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--- a/0.2/_sources/benchmark.md.txt
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 ```{sectnum}
 ---
 start: 2
 depth: 2
 suffix: .
 ---
 ```
 # Benchmarks
 MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for  `LearningSolver` with default parameters.
 ## Preliminaries
 ### Benchmark challenges
 When evaluating the performance of a conventional MIP solver, *benchmark sets*, such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the *training instances*) before solving the actual set of instances we are interested in (the *test instances*). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits.
 In MIPLearn, each optimization problem comes with a set of **benchmark challenges**, which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.
 ### Baseline results
 To illustrate the performance of `LearningSolver`, and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see [references](about.md#references). We compare three solvers:
 * **baseline:** Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver)
 * **ml-exact:** `LearningSolver` with default settings, using Gurobi 9.0 as internal MIP solver
 * **ml-heuristic:** Same as above, but with `mode="heuristic"`
 All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.
 ## Maximum Weight Stable Set Problem
 ### Problem definition
 Given a simple undirected graph $G=(V,E)$ and weights $w \in \mathbb{R}^V$, the problem is to find a stable set $S \subseteq V$ that maximizes $ \sum_{v \in V} w_v$. We recall that a subset $S \subseteq V$ is a *stable set* if no two vertices of $S$ are adjacent. This is one of Karp's 21 NP-complete problems.
 ### Random instance generator
 The class `MaxWeightStableSetGenerator` can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter `fix_graph=True` is provided, one random Erdős-Rényi graph $G_{n,p}$ is generated during 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, while the remaining parameters are sampled in the same way.
 ### Challenge A
 * Fixed random Erdős-Rényi graph $G_{n,p}$ with $n=200$ and $p=5\%$
 * Random vertex weights $w_v \sim U(100, 150)$
 * 500 training instances, 50 test instances
 ```python
 MaxWeightStableSetGenerator(w=uniform(loc=100., scale=50.),
                            n=randint(low=200, high=201),
                            p=uniform(loc=0.05, scale=0.0),
                            fix_graph=True)
 ```
 ![alt](figures/benchmark_stab_a.png)
 ## Traveling Salesman Problem
 ### Problem definition
 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.
 ### Random problem generator
 The class `TravelingSalesmanGenerator` can be used to generate random instances of this
 problem. Initially, the generator creates $n$ cities $(x_1,y_1),\ldots,(x_n,y_n) \in \mathbb{R}^2$,
 where $n, x_i$ and $y_i$ are sampled independently from the provided probability distributions `n`,
 `x` and `y`. For each 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 $\gamma_{i,j}$ is sampled from the distribution `gamma`.
 If `fix_cities=True` is provided, 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.
 ### Challenge A
 * Fixed list of 350 cities in the $[0, 1000]^2$ square
 * $\gamma_{i,j} \sim U(0.95, 1.05)$
 * 500 training instances, 50 test instances
 ```python
 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,
                          )
 ```
 ![alt](figures/benchmark_tsp_a.png)
 ## Multidimensional 0-1 Knapsack Problem
 ### Problem definition
 Given a set of $n$ items and $m$ types of resources (also called *knapsacks*), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is:
 $$
 \begin{align*}
    \text{maximize}
        & \sum_{j=1}^n p_j x_j
        \\
    \text{subject to}
        & \sum_{j=1}^n w_{ij} x_j \leq b_i
        & \forall i=1,\ldots,m \\
    & x_j \in \{0,1\}
        & \forall j=1,\ldots,n
 \end{align*}
 $$
 ### Random instance generator
 The class `MultiKnapsackGenerator` can be used to generate random instances of this problem. The number of items $n$ and knapsacks $m$ are sampled from the user-provided probability distributions `n` and `m`. The weights $w_{ij}$ are sampled independently from the provided distribution `w`. The capacity of knapsack $i$ is set to
 $$
    b_i = \alpha_i \sum_{j=1}^n w_{ij}
 $$
 where $\alpha_i$, 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 price of each
 item $j$ is set to:
 $$
    p_j = \sum_{i=1}^m \frac{w_{ij}}{m} + K  u_j,
 $$
 where $K$, the correlation coefficient, and $u_j$, the correlation multiplier, are sampled
 from the provided probability distributions `K` and `u`.
 If `fix_w=True` is provided, then $w_{ij}$ 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 $w_{ij}$, 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 $w_{ij} \gamma_{ij}$ where $\gamma_{ij}$ 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.
 !!! note "References"
    * Freville, Arnaud, and Gérard Plateau. *An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.* Discrete applied mathematics 49.1-3 (1994): 189-212.
    * Fréville, Arnaud. *The multidimensional 0–1 knapsack problem: An overview.* European Journal of Operational Research 155.1 (2004): 1-21.
 ### Challenge A
 * 250 variables, 10 constraints, fixed weights
 * $w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)$
 * $K = 500, u \sim U(0, 1), \alpha = 0.25$
 * 500 training instances, 50 test instances
 ```python
 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),
                      )
 ```
 ![alt](figures/benchmark_knapsack_a.png)
--- a/0.2/_sources/customization.md.txt
+++ b/0.2/_sources/customization.md.txt
@ -1,182 +0,0 @@
 ```{sectnum}
 ---
 start: 3
 depth: 2
 suffix: .
 ---
 ```
 # Customization
 ## Customizing solver parameters
 ### Selecting the internal MIP solver
 By default, `LearningSolver` uses [Gurobi](https://www.gurobi.com/) as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Supported solvers and modeling languages include:
 * `GurobiPyomoSolver`: Gurobi with Pyomo (default).
