asafmanela / hurdledmr.jl Goto Github PK
View Code? Open in Web Editor NEWHurdle Distributed Multinomial Regression (HDMR) implemented in Julia
License: Other
Hurdle Distributed Multinomial Regression (HDMR) implemented in Julia
License: Other
Need to check performance
In julia version 1.3, windows platform, there is an Error while running test case:
PositivePoisson: Error During Test at C:\Users\jason\.julia\dev\HurdleDMR\test\positive_poisson.jl:1
Got exception outside of a @test
OverflowError: 22 is too large to look up in the table; consider using `factorial(big(22))` instead
Hi Professor Manela:
I'm running your tutorial of HurdleDMR
for python in Jupyter Notebook, but in the data loading cell, an error pops out
---------------------------------------------------------------------------
JuliaError Traceback (most recent call last)
C:\Users\WILLIA~1\AppData\Local\Temp\15/ipykernel_4324/482917954.py in <module>
----> 1 covarsdf, counts, terms = jl.eval('include("D:/William/sotu.jl")')
C:\ProgramData\Anaconda3\lib\site-packages\julia\core.py in eval(self, src)
603 if src is None:
604 return None
--> 605 ans = self._call(src)
606 if not ans:
607 return None
C:\ProgramData\Anaconda3\lib\site-packages\julia\core.py in _call(self, src)
536 # logger.debug("_call(%s)", src)
537 ans = self.api.jl_eval_string(src.encode('utf-8'))
--> 538 self.check_exception(src)
539
540 return ans
C:\ProgramData\Anaconda3\lib\site-packages\julia\core.py in check_exception(self, src)
585 else:
586 exception = sprint(showerror, self._as_pyobj(res))
--> 587 raise JuliaError(u'Exception \'{}\' occurred while calling julia code:\n{}'
588 .format(exception, src))
589
JuliaError: Exception 'LoadError: could not load library "libdSFMT"
The specified module could not be found.
in expression starting at D:\William\sotu.jl:210' occurred while calling julia code:
include("D:/William/sotu.jl")
I think it maybe something wrong with the code in sotu.jl
?
The tutorial notebooks here need updating for Julia 1.0 syntax
dmr
and hdmr
calls by default standardize the covars
matrix, because they call fit(GammaLassoPath,...)
on each column of counts, which standardizes its X
(=covars
) matrix upon entry by default.
This means we are needlessly repeating this Lasso.standardizeX call multiple times. See relevant part of Lasso.jl
A better solution would:
standardize
in dmr/hdmr callsfit(GammaLassoPath,...)
with standardize=false
, keeping track of XnormCurrently parallelization on a local cluster uses SharedArrays to share memory between distributed cores. This is not super efficient and also sometimes fails on windows.
It would be better to replace it with multithreading, which should be getting more stable in Julia v1.2.
This issue is used to trigger TagBot; feel free to unsubscribe.
If you haven't already, you should update your TagBot.yml
to include issue comment triggers.
Please see this post on Discourse for instructions and more details.
If you'd like for me to do this for you, comment TagBot fix
on this issue.
I'll open a PR within a few hours, please be patient!
Hi Professor Manela,
I am wondering whether the existing implementation enables an estimation without lasso. It seems that I could not find the switch in the API. Besides, I also could not find any info on the fitted likelihood. May I know how to obtain it? Thanks.
Currently, all test code is sequential, and therefore many test code for a specific function depends on the result produced by previous code.
I want to organize each test case and break their dependencies using Jive.jl.
Fitting a DMR or HDMR on windows sometimes fails when trying to map memory (mmap
), with parallel=true
and local_cluster=true
specified (the default).
A workaround is to specify local_cluster=false
or to turn off parallelization altogether with parallel=false
.
This problem should go away after #6 is resolved.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.