comparison cca.py @ 0:ffcdde989859 draft

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0:ffcdde989859

#!/usr/bin/env python

import sys, string
#from rpy import *
import rpy2.robjects as robjects
import rpy2.rlike.container as rlc
from rpy2.robjects.packages import importr
r = robjects.r
grdevices = importr('grDevices')
import numpy


def stop_err(msg):
    sys.stderr.write(msg)
    sys.exit()

infile = sys.argv[1]
x_cols = sys.argv[2].split(',')
y_cols = sys.argv[3].split(',')

x_scale = x_center = "FALSE"
if sys.argv[4] == 'both':
    x_scale = x_center = "TRUE"
elif sys.argv[4] == 'center':
    x_center = "TRUE"
elif sys.argv[4] == 'scale':
    x_scale = "TRUE"
    
y_scale = y_center = "FALSE"
if sys.argv[5] == 'both':
    y_scale = y_center = "TRUE"
elif sys.argv[5] == 'center':
    y_center = "TRUE"
elif sys.argv[5] == 'scale':
    y_scale = "TRUE"

std_scores = "FALSE"   
if sys.argv[6] == "yes":
    std_scores = "TRUE"
    
outfile = sys.argv[7]
outfile2 = sys.argv[8]

fout = open(outfile,'w')
elems = []
for i, line in enumerate( file ( infile )):
    line = line.rstrip('\r\n')
    if len( line )>0 and not line.startswith( '#' ):
        elems = line.split( '\t' )
        break 
    if i == 30:
        break # Hopefully we'll never get here...

if len( elems )<1:
    stop_err( "The data in your input dataset is either missing or not formatted properly." )

x_vals = []

for k,col in enumerate(x_cols):
    x_cols[k] = int(col)-1
    #x_vals.append([])

y_vals = []

for k,col in enumerate(y_cols):
    y_cols[k] = int(col)-1
    #y_vals.append([])

skipped = 0
for ind,line in enumerate( file( infile )):
    if line and not line.startswith( '#' ):
        try:
            fields = line.strip().split("\t")
            valid_line = True
            for col in x_cols+y_cols:
                try:
                    assert float(fields[col])
                except:
                    skipped += 1
                    valid_line = False
                    break
            if valid_line:
                for k,col in enumerate(x_cols):
                    try:
                        xval = float(fields[col])
                    except:
                        xval = NaN#
                    #x_vals[k].append(xval)
                    x_vals.append(xval)
                for k,col in enumerate(y_cols):
                    try:
                        yval = float(fields[col])
                    except:
                        yval = NaN#
                    #y_vals[k].append(yval)
                    y_vals.append(yval)
        except:
            skipped += 1

#x_vals1 = numpy.asarray(x_vals).transpose()
#y_vals1 = numpy.asarray(y_vals).transpose()

#x_dat= r.list(array(x_vals1))
#y_dat= r.list(array(y_vals1))

x_dat = r['matrix'](robjects.FloatVector(x_vals),ncol=len(x_cols),byrow=True)
y_dat = r['matrix'](robjects.FloatVector(y_vals),ncol=len(y_cols),byrow=True)

try:
    r.suppressWarnings(r.library("yacca"))
except:
    stop_err("Missing R library yacca.")
    
#set_default_mode(NO_CONVERSION)
try:
    xcolnames = ["c%d" %(el+1) for el in x_cols]
    ycolnames = ["c%d" %(el+1) for el in y_cols]
    #cc = r.cca(x=x_dat, y=y_dat, xlab=xcolnames, ylab=ycolnames, xcenter=r(x_center), ycenter=r(y_center), xscale=r(x_scale), yscale=r(y_scale), standardize_scores=r(std_scores))
    cc = r.cca(x=x_dat, y=y_dat, xlab=xcolnames, ylab=ycolnames, xcenter=r(x_center), ycenter=r(y_center), xscale=r(x_scale), yscale=r(y_scale), **{'standardize.scores':r(std_scores)})
    #ftest = r.F_test_cca(cc)
    ftest = r['F.test.cca'](cc)
except RException, rex:
    stop_err("Encountered error while performing CCA on the input data: %s" %(rex))

#set_default_mode(BASIC_CONVERSION)
summary = r.summary(cc)

#ncomps = len(summary['corr'])
ncomps = len(summary.rx2('corr'))
#comps = summary['corr'].keys()
#comps = summary.rx2('corr').names
comps = (','.join(summary.rx2('corr').names)).split(',')
#corr = summary['corr'].values()
corr = summary.rx2('corr')
#xlab = summary['xlab']
xlab = summary.rx2('xlab')
#ylab = summary['ylab']
ylab = summary.rx2('ylab')

for i in range(ncomps):
    corr[comps.index('CV %s' %(i+1))] = summary.rx2('corr')[i]
    #corr[comps.index('CV %s' %(i+1))] = summary['corr'].values()[i]

#ftest=ftest.as_py()
print >>fout, "#Component\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
print >>fout, "#Correlation\t%s" %("\t".join(["%.4g" % el for el in corr]))
#print >>fout, "#F-statistic\t%s" %("\t".join(["%.4g" % el for el in ftest['statistic']]))
print >>fout, "#F-statistic\t%s" %("\t".join(["%.4g" % el for el in ftest.rx2('statistic')]))
#print >>fout, "#p-value\t%s" %("\t".join(["%.4g" % el for el in ftest['p.value']]))
print >>fout, "#p-value\t%s" %("\t".join(["%.4g" % el for el in ftest.rx2('p.value')]))


print >>fout, "#X-Coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['xcoef']):
#    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('xcoef')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(xlab[i][0], "\t".join(vals))

print >>fout, "#Y-Coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['ycoef']):
#    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('ycoef')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(ylab[i][0], "\t".join(vals))

print >>fout, "#X-Loadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['xstructcorr']):
#    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('xstructcorr')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(xlab[i][0], "\t".join(vals))

print >>fout, "#Y-Loadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['ystructcorr']):
#    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('ystructcorr')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(ylab[i][0], "\t".join(vals))

print >>fout, "#X-CrossLoadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['xcrosscorr']):
#    print >>fout, "%s\t%s" %(xlab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('xcrosscorr')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(xlab[i][0], "\t".join(vals))

print >>fout, "#Y-CrossLoadings\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)]))
#for i,val in enumerate(summary['ycrosscorr']):
#    print >>fout, "%s\t%s" %(ylab[i], "\t".join(["%.4g" % el for el in val]))
vm = summary.rx2('ycrosscorr')
for i in range(vm.nrow):
    vals = []
    for j in range(vm.ncol):
       vals.append("%.4g" % vm.rx2(i+1,j+1)[0])
    print >>fout, "%s\t%s" %(ylab[i][0], "\t".join(vals))

r.pdf( outfile2, 8, 8 )
#r.plot(cc)
for i in range(ncomps):
    r['helio.plot'](cc, cv = i+1, main = r.paste("Explained Variance for CV",i+1), type = "variance")
#r.dev_off()
grdevices.dev_off()