The following code is supposed to create a heatmap in rpy2
import numpy as np
from rpy2.robjects import r
data = np.random.random((10,10))
r.heatmap(data)
However, it results in the following error
Traceback (most recent call last):
File "z.py", line 8, in <module>
labRow=rowNames, labCol=colNames)
File "C:\Python25\lib\site-packages\rpy2\robjects\__init__.py", line 418, in __call__
new_args = [conversion.py2ri(a) for a in args]
File "C:\Python25\lib\site-packages\rpy2\robjects\__init__.py", line 93, in default_py2ri
raise(ValueError("Nothing can be done for the type %s at the moment." %(type(o))))
ValueError: Nothing can be done for the type <type 'numpy.ndarray'> at the moment.
From the documentation I learn that r.heatmap expects "a numeric matrix". How do I convert np.array to the required data type?
You need to add
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
See more in rpy2 documentation numpy section (here for the older 2.x version)
Prior to 2.2.x the import alone was sufficient.
That import alone is sufficient to
switch an automatic conversion of
numpy objects into rpy2 objects.
Why make this an optional import,
while it could have been included in
the function py2ri() (as done in the
original patch submitted for that
function) ?
Although both are valid and reasonable
options, the design decision was taken
in order to decouple rpy2 from numpy
the most, and do not assume that
having numpy installed automatically
meant that a programmer wanted to use
it.
For rpy2 2.2.4 I had to add:
import rpy2.robjects.numpy2ri
rpy2.robjects.numpy2ri.activate()
For me (2.2.1) the following also worked (as documented on http://rpy.sourceforge.net/rpy2/doc-2.2/html/numpy.html):
import rpy2.robjects as ro
from rpy2.robjects.numpy2ri import numpy2ri
ro.conversion.py2ri = numpy2ri
Related
I am using an R package within my Python code to import, process and save a GeoTIFF raster. The importing and processing works just fine, but I struggle to save the raster file again. This is a simplified version of the code I'm running:
import rpy2.rinterface as ri
import rpy2.robjects as rob
import rpy2.robjects.packages as rpackages
raster = rpackages.importr('raster')
r_raster = raster.raster(geotiff_input_path)
# r_raster = process_raster(r_raster)
raster.writeRaster(r_raster, geotiff_output_path, overwrite=True)
However, the code fails with AttributeError: module 'raster' has no attribute 'writeRaster'.
I seem to misunderstand how to call writeRaster properly.
My attempt to use rpy2 in a Jupyter (iPython) notebook fails at the point where I wish to use tidyr::nest() to make a dataframe that has some elements which are a series. (This cannot be avoided, it is necessary for the next step of the analysis.)
After trying many things (including advice from the package home I've managed to get most of the way to the end, failing to understand rpy2 with tidyr::nest at the last step. (Some of the following example may be superfluous.)
How can I fix this?
% Import packages
from rpy2 import robjects
from rpy2.robjects import Formula, Environment
from rpy2.robjects.vectors import IntVector, FloatVector
from rpy2.robjects.lib import grid
from rpy2.robjects.packages import importr, data
from rpy2.rinterface_lib.embedded import RRuntimeError
import warnings
base = importr('base')
datasets = importr('datasets')
from functools import partial
import rpy2.robjects.lib.tidyr as tidyr
from collections import OrderedDict
from rpy2.robjects.vectors import (StrVector,
IntVector)
from rpy2.robjects.lib.tidyr import DataFrame
from rpy2.robjects import rl
tidyr = importr('tidyr')
dplyr = importr('dplyr')
% Obtain the R dataset "iris"
iris = data(datasets).fetch('iris')['iris']
% Use only two columns necessary for the "tidyr::nest" trial
dataf = (
DataFrame(iris)
.select(base.c(1,5))
)
print(dataf.head())
% Failure occurs below
irisNested = tidyr.nest(dataf, data=rl('Sepal.Length'))
EDIT: This isn't a question anymore, as it turns out my original effort was correct. A fragment of another attempt to solve the problem caused it to fail. However, I asked the question as I didn't find the various sources of help available provided a clear way to address my issue, I felt I made a lucky educated guess.
import rpy2.robjects as ro
from rpy2.robjects import pandas2ri
pandas2ri.activate()
Removing this solves the problem.
