Python TransitionParser.parse方法代码示例

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
def train_model(lang,training_set='train'):
	# load and sample data
	data = get_data(lang,dataset=training_set).parsed_sents()
	if len(data) >200:
		random.seed(1234)
		subdata = random.sample(data, 200)
	else:
		subdata = data

	# train model and save
	tp = TransitionParser(Transition, FeatureExtractor)
	tp.train(subdata)
	tp.save('{0}.model'.format(lang))


	# test performance on new data
	if lang != 'english':
		testdata = get_data(lang,dataset='test').parsed_sents()
	
	# english test data not available
	# so find a subset of training data 
	# that is disjoint from data used for training 
	else:
		not_in_training = [sent for sent in data if sent not in subdata]
		testdata = random.sample(not_in_training,200)

	parsed = tp.parse(testdata)

	ev = DependencyEvaluator(testdata, parsed)

	# store and print results
	with open('results.txt','a') as results_file:
		results_file.write('{0} model:\n'.format(lang))
		results_file.write("UAS: {} \nLAS: {}\n".format(*ev.eval()))
	print '{0} model:\n'.format(lang)
	print "UAS: {} \nLAS: {}\n".format(*ev.eval())
	return ev.eval()[1]

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
        tp = TransitionParser(Transition, FeatureExtractor)
        tp.train(traindata)
	#tp.save('swedish.model')
        #tp.save('english.model')
###	tp.save('danish.model')

	#labeleddata = dataset.get_swedish_dev_corpus().parsed_sents()
        labeleddata = dataset.get_english_dev_corpus().parsed_sents()
	#labeleddata = dataset.get_danish_dev_corpus().parsed_sents()
        
	#blinddata = dataset.get_swedish_dev_blind_corpus().parsed_sents()
	blinddata = dataset.get_english_dev_blind_corpus().parsed_sents()
	#blinddata = dataset.get_danish_dev_blind_corpus().parsed_sents()
        #tp = TransitionParser.load('badfeatures.model')

        parsed = tp.parse(blinddata)

        with open('test.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')

        ev = DependencyEvaluator(labeleddata, parsed)
        print "UAS: {} \nLAS: {}".format(*ev.eval())

        # parsing arbitrary sentences (english):
        # sentence = DependencyGraph.from_sentence('Hi, this is a test')

        # tp = TransitionParser.load('english.model')
        # parsed = tp.parse([sentence])
        # print parsed[0].to_conll(10).encode('utf-8')

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
        # tp.train(SE_subdata)
        # tp.save('swedish.model')
        # SE_testdata = dataset.get_swedish_test_corpus().parsed_sents()
        # SE_tp = TransitionParser.load('swedish.model')
        # SE_parsed = SE_tp.parse(SE_testdata)
        #
        # DK
        tp = TransitionParser(Transition, FeatureExtractor)
        print('Training...')
        tp.train(DK_subdata)
        print('Ok. Saving the model...')
        tp.save('danish.model')
        print('Ok. Parsing the test corpus...')
        DK_testdata = dataset.get_danish_test_corpus().parsed_sents()
        #DK_tp = TransitionParser.load('danish.model')
        DK_parsed = tp.parse(DK_testdata)
        print('Ok.')


        # with open('english.conll', 'w') as f:
        #     for p in EN_parsed:
        #         f.write(p.to_conll(10).encode('utf-8'))
        #         f.write('\n')
        #
        # ev = DependencyEvaluator(EN_testdata, EN_parsed)
        # print('Evaluating EN model...')
        # print "LAS: {} \nUAS: {}".format(*ev.eval())

        with open('danish.conll', 'w') as f:
            for p in DK_parsed:
                f.write(p.to_conll(10).encode('utf-8'))

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
from transition import Transition

if __name__ == '__main__':
    data = dataset.get_english_train_corpus().parsed_sents()
    random.seed(1234)
    subdata = random.sample(data, 200)

    try:
        tp = TransitionParser(Transition, FeatureExtractor)
        tp.train(subdata)
        tp.save('english.model')

        testdata = dataset.get_english_dev_corpus().parsed_sents()
        #tp = TransitionParser.load('badfeatures.model')

        parsed = tp.parse(testdata)

        with open('test.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')

        ev = DependencyEvaluator(testdata, parsed)
        print "UAS: {} \nLAS: {}".format(*ev.eval())

        # parsing arbitrary sentences (english):
        # sentence = DependencyGraph.from_sentence('Hi, this is a test')

        # tp = TransitionParser.load('english.model')
        # parsed = tp.parse([sentence])
        # print parsed[0].to_conll(10).encode('utf-8')

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
    subdata = random.sample(data, 200)

    try:
        tp = TransitionParser(Transition, FeatureExtractor)
        tp.train(subdata)
        # tp.save('swedish.model')
        # tp.save('english.model')
        tp.save('danish.model')

