skdownscale.pointwise_models.AnalogRegression¶

class skdownscale.pointwise_models.AnalogRegression(n_analogs=200, thresh=None, kdtree_kwargs=None, query_kwargs=None, logistic_kwargs=None, lr_kwargs=None)[source]¶

Bases: AnalogBase

Parameters:

n_analogs (int) – Number of analogs to use when building linear regression
thresh (float or int) – Threshold value. If provided, the model will predict: 1) the probability of this threshold being exceeded, and 2) the value given the threshold is exceeded
kdtree_kwargs (dict) – Keyword arguments to pass to the sklearn.neighbors.KDTree constructor
query_kwargs (dict) – Keyword arguments to pass to the sklearn.neighbors.KDTree.query method
lr_kwargs (dict) – Keyword arguments to pass to the sklear.linear_model.LinearRegression constructor

kdtree_¶

KDTree object

Type:: sklearn.neighbors.KDTree

Notes

GARD models generates three columns in the predict function, the columns include pred, the mean prediction value; exceedance_prob, the probability of exceeding self.thresh value; and prediction_error, the RMSE associated with the mean prediction.

__init__(n_analogs=200, thresh=None, kdtree_kwargs=None, query_kwargs=None, logistic_kwargs=None, lr_kwargs=None)[source]¶

Methods

`__init__`([n_analogs, thresh, kdtree_kwargs, ...])
`fit`(X, y)	Fit Analog model using a KDTree
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict using the AnalogRegression model
`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.

Attributes

`n_outputs`
`output_names`

predict(X)[source]¶

Predict using the AnalogRegression model

Parameters:: X (DataFrame, shape (n_samples, 1)) – Samples.
Returns:: C (pd.DataFrame, shape (n_samples, self.n_outputs)) – Returns predicted values, including the mean prediction, exceedance probability, and prediction error

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.