using LowRankModels, DataFrames, Random
import RDatasets
# pick a data set
df = RDatasets.dataset("psych", "msq")
# we'll just fit four of the columns, to try out all four datatypes
dd = DataFrame([df[:,s] for s in [:TOD, :Vigorous, :Wakeful, :Alert, :Aroused, :Active]])
dd[!,end] = (dd[:,end].==1)
# help out by telling GLRM the correct data types
datatypes = [:real, :ord, :ord, :ord, :ord, :bool]
The objective value listed is different, even though I used the same random seed. What explains this?
In this particular example it is fairly stable regardless, but I tried fitting a model on some of my actual data, and I can't get reproducible results.
I'm using Julia 1.1.1. Perhaps this is a multi-threading issue? I have nthreads = 1, yet when I used ctrl-C on the fit I saw an error Error thrown in threaded loop on thread 0:.
First run:
Second run:
The objective value listed is different, even though I used the same random seed. What explains this?
In this particular example it is fairly stable regardless, but I tried fitting a model on some of my actual data, and I can't get reproducible results.
I'm using Julia 1.1.1. Perhaps this is a multi-threading issue? I have nthreads = 1, yet when I used ctrl-C on the fit I saw an error
Error thrown in threaded loop on thread 0:
.