 * `CplexPyomoSolver`: [IBM ILOG CPLEX](https://www.ibm.com/products/ilog-cplex-optimization-studio) with Pyomo.
 * `XpressPyomoSolver`: [FICO XPRESS Solver](https://www.fico.com/en/products/fico-xpress-solver) with Pyomo.
 * `GurobiSolver`: Gurobi without any modeling language.
 To switch between solvers, provide the desired class using the `solver` argument:
 ```python
 from miplearn import LearningSolver, CplexPyomoSolver
 solver = LearningSolver(solver=CplexPyomoSolver)
 ```
 To configure a particular solver, use the `params` constructor argument, as shown below.
 ```python
 from miplearn import LearningSolver, GurobiPyomoSolver
 solver = LearningSolver(
    solver=lambda: GurobiPyomoSolver(
        params={
            "TimeLimit": 900,
            "MIPGap": 1e-3,
            "NodeLimit": 1000,
        }
    ),
 )
 ```
 ## Customizing solver components
 `LearningSolver` 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:
 * `LazyConstraintComponent`: Predicts which lazy constraint to initially enforce.
 * `ObjectiveValueComponent`: Predicts the optimal value of the optimization problem, given the optimal solution to the LP relaxation.
 * `PrimalSolutionComponent`: 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.
 The following components are also available, but not enabled by default:
 * `BranchPriorityComponent`: Predicts good branch priorities for decision variables.
 ### Selecting components
 To create a `LearningSolver` with a specific set of components, the `components` constructor argument may be used, as the next example shows:
 ```python
 # Create a solver without any components
 solver1 = LearningSolver(components=[])
 # Create a solver with only two components
 solver2 = LearningSolver(components=[
    LazyConstraintComponent(...),
    PrimalSolutionComponent(...),
 ])
 ```
 ### Adjusting component aggressiveness
 The aggressiveness of classification components, such as `PrimalSolutionComponent` and `LazyConstraintComponent`, can be adjusted through the `threshold` constructor argument. Internally, these components ask the machine learning models how confident are they on each prediction they make, then automatically discard all predictions that have low confidence. The `threshold` argument specifies how confident should the ML models be for a prediction to be considered trustworthy. Lowering a component's threshold increases its aggressiveness, while raising a component's threshold makes it more conservative.
 For example, if the ML model predicts that a certain binary variable will assume value `1.0` in the optimal solution with 75% confidence, and if the `PrimalSolutionComponent` is configured to discard all predictions with less than 90% confidence, then this variable will not be included in the predicted MIP start.
 MIPLearn currently provides two types of thresholds:
 * `MinProbabilityThreshold(p: List[float])` A threshold which indicates that a prediction is trustworthy if its probability of being correct, as computed by the machine learning model, is above a fixed value.
 * `MinPrecisionThreshold(p: List[float])` A dynamic threshold which automatically adjusts itself during training to ensure that the component achieves at least a given precision on the training data set. Note that increasing a component's precision may reduce its recall.
 The example below shows how to build a `PrimalSolutionComponent` which fixes variables to zero with at least 80% precision, and to one with at least 95% precision. Other components are configured similarly.
 ```python
 from miplearn import PrimalSolutionComponent, MinPrecisionThreshold
 PrimalSolutionComponent(
    mode="heuristic",
    threshold=MinPrecisionThreshold([0.80, 0.95]),
 )
 ```
 ### Evaluating component performance
 MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
 fit `PrimalSolutionComponent` outside the solver, then evaluate its performance.
 ```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])
 ``` 
 The method `evaluate` 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:
 ```python
 import pandas as pd
 pd.DataFrame(ev["Fix one"]).mean(axis=1)
 ```
 ```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
 ```
 Regression components (such as `ObjectiveValueComponent`) can also be trained and evaluated similarly,
 as the next example shows:
 ```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)
 ```
 ```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
 ```
 ### Using customized ML classifiers and regressors
 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 `classifier` or `regressor` contructor parameters. Scikit-learn classifiers and regressors are currently supported. A future version of the package will add compatibility with Keras models.
 The example below shows how to construct a `PrimalSolutionComponent` which internally uses scikit-learn's `KNeighborsClassifiers`. Any other scikit-learn classifier or pipeline can be used. It needs to be wrapped in `ScikitLearnClassifier` to ensure that all the proper data transformations are applied.
 ```python
 from miplearn import PrimalSolutionComponent, ScikitLearnClassifier
 from sklearn.neighbors import KNeighborsClassifier
 comp = PrimalSolutionComponent(
    classifier=ScikitLearnClassifier(
        KNeighborsClassifier(n_neighbors=5),
    ),
 )
 comp.fit(train_instances)
 ``` 
--- a/0.2/_sources/instances.md.txt
+++ b/0.2/_sources/instances.md.txt
@ -11,7 +11,7 @@ Instances
 UnitCommitment.jl provides a large collection of benchmark instances collected
 from the literature and converted to a [common data format](format.md). In some cases, as indicated below, the original instances have been extended, with realistic parameters, using data-driven methods. 
-If you use these instances in your research, we request that you cite UnitCommitment.jl [UCJL], as well as the original sources.
+If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources.
 Raw instances files are [available at our GitHub repository](https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances). Benchmark instances can also be loaded with
 `UnitCommitment.read_benchmark(name)`, as explained in the [usage section](usage.md).
