def profile_method(self, scale=12):
print('Profile Method')
orig_train_input = self.ml_obj.training_input
orig_valid_input = self.ml_obj.validation_input
train_observations = self.ml_obj.training_target
valid_observations = self.ml_obj.validation_target
print(orig_train_input.min(), orig_train_input.max())
print(orig_valid_input.min(), orig_valid_input.max())
scale_array = np.linspace(orig_valid_input.min(), orig_valid_input.max(), num=scale)
fixed_array = np.linspace(orig_valid_input.min(), orig_valid_input.max(), num=10)
train_predictions = self.ml_obj.best_network[0].sim(orig_train_input)
valid_predictions = self.ml_obj.best_network[0].sim(orig_valid_input)
#if self.ml_obj.min_max_scaler_target is not None:
# predictions = self.min_max_scaler_target.inverse_transform(predictions)
# observations = self.min_max_scaler_target.inverse_transform(observations)
if True: # this can be changed to self.min_max_scaler_target is not None (cant yet due to legacy)
min_max_scaler_target = preprocessing.MinMaxScaler()
training_target = min_max_scaler_target.fit_transform(self.data_package.training_target)
train_predictions = min_max_scaler_target.inverse_transform(train_predictions)
train_observations = min_max_scaler_target.inverse_transform(train_observations)
valid_predictions = min_max_scaler_target.inverse_transform(valid_predictions)
valid_observations = min_max_scaler_target.inverse_transform(valid_observations)
orig_train_rmse = rmse(train_observations, train_predictions, 8)
orig_valid_rmse = rmse(valid_observations, valid_predictions, 8)
profile_value_list = []
for input_num in range(len(orig_train_input[0])):
input_value_list = []
for x in scale_array:
range_value = 0
for y in fixed_array:
input_list = [y] * len(orig_train_input[0])
input_list[input_num] = x
input_array = np.array(input_list).reshape(1, len(orig_train_input[0]))
#print(input_array)
train_predictions = self.ml_obj.best_network[0].sim(input_array)
train_predictions = min_max_scaler_target.inverse_transform(train_predictions)
range_value += train_predictions[0]
input_value_list.append((range_value[0] / 10.0).__round__(7))
print(input_value_list)
profile_value_list.append(input_value_list)
def perturb_method(self):
print('Perturb Method')
orig_train_input = self.ml_obj.training_input
orig_valid_input = self.ml_obj.validation_input
train_observations = self.ml_obj.training_target
valid_observations = self.ml_obj.validation_target
train_predictions = self.ml_obj.best_network[0].sim(orig_train_input)
valid_predictions = self.ml_obj.best_network[0].sim(orig_valid_input)
#if self.ml_obj.min_max_scaler_target is not None:
# predictions = self.min_max_scaler_target.inverse_transform(predictions)
# observations = self.min_max_scaler_target.inverse_transform(observations)
if True: # this can be changed to self.min_max_scaler_target is not None (cant yet due to legacy)
min_max_scaler_target = preprocessing.MinMaxScaler()
training_target = min_max_scaler_target.fit_transform(self.data_package.training_target)
train_predictions = min_max_scaler_target.inverse_transform(train_predictions)
train_observations = min_max_scaler_target.inverse_transform(train_observations)
valid_predictions = min_max_scaler_target.inverse_transform(valid_predictions)
valid_observations = min_max_scaler_target.inverse_transform(valid_observations)
orig_train_rmse = rmse(train_observations, train_predictions, 8)
orig_valid_rmse = rmse(valid_observations, valid_predictions, 8)
for input_num in range(len(orig_train_input[0])):
input_abs_val = 0
for x in np.arange(1.1, 1.5, 0.1):
# Training
y = orig_train_input.T[input_num] * x
alt_train_input = np.insert(np.delete(orig_train_input, input_num, 1), input_num, y, 1)
train_predictions = self.ml_obj.best_network[0].sim(alt_train_input)
train_predictions = min_max_scaler_target.inverse_transform(train_predictions)
alt_train_rmse = rmse(train_observations, train_predictions, 8)
# Validation
y = orig_valid_input.T[input_num] * x
alt_valid_input = np.insert(np.delete(orig_valid_input, input_num, 1), input_num, y, 1)
valid_predictions = self.ml_obj.best_network[0].sim(alt_valid_input)
valid_predictions = min_max_scaler_target.inverse_transform(valid_predictions)
alt_valid_rmse = rmse(valid_observations, valid_predictions, 8)
input_abs_val += abs((orig_train_rmse + orig_valid_rmse) - (alt_train_rmse + alt_valid_rmse))
#print('input num: {}, input_abs_val: {}'.format(input_num, input_abs_val.__round__(7)))
print('{}, {}'.format(input_num, input_abs_val.__round__(7)))
