separate binary classification and regression demo

demo/binary_classification/mapfeat.py

@@ -0,0 +1,50 @@
+#!/usr/bin/python
+import sys
+
+def loadfmap( fname ):
+    fmap = {}
+    nmap = {}
+    
+    for l in open( fname ):
+        arr = l.split()
+        if arr[0].find('.') != -1:            
+            idx = int( arr[0].strip('.') )
+            assert idx not in fmap        
+            fmap[ idx ] = {}
+            ftype = arr[1].strip(':')        
+            content = arr[2]
+        else:
+            content = arr[0]
+        for it in content.split(','):
+            if it.strip() == '':
+                continue
+            k , v = it.split('=')
+            fmap[ idx ][ v ] = len(nmap)
+            nmap[ len(nmap) ] = ftype+'='+k
+    return fmap, nmap
+
+def write_nmap( fo, nmap ):    
+    for i in xrange( len(nmap) ):
+        fo.write('%d\t%s\ti\n' % (i, nmap[i]) )
+
+# start here
+fmap, nmap = loadfmap( 'agaricus-lepiota.fmap' )
+fo = open( 'featmap.txt', 'w' )
+write_nmap( fo, nmap )
+fo.close()
+
+fo = open( 'agaricus.txt', 'w' ) 
+for l in open( 'agaricus-lepiota.data' ):
+    arr = l.split(',')
+    if arr[0] == 'p':
+        fo.write('1')
+    else:
+        assert arr[0] == 'e'
+        fo.write('0')
+    for i in xrange( 1,len(arr) ):
+        fo.write( ' %d:1' % fmap[i][arr[i].strip()] )
+    fo.write('\n')
+
+fo.close()
+
+