def classify(self, data, model, output, parse=None, task=None, goldData=None, workDir=None, fromStep=None, omitSteps=None, validate=False):
model = self.openModel(model, "r")
self.enterState(self.STATE_CLASSIFY)
self.setWorkDir(workDir)
if workDir == None:
self.setTempWorkDir()
model = self.openModel(model, "r")
if parse == None: parse = self.getStr(self.tag+"parse", model)
workOutputTag = os.path.join(self.workDir, os.path.basename(output) + "-")
xml = self.classifyToXML(data, model, None, workOutputTag,
model.get(self.tag+"classifier-model", defaultIfNotExist=None), goldData, parse, float(model.getStr("recallAdjustParameter", defaultIfNotExist=1.0)))
if (validate):
self.structureAnalyzer.load(model)
self.structureAnalyzer.validate(xml)
ETUtils.write(xml, output+"-pred.xml.gz")
else:
shutil.copy2(workOutputTag+self.tag+"pred.xml.gz", output+"-pred.xml.gz")
EvaluateInteractionXML.run(self.evaluator, xml, data, parse)
stParams = self.getBioNLPSharedTaskParams(self.bioNLPSTParams, model)
if stParams["convert"]: #self.useBioNLPSTFormat:
extension = ".zip" if (stParams["convert"] == "zip") else ".tar.gz"
Utils.STFormat.ConvertXML.toSTFormat(xml, output+"-events" + extension, outputTag=stParams["a2Tag"], writeExtra=(stParams["scores"] == True))
if stParams["evaluate"]: #self.stEvaluator != None:
if task == None:
task = self.getStr(self.tag+"task", model)
self.stEvaluator.evaluate(output+"-events" + extension, task)
self.deleteTempWorkDir()
self.exitState()
def evaluateChemProt(xml, gold):
EvaluateIXML.run(AveragingMultiClassEvaluator, xml, gold, "McCC")
preprocessor = Preprocessor(steps=["EXPORT_CHEMPROT"])
tempDir = tempfile.mkdtemp()
print >> sys.stderr, "Using temporary evaluation directory", tempDir
tsvPath = os.path.join(tempDir, "predictions.tsv")
preprocessor.process(xml, tsvPath)
ChemProtEvaluator().evaluateTSV(tsvPath, tempDir)
print >> sys.stderr, "Removing temporary evaluation directory", tempDir
shutil.rmtree(tempDir)
def evaluateChemProt(xml, gold):
EvaluateIXML.run(AveragingMultiClassEvaluator, xml, gold, "McCC")
preprocessor = Preprocessor(steps=["EXPORT_CHEMPROT"])
tempDir = tempfile.mkdtemp()
print >> sys.stderr, "Using temporary evaluation directory", tempDir
tsvPath = os.path.join(tempDir, "predictions.tsv")
preprocessor.process(xml, tsvPath)
ChemProtEvaluator().evaluateTSV(tsvPath, tempDir)
print >> sys.stderr, "Removing temporary evaluation directory", tempDir
shutil.rmtree(tempDir)
def classify(self, data, model, output, parse=None, task=None):
self.enterState(self.STATE_CLASSIFY)
model = self.openModel(model, "r")
if parse == None: parse = self.getStr(self.tag+"parse", model)
if task == None: task = self.getStr(self.tag+"task", model)
xml = self.classifyToXML(data, model, None, output + "-",
model.get(self.tag+"classifier-model"), None, parse, float(model.get("recallAdjustParameter")))
EvaluateInteractionXML.run(self.evaluator, xml, data, parse)
STFormat.ConvertXML.toSTFormat(xml, output+".tar.gz", outputTag="a2")
if self.stEvaluator != None:
self.stEvaluator.evaluate(output+".tar.gz", task)
self.exitState()
def trainUnmergingDetector(self):
xml = None
if not self.unmerging:
print >> sys.stderr, "No unmerging"
if self.checkStep("SELF-TRAIN-EXAMPLES-FOR-UNMERGING", self.unmerging) and self.unmerging:
# Self-classified train data for unmerging
if self.doUnmergingSelfTraining:
# This allows limiting to a subcorpus
triggerStyle = copy.copy(Parameters.get(self.triggerExampleStyle))
edgeStyle = copy.copy(Parameters.get(self.edgeExampleStyle))
unmergingStyle = Parameters.get(self.unmergingExampleStyle)
if "sentenceLimit" in unmergingStyle and unmergingStyle["sentenceLimit"]:
triggerStyle["sentenceLimit"] = unmergingStyle["sentenceLimit"]
edgeStyle["sentenceLimit"] = unmergingStyle["sentenceLimit"]
# Build the examples
xml = self.triggerDetector.classifyToXML(self.trainData, self.model, None, self.workDir+"unmerging-extra-", exampleStyle=triggerStyle)#, recallAdjust=0.5)
xml = self.edgeDetector.classifyToXML(xml, self.model, None, self.workDir+"unmerging-extra-", exampleStyle=edgeStyle)#, recallAdjust=0.5)
assert xml != None
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.trainData, self.parse)
else:
print >> sys.stderr, "No self-training for unmerging"
if self.checkStep("UNMERGING-EXAMPLES", self.unmerging) and self.unmerging:
# Unmerging example generation
GOLD_TEST_FILE = self.optData.replace("-nodup", "")
GOLD_TRAIN_FILE = self.trainData.replace("-nodup", "")
if self.doUnmergingSelfTraining:
if xml == None:
xml = self.workDir+"unmerging-extra-edge-pred.xml.gz"
self.unmergingDetector.buildExamples(self.model, [self.optData.replace("-nodup", ""), [self.trainData.replace("-nodup", ""), xml]],
[self.workDir+"unmerging-opt-examples.gz", self.workDir+"unmerging-train-examples.gz"],
[GOLD_TEST_FILE, [GOLD_TRAIN_FILE, GOLD_TRAIN_FILE]],
exampleStyle=self.unmergingExampleStyle, saveIdsToModel=True)
xml = None
else:
self.unmergingDetector.buildExamples(self.model, [self.optData.replace("-nodup", ""), self.trainData.replace("-nodup", "")],
[self.workDir+"unmerging-opt-examples.gz", self.workDir+"unmerging-train-examples.gz"],
[GOLD_TEST_FILE, GOLD_TRAIN_FILE],
exampleStyle=self.unmergingExampleStyle, saveIdsToModel=True)
xml = None
#UnmergingExampleBuilder.run("/home/jari/biotext/EventExtension/TrainSelfClassify/test-predicted-edges.xml", GOLD_TRAIN_FILE, UNMERGING_TRAIN_EXAMPLE_FILE, PARSE, TOK, UNMERGING_FEATURE_PARAMS, UNMERGING_IDS, append=True)
if self.checkStep("BEGIN-UNMERGING-MODEL", self.unmerging) and self.unmerging:
self.unmergingDetector.beginModel(None, self.model, self.workDir+"unmerging-train-examples.gz", self.workDir+"unmerging-opt-examples.gz")
if self.checkStep("END-UNMERGING-MODEL", self.unmerging) and self.unmerging:
self.unmergingDetector.endModel(None, self.model, self.workDir+"unmerging-opt-examples.gz")
print >> sys.stderr, "Adding unmerging classifier model to test-set event model"
if self.combinedModel != None:
self.combinedModel.addStr("unmerging-example-style", self.model.getStr("unmerging-example-style"))
self.combinedModel.insert(self.model.get("unmerging-ids.classes"), "unmerging-ids.classes")
self.combinedModel.insert(self.model.get("unmerging-ids.features"), "unmerging-ids.features")
self.unmergingDetector.addClassifierModel(self.combinedModel, self.model.get("unmerging-classifier-model", True),
self.model.getStr("unmerging-classifier-parameter"))
self.combinedModel.save()
def classify(self, data, model, output, parse=None, task=None):
self.enterState(self.STATE_CLASSIFY)
model = self.openModel(model, "r")
if parse == None: parse = self.getStr(self.tag + "parse", model)
if task == None: task = self.getStr(self.tag + "task", model)
xml = self.classifyToXML(data, model, None, output + "-",
model.get(self.tag + "classifier-model"),
None, parse,
float(model.get("recallAdjustParameter")))
EvaluateInteractionXML.run(self.evaluator, xml, data, parse)
STFormat.ConvertXML.toSTFormat(xml, output + ".tar.gz", outputTag="a2")
if self.stEvaluator != None:
self.stEvaluator.evaluate(output + ".tar.gz", task)
self.exitState()
def classify(self,
data,
model,
output,
parse=None,
task=None,
goldData=None,
workDir=None,
fromStep=None,
omitSteps=None,
validate=False):
model = self.openModel(model, "r")
self.enterState(self.STATE_CLASSIFY)
self.setWorkDir(workDir)
if workDir == None:
self.setTempWorkDir()
model = self.openModel(model, "r")
if parse == None: parse = self.getStr(self.tag + "parse", model)
workOutputTag = os.path.join(self.workDir,
os.path.basename(output) + "-")
xml = self.classifyToXML(
data, model, None, workOutputTag,
model.get(self.tag + "classifier-model", defaultIfNotExist=None),
goldData, parse,
float(model.getStr("recallAdjustParameter",
defaultIfNotExist=1.0)))
if (validate):
self.structureAnalyzer.load(model)
self.structureAnalyzer.validate(xml)
ETUtils.write(xml, output + "-pred.xml.gz")
else:
shutil.copy2(workOutputTag + self.tag + "pred.xml.gz",
output + "-pred.xml.gz")
EvaluateInteractionXML.run(self.evaluator, xml, data, parse)
stParams = self.getBioNLPSharedTaskParams(self.bioNLPSTParams, model)
if stParams["convert"]: #self.useBioNLPSTFormat:
extension = ".zip" if (stParams["convert"] == "zip") else ".tar.gz"
Utils.STFormat.ConvertXML.toSTFormat(
xml,
output + "-events" + extension,
outputTag=stParams["a2Tag"],
writeExtra=(stParams["scores"] == True))
if stParams["evaluate"]: #self.stEvaluator != None:
if task == None:
task = self.getStr(self.tag + "task", model)
self.stEvaluator.evaluate(output + "-events" + extension, task)
self.deleteTempWorkDir()
self.exitState()
def evaluateGrid(self, xml, params, bestResults):
if xml != None:
# TODO: Where should the EvaluateInteractionXML evaluator come from?
EIXMLResult = EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.optData, self.parse)
# Convert to ST-format
STFormat.ConvertXML.toSTFormat(xml, self.workDir+"grid-flat-geniaformat", "a2") #getA2FileTag(options.task, subTask))
stFormatDir = self.workDir+"grid-flat-geniaformat"
if self.unmerging:
xml = self.unmergingDetector.classifyToXML(xml, self.model, None, self.workDir+"grid-", goldData=self.optData.replace("-nodup", ""))
STFormat.ConvertXML.toSTFormat(xml, self.workDir+"grid-unmerging-geniaformat", "a2")
stFormatDir = self.workDir+"grid-unmerging-geniaformat"
stEvaluation = self.stEvaluator.evaluate(stFormatDir, self.task)
if stEvaluation != None:
if bestResults == None or stEvaluation[0] > bestResults[1][0]:
bestResults = (params, stEvaluation, stEvaluation[0])
else:
if bestResults == None or EIXMLResult.getData().fscore > bestResults[1].getData().fscore:
bestResults = (params, EIXMLResult, EIXMLResult.getData().fscore)
shutil.rmtree(self.workDir+"grid-flat-geniaformat")
if os.path.exists(self.workDir+"grid-unmerging-geniaformat"):
shutil.rmtree(self.workDir+"grid-unmerging-geniaformat")
else:
print >> sys.stderr, "No predicted edges"
return bestResults
def evaluateGrid(self, xml, params, bestResults):
if xml != None:
