src.utils.tuning.hyper_tuner

Classes

HyperTuner(**kwargs)

class src.utils.tuning.hyper_tuner.HyperTuner(**kwargs)
Author:

Alberto M. Esmoris Pena

Class for model’s hyperparameters tuning.

Variables:

  • report_path (str) – The path (OPTIONAL) to export the hyperparameter tuning report.

  • hpnames (list or tuple or np.ndarray) – The names (as strings) of the hyperparameters to be considered.

  • scores (str or list or dict or tuple or callable or None) – The scores that must be optimized during hyperparameter tuning (None means using the default behavior for each hyper tuner).

static extract_tuner_args(spec)

Extract the arguments to initialize/instantiate an HyperTuner from a key-word specification.

Parameters:

spec – The key-word specification containing the arguments.

Returns:

The arguments to initialize/instantiate an HyperTuner.

__init__(**kwargs)

Initialize/instantiate an HyperTuner.

Parameters:

kwargs – The attributes for the HyperTuner.

static search(model, search, pcloud)

Compute the search of the best hyperparameters for the given model on the given point cloud.

Parameters:

  • model – The model which hyperparameters must be tuned. See Model.

  • search – The search object (must have a fit method to be applied on a features matrix and a vector of classes, i.e., F and y).

  • pcloud – The point cloud representing the input data for the search.

Returns:

Completed search.

static update_model(model, search, pcloud=None)

Update model from result.

Parameters:

  • model – The model to be updated. See Model

  • search – The search to update the model.

  • pcloud – The input point cloud (OPTIONAL, i.e., can be None).

Returns:

The updated model.

Return type:

Model

static update_model_with_no_refit(model, search, pcloud=None)

Compute the model update when the hyperparameter tuning ends without refitting the model. See HyperTuner.update_model().

static update_model_from_refit(model, search, pcloud=None)

Compute the model update when the hyperparameter tuning refits the model after finishing. See HyperTuner.update_model().

static kwargs_hyperparameters_from_spec(kwargs, spec)

Update the key-word arguments (kwargs) to derive the hyperparameters from the specification. In case there are explicitly given hyperparameters, they must match exactly with the specification.

Parameters:

  • kwargs – The key-word arguments to be updated.

  • spec – The specification, often a dictionary contained inside the key-word arguments.

Returns:

The updated key-word arguments.