verde.base.BaseBlockCrossValidator#

class verde.base.BaseBlockCrossValidator(block_size=None, block_shape=None, n_splits=10)[source]#

Base class for spatially blocked cross-validators.

Parameters:

block_sizefloat, tuple = (s_north, s_east), or None: The block size in the South-North and West-East directions, respectively. A single value means that the size is equal in both directions.
block_shapetuple = (n_north, n_east) or None: The number of blocks in the South-North and West-East directions, respectively.
n_splitsint: Number of splitting iterations.

Methods

`get_metadata_routing`()	Get metadata routing of this object.
`get_n_splits`([X, y, groups])	Returns the number of splitting iterations in the cross-validator
`split`(X[, y, groups])	Generate indices to split data into training and test set.

Method documentation#

BaseBlockCrossValidator.get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

BaseBlockCrossValidator.get_n_splits(X=None, y=None, groups=None)[source]#

Returns the number of splitting iterations in the cross-validator

Parameters:

Returns:

n_splitsint: Returns the number of splitting iterations in the cross-validator.

BaseBlockCrossValidator.split(X, y=None, groups=None)[source]#

Generate indices to split data into training and test set.

Parameters:

Xarray_like, shape (n_samples, 2): Columns should be the easting and northing coordinates of data points, respectively.
yarray_like, shape (n_samples,): The target variable for supervised learning problems. Always ignored.
groupsarray_like, with shape (n_samples,), optional: Group labels for the samples used while splitting the dataset into train/test set. Always ignored.

Yields: