Python IdSet.getIds方法代碼示例

# 需要導入模塊: from Core.IdSet import IdSet [as 別名]
# 或者: from Core.IdSet.IdSet import getIds [as 別名]
 def writeXML(self, examples, predictions, corpus, outputFile, classSet=None, parse=None, tokenization=None, goldCorpus=None):
     #print >> sys.stderr, "Writing output to Interaction XML"
     corpus = self.loadCorpus(corpus, parse, tokenization)
     if goldCorpus != None:
         goldCorpus = self.loadCorpus(corpus, parse, tokenization)
     examples, predictions = self.loadExamples(examples, predictions)
     
     if type(classSet) == types.StringType: # class names are in file
         classSet = IdSet(filename=classSet)
     classIds = None
     if classSet != None:
         classIds = classSet.getIds()
         
     #counter = ProgressCounter(len(corpus.sentences), "Write Examples")
             
     exampleQueue = [] # One sentence's examples
     predictionsByExample = {}
     currentMajorId = None
     prevMajorIds = set()
     processedSentenceIds = set()
     xType = None
     
     count = 0
     for example in examples:
         count += 1
     assert count > 0
     progress = ProgressCounter(count, "Write Examples")
     
     for example, prediction in itertools.izip_longest(examples, predictions):
         assert example != None
         assert prediction != None
         majorId, minorId = example[0].rsplit(".x", 1)
         #if currentMajorId == "GENIA.d114.s9": print "Start"
         if majorId != currentMajorId: # new sentence
             if currentMajorId != None:
                 #if currentMajorId == "GENIA.d114.s9": print "JAA"
                 processedSentenceIds.add(currentMajorId)
                 sentenceObject = corpus.sentencesById[currentMajorId]
                 goldSentence = None
                 if goldCorpus != None:
                     goldSentence = goldCorpus.sentencesById[currentMajorId]
                 self.writeXMLSentence(exampleQueue, predictionsByExample, sentenceObject, classSet, classIds, goldSentence=goldSentence) # process queue
                 progress.update(len(exampleQueue), "Writing examples ("+exampleQueue[-1][0]+"): ")
             exampleQueue = []
             predictionsByExample = {}
             prevMajorIds.add(currentMajorId)
             assert majorId not in prevMajorIds, majorId
             currentMajorId = majorId 
         exampleQueue.append(example) # queue example
         predictionsByExample[example[0]] = prediction
         assert example[3]["xtype"] == self.xType, str(example[3]["xtype"]) + "/" + str(self.xType)
     
     # Process what is still in queue
     if currentMajorId != None:
         processedSentenceIds.add(currentMajorId)
         sentenceObject = corpus.sentencesById[currentMajorId]
         goldSentence = None
         if goldCorpus != None:
             goldSentence = goldCorpus.sentencesById[currentMajorId]
         self.writeXMLSentence(exampleQueue, predictionsByExample, sentenceObject, classSet, classIds, goldSentence=goldSentence) # process queue
         progress.update(len(exampleQueue), "Writing examples ("+exampleQueue[-1][0]+"): ")
         exampleQueue = []
         predictionsByExample = {}
     
     # Process sentences with no examples (e.g. to clear interactions)
     for sentenceId in sorted(corpus.sentencesById.keys()):
         if sentenceId not in processedSentenceIds:
             sentenceObject = corpus.sentencesById[sentenceId]
             goldSentence = None
             if goldCorpus != None:
                 goldSentence = goldCorpus.sentencesById[currentMajorId]
             self.writeXMLSentence([], {}, sentenceObject, classSet, classIds, goldSentence=goldSentence)
     
     # Print statistics
     if len(self.counts) > 0:
         print >> sys.stderr, self.counts
         self.counts = defaultdict(int)
 
     # Write corpus
     if outputFile != None:
         print >> sys.stderr, "Writing corpus to", outputFile
         ETUtils.write(corpus.rootElement, outputFile)
     return corpus.tree

