In contrast to behavioral PLS, mean-centered PLS doesn't aim to find relationships between two sets of variables. Instead, it tries to find relationships between groupings in a single set of variables. Indeed, you can think of it almost like a multivariate t-test or ANOVA (depending on how many groups you have).
>>> from pyls.examples import load_dataset >>> data = load_dataset('wine')
This is the same dataset as in :pysklearn.datasets.load_wine; the formatting has just been lightly modified to better suit our purposes.
Our data object can be treated as a dictionary, containing all the information necessary to run a PLS analysis. The keys can be accessed as attributes, so we can take a quick look at our input matrix:
>>> sorted(data.keys()) ['X', 'groups', 'n_boot', 'n_perm'] >>> data.X.shape (178, 13) >>> data.X.columns Index(['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline'], dtype='object') >>> data.groups [59, 71, 48]