'''
data['X']['mid_level_lapse_rate_ens_mean_spatial_mean'] = data['X']['mid_level_lapse_rate_ens_mean_spatial_mean'] / 2.67765
X['mid_level_lapse_rate_ens_mean_spatial_mean'] = X['mid_level_lapse_rate_ens_mean_spatial_mean'] / 2.67765
fname = join(perm_path, f'permutation_importance_all_models_{target}_{time}_training_{metric}{drop_opt}{perm_method}.nc')
explainer = InterpretToolkit(X=data['X'],y=data['targets'],estimator_output='probability',)
perm_results = explainer.load(fname)
#important_vars = perm_results['multipass_rankings__LogisticRegression'].values[:12]
#important_vars = ['low_level_lapse_rate_ens_mean_spatial_mean']
important_vars = ['mid_level_lapse_rate_ens_mean_spatial_mean']
all_vars = perm_results['singlepass_rankings__LogisticRegression'].values
display_feature_names = {f: to_readable_names([f])[0] for f in all_vars}
#display_feature_names = _fix_long_names(display_feature_names)
feature_units = {f: get_units(f)for f in all_vars}
if option == 'interaction':
interaction_index = 'auto'
y = None
elif option == 'targets':
interaction_index=None
y =data['targets']
elif option == 'interaction_and_target':
interaction_index = 'auto'
y =data['targets']
else:
interaction_index=None
y =data['targets']
y = np.ones((len(y)))
)
#examples_subset = shap.sample(examples_transformed, 1000)
myInterpreter = InterpretToolkit(model=[model.steps[-1][1]],
model_names=[model_name],
examples=examples_subset,
targets=target_subset,
feature_names=feature_names)
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}
date_subset = date_col[:len(examples_subset)].reshape(
len(examples_subset), 1)
examples_subset = np.concatenate((examples_subset, date_subset), axis=1)
examples_subset = pd.DataFrame(examples_subset,
columns=original_feature_names)
if normalize_method != None:
unnormalize = UnNormalize(model.steps[1][1], feature_names)
feature_values = unnormalize._full_inverse_transform(examples_subset)
else:
unnormalize = None
feature_values = examples_subset.values
'cin_ml_ens_mean_spatial_mean',
'lcl_ml_ens_mean_spatial_mean',
'shear_v_0to6_ens_mean_spatial_mean',
'srh_0to3_ens_mean_spatial_mean',
]
# {'w_up_time_max_ens_mean_of_90th': 'Updraft ($\\mu_e$ of P$_{90}$ of max$_t$)', 'uh_2to5_time_max_ens_mean_of_90th': '2-5 km UH ($\\mu_e$ of P$_{90}$ of max$_t$)', 'cape_ml_ens_mean_spatial_mean': 'ML CAPE ($\\mu_e$)'}
display_feature_names = {
f: to_readable_names([f])[0].split('(')[0][:-1]
for f in important_vars
}
print(display_feature_names)
display_feature_names = _fix_long_names(display_feature_names)
feature_units = {f: get_units(f) for f in important_vars}
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
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)
