mvpa2.datasets.sources.skl_data.skl_wine

mvpa2.datasets.sources.skl_data.skl_wine(return_X_y=False)

Load and return the wine dataset (classification).

New in version 0.18.

The wine dataset is a classic and very easy multi-class classification dataset.

Classes	3
Samples per class	[59,71,48]
Samples total	178
Dimensionality	13
Features	real, positive

Read more in the User Guide.

Parameters:

return_X_y : boolean, default=False.

If True, returns (data, target) instead of a Bunch object. See below for more information about the data and target object.

Returns:

data : Bunch

Dictionary-like object, the interesting attributes are: ‘data’, the data to learn, ‘target’, the classification labels, ‘target_names’, the meaning of the labels, ‘feature_names’, the meaning of the features, and ‘DESCR’, the full description of the dataset.

(data, target) : tuple if return_X_y is True

The copy of UCI ML Wine Data Set dataset is

downloaded and modified to fit standard format from:

https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

Notes

This function has been auto-generated by wrapping load_wine() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.

Examples

Let’s say you are interested in the samples 10, 80, and 140, and want to know their class name.

>>> from sklearn.datasets import load_wine
>>> data = load_wine()
>>> data.target[[10, 80, 140]]
array([0, 1, 2])
>>> list(data.target_names)
['class_0', 'class_1', 'class_2']