From ac98d8465d9b7a6faf2b51bcd18260375842f6c8 Mon Sep 17 00:00:00 2001
From: "trevor.cohn" <trevor.cohn@ec762483-ff6d-05da-a07a-a48fb63a330f>
Date: Tue, 20 Jul 2010 21:54:13 +0000
Subject: removed file
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@344 ec762483-ff6d-05da-a07a-a48fb63a330f
---
gi/evaluation/evaluate_entropy.py | 117 --------------------------------------
1 file changed, 117 deletions(-)
delete mode 100644 gi/evaluation/evaluate_entropy.py
(limited to 'gi/evaluation')
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])
--
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