finol.optimization_layer.ParametersTuner¶

class finol.optimization_layer.ParametersTuner(load_dataset_output)[source]¶

Class to tune model hyper-parameters using auto ML library.

Parameters:: load_dataset_output –

Example

>>> # Load dataset
>>> from finol.data_layer.dataset_loader import DatasetLoader
>>> load_dataset_output = DatasetLoader().load_dataset()
>>>
>>> # Tune model
>>> ParametersTuner(load_dataset_output=load_dataset_output).tune_parameters()

Methods

`objective`(trial)	Objective function for Optuna optimization.
`sample_params`(trial)	Samples model hyper-parameters for optimization.
`select_pruner`()	Selects and initializes an Optuna pruner based on the configuration.
`select_sampler`()	Selects and initializes an Optuna sampler based on the configuration.
`tune_parameters`()	Tune model hyper-parameters.

objective(trial)[source]¶

Objective function for Optuna optimization. Trains the model using the sampled parameters and returns the validation loss.

Parameters:: trial (Trial) – Optuna Trial object.
Returns:: Validation loss.
Return type:: float

sample_params(trial)[source]¶

Samples model hyper-parameters for optimization.

Parameters:: trial (Trial) – Optuna Trial object to sample the parameters.
Returns:: None
Return type:: None

select_pruner()[source]¶

Selects and initializes an Optuna pruner based on the configuration.

Returns:: Initialized Optuna pruner object.
Return type:: object

select_sampler()[source]¶

Selects and initializes an Optuna sampler based on the configuration.

Returns:: Initialized Optuna sampler object.
Return type:: object

tune_parameters()[source]¶

Tune model hyper-parameters.

This method creates an Optuna study object, optimizes the objective function, visualizes the results, and prints out the optimization results.

Returns:: None
Return type:: None