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=1,
subsample=0.5,
njobs=njobs,
nbins=30,
)
print('computing ice curves...')
ice_dict = myInterpreter.calc_ice(features=important_vars,
njobs=njobs,
nbins=30,
subsample_size=200)
fig, axes = myInterpreter.plot_ale(
display_feature_names=display_feature_names,
to_probability=True,
ice_curves=ice_dict,
display_units=feature_units,
title=
f'{plt_config.title_dict[target]} {time.replace("_", " ").title()}',
unnormalize=unnormalize_func)
fname = f'ale_{model_name}_{target}_{time}_{drop_opt}.png'
base_plot.save_figure(fig=fig, fname=fname)
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_transformed,
targets=target_values_transformed,
feature_names=feature_names)
results = myInterpreter.calc_ice(
features=feature_names,
n_bootstrap=1,
subsample=200,
n_jobs=njobs,
n_bins=35,
)
results_fname = join(
ale_path,
f'ice_results_{model_name}_{resample_method}_{target}_{time}{drop_opt}{calibrate}.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