--- a/0.2/_sources/model.md.txt
+++ b/0.2/_sources/model.md.txt
@ -190,12 +190,6 @@ JuMP.set_objective_coefficient(
 UnitCommitment.optimize!(model)
 ```
 ### Adding components to a bus
 One of the most common modifications to a unit commitment model is adding a new customized system component to a bus. The script below shows how to add 
 References
 ----------
 * [KnOsWa20] **Bernard Knueven, James Ostrowski and Jean-Paul Watson.** "On Mixed-Integer Programming Formulations for the Unit Commitment Problem". INFORMS Journal on Computing (2020). [DOI: 10.1287/ijoc.2019.0944](https://doi.org/10.1287/ijoc.2019.0944)
--- a/0.2/_sources/usage.md.txt
+++ b/0.2/_sources/usage.md.txt
@ -53,7 +53,7 @@ model = UnitCommitment.build_model(
 UnitCommitment.optimize!(model)
 # Extract solution and write it to a file
-solution = UnitCommitment.get_solution(model)
+solution = UnitCommitment.solution(model)
 open("/path/to/output.json", "w") do file
    JSON.print(file, solution, 2)
 end
--- a/0.2/benchmark/index.html
+++ b/0.2/benchmark/index.html
@ -1,472 +0,0 @@
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  <a class="reference internal nav-link" href="#preliminaries">
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    2.1.
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   Preliminaries
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   Maximum Weight Stable Set Problem
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     Challenge A
    </a>
   </li>
  </ul>
 </li>
 <li class="toc-h2 nav-item toc-entry">
  <a class="reference internal nav-link" href="#traveling-salesman-problem">
   <span class="sectnum">
    2.3.
   </span>
   Traveling Salesman Problem
  </a>
  <ul class="nav section-nav flex-column">
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#id1">
     Problem definition
    </a>
   </li>
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#random-problem-generator">
     Random problem generator
    </a>
   </li>
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#id2">
     Challenge A
    </a>
   </li>
  </ul>
 </li>
 <li class="toc-h2 nav-item toc-entry">
  <a class="reference internal nav-link" href="#multidimensional-0-1-knapsack-problem">
   <span class="sectnum">
    2.4.
   </span>
   Multidimensional 0-1 Knapsack Problem
  </a>
  <ul class="nav section-nav flex-column">
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#id3">
     Problem definition
    </a>
   </li>
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#id4">
     Random instance generator
    </a>
   </li>
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#id5">
     Challenge A
    </a>
   </li>
  </ul>
 </li>
 </ul>
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 </div>
    <div id="main-content" class="row">
        <div class="col-12 col-md-9 pl-md-3 pr-md-0">
              <div>
  <div class="section" id="benchmarks">
 <h1><span class="sectnum">2.</span> Benchmarks<a class="headerlink" href="#benchmarks" title="Permalink to this headline">¶</a></h1>
 <p>MIPLearn provides a selection of benchmark problems and random instance generators, covering applications from different fields, that can be used to evaluate new learning-enhanced MIP techniques in a measurable and reproducible way. In this page, we describe these problems, the included instance generators, and we present some benchmark results for  <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code> with default parameters.</p>
 <div class="section" id="preliminaries">
 <h2><span class="sectnum">2.1.</span> Preliminaries<a class="headerlink" href="#preliminaries" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="benchmark-challenges">
 <h3>Benchmark challenges<a class="headerlink" href="#benchmark-challenges" title="Permalink to this headline">¶</a></h3>
 <p>When evaluating the performance of a conventional MIP solver, <em>benchmark sets</em>, such as MIPLIB and TSPLIB, are typically used. The performance of newly proposed solvers or solution techniques are typically measured as the average (or total) running time the solver takes to solve the entire benchmark set. For Learning-Enhanced MIP solvers, it is also necessary to specify what instances should the solver be trained on (the <em>training instances</em>) before solving the actual set of instances we are interested in (the <em>test instances</em>). If the training instances are very similar to the test instances, we would expect a Learning-Enhanced Solver to present stronger perfomance benefits.</p>
 <p>In MIPLearn, each optimization problem comes with a set of <strong>benchmark challenges</strong>, which specify how should the training and test instances be generated. The first challenges are typically easier, in the sense that training and test instances are very similar. Later challenges gradually make the sets more distinct, and therefore harder to learn from.</p>
 </div>
 <div class="section" id="baseline-results">
 <h3>Baseline results<a class="headerlink" href="#baseline-results" title="Permalink to this headline">¶</a></h3>
 <p>To illustrate the performance of <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code>, and to set a baseline for newly proposed techniques, we present in this page, for each benchmark challenge, a small set of computational results measuring the solution speed of the solver and the solution quality with default parameters. For more detailed computational studies, see <a class="reference external" href="about.md#references">references</a>. We compare three solvers:</p>
 <ul class="simple">
 <li><p><strong>baseline:</strong> Gurobi 9.0 with default settings (a conventional state-of-the-art MIP solver)</p></li>
 <li><p><strong>ml-exact:</strong> <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code> with default settings, using Gurobi 9.0 as internal MIP solver</p></li>
 <li><p><strong>ml-heuristic:</strong> Same as above, but with <code class="docutils literal notranslate"><span class="pre">mode=&quot;heuristic&quot;</span></code></p></li>
 </ul>
