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

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Paramètres:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Renvoie:

self – The updated object.

Type renvoyé:

object