1 files changed, 68 insertions, 3 deletions
diff --git a/gi/evaluation/evaluate_entropy.py b/gi/evaluation/evaluate_entropy.py
index 55449c59..e4980ccf 100644
--- a/gi/evaluation/evaluate_entropy.py
+++ b/gi/evaluation/evaluate_entropy.py
@@ -4,8 +4,9 @@ import sys, math, itertools
 
 ginfile = open(sys.argv[1])
 pinfile = open(sys.argv[2])
-if len(sys.argv) >= 4:
+if len(sys.argv) > 3:
     slash_threshold = int(sys.argv[3])
+    #print >>sys.stderr, 'slash threshold', slash_threshold
 else:
     slash_threshold = 99999
 
@@ -28,7 +29,7 @@ for gline, pline in itertools.izip(ginfile, pinfile):
         gtag = gpart.split(':',1)[1]
         ptag = ppart.split(':',1)[1]
 
-        if gtag.count('/') <= slash_threshold:
+        if gtag.count('/') + gtag.count('\\') <= slash_threshold:
             joint_frequencies.setdefault((gtag, ptag), 0)
             joint_frequencies[gtag,ptag] += 1
 
@@ -48,4 +49,68 @@ for (gtag, ptag), cgp in joint_frequencies.items():
 hg2p /= N
 hp2g /= N
 
-print 'H(P|G)', hg2p, 'H(G|P)', hp2g, 'VI', hg2p + hp2g
+hg = 0
+for gtag, c in gold_frequencies.items():
+    hg -= c * (math.log(c, 2) - math.log(N, 2))
+hg /= N
+
+print 'H(P|G)', hg2p, 'H(G|P)', hp2g, 'VI', hg2p + hp2g, 'H(G)', hg
+
+# find top tags
+gtags = gold_frequencies.items()
+gtags.sort(lambda x,y: x[1]-y[1])
+gtags.reverse()
+#gtags = gtags[:50]
+
+print '%7s %7s' % ('pred', 'cnt'),
+for gtag, gcount in gtags: print '%7s' % gtag,
+print
+print '=' * 80
+
+preds = predict_frequencies.items()
+preds.sort(lambda x,y: x[1]-y[1])
+preds.reverse()
+for ptag, pcount in preds:
+    print '%7s %7d' % (ptag, pcount),
+    for gtag, gcount in gtags:
+	print '%7d' % joint_frequencies.get((gtag, ptag), 0),
+    print
+
+print '%7s %7d' % ('total', N),
+for gtag, gcount in gtags: print '%7d' % gcount,
+print
+
+if len(sys.argv) > 4:
+	# needs Python Image Library (PIL)
+	import Image, ImageDraw
+
+	offset=10
+
+	image = Image.new("RGB", (len(preds), len(gtags)), (255, 255, 255))
+	#hsl(hue, saturation%, lightness%)
+
+	# resort preds to get a better diagonal
+	ptags = []
+	remaining = set(predict_frequencies.keys())
+	for y, (gtag, gcount) in enumerate(gtags):
+	    best = (None, 0)
+	    for ptag in remaining:
+		#pcount = predict_frequencies[ptag]
+		p = joint_frequencies.get((gtag, ptag), 0)# / float(pcount)
+		if p > best[1]: best = (ptag, p)
+	    ptags.append(ptag)
+	    remaining.remove(ptag)
+	    if not remaining: break
+	
+	draw = ImageDraw.Draw(image)
+	for x, ptag in enumerate(ptags):
+	    pcount = predict_frequencies[ptag]
+	    minval = math.log(offset)
+	    maxval = math.log(pcount + offset)
+	    for y, (gtag, gcount) in enumerate(gtags):
+		f = math.log(offset + joint_frequencies.get((gtag, ptag), 0))
+		z = int(240. * (maxval - f) / float(maxval - minval))
+		#print x, y, z, f, maxval
+		draw.point([(x,y)], fill='hsl(%d, 100%%, 50%%)' % z)
+	del draw
+	image.save(sys.argv[4])