JustEachGroup¶

class mastml.data_splitters.JustEachGroup[source]¶

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)

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.

X : array-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.
y : array-like of shape (n_samples,): The target variable for supervised learning problems.
groups : array-like of shape (n_samples,), default=None: Group labels for the samples used while splitting the dataset into train/test set.