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<li><strong>Alinson S. Xavier,</strong> Argonne National Laboratory <<a href="mailto:axavier@anl.gov">axavier@anl.gov</a>></li>
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<li><strong>Feng Qiu,</strong> Argonne National Laboratory <<a href="mailto:fqiu@anl.gov">fqiu@anl.gov</a>></li>
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<h3 id="acknowledgments">Acknowledgments</h3>
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<li>Based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.</li>
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<h3 id="references">References</h3>
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<ul>
|
||||
<li><strong>Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems.</strong> <em>Alinson S. Xavier, Feng Qiu, Shabbir Ahmed</em>. INFORMS Journal on Computing (to appear). <a href="https://arxiv.org/abs/1902.01697">ArXiv:1902:01696</a></li>
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<h3 id="license">License</h3>
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<pre><code class="text">MIPLearn, an extensible framework for Learning-Enhanced Mixed-Integer Optimization
|
||||
Copyright © 2020, UChicago Argonne, LLC. All Rights Reserved.
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|
||||
Redistribution and use in source and binary forms, with or without modification, are permitted
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provided that the following conditions are met:
|
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1. Redistributions of source code must retain the above copyright notice, this list of
|
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conditions and the following disclaimer.
|
||||
2. Redistributions in binary form must reproduce the above copyright notice, this list of
|
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conditions and the following disclaimer in the documentation and/or other materials provided
|
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with the distribution.
|
||||
3. Neither the name of the copyright holder nor the names of its contributors may be used to
|
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endorse or promote products derived from this software without specific prior written
|
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permission.
|
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|
||||
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
|
||||
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY
|
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AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
|
||||
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
|
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CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
|
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SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
|
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
|
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OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
|
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POSSIBILITY OF SUCH DAMAGE.
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</code></pre></div>
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<li class="first-level active"><a href="#benchmarks-utilities">Benchmarks Utilities</a></li>
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<li class="second-level"><a href="#using-benchmarkrunner">Using BenchmarkRunner</a></li>
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<h1 id="benchmarks-utilities">Benchmarks Utilities</h1>
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<h3 id="using-benchmarkrunner">Using <code>BenchmarkRunner</code></h3>
|
||||
<p>MIPLearn provides the utility class <code>BenchmarkRunner</code>, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:</p>
|
||||
<pre><code class="python">from miplearn import BenchmarkRunner, LearningSolver
|
||||
|
||||
# Create train and test instances
|
||||
train_instances = [...]
|
||||
test_instances = [...]
|
||||
|
||||
# Training phase...
|
||||
training_solver = LearningSolver(...)
|
||||
training_solver.parallel_solve(train_instances, n_jobs=10)
|
||||
|
||||
# Test phase...
|
||||
test_solvers = {
|
||||
"Baseline": LearningSolver(...), # each solver may have different parameters
|
||||
"Strategy A": LearningSolver(...),
|
||||
"Strategy B": LearningSolver(...),
|
||||
"Strategy C": LearningSolver(...),
|
||||
}
|
||||
benchmark = BenchmarkRunner(test_solvers)
|
||||
benchmark.fit(train_instances)
|
||||
benchmark.parallel_solve(test_instances, n_jobs=2)
|
||||
print(benchmark.raw_results())
|
||||
</code></pre>
|
||||
|
||||
<p>The method <code>fit</code> trains the ML models for each individual solver. The method <code>parallel_solve</code> solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, <code>raw_results</code> produces a table of results (Pandas DataFrame) with the following columns:</p>
|
||||
<ul>
|
||||
<li><strong>Solver,</strong> the name of the solver.</li>
|
||||
<li><strong>Instance,</strong> the sequence number identifying the instance.</li>
|
||||
<li><strong>Wallclock Time,</strong> the wallclock running time (in seconds) spent by the solver;</li>
|
||||
<li><strong>Lower Bound,</strong> the best lower bound obtained by the solver;</li>
|
||||
<li><strong>Upper Bound,</strong> the best upper bound obtained by the solver;</li>
|
||||
<li><strong>Gap,</strong> the relative MIP integrality gap at the end of the optimization;</li>
|
||||
<li><strong>Nodes,</strong> the number of explored branch-and-bound nodes.</li>
|
||||
</ul>
|
||||
<p>In addition to the above, there is also a "Relative" version of most columns, where the raw number is compared to the solver which provided the best performance. The <em>Relative Wallclock Time</em> for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.</p>
|
||||
<h3 id="saving-and-loading-benchmark-results">Saving and loading benchmark results</h3>
|
||||
<p>When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. <code>BenchmarkRunner</code> provides the methods <code>save_results</code> and <code>load_results</code>, which can be used to avoid this repetition, as the next example shows:</p>
|
||||
<pre><code class="python"># Benchmark baseline solvers and save results to a file.
