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.