 <p>All experiments presented here were performed on a Linux server (Ubuntu Linux 18.04 LTS) with Intel Xeon Gold 6230s (2 processors, 40 cores, 80 threads) and 256 GB RAM (DDR4, 2933 MHz). All solvers were restricted to use 4 threads, with no time limits, and 10 instances were solved simultaneously at a time.</p>
 </div>
 </div>
 <div class="section" id="maximum-weight-stable-set-problem">
 <h2><span class="sectnum">2.2.</span> Maximum Weight Stable Set Problem<a class="headerlink" href="#maximum-weight-stable-set-problem" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="problem-definition">
 <h3>Problem definition<a class="headerlink" href="#problem-definition" title="Permalink to this headline">¶</a></h3>
 <p>Given a simple undirected graph <span class="math notranslate nohighlight">\(G=(V,E)\)</span> and weights <span class="math notranslate nohighlight">\(w \in \mathbb{R}^V\)</span>, the problem is to find a stable set <span class="math notranslate nohighlight">\(S \subseteq V\)</span> that maximizes <span class="math notranslate nohighlight">\( \sum_{v \in V} w_v\)</span>. We recall that a subset <span class="math notranslate nohighlight">\(S \subseteq V\)</span> is a <em>stable set</em> if no two vertices of <span class="math notranslate nohighlight">\(S\)</span> are adjacent. This is one of Karp’s 21 NP-complete problems.</p>
 </div>
 <div class="section" id="random-instance-generator">
 <h3>Random instance generator<a class="headerlink" href="#random-instance-generator" title="Permalink to this headline">¶</a></h3>
 <p>The class <code class="docutils literal notranslate"><span class="pre">MaxWeightStableSetGenerator</span></code> can be used to generate random instances of this problem, with user-specified probability distributions. When the constructor parameter <code class="docutils literal notranslate"><span class="pre">fix_graph=True</span></code> is provided, one random Erdős-Rényi graph <span class="math notranslate nohighlight">\(G_{n,p}\)</span> is generated during the constructor, where <span class="math notranslate nohighlight">\(n\)</span> and <span class="math notranslate nohighlight">\(p\)</span> are sampled from user-provided probability distributions <code class="docutils literal notranslate"><span class="pre">n</span></code> and <code class="docutils literal notranslate"><span class="pre">p</span></code>. To generate each instance, the generator independently samples each <span class="math notranslate nohighlight">\(w_v\)</span> from the user-provided probability distribution <code class="docutils literal notranslate"><span class="pre">w</span></code>. When <code class="docutils literal notranslate"><span class="pre">fix_graph=False</span></code>, a new random graph is generated for each instance, while the remaining parameters are sampled in the same way.</p>
 </div>
 <div class="section" id="challenge-a">
 <h3>Challenge A<a class="headerlink" href="#challenge-a" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Fixed random Erdős-Rényi graph <span class="math notranslate nohighlight">\(G_{n,p}\)</span> with <span class="math notranslate nohighlight">\(n=200\)</span> and <span class="math notranslate nohighlight">\(p=5\%\)</span></p></li>
 <li><p>Random vertex weights <span class="math notranslate nohighlight">\(w_v \sim U(100, 150)\)</span></p></li>
 <li><p>500 training instances, 50 test instances</p></li>
 </ul>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">MaxWeightStableSetGenerator</span><span class="p">(</span><span class="n">w</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">100.</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">50.</span><span class="p">),</span>
                            <span class="n">n</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">201</span><span class="p">),</span>
                            <span class="n">p</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.0</span><span class="p">),</span>
                            <span class="n">fix_graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </pre></div>
 </div>
 <p><img alt="alt" src="figures/benchmark_stab_a.png" /></p>
 </div>
 </div>
 <div class="section" id="traveling-salesman-problem">
 <h2><span class="sectnum">2.3.</span> Traveling Salesman Problem<a class="headerlink" href="#traveling-salesman-problem" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="id1">
 <h3>Problem definition<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
 <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>
 </div>
 <div class="section" id="random-problem-generator">
 <h3>Random problem generator<a class="headerlink" href="#random-problem-generator" title="Permalink to this headline">¶</a></h3>
 <p>The class <code class="docutils literal notranslate"><span class="pre">TravelingSalesmanGenerator</span></code> can be used to generate random instances of this
 problem. Initially, the generator creates <span class="math notranslate nohighlight">\(n\)</span> cities <span class="math notranslate nohighlight">\((x_1,y_1),\ldots,(x_n,y_n) \in \mathbb{R}^2\)</span>,
 where <span class="math notranslate nohighlight">\(n, x_i\)</span> and <span class="math notranslate nohighlight">\(y_i\)</span> are sampled independently from the provided probability distributions <code class="docutils literal notranslate"><span class="pre">n</span></code>,
 <code class="docutils literal notranslate"><span class="pre">x</span></code> and <code class="docutils literal notranslate"><span class="pre">y</span></code>. For each pair of cities <span class="math notranslate nohighlight">\((i,j)\)</span>, the distance <span class="math notranslate nohighlight">\(d_{i,j}\)</span> between them is set to:
 $<span class="math notranslate nohighlight">\(
    d_{i,j} = \gamma_{i,j} \sqrt{(x_i-x_j)^2 + (y_i - y_j)^2}
 \)</span><span class="math notranslate nohighlight">\(
 where \)</span>\gamma_{i,j}$ is sampled from the distribution <code class="docutils literal notranslate"><span class="pre">gamma</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">fix_cities=True</span></code> is provided, the list of cities is kept the same for all generated instances.