# TODO: Where should the EvaluateInteractionXML evaluator come from?
EIXMLResult = EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.optData, self.parse)
# Convert to ST-format
if self.unmerging:
xml = self.unmergingDetector.classifyToXML(xml, self.model, None, self.workDir+"grid-", goldData=self.optData)
#self.structureAnalyzer.validate(xml)
if self.bioNLPSTParams["evaluate"]:
Utils.STFormat.ConvertXML.toSTFormat(xml, self.workDir+"grid-unmerging-geniaformat", "a2")
stFormatDir = self.workDir+"grid-unmerging-geniaformat"
elif self.bioNLPSTParams["evaluate"]:
#self.structureAnalyzer.validate(xml)
Utils.STFormat.ConvertXML.toSTFormat(xml, self.workDir+"grid-flat-geniaformat", "a2") #getA2FileTag(options.task, subTask))
stFormatDir = self.workDir+"grid-flat-geniaformat"
# Evaluation
# Attempt shared task evaluation
stEvaluation = None
if self.bioNLPSTParams["evaluate"]:
stEvaluation = self.stEvaluator.evaluate(stFormatDir, self.task)
if stEvaluation != None:
if bestResults == None or stEvaluation[0] > bestResults[1][0]:
bestResults = (params, stEvaluation, stEvaluation[0])
else: # If shared task evaluation was not done (failed or not requested) fall back to internal evaluation
if bestResults == None or EIXMLResult.getData().fscore > bestResults[1].getData().fscore:
bestResults = (params, EIXMLResult, EIXMLResult.getData().fscore)
# Remove ST-format files
if os.path.exists(self.workDir+"grid-flat-geniaformat"):
shutil.rmtree(self.workDir+"grid-flat-geniaformat")
if os.path.exists(self.workDir+"grid-unmerging-geniaformat"):
shutil.rmtree(self.workDir+"grid-unmerging-geniaformat")
else:
print >> sys.stderr, "No predicted edges"
return bestResults
def classify(self, data, model, output, parse=None, task=None, fromStep=None, toStep=None):
BINARY_RECALL_MODE = False # TODO: make a parameter
xml = None
self.initVariables(classifyData=data, model=model, xml=None, task=task, parse=parse)
self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep)
#self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "RECALL-ADJUST", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep)
self.model = self.openModel(self.model, "r")
if self.checkStep("TRIGGERS"):
xml = self.triggerDetector.classifyToXML(self.classifyData, self.model, None, output + "-", parse=self.parse, recallAdjust=float(self.getStr("recallAdjustParameter", self.model)))
if self.checkStep("EDGES"):
xml = self.getWorkFile(xml, output + "-recall-adjusted.xml.gz")
xml = self.edgeDetector.classifyToXML(xml, self.model, None, output + "-", parse=self.parse)
assert xml != None
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag+"parse", self.model)
else:
edgeParse = self.parse
#EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, None, edgeParse)
if self.checkStep("UNMERGING"):
if self.model.hasMember("unmerging-classifier-model"):
#xml = self.getWorkFile(xml, output + "-edge-pred.xml.gz")
# To avoid running out of memory, always use file on disk
xml = self.getWorkFile(None, output + "-edge-pred.xml.gz")
goldData = None
if type(self.classifyData) in types.StringTypes:
if os.path.exists(self.classifyData.replace("-nodup", "")):
goldData = self.classifyData.replace("-nodup", "")
xml = self.unmergingDetector.classifyToXML(xml, self.model, None, output + "-", goldData=goldData, parse=self.parse)
else:
print >> sys.stderr, "No model for unmerging"
if self.checkStep("MODIFIERS"):
if self.model.hasMember("modifier-classifier-model"):
xml = self.getWorkFile(xml, [output + "-unmerging-pred.xml.gz", output + "-edge-pred.xml.gz"])
xml = self.modifierDetector.classifyToXML(xml, self.model, None, output + "-", parse=self.parse)
else:
print >> sys.stderr, "No model for modifier detection"
if self.checkStep("ST-CONVERT"):
xml = self.getWorkFile(xml, [output + "-modifier-pred.xml.gz", output + "-unmerging-pred.xml.gz", output + "-edge-pred.xml.gz"])
STFormat.ConvertXML.toSTFormat(xml, output+"-events.tar.gz", outputTag="a2", writeScores=self.stWriteScores)
if self.stEvaluator != None:
task = self.task
if task == None:
task = self.getStr(self.edgeDetector.tag+"task", self.model)
self.stEvaluator.evaluate(output + "-events.tar.gz", task)
self.exitState()
def classify(self, data, model, output, parse=None, task=None, goldData=None, workDir=None, fromStep=None, omitSteps=None):
model = self.openModel(model, "r")
self.enterState(self.STATE_CLASSIFY)
self.setWorkDir(workDir)
if workDir == None:
self.setTempWorkDir()
model = self.openModel(model, "r")
if parse == None: parse = self.getStr(self.tag+"parse", model)
if task == None: task = self.getStr(self.tag+"task", model)
workOutputTag = os.path.join(self.workDir, os.path.basename(output) + "-")
xml = self.classifyToXML(data, model, None, workOutputTag,
model.get(self.tag+"classifier-model"), goldData, parse, float(model.getStr("recallAdjustParameter", defaultIfNotExist=1.0)))
shutil.copy2(workOutputTag+self.tag+"pred.xml.gz", output+"-pred.xml.gz")
EvaluateInteractionXML.run(self.evaluator, xml, data, parse)
stParams = self.getBioNLPSharedTaskParams(self.bioNLPSTParams, model)
if stParams["convert"]: #self.useBioNLPSTFormat:
Utils.STFormat.ConvertXML.toSTFormat(xml, output+"-events.tar.gz", outputTag="a2")
if stParams["evaluate"]: #self.stEvaluator != None:
self.stEvaluator.evaluate(output+"-events.tar.gz", task)
self.deleteTempWorkDir()
self.exitState()
def evaluateGrid(self, xml, params, bestResults):
#traceback.print_stack()
#pdb.set_trace()
if xml != None:
# TODO: Where should the EvaluateInteractionXML evaluator come from?
EIXMLResult = EvaluateInteractionXML.run(
self.edgeDetector.evaluator, xml, self.optData, self.parse)
# Convert to ST-format
if self.unmerging:
xml = self.unmergingDetector.classifyToXML(
xml,
self.model,
None,
self.workDir + "grid-",
goldData=self.optData)
#self.structureAnalyzer.validate(xml)
if self.bioNLPSTParams["evaluate"]:
Utils.STFormat.ConvertXML.toSTFormat(
xml, self.workDir + "grid-unmerging-geniaformat", "a2")
stFormatDir = self.workDir + "grid-unmerging-geniaformat"
elif self.bioNLPSTParams["evaluate"]:
#self.structureAnalyzer.validate(xml)
Utils.STFormat.ConvertXML.toSTFormat(
xml, self.workDir + "grid-flat-geniaformat",
"a2") #getA2FileTag(options.task, subTask))
stFormatDir = self.workDir + "grid-flat-geniaformat"
# Evaluation
# Attempt shared task evaluation
stEvaluation = None
if self.bioNLPSTParams["evaluate"]:
stEvaluation = self.stEvaluator.evaluate(
stFormatDir, self.task)
if stEvaluation != None:
if bestResults == None or stEvaluation[0] > bestResults[1][0]:
bestResults = (params, stEvaluation, stEvaluation[0])
else: # If shared task evaluation was not done (failed or not requested) fall back to internal evaluation
if bestResults == None or EIXMLResult.getData(
).fscore > bestResults[1].getData().fscore:
bestResults = (params, EIXMLResult,
EIXMLResult.getData().fscore)
# Remove ST-format files
if os.path.exists(self.workDir + "grid-flat-geniaformat"):
shutil.rmtree(self.workDir + "grid-flat-geniaformat")
if os.path.exists(self.workDir + "grid-unmerging-geniaformat"):
shutil.rmtree(self.workDir + "grid-unmerging-geniaformat")
else:
print >> sys.stderr, "No predicted edges"
return bestResults
def evaluateGrid(self, xml, params, bestResults):
if xml != None:
# TODO: Where should the EvaluateInteractionXML evaluator come from?
EIXMLResult = EvaluateInteractionXML.run(
self.edgeDetector.evaluator, xml, self.optData, self.parse)
# Convert to ST-format
STFormat.ConvertXML.toSTFormat(
xml, self.workDir + "grid-flat-geniaformat",
"a2") #getA2FileTag(options.task, subTask))
stFormatDir = self.workDir + "grid-flat-geniaformat"
if self.unmerging:
xml = self.unmergingDetector.classifyToXML(
xml,
self.model,
None,
self.workDir + "grid-",
goldData=self.optData.replace("-nodup", ""))
STFormat.ConvertXML.toSTFormat(
xml, self.workDir + "grid-unmerging-geniaformat", "a2")
stFormatDir = self.workDir + "grid-unmerging-geniaformat"
stEvaluation = self.stEvaluator.evaluate(stFormatDir, self.task)
if stEvaluation != None:
if bestResults == None or stEvaluation[0] > bestResults[1][0]:
bestResults = (params, stEvaluation, stEvaluation[0])
else:
if bestResults == None or EIXMLResult.getData(
).fscore > bestResults[1].getData().fscore:
bestResults = (params, EIXMLResult,
EIXMLResult.getData().fscore)
shutil.rmtree(self.workDir + "grid-flat-geniaformat")
if os.path.exists(self.workDir + "grid-unmerging-geniaformat"):
shutil.rmtree(self.workDir + "grid-unmerging-geniaformat")
else:
print >> sys.stderr, "No predicted edges"
return bestResults
def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph=None):
"""
Build one example for each token of the sentence
"""
examples = []
exampleIndex = 0
self.tokenFeatures = {}
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(
sentenceGraph.entities, goldGraph.entities)
namedEntityCount = 0
entityCount = 0
for entity in sentenceGraph.entities:
if entity.get(
"isName"
) == "True": # known data which can be used for features
namedEntityCount += 1
else: # known data which can be used for features
entityCount += 1
namedEntityCountFeature = "nameCount_" + str(namedEntityCount)
entityCountFeature = "entityCount_" + str(entityCount)
bagOfWords = {}
for token in sentenceGraph.tokens:
text = "bow_" + token.get("text")
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
if sentenceGraph.tokenIsName[token]:
text = "ne_" + text
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
if len(sentenceGraph.tokenIsEntityHead) > 0:
text = "ge_" + text
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
text = token.get("text")
if self.styles["speculation_words"] and text in self.specWords:
if not bagOfWords.has_key("spec_bow_" + text):
bagOfWords["spec_bow_" + text] = 0
bagOfWords["spec_bow_" + text] += 1
bagOfWords["spec_sentence"] = 1
bowFeatures = {}
for k, v in bagOfWords.iteritems():
bowFeatures[self.featureSet.getId(k)] = v
self.inEdgesByToken = {}
self.outEdgesByToken = {}
self.edgeSetByToken = {}
for token in sentenceGraph.tokens:
inEdges = sentenceGraph.dependencyGraph.getInEdges(token)
self.inEdgesByToken[token] = inEdges
outEdges = sentenceGraph.dependencyGraph.getOutEdges(token)
self.outEdgesByToken[token] = outEdges
self.edgeSetByToken[token] = set(inEdges + outEdges)
for entity in sentenceGraph.entities:
#token = sentenceGraph.tokens[i]
token = sentenceGraph.entityHeadTokenByEntity[entity]
# Recognize only non-named entities (i.e. interaction words)
if entity.get("isName") == "True":
continue
# CLASS
if self.styles["classification"] == "multiclass":
task3Type = "multiclass"
categoryName = ""
if entity.get("negation") == "True":
categoryName += "negation"
if entity.get("speculation") == "True":
if categoryName != "":
categoryName += "---"
categoryName += "speculation"
if categoryName == "":
categoryName = "neg"
category = self.classSet.getId(categoryName)
elif self.styles["classification"] == "speculation":
task3Type = "speculation"
if entity.get("speculation") == "True":
category = self.classSet.getId("speculation")
else:
category = 1
if goldGraph != None:
if len(entityToGold[entity]) > 0 and entityToGold[entity][
0].get("speculation") == "True":
category = self.classSet.getId("speculation")
else:
category = 1
categoryName = self.classSet.getName(category)
elif self.styles["classification"] == "negation":
task3Type = "negation"
if entity.get("negation") == "True":
category = self.classSet.getId("negation")
else:
category = 1
if goldGraph != None:
if len(entityToGold[entity]) > 0 and entityToGold[entity][
0].get("negation") == "True":
category = self.classSet.getId("negation")
else:
category = 1
categoryName = self.classSet.getName(category)
self.exampleStats.beginExample(categoryName)
# FEATURES
features = {}
# ENTITY TYPE
#entityType = self.classSet.getId(self.getMergedEntityType(entity))
#del self.classSet.Ids[self.getMergedEntityType(entity)]
