def buildExamples(self, sentenceGraph):
examples = []
exampleIndex = 0
if not self.tokenCounts.has_key(len(sentenceGraph.tokens)):
self.tokenCounts[len(sentenceGraph.tokens)] = 0
self.tokenCounts[len(sentenceGraph.tokens)] += 1
for token in sentenceGraph.tokens:
entityCounts = {}
for entity in sentenceGraph.tokenIsEntityHead[token]:
t = entity.get("type")
if not entityCounts.has_key(t): entityCounts[t] = 0
entityCounts[t] += 1
for k, v in entityCounts.iteritems():
if not self.counts.has_key(v): self.counts[v] = 0
self.counts[v] += 1
# per type
if not self.countsPerType.has_key(k):
self.countsPerType[k] = {}
if not self.countsPerType[k].has_key(v):
self.countsPerType[k][v] = 0
self.countsPerType[k][v] += 1
numEntities = len(sentenceGraph.tokenIsEntityHead[token])
if not self.untypedCounts.has_key(numEntities):
self.untypedCounts[numEntities] = 0
self.untypedCounts[numEntities] += 1
#count = len(sentenceGraph.tokenIsEntityHead[token])
#if not self.counts.has_key(count): self.counts[count] = 0
#self.counts[count] += 1
if max(entityCounts.values() + [0]) >= 8:
print "======================================"
print "Entity", token.get("id")
for e in sentenceGraph.tokenIsEntityHead[token]:
print ETUtils.toStr(e)
print "======================================"
return []
# for entity in sentenceGraph.entities:
# if entity
# #undirected = sentenceGraph.getUndirectedDependencyGraph()
# undirected = self.nxMultiDiGraphToUndirected(sentenceGraph.dependencyGraph)
# ##undirected = sentenceGraph.dependencyGraph.to_undirected()
# ###undirected = NX10.MultiGraph(sentenceGraph.dependencyGraph) This didn't work
# paths = NX10.all_pairs_shortest_path(undirected, cutoff=999)
#
# # Generate examples based on interactions between entities or interactions between tokens
# if "entities" in self.styles:
# loopRange = len(sentenceGraph.entities)
# else:
# loopRange = len(sentenceGraph.tokens)
# for i in range(loopRange-1):
# for j in range(i+1,loopRange):
# eI = None
# eJ = None
# if "entities" in self.styles:
# eI = sentenceGraph.entities[i]
def buildExamples(self, sentenceGraph):
examples = []
exampleIndex = 0
if not self.tokenCounts.has_key(len(sentenceGraph.tokens)):
self.tokenCounts[len(sentenceGraph.tokens)] = 0
self.tokenCounts[len(sentenceGraph.tokens)] += 1
for token in sentenceGraph.tokens:
entityCounts = {}
for entity in sentenceGraph.tokenIsEntityHead[token]:
t = entity.get("type")
if not entityCounts.has_key(t): entityCounts[t] = 0
entityCounts[t] += 1
for k,v in entityCounts.iteritems():
if not self.counts.has_key(v): self.counts[v] = 0
self.counts[v] += 1
# per type
if not self.countsPerType.has_key(k): self.countsPerType[k] = {}
if not self.countsPerType[k].has_key(v): self.countsPerType[k][v] = 0
self.countsPerType[k][v] += 1
numEntities = len(sentenceGraph.tokenIsEntityHead[token])
if not self.untypedCounts.has_key(numEntities): self.untypedCounts[numEntities] = 0
self.untypedCounts[numEntities] += 1
#count = len(sentenceGraph.tokenIsEntityHead[token])
#if not self.counts.has_key(count): self.counts[count] = 0
#self.counts[count] += 1
if max(entityCounts.values() + [0]) >= 8:
print "======================================"
print "Entity", token.get("id")
for e in sentenceGraph.tokenIsEntityHead[token]:
print ETUtils.toStr(e)
print "======================================"
return []
