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<a class="reference internal nav-link" href="#Configuring-the-solver">
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9.1. Configuring the solver
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9.2. Training and solving new instances
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<div class="section" id="Learning-Solver">
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<h1><span class="section-number">9. </span>Learning Solver<a class="headerlink" href="#Learning-Solver" title="Permalink to this headline">¶</a></h1>
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<p>On previous pages, we discussed various components of the MIPLearn framework, including training data collectors, feature extractors, and individual machine learning components. In this page, we introduce <strong>LearningSolver</strong>, the main class of the framework which integrates all the aforementioned components into a cohesive whole. Using <strong>LearningSolver</strong> involves three steps: (i) configuring the solver; (ii) training the ML components; and (iii) solving new MIP instances. In the following, we
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describe each of these steps, then conclude with a complete runnable example.</p>
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<div class="section" id="Configuring-the-solver">
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<h2><span class="section-number">9.1. </span>Configuring the solver<a class="headerlink" href="#Configuring-the-solver" title="Permalink to this headline">¶</a></h2>
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<p><strong>LearningSolver</strong> is composed by multiple individual machine learning components, each targeting a different part of the solution process, or implementing a different machine learning strategy. This architecture allows strategies to be easily enabled, disabled or customized, making the framework flexible. By default, no components are provided and <strong>LearningSolver</strong> is equivalent to a traditional MIP solver. To specify additional components, the <code class="docutils literal notranslate"><span class="pre">components</span></code> constructor argument may be used:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">solver</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span>
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<span class="n">components</span><span class="o">=</span><span class="p">[</span>
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<span class="n">comp1</span><span class="p">,</span>
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<span class="n">comp2</span><span class="p">,</span>
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<span class="n">comp3</span><span class="p">,</span>
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<span class="p">]</span>
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<span class="p">)</span>
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</pre></div>
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</div>
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<p>In this example, three components <code class="docutils literal notranslate"><span class="pre">comp1</span></code>, <code class="docutils literal notranslate"><span class="pre">comp2</span></code> and <code class="docutils literal notranslate"><span class="pre">comp3</span></code> are provided. The strategies implemented by these components are applied sequentially when solving the problem. For example, <code class="docutils literal notranslate"><span class="pre">comp1</span></code> and <code class="docutils literal notranslate"><span class="pre">comp2</span></code> could fix a subset of decision variables, while <code class="docutils literal notranslate"><span class="pre">comp3</span></code> constructs a warm start for the remaining problem.</p>
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</div>
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<div class="section" id="Training-and-solving-new-instances">
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<h2><span class="section-number">9.2. </span>Training and solving new instances<a class="headerlink" href="#Training-and-solving-new-instances" title="Permalink to this headline">¶</a></h2>
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<p>Once a solver is configured, its ML components need to be trained. This can be achieved by the <code class="docutils literal notranslate"><span class="pre">solver.fit</span></code> method, as illustrated below. The method accepts a list of HDF5 files and trains each individual component sequentially. Once the solver is trained, new instances can be solved using <code class="docutils literal notranslate"><span class="pre">solver.optimize</span></code>. The method returns a dictionary of statistics collected by each component, such as the number of variables fixed.</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build instances</span>
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<span class="n">train_data</span> <span class="o">=</span> <span class="o">...</span>
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<span class="n">test_data</span> <span class="o">=</span> <span class="o">...</span>
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<span class="c1"># Collect training data</span>
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<span class="n">bc</span> <span class="o">=</span> <span class="n">BasicCollector</span><span class="p">()</span>
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<span class="n">bc</span><span class="o">.</span><span class="n">collect</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">build_model</span><span class="p">)</span>
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<span class="c1"># Build solver</span>
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<span class="n">solver</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="o">...</span><span class="p">)</span>
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<span class="c1"># Train components</span>
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<span class="n">solver</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_data</span><span class="p">)</span>
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<span class="c1"># Solve a new test instance</span>
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<span class="n">stats</span> <span class="o">=</span> <span class="n">solver</span><span class="o">.</span><span class="n">optimize</span><span class="p">(</span><span class="n">test_data</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">build_model</span><span class="p">)</span>
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</pre></div>
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</div>
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</div>
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<div class="section" id="Complete-example">
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<h2><span class="section-number">9.3. </span>Complete example<a class="headerlink" href="#Complete-example" title="Permalink to this headline">¶</a></h2>
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<p>In the example below, we illustrate the usage of <strong>LearningSolver</strong> by building instances of the Traveling Salesman Problem, collecting training data, training the ML components, then solving a new instance.</p>
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<div class="nbinput docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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</pre></div>
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</div>
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>import random
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import numpy as np
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from scipy.stats import uniform, randint
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from sklearn.linear_model import LogisticRegression
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from miplearn.classifiers.minprob import MinProbabilityClassifier
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from miplearn.classifiers.singleclass import SingleClassFix
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from miplearn.collectors.basic import BasicCollector
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from miplearn.components.primal.actions import SetWarmStart
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from miplearn.components.primal.indep import IndependentVarsPrimalComponent
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from miplearn.extractors.AlvLouWeh2017 import AlvLouWeh2017Extractor
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from miplearn.io import write_pkl_gz
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from miplearn.problems.tsp import (
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TravelingSalesmanGenerator,
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build_tsp_model_gurobipy,
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)
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from miplearn.solvers.learning import LearningSolver
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# Set random seed to make example reproducible.
