ForeTiS.model.evars-gpr

Module Contents

Classes

Evars_gpr

Implementation of a class for Gpr.

class ForeTiS.model.evars-gpr.Evars_gpr(optuna_trial, datasets, featureset_name, pca_transform=None, target_column=None, optimize_featureset=None, scale_thr=None, scale_seasons=None, scale_window_factor=None, cf_r=None, cf_order=None, cf_smooth=None, cf_thr_perc=None, scale_window_minimum=None, max_samples_factor=None)

Bases: ForeTiS.model._tensorflow_model.TensorflowModel

Implementation of a class for Gpr.

See BaseModel for more information on the attributes.

Parameters:

• optuna_trial (optuna.trial.Trial)

• datasets (list)

• featureset_name (str)

• pca_transform (bool)

• target_column (str)

• optimize_featureset (bool)

• scale_thr (float)

• scale_seasons (int)

• scale_window_factor (float)

• cf_r (float)

• cf_order (int)

• cf_smooth (int)

• cf_thr_perc (int)

• scale_window_minimum (int)

• max_samples_factor (int)

get_augmented_data()

get augmented data

Returns:

augmented dataset

retrain(retrain)

Implementation of the retraining for models with sklearn-like API. See BaseModel for more information

Parameters:

retrain (pandas.DataFrame)

update(update, period)

Implementation of the retraining for models with sklearn-like API. See BaseModel for more information

Parameters:

• update (pandas.DataFrame)

• period (int)

predict(X_in)

Implementation of a prediction based on input features for models with sklearn-like API. See BaseModel for more information

Parameters:

X_in (pandas.DataFrame)

Return type:

numpy.array

pca_transform_train_test(train)

Deliver PCA transformed train and test set

Parameters:

train (pandas.DataFrame) – data for the training

Returns:

tuple of transformed train and test dataset

Return type:

tuple

define_hyperparams_to_tune()

See BaseModel for more information on the format.

Return type:

dict