# for entity in sentenceGraph.entities:
# if entity
# #undirected = sentenceGraph.getUndirectedDependencyGraph()
# undirected = self.nxMultiDiGraphToUndirected(sentenceGraph.dependencyGraph)
# ##undirected = sentenceGraph.dependencyGraph.to_undirected()
# ###undirected = NX10.MultiGraph(sentenceGraph.dependencyGraph) This didn't work
# paths = NX10.all_pairs_shortest_path(undirected, cutoff=999)
#
# # Generate examples based on interactions between entities or interactions between tokens
# if "entities" in self.styles:
# loopRange = len(sentenceGraph.entities)
# else:
# loopRange = len(sentenceGraph.tokens)
# for i in range(loopRange-1):
# for j in range(i+1,loopRange):
# eI = None
# eJ = None
# if "entities" in self.styles:
# eI = sentenceGraph.entities[i]
def loadEventXML(path, verbose=False):
xml = ETUtils.ETFromObj(path)
sentDict = {}
for sentence in xml.getiterator("sentence"):
sentenceText = getText(sentence).strip()
if not sentDict.has_key(sentenceText):
sentDict[sentenceText] = []
for event in xml.getiterator("event"):
sentenceText = getText(event).strip()
if not sentDict.has_key(sentenceText):
sentDict[sentenceText] = []
events = sentDict[sentenceText]
clue = event.find("clue")
clueTuple = getClue(clue)
eventType = event.find("type").get("class")
if eventType == "Protein_amino_acid_phosphorylation":
eventType = "Phosphorylation"
if type(clueTuple) == types.StringType:
if verbose:
print "Event", eventType, "clue with no clueType:", ETUtils.toStr(
clue)
else:
assert sentenceText[clueTuple[1]:clueTuple[2] +
1] == clueTuple[0], (
sentenceText,
sentenceText[clueTuple[1]:clueTuple[2] +
1], clueTuple)
event = (clueTuple[1], clueTuple[2], eventType, clueTuple[0])
if event not in events:
events.append(event)
return sentDict
def loadEventXML(path, verbose=False):
xml = ETUtils.ETFromObj(path)
sentDict = {}
for sentence in xml.getiterator("sentence"):
sentenceText = getText(sentence).strip()
if not sentDict.has_key(sentenceText):
sentDict[sentenceText] = []
for event in xml.getiterator("event"):
sentenceText = getText(event).strip()
if not sentDict.has_key(sentenceText):
sentDict[sentenceText] = []
events = sentDict[sentenceText]
clue = event.find("clue")
clueTuple = getClue(clue)
eventType = event.find("type").get("class")
if eventType == "Protein_amino_acid_phosphorylation":
eventType = "Phosphorylation"
if type(clueTuple) == types.StringType:
if verbose: print "Event", eventType, "clue with no clueType:", ETUtils.toStr(clue)
else:
assert sentenceText[clueTuple[1]:clueTuple[2]+1] == clueTuple[0], (sentenceText, sentenceText[clueTuple[1]:clueTuple[2]+1], clueTuple)
event = (clueTuple[1], clueTuple[2], eventType, clueTuple[0])
if event not in events:
events.append(event)
return sentDict
def processCorpus(input, parserName):
print >> sys.stderr, "Loading corpus file", input
corpusRoot = ETUtils.ETFromObj(input).getroot()
documents = corpusRoot.findall("document")
counts = defaultdict(int)
matchByType = defaultdict(lambda: [0, 0])
filteredMatchByType = defaultdict(lambda: [0, 0])
filter = set(
["NP", "TOK-tIN", "WHADVP", "WHNP", "TOK-tWP$", "TOK-tPRP$", "NP-IN"])
# # fix spans
# for document in documents:
# for sentence in document.findall("sentence"):
# sentOffset = Range.charOffsetToSingleTuple(sentence.get("charOffset"))
# for entity in sentence.findall("entity"):
# altOffsetString = entity.get("altOffset")
# if altOffsetString == None:
# continue
# #print altOffsetString
# altOffsets = Range.charOffsetToTuples(altOffsetString)
# assert len(altOffsets) == 1
# for i in range(len(altOffsets)):
# altOffset = altOffsets[i]
# altOffsets[i] = (altOffset[0] - sentOffset[0], altOffset[1] - sentOffset[0])
# entity.set("altOffset", Range.tuplesToCharOffset(altOffsets))
#counter = ProgressCounter(len(documents), "Documents")