 The <span class="math notranslate nohighlight">\(gamma\)</span> values, and therefore also the distances, are still different.</p>
 <p>By default, all distances <span class="math notranslate nohighlight">\(d_{i,j}\)</span> are rounded to the nearest integer.  If <code class="docutils literal notranslate"><span class="pre">round=False</span></code>
 is provided, this rounding will be disabled.</p>
 </div>
 <div class="section" id="id2">
 <h3>Challenge A<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Fixed list of 350 cities in the <span class="math notranslate nohighlight">\([0, 1000]^2\)</span> square</p></li>
 <li><p><span class="math notranslate nohighlight">\(\gamma_{i,j} \sim U(0.95, 1.05)\)</span></p></li>
 <li><p>500 training instances, 50 test instances</p></li>
 </ul>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">TravelingSalesmanGenerator</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">),</span>
                           <span class="n">y</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">),</span>
                           <span class="n">n</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">350</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">351</span><span class="p">),</span>
                           <span class="n">gamma</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
                           <span class="n">fix_cities</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                           <span class="nb">round</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                          <span class="p">)</span>
 </pre></div>
 </div>
 <p><img alt="alt" src="figures/benchmark_tsp_a.png" /></p>
 </div>
 </div>
 <div class="section" id="multidimensional-0-1-knapsack-problem">
 <h2><span class="sectnum">2.4.</span> Multidimensional 0-1 Knapsack Problem<a class="headerlink" href="#multidimensional-0-1-knapsack-problem" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="id3">
 <h3>Problem definition<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
 <p>Given a set of <span class="math notranslate nohighlight">\(n\)</span> items and <span class="math notranslate nohighlight">\(m\)</span> types of resources (also called <em>knapsacks</em>), the problem is to find a subset of items that maximizes profit without consuming more resources than it is available. More precisely, the problem is:</p>
 <div class="math notranslate nohighlight">
 \[\begin{split}
 \begin{align*}
    \text{maximize}
        &amp; \sum_{j=1}^n p_j x_j
        \\
    \text{subject to}
        &amp; \sum_{j=1}^n w_{ij} x_j \leq b_i
        &amp; \forall i=1,\ldots,m \\
    &amp; x_j \in \{0,1\}
        &amp; \forall j=1,\ldots,n
 \end{align*}
 \end{split}\]</div>
 </div>
 <div class="section" id="id4">
 <h3>Random instance generator<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h3>
 <p>The class <code class="docutils literal notranslate"><span class="pre">MultiKnapsackGenerator</span></code> can be used to generate random instances of this problem. The number of items <span class="math notranslate nohighlight">\(n\)</span> and knapsacks <span class="math notranslate nohighlight">\(m\)</span> are sampled from the user-provided probability distributions <code class="docutils literal notranslate"><span class="pre">n</span></code> and <code class="docutils literal notranslate"><span class="pre">m</span></code>. The weights <span class="math notranslate nohighlight">\(w_{ij}\)</span> are sampled independently from the provided distribution <code class="docutils literal notranslate"><span class="pre">w</span></code>. The capacity of knapsack <span class="math notranslate nohighlight">\(i\)</span> is set to</p>
 <div class="math notranslate nohighlight">
 \[
    b_i = \alpha_i \sum_{j=1}^n w_{ij}
 \]</div>
 <p>where <span class="math notranslate nohighlight">\(\alpha_i\)</span>, the tightness ratio, is sampled from the provided probability
 distribution <code class="docutils literal notranslate"><span class="pre">alpha</span></code>. To make the instances more challenging, the costs of the items
 are linearly correlated to their average weights. More specifically, the price of each
 item <span class="math notranslate nohighlight">\(j\)</span> is set to:</p>
 <div class="math notranslate nohighlight">
 \[
    p_j = \sum_{i=1}^m \frac{w_{ij}}{m} + K  u_j,
 \]</div>
 <p>where <span class="math notranslate nohighlight">\(K\)</span>, the correlation coefficient, and <span class="math notranslate nohighlight">\(u_j\)</span>, the correlation multiplier, are sampled
 from the provided probability distributions <code class="docutils literal notranslate"><span class="pre">K</span></code> and <code class="docutils literal notranslate"><span class="pre">u</span></code>.</p>
 <p>If <code class="docutils literal notranslate"><span class="pre">fix_w=True</span></code> is provided, then <span class="math notranslate nohighlight">\(w_{ij}\)</span> are kept the same in all generated instances. This also implies that <span class="math notranslate nohighlight">\(n\)</span> and <span class="math notranslate nohighlight">\(m\)</span> are kept fixed. Although the prices and capacities are derived from <span class="math notranslate nohighlight">\(w_{ij}\)</span>, as long as <code class="docutils literal notranslate"><span class="pre">u</span></code> and <code class="docutils literal notranslate"><span class="pre">K</span></code> are not constants, the generated instances will still not be completely identical.</p>
 <p>If a probability distribution <code class="docutils literal notranslate"><span class="pre">w_jitter</span></code> is provided, then item weights will be set to <span class="math notranslate nohighlight">\(w_{ij} \gamma_{ij}\)</span> where <span class="math notranslate nohighlight">\(\gamma_{ij}\)</span> is sampled from <code class="docutils literal notranslate"><span class="pre">w_jitter</span></code>. When combined with <code class="docutils literal notranslate"><span class="pre">fix_w=True</span></code>, 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 class="docutils literal notranslate"><span class="pre">round=False</span></code> is provided, this rounding will be disabled.</p>
 <p>!!! note “References”
 * Freville, Arnaud, and Gérard Plateau. <em>An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem.</em> Discrete applied mathematics 49.1-3 (1994): 189-212.
