verde.base.BaseBlockCrossValidator

class verde.base.BaseBlockCrossValidator(spacing=None, shape=None, n_splits=10)

Base class for spatially blocked cross-validators.

Parameters

• spacing (float, 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 spacing is equal in both directions. If None, then shape must be provided.

• shape (tuple = (n_north, n_east) or None) – The number of blocks in the South-North and West-East directions, respectively. If None, then spacing must be provided.

• n_splits (int) – Number of splitting iterations.

Methods Summary

BaseBlockCrossValidator.get_n_splits([X, y, ...])	Returns the number of splitting iterations in the cross-validator
BaseBlockCrossValidator.split(X[, y, groups])	Generate indices to split data into training and test set.

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BaseBlockCrossValidator.get_n_splits(X=None, y=None, groups=None)

Returns the number of splitting iterations in the cross-validator

Parameters

• X (object) – Always ignored, exists for compatibility.

• y (object) – Always ignored, exists for compatibility.

• groups (object) – Always ignored, exists for compatibility.

Returns

n_splits (int) – Returns the number of splitting iterations in the cross-validator.

BaseBlockCrossValidator.split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters

• X (array-like, shape (n_samples, 2)) – Columns should be the easting and northing coordinates of data points, respectively.

• y (array-like, shape (n_samples,)) – The target variable for supervised learning problems. Always ignored.

• groups (array-like, with shape (n_samples,), optional) – Group labels for the samples used while splitting the dataset into train/test set. Always ignored.

Yields

• train (ndarray) – The training set indices for that split.

• test (ndarray) – The testing set indices for that split.

Examples using verde.base.BaseBlockCrossValidator

K-Fold cross-validation with blocks