#IF LOCAL
# There's a mistake here. The entityType should be the string, not
# the id of the type. But there's also another issue. getMergedEntityType
# expects a list, not an item. Therefore the type is always empty ->
# types don't get used in classification. But this is the code used in
# the publication, so it will now be published as is, and fixed in a later
# release.
#
# Besides, using the classSet here generates an unneeded
# additional class, that shows up in evaluations etc. However, to be
# able to publish the exact models used for the publication experiments,
# this can't be fixed so it breaks feature id consistency. Therefore I'll
# now just remove the redundant class id from the classSet.
#ENDIF
#features[self.featureSet.getId(entityType)] = 1
features[self.featureSet.getId(namedEntityCountFeature)] = 1
features[self.featureSet.getId(entityCountFeature)] = 1
#for k,v in bagOfWords.iteritems():
# features[self.featureSet.getId(k)] = v
# pre-calculate bow _features_
features.update(bowFeatures)
# for j in range(len(sentenceGraph.tokens)):
# text = "bow_" + sentenceGraph.tokens[j].get("text")
# if j < i:
# features[self.featureSet.getId("bf_" + text)] = 1
# elif j > i:
# features[self.featureSet.getId("af_" + text)] = 1
# Main features
text = token.get("text")
features[self.featureSet.getId("txt_" + text)] = 1
features[self.featureSet.getId("POS_" + token.get("POS"))] = 1
stem = PorterStemmer.stem(text)
features[self.featureSet.getId("stem_" + stem)] = 1
features[self.featureSet.getId("nonstem_" + text[len(stem):])] = 1
if self.styles["speculation_words"]:
if text in self.specWords:
features[self.featureSet.getId("ent_spec")] = 1
if stem in self.specWordStems:
features[self.featureSet.getId("ent_spec_stem")] = 1
# Linear order features
for i in range(len(sentenceGraph.tokens)):
if token == sentenceGraph.tokens[i]:
break
for index in [-3, -2, -1, 1, 2, 3]:
if i + index > 0 and i + index < len(sentenceGraph.tokens):
self.buildLinearOrderFeatures(sentenceGraph, i + index,
str(index), features)
# Content
if i > 0 and text[0].isalpha() and text[0].isupper():
features[self.featureSet.getId("upper_case_start")] = 1
for j in range(len(text)):
if j > 0 and text[j].isalpha() and text[j].isupper():
features[self.featureSet.getId("upper_case_middle")] = 1
# numbers and special characters
if text[j].isdigit():
features[self.featureSet.getId("has_digits")] = 1
if j > 0 and text[j - 1] == "-":
features[self.featureSet.getId(
"has_hyphenated_digit")] = 1
elif text[j] == "-":
features[self.featureSet.getId("has_hyphen")] = 1
elif text[j] == "/":
features[self.featureSet.getId("has_fslash")] = 1
elif text[j] == "\\":
features[self.featureSet.getId("has_bslash")] = 1
# duplets
if j > 0:
features[self.featureSet.getId("dt_" +
text[j - 1:j +
1].lower())] = 1
# triplets
if j > 1:
features[self.featureSet.getId("tt_" +
text[j - 2:j +
1].lower())] = 1
# Attached edges (Hanging in and out edges)
t1InEdges = self.inEdgesByToken[token]
for edge in t1InEdges:
edgeType = edge[2].get("type")
features[self.featureSet.getId("t1HIn_" + edgeType)] = 1
features[self.featureSet.getId("t1HIn_" +
edge[0].get("POS"))] = 1
features[self.featureSet.getId("t1HIn_" + edgeType + "_" +
edge[0].get("POS"))] = 1
tokenText = sentenceGraph.getTokenText(edge[0])
features[self.featureSet.getId("t1HIn_" + tokenText)] = 1
features[self.featureSet.getId("t1HIn_" + edgeType + "_" +
tokenText)] = 1
t1OutEdges = self.outEdgesByToken[token]
for edge in t1OutEdges:
edgeType = edge[2].get("type")
features[self.featureSet.getId("t1HOut_" + edgeType)] = 1
features[self.featureSet.getId("t1HOut_" +
edge[1].get("POS"))] = 1
features[self.featureSet.getId("t1HOut_" + edgeType + "_" +
edge[1].get("POS"))] = 1
tokenText = sentenceGraph.getTokenText(edge[1])
features[self.featureSet.getId("t1HOut_" + tokenText)] = 1
features[self.featureSet.getId("t1HOut_" + edgeType + "_" +
tokenText)] = 1
self.buildChains(token, sentenceGraph, features)
extra = {
"xtype": "task3",
"t3type": task3Type,
"t": token.get("id"),
"entity": entity.get("id")
}
#examples.append( (sentenceGraph.getSentenceId()+".x"+str(exampleIndex),category,features,extra) )
example = (sentenceGraph.getSentenceId() + ".x" +
str(exampleIndex), category, features, extra)
ExampleUtils.appendExamples([example], outfile)
exampleIndex += 1
self.exampleStats.endExample()
#return examples
return exampleIndex
def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph = None):
"""
Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format.
"""
#examples = []
exampleIndex = 0
if self.styles["trigger_features"]:
self.triggerFeatureBuilder.initSentence(sentenceGraph)
if self.styles["evex"]:
self.evexFeatureBuilder.initSentence(sentenceGraph)
# Filter entities, if needed
#mergedIds = None
#duplicateEntities = None
#entities = sentenceGraph.entities
#entities, mergedIds, duplicateEntities = self.mergeEntities(sentenceGraph, False) # "no_duplicates" in self.styles)
sentenceGraph.mergeInteractionGraph(True)
entities = sentenceGraph.mergedEntities
entityToDuplicates = sentenceGraph.mergedEntityToDuplicates
self.exampleStats.addValue("Duplicate entities skipped", len(sentenceGraph.entities) - len(entities))
# Connect to optional gold graph
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(entities, goldGraph.entities)
paths = None
if not self.styles["no_path"]:
##undirected = sentenceGraph.getUndirectedDependencyGraph()
#undirected = self.nxMultiDiGraphToUndirected(sentenceGraph.dependencyGraph)
###undirected = sentenceGraph.dependencyGraph.to_undirected()
####undirected = NX10.MultiGraph(sentenceGraph.dependencyGraph) This didn't work
undirected = sentenceGraph.dependencyGraph.toUndirected()
#paths = NX10.all_pairs_shortest_path(undirected, cutoff=999)
paths = undirected
#for edge in sentenceGraph.dependencyGraph.edges:
# assert edge[2] != None
#for edge in undirected.edges:
# assert edge[2] != None
#if sentenceGraph.sentenceElement.get("id") == "GENIA.d70.s5":
# print [(x[0].get("id"), x[1].get("id"), x[2].get("id")) for x in sentenceGraph.dependencyGraph.edges]
# Generate examples based on interactions between entities or interactions between tokens
if self.styles["entities"]:
loopRange = len(entities)
else:
loopRange = len(sentenceGraph.tokens)
for i in range(loopRange-1):
for j in range(i+1,loopRange):
eI = None
eJ = None
if self.styles["entities"]:
eI = entities[i]
eJ = entities[j]
tI = sentenceGraph.entityHeadTokenByEntity[eI]
tJ = sentenceGraph.entityHeadTokenByEntity[eJ]
#if "no_ne_interactions" in self.styles and eI.get("isName") == "True" and eJ.get("isName") == "True":
# continue
if eI.get("type") == "neg" or eJ.get("type") == "neg":
continue
if self.styles["skip_extra_triggers"]:
if eI.get("source") != None or eJ.get("source") != None:
continue
else:
tI = sentenceGraph.tokens[i]
tJ = sentenceGraph.tokens[j]
# only consider paths between entities (NOTE! entities, not only named entities)
if self.styles["headsOnly"]:
if (len(sentenceGraph.tokenIsEntityHead[tI]) == 0) or (len(sentenceGraph.tokenIsEntityHead[tJ]) == 0):
continue
if self.styles["directed"]:
# define forward
if self.styles["entities"]:
categoryName = self.getCategoryName(sentenceGraph, eI, eJ, True)
if goldGraph != None:
categoryName = self.getGoldCategoryName(goldGraph, entityToGold, eI, eJ, True)
else:
categoryName = self.getCategoryNameFromTokens(sentenceGraph, tI, tJ, True)
# make forward
self.exampleStats.beginExample(categoryName)
makeExample = True
if self.styles["genia_limits"] and not self.isPotentialGeniaInteraction(eI, eJ):
makeExample = False
self.exampleStats.filter("genia_limits")
if self.styles["genia_task1"] and (eI.get("type") == "Entity" or eJ.get("type") == "Entity"):
makeExample = False
self.exampleStats.filter("genia_task1")
if self.styles["rel_limits"] and not self.isPotentialRELInteraction(eI, eJ):
makeExample = False
self.exampleStats.filter("rel_limits")
if self.styles["co_limits"] and not self.isPotentialCOInteraction(eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("co_limits")
if self.styles["bb_limits"] and not self.isPotentialBBInteraction(eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("bb_limits")
if categoryName != "neg":
self.exampleStats.filter("bb_limits(" + categoryName + ":" + eI.get("type") + "/" + eJ.get("type") + ")")
if self.styles["bi_limits"] and not self.isPotentialBIInteraction(eI, eJ, sentenceGraph, self.exampleStats):
makeExample = False
#self.exampleStats.filter("bi_limits")
if self.styles["epi_limits"] and not self.isPotentialEPIInteraction(eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("epi_limits")
if self.styles["id_limits"] and not self.isPotentialIDInteraction(eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("id_limits")
# if self.styles["selftrain_limits"] and (eI.get("selftrain") == "False" or eJ.get("selftrain") == "False"):
# makeExample = False
# self.exampleStats.filter("selftrain_limits")
# if self.styles["selftrain_group"] and (eI.get("selftraingroup") not in self.selfTrainGroups or eJ.get("selftraingroup") not in self.selfTrainGroups):
# makeExample = False
# self.exampleStats.filter("selftrain_group")
if self.styles["pos_only"] and categoryName == "neg":
makeExample = False
self.exampleStats.filter("pos_only")
if makeExample:
#examples.append( self.buildExample(tI, tJ, paths, sentenceGraph, categoryName, exampleIndex, eI, eJ) )
ExampleUtils.appendExamples([self.buildExample(tI, tJ, paths, sentenceGraph, categoryName, exampleIndex, eI, eJ)], outfile)
exampleIndex += 1
self.exampleStats.endExample()
# define reverse
if self.styles["entities"]:
categoryName = self.getCategoryName(sentenceGraph, eJ, eI, True)
if goldGraph != None:
categoryName = self.getGoldCategoryName(goldGraph, entityToGold, eJ, eI, True)
else:
categoryName = self.getCategoryNameFromTokens(sentenceGraph, tJ, tI, True)
# make reverse
self.exampleStats.beginExample(categoryName)
makeExample = True
if self.styles["genia_limits"] and not self.isPotentialGeniaInteraction(eJ, eI):
makeExample = False
self.exampleStats.filter("genia_limits")
if self.styles["genia_task1"] and (eI.get("type") == "Entity" or eJ.get("type") == "Entity"):
makeExample = False
self.exampleStats.filter("genia_task1")
if self.styles["rel_limits"] and not self.isPotentialRELInteraction(eJ, eI):
makeExample = False
self.exampleStats.filter("rel_limits")
if self.styles["co_limits"] and not self.isPotentialCOInteraction(eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("co_limits")
if self.styles["bb_limits"] and not self.isPotentialBBInteraction(eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("bb_limits")
if categoryName != "neg":
self.exampleStats.filter("bb_limits(" + categoryName + ":" + eJ.get("type") + "/" + eI.get("type") + ")")
if self.styles["bi_limits"] and not self.isPotentialBIInteraction(eJ, eI, sentenceGraph, self.exampleStats):
makeExample = False
#self.exampleStats.filter("bi_limits")
if self.styles["epi_limits"] and not self.isPotentialEPIInteraction(eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("epi_limits")
if self.styles["id_limits"] and not self.isPotentialIDInteraction(eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("id_limits")
