Customization

Customizing solver parameters

Selecting the internal MIP solver

By default, LearningSolver uses Gurobi as its internal MIP solver. Another supported solver is IBM ILOG CPLEX. To switch between solvers, use the solver constructor argument, as shown below. It is also possible to specify a time limit (in seconds) and a relative MIP gap tolerance.

from miplearn import LearningSolver
solver = LearningSolver(solver="cplex",
                        time_limit=300,
                        gap_tolerance=1e-3)

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:

The following components are also available, but not enabled by default:

Selecting components

To create a LearningSolver with a specific set of components, the components constructor argument may be used, as the next example shows:

# Create a solver without any components
solver1 = LearningSolver(components=[])

# Create a solver with only two components
solver2 = LearningSolver(components=[
    LazyConstraintComponent(...),
    PrimalSolutionComponent(...),
])

It is also possible to add components to an existing solver using the solver.add method, as shown below. If the solver already holds another component of that type, the new component will replace the previous one.

# Create solver with default components
solver = LearningSolver()

# Replace the default LazyConstraintComponent by one with custom parameters 
solver.add(LazyConstraintComponent(...))

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 ML models how confident they are on each prediction (through the predict_proba method in the sklearn API), and only take into account predictions which have probabilities above the threshold. Lowering a component's threshold increases its aggressiveness, while raising a component's threshold makes it more conservative.

MIPLearn also includes MinPrecisionThreshold, a dynamic threshold which adjusts itself automatically during training to achieve a minimum desired true positive rate (also known as precision). The example below shows how to initialize a PrimalSolutionComponent which achieves 95% precision, possibly at the cost of a lower recall. To make the component more aggressive, this precision may be lowered.

PrimalSolutionComponent(threshold=MinPrecisionThreshold(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.

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:

import pandas as pd
pd.DataFrame(ev["Fix one"]).mean(axis=1)

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:

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)

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. The provided classifiers and regressors must follow the sklearn API. In particular, classifiers must provide the methods fit, predict_proba and predict, while regressors must provide the methods fit and predict

Danger

MIPLearn must be able to generate a copy of any custom ML classifiers and regressors through the standard copy.deepcopy method. This currently makes it incompatible with Keras and TensorFlow predictors. This is a known limitation, which will be addressed in a future version.

The example below shows how to construct a PrimalSolutionComponent which internally uses sklearn's KNeighborsClassifiers. Any other sklearn classifier or pipeline can be used. 

from miplearn import PrimalSolutionComponent
from sklearn.neighbors import KNeighborsClassifier

comp = PrimalSolutionComponent(classifier=KNeighborsClassifier(n_neighbors=5))
comp.fit(train_instances)