oraclesai.preprocessing.SpatialImputer

class SpatialImputer(missing_values=nan, spatial_weights_definition=None, strategy='mean')

Fill all the missing values using the values from the neighbors for each observation.

Parameters:

• missing_values – int, float, str, np.nan, None or pandas.NA, default=np.nan. The placeholder for the missing values. All occurrences of missing_values will be imputed

• spatial_weights_definition – SpatialWeightsDefinition, default=None. Spatial relationship specification

• strategy – {“mean”, “median”, “maximum”, “minimum”}, default=”mean”. It calculates the specified statistic from the neighbors to fill the missing value.

Methods

__init__([missing_values, ...])
fit(X[, y, geometries, spatial_weights])	Calculate the spatial weights according to spatial_weights_definition.
fit_transform(X[, y, geometries])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X[, y, geometries, ...])	Returns a NumPy array with the data passed as parameter filled according to the specified strategy.

Attributes

mask_

A boolean array with True in those cells with missing values, and False anywhere else