for document in documents:
for sentence in document.findall("sentence"):
entities = sentence.findall("entity")
parse = ETUtils.getElementByAttrib(
sentence.find("sentenceanalyses"), "parse",
{"parser": parserName})
if parse == None:
continue
tokenization = ETUtils.getElementByAttrib(
sentence.find("sentenceanalyses"), "tokenization",
{"tokenizer": parse.get("tokenizer")})
phrases, phraseDict = makePhrases(parse, tokenization, entities)
phraseOffsets = phraseDict.keys()
#phraseOffsets.sort()
phraseNECounts = getNECounts(phrases, entities)
for value in phraseDict.values():
counts["phrases"] += len(value)
for phrase in value:
matchByType[phrase.get("type")][0] += 1
if phrase.get("type") in filter:
filteredMatchByType[phrase.get("type")][0] += 1
counts["phrases-filtered"] += 1
if phrase.get("type").find("NP") != -1:
matchByType[phrase.get("type") + "_NE" +
str(phraseNECounts[phrase])][0] += 1
counts["tokens"] += len(tokenization.findall("token"))
corefType = {}
for interaction in sentence.findall("interaction"):
if interaction.get("type") == "Coref":
corefType[interaction.get("e1")] = "Anaphora"
corefType[interaction.get("e2")] = "Antecedent"
for entity in entities:
if entity.get("isName") == "True":
continue
counts["entity"] += 1
print "entity", entity.get("id")
print ETUtils.toStr(entity)
matches = getMatchingPhrases(entity, phraseOffsets, phraseDict)
count = 0
filteredCount = 0
for phrase in matches:
cType = "UNKNOWN"
if corefType.has_key(entity.get("id")):
cType = corefType[entity.get("id")]
print " match", count, ETUtils.toStr(phrase), "NE" + str(
phraseNECounts[phrase]
), "ctype:" + cType, "ent:" + ETUtils.toStr(entity)
count += 1
matchByType[phrase.get("type")][1] += 1
matchByType[phrase.get("type") + "_" + cType][1] += 1
matchByType[phrase.get("type") + "_" + cType + "_NE" +
str(phraseNECounts[phrase])][1] += 1
if phrase.get("type") in filter:
filteredCount += 1
filteredMatchByType[phrase.get("type")][1] += 1
# Matching
if count == 0:
print " NO MATCH", ETUtils.toStr(entity)
counts["no-match"] += 1
else:
counts["match"] += 1
# Multimatching
if len(matches) > 1:
bestMatch = selectBestMatch(entity, matches)
print " MULTIMATCH(" + entity.get(
"charOffset"
) + "," + str(entity.get("altOffset")) + ")", ", ".join([
x.get("type") + "_" + x.get("charOffset")
for x in matches
]), "SEL(" + bestMatch.get("type") + "_" + bestMatch.get(
"charOffset") + ")"
# Filtered matching
if filteredCount == 0: counts["no-match-filtered"] += 1
else: counts["match-filtered"] += 1
print "Match"
for key in sorted(matchByType.keys()):
print " ", key, " ", matchByType[key]
print "Filtered", filteredMatchByType
print "Counts", counts
def processCorpus(input, parserName):
print >> sys.stderr, "Loading corpus file", input
corpusRoot = ETUtils.ETFromObj(input).getroot()
documents = corpusRoot.findall("document")
counts = defaultdict(int)
matchByType = defaultdict(lambda : [0,0])
filteredMatchByType = defaultdict(lambda : [0,0])
#filter = set(["NP", "TOK-tIN", "WHADVP", "WHNP", "TOK-tWP$", "TOK-tPRP$", "NP-IN"])
filter = set(["ADJP",
"DT(-)-NP-IN",
"DT(-)-NP",
"NP",
"NP-IN",
"PP",
"S",
"S1",