# if self.styles["selftrain_limits"] and (eI.get("selftrain") == "False" or eJ.get("selftrain") == "False"):
# makeExample = False
# self.exampleStats.filter("selftrain_limits")
# if self.styles["selftrain_group"] and (eI.get("selftraingroup") not in self.selfTrainGroups or eJ.get("selftraingroup") not in self.selfTrainGroups):
# makeExample = False
# self.exampleStats.filter("selftrain_group")
if self.styles["pos_only"] and categoryName == "neg":
makeExample = False
self.exampleStats.filter("pos_only")
if makeExample:
#examples.append( self.buildExample(tJ, tI, paths, sentenceGraph, categoryName, exampleIndex, eJ, eI) )
ExampleUtils.appendExamples([self.buildExample(tJ, tI, paths, sentenceGraph, categoryName, exampleIndex, eJ, eI)], outfile)
exampleIndex += 1
self.exampleStats.endExample()
else:
if self.styles["entities"]:
categoryName = self.getCategoryName(sentenceGraph, eI, eJ, False)
else:
categoryName = self.getCategoryNameFromTokens(sentenceGraph, tI, tJ, False)
self.exampleStats.beginExample(categoryName)
forwardExample = self.buildExample(tI, tJ, paths, sentenceGraph, categoryName, exampleIndex, eI, eJ)
if not self.styles["graph_kernel"]:
reverseExample = self.buildExample(tJ, tI, paths, sentenceGraph, categoryName, exampleIndex, eJ, eI)
forwardExample[2].update(reverseExample[2])
#examples.append(forwardExample)
ExampleUtils.appendExamples([forwardExample], outfile)
exampleIndex += 1
self.exampleStats.endExample()
#return examples
return exampleIndex
def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph=None, structureAnalyzer=None):
"""
Build one example for each token of the sentence
"""
examples = []
exampleIndex = 0
self.tokenFeatures = {}
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(sentenceGraph.entities, goldGraph.entities)
namedEntityCount = 0
entityCount = 0
for entity in sentenceGraph.entities:
if entity.get("given") == "True": # known data which can be used for features
namedEntityCount += 1
else: # known data which can be used for features
entityCount += 1
namedEntityCountFeature = "nameCount_" + str(namedEntityCount)
entityCountFeature = "entityCount_" + str(entityCount)
bagOfWords = {}
for token in sentenceGraph.tokens:
text = "bow_" + token.get("text")
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
if sentenceGraph.tokenIsName[token]:
text = "ne_" + text
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
if len(sentenceGraph.tokenIsEntityHead) > 0:
text = "ge_" + text
if not bagOfWords.has_key(text):
bagOfWords[text] = 0
bagOfWords[text] += 1
text = token.get("text")
if self.styles["speculation_words"] and text in self.specWords:
if not bagOfWords.has_key("spec_bow_"+text):
bagOfWords["spec_bow_"+text] = 0
bagOfWords["spec_bow_"+text] += 1
bagOfWords["spec_sentence"] = 1
bowFeatures = {}
for k,v in bagOfWords.iteritems():
bowFeatures[self.featureSet.getId(k)] = v
self.inEdgesByToken = {}
self.outEdgesByToken = {}
self.edgeSetByToken = {}
for token in sentenceGraph.tokens:
inEdges = sentenceGraph.dependencyGraph.getInEdges(token)
self.inEdgesByToken[token] = inEdges
outEdges = sentenceGraph.dependencyGraph.getOutEdges(token)
self.outEdgesByToken[token] = outEdges
self.edgeSetByToken[token] = set(inEdges + outEdges)
for entity in sentenceGraph.entities:
#token = sentenceGraph.tokens[i]
token = sentenceGraph.entityHeadTokenByEntity[entity]
# Recognize only non-named entities (i.e. interaction words)
if entity.get("given") == "True":
continue
# CLASS
if self.styles["classification"] == "multiclass":
task3Type = "multiclass"
categoryName = ""
if entity.get("negation") == "True":
categoryName += "negation"
if entity.get("speculation") == "True":
if categoryName != "":
categoryName += "---"
categoryName += "speculation"
if categoryName == "":
categoryName = "neg"
category = self.classSet.getId(categoryName)
elif self.styles["classification"] == "speculation":
task3Type = "speculation"
if entity.get("speculation") == "True":
category = self.classSet.getId("speculation")
else:
category = 1
if goldGraph != None:
if len(entityToGold[entity]) > 0 and entityToGold[entity][0].get("speculation") == "True":
category = self.classSet.getId("speculation")
else:
category = 1
categoryName = self.classSet.getName(category)
elif self.styles["classification"] == "negation":
task3Type = "negation"
if entity.get("negation") == "True":
category = self.classSet.getId("negation")
else:
category = 1
if goldGraph != None:
if len(entityToGold[entity]) > 0 and entityToGold[entity][0].get("negation") == "True":
category = self.classSet.getId("negation")
else:
category = 1
categoryName = self.classSet.getName(category)
self.exampleStats.beginExample(categoryName)
# FEATURES
features = {}
# ENTITY TYPE
#entityType = self.classSet.getId(self.getMergedEntityType(entity))
#del self.classSet.Ids[self.getMergedEntityType(entity)]
#IF LOCAL
# There's a mistake here. The entityType should be the string, not
# the id of the type. But there's also another issue. getMergedEntityType
# expects a list, not an item. Therefore the type is always empty ->
# types don't get used in classification. But this is the code used in
# the publication, so it will now be published as is, and fixed in a later
# release.
#
# Besides, using the classSet here generates an unneeded
# additional class, that shows up in evaluations etc. However, to be
# able to publish the exact models used for the publication experiments,
# this can't be fixed so it breaks feature id consistency. Therefore I'll
# now just remove the redundant class id from the classSet.
#ENDIF
#features[self.featureSet.getId(entityType)] = 1
features[self.featureSet.getId(namedEntityCountFeature)] = 1
features[self.featureSet.getId(entityCountFeature)] = 1
#for k,v in bagOfWords.iteritems():
# features[self.featureSet.getId(k)] = v
# pre-calculate bow _features_
features.update(bowFeatures)
# for j in range(len(sentenceGraph.tokens)):
# text = "bow_" + sentenceGraph.tokens[j].get("text")
# if j < i:
# features[self.featureSet.getId("bf_" + text)] = 1
# elif j > i:
# features[self.featureSet.getId("af_" + text)] = 1
# Main features
text = token.get("text")
features[self.featureSet.getId("txt_"+text)] = 1
features[self.featureSet.getId("POS_"+token.get("POS"))] = 1
stem = PorterStemmer.stem(text)
features[self.featureSet.getId("stem_"+stem)] = 1
features[self.featureSet.getId("nonstem_"+text[len(stem):])] = 1
if self.styles["speculation_words"]:
if text in self.specWords:
features[self.featureSet.getId("ent_spec")] = 1
if stem in self.specWordStems:
features[self.featureSet.getId("ent_spec_stem")] = 1
# Linear order features
for i in range(len(sentenceGraph.tokens)):
if token == sentenceGraph.tokens[i]:
break
for index in [-3,-2,-1,1,2,3]:
if i + index > 0 and i + index < len(sentenceGraph.tokens):
self.buildLinearOrderFeatures(sentenceGraph, i + index, str(index), features)
# Content
if i > 0 and text[0].isalpha() and text[0].isupper():
features[self.featureSet.getId("upper_case_start")] = 1
for j in range(len(text)):
if j > 0 and text[j].isalpha() and text[j].isupper():
features[self.featureSet.getId("upper_case_middle")] = 1
# numbers and special characters
if text[j].isdigit():
features[self.featureSet.getId("has_digits")] = 1
if j > 0 and text[j-1] == "-":
features[self.featureSet.getId("has_hyphenated_digit")] = 1
elif text[j] == "-":
features[self.featureSet.getId("has_hyphen")] = 1
elif text[j] == "/":
features[self.featureSet.getId("has_fslash")] = 1
elif text[j] == "\\":
features[self.featureSet.getId("has_bslash")] = 1
# duplets
if j > 0:
features[self.featureSet.getId("dt_"+text[j-1:j+1].lower())] = 1
# triplets
if j > 1:
features[self.featureSet.getId("tt_"+text[j-2:j+1].lower())] = 1
# Attached edges (Hanging in and out edges)
t1InEdges = self.inEdgesByToken[token]
for edge in t1InEdges:
edgeType = edge[2].get("type")
features[self.featureSet.getId("t1HIn_"+edgeType)] = 1
features[self.featureSet.getId("t1HIn_"+edge[0].get("POS"))] = 1
features[self.featureSet.getId("t1HIn_"+edgeType+"_"+edge[0].get("POS"))] = 1
tokenText = sentenceGraph.getTokenText(edge[0])
features[self.featureSet.getId("t1HIn_"+tokenText)] = 1
features[self.featureSet.getId("t1HIn_"+edgeType+"_"+tokenText)] = 1
t1OutEdges = self.outEdgesByToken[token]
for edge in t1OutEdges:
edgeType = edge[2].get("type")
features[self.featureSet.getId("t1HOut_"+edgeType)] = 1
features[self.featureSet.getId("t1HOut_"+edge[1].get("POS"))] = 1
features[self.featureSet.getId("t1HOut_"+edgeType+"_"+edge[1].get("POS"))] = 1
tokenText = sentenceGraph.getTokenText(edge[1])
features[self.featureSet.getId("t1HOut_"+tokenText)] = 1
features[self.featureSet.getId("t1HOut_"+edgeType+"_"+tokenText)] = 1
self.buildChains(token, sentenceGraph, features)
extra = {"xtype":"task3","t3type":task3Type,"t":token.get("id"),"entity":entity.get("id")}
#examples.append( (sentenceGraph.getSentenceId()+".x"+str(exampleIndex),category,features,extra) )
example = (sentenceGraph.getSentenceId()+".x"+str(exampleIndex),category,features,extra)
ExampleUtils.appendExamples([example], outfile)
exampleIndex += 1
self.exampleStats.endExample()
#return examples
return exampleIndex
def train(output, task=None, detector=None, inputFiles=None, models=None, parse=None,
processUnmerging=None, processModifiers=None,
bioNLPSTParams=None, preprocessorParams=None, exampleStyles=None,
classifierParams=None, doFullGrid=False, deleteOutput=False, copyFrom=None,
log="log.txt", step=None, omitSteps=None, debug=False, connection=None, subset=None,
folds=None, corpusDir=None, corpusPreprocessing=None, evaluator=None):
"""
Train a new model for event or relation detection.
@param output: A directory where output files will appear.
@param task: If defined, overridable default settings are used for many of the training parameters. Must be one of the supported TEES tasks.
@param detector: a Detector object, or a string defining one to be imported
@param inputFiles: A dictionary of file names, with keys "train", "devel" and, "test"
@param models: A dictionary of file names defining the place for the new models, with keys "devel" and, "test"
@param parse: The parse element name in the training interaction XML
@param processUnmerging: Use the unmerging step of EventDetector. True, False or None for task default.
@param processModifiers: Use the modifier detection step of EventDetector. True, False or None for task default.
@param bioNLPSTParams: Parameters controlling BioNLP ST format output.
@param preprocessorParams: Parameters controlling the preprocessor. Not used for training, but saved to the model for use when classifying.
@param exampleStyles: A parameter set for controlling example builders.
@param classifierParams: A parameter set for controlling classifiers.
@param doFullGrid: Whether all parameters, as opposed to just recall adjustment, are tested in the EventDetector grid search.
@param deleteOutput: Remove an existing output directory
@param copyFrom: Copy an existing output directory for use as a template
@param log: An optional alternative name for the log file. None is for no logging.
@param step: A step=substep pair, where the steps are "TRAIN", "DEVEL", "EMPTY" and "TEST"
@param omitSteps: step=substep parameters, where multiple substeps can be defined.
