Hi Paul, PLSRegression in sklearn uses an iterative method to estimate the eigen vectors and values (I think it is the power method) , which mostly varies depending on the underlying library that you use, I would suggest to use SVD instead if you want to get stable results and your dataset is small
I have wrote also wrote a Kernal PLS which you can find here https://gist.github.com/aeweiwi/7788156 Cheers, On Tue, Feb 14, 2017 at 11:54 AM, Paul Anton Letnes <[email protected]> wrote: > Hi! > > Versions: > sklearn 0.18.1 > numpy 1.11.3 > Anaconda python 3.5 on ubuntu 16.04 > > What range is the cross_val_score supposed to be in? I was under the > impression from the documentation, although I cannot find it stated > explicitly anywhere, that it should be a number in the range [0, 1]. > However, it appears that one can get large negative values; see the ipython > session below. > > Cheers > Paul > > In [2]: import numpy as np > > In [3]: y = np.random.random((10, 3)) > > In [4]: x = np.random.random((10, 17)) > > In [5]: from sklearn.cross_decomposition import PLSRegression > > In [6]: pls = PLSRegression(n_components=3) > > In [7]: from sklearn.cross_validation import cross_val_score > > In [8]: from sklearn.model_selection import cross_val_score > > In [9]: cross_val_score(pls, x, y) > Out[9]: array([-32.52217837, -4.17228083, -5.88632365]) > > > PS: > This happens even if I cheat by setting y to the predicted value, and > cross validate on that. > > In [29]: y = x @ pls.coef_ > > In [30]: cross_val_score(pls, x, y) > /home/paul/anaconda3/envs/wp3-paper/lib/python3.5/site- > packages/sklearn/cross_decomposition/pls_.py:293: UserWarning: Y residual > constant at iteration 5 > warnings.warn('Y residual constant at iteration %s' % k) > /home/paul/anaconda3/envs/wp3-paper/lib/python3.5/site- > packages/sklearn/cross_decomposition/pls_.py:293: UserWarning: Y residual > constant at iteration 6 > warnings.warn('Y residual constant at iteration %s' % k) > /home/paul/anaconda3/envs/wp3-paper/lib/python3.5/site- > packages/sklearn/cross_decomposition/pls_.py:293: UserWarning: Y residual > constant at iteration 6 > warnings.warn('Y residual constant at iteration %s' % k) > Out[30]: array([-35.01267353, -4.94806383, -5.9619526 ]) > > In [34]: np.max(np.abs(y - x @ pls.coef_)) > Out[34]: 0.0 > > > > _______________________________________________ > scikit-learn mailing list > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > >
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