madeleineudell / parallelsparseregression.jl Goto Github PK

Start with a very high regularizer, so the problem is easy to solve, and decrease it. Each iteration should be very fast. Design make_prox_* so that changing lambda incurs little overhead. Allow user to choose lambda_min and lambda_max, but also include good defaults to encourage interesting (eg non-zero) solutions.

This should be easy to do using cvxpy. In test/test.jl, just add

using PyCall
@pyimport cvxpy

to compute the solution using a verified method and compare the results.

When I run test/test.jl, I get the error:

ERROR: pids not defined
in make_prox_lsq at /Users/stevend2/.julia/ParallelSparseRegression/src/prox.jl:51
in nnlsq at /Users/stevend2/.julia/ParallelSparseRegression/src/regression.jl:6
in include_from_node1 at loading.jl:120
while loading /Users/stevend2/PythonProjects/ParallelSparseRegression.jl/test/test.jl, in expression starting on line 15

This also showed up when I was using ParallelSparseMatMul. How do I fix this?

Cannot run the tests after latest updates. (My system does not show a stack traceback, looking to replicate in one that does)

➜  test git:(master) julia test.jl
ERROR: access to undefined reference
while loading /Users/x/.julia/ParallelSparseRegression/test/test.jl, in expression starting on line 15

@madeleineudell Thanks for this package, here is the output of my initial testing:

Sample program:

using ParallelSparseRegression

m,n,p = 2048,1024,.1
A = sprand(m,n,p)
x0 = Base.shmem_randn(n)
b = A*x0
rho = 1
lambda = 1
quiet = false
maxiters = 100

params = Params(rho,quiet,maxiters)

# Lasso
@time z_lasso = lasso(A,b,lambda; params=params)

Calling the following program with different addprocs values gives the following:

Output without addprocs:

1000 : 1.76e+00 1.27e-01 5.54e-03 4.09e+01
elapsed time: 24.422318823 seconds (6440755392 bytes allocated)

Output with addprocs(3):

1000 : 2.15e+00 1.12e-01 6.07e-03 4.65e+01
elapsed time: 90.979009048 seconds (12805856436 bytes allocated)

Output with addprocs(7):

1000 : 1.75e+00 1.47e-01 5.74e-03 4.21e+01
elapsed time: 228.324713722 seconds (28927210844 bytes allocated)

Full output with values for every iteration:

https://gist.github.com/ingenieroariel/9095001

m,n,p = 100,20,.1
A = sprand(m,n,p)
x0 = Base.shmem_randn(n)
b = A*x0
rho = 1
quiet = false
maxiters = 100

params = Params(rho,quiet,maxiters)
z = nnlsq(A,b; params=params)

Running the above file returns now:

ERROR: params not defined

Here is an example that could be added to the test suite, for my problem I need to send matrices instead of vectors to the lasso solver:

using ParallelSparseRegression

dimensions, components, samples = 180, 100, 1500

dictionary = sprandn(dimensions, components, 0.1)
original_code = sprandn(components, samples, 0.1)
data = dictionary * original_code

# minimize \|data - dictionary*code\|_2^2 + \lambda\|code\|_1
# for variable code.
code = lasso(data, dictionary, 1)

# FIXME(Ariel): Is this the best way to check it found the real solution?
@assert(code = original_code)

IterativeSolvers uses "maxiter", ParallelSparseRegression uses "maxiters". Should we harmonize the two to avoid confusion for end users?