teachpyx.practice.ml_skl

class teachpyx.practice.ml_skl.PositiveOrNegativeLinearRegression(epsilon: float = 1.0, max_iter: int = 100, positive: bool = True)

Trains a linear regression with coefficients of the same sign. The order of inheritance must be RegressorMixin, BaseEstimator otherwise the tags are wrong.

Paramètres:

• epsilon – gradient step

• max_iter – number maximum of iterations

• positive – only positive weights (or negative if False)

Tags can be changed.

def __sklearn_tags__(self):
    tags = RegressorMixin.__sklearn_tags__(self)
    return tags

    return Tags(
        estimator_type=None,
        target_tags=TargetTags(required=False),
        transformer_tags=None,
        regressor_tags=None,
        classifier_tags=None,
    )

Or:

def __sklearn_tags__(self):
    return Tags(
        estimator_type="regressor",
        classifier_tags=None,
        regressor_tags=RegressorTags(),
        transformer_tags=None,
        target_tags=TargetTags(required=True),
    )

fit(X, y)

Trains.

predict(X)

Predicts.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → PositiveOrNegativeLinearRegression

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to score.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Ajouté dans la version 1.3.

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

selfobject

The updated object.