def _iter_scorer(train_inds, test_inds, flux_arr, model__and__model_flux_mean, method, score_methods, include_mle):
model = model__and__model_flux_mean[0]
model_flux_mean = model__and__model_flux_mean[1]
flux_test = flux_arr[test_inds]
flux_conv_test = None
if score_methods != ['LL']:
for pca_model in model:
if flux_conv_test is None:
flux_conv_test = iz.transform_inverse_transform(flux_test, pca_model, model_flux_mean, method)
flux_test -= flux_conv_test
else:
residual = iz.transform_inverse_transform(flux_test, pca_model, model_flux_mean, method)
flux_conv_test += residual
flux_test -= residual
scores = {}
for score_method in score_methods:
#print("Calculating score:" + score_method)
score_func = iz.get_score_func(score_method)
if score_func is not None:
if score_method != 'MAE':
scores[score_method] = score_func(flux_test, flux_conv_test, multioutput='uniform_average')
else:
scores[score_method] = np.mean(np.median(np.abs(flux_test - flux_conv_test), axis=1))
if (include_mle or score_method == 'LL') and method in ['FA', 'PCA']:
try:
scores['mle'] = model.score(flux_test)
except np.linalg.linalg.LinAlgError:
scores['mle'] = 0 #-2**10 #float("-inf")
except ValueError:
scores['mle'] = 0 #-2**10 #float("-inf")
#print("Scores: " + str(scores))
return scores
def _iter_modeler(train_inds, test_inds, flux_arr, model, method):
model_list = []
flux_train = flux_arr[train_inds]
flux_avg = np.mean(flux_train, axis=0)
for i in range(2):
new_model = est_clone(model)
new_model.fit(flux_train)
back_train = iz.transform_inverse_transform(flux_train, new_model, flux_avg, method)
flux_train -= back_train
model_list.append(new_model)
return model_list, flux_avg
