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View Code? Open in Web Editor NEWPenalized Sparse Learning Solver - Unleash the Power of Nonconvex Penalty
Home Page: https://jasonge27.github.io/picasso/
License: GNU General Public License v3.0
Penalized Sparse Learning Solver - Unleash the Power of Nonconvex Penalty
Home Page: https://jasonge27.github.io/picasso/
License: GNU General Public License v3.0
I'm wondering if there's any possiblity that picasso supports long vectors so that it works with big matrices of many columns and rows (.e.g 22000000 rows, 120 columns, more elements than max integer).
It is unfortunate that glmnet
does not support long vectors since it's not trivial to adapt its Fortran implementation to work with long vectors that uses double
length.
In
picasso/include/picasso/objective.hpp
Lines 92 to 103 in 10ba007
Shouldn't it be like this:
`double soft_thresh_mcp(double y, double lamb, double gamma){
if(fabs(y)>fabs(gamma*lamb)) {
return y;
}else{
return soft_thresh_l1(y, lamb)/(1-1/gamma);
}
}`
Thanks for such a fast package. I am using the python pycasso on a subset of big data for design matrix X. It works for X with dimensions 210,000 x 123,916 , but it fails with Segmentation fault for X with dimensions 235,495 x 123,916 (X is always a subset of the same data). Debugging with gdb shows
In the python the error is thrown in pycasso/core.py, line 209 in wrapper.
What do you suggest?
Great library. There is feature though that is stopping me from using it and that is support for something like the penalty.factor
in glmnet.
I've accidentally noticed that the default stopping precision differs across the files. The documentation states that the default value is 1e-7. At the same time the python package and some of the R routines set it to 1e-4. What should the default value be?
Hello, it is a nice work!
I found there is a spelling mistake in core.py line 130 which "lamdas.size" should be "lambdas.size". Would you please check this? Thanks!
Greetings.
According to python-package/pycasso/lib/core.py, the mapping of regularizer is as follows:
However, in src/c_api/c_api.cpp, the mapping of regularizer is as follows:
This will cause incompatible situations. An exception should be raised for number not in 1~3, and mapping logic should be the same.
On the other hand, when it comes to ActGDSolver::solve(), regfunc is determined by enum defined in include/picasso/solver_params.hpp. However, according to the cpp standard, the default value of enum is set to 0, which means L1 = 0, SCAD =1 and MCP = 2, and this will cause wrong regfunc to be applied.
Please correct me if I was wrong, thank you.
Wenny
Would it be possible for picasso in the future to also support box constraints on the fitted coefficients by any chance, similar to what glmnet can do using arguments lower.limits and upper.limits? E.g. to support nonnegativity constraints?
HI,
First off, thank you for putting this together. It has helped me understand SCAD and play around with the model.
I noted that after fitting the model using the Python module, running predict multiple times will change the model.
For example
>>> scadpath.result['beta'][4]
[ 6.56925953e-02 1.01571611e+03 -3.36603532e-11 3.20955197e-03
4.31731825e+03 -7.09569678e-03 -9.05913159e-01 2.02699282e-17]
>>> predictionPath = scadpath.predict(X_train, lambdidx=4)
>>> scadpath.result['beta'][4]
[ 5.28674587e-02 2.13116762e+03 -4.48672601e-12 2.19283383e-03
1.02795980e+04 -5.06569449e-03 -1.27797957e+00 6.91764767e-19]
Please let me know if I am misinterpreting the results in any way.
Thank you,
predictionScad = scad.predict(X_train)
I was wondering what link scale is assumed for Poisson models? Is this a log link? If so, I was wondering if it would be hard to also support Poisson models with an identity link? I am asking because I am dealing with models where my noise is Poisson but where my covariates act additively rather than multiplicatively...
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