skdownscale.pointwise_models.QuantileMappingReressor

class skdownscale.pointwise_models.QuantileMappingReressor(extrapolate=None, n_endpoints=10)

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)

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)

Fit the quantile mapping regression model.

Parameters:

X (array-like, shape  [n_samples, 1]) – Training data.

Returns:

self (object)

predict(X, **kwargs)

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.