@param debug: In debug mode, more output is shown, and some temporary intermediate files are saved
@param connection: A parameter set defining a local or remote connection for training the classifier
@param subset: A parameter set for making subsets of input files
"""
# Insert default arguments where needed
inputFiles = setDictDefaults(inputFiles, {"train":None, "devel":None, "test":None})
models = setDictDefaults(models, {"devel":"model-devel", "test":"model-test"})
exampleStyles = setDictDefaults(exampleStyles, {"examples":None, "trigger":None, "edge":None, "unmerging":None, "modifiers":None})
classifierParams = setDictDefaults(classifierParams, {"examples":None, "trigger":None, "recall":None, "edge":None, "unmerging":None, "modifiers":None})
subset = setDictDefaults(Parameters.get(subset), {"train":None, "devel":None, "test":None, "seed":0, "all":None})
folds = setDictDefaults(folds, {"train":None, "devel":None, "test":None})
processUnmerging = getDefinedBool(processUnmerging)
processModifiers = getDefinedBool(processModifiers)
# Initialize working directory
workdir(output, deleteOutput, copyFrom, log)
# Get task specific parameters
useKerasDetector = False
if detector != None and "keras" in detector.lower():
print >> sys.stderr, "Using a Keras Detector"
useKerasDetector = True
if detector.lower() == "keras":
detector = None
detector, bioNLPSTParams, preprocessorParams, folds = getTaskSettings(task, detector,
bioNLPSTParams, preprocessorParams, inputFiles, exampleStyles, classifierParams, folds, corpusDir=corpusDir, useKerasDetector=useKerasDetector)
# Learn training settings from input files
detector = learnSettings(inputFiles, detector, classifierParams, task, exampleStyles, useKerasDetector=useKerasDetector)
# Get corpus subsets
getFolds(inputFiles, folds)
getSubsets(inputFiles, subset)
if task != None:
task = task.replace("-FULL", "")
if "." in task:
_, subTask = getSubTask(task)
if subTask != 3:
processModifiers = False
# Preprocess the corpus if required
if corpusPreprocessing != None:
preprocessor = Preprocessor(steps=corpusPreprocessing)
assert preprocessor.steps[0].name == "MERGE_SETS"
assert preprocessor.steps[-1].name == "DIVIDE_SETS"
preprocessedCorpusDir = os.path.join(output, "corpus")
#outputFiles = {x:os.path.join(preprocessedCorpusDir, os.path.basename(inputFiles[x])) for x in inputFiles}
preprocessor.process(inputFiles, os.path.join(preprocessedCorpusDir, task))
#inputFiles = outputFiles
for setName in inputFiles.keys():
if inputFiles[setName] != None:
inputFiles[setName] = os.path.join(preprocessedCorpusDir, task + "-" + setName + ".xml")
# Define processing steps
selector, detectorSteps, omitDetectorSteps = getSteps(step, omitSteps, ["TRAIN", "DEVEL", "EMPTY", "TEST"])
# Initialize the detector
detector, detectorName = getDetector(detector, evaluator=evaluator)
evaluator, evaluatorName = importClass(evaluator, "evaluator")
detector = detector() # initialize object
if evaluator != None:
print >> sys.stderr, "Using evaluator", evaluator.__name__
detector.evaluator = evaluator
detector.debug = debug
detector.bioNLPSTParams = detector.getBioNLPSharedTaskParams(bioNLPSTParams)
#detector.useBioNLPSTFormat = useBioNLPSTFormat # classify-output and grid evaluation in ST-format
#detector.stWriteScores = True # write confidence scores into additional st-format files
connection = getConnection(connection)
detector.setConnection(connection)
connection.debug = debug
if deleteOutput:
connection.clearWorkDir()
# Train
if selector.check("TRAIN"):
print >> sys.stderr, "----------------------------------------------------"
print >> sys.stderr, "------------------ Train Detector ------------------"
print >> sys.stderr, "----------------------------------------------------"
if not isinstance(detector, EventDetector):
detector.train(inputFiles["train"], inputFiles["devel"], models["devel"], models["test"],
exampleStyles["examples"], classifierParams["examples"], parse, None, task,
fromStep=detectorSteps["TRAIN"], workDir="training", testData=inputFiles["test"])
else:
detector.train(inputFiles["train"], inputFiles["devel"], models["devel"], models["test"],
exampleStyles["trigger"], exampleStyles["edge"], exampleStyles["unmerging"], exampleStyles["modifiers"],
classifierParams["trigger"], classifierParams["edge"], classifierParams["unmerging"], classifierParams["modifiers"],
classifierParams["recall"], processUnmerging, processModifiers,
doFullGrid, task, parse, None,
fromStep=detectorSteps["TRAIN"], workDir="training", testData=inputFiles["test"])
# Save the detector type
for model in [models["devel"], models["test"]]:
if model != None and os.path.exists(model):
model = Model(model, "a")
model.addStr("detector", detectorName)
if evaluatorName != None:
model.addStr("detector", evaluatorName)
if preprocessorParams != None:
preprocessor = Preprocessor()
model.addStr("preprocessorParams", Parameters.toString(preprocessor.getParameters(preprocessorParams)))
model.save()
model.close()
if selector.check("DEVEL"):
print >> sys.stderr, "----------------------------------------------------"
print >> sys.stderr, "------------ Check devel classification ------------"
print >> sys.stderr, "----------------------------------------------------"
#detector.bioNLPSTParams["scores"] = False # the evaluation server doesn't like additional files
detector.classify(inputFiles["devel"], models["devel"], "classification-devel/devel", goldData=inputFiles["devel"], fromStep=detectorSteps["DEVEL"], workDir="classification-devel")
if selector.check("EMPTY"):
# By passing an emptied devel set through the prediction system, we can check that we get the same predictions
# as in the DEVEL step, ensuring the model does not use leaked information.
print >> sys.stderr, "----------------------------------------------------"
print >> sys.stderr, "------------ Empty devel classification ------------"
print >> sys.stderr, "----------------------------------------------------"
#detector.bioNLPSTParams["scores"] = False # the evaluation server doesn't like additional files
removalScope = "non-given"
if "names" in str(exampleStyles["examples"]) or "names" in str(exampleStyles["trigger"]):
removalScope = "all"
elif "Edge" in detector.__class__.__name__:
removalScope = "interactions"
detector.classify(getEmptyCorpus(inputFiles["devel"], scope=removalScope), models["devel"], "classification-empty/devel-empty", fromStep=detectorSteps["EMPTY"], workDir="classification-empty")
print >> sys.stderr, "*** Evaluate empty devel classification ***"
if os.path.exists("classification-empty/devel-empty-pred.xml.gz"):
EvaluateInteractionXML.run(detector.evaluator, "classification-empty/devel-empty-pred.xml.gz", inputFiles["devel"], parse)
else:
print >> sys.stderr, "No output file for evaluation"
if selector.check("TEST"):
print >> sys.stderr, "----------------------------------------------------"
print >> sys.stderr, "------------- Test set classification --------------"
print >> sys.stderr, "----------------------------------------------------"
if inputFiles["test"] == None or not os.path.exists(inputFiles["test"]):
print >> sys.stderr, "Skipping, test file", inputFiles["test"], "does not exist"
else:
#detector.bioNLPSTParams["scores"] = False # the evaluation server doesn't like additional files
detector.classify(inputFiles["test"], models["test"] if models["test"] != None else models["devel"], "classification-test/test", fromStep=detectorSteps["TEST"], workDir="classification-test")
if detector.bioNLPSTParams["convert"]:
extension = ".zip" if (detector.bioNLPSTParams["convert"] == "zip") else ".tar.gz"
Utils.STFormat.Compare.compare("classification-test/test-events" + extension, "classification-devel/devel-events" + extension, "a2")
# Stop logging
if log != None:
Stream.closeLog(log)
def classify(self, data, model, output, parse=None, task=None, goldData=None, fromStep=None, toStep=None, omitSteps=None, workDir=None):
#BINARY_RECALL_MODE = False # TODO: make a parameter
xml = None
model = self.openModel(model, "r")
self.initVariables(classifyData=data, model=model, xml=None, task=task, parse=parse)
self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep, omitSteps)
#self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "RECALL-ADJUST", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep)
self.setWorkDir(workDir)
if workDir == None:
self.setTempWorkDir()
workOutputTag = os.path.join(self.workDir, os.path.basename(output) + "-")
self.model = self.openModel(self.model, "r")
stParams = self.getBioNLPSharedTaskParams(self.bioNLPSTParams, model)
if self.checkStep("TRIGGERS"):
xml = self.triggerDetector.classifyToXML(self.classifyData, self.model, None, workOutputTag, goldData=goldData, parse=self.parse, recallAdjust=float(self.getStr("recallAdjustParameter", self.model)))
if self.checkStep("EDGES"):
xml = self.getWorkFile(xml, workOutputTag + "trigger-pred.xml.gz")
xml = self.edgeDetector.classifyToXML(xml, self.model, None, workOutputTag, goldData=goldData, parse=self.parse)
assert xml != None
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag+"parse", self.model)
else:
edgeParse = self.parse
#EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
if goldData != None:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, goldData, edgeParse)
else:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
if self.checkStep("UNMERGING"):
if self.model.getStr("unmerging-classifier-parameter", None) != None: #self.model.hasMember("unmerging-classifier-model"):
#xml = self.getWorkFile(xml, output + "-edge-pred.xml.gz")
# To avoid running out of memory, always use file on disk
xml = self.getWorkFile(None, workOutputTag + "edge-pred.xml.gz")
#goldData = None
#if type(self.classifyData) in types.StringTypes:
# if os.path.exists(self.classifyData.replace("-nodup", "")):
# goldData = self.classifyData.replace("-nodup", "")
xml = self.unmergingDetector.classifyToXML(xml, self.model, None, workOutputTag, goldData=goldData, parse=self.parse)
# Evaluate after unmerging
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag+"parse", self.model)
else:
edgeParse = self.parse
if goldData != None:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, goldData, edgeParse)
else:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
else:
print >> sys.stderr, "No model for unmerging"
if self.checkStep("MODIFIERS"):
if self.model.hasMember("modifier-classifier-model"):
xml = self.getWorkFile(xml, [workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
xml = self.modifierDetector.classifyToXML(xml, self.model, None, workOutputTag, goldData=goldData, parse=self.parse)
else:
print >> sys.stderr, "No model for modifier detection"
# if self.checkStep("VALIDATE"):
# xml = self.getWorkFile(xml, [workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
# self.structureAnalyzer.load(model)
# self.structureAnalyzer.validate(xml)
# ETUtils.write(xml, workOutputTag + "validate-pred.xml.gz")
if self.checkStep("ST-CONVERT"):
if stParams["convert"]:
#xml = self.getWorkFile(xml, [workOutputTag + "validate-pred.xml.gz", workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
xml = self.getWorkFile(xml, [workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
Utils.STFormat.ConvertXML.toSTFormat(xml, output+"-events.tar.gz", outputTag=stParams["a2Tag"], writeExtra=(stParams["scores"] == True))
if stParams["evaluate"]: #self.stEvaluator != None:
task = self.task
if task == None:
task = self.getStr(self.edgeDetector.tag+"task", self.model)
self.stEvaluator.evaluate(output + "-events.tar.gz", task)
else:
print >> sys.stderr, "No BioNLP shared task format conversion"
finalXMLFile = self.getWorkFile(None, [workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
if finalXMLFile != None:
shutil.copy2(finalXMLFile, output+"-pred.xml.gz")
self.deleteTempWorkDir()
self.exitState()
CLASSIFIER_PARAMS="c:25000,50000,87500"
WORKDIR="/usr/share/biotext/GeniaChallenge/SharedTaskTriggerTest"
PARSE_TOK="split-Charniak-Lease"
workdir(WORKDIR, False)
log()
# Trigger detection
#Gazetteer.run(TRAIN_FILE, "gazetteer-train")
#GeneralEntityTypeRecognizer.run(TRAIN_FILE, "trigger-train-examples", PARSE_TOK, PARSE_TOK, "style:typed", "trigger-ids")
GeneralEntityTypeRecognizer.run(TEST_FILE, "trigger-test-examples", PARSE_TOK, PARSE_TOK, "style:typed", "trigger-ids")
Cls.test("trigger-test-examples", "trigger-param-opt/model-c_75000", "trigger-test-classifications")
evaluator = Ev.evaluate("trigger-test-examples", "trigger-test-classifications", "trigger-ids.class_names")
#evaluator = optimize(Cls, Ev, "trigger-train-examples", "trigger-test-examples",\
# "trigger-ids.class_names", CLASSIFIER_PARAMS, "trigger-param-opt")[0]
ExampleUtils.writeToInteractionXML(evaluator.classifications, TEST_FILE, "test-predicted-triggers.xml", "trigger-ids.class_names", PARSE_TOK, PARSE_TOK)
# RecallAdjust.run("test-predicted-triggers.xml",1.0,"test-predicted-triggers-adj.xml")
# ix.splitMergedElements("test-predicted-triggers-adj.xml", "test-predicted-triggers-adj-split.xml")
# ix.recalculateIds("test-predicted-triggers-adj-split.xml", "test-predicted-triggers-adj-split-recids.xml", True)
# EvaluateInteractionXML.run(Ev, "test-predicted-triggers-adj-split-recids.xml", GOLD_TEST_FILE, PARSE_TOK, PARSE_TOK)
ix.splitMergedElements("test-predicted-triggers.xml", "test-predicted-triggers-split.xml")
ix.recalculateIds("test-predicted-triggers-split.xml", "test-predicted-triggers-split-recids.xml", True)
EvaluateInteractionXML.run(Ev, "test-predicted-triggers-split-recids.xml", GOLD_TEST_FILE, PARSE_TOK, PARSE_TOK)
def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph = None, structureAnalyzer=None):
"""
Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format.