I have a python snippet which works just fine to run GLMNET on np.array X and y. However, when X is a column sparse matrix from scipy, the code fails as rpy2 is not able to convert X. Am I making an obvious mistake?
A MCVE is:
import numpy as np
from scipy import sparse
from rpy2 import robjects
import rpy2.robjects.packages as rpackages
from rpy2.robjects import numpy2ri
from rpy2.robjects import pandas2ri
if __name__ == "__main__":
X = sparse.rand(5, 20, density=0.1)
y = np.random.randn(5)
numpy2ri.activate()
pandas2ri.activate()
utils = rpackages.importr('utils')
utils.chooseCRANmirror(ind=1)
if not rpackages.isinstalled('glmnet'):
utils.install_packages("glmnet")
glmnet = rpackages.importr('glmnet')
glmnet = robjects.r['glmnet']
glmnet_fit = glmnet(X, y, intercept=False, standardize=False)
And when I run it I get a NotImplementedError:
Conversion 'py2ri' not defined for objects of type '<class 'scipy.sparse.csc.csc_matrix'>'
Could I provide X in a different way? I'd be surprised if rpy2 could not handle sparse matrices.
You can create a sparse matrix with rpy2 as follows:
import numpy as np
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from scipy import sparse
X = sparse.rand(5, 20, density=0.1).tocoo()
r_Matrix = importr("Matrix")
r_Matrix.sparseMatrix(
i=ro.IntVector(X.row + 1),
j=ro.IntVector(X.col + 1),
x=ro.FloatVector(X.data),
dims=ro.IntVector(X.shape))
There is indeed no converter Python -> R for your object type included in rpy2. Your Python object is not a conventional arrays but a sparse matrix as you note it (scipy.sparse.csc.csc_matrix to be specific), implemented as one of the numerical extensions available for numpy. As numpy itself is not even required to use rpy2 the support for extension of numpy is rather sparse, at the notable exception of pandas since data tables are ubiquitous.
You may want to write your own converter from css_matrix to gcCMatrix in the R package Matrix (https://stat.ethz.ch/R-manual/R-devel/library/Matrix/html/dgCMatrix-class.html) as the package glmnet appears to be able to handle them.
Writing a custom converter will require how to map or copy the content of the Python object to its chosen R counterpart, but once done plugging the code into rpy2 should be quite easy:
https://rpy2.github.io/doc/v2.9.x/html/generated_rst/s4class.html#custom-conversion
Consider opening an issue as a "feature request" on the rpy2 issue tracker, and reporting progress and outcome, with the hope to see this turn into a pull request complete with unit tests
Also a quick solution that might work would be to save the sparse matrix file temporarily.
import numpy as np
import rpy2.robjects as ro
import warnings
from rpy2.rinterface import RRuntimeWarning
import rpy2.robjects.numpy2ri as numpy2ri
from scipy.io import mmwrite
mmwrite('temp.mtx',matrix)
ro.r('X <- readMM("temp.mtx")')
I would be very interested though, if someone comes with a custom converter for avoiding that copy to disk.
I'm trying to figure out rpy2 for plotting some graphs. I'd like to be able to use the with function that's part of R's base like it's used it the following R code:
with(res, plot(log2FoldChange, -log10(pvalue), pch=20, main="Volcano plot", xlim=c(-2.5,2)))
with(subset(res, padj<.05 ), points(log2FoldChange, -log10(pvalue), pch=20, col="red"))
Where res is a dataframe and log2FoldChange and pvalue are columns from that dataframe.