# 需要導入模塊: from Core.IdSet import IdSet [as 別名]
# 或者: from Core.IdSet.IdSet import getIds [as 別名]
    def writeXML(self, examples, predictions, corpus, outputFile, classSet=None, parse=None, tokenization=None, goldCorpus=None, exampleStyle=None):
        """
        Writes task 3 examples to interaction XML. Assumes task 3 classification
        is done with SVMMulticlass Classifier, used for two classes.
        """
        print >> sys.stderr, "Adding task 3 to Interaction XML"
        examples, predictions = self.loadExamples(examples, predictions)
        
        if type(classSet) == types.StringType: # class names are in file
            classSet = IdSet(filename=classSet)
        classIds = None
        if classSet != None:
            classIds = classSet.getIds()

        corpusTree = ETUtils.ETFromObj(corpus)
        corpusRoot = corpusTree.getroot()
        
        # Determine subtask
        task3Type = None
        for example in examples:
            assert example[3].has_key("t3type")
            task3Type = example[3]["t3type"]
            break        
        if task3Type == None:
            if outputFile != None:
                print >> sys.stderr, "Writing corpus to", outputFile
                ETUtils.write(corpusRoot, outputFile)
            return corpusTree
        assert task3Type in ["multiclass", "speculation", "negation"]
        
        # Remove the task 3 subtask information if it already exists
        for entity in corpusRoot.getiterator("entity"):
            if task3Type == "multiclass":
                entity.set("speculation", "False")
                entity.set("negation", "False")
            elif task3Type == "speculation":
                entity.set("speculation", "False")
            else: # task3Type == "negation"
                entity.set("negation", "False")
        
        specMap = {}
        negMap = {}
        for example, prediction in itertools.izip(examples, predictions):
            assert example[3]["xtype"] == "task3"
            if example[3]["t3type"] == "multiclass":
                predictedClassName = classSet.getName(prediction[0])
                if predictedClassName != "neg":
                    predictedModifiers = predictedClassName.split("---")
                    if "negation" in predictedModifiers:
                        assert not negMap.has_key(example[3]["entity"])
                        negMap[example[3]["entity"]] = (True, prediction)
                    if "speculation" in predictedModifiers:
                        assert not specMap.has_key(example[3]["entity"])
                        specMap[example[3]["entity"]] = (True, prediction)
            else:
                if example[3]["t3type"] == "speculation":
                    map = specMap
                else:
                    map = negMap
                if prediction[0] != 1:
                    assert not map.has_key(example[3]["entity"])
                    map[example[3]["entity"]] = (True, prediction)
                else:
                    assert not map.has_key(example[3]["entity"])
                    map[example[3]["entity"]] = (False, prediction)
        
        for entity in corpusRoot.getiterator("entity"):
            eId = entity.get("id")
            if task3Type == "multiclass":
                if specMap.has_key(eId):
                    entity.set("speculation", str(specMap[eId][0]))
                    entity.set("modPred", self.getPredictionStrengthString(specMap[eId][1], classSet, classIds))
                if negMap.has_key(eId):
                    entity.set("negation", str(negMap[eId][0]))
                    entity.set("modPred", self.getPredictionStrengthString(negMap[eId][1], classSet, classIds))
            else:
                if task3Type == "speculation":
                    if specMap.has_key(eId):
                        entity.set("speculation", str(specMap[eId][0]))
                        entity.set("specPred", self.getPredictionStrengthString(specMap[eId][1], classSet, classIds, [""]))
                elif task3Type == "negation":
                    if negMap.has_key(eId):
                        entity.set("negation", str(negMap[eId][0]))
                        entity.set("negPred", self.getPredictionStrengthString(negMap[eId][1], classSet, classIds, ["","speculation"]))
        
        # Write corpus
        if outputFile != None:
            print >> sys.stderr, "Writing corpus to", outputFile
            ETUtils.write(corpusRoot, outputFile)
        return corpusTree