[2] | 1 | #!/usr/bin/env python |
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| 2 | |
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| 3 | from galaxy import eggs |
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| 4 | |
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| 5 | import sys, string |
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| 6 | from rpy import * |
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| 7 | import numpy |
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| 8 | |
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| 9 | def stop_err(msg): |
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| 10 | sys.stderr.write(msg) |
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| 11 | sys.exit() |
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| 12 | |
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| 13 | infile = sys.argv[1] |
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| 14 | y_col = int(sys.argv[2])-1 |
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| 15 | x_cols = sys.argv[3].split(',') |
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| 16 | outfile = sys.argv[4] |
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| 17 | outfile2 = sys.argv[5] |
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| 18 | print "Predictor columns: %s; Response column: %d" %(x_cols,y_col+1) |
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| 19 | fout = open(outfile,'w') |
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| 20 | |
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| 21 | for i, line in enumerate( file ( infile )): |
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| 22 | line = line.rstrip('\r\n') |
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| 23 | if len( line )>0 and not line.startswith( '#' ): |
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| 24 | elems = line.split( '\t' ) |
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| 25 | break |
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| 26 | if i == 30: |
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| 27 | break # Hopefully we'll never get here... |
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| 28 | |
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| 29 | if len( elems )<1: |
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| 30 | stop_err( "The data in your input dataset is either missing or not formatted properly." ) |
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| 31 | |
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| 32 | y_vals = [] |
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| 33 | x_vals = [] |
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| 34 | |
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| 35 | for k,col in enumerate(x_cols): |
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| 36 | x_cols[k] = int(col)-1 |
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| 37 | x_vals.append([]) |
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| 38 | |
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| 39 | NA = 'NA' |
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| 40 | for ind,line in enumerate( file( infile )): |
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| 41 | if line and not line.startswith( '#' ): |
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| 42 | try: |
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| 43 | fields = line.split("\t") |
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| 44 | try: |
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| 45 | yval = float(fields[y_col]) |
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| 46 | except Exception, ey: |
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| 47 | yval = r('NA') |
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| 48 | y_vals.append(yval) |
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| 49 | for k,col in enumerate(x_cols): |
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| 50 | try: |
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| 51 | xval = float(fields[col]) |
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| 52 | except Exception, ex: |
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| 53 | xval = r('NA') |
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| 54 | x_vals[k].append(xval) |
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| 55 | except: |
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| 56 | pass |
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| 57 | |
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| 58 | response_term = "" |
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| 59 | |
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| 60 | x_vals1 = numpy.asarray(x_vals).transpose() |
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| 61 | |
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| 62 | dat= r.list(x=array(x_vals1), y=y_vals) |
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| 63 | |
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| 64 | r.library("leaps") |
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| 65 | |
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| 66 | set_default_mode(NO_CONVERSION) |
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| 67 | try: |
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| 68 | leaps = r.regsubsets(r("y ~ x"), data= r.na_exclude(dat)) |
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| 69 | except RException, rex: |
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| 70 | stop_err("Error performing linear regression on the input data.\nEither the response column or one of the predictor columns contain no numeric values.") |
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| 71 | set_default_mode(BASIC_CONVERSION) |
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| 72 | |
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| 73 | summary = r.summary(leaps) |
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| 74 | tot = len(x_vals) |
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| 75 | pattern = "[" |
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| 76 | for i in range(tot): |
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| 77 | pattern = pattern + 'c' + str(int(x_cols[int(i)]) + 1) + ' ' |
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| 78 | pattern = pattern.strip() + ']' |
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| 79 | print >>fout, "#Vars\t%s\tR-sq\tAdj. R-sq\tC-p\tbic" %(pattern) |
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| 80 | for ind,item in enumerate(summary['outmat']): |
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| 81 | 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]) |
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| 82 | |
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| 83 | |
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| 84 | r.pdf( outfile2, 8, 8 ) |
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| 85 | r.plot(leaps, scale="Cp", main="Best subsets using Cp Criterion") |
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| 86 | r.plot(leaps, scale="r2", main="Best subsets using R-sq Criterion") |
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| 87 | r.plot(leaps, scale="adjr2", main="Best subsets using Adjusted R-sq Criterion") |
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| 88 | r.plot(leaps, scale="bic", main="Best subsets using bic Criterion") |
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| 89 | |
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| 90 | r.dev_off() |
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