lale.lib.autogen.transformed_target_regressor module

class lale.lib.autogen.transformed_target_regressor.TransformedTargetRegressor(*, regressor=None, transformer=None, func=None, inverse_func=None, check_inverse=True)

Bases: PlannedIndividualOp

Combined schema for expected data and hyperparameters.

This documentation is auto-generated from JSON schemas.

Parameters

  • regressor (None, not for optimizer, default None) – Regressor object such as derived from RegressorMixin

  • transformer (union type, not for optimizer, default None) –

    Estimator object such as derived from TransformerMixin

    • dict

    • or None

    See also constraint-1, constraint-2.

  • func (None, not for optimizer, default None) –

    Function to apply to y before passing to fit

    See also constraint-2.

  • inverse_func (None, not for optimizer, default None) –

    Function to apply to the prediction of the regressor

    See also constraint-2.

  • check_inverse (boolean, not for optimizer, default True) – Whether to check that transform followed by inverse_transform or func followed by inverse_func leads to the original targets.

Notes

constraint-1 : union type

transformer’ and functions ‘func’/’inverse_func’ cannot both be set.

  • transformer : None

  • or intersection type

    • dict of func : None

    • and dict of inverse_func : None

constraint-2 : union type

When ‘func’ is provided, ‘inverse_func’ must also be provided

  • intersection type

    • dict of transformer : negated type of None

    • and union type

      • dict of func : negated type of None

      • or dict of inverse_func : negated type of None

  • or transformer : negated type of None

  • or func : None

  • or inverse_func : negated type of None

fit(X, y=None, **fit_params)

Train the operator.

Note: The fit method is not available until this operator is trainable.

Once this method is available, it will have the following signature:

Parameters

  • X (array of items : array of items : float) – Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y (array of items : float) – Target values.

  • sample_weight (Any, optional) – Array of weights that are assigned to individual samples

predict(X, **predict_params)

Make predictions.

Note: The predict method is not available until this operator is trained.

Once this method is available, it will have the following signature:

Parameters

X (array of items : array of items : float) – Samples.

Returns

result – Predicted values.

Return type

array of items : float