verde.base.BaseBlockCrossValidator¶
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class
verde.base.BaseBlockCrossValidator(spacing=None, shape=None, n_splits=10)[source]¶ 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
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Returns the number of splitting iterations in the cross-validator |
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Generate indices to split data into training and test set. |
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BaseBlockCrossValidator.get_n_splits(X=None, y=None, groups=None)[source]¶ Returns the number of splitting iterations in the cross-validator
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BaseBlockCrossValidator.split(X, y=None, groups=None)[source]¶ 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.
