if normalize_method != None:
unnormalize_func = UnNormalize(model.steps[1][1], feature_names)
else:
unnormalize_func = None
result_dict = myInterpreter.calc_ale(
features=important_vars,
nbootstrap=100,
subsample=1.0,
njobs=njobs,
nbins=30,
)
ale_results.append(result_dict)
myInterpreter.model_names = model_set
ale_results = merge_nested_dict(ale_results)
myInterpreter.set_results(ale_results, option='ale')
fig, axes = myInterpreter.plot_ale(
display_feature_names=display_feature_names,
to_probability=True,
display_units=feature_units,
title=
f'{plt_config.title_dict[target]} {time.replace("_", " ").title()}',
unnormalize=unnormalize_func,
n_columns=n_columns,
figsize=figsize)
fname = f'ale_{target}_{time}_{drop_opt}_special.png'
base_plot.save_figure(fig=fig, fname=fname)
njobs = 1 if model_name == 'XGBoost' else len(important_vars)
if normalize_method != None:
unnormalize_func = UnNormalize(model.steps[1][1], feature_names)
else:
unnormalize_func = None
result_dict = myInterpreter.calc_pd(
features=important_vars,
nbootstrap=10,
subsample=0.1,
njobs=njobs,
nbins=20,
)
pd_results.append(result_dict)
myInterpreter.model_names = model_set
pd_results = merge_nested_dict(pd_results)
myInterpreter.set_results(pd_results, option='pdp')
fig, axes = myInterpreter.plot_pd(
display_feature_names=display_feature_names,
to_probability=True,
display_units=feature_units,
title=
f'{plt_config.title_dict[target]} {time.replace("_", " ").title()}',
unnormalize=unnormalize_func)
fname = f'pdp_{target}_{time}_{drop_opt}.png'
base_plot.save_figure(fig=fig, fname=fname)
ale_results = []
for i, model_name in enumerate(model_set):
parameters['model_name'] = model_name
calibrated_pipeline = _load_model(**parameters)
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)
stp_examples = stp_examples.values
print(stp_examples.shape)
base_clf = model.steps[-1][1]
myInterpreter = InterpretToolkit(model=[base_clf],
model_names=[model_name],
examples=stp_examples,
targets=stp_target_values,
feature_names=feature_names)
out_dict = myInterpreter.calc_contributions(
method='shap',
data_for_shap=examples_transformed,
performance_based=True,
n_examples=250,
shap_sample_size=200)
results.append(out_dict)
myInterpreter.model_names = model_set
results = merge_nested_dict(results)
myInterpreter.set_results(results, option='contributions')
fig = myInterpreter.plot_contributions(to_only_varname=to_only_varname,
model_names=model_set,
wspace=2.0)
fname = f'contributions_stp_geq_2_performance_based_all_models_{target}_{time}{drop_opt}.png'
print(f'Saving {fname}..')
base_plot.save_figure(fig=fig, fname=fname)