"""
#examples = []
exampleIndex = 0
# example directionality
if self.styles["directed"] == None and self.styles["undirected"] == None: # determine directedness from corpus
examplesAreDirected = structureAnalyzer.hasDirectedTargets() if structureAnalyzer != None else True
elif self.styles["directed"]:
assert self.styles["undirected"] in [None, False]
examplesAreDirected = True
elif self.styles["undirected"]:
assert self.styles["directed"] in [None, False]
examplesAreDirected = False
if not self.styles["no_trigger_features"]:
self.triggerFeatureBuilder.initSentence(sentenceGraph)
if self.styles["evex"]:
self.evexFeatureBuilder.initSentence(sentenceGraph)
# if self.styles["sdb_merge"]:
# self.determineNonOverlappingTypes(structureAnalyzer)
# Filter entities, if needed
sentenceGraph.mergeInteractionGraph(True)
entities = sentenceGraph.mergedEntities
entityToDuplicates = sentenceGraph.mergedEntityToDuplicates
self.exampleStats.addValue("Duplicate entities skipped", len(sentenceGraph.entities) - len(entities))
# Connect to optional gold graph
entityToGold = None
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(entities, goldGraph.entities)
paths = None
if not self.styles["no_path"]:
undirected = sentenceGraph.dependencyGraph.toUndirected()
paths = undirected
if self.styles["filter_shortest_path"] != None: # For DDI use filter_shortest_path=conj_and
paths.resetAnalyses() # just in case
paths.FloydWarshall(self.filterEdge, {"edgeTypes":self.styles["filter_shortest_path"]})
# Generate examples based on interactions between entities or interactions between tokens
if self.styles["token_nodes"]:
loopRange = len(sentenceGraph.tokens)
else:
loopRange = len(entities)
for i in range(loopRange-1):
for j in range(i+1,loopRange):
eI = None
eJ = None
if self.styles["token_nodes"]:
tI = sentenceGraph.tokens[i]
tJ = sentenceGraph.tokens[j]
else:
eI = entities[i]
eJ = entities[j]
tI = sentenceGraph.entityHeadTokenByEntity[eI]
tJ = sentenceGraph.entityHeadTokenByEntity[eJ]
if eI.get("type") == "neg" or eJ.get("type") == "neg":
continue
if self.styles["skip_extra_triggers"]:
if eI.get("source") != None or eJ.get("source") != None:
continue
# only consider paths between entities (NOTE! entities, not only named entities)
if self.styles["headsOnly"]:
if (len(sentenceGraph.tokenIsEntityHead[tI]) == 0) or (len(sentenceGraph.tokenIsEntityHead[tJ]) == 0):
continue
examples = self.buildExamplesForPair(tI, tJ, paths, sentenceGraph, goldGraph, entityToGold, eI, eJ, structureAnalyzer, examplesAreDirected)
for categoryName, features, extra in examples:
# make example
if self.styles["binary"]:
if categoryName != "neg":
category = 1
else:
category = -1
extra["categoryName"] = "i"
else:
category = self.classSet.getId(categoryName)
example = [sentenceGraph.getSentenceId()+".x"+str(exampleIndex), category, features, extra]
ExampleUtils.appendExamples([example], outfile)
exampleIndex += 1
return exampleIndex
def makeExampleGraphWithGold(self, builder, sentenceGraph, goldGraph, sentenceIndex):
exampleGraph = NX10.MultiDiGraph()
for token in goldGraph.tokens:
exampleGraph.add_node(token)
arcStyles = {}
labelStyles = {}
extraByToken = {}
edgeTypes = {}
stats = {"entities":0,"edges":0,"tp":0,"fp":0,"tn":0,"fn":0}
entityMap = EvaluateInteractionXML.mapEntities(sentenceGraph.entities, goldGraph.entities, goldGraph.tokens)
tokenMap = self.getTokenMap(sentenceGraph, goldGraph)
toEntitiesWithPredictions = set()
for entityFrom, entitiesTo in entityMap.iteritems():
stats["entities"] += 1
entityFromHeadToken = sentenceGraph.entityHeadTokenByEntity[entityFrom]
for entityTo in entitiesTo:
toEntitiesWithPredictions.add(entityTo)
entityToHeadToken = goldGraph.entityHeadTokenByEntity[entityTo]
style = None
eFromType = entityFrom.get("type")
eToType = entityTo.get("type")
if extraByToken.has_key(entityToHeadToken):
style = extraByToken[entityToHeadToken]
if eFromType == eToType:
if eToType != "neg":
if style == None:
style = [entityTo.get("type"),{"fill":"green"}]
elif style[1]["fill"] == "#79BAEC":
style = [entityTo.get("type"),{"fill":"green"}]
if entityTo.get("isName") == "True":
style = [entityTo.get("type"),{"fill":"brown"}]
else:
stats["tp"] += 1
else:
if eToType == "neg":
pass
extraByToken[entityToHeadToken] = style
if len(entitiesTo) == 0:
stats["fp"] += 1
if extraByToken.has_key(tokenMap[entityFromHeadToken]):
style = extraByToken[tokenMap[entityFromHeadToken]]
if style[1]["fill"] != "green":
style = [entityFrom.get("type"),{"fill":"red"}]
extraByToken[tokenMap[entityFromHeadToken]] = style
else:
extraByToken[tokenMap[entityFromHeadToken]] = [entityFrom.get("type"),{"fill":"red"}]
for entity in goldGraph.entities:
if entity not in toEntitiesWithPredictions:
stats["fn"] += 1
extraByToken[goldGraph.entityHeadTokenByEntity[entity]] = [entity.get("type"),{"fill":"#79BAEC"}]
toInteractionsWithPredictions = set()
for interactionFrom in sentenceGraph.interactions:
if interactionFrom.get("type") == "neg":
continue
stats["edges"] += 1
e1s = entityMap[sentenceGraph.entitiesById[interactionFrom.get("e1")]]
e1Ids = []
for e1 in e1s:
e1Ids.append(e1.get("id"))
e2s = entityMap[sentenceGraph.entitiesById[interactionFrom.get("e2")]]
e2Ids = []
for e2 in e2s:
e2Ids.append(e2.get("id"))
t1 = tokenMap[sentenceGraph.entityHeadTokenByEntity[sentenceGraph.entitiesById[interactionFrom.get("e1")]]]
t2 = tokenMap[sentenceGraph.entityHeadTokenByEntity[sentenceGraph.entitiesById[interactionFrom.get("e2")]]]
iFromType = interactionFrom.get("type")
found = False
for interactionTo in goldGraph.interactions:
if interactionTo.get("e1") in e1Ids and interactionTo.get("e2") in e2Ids:
toInteractionsWithPredictions.add(interactionTo)
iToType = interactionTo.get("type")
exampleGraph.add_edge(t1, t2, element=interactionFrom)
#edge = exampleGraph.get_edge(t1, t2, data=True)
edge = self.getNXEdge(exampleGraph, t1, t2, interactionFrom)
if t1 != t2:
if iToType == iFromType:
edge[2]["arcStyles"] = {"stroke":"green"}
edge[2]["labelStyles"] = {"fill":"green"}
stats["tp"] += 1
else:
edge[2]["arcStyles"] = {"stroke":"red"}
edge[2]["labelStyles"] = {"fill":"red"}
stats["fp"] += 1
found = True
if not found: # false positive prediction
if t1 != t2:
exampleGraph.add_edge(t1, t2, element=interactionFrom)
edge = self.getNXEdge(exampleGraph, t1, t2, interactionFrom)
edge[2]["arcStyles"] = {"stroke":"red"}
edge[2]["labelStyles"] = {"fill":"red"}
stats["fp"] += 1
for interactionTo in goldGraph.interactions:
if interactionTo not in toInteractionsWithPredictions: # false negative gold
t1 = goldGraph.entityHeadTokenByEntity[goldGraph.entitiesById[interactionTo.get("e1")]]
t2 = goldGraph.entityHeadTokenByEntity[goldGraph.entitiesById[interactionTo.get("e2")]]
if t1 != t2:
exampleGraph.add_edge(t1, t2, element=interactionTo)
edge = self.getNXEdge(exampleGraph, t1, t2, interactionTo)
edge[2]["arcStyles"] = {"stroke":"#79BAEC"}
edge[2]["labelStyles"] = {"fill":"#79BAEC"}
stats["fn"] += 1
builder.header("Classification",4)
svgTokens = GraphToSVG.tokensToSVG(goldGraph.tokens,False,None,extraByToken)
#arcStyles, labelStyles = self.getMatchingEdgeStyles(exampleGraph, sentenceGraph.interactionGraph, "green", "red" )
svgEdges = GraphToSVG.edgesToSVG(svgTokens, exampleGraph, "type", None)
sentenceId = sentenceGraph.getSentenceId()
svgElement = GraphToSVG.writeSVG(svgTokens, svgEdges, self.outDir+"/svg/"+sentenceId+"-"+str(sentenceIndex)+"_learned.svg")
builder.svg("../svg/" + sentenceId + "-"+str(sentenceIndex)+"_learned.svg",svgElement.attrib["width"],svgElement.attrib["height"],id="learned_graph")
builder.lineBreak()
return stats
def buildExamplesFromGraph(self, sentenceGraph, outfile, goldGraph=None):
"""
Build examples for a single sentence. Returns a list of examples.
See Core/ExampleUtils for example format.