 * Fréville, Arnaud. <em>The multidimensional 0–1 knapsack problem: An overview.</em> European Journal of Operational Research 155.1 (2004): 1-21.</p>
 </div>
 <div class="section" id="id5">
 <h3>Challenge A<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>250 variables, 10 constraints, fixed weights</p></li>
 <li><p><span class="math notranslate nohighlight">\(w \sim U(0, 1000), \gamma \sim U(0.95, 1.05)\)</span></p></li>
 <li><p><span class="math notranslate nohighlight">\(K = 500, u \sim U(0, 1), \alpha = 0.25\)</span></p></li>
 <li><p>500 training instances, 50 test instances</p></li>
 </ul>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">MultiKnapsackGenerator</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">250</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">251</span><span class="p">),</span>
                       <span class="n">m</span><span class="o">=</span><span class="n">randint</span><span class="p">(</span><span class="n">low</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">high</span><span class="o">=</span><span class="mi">11</span><span class="p">),</span>
                       <span class="n">w</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1000.0</span><span class="p">),</span>
                       <span class="n">K</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">500.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.0</span><span class="p">),</span>
                       <span class="n">u</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.0</span><span class="p">),</span>
                       <span class="n">alpha</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.0</span><span class="p">),</span>
                       <span class="n">fix_w</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                       <span class="n">w_jitter</span><span class="o">=</span><span class="n">uniform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
                      <span class="p">)</span>
 </pre></div>
 </div>
 <p><img alt="alt" src="figures/benchmark_knapsack_a.png" /></p>
 </div>
 </div>
 </div>
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 <li class="toc-h2 nav-item toc-entry">
  <a class="reference internal nav-link" href="#customizing-solver-parameters">
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   Customizing solver parameters
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   Customizing solver components
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     Selecting components
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     Adjusting component aggressiveness
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     Evaluating component performance
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    <a class="reference internal nav-link" href="#using-customized-ml-classifiers-and-regressors">
     Using customized ML classifiers and regressors
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              <div>
  <div class="section" id="customization">
 <h1><span class="sectnum">3.</span> Customization<a class="headerlink" href="#customization" title="Permalink to this headline">¶</a></h1>
 <div class="section" id="customizing-solver-parameters">
 <h2><span class="sectnum">3.1.</span> Customizing solver parameters<a class="headerlink" href="#customizing-solver-parameters" title="Permalink to this headline">¶</a></h2>
 <div class="section" id="selecting-the-internal-mip-solver">
 <h3>Selecting the internal MIP solver<a class="headerlink" href="#selecting-the-internal-mip-solver" title="Permalink to this headline">¶</a></h3>
 <p>By default, <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code> uses <a class="reference external" href="https://www.gurobi.com/">Gurobi</a> as its internal MIP solver, and expects models to be provided using the Pyomo modeling language. Supported solvers and modeling languages include:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">GurobiPyomoSolver</span></code>: Gurobi with Pyomo (default).</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">CplexPyomoSolver</span></code>: <a class="reference external" href="https://www.ibm.com/products/ilog-cplex-optimization-studio">IBM ILOG CPLEX</a> with Pyomo.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">XpressPyomoSolver</span></code>: <a class="reference external" href="https://www.fico.com/en/products/fico-xpress-solver">FICO XPRESS Solver</a> with Pyomo.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">GurobiSolver</span></code>: Gurobi without any modeling language.</p></li>
 </ul>
 <p>To switch between solvers, provide the desired class using the <code class="docutils literal notranslate"><span class="pre">solver</span></code> argument:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">LearningSolver</span><span class="p">,</span> <span class="n">CplexPyomoSolver</span>
 <span class="n">solver</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="n">solver</span><span class="o">=</span><span class="n">CplexPyomoSolver</span><span class="p">)</span>
 </pre></div>
 </div>
 <p>To configure a particular solver, use the <code class="docutils literal notranslate"><span class="pre">params</span></code> constructor argument, as shown below.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">LearningSolver</span><span class="p">,</span> <span class="n">GurobiPyomoSolver</span>
 <span class="n">solver</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span>
    <span class="n">solver</span><span class="o">=</span><span class="k">lambda</span><span class="p">:</span> <span class="n">GurobiPyomoSolver</span><span class="p">(</span>
        <span class="n">params</span><span class="o">=</span><span class="p">{</span>
            <span class="s2">&quot;TimeLimit&quot;</span><span class="p">:</span> <span class="mi">900</span><span class="p">,</span>
            <span class="s2">&quot;MIPGap&quot;</span><span class="p">:</span> <span class="mf">1e-3</span><span class="p">,</span>
            <span class="s2">&quot;NodeLimit&quot;</span><span class="p">:</span> <span class="mi">1000</span><span class="p">,</span>
        <span class="p">}</span>
    <span class="p">),</span>
 <span class="p">)</span>
 </pre></div>
 </div>
 </div>
 </div>
 <div class="section" id="customizing-solver-components">
 <h2><span class="sectnum">3.2.</span> Customizing solver components<a class="headerlink" href="#customizing-solver-components" title="Permalink to this headline">¶</a></h2>
