}
#aupdc = make_scorer(score_func=average_precision_score, greater_is_better=True,needs_proba=True,)
#results = permutation_importance(rf, X, y, scorer=aupdc, n_vars=10, n_jobs=50)
from mintpy.mintpy.main.interpret_toolkit import InterpretToolkit
myInterpreter = InterpretToolkit(model=[rf],
model_names=['Random Forest'],
examples=X,
targets=y)
start_time = datetime.datetime.now()
results = myInterpreter.calc_permutation_importance(n_vars=10,
evaluation_fn='auprc',
subsample=1.0,
njobs=50,
nbootstrap=10)
duration = datetime.datetime.now() - start_time
seconds = duration.total_seconds()
hours = seconds // 3600
minutes = (seconds % 3600) // 60
seconds = seconds % 60
message = f"""
Duration : {hours:.2f} hours : {minutes:.2f} minutes : {seconds:.2f} seconds
"""
print(message)
# examples_transformed, target_values_transformed = rus.fit_resample(examples, target_values)
# else:
# examples_transformed = examples
# target_values_transformed = target_values
#else:
# models=[model.steps[-1][1]]
# examples_transformed, target_values_transformed = just_transforms(model, examples, target_values)
# feature_names.remove('Run Date')
myInterpreter = InterpretToolkit(models=models,
model_names=model_names,
examples=examples,
targets=targets,
)
results = myInterpreter.calc_permutation_importance(n_vars=n_vars, evaluation_fn=metric,
subsample=subsample, n_jobs=njobs, n_bootstrap=nbootstrap, verbose=True, perm_method=method)
results_fname = join(perm_imp_path, f'permutation_importance_{model_name}_{target}_{time}_{data_mode}_{metric}{drop_opt}{resample}{calibrate}{method}.nc')
print(f'Saving {results_fname}...')
myInterpreter.save_results(fname=results_fname, data=results)
duration = datetime.datetime.now() - start_time
seconds = duration.total_seconds()
hours = seconds // 3600
minutes = (seconds % 3600) // 60
seconds = seconds % 60
message = f"""
{model_name} {target} {time}
Permutation important results are done!
