fixed typo

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@343 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 117 insertions, 0 deletions

diff --git a/gi/evaluation/evaluate_entropy.py b/gi/evaluation/evaluate_entropy.py
new file mode 100644
index 00000000..43edc376
--- /dev/null
+++ b/gi/evaluation/evaluate_entropy.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python
+
+import sys, math, itertools
+
+ginfile = open(sys.argv[1])
+pinfile = open(sys.argv[2])
+if len(sys.argv) > 3:
+    slash_threshold = int(sys.argv[3])
+    #print >>sys.stderr, 'slash threshold', slash_threshold
+else:
+    slash_threshold = 99999
+
+# evaluating: H(G | P) = sum_{g,p} p(g,p) log { p(p) / p(g,p) }
+#                      = sum_{g,p} c(g,p)/N { log c(p) - log N - log c(g,p) + log N }
+#                      = 1/N sum_{g,p} c(g,p) { log c(p) - log c(g,p) }
+# where G = gold, P = predicted, N = number of events
+
+N = 0
+gold_frequencies = {}
+predict_frequencies = {}
+joint_frequencies = {}
+
+for gline, pline in itertools.izip(ginfile, pinfile):
+    gparts = gline.split('||| ')[1].split()
+    pparts = pline.split('||| ')[1].split()
+    assert len(gparts) == len(pparts)
+
+    for gpart, ppart in zip(gparts, pparts):
+        gtag = gpart.split(':',1)[1]
+        ptag = ppart.split(':',1)[1]
+
+        if gtag.count('/') + gtag.count('\\') <= slash_threshold:
+            joint_frequencies.setdefault((gtag, ptag), 0)
+            joint_frequencies[gtag,ptag] += 1
+
+            predict_frequencies.setdefault(ptag, 0)
+            predict_frequencies[ptag] += 1
+
+            gold_frequencies.setdefault(gtag, 0)
+            gold_frequencies[gtag] += 1
+
+            N += 1
+
+hg2p = 0
+hp2g = 0
+for (gtag, ptag), cgp in joint_frequencies.items():
+    hp2g += cgp * (math.log(predict_frequencies[ptag], 2) - math.log(cgp, 2))
+    hg2p += cgp * (math.log(gold_frequencies[gtag], 2) - math.log(cgp, 2))
+hg2p /= N
+hp2g /= N
+
+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
+#sys.exit(0)
+
+# 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])