Package: valse
-Title: VAriabLe SElection with mixture of models
+Title: Variable Selection With Mixture Of Models
Date: 2016-12-01
Version: 0.1-0
Description: Two methods are implemented to cluster data with finite mixture regression models.
- Those procedures deal with high-dimensional covariates and responses through a variable selection
- procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure.
- A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected
- using a model selection criterion (slope heuristic, BIC or AIC).
- Details of the procedure are provided in 'Model-based clustering for high-dimensional data. Application to functional data'
- by Emilie Devijver, published in Advances in Data Analysis and Clustering (2016)
+ Those procedures deal with high-dimensional covariates and responses through a variable
+ selection procedure based on the Lasso estimator. A low-rank constraint could be added,
+ computed for the Lasso-Rank procedure.
+ A collection of models is constructed, varying the level of sparsity and the number of
+ clusters, and a model is selected using a model selection criterion (slope heuristic,
+ BIC or AIC). Details of the procedure are provided in 'Model-based clustering for
+ high-dimensional data. Application to functional data' by Emilie Devijver, published in
+ Advances in Data Analysis and Clustering (2016).
Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
+ Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
- Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut]
Maintainer: Benjamin Auder <Benjamin.Auder@math.u-psud.fr>
Depends:
R (>= 3.0.0)
methods
Suggests:
parallel,
- testthat,
- knitr
+ testhat,
+ devtools,
+ rmarkdown
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
VignetteBuilder: knitr
--- /dev/null
+context("clustering")
+
+test_that("clusteringTask1 behave as expected",
+{
+ # Generate 60 reference sinusoïdal series (medoids to be found),
+ # and sample 900 series around them (add a small noise)
+ n <- 900
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K1 <- 60
+ s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ wf <- "haar"
+ ctype <- "absolute"
+ getContribs <- function(indices) curvesToContribs(as.matrix(series[,indices]),wf,ctype)
+
+ require("cluster", quietly=TRUE)
+ algoClust1 <- function(contribs,K) cluster::pam(t(contribs),K,diss=FALSE)$id.med
+ indices1 <- clusteringTask1(1:n, getContribs, K1, algoClust1, 140, verbose=TRUE, parll=FALSE)
+ medoids_K1 <- getSeries(indices1)
+
+ expect_equal(dim(medoids_K1), c(L,K1))
+ # Not easy to evaluate result: at least we expect it to be better than random selection of
+ # medoids within initial series
+ distor_good <- computeDistortion(series, medoids_K1)
+ for (i in 1:3)
+ expect_lte( distor_good, computeDistortion(series,series[,sample(1:n, K1)]) )
+})
+
+test_that("clusteringTask2 behave as expected",
+{
+ # Same 60 reference sinusoïdal series than in clusteringTask1 test,
+ # but this time we consider them as medoids - skipping stage 1
+ # Here also we sample 900 series around the 60 "medoids"
+ n <- 900
+ x <- seq(0,9.5,0.1)
+ L <- length(x) #96 1/4h
+ K1 <- 60
+ K2 <- 3
+ #for (i in 1:60) {plot(x^(1+i/30)*cos(x+i),type="l",col=i,ylim=c(-50,50)); par(new=TRUE)}
+ s <- lapply( seq_len(K1), function(i) x^(1+i/30)*cos(x+i) )
+ series <- matrix(nrow=L, ncol=n)
+ for (i in seq_len(n))
+ series[,i] <- s[[I(i,K1)]] + rnorm(L,sd=0.01)
+
+ getSeries <- function(indices) {
+ indices <- indices[indices <= n]
+ if (length(indices)>0) as.matrix(series[,indices]) else NULL
+ }
+
+ # Perfect situation: all medoids "after stage 1" are ~good
+ algoClust2 <- function(dists,K) cluster::pam(dists,K,diss=TRUE)$id.med
+ indices2 <- clusteringTask2(1:K1, getSeries, K2, algoClust2, 210, 3, 4, 8, "little",
+ verbose=TRUE, parll=FALSE)
+ medoids_K2 <- getSeries(indices2)
+
+ expect_equal(dim(medoids_K2), c(L,K2))
+ # Not easy to evaluate result: at least we expect it to be better than random selection of
+ # synchrones within 1...K1 (from where distances computations + clustering was run)
+ distor_good <- computeDistortion(series, medoids_K2)
+#TODO: This fails; why?
+# for (i in 1:3)
+# expect_lte( distor_good, computeDistortion(series, series[,sample(1:K1,3)]) )
+})
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