|
||||
benchmark = BenchmarkRunner(baseline_solvers)
|
||||
benchmark.parallel_solve(test_instances)
|
||||
benchmark.save_results("baseline_results.csv")
|
||||
|
||||
# Benchmark remaining solvers, loading baseline results from file.
|
||||
benchmark = BenchmarkRunner(alternative_solvers)
|
||||
benchmark.load_results("baseline_results.csv")
|
||||
benchmark.fit(training_instances)
|
||||
benchmark.parallel_solve(test_instances)
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</code></pre></div>
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|
||||
/* Added to support >2 level nav in drop down */
|
||||
|
||||
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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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|
||||
|
||||
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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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|
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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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|
||||
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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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|
||||
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|
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/* Start Bootstrap Callouts CSS Source by Chris Pratt (https://codepen.io/chrisdpratt/pen/IAymB) MIT License*/
|
||||
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|
||||
padding: 20px;
|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
/* End Bootstrap Callouts CSS Source by Chris Pratt */
|
||||
|
||||
/* Admonitions */
|
||||
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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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|
||||
|
||||
.admonition p:last-child {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
margin-top: -5px;
|
||||
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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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|
||||
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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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|
||||
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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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|
||||
@ -1 +0,0 @@
|
||||
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|
||||
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Load Diff
File diff suppressed because one or more lines are too long
@ -1,82 +0,0 @@
|
||||
/*
|
||||
Cinder Theme for MkDocs | Copyright 2015 Christopher Simpkins | MIT License
|
||||
*/
|
||||
|
||||
body {
|
||||
font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
|
||||
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|
||||
line-height: 1.7;
|
||||
background-color: #FFF;
|
||||
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|
||||
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|
||||
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|
||||
font-family:'PT Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif;
|
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|
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|
||||
h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small {
|
||||
color: #B1B7B9;
|
||||
}
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
margin-top: 20px;
|
||||
font-style: italic;
|
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|
||||
h5 {
|
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|
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|
||||
font-variant: small-caps;
|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
pre {
|
||||
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|
||||
margin-bottom: 25px;
|
||||
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|
||||
.lead {
|
||||
font-family:"Open Sans", "Helvetica Neue", Helvetica, Arial, sans-serif;
|
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|
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|
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|
||||
color: #B1B7B9;
|
||||
}
|
||||
.navbar-default {
|
||||
background-color: #343838;
|
||||
border-bottom: 8px #EBF2F2 solid;
|
||||
}
|
||||
.bs-sidenav {
|
||||
background-image: url("../img/grid11.png");
|
||||
background-repeat: repeat;
|
||||
font-size: 12px;
|
||||
}
|
||||
.well {
|
||||
background-color: #FCFDFF;
|
||||
}
|
||||
.btn-default {
|
||||
background-color:#FCFDFF;
|
||||
}
|
||||
.table-striped > tbody > tr:nth-child(2n+1) > td, .table-striped > tbody > tr:nth-child(2n+1) > th {
|
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|
||||
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|
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#mkdocs-search-query:focus {
|
||||
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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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|
||||
color: #343838;
|
||||
height: 45px;
|
||||
}
|
||||
footer > hr {
|
||||
width: 35%;
|
||||
}
|
||||
@ -1 +0,0 @@
|
||||
