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.evaluate_tuning module#

codes.tune.evaluate_tuning.evaluate_tuning(study_prefix, top_n=10, storage_name='optuna_db')#

For all surrogate studies named ‘<study_prefix>_<surrogate>’, plot the top_n test-loss trajectories.

Return type:

None

codes.tune.evaluate_tuning.get_best_trials(study, top_n)#

Return trial numbers of best ‘top_n’ trials. Single-objective: lowest value. Multi-objective: Pareto front; if fewer than top_n, pad with closest-to-origin among the rest.

Return type:

list[int]

codes.tune.evaluate_tuning.load_loss_history(model_path)#

Load loss histories from a saved model file (.pth). Returns (train_loss, test_loss, n_epochs).

Return type:

tuple[ndarray, ndarray, int]

codes.tune.evaluate_tuning.main()#
codes.tune.evaluate_tuning.parse_args()#
codes.tune.evaluate_tuning.plot_losses(loss_histories, epochs, labels, title='Losses', save=False, out_dir=None, mode='')#

Plot multiple loss trajectories on a log scale.

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.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)#
Return type:

dict

codes.tune.optuna_fcts.maybe_set_runtime_threshold(study, warmup_target, include_pruned=False)#
Return type:

None

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

Run the training for a single Optuna trial and return the loss. In multi-objective mode, also returns the mean inference time.

Parameters:

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

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

  • slot_id (int) – Slot ID for the position of the progress bar.

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

Returns:

Loss value in single objective mode. tuple[float, float]: (loss, mean_inference_time) in multi objective mode.

Return type:

float

codes.tune.postgres_fcts module#

codes.tune.postgres_fcts.initialize_optuna_database(config, study_folder_name)#
Return type:

str

codes.tune.tune_utils module#

codes.tune.tune_utils.build_study_names(config, main_study_name)#
Return type:

list[str]

codes.tune.tune_utils.copy_config(src, dst_folder)#
Return type:

None

codes.tune.tune_utils.delete_studies_if_requested(config, main_study_name, db_url)#

Delete studies if user overwrote run folder.

codes.tune.tune_utils.prepare_workspace(master_cfg_path, config)#
Return type:

None

codes.tune.tune_utils.yes_no(prompt, default=False)#

Simple Y/N prompt. default=False -> [y/N], True -> [Y/n].

Return type:

bool

Module contents#

codes.tune.build_study_names(config, main_study_name)#
Return type:

list[str]

codes.tune.copy_config(src, dst_folder)#
Return type:

None

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.delete_studies_if_requested(config, main_study_name, db_url)#

Delete studies if user overwrote run folder.

codes.tune.initialize_optuna_database(config, study_folder_name)#
Return type:

str

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)#
Return type:

dict

codes.tune.maybe_set_runtime_threshold(study, warmup_target, include_pruned=False)#
Return type:

None

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.prepare_workspace(master_cfg_path, config)#
Return type:

None

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

Run the training for a single Optuna trial and return the loss. In multi-objective mode, also returns the mean inference time.

Parameters:

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

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

  • slot_id (int) – Slot ID for the position of the progress bar.

  • config (dict) – Configuration dictionary.

  • study_name (str) – Name of the study.

Returns:

Loss value in single objective mode. tuple[float, float]: (loss, mean_inference_time) in multi objective mode.

Return type:

float

codes.tune.yes_no(prompt, default=False)#

Simple Y/N prompt. default=False -> [y/N], True -> [Y/n].

Return type:

bool

Contents