When I import the base package using rpy2's importr I can see that 'with' is in the object by doing:
from rpy2.robjects.packages import importr
base = importr('base')
dir(base)
However, I can't seem to figure out the correct syntax:
from rpy2.robjects.packages import importr
from rpy2 import robjects
base = importr('base')
base.with(res, robjects.r.plot(log2FoldChange, padj))
File "<stdin>", line 1
base.with(res, robjects.r.plot(log2FoldChange, padj))
^
SyntaxError: invalid syntax
Unfortunately, searching for something like 'base.with' has proven intractable. My question: what is the syntax for using 'base.with' in rpy2 python code?
Alternatively, while using 'with' is the most R forward approach to doing this, perhaps there's a more rpy2 friendly approach to this same problem that I'm unaware of.
Python might be getting a conflict with its own with() command which requires a space right after it. This is the challenge of interfacing with another language.
Try running the command natively in R syntax wrapped around the robjects function. Below I pass Python objects into R's global environment scope.
import rpy2.robjects as ro
ro.globalenv['res'] = res_frompy
ro.globalenv['log2FoldChang'] = log2FoldChang_frompy
ro.globalenv['padj'] = padj_frompy
ro.r('with(res, plot(log2FoldChange, padj))')
I am using rpy2-2.2.2 with the new free Enthought python distribution that includes numpy 1.6.0 and python 2.7.2. I easy_installed rpy2 which resulted in v. 2.2.2 being installed and all tests were successful.
The problem I'm having is with code I wrote that worked fine with rpy2 2.1.8 and python 2.6. The issue is in converting from numpy to R for arrays.
Here is a snippet of the relevant code:
import rpy2
import rpy2.rinterface as rinterface
import rpy2.robjects as rob
import rpy2.rlike.container as rlc
import numpy as np
import rpy2.robjects.numpy2ri
r = rob.r
...
HGr = rob.conversion.py2ri(HG_reg)
RHSr = rob.conversion.py2ri(RHS)
#
CalData = rlc.TaggedList([HGr,RHSr],tags=('hg','rhs'))
CalData = rob.DataFrame(CalData)
r('''library(pls)''')
#rob.globalEnv["HGr"] = HGr
#rob.globalEnv["RHSr"] = RHSr
rob.globalenv["CalData"] = CalData
# perform the PLS regression
if wetlflag:
HGresults = r.plsr(r("hg ~ rhs.1 + rhs.2 + rhs.3 + rhs.4"),data=CalData,validation="LOO")
I will gladly admit it's not the most elegant way to do things, but it worked before and now when I need to provide results all is broken (!). The error I get is the following:
Traceback (most recent call last):
File "Mercury_PLS_WL_DF.py", line 224, in <module>
HGr = rob.conversion.py2ri(HG_reg)
File "/Library/Frameworks/Python.framework/Versions/7.1/lib/python2.7/site-packages/rpy2-2.2.2dev_20110726-py2.7-macosx-10.5-i386.egg/rpy2/robjects/__init__.py", line 134, in default_py2ri
raise(ValueError("Nothing can be done for the type %s at the moment." %(type(o))))
ValueError: Nothing can be done for the type <type 'numpy.ndarray'> at the moment.
I found the discussion here and got the impression that numpy arrays are now automatically converted to R arrays, but commenting out the rob.conversion.py2ri(HG_reg) statements and using the numpy arrays directly also seems to fail. Am I missing something obvious? Why would this break between 2.1.8 and 2.2.2?
From http://rpy.sourceforge.net/rpy2/doc-2.2/html/numpy.html#from-numpy-to-rpy2:
Warning
In earlier versions of rpy2, the import was all that was needed to have the conversion. A side-effect when importing a module can lead to problems, and there is now an extra step to make the conversion active: call the function rpy2.robjects.activate().
So put rpy2.robjects.activate() after the import and you should be fine.