"TOK-tJJ",
"TOK-tNN",
"TOK-tNNP",
"TOK-tNNS",
"VP",
"VP-IN"])
# # fix spans
# for document in documents:
# for sentence in document.findall("sentence"):
# sentOffset = Range.charOffsetToSingleTuple(sentence.get("charOffset"))
# for entity in sentence.findall("entity"):
# altOffsetString = entity.get("altOffset")
# if altOffsetString == None:
# continue
# #print altOffsetString
# altOffsets = Range.charOffsetToTuples(altOffsetString)
# assert len(altOffsets) == 1
# for i in range(len(altOffsets)):
# altOffset = altOffsets[i]
# altOffsets[i] = (altOffset[0] - sentOffset[0], altOffset[1] - sentOffset[0])
# entity.set("altOffset", Range.tuplesToCharOffset(altOffsets))
#counter = ProgressCounter(len(documents), "Documents")
for document in documents:
for sentence in document.findall("sentence"):
entities = sentence.findall("entity")
parse = ETUtils.getElementByAttrib(sentence.find("sentenceanalyses"), "parse", {"parser":parserName})
if parse == None:
continue
tokenization = ETUtils.getElementByAttrib(sentence.find("sentenceanalyses"), "tokenization", {"tokenizer":parse.get("tokenizer")})
phrases, phraseDict = makePhrases(parse, tokenization, entities)
phraseOffsets = phraseDict.keys()
#phraseOffsets.sort()
phraseNECounts = getNECounts(phrases, entities)
for value in phraseDict.values():
counts["phrases"] += len(value)
for phrase in value:
matchByType[phrase.get("type")][0] += 1
if phrase.get("type") in filter:
filteredMatchByType[phrase.get("type")][0] += 1
counts["phrases-filtered"] += 1
if phrase.get("type").find("NP") != -1:
matchByType[phrase.get("type")+"_NE"+str(phraseNECounts[phrase])][0] += 1
counts["tokens"] += len(tokenization.findall("token"))
corefType = {}
for interaction in sentence.findall("interaction"):
if interaction.get("type") == "Coref":
corefType[interaction.get("e1")] = "Anaphora"
corefType[interaction.get("e2")] = "Antecedent"
for entity in entities:
if entity.get("isName") == "True":
continue
counts["entity"] += 1
print "entity", entity.get("id")
print ETUtils.toStr(entity)
matches = getMatchingPhrases(entity, phraseOffsets, phraseDict)
count = 0
filteredCount = 0
for phrase in matches:
cType = "UNKNOWN"
if corefType.has_key(entity.get("id")):
cType = corefType[entity.get("id")]
print " match", count, ETUtils.toStr(phrase), "NE" + str(phraseNECounts[phrase]), "ctype:" + cType, "ent:" + ETUtils.toStr(entity)
count += 1
matchByType[phrase.get("type")][1] += 1
matchByType[phrase.get("type")+"_"+cType][1] += 1
matchByType[phrase.get("type")+"_"+cType+"_NE"+str(phraseNECounts[phrase])][1] += 1
if phrase.get("type") in filter:
filteredCount += 1
filteredMatchByType[phrase.get("type")][1] += 1
# Matching
if count == 0:
print " NO MATCH", ETUtils.toStr(entity)
for phrase in phrases:
print " " + phraseToStr(phrase)
counts["no-match"] += 1
else:
counts["match"] += 1
# Multimatching
if len(matches) > 1:
bestMatch = selectBestMatch(entity, matches)
print " MULTIMATCH("+ entity.get("charOffset")+","+str(entity.get("altOffset")) + ")", ", ".join([x.get("type") + "_" + x.get("charOffset") for x in matches]), "SEL(" + bestMatch.get("type") + "_" + bestMatch.get("charOffset") + ")"
# Filtered matching
if filteredCount == 0: counts["no-match-filtered"] += 1
else: counts["match-filtered"] += 1
print "Match"
for key in sorted(matchByType.keys()):
print " ", key, " ", matchByType[key]
print "Filtered", filteredMatchByType
print "Counts", counts
