Mercurial > repos > devteam > rcve
view rcve.py @ 0:d8c414b9d774 draft default tip
Imported from capsule None
author | devteam |
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date | Tue, 01 Apr 2014 09:13:06 -0400 |
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#!/usr/bin/env python import sys from rpy import * import numpy def stop_err(msg): sys.stderr.write(msg) sys.exit() def sscombs(s): if len(s) == 1: return [s] else: ssc = sscombs(s[1:]) return [s[0]] + [s[0]+comb for comb in ssc] + ssc infile = sys.argv[1] y_col = int(sys.argv[2])-1 x_cols = sys.argv[3].split(',') outfile = sys.argv[4] print "Predictor columns: %s; Response column: %d" % ( x_cols, y_col+1 ) fout = open(outfile,'w') 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." ) y_vals = [] x_vals = [] for k, col in enumerate(x_cols): x_cols[k] = int(col)-1 x_vals.append([]) """ try: float( elems[x_cols[k]] ) except: try: msg = "This operation cannot be performed on non-numeric column %d containing value '%s'." % ( col, elems[x_cols[k]] ) except: msg = "This operation cannot be performed on non-numeric data." stop_err( msg ) """ NA = 'NA' for ind, line in enumerate( file( infile )): if line and not line.startswith( '#' ): try: fields = line.split("\t") try: yval = float(fields[y_col]) except Exception, ey: yval = r('NA') #print >>sys.stderr, "ey = %s" %ey y_vals.append(yval) for k, col in enumerate(x_cols): try: xval = float(fields[col]) except Exception, ex: xval = r('NA') #print >>sys.stderr, "ex = %s" %ex x_vals[k].append(xval) except: pass x_vals1 = numpy.asarray(x_vals).transpose() dat = r.list( x=array(x_vals1), y=y_vals ) set_default_mode(NO_CONVERSION) try: full = r.lm( r("y ~ x"), data=r.na_exclude(dat) ) #full model includes all the predictor variables specified by the user except RException, rex: stop_err("Error performing linear regression on the input data.\nEither the response column or one of the predictor columns contain no numeric values.") set_default_mode(BASIC_CONVERSION) summary = r.summary(full) fullr2 = summary.get('r.squared','NA') if fullr2 == 'NA': stop_err("Error in linear regression") if len(x_vals) < 10: s = "" for ch in range(len(x_vals)): s += str(ch) else: stop_err("This tool only works with less than 10 predictors.") print >> fout, "#Model\tR-sq\tRCVE_Terms\tRCVE_Value" all_combos = sorted(sscombs(s), key=len) all_combos.reverse() for j, cols in enumerate(all_combos): #if len(cols) == len(s): #Same as the full model above # continue if len(cols) == 1: x_vals1 = x_vals[int(cols)] else: x_v = [] for col in cols: x_v.append(x_vals[int(col)]) x_vals1 = numpy.asarray(x_v).transpose() dat = r.list(x=array(x_vals1), y=y_vals) set_default_mode(NO_CONVERSION) red = r.lm(r("y ~ x"), data= dat) #Reduced model set_default_mode(BASIC_CONVERSION) summary = r.summary(red) redr2 = summary.get('r.squared','NA') try: rcve = (float(fullr2)-float(redr2))/float(fullr2) except: rcve = 'NA' col_str = "" for col in cols: col_str = col_str + str(int(x_cols[int(col)]) + 1) + " " col_str.strip() rcve_col_str = "" for col in s: if col not in cols: rcve_col_str = rcve_col_str + str(int(x_cols[int(col)]) + 1) + " " rcve_col_str.strip() if len(cols) == len(s): #full model rcve_col_str = "-" rcve = "-" try: redr2 = "%.4f" % (float(redr2)) except: pass try: rcve = "%.4f" % (float(rcve)) except: pass print >> fout, "%s\t%s\t%s\t%s" % ( col_str, redr2, rcve_col_str, rcve )