body{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:16px;line-height:1.7;background-color:#FFF;color:#343838}h1,h2,h3,h4,h5,h6{font-family:'PT Sans','Helvetica Neue',Helvetica,Arial,sans-serif;color:#222}h1 small,h2 small,h3 small,h4 small,h5 small,h6 small,.h1 small,.h2 small,.h3 small,.h4 small,.h5 small,.h6 small,h1 .small,h2 .small,h3 .small,h4 .small,h5 .small,h6 .small,.h1 .small,.h2 .small,.h3 .small,.h4 .small,.h5 .small,.h6 .small{color:#b1b7b9}h1,h2{font-weight:700}h4{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;margin-top:20px;font-style:italic}h5{font-family:'Open Sans','Helvetica Neue',Helvetica,Arial,sans-serif;font-weight:300;font-variant:small-caps}pre,code{background-color:#fcfdff}pre>code{font-size:13px}pre{margin-top:25px;margin-bottom:25px}.lead{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-weight:400;line-height:1.4;letter-spacing:.0312em;color:#b1b7b9}.navbar-default{background-color:#343838;border-bottom:8px #ebf2f2 solid}.bs-sidenav{background-image:url("../img/grid11.png");background-repeat:repeat;font-size:12px}.well{background-color:#fcfdff}.btn-default{background-color:#fcfdff}.table-striped>tbody>tr:nth-child(2n+1)>td,.table-striped>tbody>tr:nth-child(2n+1)>th{background-color:#fcfdff}#mkdocs-search-query:focus{outline:0;-webkit-box-shadow:none;box-shadow:none}#mkdocs-search-query{font-family:"Open Sans","Helvetica Neue",Helvetica,Arial,sans-serif;font-size:20px;font-weight:700;color:#343838;height:45px}footer>hr{width:35%}
|
||||
@ -1,99 +0,0 @@
|
||||
/*
|
||||
|
||||
github.com style (c) Vasily Polovnyov <vast@whiteants.net>
|
||||
|
||||
*/
|
||||
|
||||
.hljs {
|
||||
display: block;
|
||||
overflow-x: auto;
|
||||
padding: 0.5em;
|
||||
color: #333;
|
||||
background: #FCFDFF;
|
||||
}
|
||||
|
||||
.hljs-comment,
|
||||
.hljs-quote {
|
||||
color: #998;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.hljs-keyword,
|
||||
.hljs-selector-tag,
|
||||
.hljs-subst {
|
||||
color: #333;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.hljs-number,
|
||||
.hljs-literal,
|
||||
.hljs-variable,
|
||||
.hljs-template-variable,
|
||||
.hljs-tag .hljs-attr {
|
||||
color: #008080;
|
||||
}
|
||||
|
||||
.hljs-string,
|
||||
.hljs-doctag {
|
||||
color: #d14;
|
||||
}
|
||||
|
||||
.hljs-title,
|
||||
.hljs-section,
|
||||
.hljs-selector-id {
|
||||
color: #900;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.hljs-subst {
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.hljs-type,
|
||||
.hljs-class .hljs-title {
|
||||
color: #458;
|
||||
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|
||||
}
|
||||
|
||||
.hljs-tag,
|
||||
.hljs-name,
|
||||
.hljs-attribute {
|
||||
color: #000080;
|
||||
font-weight: normal;
|
||||
}
|
||||
|
||||
.hljs-regexp,
|
||||
.hljs-link {
|
||||
color: #009926;
|
||||
}
|
||||
|
||||
.hljs-symbol,
|
||||
.hljs-bullet {
|
||||
color: #990073;
|
||||
}
|
||||
|
||||
.hljs-built_in,
|
||||
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|
||||
color: #0086b3;
|
||||
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|
||||
|
||||
.hljs-meta {
|
||||
color: #999;
|
||||
font-weight: bold;
|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
||||
.hljs-addition {
|
||||
background: #dfd;
|
||||
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|
||||
|
||||
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|
||||
font-style: italic;
|
||||
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|
||||
|
||||
.hljs-strong {
|
||||
font-weight: bold;
|
||||
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|
||||
@ -1 +0,0 @@
|
||||
.hljs{display:block;overflow-x:auto;padding:.5em;color:#333;background:#fcfdff}.hljs-comment,.hljs-quote{color:#998;font-style:italic}.hljs-keyword,.hljs-selector-tag,.hljs-subst{color:#333;font-weight:bold}.hljs-number,.hljs-literal,.hljs-variable,.hljs-template-variable,.hljs-tag .hljs-attr{color:teal}.hljs-string,.hljs-doctag{color:#d14}.hljs-title,.hljs-section,.hljs-selector-id{color:#900;font-weight:bold}.hljs-subst{font-weight:normal}.hljs-type,.hljs-class .hljs-title{color:#458;font-weight:bold}.hljs-tag,.hljs-name,.hljs-attribute{color:navy;font-weight:normal}.hljs-regexp,.hljs-link{color:#009926}.hljs-symbol,.hljs-bullet{color:#990073}.hljs-built_in,.hljs-builtin-name{color:#0086b3}.hljs-meta{color:#999;font-weight:bold}.hljs-deletion{background:#fdd}.hljs-addition{background:#dfd}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}
|
||||
@ -1,384 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
|
||||
|
||||
<link rel="../img/favicon.ico">
|
||||
|
||||
|
||||
<title>Customization - MIPLearn</title>
|
||||
|
||||
|
||||
<link rel="stylesheet" href="//use.fontawesome.com/releases/v5.5.0/css/all.css" integrity="sha384-B4dIYHKNBt8Bc12p+WXckhzcICo0wtJAoU8YZTY5qE0Id1GSseTk6S+L3BlXeVIU" crossorigin="anonymous">
|
||||
<link rel="stylesheet" href="//cdn.jsdelivr.net/npm/hack-font@3.3.0/build/web/hack.min.css">
|
||||
<link href='//fonts.googleapis.com/css?family=PT+Sans:400,400italic,700,700italic&subset=latin-ext,latin' rel='stylesheet' type='text/css'>
|
||||
<link href='//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,700italic,400,300,600,700&subset=latin-ext,latin' rel='stylesheet' type='text/css'>
|
||||
<link href="../css/bootstrap-custom.min.css" rel="stylesheet">
|
||||
<link href="../css/base.min.css" rel="stylesheet">
|
||||
<link href="../css/cinder.min.css" rel="stylesheet">
|
||||
<link href="../css/highlight.min.css" rel="stylesheet">
|
||||
<link href="../css/custom.css" rel="stylesheet">
|
||||
|
||||
<!-- HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries -->
|
||||
<!--[if lt IE 9]>
|
||||
<script src="https://cdn.jsdelivr.net/npm/html5shiv@3.7.3/dist/html5shiv.min.js"></script>
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<li class="first-level active"><a href="#customization">Customization</a></li>
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<li class="second-level"><a href="#customizing-solver-parameters">Customizing solver parameters</a></li>
|
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|