 <p><code class="docutils literal notranslate"><span class="pre">LearningSolver</span></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 class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">LazyConstraintComponent</span></code>: Predicts which lazy constraint to initially enforce.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">ObjectiveValueComponent</span></code>: Predicts the optimal value of the optimization problem, given the optimal solution to the LP relaxation.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></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.</p></li>
 </ul>
 <p>The following components are also available, but not enabled by default:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">BranchPriorityComponent</span></code>: Predicts good branch priorities for decision variables.</p></li>
 </ul>
 <div class="section" id="selecting-components">
 <h3>Selecting components<a class="headerlink" href="#selecting-components" title="Permalink to this headline">¶</a></h3>
 <p>To create a <code class="docutils literal notranslate"><span class="pre">LearningSolver</span></code> with a specific set of components, the <code class="docutils literal notranslate"><span class="pre">components</span></code> constructor argument may be used, as the next example shows:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create a solver without any components</span>
 <span class="n">solver1</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="n">components</span><span class="o">=</span><span class="p">[])</span>
 <span class="c1"># Create a solver with only two components</span>
 <span class="n">solver2</span> <span class="o">=</span> <span class="n">LearningSolver</span><span class="p">(</span><span class="n">components</span><span class="o">=</span><span class="p">[</span>
    <span class="n">LazyConstraintComponent</span><span class="p">(</span><span class="o">...</span><span class="p">),</span>
    <span class="n">PrimalSolutionComponent</span><span class="p">(</span><span class="o">...</span><span class="p">),</span>
 <span class="p">])</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="adjusting-component-aggressiveness">
 <h3>Adjusting component aggressiveness<a class="headerlink" href="#adjusting-component-aggressiveness" title="Permalink to this headline">¶</a></h3>
 <p>The aggressiveness of classification components, such as <code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></code> and <code class="docutils literal notranslate"><span class="pre">LazyConstraintComponent</span></code>, can be adjusted through the <code class="docutils literal notranslate"><span class="pre">threshold</span></code> constructor argument. Internally, these components ask the machine learning models how confident are they on each prediction they make, then automatically discard all predictions that have low confidence. The <code class="docutils literal notranslate"><span class="pre">threshold</span></code> argument specifies how confident should the ML models be for a prediction to be considered trustworthy. Lowering a component’s threshold increases its aggressiveness, while raising a component’s threshold makes it more conservative.</p>
 <p>For example, if the ML model predicts that a certain binary variable will assume value <code class="docutils literal notranslate"><span class="pre">1.0</span></code> in the optimal solution with 75% confidence, and if the <code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></code> is configured to discard all predictions with less than 90% confidence, then this variable will not be included in the predicted MIP start.</p>
 <p>MIPLearn currently provides two types of thresholds:</p>
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">MinProbabilityThreshold(p:</span> <span class="pre">List[float])</span></code> A threshold which indicates that a prediction is trustworthy if its probability of being correct, as computed by the machine learning model, is above a fixed value.</p></li>
 <li><p><code class="docutils literal notranslate"><span class="pre">MinPrecisionThreshold(p:</span> <span class="pre">List[float])</span></code> A dynamic threshold which automatically adjusts itself during training to ensure that the component achieves at least a given precision on the training data set. Note that increasing a component’s precision may reduce its recall.</p></li>
 </ul>
 <p>The example below shows how to build a <code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></code> which fixes variables to zero with at least 80% precision, and to one with at least 95% precision. Other components are configured similarly.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">PrimalSolutionComponent</span><span class="p">,</span> <span class="n">MinPrecisionThreshold</span>
 <span class="n">PrimalSolutionComponent</span><span class="p">(</span>
    <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;heuristic&quot;</span><span class="p">,</span>
    <span class="n">threshold</span><span class="o">=</span><span class="n">MinPrecisionThreshold</span><span class="p">([</span><span class="mf">0.80</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">]),</span>
 <span class="p">)</span>
 </pre></div>
 </div>
 </div>
 <div class="section" id="evaluating-component-performance">
 <h3>Evaluating component performance<a class="headerlink" href="#evaluating-component-performance" title="Permalink to this headline">¶</a></h3>
 <p>MIPLearn allows solver components to be modified, trained and evaluated in isolation. In the following example, we build and
 fit <code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></code> outside the solver, then evaluate its performance.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">PrimalSolutionComponent</span>
 <span class="c1"># User-provided set of previously-solved instances</span>
 <span class="n">train_instances</span> <span class="o">=</span> <span class="p">[</span><span class="o">...</span><span class="p">]</span>
 <span class="c1"># Construct and fit component on a subset of training instances</span>
 <span class="n">comp</span> <span class="o">=</span> <span class="n">PrimalSolutionComponent</span><span class="p">()</span>
 <span class="n">comp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_instances</span><span class="p">[:</span><span class="mi">100</span><span class="p">])</span>
 <span class="c1"># Evaluate performance on an additional set of training instances</span>
 <span class="n">ev</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">train_instances</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">])</span>
 </pre></div>
 </div>
 <p>The method <code class="docutils literal notranslate"><span class="pre">evaluate</span></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>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ev</span><span class="p">[</span><span class="s2">&quot;Fix one&quot;</span><span class="p">])</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>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
