'lcl_ml_ens_mean_spatial_mean',
'w_up_time_max_ens_mean_of_90th']
'''
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']
})
examples_subset, target_subset = rus.fit_resample(
examples_transformed,
target_values_transformed,
)
#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)
print('First load of the data...')
examples, target_values = _load_train_data(**parameters)
feature_names = list(examples.columns)
feature_names.remove('Run Date')
important_vars = [
'cape_ml_ens_mean_spatial_mean',
'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(
myInterpreter = InterpretToolkit(examples=examples, targets=target_values)
results = []
for target in targets:
fnames = [
get_fnames(model_name, target, time, drop_opt)
for model_name in ml_models
]
results.append(myInterpreter.load_results(fnames))
fname = '/work/mflora/ML_DATA/INPUT_DATA/20180501/PROBABILITY_OBJECTS_20180501-2330_10.nc'
ds = xr.open_dataset(fname)
features = [var for var in list(ds.data_vars) if 'matched' not in var
] + ['Run Date']
readable_feature_names = {
feature: to_readable_names([feature])[0]
for feature in features
}
feature_colors = {
feature: to_readable_names([feature])[1]
for feature in features
}
adict = readable_feature_names
display_feature_names1 = {
f'{f[0]}__{f[1]}': f'{adict[f[0]]} & {adict[f[1]]}'
for f in list(itertools.combinations(features, r=2))
}
display_feature_names2 = {
f'{f[1]}__{f[0]}': f'{adict[f[0]]} & {adict[f[1]]}'
# Load the most important variables
path = '/work/mflora/ML_DATA/permutation_importance'
perm_imp_fname = join(
path,
f'permutation_importance_all_models_{target}_{time}_training_{metric}{drop_opt}{perm_method}.nc'
)
explainer = InterpretToolkit()
perm_imp_results = explainer.load(perm_imp_fname)
important_vars = perm_imp_results[
f'{mode}_rankings__LogisticRegression'].values
important_vars = important_vars[:n_vars]
# Convert to pretty feature names
readable_feature_names = {
feature: to_readable_names([feature])[0] + f' ({get_units(feature)})'
for feature in important_vars
}
parameters = {
'time': time,
'target': target,
'drop_opt': drop_opt,
}
X, y = _load_train_data(**parameters)
n_panels = len(important_vars)
fig, axes = base_plt.create_subplots(n_panels,
figsize=(10, 6),
sharey=True,
n_columns=4,
resample_method = resample_dict[time][target][model_name]
return join(
ale_path,
f'ale_2d_results_{model_name}_{target}_{time}{drop_opt}{resample_method}.nc'
)
########################################
fname = '/work/mflora/ML_DATA/INPUT_DATA/20180501/PROBABILITY_OBJECTS_20180501-2330_10.nc'
ds = xr.open_dataset(fname)
features = [var for var in list(ds.data_vars) if 'matched' not in var
] + ['Run Date']
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]
return join(path, f'permutation_importance_{atype}_{target}_{time}_{mode}_{metric}{drop_opt}{perm_method}{resample}.nc')
explainer = InterpretToolkit() #X=X,y=y)
results =[]
for target in targets:
fname = get_fnames(target, time, mode, metric, drop_opt, perm_method, resample)
results.append(explainer.load(fname))
results[0].attrs['estimator_output'] = ['LogisticRegression']
fname = '/work/mflora/ML_DATA/INPUT_DATA/20180501/PROBABILITY_OBJECTS_20180501-2330_10.nc'
ds = xr.open_dataset(fname)
features = [ var for var in list(ds.data_vars) if 'matched' not in var] + ['Run Date']
readable_feature_names = {feature: to_readable_names([feature])[0] for feature in features}
feature_colors = {feature: to_readable_names([feature])[1] for feature in features}
p_values=None
fig = explainer.plot_importance(
data=results,
method=method,
display_feature_names=readable_feature_names,
feature_colors=feature_colors,
num_vars_to_plot=num_vars_to_plot,
rows = rows,
columns = columns,
plot_correlated_features=False,
estimator_names = ml_models,
p_values =p_values,