Scientific computing in python is well-established. This package takes advantage of new work at Rstudio that fosters python-R interoperability. Identifying good practices of interface design will require extensive discussion and experimentation, and this package takes an initial step in this direction.
A key motivation is experimenting with an incremental PCA implementation with very large out-of-memory data. We have also provided an interface to the sklearn.cluster.KMeans procedure.
The package includes a list of references to python modules.
library(BiocSklearn)
skels = SklearnEls()
skels## $np
## Module(numpy)
##
## $pd
## Module(pandas)
##
## $h5py
## Module(h5py)
##
## $skd
## Module(sklearn.decomposition)
##
## $skcl
## Module(sklearn.cluster)
##
## $joblib
## Module(sklearn.externals.joblib)We can acquire python documentation of included modules with
reticulate’s py_help:
# py_help(skels$skd)
Help on package sklearn.decomposition in sklearn:
NAME
sklearn.decomposition
FILE
/Users/stvjc/anaconda2/lib/python2.7/site-packages/sklearn/decomposition/__init__.py
DESCRIPTION
The :mod:`sklearn.decomposition` module includes matrix decomposition
algorithms, including among others PCA, NMF or ICA. Most of the algorithms of
this module can be regarded as dimensionality reduction techniques.
PACKAGE CONTENTS
_online_lda
base
cdnmf_fast
dict_learning
factor_analysis
fastica_
incremental_pca
...The reticulate package is designed to limit the amount of effort required to convert data from R to python for natural use in each language.
irloc = system.file("csv/iris.csv", package="BiocSklearn")
irismat = skels$np$genfromtxt(irloc, delimiter=',')To examine a submatrix, we use the take method from numpy. The bracket format seen below notifies us that we are not looking at data native to R.
skels$np$take(irismat, 0:2, 0L )## [[5.1 3.5 1.4 0.2]
## [4.9 3. 1.4 0.2]
## [4.7 3.2 1.3 0.2]]We’ll use R’s prcomp as a first test to demonstrate performance of the sklearn modules with the iris data.
fullpc = prcomp(data.matrix(iris[,1:4]))$xWe have a python representation of the iris data. We compute the PCA as follows:
ppca = skPCA(irismat)
ppca## SkDecomp instance, method: PCA
## retrieve transformed data with getTransformed(),
## python reference with pyobj()This returns an object that can be reused through python methods.
The numerical transformation is accessed via getTransformed.
tx = getTransformed(ppca)
dim(tx)## [1] 150 4head(tx)## [,1] [,2] [,3] [,4]
## [1,] -2.684126 0.3193972 -0.02791483 -0.002262437
## [2,] -2.714142 -0.1770012 -0.21046427 -0.099026550
## [3,] -2.888991 -0.1449494 0.01790026 -0.019968390
## [4,] -2.745343 -0.3182990 0.03155937 0.075575817
## [5,] -2.728717 0.3267545 0.09007924 0.061258593
## [6,] -2.280860 0.7413304 0.16867766 0.024200858The native methods can be applied to the pyobj output.
pyobj(ppca)$fit_transform(irismat)[1:3,]## [,1] [,2] [,3] [,4]
## [1,] -2.684126 0.3193972 -0.02791483 -0.002262437
## [2,] -2.714142 -0.1770012 -0.21046427 -0.099026550
## [3,] -2.888991 -0.1449494 0.01790026 -0.019968390Concordance with the R computation can be checked:
round(cor(tx, fullpc),3)## PC1 PC2 PC3 PC4
## [1,] 1 0 0 0
## [2,] 0 -1 0 0
## [3,] 0 0 -1 0
## [4,] 0 0 0 -1A computation supporting a priori bounding of memory consumption is available. In this procedure one can also select the number of principal components to compute.
ippca = skIncrPCA(irismat) #
ippcab = skIncrPCA(irismat, batch_size=25L)
round(cor(getTransformed(ippcab), fullpc),3)## PC1 PC2 PC3 PC4
## [1,] 1 0 0 0
## [2,] 0 -1 0 0
## [3,] 0 0 -1 0
## [4,] 0 0 0 1This procedure can be used when data are provided in chunks, perhaps from a stream. We iteratively update the object, for which there is no container at present. Again the number of components computed can be specified.
ta = skels$np$take # provide slicer utility
ipc = skPartialPCA_step(ta(irismat,0:49,0L))
ipc = skPartialPCA_step(ta(irismat,50:99,0L), obj=ipc)
ipc = skPartialPCA_step(ta(irismat,100:149,0L), obj=ipc)
ipc$transform(ta(irismat,0:5,0L))## [,1] [,2] [,3] [,4]
## [1,] -2.684126 0.3193972 -0.02791483 0.002262437
## [2,] -2.714142 -0.1770012 -0.21046427 0.099026550
## [3,] -2.888991 -0.1449494 0.01790026 0.019968390
## [4,] -2.745343 -0.3182990 0.03155937 -0.075575817
## [5,] -2.728717 0.3267545 0.09007924 -0.061258593
## [6,] -2.280860 0.7413304 0.16867766 -0.024200858fullpc[1:5,]## PC1 PC2 PC3 PC4
## [1,] -2.684126 -0.3193972 0.02791483 0.002262437
## [2,] -2.714142 0.1770012 0.21046427 0.099026550
## [3,] -2.888991 0.1449494 -0.01790026 0.019968390
## [4,] -2.745343 0.3182990 -0.03155937 -0.075575817
## [5,] -2.728717 -0.3267545 -0.09007924 -0.061258593We have extracted methylation data for the Yoruban
subcohort of CEPH from the yriMulti package. Data
from chr6 and chr17 are available in an HDF5 matrix
in this BiocSklearn package. A reference to the
dataset through the h5py File interface is created by
H5matref.
fn = system.file("ban_6_17/assays.h5", package="BiocSklearn")
ban = H5matref(fn)
ban ## <HDF5 dataset "assay001": shape (64, 44560), type "<f8">We will explicitly define the numpy matrix.
np = import("numpy", convert=FALSE) # ensure
ban$shape## [[1]]
## [1] 64
##
## [[2]]
## [1] 44560We’ll treat genes as records and individuals as features.
ban2 = np$matrix(ban)$TWe’ll define three chunks of the data and update
the partial PCA contributions in the object st.
st = skPartialPCA_step(ta(ban2, 0:999, 0L))
st = skPartialPCA_step(ta(ban2, 1000:10999, 0L), obj=st)
st = skPartialPCA_step(ta(ban2, 11000:44559, 0L), obj=st)
sss = st$transform(ban2)Verify against the standard PCA, checking correlation between the projections to the first four PCs.
iii = skPCA(ban2)
dim(getTransformed(iii))## [1] 44560 64round(cor(sss[,1:4], getTransformed(iii)[,1:4]),3)## [,1] [,2] [,3] [,4]
## [1,] -1 0 0 0
## [2,] 0 1 0 0
## [3,] 0 0 1 0
## [4,] 0 0 0 1We need more applications and profiling.