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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="#selecting-the-internal-mip-solver">Selecting the internal MIP solver</a></li>
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<li class="second-level"><a href="#selecting-solver-components">Selecting solver components</a></li>
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<li class="second-level"><a href="#adjusting-component-aggresiveness">Adjusting component aggresiveness</a></li>
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<li class="second-level"><a href="#evaluating-component-performance">Evaluating component performance</a></li>
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<h1 id="customization">Customization</h1>
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<h2 id="selecting-the-internal-mip-solver">Selecting the internal MIP solver</h2>
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<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>
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<pre><code class="python">from miplearn import LearningSolver
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solver = LearningSolver(solver="cplex",
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time_limit=300,
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gap_tolerance=1e-3)
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</code></pre>
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<h2 id="selecting-solver-components">Selecting solver components</h2>
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<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>
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<ul>
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<li><code>LazyConstraintComponent</code>: Predicts which lazy constraint to initially enforce.</li>
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<li><code>ObjectiveValueComponent</code>: Predicts the optimal value of the optimization problem, given the optimal solution to the LP relaxation.</li>
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<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>
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</ul>
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<p>The following components are also available, but not enabled by default:</p>
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<ul>
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<li><code>BranchPriorityComponent</code>: Predicts good branch priorities for decision variables.</li>
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</ul>
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<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>
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<pre><code class="python"># Create a solver without any components
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solver1 = LearningSolver(components=[])
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# Create a solver with only two components
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solver2 = LearningSolver(components=[
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LazyConstraintComponent(...),
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PrimalSolutionComponent(...),
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])
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</code></pre>
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<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>
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<pre><code class="python"># Create solver with default components
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solver = LearningSolver()
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# Replace the default LazyConstraintComponent by one with custom parameters
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solver.add(LazyConstraintComponent(...))
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</code></pre>
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<h2 id="adjusting-component-aggresiveness">Adjusting component aggresiveness</h2>
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<p>The aggressiveness of classification components (such as <code>PrimalSolutionComponent</code> and <code>LazyConstraintComponent</code>) can
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be adjusted through the <code>threshold</code> constructor argument. Internally, these components ask the ML models how confident
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they are on each prediction (through the <code>predict_proba</code> method in the sklearn API), and only take into account
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predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggresiveness,
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while raising a component's threshold makes it more conservative. </p>
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<p>MIPLearn also includes <code>MinPrecisionThreshold</code>, a dynamic threshold which adjusts itself automatically during training
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to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize
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a <code>PrimalSolutionComponent</code> which achieves 95% precision, possibly at the cost of a lower recall. To make the component
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more aggressive, this precision may be lowered.</p>
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<pre><code class="python">PrimalSolutionComponent(threshold=MinPrecisionThreshold(0.95))
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</code></pre>
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<h2 id="evaluating-component-performance">Evaluating component performance</h2>
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<p>MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
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fit <code>PrimalSolutionComponent</code> outside the solver, then evaluate its performance.</p>
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<pre><code class="python">from miplearn import PrimalSolutionComponent
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# User-provided set of previously-solved instances
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train_instances = [...]
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# Construct and fit component on a subset of training instances
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comp = PrimalSolutionComponent()
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comp.fit(train_instances[:100])
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# Evaluate performance on an additional set of training instances
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ev = comp.evaluate(train_instances[100:150])
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</code></pre>
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<p>The method <code>evaluate</code> returns a dictionary with performance evaluation statistics for each training instance provided,
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and for each type of prediction the component makes. To obtain a summary across all instances, pandas may be used, as below:</p>
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<pre><code class="python">import pandas as pd
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pd.DataFrame(ev["Fix one"]).mean(axis=1)
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</code></pre>
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<pre><code class="text">Predicted positive 3.120000
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Predicted negative 196.880000
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Condition positive 62.500000
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Condition negative 137.500000
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True positive 3.060000
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True negative 137.440000
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False positive 0.060000
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False negative 59.440000
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Accuracy 0.702500
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F1 score 0.093050
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Recall 0.048921
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Precision 0.981667
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Predicted positive (%) 1.560000
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Predicted negative (%) 98.440000
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Condition positive (%) 31.250000
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Condition negative (%) 68.750000
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True positive (%) 1.530000
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True negative (%) 68.720000
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False positive (%) 0.030000
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False negative (%) 29.720000
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dtype: float64
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</code></pre>
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<p>Regression components (such as <code>ObjectiveValueComponent</code>) can also be trained and evaluated similarly,
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as the next example shows:</p>
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<pre><code class="python">from miplearn import ObjectiveValueComponent
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comp = ObjectiveValueComponent()
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comp.fit(train_instances[:100])
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ev = comp.evaluate(train_instances[100:150])
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import pandas as pd
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pd.DataFrame(ev).mean(axis=1)
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</code></pre>
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<pre><code class="text">Mean squared error 7001.977827
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Explained variance 0.519790
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Max error 242.375804
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Mean absolute error 65.843924
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R2 0.517612
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Median absolute error 65.843924
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dtype: float64
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</code></pre></div>
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