JustEachGroup¶
- class mastml.data_splitters.JustEachGroup(**kwargs)[source]¶
Bases:
BaseSplitter
Class to train the model on one group at a time and test it on the rest of the data This class wraps scikit-learn’s LeavePGroupsOut with P set to n-1. More information is available at: http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.LeavePGroupsOut.html
- Args:
None (only object instance)
- Attributes:
parallel_run: an attribute definining wheteher to run splits with all available computer cores
- Methods:
- get_n_splits: method to calculate the number of splits to perform
- Args:
groups: (numpy array), array of group labels
- Returns:
(int), number of unique groups, indicating number of splits to perform
- split: method to perform split into train indices and test indices
- Args:
X: (numpy array), array of X features
y: (numpy array), array of y data
groups: (numpy array), array of group labels
- Returns:
(numpy array), array of train and test indices
Methods Summary
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.
Methods Documentation
- get_n_splits(X=None, y=None, groups=None)[source]¶
Returns the number of splitting iterations in the cross-validator
- split(X, y, groups)[source]¶
Generate indices to split data into training and test set.
Parameters¶
- Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
- yarray-like of shape (n_samples,)
The target variable for supervised learning problems.
- groupsarray-like of shape (n_samples,), default=None
Group labels for the samples used while splitting the dataset into train/test set.
Yields¶
- trainndarray
The training set indices for that split.
- testndarray
The testing set indices for that split.