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-rw-r--r--gi/evaluation/conditional_entropy.py61
-rw-r--r--gi/evaluation/confusion_matrix.py118
-rw-r--r--gi/evaluation/entropy.py38
-rw-r--r--gi/evaluation/evaluate_entropy.py116
4 files changed, 217 insertions, 116 deletions
diff --git a/gi/evaluation/conditional_entropy.py b/gi/evaluation/conditional_entropy.py
new file mode 100644
index 00000000..356d3b1d
--- /dev/null
+++ b/gi/evaluation/conditional_entropy.py
@@ -0,0 +1,61 @@
+#!/usr/bin/env python
+
+import sys, math, itertools, getopt
+
+def usage():
+ print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] input-1 input-2'
+ sys.exit(0)
+
+optlist, args = getopt.getopt(sys.argv[1:], 'hs:')
+slash_threshold = None
+for opt, arg in optlist:
+ if opt == '-s':
+ slash_threshold = int(arg)
+ else:
+ usage()
+if len(args) != 2:
+ usage()
+
+ginfile = open(args[0])
+pinfile = open(args[1])
+
+# 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 slash_threshold == None or 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
+
+print 'H(P|G)', hg2p, 'H(G|P)', hp2g, 'VI', hg2p + hp2g
diff --git a/gi/evaluation/confusion_matrix.py b/gi/evaluation/confusion_matrix.py
new file mode 100644
index 00000000..c5e2a379
--- /dev/null
+++ b/gi/evaluation/confusion_matrix.py
@@ -0,0 +1,118 @@
+#!/usr/bin/env python
+
+import sys, math, itertools, getopt
+
+def usage():
+ print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] [-p output] [-m] input-1 input-2'
+ sys.exit(0)
+
+optlist, args = getopt.getopt(sys.argv[1:], 'hs:')
+slash_threshold = None
+output_fname = None
+show_matrix = False
+for opt, arg in optlist:
+ if opt == '-s':
+ slash_threshold = int(arg)
+ elif opt == '-p':
+ output_fname = arg
+ elif opt == '-m':
+ show_matrix = True
+ else:
+ usage()
+if len(args) != 2 or (not show_matrix and not output_fname):
+ usage()
+
+ginfile = open(args[0])
+pinfile = open(args[1])
+
+if output_fname:
+ try:
+ import Image, ImageDraw
+ except ImportError:
+ print >>sys.stderr, "Error: Python Image Library not available. Did you forget to set your PYTHONPATH environment variable?"
+ sys.exit(1)
+
+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 slash_threshold == None or 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
+
+# find top tags
+gtags = gold_frequencies.items()
+gtags.sort(lambda x,y: x[1]-y[1])
+gtags.reverse()
+#gtags = gtags[:50]
+
+preds = predict_frequencies.items()
+preds.sort(lambda x,y: x[1]-y[1])
+preds.reverse()
+
+if show_matrix:
+ print '%7s %7s' % ('pred', 'cnt'),
+ for gtag, gcount in gtags: print '%7s' % gtag,
+ print
+ print '=' * 80
+
+ 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 output_fname:
+ offset=10
+
+ image = Image.new("RGB", (len(preds), len(gtags)), (255, 255, 255))
+ #hsl(hue, saturation%, lightness%)
+
+ # re-sort preds to get a better diagonal
+ if False:
+ 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(output_fname)
diff --git a/gi/evaluation/entropy.py b/gi/evaluation/entropy.py
new file mode 100644
index 00000000..cef0dbb4
--- /dev/null
+++ b/gi/evaluation/entropy.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python
+
+import sys, math, itertools, getopt
+
+def usage():
+ print >>sys.stderr, 'Usage:', sys.argv[0], '[-s slash_threshold] input file'
+ sys.exit(0)
+
+optlist, args = getopt.getopt(sys.argv[1:], 'hs:')
+slash_threshold = None
+for opt, arg in optlist:
+ if opt == '-s':
+ slash_threshold = int(arg)
+ else:
+ usage()
+if len(args) != 1:
+ usage()
+
+infile = open(args[0])
+N = 0
+frequencies = {}
+
+for line in infile:
+
+ for part in line.split('||| ')[1].split():
+ tag = part.split(':',1)[1]
+
+ if slash_threshold == None or tag.count('/') + tag.count('\\') <= slash_threshold:
+ frequencies.setdefault(gtag, 0)
+ frequencies[gtag] += 1
+ N += 1
+
+h = 0
+for tag, c in frequencies.items():
+ h -= c * (math.log(c, 2) - math.log(N, 2))
+h /= N
+
+print 'entropy', h
diff --git a/gi/evaluation/evaluate_entropy.py b/gi/evaluation/evaluate_entropy.py
deleted file mode 100644
index e4980ccf..00000000
--- a/gi/evaluation/evaluate_entropy.py
+++ /dev/null
@@ -1,116 +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
-
-# 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])
generated by cgit v1.2.3 (git 2.39.1) at 2024-09-21 21:45:01 +0000