diff kcca.py @ 0:ffcdde989859 draft

Uploaded

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/kcca.py	Tue Jul 29 06:30:45 2014 -0400
@@ -0,0 +1,166 @@
+#!/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)
+"""
+
+from galaxy import eggs
+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
+import numpy
+import pkg_resources; pkg_resources.require( "bx-python" )
+from bx.cookbook import doc_optparse
+
+
+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)
+                    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('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:
+    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]))
+for i in range(1,xcoef.nrow+1):
+    vals = []
+    for j in range(1,xcoef.ncol+1):
+       vals.append("%.4g" % xcoef.rx2(i,j)[0])
+    print >>fout, "%s\t%s" %(i, "\t".join(vals))
+
+
+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]))
+for i in range(1,ycoef.nrow+1):
+    vals = []
+    for j in range(1,ycoef.ncol+1):
+       vals.append("%.4g" % ycoef.rx2(i,j)[0])
+    print >>fout, "%s\t%s" %(i, "\t".join(vals))