skdownscale.pointwise_models.QuantileMappingReressor¶

class skdownscale.pointwise_models.QuantileMappingReressor(extrapolate=None, n_endpoints=10)[source]¶

Bases: RegressorMixin, BaseEstimator

Transform features using quantile mapping.

Parameters:

extrapolate (str, optional) – How to extend the cdfs at the tails. Valid options include {‘min’, ‘max’, ‘both’, ‘1to1’, None}
n_endpoints (int) – Number of endpoints to include when extrapolating the tails of the cdf

_X_cdf¶

NamedTuple representing the fit’s X cdf

Type:: Cdf

_y_cdf¶

NamedTuple representing the fit’s y cdf

Type:: Cdf

__init__(extrapolate=None, n_endpoints=10)[source]¶

Methods

`__init__`([extrapolate, n_endpoints])
`fit`(X, y, **kwargs)	Fit the quantile mapping regression model.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X, **kwargs)	Predict regression for target X.
`score`(X, y[, sample_weight])	Return coefficient of determination on test data.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

fit(X, y, **kwargs)[source]¶

Fit the quantile mapping regression model.

Parameters:: X (array-like, shape [n_samples, 1]) – Training data.
Returns:: self (object)

predict(X, **kwargs)[source]¶

Predict regression for target X.

Parameters:: X (array_like, shape [n_samples, 1]) – Samples.
Returns:: y (ndarray of shape (n_samples, )) – Predicted data.

set_score_request(*, sample_weight='$UNCHANGED$')¶

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.

Added in version 1.3.

Parameters:: sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.
Returns:: self (object) – The updated object.