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| -rw-r--r-- | gi/evaluation/evaluate_entropy.py | 117 |
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]) |