removed file

git-svn-id: https://ws10smt.googlecode.com/svn/trunk@344 ec762483-ff6d-05da-a07a-a48fb63a330f

1 files changed, 0 insertions, 117 deletions

diff --git a/gi/evaluation/evaluate_entropy.py b/gi/evaluation/evaluate_entropy.py
deleted file mode 100644
index 43edc376..00000000
--- a/gi/evaluation/evaluate_entropy.py
+++ /dev/null
@@ -1,117 +0,0 @@
-#!/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])