transbiozi / rmtl Goto Github PK

View Code? Open in Web Editor NEW

19.0 3.0 12.0 339 KB

Regularized Multi-task Learning in R

Home Page: https://CRAN.R-project.org/package=RMTL

R 100.00%

multi-task-learning sparse-coding low-rank-representaion regularization

rmtl's People

Contributors

Stargazers

Watchers

Forkers

hank9cao riverdarda haithamsghaier fyc0803 mosaddek-hossain yongchangliugit vishalbelsare ghaithcode jinjun-ama bluevale rnaimehaom imalenica

rmtl's Issues

the error in seq.default(cv_idx, task_sample_size, by = cv_fold)

I used this package for the multi-task learning of regression problem. The dataset is (160row, 31 column), and the X is (160row, 28column), Y is (160row, 3 column).

The model and the parameter are that:

cvfitr1 <- cvMTL(X, Y, type="Regression", Regularization="L21",

            Lam1_seq=10^seq(1,-4, -1),  Lam2=0, opts=list(init=0,  tol=10^-6, maxIter=1500),

            nfolds= 10, stratify=F, parallel=F)

The error is that:
Error in seq.default(cv_idx, task_sample_size, by = cv_fold) :
wrong sign in 'by' argument.

Another model and parameter:

model <- MTL(X, Y, type = "Regression",Regularization="L21",

         Lam1=0.1, Lam2=0.1, opts=list(init=0, tol=10^-6, maxIter=500))

Error is that:
Error in Y[[x]] : 下标出界(the subscript out of size)

My own datasets

Hello,
I like to use this package for the multi-task learning of classification problem. However, I don't know how to import my datasets ( three tasks) in this package.
Please help me in this regard

thank you for your works, but maybe there have bug in parallel functions,:
1 need add stopImplicitCluster() to stop cluster when compute finished, otherwise r background process will not close.
2 some times parallel cvMTL and single thread cvMTL got different result Lam1.min

set.seed(202000)
library(RMTL)
data <- Create_simulated_data(t=1,p=50,n=200000,
                              Regularization="Lasso",
                              type="Classification")

cvfit<-cvMTL(data$X, data$Y, type="Classification", 
                         Regularization="Lasso",nfolds = 10)

cvfit1<-cvMTL(data$X, data$Y, type="Classification", 
                         Regularization="Lasso",parallel = T,
                         ncores = 2,nfolds = 10)
cvfit$Lam1.min==cvfit1$Lam1.min

#run it 
> set.seed(202000)
> library(RMTL)
> data <- Create_simulated_data(t=1,p=50,n=200000,
+                               Regularization="Lasso",
+                               type="Classification")
> 
> cvfit<-cvMTL(data$X, data$Y, type="Classification", 
+                          Regularization="Lasso",nfolds = 10)
> 
> cvfit1<-cvMTL(data$X, data$Y, type="Classification", 
+                          Regularization="Lasso",parallel = T,
+                          ncores = 2,nfolds = 10)
> cvfit$Lam1.min==cvfit1$Lam1.min
[1] FALSE
> cvfit$Lam1.min
[1] 1e-04
> cvfit1$Lam1.min
[1] 0.001
> cvfit
$Lam1_seq
[1] 1e+01 1e+00 1e-01 1e-02 1e-03 1e-04

$Lam1.min
[1] 1e-04

$Lam2
[1] 0

$cvm
[1] 0.499590 0.499590 0.221030 0.041510 0.035485 0.035435

attr(,"class")
[1] "cvMTL"
> cvfit1
$Lam1_seq
[1] 1e+01 1e+00 1e-01 1e-02 1e-03 1e-04

$Lam1.min
[1] 0.001

$Lam2
[1] 0

$cvm
[1] 0.500890 0.500890 0.220945 0.041620 0.035445 0.035445

attr(,"class")
[1] "cvMTL"
> sessionInfo()
R version 3.5.3 (2019-03-11)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17763)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 
[2] LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] RMTL_0.9

loaded via a namespace (and not attached):
[1] compiler_3.5.3    parallel_3.5.3    tools_3.5.3       codetools_0.2-16 
[5] doParallel_1.0.14 iterators_1.0.10  foreach_1.4.4    
>

Recommend Projects

React

A declarative, efficient, and flexible JavaScript library for building user interfaces.
Vue.js

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
Typescript

TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
TensorFlow

An Open Source Machine Learning Framework for Everyone
Django

The Web framework for perfectionists with deadlines.
Laravel

A PHP framework for web artisans
D3

Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

javascript

JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
web

Some thing interesting about web. New door for the world.
server

A server is a program made to process requests and deliver data to clients.
Machine learning

Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Visualization

Some thing interesting about visualization, use data art
Game

Some thing interesting about game, make everyone happy.

Recommend Org

Facebook

We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft

Open source projects and samples from Microsoft.
Google

Google ❤️ Open Source for everyone.
Alibaba

Alibaba Open Source for everyone
D3

Data-Driven Documents codes.
Tencent

China tencent open source team.