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<li class="third-level"><a href="#selecting-the-internal-mip-solver">Selecting the internal MIP solver</a></li>
|
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<li class="second-level"><a href="#customizing-solver-components">Customizing solver components</a></li>
|
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<li class="third-level"><a href="#selecting-components">Selecting components</a></li>
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<li class="third-level"><a href="#adjusting-component-aggressiveness">Adjusting component aggressiveness</a></li>
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<li class="third-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
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<li class="third-level"><a href="#using-customized-ml-classifiers-and-regressors">Using customized ML classifiers and regressors</a></li>
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<div class="col-md-9" role="main">
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||||
<h1 id="customization">Customization</h1>
|
||||
<h2 id="customizing-solver-parameters">Customizing solver parameters</h2>
|
||||
<h3 id="selecting-the-internal-mip-solver">Selecting the internal MIP solver</h3>
|
||||
<p>By default, <code>LearningSolver</code> uses <a href="https://www.gurobi.com/">Gurobi</a> as its internal MIP solver. Another supported solver is <a href="https://www.ibm.com/products/ilog-cplex-optimization-studio">IBM ILOG CPLEX</a>. To switch between solvers, use the <code>solver</code> constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.</p>
|
||||
<pre><code class="python">from miplearn import LearningSolver
|
||||
solver = LearningSolver(solver="cplex",
|
||||
time_limit=300,
|
||||
gap_tolerance=1e-3)
|
||||
</code></pre>
|
||||
|
||||
<h2 id="customizing-solver-components">Customizing solver components</h2>
|
||||
<p><code>LearningSolver</code> is composed by a number of individual machine-learning components, each targeting a different part of the solution process. Each component can be individually enabled, disabled or customized. The following components are enabled by default:</p>
|
||||
<ul>
|
||||
<li><code>LazyConstraintComponent</code>: Predicts which lazy constraint to initially enforce.</li>
|
||||
<li><code>ObjectiveValueComponent</code>: Predicts the optimal value of the optimization problem, given the optimal solution to the LP relaxation.</li>
|
||||
<li><code>PrimalSolutionComponent</code>: Predicts optimal values for binary decision variables. In heuristic mode, this component fixes the variables to their predicted values. In exact mode, the predicted values are provided to the solver as a (partial) MIP start.</li>
|
||||
</ul>
|
||||
<p>The following components are also available, but not enabled by default:</p>
|
||||
<ul>
|
||||
<li><code>BranchPriorityComponent</code>: Predicts good branch priorities for decision variables.</li>
|
||||
</ul>
|
||||
<h3 id="selecting-components">Selecting components</h3>
|
||||
<p>To create a <code>LearningSolver</code> with a specific set of components, the <code>components</code> constructor argument may be used, as the next example shows:</p>
|
||||
<pre><code class="python"># Create a solver without any components
|
||||
solver1 = LearningSolver(components=[])
|
||||
|
||||
# Create a solver with only two components
|
||||
solver2 = LearningSolver(components=[
|
||||
LazyConstraintComponent(...),
|
||||
PrimalSolutionComponent(...),
|
||||
])
|
||||
</code></pre>
|
||||
|
||||
<p>It is also possible to add components to an existing solver using the <code>solver.add</code> method, as shown below. If the solver already holds another component of that type, the new component will replace the previous one.</p>
|
||||
<pre><code class="python"># Create solver with default components
|
||||
solver = LearningSolver()
|
||||
|
||||
# Replace the default LazyConstraintComponent by one with custom parameters
|
||||
solver.add(LazyConstraintComponent(...))
|
||||
</code></pre>
|
||||
|
||||
<h3 id="adjusting-component-aggressiveness">Adjusting component aggressiveness</h3>
|
||||
<p>The aggressiveness of classification components (such as <code>PrimalSolutionComponent</code> and <code>LazyConstraintComponent</code>) can
|
||||
be adjusted through the <code>threshold</code> constructor argument. Internally, these components ask the ML models how confident
|
||||
they are on each prediction (through the <code>predict_proba</code> method in the sklearn API), and only take into account
|
||||
predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggressiveness,
|
||||
while raising a component's threshold makes it more conservative. </p>
|
||||
<p>MIPLearn also includes <code>MinPrecisionThreshold</code>, a dynamic threshold which adjusts itself automatically during training
|
||||
to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize
|
||||
a <code>PrimalSolutionComponent</code> which achieves 95% precision, possibly at the cost of a lower recall. To make the component
|
||||
more aggressive, this precision may be lowered.</p>
|
||||
<pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
|
||||
</code></pre>
|
||||
|
||||
<h3 id="evaluating-component-performance">Evaluating component performance</h3>
|
||||
<p>MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
|
||||
fit <code>PrimalSolutionComponent</code> outside the solver, then evaluate its performance.</p>
|
||||
<pre><code class="python">from miplearn import PrimalSolutionComponent
|
||||
|
||||
# User-provided set of previously-solved instances
|
||||
train_instances = [...]
