def sdcToClassifier(self, sdc):
"""
Finds the classifier for an sdc.
"""
vIndexes, vTokens = chunker.tokenize(sdc.verb.text)
srIndexes, srTokens = chunker.tokenize(sdc.spatialRelation.text)
vTokens = [x.lower() for x in vTokens]
srTokens = [x.lower() for x in srTokens]
if "pass" in vTokens and "past" in self.tokenToEngine:
return self.tokenToEngine["past"]
else:
for sp, engine in self.tokenToEngine.iteritems():
if sp in srTokens:
if sp == "to" and sdc.spatialRelation.text.lower(
) != "to" and len(sdc.spatialRelation.text.split()) > 2:
# if they say "with your back to" or "to the left of", don't use "to."
continue
else:
return self.tokenToEngine[sp]
if "stop" in vTokens:
if not sdc.landmark.isNull() and "until" in self.tokenToEngine:
return self.tokenToEngine["until"]
# if "exit" in vTokens or "leave" in vTokens:
# if not sdc.landmark.isNull():
# return self.tokenToEngine["out"]
return None
def main():
dialog_fname = "sdc_annotations/stefie10.dialog.xml"
dialogs = readDialogs(dialog_fname)
figsize = (6, 4.5)
bottomAdjust = 0.14
topAdjust = 0.9
histograms = {
"landmark": Histogram(),
"verb": Histogram(),
"figure": Histogram(),
"spatialRelation": Histogram()
}
for dialog in dialogs:
for turn in dialog.turns:
for sdc in turn.sdcs:
indexes, allTokens = chunker.tokenize(sdc.text)
movementWords = set(["go", "going", "walk", "walking"])
lowerTokens = [t.lower() for t in allTokens]
if len(movementWords.intersection(lowerTokens)) != 0:
print sdc.text
for key in sdc.keys:
if key in histograms:
histogram = histograms[key]
elif key[:-1] in histograms:
histogram = histograms[key[:-1]]
else:
raise ValueError("No histogram for: " + ` key `)
text = sdc.annotationMap[key].text
indexes, tokens = chunker.tokenize(text)
for t in tokens:
histogram.add(t)
for key, histogram in histograms.iteritems():
print "doing", key, histogram
title = "%s" % key
graphStacked({"all": histogram},
"histogram",
title,
maxCols=10,
xticks=None,
figsize=figsize,
topAdjust=topAdjust,
bottomAdjust=bottomAdjust)
mpl.legend()
mpl.show()
def gloss_map(self):
gloss_dist = nltk.FreqDist()
for i, word in enumerate(self.words):
synset = self.synset(i)
indexes, tokens = chunker.tokenize(synset.definition)
for t in tokens:
gloss_dist.inc(t)
return gloss_dist
def writeTrainingForText(self, text, annotations, out):
for sentenceStandoff in self.sentenceTokenizer.tokenize(text):
indexes, tokens = tokenize(sentenceStandoff.text)
tags = self.tagger.tag(tokens)
for startIndex, (word, posTag) in zip(indexes, tags):
startIndex = startIndex + sentenceStandoff.start
wordRange = startIndex, startIndex + len(word)
annotation, key = containingAnnotations(annotations, wordRange)
if not (annotation is None):
chunk = key
else:
chunk = "None"
out.write("\t".join([word, posTag, chunk]) + "\n")
out.write("\n")
def chunk(self, string):
import CRFPP
tagger = CRFPP.Tagger("-m " + self.modelFile)
indexes, tokens = tokenize(string)
tags = self.tagger.tag(tokens)
tagger.clear()
for word, posTag in tags:
tagger.add(str("%s %s" % (word, posTag)))
tagger.parse()
for i, (index, token) in enumerate(zip(indexes, tokens)):
label = tagger.y2(i)
#print index, token, label
labels = [tagger.y2(i) for i in range(len(tokens))]
return indexes, tokens, labels
def score(groundTruthSessions, testSessions):
tagger = makeTagger()
cm = ConfusionMatrix()
for groundTruth in groundTruthSessions:
testSession = testSessions[groundTruth]
for instructionIdx, instruction in enumerate(
groundTruth.routeInstructions):
groundTruthAnnotations = groundTruth.routeAnnotations[
instructionIdx]
indexes, tokens = tokenize(instruction)
print "tokens", tokens
tags = tagger.tag(tokens)
print " ".join(["%s/%s" % (word, tag) for word, tag in tags])
matchedIndexes = [False for g in groundTruthAnnotations]
if len(groundTruthAnnotations) != 0:
print "considering", groundTruth.key, "instruction", instructionIdx
for testAnnotation in testSession.routeAnnotations[
instructionIdx]:
idx, groundTruthMatch = findMatch(testAnnotation,
groundTruthAnnotations,
npMatch)
if groundTruthMatch is None:
print "fp", testAnnotation
cm.FP += 1
else:
print "tp", testAnnotation
print "\tmatched", groundTruthMatch
cm.TP += 1
matchedIndexes[idx] = True
for i, hasMatch in enumerate(matchedIndexes):
if not hasMatch:
cm.FN += 1
print "fn", groundTruthAnnotations[i]
#else:
# what to do with true negatives
print "precision", cm.precision
print "recall", cm.recall
print "f1", cm.f1
def spellcheck(str):
from chunker import tokenize
indexes, tokens = tokenize(str)
result = ""
first = True
for idx, token in zip(indexes, tokens):
if not first:
result += " "
if (not token in [",", ".", "?", "!"] and not checker.check(token)):
suggestions = checker.suggest(token)
if len(suggestions) != 0:
result += checker.suggest(token)[0]
else:
result += token
else:
result += token
first = False
return result
def main():
corpus = Corpus("%s/data/verbs/corpus-11-2009.ods" % TKLIB_HOME)
posTagger = chunker.makeTagger()
print len(corpus.sessions), "subjects", len(corpus.commands), "commands"
print len([x.command for x in corpus.commands if commandFilter(x.command)]),
print "filtered commands"
print na.mean([len([x for x in s.commands if commandFilter(x.command)])
for s in corpus.sessions]), "commands per subject"
figsize=(6,10)
bottomAdjust=0.07
topAdjust=0.9
histograms = {}
commandTypes = corpus.commandTypes
#commandTypes = ["Guiding people"]
#commandTypes = ["Surveillance"]
for commandType in commandTypes:
histogram = Histogram()
histograms[commandType] = histogram
for command in corpus.commandsForType(commandType):
indexes, tokens = chunker.tokenize(command.command)
tokens = [t.lower() for t in tokens]
tags = posTagger.tag(tokens)
for token, tag in tags:
if tag[0] == "V":
histogram.add(token)
graphStacked({"All":histogram}, "histogram", commandType, maxCols=50,
xticks=None, figsize=figsize, topAdjust=topAdjust,
bottomAdjust=bottomAdjust)
graphStacked(histograms, "histogram", "All", maxCols=10, figsize=figsize,
topAdjust=topAdjust, bottomAdjust=bottomAdjust)
mpl.legend()
mpl.show()
def testEmpty(self):
tokens, indexes = chunker.tokenize(" ")
self.assertEqual(len(tokens), 0)
self.assertEqual(len(indexes), 0)