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random.seed(42)
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np.random.seed(42)
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# Generate a few instances of the traveling salesman problem.
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data = TravelingSalesmanGenerator(
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n=randint(low=10, high=11),
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x=uniform(loc=0.0, scale=1000.0),
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y=uniform(loc=0.0, scale=1000.0),
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gamma=uniform(loc=0.90, scale=0.20),
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fix_cities=True,
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round=True,
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).generate(50)
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# Save instance data to data/tsp/00000.pkl.gz, data/tsp/00001.pkl.gz, ...
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all_data = write_pkl_gz(data, "data/tsp")
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# Split train/test data
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train_data = all_data[:40]
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test_data = all_data[40:]
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# Collect training data
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bc = BasicCollector()
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bc.collect(train_data, build_tsp_model_gurobipy, n_jobs=4)
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# Build learning solver
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solver = LearningSolver(
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components=[
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IndependentVarsPrimalComponent(
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base_clf=SingleClassFix(
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MinProbabilityClassifier(
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base_clf=LogisticRegression(),
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thresholds=[0.95, 0.95],
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),
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),
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extractor=AlvLouWeh2017Extractor(),
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action=SetWarmStart(),
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)
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]
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)
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# Train ML models
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solver.fit(train_data)
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# Solve a test instance
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solver.optimize(test_data[0], build_tsp_model_gurobipy)
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</pre></div>
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</div>
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</div>
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<div class="nboutput docutils container">
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<div class="prompt empty docutils container">
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</div>
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<div class="output_area docutils container">
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<div class="highlight"><pre>
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Restricted license - for non-production use only - expires 2024-10-28
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Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (linux64)
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CPU model: 13th Gen Intel(R) Core(TM) i7-13800H, instruction set [SSE2|AVX|AVX2]
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Thread count: 10 physical cores, 20 logical processors, using up to 20 threads
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Optimize a model with 10 rows, 45 columns and 90 nonzeros
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Model fingerprint: 0x6ddcd141
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Coefficient statistics:
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Matrix range [1e+00, 1e+00]
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Objective range [4e+01, 1e+03]
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Bounds range [1e+00, 1e+00]
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RHS range [2e+00, 2e+00]
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Presolve time: 0.00s
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Presolved: 10 rows, 45 columns, 90 nonzeros
|
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|
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Iteration Objective Primal Inf. Dual Inf. Time
|
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0 6.3600000e+02 1.700000e+01 0.000000e+00 0s
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15 2.7610000e+03 0.000000e+00 0.000000e+00 0s
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Solved in 15 iterations and 0.00 seconds (0.00 work units)
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Optimal objective 2.761000000e+03
|
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|
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User-callback calls 56, time in user-callback 0.00 sec
|
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Set parameter PreCrush to value 1
|
|
Set parameter LazyConstraints to value 1
|
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Gurobi Optimizer version 10.0.3 build v10.0.3rc0 (linux64)
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|
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CPU model: 13th Gen Intel(R) Core(TM) i7-13800H, instruction set [SSE2|AVX|AVX2]
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Thread count: 10 physical cores, 20 logical processors, using up to 20 threads
|
|
|
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Optimize a model with 10 rows, 45 columns and 90 nonzeros
|
|
Model fingerprint: 0x74ca3d0a
|
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Variable types: 0 continuous, 45 integer (45 binary)
|
|
Coefficient statistics:
|
|
Matrix range [1e+00, 1e+00]
|
|
Objective range [4e+01, 1e+03]
|
|
Bounds range [1e+00, 1e+00]
|
|
RHS range [2e+00, 2e+00]
|
|
|
|
User MIP start produced solution with objective 2796 (0.00s)
|
|
Loaded user MIP start with objective 2796
|
|
|
|
Presolve time: 0.00s
|
|
Presolved: 10 rows, 45 columns, 90 nonzeros
|
|
Variable types: 0 continuous, 45 integer (45 binary)
|
|
|
|
Root relaxation: objective 2.761000e+03, 14 iterations, 0.00 seconds (0.00 work units)
|
|
|
|
Nodes | Current Node | Objective Bounds | Work
|
|
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
|
|
|
|
0 0 2761.00000 0 - 2796.00000 2761.00000 1.25% - 0s
|
|
0 0 cutoff 0 2796.00000 2796.00000 0.00% - 0s
|
|
|
|
Cutting planes:
|
|
Lazy constraints: 3
|
|
|
|
Explored 1 nodes (16 simplex iterations) in 0.01 seconds (0.00 work units)
|
|
Thread count was 20 (of 20 available processors)
|
|
|
|
Solution count 1: 2796
|
|
|
|
Optimal solution found (tolerance 1.00e-04)
|
|
Best objective 2.796000000000e+03, best bound 2.796000000000e+03, gap 0.0000%
|
|
|
|
User-callback calls 110, time in user-callback 0.00 sec
|
|
</pre></div></div>
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</div>
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<div class="nboutput nblast docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
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</pre></div>
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</div>
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<div class="output_area docutils container">
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<div class="highlight"><pre>
|
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{'WS: Count': 1, 'WS: Number of variables set': 41.0}
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</pre></div></div>
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</div>
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<div class="nbinput nblast docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
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</pre></div>
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</div>
|
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<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
|
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</pre></div>
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</div>
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</div>
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