Benchmarks Utilities

Using BenchmarkRunner

MIPLearn provides the utility class BenchmarkRunner, which simplifies the task of comparing the performance of different solvers. The snippet below shows its basic usage:

from miplearn import BenchmarkRunner, LearningSolver

# Create train and test instances
train_instances = [...]
test_instances  = [...]

# Training phase...
training_solver = LearningSolver(...)
training_solver.parallel_solve(train_instances, n_jobs=10)

# Test phase...
test_solvers = {
    "Baseline": LearningSolver(...), # each solver may have different parameters
    "Strategy A": LearningSolver(...), 
    "Strategy B": LearningSolver(...),
    "Strategy C": LearningSolver(...),
}
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2)
print(benchmark.raw_results())

The method fit trains the ML models for each individual solver. The method parallel_solve solves the test instances in parallel, and collects solver statistics such as running time and optimal value. Finally, raw_results produces a table of results (Pandas DataFrame) with the following columns:

• Solver, the name of the solver.
• Instance, the sequence number identifying the instance.
• Wallclock Time, the wallclock running time (in seconds) spent by the solver;
• Lower Bound, the best lower bound obtained by the solver;
• Upper Bound, the best upper bound obtained by the solver;
• Gap, the relative MIP integrality gap at the end of the optimization;
• Nodes, the number of explored branch-and-bound nodes.

In addition to the above, there is also a "Relative" version of most columns, where the raw number is compared to the solver which provided the best performance. The Relative Wallclock Time for example, indicates how many times slower this run was when compared to the best time achieved by any solver when processing this instance. For example, if this run took 10 seconds, but the fastest solver took only 5 seconds to solve the same instance, the relative wallclock time would be 2.

Saving and loading benchmark results

When iteratively exploring new formulations, encoding and solver parameters, it is often desirable to avoid repeating parts of the benchmark suite. For example, if the baseline solver has not been changed, there is no need to evaluate its performance again and again when making small changes to the remaining solvers. BenchmarkRunner provides the methods save_results and load_results, which can be used to avoid this repetition, as the next example shows:

# Benchmark baseline solvers and save results to a file.
benchmark = BenchmarkRunner(baseline_solvers)
benchmark.parallel_solve(test_instances)
benchmark.save_results("baseline_results.csv")

# Benchmark remaining solvers, loading baseline results from file.
benchmark = BenchmarkRunner(alternative_solvers)
benchmark.load_results("baseline_results.csv")
benchmark.fit(training_instances)
benchmark.parallel_solve(test_instances)