 </pre></div>
 </div>
 <p>Regression components (such as <code class="docutils literal notranslate"><span class="pre">ObjectiveValueComponent</span></code>) can also be trained and evaluated similarly,
 as the next example shows:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">ObjectiveValueComponent</span>
 <span class="n">comp</span> <span class="o">=</span> <span class="n">ObjectiveValueComponent</span><span class="p">()</span>
 <span class="n">comp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_instances</span><span class="p">[:</span><span class="mi">100</span><span class="p">])</span>
 <span class="n">ev</span> <span class="o">=</span> <span class="n">comp</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">train_instances</span><span class="p">[</span><span class="mi">100</span><span class="p">:</span><span class="mi">150</span><span class="p">])</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ev</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
 </pre></div>
 </div>
 <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>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
 </pre></div>
 </div>
 </div>
 <div class="section" id="using-customized-ml-classifiers-and-regressors">
 <h3>Using customized ML classifiers and regressors<a class="headerlink" href="#using-customized-ml-classifiers-and-regressors" title="Permalink to this headline">¶</a></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 class="docutils literal notranslate"><span class="pre">classifier</span></code> or <code class="docutils literal notranslate"><span class="pre">regressor</span></code> contructor parameters. Scikit-learn classifiers and regressors are currently supported. A future version of the package will add compatibility with Keras models.</p>
 <p>The example below shows how to construct a <code class="docutils literal notranslate"><span class="pre">PrimalSolutionComponent</span></code> which internally uses scikit-learn’s <code class="docutils literal notranslate"><span class="pre">KNeighborsClassifiers</span></code>. Any other scikit-learn classifier or pipeline can be used. It needs to be wrapped in <code class="docutils literal notranslate"><span class="pre">ScikitLearnClassifier</span></code> to ensure that all the proper data transformations are applied.</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">miplearn</span> <span class="kn">import</span> <span class="n">PrimalSolutionComponent</span><span class="p">,</span> <span class="n">ScikitLearnClassifier</span>
 <span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="kn">import</span> <span class="n">KNeighborsClassifier</span>
 <span class="n">comp</span> <span class="o">=</span> <span class="n">PrimalSolutionComponent</span><span class="p">(</span>
    <span class="n">classifier</span><span class="o">=</span><span class="n">ScikitLearnClassifier</span><span class="p">(</span>
        <span class="n">KNeighborsClassifier</span><span class="p">(</span><span class="n">n_neighbors</span><span class="o">=</span><span class="mi">5</span><span class="p">),</span>
    <span class="p">),</span>
 <span class="p">)</span>
 <span class="n">comp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_instances</span><span class="p">)</span>
 </pre></div>
 </div>
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 <h1><span class="sectnum">3.</span> Instances<a class="headerlink" href="#instances" title="Permalink to this headline">¶</a></h1>
 <p>UnitCommitment.jl provides a large collection of benchmark instances collected
 from the literature and converted to a <a class="reference internal" href="../format/"><span class="doc std std-doc">common data format</span></a>. In some cases, as indicated below, the original instances have been extended, with realistic parameters, using data-driven methods.
-If you use these instances in your research, we request that you cite UnitCommitment.jl [UCJL], as well as the original sources.</p>
+If you use these instances in your research, we request that you cite UnitCommitment.jl, as well as the original sources.</p>
 <p>Raw instances files are <a class="reference external" href="https://github.com/ANL-CEEESA/UnitCommitment.jl/tree/dev/instances">available at our GitHub repository</a>. Benchmark instances can also be loaded with
 <code class="docutils literal notranslate"><span class="pre">UnitCommitment.read_benchmark(name)</span></code>, as explained in the <a class="reference internal" href="../usage/"><span class="doc std std-doc">usage section</span></a>.</p>
 <div class="section" id="matpower">
--- a/0.2/model/index.html
+++ b/0.2/model/index.html
@ -242,11 +242,6 @@
     Modifying the model
    </a>
   </li>
   <li class="toc-h3 nav-item toc-entry">
    <a class="reference internal nav-link" href="#adding-components-to-a-bus">
     Adding components to a bus
    </a>
   </li>
  </ul>
 </li>
 <li class="toc-h2 nav-item toc-entry">
@ -527,10 +522,6 @@
 </pre></div>
 </div>
 </div>
 <div class="section" id="adding-components-to-a-bus">
 <h3>Adding components to a bus<a class="headerlink" href="#adding-components-to-a-bus" title="Permalink to this headline">¶</a></h3>
 <p>One of the most common modifications to a unit commitment model is adding a new customized system component to a bus. The script below shows how to add</p>
 </div>
 </div>
 <div class="section" id="references">
 <h2><span class="sectnum">4.5.</span> References<a class="headerlink" href="#references" title="Permalink to this headline">¶</a></h2>
--- a/0.2/objects.inv
+++ b/0.2/objects.inv
--- a/0.2/searchindex.js
+++ b/0.2/searchindex.js
--- a/0.2/usage/index.html
+++ b/0.2/usage/index.html
@ -278,7 +278,7 @@
 <span class="n">UnitCommitment</span><span class="o">.</span><span class="n">optimize!</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 <span class="c"># Extract solution and write it to a file</span>
-<span class="n">solution</span> <span class="o">=</span> <span class="n">UnitCommitment</span><span class="o">.</span><span class="n">get_solution</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+<span class="n">solution</span> <span class="o">=</span> <span class="n">UnitCommitment</span><span class="o">.</span><span class="n">solution</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
 <span class="n">open</span><span class="p">(</span><span class="s">&quot;/path/to/output.json&quot;</span><span class="p">,</span> <span class="s">&quot;w&quot;</span><span class="p">)</span> <span class="k">do</span> <span class="n">file</span>
    <span class="n">JSON</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">solution</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
 <span class="k">end</span>