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 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. ------------------------------------------------------------------------------ Learn Graph Databases - Download FREE O'Reilly Book "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 _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
