From e1aabfcdf88ea95b70ae60e9f07d5c206e1361be Mon Sep 17 00:00:00 2001 From: autopep8 bot Date: Thu, 24 Oct 2024 13:21:12 +0000 Subject: [PATCH] Automated autopep8 fixes --- .../industrial_implementations/ml_optimisation.py | 8 ++++---- .../repository/industrial_implementations/optimisation.py | 1 - 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py b/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py index d941a27cc..7f7031c77 100644 --- a/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py +++ b/fedot_ind/core/repository/industrial_implementations/ml_optimisation.py @@ -104,13 +104,13 @@ def _get_parameters_from_trial(self, graph: OptGraph, trial: Trial) -> dict: sampling_scope = parameter_properties.get('sampling-scope') if parameter_type == 'discrete': new_parameters.update({node_op_parameter_name: - trial.suggest_int(node_op_parameter_name, *sampling_scope)}) + trial.suggest_int(node_op_parameter_name, *sampling_scope)}) elif parameter_type == 'continuous': new_parameters.update({node_op_parameter_name: - trial.suggest_float(node_op_parameter_name, *sampling_scope)}) + trial.suggest_float(node_op_parameter_name, *sampling_scope)}) elif parameter_type == 'categorical': new_parameters.update({node_op_parameter_name: - trial.suggest_categorical(node_op_parameter_name, *sampling_scope)}) + trial.suggest_categorical(node_op_parameter_name, *sampling_scope)}) return new_parameters def _get_initial_point(self, graph: OptGraph) -> Tuple[dict, bool]: @@ -125,7 +125,7 @@ def _get_initial_point(self, graph: OptGraph) -> Tuple[dict, bool]: if tunable_node_params: has_parameters_to_optimize = True tunable_initial_params = {get_node_operation_parameter_label(node_id, operation_name, p): - node.parameters[p] for p in node.parameters if p in tunable_node_params} + node.parameters[p] for p in node.parameters if p in tunable_node_params} if tunable_initial_params: initial_parameters.update(tunable_initial_params) return initial_parameters, has_parameters_to_optimize diff --git a/fedot_ind/core/repository/industrial_implementations/optimisation.py b/fedot_ind/core/repository/industrial_implementations/optimisation.py index 1888c61d5..95f8ba4f5 100644 --- a/fedot_ind/core/repository/industrial_implementations/optimisation.py +++ b/fedot_ind/core/repository/industrial_implementations/optimisation.py @@ -622,4 +622,3 @@ def has_no_data_flow_conflicts_in_industrial_pipeline(pipeline: Pipeline): def _crossover_by_type(self, crossover_type: CrossoverTypesEnum) -> None: IndustrialCrossover() return None -