|
||||
|
||||
# Construct and fit component on a subset of training instances
|
||||
comp = PrimalSolutionComponent()
|
||||
comp.fit(train_instances[:100])
|
||||
|
||||
# Evaluate performance on an additional set of training instances
|
||||
ev = comp.evaluate(train_instances[100:150])
|
||||
</code></pre>
|
||||
|
||||
<p>The method <code>evaluate</code> returns a dictionary with performance evaluation statistics for each training instance provided,
|
||||
and for each type of prediction the component makes. To obtain a summary across all instances, pandas may be used, as below:</p>
|
||||
<pre><code class="python">import pandas as pd
|
||||
pd.DataFrame(ev["Fix one"]).mean(axis=1)
|
||||
</code></pre>
|
||||
|
||||
<pre><code class="text">Predicted positive 3.120000
|
||||
Predicted negative 196.880000
|
||||
Condition positive 62.500000
|
||||
Condition negative 137.500000
|
||||
True positive 3.060000
|
||||
True negative 137.440000
|
||||
False positive 0.060000
|
||||
False negative 59.440000
|
||||
Accuracy 0.702500
|
||||
F1 score 0.093050
|
||||
Recall 0.048921
|
||||
Precision 0.981667
|
||||
Predicted positive (%) 1.560000
|
||||
Predicted negative (%) 98.440000
|
||||
Condition positive (%) 31.250000
|
||||
Condition negative (%) 68.750000
|
||||
True positive (%) 1.530000
|
||||
True negative (%) 68.720000
|
||||
False positive (%) 0.030000
|
||||
False negative (%) 29.720000
|
||||
dtype: float64
|
||||
</code></pre>
|
||||
|
||||
<p>Regression components (such as <code>ObjectiveValueComponent</code>) can also be trained and evaluated similarly,
|
||||
as the next example shows:</p>
|
||||
<pre><code class="python">from miplearn import ObjectiveValueComponent
|
||||
comp = ObjectiveValueComponent()
|
||||
comp.fit(train_instances[:100])
|
||||
ev = comp.evaluate(train_instances[100:150])
|
||||
|
||||
import pandas as pd
|
||||
pd.DataFrame(ev).mean(axis=1)
|
||||
</code></pre>
|
||||
|
||||
<pre><code class="text">Mean squared error 7001.977827
|
||||
Explained variance 0.519790
|
||||
Max error 242.375804
|
||||
Mean absolute error 65.843924
|
||||
R2 0.517612
|
||||
Median absolute error 65.843924
|
||||
dtype: float64
|
||||
</code></pre>
|
||||
|
||||
<h3 id="using-customized-ml-classifiers-and-regressors">Using customized ML classifiers and regressors</h3>
|
||||
<p>By default, given a training set of instantes, MIPLearn trains a fixed set of ML classifiers and regressors, then
|
||||
selects the best one based on cross-validation performance. Alternatively, the user may specify which ML model a component
|
||||
should use through the <code>classifier</code> or <code>regressor</code> contructor parameters. The provided classifiers and regressors must
|
||||
follow the sklearn API. In particular, classifiers must provide the methods <code>fit</code>, <code>predict_proba</code> and <code>predict</code>,
|
||||
while regressors must provide the methods <code>fit</code> and <code>predict</code></p>
|
||||
<div class="admonition danger">
|
||||
<p class="admonition-title">Danger</p>
|
||||
<p>MIPLearn must be able to generate a copy of any custom ML classifiers and regressors through
|
||||
the standard <code>copy.deepcopy</code> method. This currently makes it incompatible with Keras and TensorFlow
|
||||
predictors. This is a known limitation, which will be addressed in a future version.</p>
|
||||
</div>
|
||||
<p>The example below shows how to construct a <code>PrimalSolutionComponent</code> which internally uses
|
||||
sklearn's <code>KNeighborsClassifiers</code>. Any other sklearn classifier or pipeline can be used. </p>
|
||||
<pre><code class="python">from miplearn import PrimalSolutionComponent
|
||||
from sklearn.neighbors import KNeighborsClassifier
|
||||
|
||||
comp = PrimalSolutionComponent(classifier=KNeighborsClassifier(n_neighbors=5))
|
||||
comp.fit(train_instances)
|
||||
</code></pre></div>
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<h1 id="miplearn">MIPLearn</h1>
|
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<p><strong>MIPLearn</strong> is an extensible framework for <strong>Learning-Enhanced Mixed-Integer Optimization</strong>, an approach targeted at discrete optimization problems that need to be repeatedly solved with only minor changes to input data.</p>
|
||||
<p>The package uses Machine Learning (ML) to automatically identify patterns in previously solved instances of the problem, or in the solution process itself, and produces hints that can guide a conventional MIP solver towards the optimal solution faster. For particular classes of problems, this approach has been shown to provide significant performance benefits (see <a href="problems/">benchmark results</a> and <a href="about/#references">references</a> for more details).</p>
|
||||
<h3 id="features">Features</h3>
|
||||
<ul>
|
||||
<li>
|
||||
<p><strong>MIPLearn proposes a flexible problem specification format,</strong> which allows users to describe their particular optimization problems to a Learning-Enhanced MIP solver, both from the MIP perspective and from the ML perspective, without making any assumptions on the problem being modeled, the mathematical formulation of the problem, or ML encoding. While the format is very flexible, some constraints are enforced to ensure that it is usable by an actual solver.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>MIPLearn provides a reference implementation of a <em>Learning-Enhanced Solver</em>,</strong> which can use the above problem specification format to automatically predict, based on previously solved instances, a number of hints to accelerate MIP performance. Currently, the reference solver is able to predict: (i) partial solutions which are likely to work well as MIP starts; (ii) an initial set of lazy constraints to enforce; (iii) variable branching priorities to accelerate the exploration of the branch-and-bound tree; (iv) the optimal objective value based on the solution to the LP relaxation. The usage of the solver is very straightforward. The most suitable ML models are automatically selected, trained, cross-validated and applied to the problem with no user intervention.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>MIPLearn provides a set of benchmark problems and random instance generators,</strong> covering applications from different domains, which can be used to quickly evaluate new learning-enhanced MIP techniques in a measurable and reproducible way.</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>MIPLearn is customizable and extensible</strong>. For MIP and ML researchers exploring new techniques to accelerate MIP performance based on historical data, each component of the reference solver can be individually replaced, extended or customized.</p>
|
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</li>
|
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</ul>
|
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<h3 id="documentation">Documentation</h3>
|
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<ul>
|
||||
<li><a href="usage/">Installation and typical usage</a></li>
|
||||
<li><a href="benchmark/">Benchmark utilities</a></li>
|
||||
<li><a href="problems/">Benchmark problems, challenges and results</a></li>
|
||||
<li><a href="customization/">Customizing the solver</a></li>
|
||||
<li><a href="about/">License, authors, references and acknowledgments</a></li>
|
||||
</ul>
|
||||
<h3 id="source-code">Source Code</h3>
|
||||
<ul>
|
||||
<li><a href="https://github.com/ANL-CEEESA/MIPLearn">https://github.com/ANL-CEEESA/MIPLearn</a></li>
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|
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function onJSONLoaded () {
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||||
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function init () {
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<?xml version="1.0" encoding="UTF-8"?>
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||||
<urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9"><url>
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<lastmod>2020-05-05</lastmod>
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<changefreq>daily</changefreq>
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<lastmod>2020-05-05</lastmod>
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<changefreq>daily</changefreq>
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||||
</url><url>
|
||||
<loc>None</loc>
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||||
<lastmod>2020-05-05</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-05-05</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-05-05</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url><url>
|
||||
<loc>None</loc>
|
||||
<lastmod>2020-05-05</lastmod>
|
||||
<changefreq>daily</changefreq>
|
||||
</url>
|
||||
</urlset>
|
||||
Binary file not shown.
