def check_oversamplers_classifiers(oversamplers, classifiers, random_state,
n_runs):
"""Extract estimators and parameters grids."""
# Create random states
random_states = check_random_states(random_state, n_runs)
# Create estimators and parameter grids
estimators, param_grids = [], []
for oversampler, classifier in product(oversamplers, classifiers):
# Unpack oversamplers and classifiers
ovs_name, ovs, ovs_param_grid = oversampler
clf_name, clf, clf_param_grid = classifier
if ovs is None:
ovs = FunctionTransformer()
# Create estimator
name = f'{ovs_name}|{clf_name}'
ovs_steps = ovs.steps if isinstance(ovs, Pipeline) else [(ovs_name,
ovs)]
clf_steps = clf.steps if isinstance(clf, Pipeline) else [(clf_name,
clf)]
steps = ovs_steps + clf_steps
estimators.append((name, Pipeline(steps)))
# Create parameter grid
ovs_prefix = f'{name}' if isinstance(
ovs, Pipeline) else f'{name}__{ovs_name}'
ovs_param_grid = [{
f'{ovs_prefix}__{param}': val
for param, val in param_grid.items()
} for param_grid in ParameterGrid(ovs_param_grid)]
clf_prefix = f'{name}' if isinstance(
clf, Pipeline) else f'{name}__{clf_name}'
clf_param_grid = [{
f'{clf_prefix}__{param}': val
for param, val in param_grid.items()
} for param_grid in ParameterGrid(clf_param_grid)]
combinations = product(ovs_param_grid, clf_param_grid, random_states)
for param_grid1, param_grid2, random_state in combinations:
param_grid1.update(param_grid2)
param_grid = {'est_name': [name]}
for param in ovs.get_params().keys():
if 'random_state' in param:
param_grid.update(
{f'{ovs_prefix}__{param}': [random_state]})
for param in clf.get_params().keys():
if 'random_state' in param:
param_grid.update(
{f'{clf_prefix}__{param}': [random_state]})
param_grid.update(
{param: [val]
for param, val in param_grid1.items()})
param_grids.append(param_grid)
return estimators, param_grids
