time,
'training',
'norm_aupdc',
drop_opt,
resample='')
display_feature_names = {f: to_readable_names([f])[0] for f in feature_names}
display_feature_names = _fix_long_names(display_feature_names)
feature_units = {f: get_units(f) for f in feature_names}
unnormalize_func = None
myInterpreter = InterpretToolkit()
fnames = get_fnames(model_name, target, time, resample_method, drop_opt,
calibrate)
myInterpreter.load_results(fnames=fnames)
ice_fnames = get_ice_fnames(model_name, target, time, resample_method,
drop_opt, calibrate)
ice_dict = myInterpreter.load_results(fnames=ice_fnames)
fig, axes = myInterpreter.plot_ale(
features=feature_names[:10],
display_feature_names=display_feature_names,
display_units=feature_units,
title=f'{plt_config.title_dict[target]} {time.replace("_", " ").title()}',
unnormalize=unnormalize_func,
ice_curves=ice_dict,
)
fname = f'ale_{model_name}_{target}_{time}_{drop_opt}_{resample_method}{calibrate}.png'
base_plot.save_figure(fig=fig, fname=fname)
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)
ds.close()
display_feature_names = {f: to_readable_names([f])[0] for f in features}
display_feature_names = _fix_long_names(display_feature_names)
###feature_units = {f: get_units(f)for f in features}
myInterpreter = InterpretToolkit()
fnames = [get_fnames(m, target, time, drop_opt) for m in model_names]
results = myInterpreter.load_results(fnames=fnames)
feature_names = results[
'ale_variance_interactions_rankings__LogisticRegression'].values
feature_names = feature_names[:3]
fnames = [get_2d_ale(m, target, time, drop_opt) for m in model_names]
ale_data = myInterpreter.load_results(fnames=fnames)
feature_names = [tuple(f.split('__')) for f in feature_names]
print(feature_names)
fig, axes = myInterpreter.plot_ale(
ale_data=ale_data,
features=feature_names,
display_feature_names=display_feature_names,
title=f'{plt_config.title_dict[target]} {time.replace("_", " ").title()}',
hspace=1.0)
fname = f'ale_2D_{target}_{time}_{drop_opt}.png'
base_plot.save_figure(fig=fig, fname=fname)