@ -1,359 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
|
||||
<head>
|
||||
<meta charset="utf-8">
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
|
||||
|
||||
<link rel="../img/favicon.ico">
|
||||
|
||||
|
||||
<title>Usage - MIPLearn</title>
|
||||
|
||||
|
||||
<link rel="stylesheet" href="//use.fontawesome.com/releases/v5.5.0/css/all.css" integrity="sha384-B4dIYHKNBt8Bc12p+WXckhzcICo0wtJAoU8YZTY5qE0Id1GSseTk6S+L3BlXeVIU" crossorigin="anonymous">
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<link rel="stylesheet" href="//cdn.jsdelivr.net/npm/hack-font@3.3.0/build/web/hack.min.css">
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||||
<link href='//fonts.googleapis.com/css?family=PT+Sans:400,400italic,700,700italic&subset=latin-ext,latin' rel='stylesheet' type='text/css'>
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||||
<link href='//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,700italic,400,300,600,700&subset=latin-ext,latin' rel='stylesheet' type='text/css'>
|
||||
<link href="../css/bootstrap-custom.min.css" rel="stylesheet">
|
||||
<link href="../css/base.min.css" rel="stylesheet">
|
||||
<link href="../css/cinder.min.css" rel="stylesheet">
|
||||
<link href="../css/highlight.min.css" rel="stylesheet">
|
||||
<link href="../css/custom.css" rel="stylesheet">
|
||||
|
||||
<!-- HTML5 shim and Respond.js IE8 support of HTML5 elements and media queries -->
|
||||
<!--[if lt IE 9]>
|
||||
<script src="https://cdn.jsdelivr.net/npm/html5shiv@3.7.3/dist/html5shiv.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/npm/respond.js@1.4.2/dest/respond.min.js"></script>
|
||||
<![endif]-->
|
||||
|
||||
<script src="//ajax.googleapis.com/ajax/libs/webfont/1.6.26/webfont.js"></script>
|
||||
<script>
|
||||
WebFont.load({
|
||||
google: {
|
||||
families: ['Open Sans', 'PT Sans']
|
||||
}
|
||||
});
|
||||
</script>
|
||||
|
||||
|
||||
|
||||
|
||||
</head>
|
||||
|
||||
<body>
|
||||
|
||||
<div class="navbar navbar-default navbar-fixed-top" role="navigation">
|
||||
<div class="container">
|
||||
|
||||
<!-- Collapsed navigation -->
|
||||
<div class="navbar-header">
|
||||
<!-- Expander button -->
|
||||
<button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-collapse">
|
||||
<span class="sr-only">Toggle navigation</span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
</button>
|
||||
|
||||
|
||||
<!-- Main title -->
|
||||
|
||||
<a class="navbar-brand" href="..">MIPLearn</a>
|
||||
</div>
|
||||
|
||||
<!-- Expanded navigation -->
|
||||
<div class="navbar-collapse collapse">
|
||||
<!-- Main navigation -->
|
||||
<ul class="nav navbar-nav">
|
||||
|
||||
|
||||
<li >
|
||||
<a href="..">Home</a>
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li class="active">
|
||||
<a href="./">Usage</a>
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li >
|
||||
<a href="../benchmark/">Benchmark</a>
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li >
|
||||
<a href="../problems/">Problems</a>
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li >
|
||||
<a href="../customization/">Customization</a>
|
||||
</li>
|
||||
|
||||
|
||||
|
||||
<li >
|
||||
<a href="../about/">About</a>
|
||||
</li>
|
||||
|
||||
|
||||
</ul>
|
||||
|
||||
<ul class="nav navbar-nav navbar-right">
|
||||
<li>
|
||||
<a href="#" data-toggle="modal" data-target="#mkdocs_search_modal">
|
||||
<i class="fas fa-search"></i> Search
|
||||
</a>
|
||||
</li>
|
||||
<li >
|
||||
<a rel="prev" href="..">
|
||||
<i class="fas fa-arrow-left"></i> Previous
|
||||
</a>
|
||||
</li>
|
||||
<li >
|
||||
<a rel="next" href="../benchmark/">
|
||||
Next <i class="fas fa-arrow-right"></i>
|
||||
</a>
|
||||
</li>
|
||||
<li>
|
||||
<a href="https://github.com/ANL-CEEESA/MIPLearn/edit/master/src/docs/usage.md"><i class="fab fa-github"></i> Edit on GitHub</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="container">
|
||||
|
||||
|
||||
<div class="col-md-3"><div class="bs-sidebar hidden-print affix well" role="complementary">
|
||||
<ul class="nav bs-sidenav">
|
||||
<li class="first-level active"><a href="#usage">Usage</a></li>
|
||||
<li class="second-level"><a href="#installation">Installation</a></li>
|
||||
|
||||
<li class="second-level"><a href="#using-learningsolver">Using LearningSolver</a></li>
|
||||
|
||||
<li class="second-level"><a href="#describing-problem-instances">Describing problem instances</a></li>
|
||||
|
||||
<li class="second-level"><a href="#obtaining-heuristic-solutions">Obtaining heuristic solutions</a></li>
|
||||
|
||||
<li class="second-level"><a href="#saving-and-loading-solver-state">Saving and loading solver state</a></li>
|
||||
|
||||
<li class="second-level"><a href="#solving-training-instances-in-parallel">Solving training instances in parallel</a></li>
|
||||
|
||||
<li class="second-level"><a href="#current-limitations">Current Limitations</a></li>
|
||||
|
||||
</ul>
|
