skdownscale.pointwise_models.PureAnalog

class skdownscale.pointwise_models.PureAnalog(n_analogs=200, kind='best_analog', thresh=None, kdtree_kwargs=None, query_kwargs=None)

Parameters

n_analogsint

Number of analogs to use

threshfloat

Subset analogs based on threshold

statsbool

Calculate fit statistics during predict step

kdtree_kwargsdict

Dictionary of keyword arguments to pass to cKDTree constructor

query_kwargsdict

Dictionary of keyword arguments to pass to cKDTree.query

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.

Attributes

kdtree_sklearn.neighbors.KDTree

KDTree object

Methods

fit(X, y)	Fit Analog model using a KDTree
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the PureAnalog model
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_params(**params)	Set the parameters of this estimator.

__init__(n_analogs=200, kind='best_analog', thresh=None, kdtree_kwargs=None, query_kwargs=None)

Methods

__init__([n_analogs, kind, thresh, ...])
fit(X, y)	Fit Analog model using a KDTree
get_params([deep])	Get parameters for this estimator.
predict(X)	Predict using the PureAnalog model
score(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
set_params(**params)	Set the parameters of this estimator.

Attributes

n_outputs
output_names