train_test_split¶
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museotoolbox.cross_validation.train_test_split(cv, X, y, random_state=False, **kwargs)[source]¶ Split arrays into random train and test subsets according to your choosen cross_validation method.
Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.
- Parameters
Parameters –
- cvcross-validation function.
Allowed function from museotoolbox as scikit-learn.
- Xarray-like, shape (n_samples, n_features), optional
Training data, where n_samples is the number of samples and n_features is the number of features.
- yarray-like, of length n_samples
The target variable for supervised learning problems.
- random_stateint, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
Examples
import numpy as np import museotoolbox as mtb
X, y = np.arange(10).reshape((5, 2)), range(5) cv = mtb.cross_validation.LeaveOneOut X_train, y_train, X_test, y_test = mtb.cross_validation.train_test_split(cv,X,y,random_state=42)
