Added to my todo-list ;)
2013/5/13 Alexandre Gramfort <alexandre.gramf...@inria.fr>
> PR welcome on this. I think Jaques you have it ready.
>
> Best,
> Alex
>
> On Tue, May 7, 2013 at 11:42 AM, Jaques Grobler <jaquesgrob...@gmail.com>
> wrote:
> >
> >
> >>
> >> 2013/5/7 James D Jensen <jdjen...@ucsd.edu>
> >> Thanks. You mentioned that I could "[add] positive to LassoCV and [pass]
> >> it to the Lasso estimators used in the cross-val." In the directory of
> my
> >> own installation of scikit-learn, I modified
> >> sklearn/linear_model/coordinate_descent.py to include "positive=False"
> to
> >> the parameter list of __init__ for the classes LassoCV, ElasticNetCV,
> and
> >> LinearModelCV, and added "self.positive=positive" in the body of the
> >> __init__ methods. However, calling LassoCV("positive=True", cv=20) still
> >> gives me the error "TypeError: __init__() got an unexpected keyword
> >> argument 'positive'".
> >
> >
> > I tried that quickly and got no error. Just with the parameter lists of
> > LinearModelCV
> > and LassoCV, i changed this:
> >
> > in class LinearModelCV(LinearModel)
> >
> > def __init__(self, eps=1e-3, n_alphas=100, alphas=None,
> fit_intercept=True,
> > normalize=False, precompute='auto', max_iter=1000,
> > tol=1e-4,
> > copy_X=True, cv=None, verbose=False, positive=False):
> >
> > .....
> > self.positive = positive
> >
> > within LassoCV(LinearModelCV, RegressorMixin):
> >
> > def __init__(self, eps=1e-3, n_alphas=100, alphas=None,
> fit_intercept=True,
> > normalize=False, precompute='auto', max_iter=1000,
> > tol=1e-4,
> > copy_X=True, cv=None, verbose=False, positive=False):
> > super(LassoCV, self).__init__(
> > eps=eps, n_alphas=n_alphas, alphas=alphas,
> > fit_intercept=fit_intercept, normalize=normalize,
> > precompute=precompute, max_iter=max_iter, tol=tol,
> > copy_X=copy_X,
> > cv=cv, verbose=verbose, positive=positive)
> >
> > Then in ipython
> >
> > In [3]: coordinate_descent.LassoCV(positive=True, cv=20)
> > Out[3]:
> > LassoCV(alphas=None, copy_X=True, cv=20, eps=0.001, fit_intercept=True,
> > max_iter=1000, n_alphas=100, normalize=False, positive=True,
> > precompute='auto', tol=0.0001, verbose=False)
> >
> > Just have a look if you don't have any typos or you're missing something
> > small.
> > Goodluck!
> >
> >
> >
> >
> >
> >
> >
> >>
> >> I appreciate your patience with me. I have been programming in Python
> for
> >> only a few months and am no expert in machine learning. I imagine that
> I'm
> >> overlooking or misunderstanding some things that are obvious to those
> with
> >> more experience.
> >>
> >> I notice that Lasso inherits from ElasticNet, and that ElasticNet
> includes
> >> the "positive" option, although some of the documentation for ElasticNet
> >> doesn't seem to reflect this. I imagine that this means it would be at
> >> least as straightforward for me to add the "positive" option to
> >> ElasticNetCV as to LassoCV. ElasticNetCV may be even better for my
> problem
> >> than LassoCV, since I expect many of my regressors to be correlated.
> >>
> >> I'm using these regularized regression methods as part of an iterative
> >> solver for non-negative canonical correlation. CCA can be done by
> finding
> >> w that minimizes ||Yv-Xw||^2, then scaling w by ||Xw||, then doing the
> >> same for v, and so on back and forth until convergence. Lasso and
> >> ElasticNet can be used for the minimization step. I'm realizing,
> however,
> >> that the objective function I need to minimize will require an
> additional
> >> quadratic term to enforce the orthogonality of each projection direction
> >> to all previous directions. These methods from scikit-learn could give
> me
> >> the first pair of canonical variables, but if I want to get subsequent
> >> ones (and I do) I may have to use a more general-purpose optimization
> >> library like scipy.optimize and define my own objective function.
> >>
> >>
> >>
> >>
> >>
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> >
> >
> >
> >
> ------------------------------------------------------------------------------
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>
>
> ------------------------------------------------------------------------------
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> "Graph Databases" is the definitive new guide to graph databases and
> their applications. This 200-page book is written by three acclaimed
> leaders in the field. The early access version is available now.
> Download your free book today! http://p.sf.net/sfu/neotech_d2d_may
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