diff best_regression_subsets.py @ 0:54c7a01a2cc7

Imported from capsule None

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/best_regression_subsets.py	Tue Apr 01 10:50:17 2014 -0400
@@ -0,0 +1,91 @@
+#!/usr/bin/env python
+
+from galaxy import eggs
+
+import sys
+from rpy import *
+import numpy
+
+def stop_err(msg):
+    sys.stderr.write(msg)
+    sys.exit()
+
+
+infile = sys.argv[1]
+y_col = int(sys.argv[2])-1
+x_cols = sys.argv[3].split(',')
+outfile = sys.argv[4]
+outfile2 = sys.argv[5]
+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([])
+    
+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')
+            y_vals.append(yval)
+            for k, col in enumerate(x_cols):
+                try:
+                    xval = float(fields[col])
+                except Exception, ex:
+                    xval = r('NA')
+                x_vals[k].append(xval)
+        except:
+            pass
+
+response_term = ""
+
+x_vals1 = numpy.asarray(x_vals).transpose()
+
+dat = r.list(x=array(x_vals1), y=y_vals)
+
+r.library("leaps")
+
+set_default_mode(NO_CONVERSION)
+try:
+    leaps = r.regsubsets(r("y ~ x"), data= r.na_exclude(dat))
+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(leaps)
+tot = len(x_vals)
+pattern = "["
+for i in range(tot):
+    pattern = pattern + 'c' + str(int(x_cols[int(i)]) + 1) + ' '
+pattern = pattern.strip() + ']'
+print >> fout, "#Vars\t%s\tR-sq\tAdj. R-sq\tC-p\tbic" % (pattern)
+for ind, item in enumerate(summary['outmat']):
+    print >> fout, "%s\t%s\t%s\t%s\t%s\t%s" % (str(item).count('*'), item, summary['rsq'][ind], summary['adjr2'][ind], summary['cp'][ind], summary['bic'][ind])
+
+
+r.pdf( outfile2, 8, 8 )
+r.plot(leaps, scale="Cp", main="Best subsets using Cp Criterion")
+r.plot(leaps, scale="r2", main="Best subsets using R-sq Criterion")
+r.plot(leaps, scale="adjr2", main="Best subsets using Adjusted R-sq Criterion")
+r.plot(leaps, scale="bic", main="Best subsets using bic Criterion")
+
+r.dev_off()