class museotoolbox.cross_validation.LeavePSubGroupOut(valid_size=0.5, n_repeats=False, random_state=False, verbose=False)[source]

Generate a Cross-Validation using subgroup (each group belong to a unique label).

  • valid_size (float, default 0.5.) – From 0 to 1.

  • n_repeats (int or bool, optional (default=False)) – If False, n_splits is 1/valid_size (default : 1/0.5 = 2). If int : will iterate the number of times given in n_splits.

  • random_state (integer or None, optional (default=False)) – If int, random_state is the seed used by the random number generator; If None, the random number generator is created with time.time().

  • verbose (integer or False, optional (default=False)) – Controls the verbosity: the higher the value is, the more the messages are detailed.

Manage cross-validartion methods to generate the duo valid/train samples.

split(X,y,g) : Function.

Get a memory cross validation to use directly in Scikit-Learn.

saveVectorFiles() : Need default output name (str).

To save as many vector files (train/valid) as your Cross Validation method outputs.

__get_supported_extensions() : Function.

Show you the list of supported vector extensions type when using saveVectorFiles function.

reinitialize() : Function.

If you need to regenerate the cross validation, you need to reinitialize it.


__init__([valid_size, n_repeats, …])

Manage cross-validartion methods to generate the duo valid/train samples.

get_n_splits([X, y, groups])

Returns the number of splitting iterations in the cross-validator.



save_to_vector(vector, field[, group, …])

Save to vector files each fold from the cross-validation.

split(X, y[, groups])

Split the vector/array according to y and groups.