"""
#examples = []
exampleIndex = 0
if self.styles["trigger_features"]:
self.triggerFeatureBuilder.initSentence(sentenceGraph)
if self.styles["evex"]:
self.evexFeatureBuilder.initSentence(sentenceGraph)
# Filter entities, if needed
#mergedIds = None
#duplicateEntities = None
#entities = sentenceGraph.entities
#entities, mergedIds, duplicateEntities = self.mergeEntities(sentenceGraph, False) # "no_duplicates" in self.styles)
sentenceGraph.mergeInteractionGraph(True)
entities = sentenceGraph.mergedEntities
entityToDuplicates = sentenceGraph.mergedEntityToDuplicates
self.exampleStats.addValue("Duplicate entities skipped",
len(sentenceGraph.entities) - len(entities))
# Connect to optional gold graph
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(
entities, goldGraph.entities)
paths = None
if not self.styles["no_path"]:
##undirected = sentenceGraph.getUndirectedDependencyGraph()
#undirected = self.nxMultiDiGraphToUndirected(sentenceGraph.dependencyGraph)
###undirected = sentenceGraph.dependencyGraph.to_undirected()
####undirected = NX10.MultiGraph(sentenceGraph.dependencyGraph) This didn't work
undirected = sentenceGraph.dependencyGraph.toUndirected()
#paths = NX10.all_pairs_shortest_path(undirected, cutoff=999)
paths = undirected
#for edge in sentenceGraph.dependencyGraph.edges:
# assert edge[2] != None
#for edge in undirected.edges:
# assert edge[2] != None
#if sentenceGraph.sentenceElement.get("id") == "GENIA.d70.s5":
# print [(x[0].get("id"), x[1].get("id"), x[2].get("id")) for x in sentenceGraph.dependencyGraph.edges]
# Generate examples based on interactions between entities or interactions between tokens
if self.styles["entities"]:
loopRange = len(entities)
else:
loopRange = len(sentenceGraph.tokens)
for i in range(loopRange - 1):
for j in range(i + 1, loopRange):
eI = None
eJ = None
if self.styles["entities"]:
eI = entities[i]
eJ = entities[j]
tI = sentenceGraph.entityHeadTokenByEntity[eI]
tJ = sentenceGraph.entityHeadTokenByEntity[eJ]
#if "no_ne_interactions" in self.styles and eI.get("isName") == "True" and eJ.get("isName") == "True":
# continue
if eI.get("type") == "neg" or eJ.get("type") == "neg":
continue
if self.styles["skip_extra_triggers"]:
if eI.get("source") != None or eJ.get(
"source") != None:
continue
else:
tI = sentenceGraph.tokens[i]
tJ = sentenceGraph.tokens[j]
# only consider paths between entities (NOTE! entities, not only named entities)
if self.styles["headsOnly"]:
if (len(sentenceGraph.tokenIsEntityHead[tI]) == 0) or (len(
sentenceGraph.tokenIsEntityHead[tJ]) == 0):
continue
if self.styles["directed"]:
# define forward
if self.styles["entities"]:
categoryName = self.getCategoryName(
sentenceGraph, eI, eJ, True)
if goldGraph != None:
categoryName = self.getGoldCategoryName(
goldGraph, entityToGold, eI, eJ, True)
else:
categoryName = self.getCategoryNameFromTokens(
sentenceGraph, tI, tJ, True)
# make forward
self.exampleStats.beginExample(categoryName)
makeExample = True
if self.styles[
"genia_limits"] and not self.isPotentialGeniaInteraction(
eI, eJ):
makeExample = False
self.exampleStats.filter("genia_limits")
if self.styles["genia_task1"] and (
eI.get("type") == "Entity"
or eJ.get("type") == "Entity"):
makeExample = False
self.exampleStats.filter("genia_task1")
if self.styles[
"rel_limits"] and not self.isPotentialRELInteraction(
eI, eJ):
makeExample = False
self.exampleStats.filter("rel_limits")
if self.styles[
"co_limits"] and not self.isPotentialCOInteraction(
eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("co_limits")
if self.styles[
"bb_limits"] and not self.isPotentialBBInteraction(
eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("bb_limits")
if categoryName != "neg":
self.exampleStats.filter("bb_limits(" +
categoryName + ":" +
eI.get("type") + "/" +
eJ.get("type") + ")")
if self.styles[
"bi_limits"] and not self.isPotentialBIInteraction(
eI, eJ, sentenceGraph, self.exampleStats):
makeExample = False
#self.exampleStats.filter("bi_limits")
if self.styles[
"epi_limits"] and not self.isPotentialEPIInteraction(
eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("epi_limits")
if self.styles[
"id_limits"] and not self.isPotentialIDInteraction(
eI, eJ, sentenceGraph):
makeExample = False
self.exampleStats.filter("id_limits")
# if self.styles["selftrain_limits"] and (eI.get("selftrain") == "False" or eJ.get("selftrain") == "False"):
# makeExample = False
# self.exampleStats.filter("selftrain_limits")
# if self.styles["selftrain_group"] and (eI.get("selftraingroup") not in self.selfTrainGroups or eJ.get("selftraingroup") not in self.selfTrainGroups):
# makeExample = False
# self.exampleStats.filter("selftrain_group")
if self.styles["pos_only"] and categoryName == "neg":
makeExample = False
self.exampleStats.filter("pos_only")
if makeExample:
#examples.append( self.buildExample(tI, tJ, paths, sentenceGraph, categoryName, exampleIndex, eI, eJ) )
ExampleUtils.appendExamples([
self.buildExample(tI, tJ, paths, sentenceGraph,
categoryName, exampleIndex, eI,
eJ)
], outfile)
exampleIndex += 1
self.exampleStats.endExample()
# define reverse
if self.styles["entities"]:
categoryName = self.getCategoryName(
sentenceGraph, eJ, eI, True)
if goldGraph != None:
categoryName = self.getGoldCategoryName(
goldGraph, entityToGold, eJ, eI, True)
else:
categoryName = self.getCategoryNameFromTokens(
sentenceGraph, tJ, tI, True)
# make reverse
self.exampleStats.beginExample(categoryName)
makeExample = True
if self.styles[
"genia_limits"] and not self.isPotentialGeniaInteraction(
eJ, eI):
makeExample = False
self.exampleStats.filter("genia_limits")
if self.styles["genia_task1"] and (
eI.get("type") == "Entity"
or eJ.get("type") == "Entity"):
makeExample = False
self.exampleStats.filter("genia_task1")
if self.styles[
"rel_limits"] and not self.isPotentialRELInteraction(
eJ, eI):
makeExample = False
self.exampleStats.filter("rel_limits")
if self.styles[
"co_limits"] and not self.isPotentialCOInteraction(
eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("co_limits")
if self.styles[
"bb_limits"] and not self.isPotentialBBInteraction(
eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("bb_limits")
if categoryName != "neg":
self.exampleStats.filter("bb_limits(" +
categoryName + ":" +
eJ.get("type") + "/" +
eI.get("type") + ")")
if self.styles[
"bi_limits"] and not self.isPotentialBIInteraction(
eJ, eI, sentenceGraph, self.exampleStats):
makeExample = False
#self.exampleStats.filter("bi_limits")
if self.styles[
"epi_limits"] and not self.isPotentialEPIInteraction(
eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("epi_limits")
if self.styles[
"id_limits"] and not self.isPotentialIDInteraction(
eJ, eI, sentenceGraph):
makeExample = False
self.exampleStats.filter("id_limits")
# if self.styles["selftrain_limits"] and (eI.get("selftrain") == "False" or eJ.get("selftrain") == "False"):
# makeExample = False
# self.exampleStats.filter("selftrain_limits")
# if self.styles["selftrain_group"] and (eI.get("selftraingroup") not in self.selfTrainGroups or eJ.get("selftraingroup") not in self.selfTrainGroups):
# makeExample = False
# self.exampleStats.filter("selftrain_group")
if self.styles["pos_only"] and categoryName == "neg":
makeExample = False
self.exampleStats.filter("pos_only")
if makeExample:
#examples.append( self.buildExample(tJ, tI, paths, sentenceGraph, categoryName, exampleIndex, eJ, eI) )
ExampleUtils.appendExamples([
self.buildExample(tJ, tI, paths, sentenceGraph,
categoryName, exampleIndex, eJ,
eI)
], outfile)
exampleIndex += 1
self.exampleStats.endExample()
else:
if self.styles["entities"]:
categoryName = self.getCategoryName(
sentenceGraph, eI, eJ, False)
else:
categoryName = self.getCategoryNameFromTokens(
sentenceGraph, tI, tJ, False)
self.exampleStats.beginExample(categoryName)
forwardExample = self.buildExample(tI, tJ, paths,
sentenceGraph,
categoryName,
exampleIndex, eI, eJ)
if not self.styles["graph_kernel"]:
reverseExample = self.buildExample(
tJ, tI, paths, sentenceGraph, categoryName,
exampleIndex, eJ, eI)
forwardExample[2].update(reverseExample[2])
#examples.append(forwardExample)
ExampleUtils.appendExamples([forwardExample], outfile)
exampleIndex += 1
self.exampleStats.endExample()
#return examples
return exampleIndex
def classify(self,
data,
model,
output,
parse=None,
task=None,
goldData=None,
fromStep=None,
toStep=None,
omitSteps=None,
workDir=None):
#BINARY_RECALL_MODE = False # TODO: make a parameter
xml = None
model = self.openModel(model, "r")
self.initVariables(classifyData=data,
model=model,
xml=None,
task=task,
parse=parse)
self.enterState(
self.STATE_CLASSIFY,
["TRIGGERS", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"],
fromStep, toStep, omitSteps)
#self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "RECALL-ADJUST", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep)
self.setWorkDir(workDir)
if workDir == None:
self.setTempWorkDir()
workOutputTag = os.path.join(self.workDir,
os.path.basename(output) + "-")
self.model = self.openModel(self.model, "r")
stParams = self.getBioNLPSharedTaskParams(self.bioNLPSTParams, model)
if self.checkStep("TRIGGERS"):
xml = self.triggerDetector.classifyToXML(
self.classifyData,
self.model,
None,
workOutputTag,
goldData=goldData,
parse=self.parse,
recallAdjust=float(
self.getStr("recallAdjustParameter", self.model)))
if self.checkStep("EDGES"):
xml = self.getWorkFile(xml, workOutputTag + "trigger-pred.xml.gz")
xml = self.edgeDetector.classifyToXML(xml,
self.model,
None,
workOutputTag,
goldData=goldData,
parse=self.parse)
assert xml != None
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag + "parse",
self.model)
else:
edgeParse = self.parse
#EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
if goldData != None:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml,
goldData, edgeParse)
else:
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml,
self.classifyData, edgeParse)
if self.checkStep("UNMERGING"):
if self.model.getStr(
"unmerging-classifier-parameter", None
) != None: #self.model.hasMember("unmerging-classifier-model"):
#xml = self.getWorkFile(xml, output + "-edge-pred.xml.gz")
# To avoid running out of memory, always use file on disk
xml = self.getWorkFile(None,
workOutputTag + "edge-pred.xml.gz")
#goldData = None
#if type(self.classifyData) in types.StringTypes:
# if os.path.exists(self.classifyData.replace("-nodup", "")):
# goldData = self.classifyData.replace("-nodup", "")
xml = self.unmergingDetector.classifyToXML(xml,
self.model,
None,
workOutputTag,
goldData=goldData,
parse=self.parse)
# Evaluate after unmerging
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag + "parse",
self.model)
else:
edgeParse = self.parse
if goldData != None:
EvaluateInteractionXML.run(self.edgeDetector.evaluator,
xml, goldData, edgeParse)
else:
EvaluateInteractionXML.run(self.edgeDetector.evaluator,
xml, self.classifyData,
edgeParse)
else:
print >> sys.stderr, "No model for unmerging"
if self.checkStep("MODIFIERS"):
if self.model.hasMember("modifier-classifier-model"):
xml = self.getWorkFile(xml, [
workOutputTag + "unmerging-pred.xml.gz",
workOutputTag + "edge-pred.xml.gz"
])
xml = self.modifierDetector.classifyToXML(xml,
self.model,
None,
workOutputTag,
goldData=goldData,
parse=self.parse)
else:
print >> sys.stderr, "No model for modifier detection"
# if self.checkStep("VALIDATE"):
# xml = self.getWorkFile(xml, [workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
# self.structureAnalyzer.load(model)
# self.structureAnalyzer.validate(xml)
# ETUtils.write(xml, workOutputTag + "validate-pred.xml.gz")
if self.checkStep("ST-CONVERT"):
if stParams["convert"]:
