> 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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