skKMeans: interface to sklearn.cluster.KMeans using basilisk discipline

In BiocSklearn: interface to python sklearn via Rstudio reticulate

Description

interface to sklearn.cluster.KMeans using basilisk discipline

Usage

skKMeans(mat, ...)

Arguments

mat	a matrix-like datum or reference to such
...	arguments to sklearn.cluster.KMeans

Value

a list with cluster assignments (integers starting with zero) and asserted cluster centers.

Note

You can use py_help(SklearnEls()$skcl$KMeans) to get python documentation on parameters and return structure. This is a demonstrative interface to the resources of sklearn.cluster. In this particular interface, we are using sklearn.cluster.k_means_.KMeans. There are many other possibilities in sklearn.cluster: _dbscan_inner, feature_agglomeration, hierarchical, k_means, k_means_elkan, affinity_propagation, bicluster, birch, dbscan, hierarchical, k_means, mean_shift, setup, spectral.

Examples

## Not run: 
# This blocked example shows a risky approach evading basilisk discipline and is
# to be used at your own risk.
# start with numpy array reference as data
irloc = system.file("csv/iris.csv", package="BiocSklearn")
skels = SklearnEls()
irismat = skels$np$genfromtxt(irloc, delimiter=',')
ans = skKMeans(irismat, n_clusters=2L)
names(ans) # names of available result components
table(iris$Species, ans$labels_)
# now use an HDF5 reference
irh5 = system.file("hdf5/irmat.h5", package="BiocSklearn")
fref = skels$h5py$File(irh5)
ds = fref$`__getitem__`("quants") # thanks Samuela Pollack!
ans2 = skKMeans(skels$np$array(ds)$T, n_clusters=2L) # HDF5 matrix is transposed relative to python array layout!  Is the np$array conversion unduly costly?
table(ans$labels_, ans2$labels_)
ans3 = skKMeans(skels$np$array(ds)$T, 
   n_clusters=8L, max_iter=200L, 
   algorithm="full", random_state=20L)

## End(Not run)
dem = skKMeans(iris[,1:4], n_clusters=3L, max_iter=100L, algorithm="full",
   random_state=20L)
str(dem)
tab = table(iris$Species, dem$labels)
tab
plot(iris[,1], iris[,3], col=as.numeric(factor(iris$Species)))
points(dem$centers[,1], dem$centers[,3], pch=19, col=apply(tab,2,which.max))

BiocSklearn documentation built on Nov. 8, 2020, 7:52 p.m.