#xml = self.getWorkFile(xml, [workOutputTag + "validate-pred.xml.gz", workOutputTag + "modifier-pred.xml.gz", workOutputTag + "unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"])
xml = self.getWorkFile(xml, [
workOutputTag + "modifier-pred.xml.gz", workOutputTag +
"unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"
])
Utils.STFormat.ConvertXML.toSTFormat(
xml,
output + "-events.tar.gz",
outputTag=stParams["a2Tag"],
writeExtra=(stParams["scores"] == True))
if stParams["evaluate"]: #self.stEvaluator != None:
task = self.task
if task == None:
task = self.getStr(self.edgeDetector.tag + "task",
self.model)
self.stEvaluator.evaluate(output + "-events.tar.gz", task)
else:
print >> sys.stderr, "No BioNLP shared task format conversion"
finalXMLFile = self.getWorkFile(None, [
workOutputTag + "modifier-pred.xml.gz", workOutputTag +
"unmerging-pred.xml.gz", workOutputTag + "edge-pred.xml.gz"
])
if finalXMLFile != None:
shutil.copy2(finalXMLFile, output + "-pred.xml.gz")
self.deleteTempWorkDir()
self.exitState()
def trainUnmergingDetector(self):
xml = None
if not self.unmerging:
print >> sys.stderr, "No unmerging"
if self.checkStep("SELF-TRAIN-EXAMPLES-FOR-UNMERGING",
self.unmerging) and self.unmerging:
# Self-classified train data for unmerging
if self.doUnmergingSelfTraining:
# This allows limiting to a subcorpus
triggerStyle = copy.copy(
Parameters.get(self.triggerExampleStyle))
edgeStyle = copy.copy(Parameters.get(self.edgeExampleStyle))
unmergingStyle = Parameters.get(self.unmergingExampleStyle)
if "sentenceLimit" in unmergingStyle and unmergingStyle[
"sentenceLimit"]:
triggerStyle["sentenceLimit"] = unmergingStyle[
"sentenceLimit"]
edgeStyle["sentenceLimit"] = unmergingStyle[
"sentenceLimit"]
# Build the examples
xml = self.triggerDetector.classifyToXML(
self.trainData,
self.model,
None,
self.workDir + "unmerging-extra-",
exampleStyle=triggerStyle) #, recallAdjust=0.5)
xml = self.edgeDetector.classifyToXML(
xml,
self.model,
None,
self.workDir + "unmerging-extra-",
exampleStyle=edgeStyle) #, recallAdjust=0.5)
assert xml != None
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml,
self.trainData, self.parse)
else:
print >> sys.stderr, "No self-training for unmerging"
if self.checkStep("UNMERGING-EXAMPLES",
self.unmerging) and self.unmerging:
# Unmerging example generation
GOLD_TEST_FILE = self.optData.replace("-nodup", "")
GOLD_TRAIN_FILE = self.trainData.replace("-nodup", "")
if self.doUnmergingSelfTraining:
if xml == None:
xml = self.workDir + "unmerging-extra-edge-pred.xml.gz"
self.unmergingDetector.buildExamples(
self.model, [
self.optData.replace("-nodup", ""),
[self.trainData.replace("-nodup", ""), xml]
], [
self.workDir + "unmerging-opt-examples.gz",
self.workDir + "unmerging-train-examples.gz"
], [GOLD_TEST_FILE, [GOLD_TRAIN_FILE, GOLD_TRAIN_FILE]],
exampleStyle=self.unmergingExampleStyle,
saveIdsToModel=True)
xml = None
else:
self.unmergingDetector.buildExamples(
self.model, [
self.optData.replace("-nodup", ""),
self.trainData.replace("-nodup", "")
], [
self.workDir + "unmerging-opt-examples.gz",
self.workDir + "unmerging-train-examples.gz"
], [GOLD_TEST_FILE, GOLD_TRAIN_FILE],
exampleStyle=self.unmergingExampleStyle,
saveIdsToModel=True)
xml = None
#UnmergingExampleBuilder.run("/home/jari/biotext/EventExtension/TrainSelfClassify/test-predicted-edges.xml", GOLD_TRAIN_FILE, UNMERGING_TRAIN_EXAMPLE_FILE, PARSE, TOK, UNMERGING_FEATURE_PARAMS, UNMERGING_IDS, append=True)
if self.checkStep("BEGIN-UNMERGING-MODEL",
self.unmerging) and self.unmerging:
self.unmergingDetector.beginModel(
None, self.model, self.workDir + "unmerging-train-examples.gz",
self.workDir + "unmerging-opt-examples.gz")
if self.checkStep("END-UNMERGING-MODEL",
self.unmerging) and self.unmerging:
self.unmergingDetector.endModel(
None, self.model, self.workDir + "unmerging-opt-examples.gz")
print >> sys.stderr, "Adding unmerging classifier model to test-set event model"
if self.combinedModel != None:
self.combinedModel.addStr(
"unmerging-example-style",
self.model.getStr("unmerging-example-style"))
self.combinedModel.insert(
self.model.get("unmerging-ids.classes"),
"unmerging-ids.classes")
self.combinedModel.insert(
self.model.get("unmerging-ids.features"),
"unmerging-ids.features")
self.unmergingDetector.addClassifierModel(
self.combinedModel,
self.model.get("unmerging-classifier-model", True),
self.model.getStr("unmerging-classifier-parameter"))
self.combinedModel.save()
def classify(self,
data,
model,
output,
parse=None,
task=None,
fromStep=None,
toStep=None):
BINARY_RECALL_MODE = False # TODO: make a parameter
xml = None
self.initVariables(classifyData=data,
model=model,
xml=None,
task=task,
parse=parse)
self.enterState(
self.STATE_CLASSIFY,
["TRIGGERS", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"],
fromStep, toStep)
#self.enterState(self.STATE_CLASSIFY, ["TRIGGERS", "RECALL-ADJUST", "EDGES", "UNMERGING", "MODIFIERS", "ST-CONVERT"], fromStep, toStep)
self.model = self.openModel(self.model, "r")
if self.checkStep("TRIGGERS"):
xml = self.triggerDetector.classifyToXML(
self.classifyData,
self.model,
None,
output + "-",
parse=self.parse,
recallAdjust=float(
self.getStr("recallAdjustParameter", self.model)))
if self.checkStep("EDGES"):
xml = self.getWorkFile(xml, output + "-recall-adjusted.xml.gz")
xml = self.edgeDetector.classifyToXML(xml,
self.model,
None,
output + "-",
parse=self.parse)
assert xml != None
if self.parse == None:
edgeParse = self.getStr(self.edgeDetector.tag + "parse",
self.model)
else:
edgeParse = self.parse
#EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, self.classifyData, edgeParse)
EvaluateInteractionXML.run(self.edgeDetector.evaluator, xml, None,
edgeParse)
if self.checkStep("UNMERGING"):
if self.model.hasMember("unmerging-classifier-model"):
#xml = self.getWorkFile(xml, output + "-edge-pred.xml.gz")
# To avoid running out of memory, always use file on disk
xml = self.getWorkFile(None, output + "-edge-pred.xml.gz")
goldData = None
if type(self.classifyData) in types.StringTypes:
if os.path.exists(self.classifyData.replace("-nodup", "")):
goldData = self.classifyData.replace("-nodup", "")
xml = self.unmergingDetector.classifyToXML(xml,
self.model,
None,
output + "-",
goldData=goldData,
parse=self.parse)
else:
print >> sys.stderr, "No model for unmerging"
if self.checkStep("MODIFIERS"):
if self.model.hasMember("modifier-classifier-model"):
xml = self.getWorkFile(xml, [
output + "-unmerging-pred.xml.gz",
output + "-edge-pred.xml.gz"
])
xml = self.modifierDetector.classifyToXML(xml,
self.model,
None,
output + "-",
parse=self.parse)
else:
print >> sys.stderr, "No model for modifier detection"
if self.checkStep("ST-CONVERT"):
xml = self.getWorkFile(xml, [
output + "-modifier-pred.xml.gz",
output + "-unmerging-pred.xml.gz", output + "-edge-pred.xml.gz"
])
STFormat.ConvertXML.toSTFormat(xml,
output + "-events.tar.gz",
outputTag="a2",
writeScores=self.stWriteScores)
if self.stEvaluator != None:
task = self.task
if task == None:
task = self.getStr(self.edgeDetector.tag + "task",
self.model)
self.stEvaluator.evaluate(output + "-events.tar.gz", task)
self.exitState()
def buildExamplesFromGraph(self, sentenceGraph, examples, goldGraph=None):
# example directionality
if self.styles.get("directed") == None and self.styles.get("undirected") == None: # determine directedness from corpus
examplesAreDirected = self.structureAnalyzer.hasDirectedTargets() if self.structureAnalyzer != None else True
elif self.styles.get("directed"):
assert self.styles.get("undirected") in [None, False]
examplesAreDirected = True
elif self.styles.get("undirected"):
assert self.styles.get("directed") in [None, False]
examplesAreDirected = False
# Filter entities, if needed
sentenceGraph.mergeInteractionGraph(True)
entities = sentenceGraph.mergedEntities
#entityToDuplicates = sentenceGraph.mergedEntityToDuplicates
self.exampleStats.addValue("Duplicate entities skipped", len(sentenceGraph.entities) - len(entities))
# Connect to optional gold graph
entityToGold = None
if goldGraph != None:
entityToGold = EvaluateInteractionXML.mapEntities(entities, goldGraph.entities)
# paths = None
# if not self.styles.get("no_path"):
# undirected = sentenceGraph.dependencyGraph.toUndirected()
# paths = undirected
# if self.styles.get("filter_shortest_path") != None: # For DDI use filter_shortest_path=conj_and
# paths.resetAnalyses() # just in case
# paths.FloydWarshall(self.filterEdge, {"edgeTypes":self.styles["filter_shortest_path"]})
dg = sentenceGraph.dependencyGraph
undirected = dg.toUndirected()
edgeCounts = {x:len(dg.getInEdges(x) + dg.getOutEdges(x)) for x in sentenceGraph.tokens}
tokens, tokenMap = self.getTokenFeatures(sentenceGraph)
# Generate examples based on interactions between entities or interactions between tokens
if self.styles.get("token_nodes"):
loopRange = len(tokens)
else:
loopRange = len(entities)
for i in range(loopRange-1):
for j in range(i+1,loopRange):
eI = None
eJ = None
if self.styles.get("token_nodes"):
tI = tokens[i]["element"]
tJ = tokens[j]["element"]
else:
eI = entities[i]
eJ = entities[j]
tI = sentenceGraph.entityHeadTokenByEntity[eI]
tJ = sentenceGraph.entityHeadTokenByEntity[eJ]
if eI.get("type") == "neg" or eJ.get("type") == "neg":
continue
if self.styles.get("skip_extra_triggers"):
if eI.get("source") != None or eJ.get("source") != None:
continue
# only consider paths between entities (NOTE! entities, not only named entities)
if self.styles.get("headsOnly"):
if (len(sentenceGraph.tokenIsEntityHead[tI]) == 0) or (len(sentenceGraph.tokenIsEntityHead[tJ]) == 0):
continue
if examplesAreDirected:
self.buildExample(examples, tI, tJ, eI, eJ, tokens, tokenMap, sentenceGraph, goldGraph, entityToGold, undirected, edgeCounts)
self.buildExample(examples, tJ, tI, eJ, eI, tokens, tokenMap, sentenceGraph, goldGraph, entityToGold, undirected, edgeCounts)
else:
if tokenMap[tJ]["index"] < tokenMap[tI]["index"]:
tI, tJ = tJ, tI
eI, eJ = eJ, eI
self.buildExample(examples, tI, tJ, eI, eJ, tokens, tokenMap, sentenceGraph, goldGraph, entityToGold, undirected, edgeCounts, False)
#Gazetteer.run(TRAIN_FILE, "gazetteer-train")
#GeneralEntityTypeRecognizer.run(TRAIN_FILE, "trigger-train-examples", PARSE_TOK, PARSE_TOK, "style:typed", "trigger-ids")
GeneralEntityTypeRecognizer.run(TEST_FILE, "trigger-test-examples", PARSE_TOK,
PARSE_TOK, "style:typed", "trigger-ids")
Cls.test("trigger-test-examples", "trigger-param-opt/model-c_75000",
"trigger-test-classifications")
evaluator = Ev.evaluate("trigger-test-examples",
"trigger-test-classifications",
"trigger-ids.class_names")
#evaluator = optimize(Cls, Ev, "trigger-train-examples", "trigger-test-examples",\
# "trigger-ids.class_names", CLASSIFIER_PARAMS, "trigger-param-opt")[0]
ExampleUtils.writeToInteractionXML(evaluator.classifications, TEST_FILE,
"test-predicted-triggers.xml",
"trigger-ids.class_names", PARSE_TOK,
PARSE_TOK)
# RecallAdjust.run("test-predicted-triggers.xml",1.0,"test-predicted-triggers-adj.xml")
# ix.splitMergedElements("test-predicted-triggers-adj.xml", "test-predicted-triggers-adj-split.xml")
# ix.recalculateIds("test-predicted-triggers-adj-split.xml", "test-predicted-triggers-adj-split-recids.xml", True)
# EvaluateInteractionXML.run(Ev, "test-predicted-triggers-adj-split-recids.xml", GOLD_TEST_FILE, PARSE_TOK, PARSE_TOK)
ix.splitMergedElements("test-predicted-triggers.xml",
"test-predicted-triggers-split.xml")
ix.recalculateIds("test-predicted-triggers-split.xml",
"test-predicted-triggers-split-recids.xml", True)
EvaluateInteractionXML.run(Ev, "test-predicted-triggers-split-recids.xml",
GOLD_TEST_FILE, PARSE_TOK, PARSE_TOK)
