="section-intro">
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python"># MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
|
||||
# Released under the modified BSD license. See COPYING.md for more details.
|
||||
|
||||
from typing import Optional, Dict, Callable, Any, Union, List
|
||||
|
||||
from mypy_extensions import TypedDict
|
||||
|
||||
VarIndex = Union[str, int, List[Union[str, int]]]
|
||||
|
||||
Solution = Dict[str, Dict[VarIndex, Optional[float]]]
|
||||
|
||||
TrainingSample = TypedDict(
|
||||
"TrainingSample",
|
||||
{
|
||||
"LP log": str,
|
||||
"LP solution": Optional[Solution],
|
||||
"LP value": Optional[float],
|
||||
"Lower bound": Optional[float],
|
||||
"MIP log": str,
|
||||
"Solution": Optional[Solution],
|
||||
"Upper bound": Optional[float],
|
||||
"slacks": Dict,
|
||||
},
|
||||
total=False,
|
||||
)
|
||||
|
||||
LPSolveStats = TypedDict(
|
||||
"LPSolveStats",
|
||||
{
|
||||
"Optimal value": Optional[float],
|
||||
"Log": str,
|
||||
},
|
||||
)
|
||||
|
||||
MIPSolveStats = TypedDict(
|
||||
"MIPSolveStats",
|
||||
{
|
||||
"Lower bound": Optional[float],
|
||||
"Upper bound": Optional[float],
|
||||
"Wallclock time": float,
|
||||
"Nodes": Optional[int],
|
||||
"Sense": str,
|
||||
"Log": str,
|
||||
"Warm start value": Optional[float],
|
||||
"LP value": Optional[float],
|
||||
},
|
||||
)
|
||||
|
||||
IterationCallback = Callable[[], bool]
|
||||
|
||||
LazyCallback = Callable[[Any, Any], None]
|
||||
|
||||
SolverParams = Dict[str, Any]
|
||||
|
||||
BranchPriorities = Solution
|
||||
|
||||
|
||||
class Constraint:
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
</section>
|
||||
<section>
|
||||
<h2 class="section-title" id="header-classes">Classes</h2>
|
||||
<dl>
|
||||
<dt id="miplearn.types.Constraint"><code class="flex name class">
|
||||
<span>class <span class="ident">Constraint</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"></section>
|
||||
<details class="source">
|
||||
<summary>
|
||||
<span>Expand source code</span>
|
||||
</summary>
|
||||
<pre><code class="python">class Constraint:
|
||||
pass</code></pre>
|
||||
</details>
|
||||
</dd>
|
||||
<dt id="miplearn.types.LPSolveStats"><code class="flex name class">
|
||||
<span>class <span class="ident">LPSolveStats</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>dict() -> new empty dictionary
|
||||
dict(mapping) -> new dictionary initialized from a mapping object's
|
||||
(key, value) pairs
|
||||
dict(iterable) -> new dictionary initialized as if via:
|
||||
d = {}
|
||||
for k, v in iterable:
|
||||
d[k] = v
|
||||
dict(**kwargs) -> new dictionary initialized with the name=value pairs
|
||||
in the keyword argument list.
|
||||
For example:
|
||||
dict(one=1, two=2)</p></section>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>builtins.dict</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.types.MIPSolveStats"><code class="flex name class">
|
||||
<span>class <span class="ident">MIPSolveStats</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>dict() -> new empty dictionary
|
||||
dict(mapping) -> new dictionary initialized from a mapping object's
|
||||
(key, value) pairs
|
||||
dict(iterable) -> new dictionary initialized as if via:
|
||||
d = {}
|
||||
for k, v in iterable:
|
||||
d[k] = v
|
||||
dict(**kwargs) -> new dictionary initialized with the name=value pairs
|
||||
in the keyword argument list.
|
||||
For example:
|
||||
dict(one=1, two=2)</p></section>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>builtins.dict</li>
|
||||
</ul>
|
||||
</dd>
|
||||
<dt id="miplearn.types.TrainingSample"><code class="flex name class">
|
||||
<span>class <span class="ident">TrainingSample</span></span>
|
||||
<span>(</span><span>*args, **kwargs)</span>
|
||||
</code></dt>
|
||||
<dd>
|
||||
<section class="desc"><p>dict() -> new empty dictionary
|
||||
dict(mapping) -> new dictionary initialized from a mapping object's
|
||||
(key, value) pairs
|
||||
dict(iterable) -> new dictionary initialized as if via:
|
||||
d = {}
|
||||
for k, v in iterable:
|
||||
d[k] = v
|
||||
dict(**kwargs) -> new dictionary initialized with the name=value pairs
|
||||
in the keyword argument list.
|
||||
For example:
|
||||
dict(one=1, two=2)</p></section>
|
||||
<h3>Ancestors</h3>
|
||||
<ul class="hlist">
|
||||
<li>builtins.dict</li>
|
||||
</ul>
|
||||
</dd>
|
||||
</dl>
|
||||
</section>
|
||||
</article>
|
||||
<nav id="sidebar">
|
||||
<h1>Index</h1>
|
||||
<div class="toc">
|
||||
<ul></ul>
|
||||
</div>
|
||||
<ul id="index">
|
||||
<li><h3>Super-module</h3>
|
||||
<ul>
|
||||
<li><code><a title="miplearn" href="index.html">miplearn</a></code></li>
|
||||
</ul>
|
||||
</li>
|
||||
<li><h3><a href="#header-classes">Classes</a></h3>
|
||||
<ul>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.types.Constraint" href="#miplearn.types.Constraint">Constraint</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.types.LPSolveStats" href="#miplearn.types.LPSolveStats">LPSolveStats</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.types.MIPSolveStats" href="#miplearn.types.MIPSolveStats">MIPSolveStats</a></code></h4>
|
||||
</li>
|
||||
<li>
|
||||
<h4><code><a title="miplearn.types.TrainingSample" href="#miplearn.types.TrainingSample">TrainingSample</a></code></h4>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</nav>
|
||||
</main>
|
||||
<footer id="footer">
|
||||
<p>Generated by <a href="https://pdoc3.github.io/pdoc"><cite>pdoc</cite> 0.7.0</a>.</p>
|
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</footer>
|
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<script src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.12.0/highlight.min.js"></script>
|
||||
<script>hljs.initHighlightingOnLoad()</script>
|
||||
</body>
|
||||
</html>
|
||||
File diff suppressed because one or more lines are too long
@ -1,27 +1,27 @@
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-12-03</lastmod>
|
||||
<lastmod>2021-01-22</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url>
|
||||
</urlset>
|
||||
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