||||
</div></div>
|
||||
<div class="col-md-9" role="main">
|
||||
|
||||
<h1 id="usage">Usage</h1>
|
||||
<h3 id="installation">Installation</h3>
|
||||
<p>MIPLearn is mainly written in Python, with some components written in Julia. For this
|
||||
reason, both Python 3.6+ and Julia 1.3+ are required. A mixed-integer solver is also required, and
|
||||
its Python bindings must be properly installed. Supported solvers are CPLEX and
|
||||
Gurobi. Optimization problems currently need to be specified in the Pyomo modeling language.
|
||||
A JuMP interface to the package is currently under development.</p>
|
||||
<p>To install MIPLearn, run the following commands: </p>
|
||||
<pre><code class="bash">git clone https://github.com/ANL-CEEESA/MIPLearn.git
|
||||
cd MIPLearn
|
||||
make install
|
||||
</code></pre>
|
||||
|
||||
<p>After installation, the package <code>miplearn</code> should become available to Python. It can be imported
|
||||
as follows:</p>
|
||||
<pre><code class="python">import miplearn
|
||||
</code></pre>
|
||||
|
||||
<div class="admonition note">
|
||||
<p class="admonition-title">Note</p>
|
||||
<p>To install MIPLearn in another Python environment, switch to that environment before running <code>make install</code>. To install the package in development mode, run <code>make develop</code> instead.</p>
|
||||
</div>
|
||||
<h3 id="using-learningsolver">Using <code>LearningSolver</code></h3>
|
||||
<p>The main class provided by this package is <code>LearningSolver</code>, a learning-enhanced MIP solver which uses information from previously solved instances to accelerate the solution of new instances. The following example shows its basic usage:</p>
|
||||
<pre><code class="python">from miplearn import LearningSolver
|
||||
|
||||
# List of user-provided instances
|
||||
training_instances = [...]
|
||||
test_instances = [...]
|
||||
|
||||
# Create solver
|
||||
solver = LearningSolver()
|
||||
|
||||
# Solve all training instances
|
||||
for instance in training_instances:
|
||||
solver.solve(instance)
|
||||
|
||||
# Learn from training instances
|
||||
solver.fit(training_instances)
|
||||
|
||||
# Solve all test instances
|
||||
for instance in test_instances:
|
||||
solver.solve(instance)
|
||||
</code></pre>
|
||||
|
||||
<p>In this example, we have two lists of user-provided instances: <code>training_instances</code> and <code>test_instances</code>. We start by solving all training instances. Since there is no historical information available at this point, the instances will be processed from scratch, with no ML acceleration. After solving each instance, the solver stores within each <code>instance</code> object the optimal solution, the optimal objective value, and other information that can be used to accelerate future solves. After all training instances are solved, we call <code>solver.fit(training_instances)</code>. This instructs the solver to train all its internal machine-learning models based on the solutions of the (solved) trained instances. Subsequent calls to <code>solver.solve(instance)</code> will automatically use the trained Machine Learning models to accelerate the solution process.</p>
|
||||
<h3 id="describing-problem-instances">Describing problem instances</h3>
|
||||
<p>Instances to be solved by <code>LearningSolver</code> must derive from the abstract class <code>miplearn.Instance</code>. The following three abstract methods must be implemented:</p>
|
||||
<ul>
|
||||
<li><code>instance.to_model()</code>, which returns a concrete Pyomo model corresponding to the instance;</li>
|
||||
<li><code>instance.get_instance_features()</code>, which returns a 1-dimensional Numpy array of (numerical) features describing the entire instance;</li>
|
||||
<li><code>instance.get_variable_features(var_name, index)</code>, which returns a 1-dimensional array of (numerical) features describing a particular decision variable.</li>
|
||||
</ul>
|
||||
<p>The first method is used by <code>LearningSolver</code> to construct a concrete Pyomo model, which will be provided to the internal MIP solver. The second and third methods provide an encoding of the instance, which can be used by the ML models to make predictions. In the knapsack problem, for example, an implementation may decide to provide as instance features the average weights, average prices, number of items and the size of the knapsack. The weight and the price of each individual item could be provided as variable features. See <code>src/python/miplearn/problems/knapsack.py</code> for a concrete example.</p>
|
||||
<p>An optional method which can be implemented is <code>instance.get_variable_category(var_name, index)</code>, which returns a category (a string, an integer or any hashable type) for each decision variable. If two variables have the same category, <code>LearningSolver</code> will use the same internal ML model to predict the values of both variables. By default, all variables belong to the <code>"default"</code> category, and therefore only one ML model is used for all variables. If the returned category is <code>None</code>, ML predictors will ignore the variable.</p>
