model = calibrated_pipeline.calibrated_classifiers_[0].base_estimator
path = '/work/mflora/ML_DATA/permutation_importance/'
fnames = [
join(
path,
f'permutation_importance_{model_name}_{target}_{time}_training_norm_aupdc{drop_opt}.pkl'
)
]
perm_imp_results = load_pickle(fnames)
myInterpreter = InterpretToolkit(model=[None])
myInterpreter.set_results(perm_imp_results,
option='permutation_importance')
important_vars = myInterpreter.get_important_vars(
perm_imp_results,
multipass=True,
combine=False,
)[model_name][:9]
important_vars = ['srh_0to1_ens_mean_spatial_mean']
examples_transformed, target_values_transformed = just_transforms(
model, examples, target_values)
myInterpreter = InterpretToolkit(model=[model.steps[-1][1]],
model_names=[model_name],
examples=examples_transformed,
targets=target_values_transformed,
feature_names=feature_names)
njobs = len(important_vars)
import itertools
targets = ['tornado', 'severe_wind', 'severe_hail']
times = ['first_hour', 'second_hour']
drop_opt = '_manual_drop_time_max_spatial_mean' #'_drop_high_corr_pred'
model_set = ['RandomForest', 'XGBoost', 'LogisticRegression']
iterator = itertools.product(targets, times)
for pair in iterator:
target, time = pair
path = '/work/mflora/ML_DATA/permutation_importance/'
fnames = [
join(
path,
f'permutation_importance_{model_name}_{target}_{time}_training_norm_aupdc{drop_opt}.pkl'
) for model_name in model_set
]
perm_imp_results = load_pickle(fnames)
myInterpreter = InterpretToolkit(model=[None])
myInterpreter.set_results(perm_imp_results,
option='permutation_importance')
important_vars = myInterpreter.get_important_vars(perm_imp_results,
multipass=True,
combine=True,
nvars=9)
fname = f'important_vars_all_models_{target}_{time}_{drop_opt}.pkl'
with open(fname, 'wb') as pkl_file:
pickle.dump(important_vars, pkl_file)
