codes.tune package

codes.tune package#

Submodules#

codes.tune.evaluate_study module#

codes.tune.evaluate_study.load_model_test_losses(model_path)#

Load the test losses from the model checkpoint.

Parameters:

model_path (str) – Path to the model checkpoint.

Returns:

Test losses.

Return type:

np.ndarray

codes.tune.evaluate_study.load_study_config(study_name)#

Load the YAML config used by the study (optuna_config.yaml).

Return type:

dict

codes.tune.evaluate_study.main()#

Main function to evaluate an Optuna study and its top models. Usually, viewing the study database with Optuna Dashboard is more informative.

codes.tune.evaluate_study.moving_average(data, window_size)#

Compute the moving average of a 1D array.

Parameters:

  • data (np.ndarray) – 1D array to compute the moving average.

  • window_size (int) – Size of the window for the moving average.

Returns:

Moving average of the input data.

Return type:

np.ndarray

Raises:

ValueError – If the window size is not a positive integer.

codes.tune.evaluate_study.parse_arguments()#

Parse command-line arguments.

codes.tune.evaluate_study.plot_test_losses(test_losses, labels, study_name, window_size=5)#

Plot the test losses of the top models.

Parameters:

  • test_losses (list[np.ndarray]) – List of test losses.

  • labels (list[str]) – List of labels for the test losses.

  • study_name (str) – Name of the study.

  • window_size (int, optional) – Size of the window for the moving average. Defaults to 5.

Return type:

None

codes.tune.optuna_fcts module#

codes.tune.optuna_fcts.create_objective(config, study_name, device_queue)#

Create the objective function for Optuna.

Parameters:

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

  • device_queue (queue.Queue) – Queue of available devices.

Returns:

Objective function for Optuna.

Return type:

function

codes.tune.optuna_fcts.get_activation_function(name)#

Get the activation function module from its name. Required for Optuna to suggest activation functions.

Parameters:

name (str) – Name of the activation function.

Returns:

Activation function module.

Return type:

nn.Module

codes.tune.optuna_fcts.load_yaml_config(config_path)#

Load a YAML configuration file.

Parameters:

config_path (str) – Path to the YAML configuration file.

Returns:

Configuration dictionary.

Return type:

dict

codes.tune.optuna_fcts.make_optuna_params(trial, optuna_params)#

Make Optuna suggested parameters from the optuna_config.yaml file.

Parameters:

  • trial (optuna.Trial) – Optuna trial object.

  • optuna_params (dict) – Optuna parameters dictionary.

Returns:

Suggested parameters.

Return type:

dict

codes.tune.optuna_fcts.training_run(trial, device, config, study_name)#

Run the training for a single Optuna trial and return the loss.

Parameters:

  • trial (optuna.Trial) – Optuna trial object.

  • device (str) – Device to run the training on.

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

Returns:

Loss value.

Return type:

float

Module contents#

codes.tune.create_objective(config, study_name, device_queue)#

Create the objective function for Optuna.

Parameters:

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

  • device_queue (queue.Queue) – Queue of available devices.

Returns:

Objective function for Optuna.

Return type:

function

codes.tune.get_activation_function(name)#

Get the activation function module from its name. Required for Optuna to suggest activation functions.

Parameters:

name (str) – Name of the activation function.

Returns:

Activation function module.

Return type:

nn.Module

codes.tune.load_model_test_losses(model_path)#

Load the test losses from the model checkpoint.

Parameters:

model_path (str) – Path to the model checkpoint.

Returns:

Test losses.

Return type:

np.ndarray

codes.tune.load_study_config(study_name)#

Load the YAML config used by the study (optuna_config.yaml).

Return type:

dict

codes.tune.load_yaml_config(config_path)#

Load a YAML configuration file.

Parameters:

config_path (str) – Path to the YAML configuration file.

Returns:

Configuration dictionary.

Return type:

dict

codes.tune.make_optuna_params(trial, optuna_params)#

Make Optuna suggested parameters from the optuna_config.yaml file.

Parameters:

  • trial (optuna.Trial) – Optuna trial object.

  • optuna_params (dict) – Optuna parameters dictionary.

Returns:

Suggested parameters.

Return type:

dict

codes.tune.moving_average(data, window_size)#

Compute the moving average of a 1D array.

Parameters:

  • data (np.ndarray) – 1D array to compute the moving average.

  • window_size (int) – Size of the window for the moving average.

Returns:

Moving average of the input data.

Return type:

np.ndarray

Raises:

ValueError – If the window size is not a positive integer.

codes.tune.plot_test_losses(test_losses, labels, study_name, window_size=5)#

Plot the test losses of the top models.

Parameters:

  • test_losses (list[np.ndarray]) – List of test losses.

  • labels (list[str]) – List of labels for the test losses.

  • study_name (str) – Name of the study.

  • window_size (int, optional) – Size of the window for the moving average. Defaults to 5.

Return type:

None

codes.tune.training_run(trial, device, config, study_name)#

Run the training for a single Optuna trial and return the loss.

Parameters:

  • trial (optuna.Trial) – Optuna trial object.

  • device (str) – Device to run the training on.

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

Returns:

Loss value.

Return type:

float