Mercurial > repos > devteam > kernel_canonical_correlation_analysis
diff kcca.py @ 0:7a092113eb8c draft
Imported from capsule None
author | devteam |
---|---|
date | Mon, 19 May 2014 12:34:54 -0400 |
parents | |
children |
line wrap: on
line diff
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/kcca.py Mon May 19 12:34:54 2014 -0400 @@ -0,0 +1,147 @@ +#!/usr/bin/env python + +""" +Run kernel CCA using kcca() from R 'kernlab' package + +usage: %prog [options] + -i, --input=i: Input file + -o, --output1=o: Summary output + -x, --x_cols=x: X-Variable columns + -y, --y_cols=y: Y-Variable columns + -k, --kernel=k: Kernel function + -f, --features=f: Number of canonical components to return + -s, --sigma=s: sigma + -d, --degree=d: degree + -l, --scale=l: scale + -t, --offset=t: offset + -r, --order=r: order + +usage: %prog input output1 x_cols y_cols kernel features sigma(or_None) degree(or_None) scale(or_None) offset(or_None) order(or_None) +""" + +import sys, string +from rpy import * +import numpy +from bx.cookbook import doc_optparse +import logging +log = logging.getLogger('kcca') + +def stop_err(msg): + sys.stderr.write(msg) + sys.exit() + +#Parse Command Line +options, args = doc_optparse.parse( __doc__ ) +#{'options= kernel': 'rbfdot', 'var_cols': '1,2,3,4', 'degree': 'None', 'output2': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_260.dat', 'output1': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_259.dat', 'scale': 'None', 'offset': 'None', 'input': '/afs/bx.psu.edu/home/gua110/workspace/galaxy_bitbucket/database/files/000/dataset_256.dat', 'sigma': '1.0', 'order': 'None'} + +infile = options.input +x_cols = options.x_cols.split(',') +y_cols = options.y_cols.split(',') +kernel = options.kernel +outfile = options.output1 +ncomps = int(options.features) +fout = open(outfile,'w') + +if ncomps < 1: + print "You chose to return '0' canonical components. Please try rerunning the tool with number of components = 1 or more." + sys.exit() +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([]) +NA = 'NA' +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) + for k,col in enumerate(y_cols): + try: + yval = float(fields[col]) + except: + yval = NaN + y_vals[k].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)) + +try: + r.suppressWarnings(r.library('kernlab')) +except: + stop_err('Missing R library kernlab') + +set_default_mode(NO_CONVERSION) +if kernel=="rbfdot" or kernel=="anovadot": + pars = r.list(sigma=float(options.sigma)) +elif kernel=="polydot": + pars = r.list(degree=float(options.degree),scale=float(options.scale),offset=float(options.offset)) +elif kernel=="tanhdot": + pars = r.list(scale=float(options.scale),offset=float(options.offset)) +elif kernel=="besseldot": + pars = r.list(degree=float(options.degree),sigma=float(options.sigma),order=float(options.order)) +elif kernel=="anovadot": + pars = r.list(degree=float(options.degree),sigma=float(options.sigma)) +else: + pars = rlist() + +try: + kcc = r.kcca(x=x_dat, y=y_dat, kernel=kernel, kpar=pars, ncomps=ncomps) +except RException, rex: + raise + log.exception( rex ) + stop_err("Encountered error while performing kCCA on the input data: %s" %(rex)) + +set_default_mode(BASIC_CONVERSION) +kcor = r.kcor(kcc) +if ncomps == 1: + kcor = [kcor] +xcoef = r.xcoef(kcc) +ycoef = r.ycoef(kcc) + +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 kcor])) + +print >>fout, "#Estimated X-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)])) +for obs,val in enumerate(xcoef): + print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val])) + +print >>fout, "#Estimated Y-coefficients\t%s" %("\t".join(["%s" % el for el in range(1,ncomps+1)])) +for obs,val in enumerate(ycoef): + print >>fout, "%s\t%s" %(obs+1, "\t".join(["%.4g" % el for el in val]))