Setting solver parameters and hyperparameter search

Calling a solver by name

You can see the list of available solvers (and subsolvers) as follows:

from cpmpy import *

print(SolverLookup.solvernames())

On my system, with pysat and minizinc installed, this gives `[‘ortools’, ‘minizinc’, ‘minizinc:chuffed’, ‘minizinc:coin-bc’, …, ‘pysat:minicard’, ‘pysat:minisat22’, ‘pysat:minisat-gh’]

You can use any of these solvers by passing its name to the Model.solve() parameter ‘solver’ as such:

a,b = boolvar(2)
Model(a|b).solve(solver='minizinc:chuffed')

Direct solver access

CPMpy also offers direct access to its solver API, as well as to the underlying native solver API. For most cases, including setting solver parameters, access to CPMpy’s solver API will be sufficient.

In the following, we will use the or-tools CP-SAT solver. The corresponding CPMpy class is CPM_ortools and can be included as follows:

from cpmpy.solvers import CPM_ortools

The same principles will apply to the other solver interfaces too.

Setting solver parameters

or-tools has many solver parameters, documented here.

CPMpy’s interface to ortools accepts keyword arguments to solve(), and will set the corresponding or-tools parameters if the name matches. We documented some of the frequent once in our CPM_ortools API.

For example, with model a CPMpy Model(), you can do the following to make or-tools use 8 parallel cores and print search progress:

from cpmpy import *
from cpmpy.solvers import CPM_ortools

s = CPM_ortools(model)
s.solve(num_search_workers=8, log_search_progress=True)

Hyperparameter search across different parameters

Because CPMpy offers programmatic access to the solver API, hyperparameter search can be straightforwardly done with little overhead between the calls.

The cpmpy.solvers module has a helper function param_combinations that generates all parameter combinations of an input, which can then be looped over.

The example is in examples/advanced/hyperparameter_search.py, the key part is:

from cpmpy.solvers import CPM_ortools, param_combinations

params = {'cp_model_probing_level': [0,1,2,3],
          'linearization_level': [0,1,2],
          'symmetry_level': [0,1,2]}

for params in param_combinations(all_params):
    s = CPM_ortools(model)
    s.solve(**params)
    print(s.status().runtime, "seconds for config", params)