|
||||
<p>It is not necessary to have a one-to-one correspondence between features and problem instances. One important (and deliberate) limitation of MIPLearn, however, is that <code>get_instance_features()</code> must always return arrays of same length for all relevant instances of the problem. Similarly, <code>get_variable_features(var_name, index)</code> must also always return arrays of same length for all variables in each category. It is up to the user to decide how to encode variable-length characteristics of the problem into fixed-length vectors. In graph problems, for example, graph embeddings can be used to reduce the (variable-length) lists of nodes and edges into a fixed-length structure that still preserves some properties of the graph. Different instance encodings may have significant impact on performance.</p>
|
||||
<h3 id="obtaining-heuristic-solutions">Obtaining heuristic solutions</h3>
|
||||
<p>By default, <code>LearningSolver</code> uses Machine Learning to accelerate the MIP solution process, while maintaining all optimality guarantees provided by the MIP solver. In the default mode of operation, for example, predicted optimal solutions are used only as MIP starts.</p>
|
||||
<p>For more significant performance benefits, <code>LearningSolver</code> can also be configured to place additional trust in the Machine Learning predictors, by using the <code>mode="heuristic"</code> constructor argument. When operating in this mode, if a ML model is statistically shown (through <em>stratified k-fold cross validation</em>) to have exceptionally high accuracy, the solver may decide to restrict the search space based on its predictions. The parts of the solution which the ML models cannot predict accurately will still be explored using traditional (branch-and-bound) methods. For particular applications, this mode has been shown to quickly produce optimal or near-optimal solutions (see <a href="../about/#references">references</a> and <a href="../benchmark/">benchmark results</a>).</p>
|
||||
<div class="admonition danger">
|
||||
<p class="admonition-title">Danger</p>
|
||||
<p>The <code>heuristic</code> mode provides no optimality guarantees, and therefore should only be used if the solver is first trained on a large and representative set of training instances. Training on a small or non-representative set of instances may produce low-quality solutions, or make the solver incorrectly classify new instances as infeasible.</p>
|
||||
</div>
|
||||
<h3 id="saving-and-loading-solver-state">Saving and loading solver state</h3>
|
||||
<p>After solving a large number of training instances, it may be desirable to save the current state of <code>LearningSolver</code> to disk, so that the solver can still use the acquired knowledge after the application restarts. This can be accomplished by using the standard <code>pickle</code> module, as the following example illustrates:</p>
|
||||
<pre><code class="python">from miplearn import LearningSolver
|
||||
import pickle
|
||||
|
||||
# Solve training instances
|
||||
training_instances = [...]
|
||||
solver = LearningSolver()
|
||||
for instance in training_instances:
|
||||
solver.solve(instance)
|
||||
|
||||
# Train machine-learning models
|
||||
solver.fit(training_instances)
|
||||
|
||||
# Save trained solver to disk
|
||||
pickle.dump(solver, open("solver.pickle", "wb"))
|
||||
|
||||
# Application restarts...
|
||||
|
||||
# Load trained solver from disk
|
||||
solver = pickle.load(open("solver.pickle", "rb"))
|
||||
|
||||
# Solve additional instances
|
||||
test_instances = [...]
|
||||
for instance in test_instances:
|
||||
solver.solve(instance)
|
||||
</code></pre>
|
||||
|
||||
<h3 id="solving-training-instances-in-parallel">Solving training instances in parallel</h3>
|
||||
<p>In many situations, training and test instances can be solved in parallel to accelerate the training process. <code>LearningSolver</code> provides the method <code>parallel_solve(instances)</code> to easily achieve this:</p>
|
||||
<pre><code class="python">from miplearn import LearningSolver
|
||||
|
||||
training_instances = [...]
|
||||
solver = LearningSolver()
|
||||
solver.parallel_solve(training_instances, n_jobs=4)
|
||||
solver.fit(training_instances)
|
||||
|
||||
# Test phase...
|
||||
test_instances = [...]
|
||||
solver.parallel_solve(test_instances)
|
||||
</code></pre>
|
||||
|
||||
<h3 id="current-limitations">Current Limitations</h3>
|
||||
<ul>
|
||||
<li>Only binary and continuous decision variables are currently supported.</li>
|
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</ul></div>
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