        # testdata = dataset.get_swedish_test_corpus().parsed_sents()
        testdata = dataset.get_danish_test_corpus().parsed_sents()
        # tp = TransitionParser.load('badfeatures.model')
        # testdata = dataset.get_english_test_corpus().parsed_sents()
        # tp = TransitionParser.load('english.model')

        parsed = tp.parse(testdata)

        with open('test.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')

        ev = DependencyEvaluator(testdata, parsed)
        print "LAS: {} \nUAS: {}".format(*ev.eval())

        # parsing arbitrary sentences (english):
        sentence = DependencyGraph.from_sentence('Hi, this is a test')

        tp = TransitionParser.load('english.model')
        parsed = tp.parse([sentence])
        print parsed[0].to_conll(10).encode('utf-8')

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
    danishsubdata = random.sample(danishdata, 235)

    try:
        
        #SWEDISH TESTING
        tp = TransitionParser(Transition, FeatureExtractor)
        tp.train(swedishsubdata)
        tp.save('swedish.model')
        
        
        #badfeatures.model...don't use for real testing
        #tp = TransitionParser.load('badfeatures.model')

 
        testdata = dataset.get_swedish_test_corpus().parsed_sents()
        parsed = tp.parse(testdata)
        
        #to write output...for badfeatures.model
        '''
        with open('test.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')
        '''

        ev = DependencyEvaluator(testdata, parsed)
        print "SWEDISH UAS: {} \nLAS: {}".format(*ev.eval())
         
        
        #DANISH TESTING
        tp.train(danishsubdata)

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
    try:
        model_path = sys.argv[1]
        # print 'ModelPath', model_path
    except IndexError as ie:
        print 'Model Path Not Specified! Exiting...', ie
        sys.exit(-1)

    try:
        tp = TransitionParser(Transition, FeatureExtractor)
        tp = TransitionParser.load(model_path)   # load the trained model for parsing.

        for line in sys.stdin:
            # print 'Processing:', line
            sentence = DependencyGraph.from_sentence(line)
            parsed = tp.parse([sentence]) # parse the input line
            print parsed[0].to_conll(10).encode('utf-8')

        # with open('test.conll', 'w') as f:
        #     for p in parsed:
        #         f.write(p.to_conll(10).encode('utf-8'))
        #         f.write('\n')

        # parsing arbitrary sentences (english):
        # sentence = DependencyGraph.from_sentence('Hi, this is a test')

        # tp = TransitionParser.load('english.model')
        # parsed = tp.parse([sentence])
        # print parsed[0].to_conll(10).encode('utf-8')

    except Exception as e:

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
    random.seed()
    danish_subdata = random.sample(danish_data, 200)

    try:
        print 'training swedish'

        # swedish
        tp = TransitionParser(Transition, FeatureExtractor)
        tp.train(swedish_subdata)
        tp.save('swedish.model')

        testdata = dataset.get_swedish_test_corpus().parsed_sents()
        tp = TransitionParser.load('swedish.model')

        print 'testing swedish'
        parsed = tp.parse(testdata)

        with open('test.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')

        ev = DependencyEvaluator(testdata, parsed)
        print 'Swedish results'
        print "UAS: {} \nLAS: {}".format(*ev.eval())


        # english
        print '\n----------------------\n'
        print 'Training english'
        tpe = TransitionParser(Transition, FeatureExtractor)

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
        ev = DependencyEvaluator(testdata, parsed)
        print "Bad Features Results"
        print "UAS: {} \nLAS: {}".format(*ev.eval())
        t1 = time.time()
        print "Time: "+str(t1 - t0) + '\n'

        # SWEDISH FEATURE MODELS
        print 'Starting Swedish'
        tp_s = TransitionParser(Transition, FeatureExtractor)
        tp_s.train(subdata)
        tp_s.save('swedish.model')

        testdata = dataset.get_swedish_test_corpus().parsed_sents()
        tp_s = TransitionParser.load('swedish.model')

        parsed = tp_s.parse(testdata)

        with open('swedish.conll', 'w') as f:
            for p in parsed:
                f.write(p.to_conll(10).encode('utf-8'))
                f.write('\n')

        ev = DependencyEvaluator(testdata, parsed)
        print "Swedish Results"
        print "UAS: {} \nLAS: {}".format(*ev.eval())
        t2 = time.time()
        print "Time: " + str(t2 - t1) + "\n"

        # NEED TO ALSO DO DANISH AND ENGLISH, BUT IT'S PRACTICALLY THE SAME

        # ENGLISH FEATURE MODELS

# 需要导入模块: from providedcode.transitionparser import TransitionParser [as 别名]
# 或者: from providedcode.transitionparser.TransitionParser import parse [as 别名]
            'swedish' : ('swedish.model', dataset.get_swedish_train_corpus, dataset.get_swedish_test_corpus)
    }
    for model_type, model_tuple in model_dict.iteritems():
        model, data, testdata = model_tuple[0], model_tuple[1]().parsed_sents(), model_tuple[2]().parsed_sents()

        random.seed(1234)
        subdata = random.sample(data, 200)  # 200 randomly selected DependencyGraphs(sentences) for model training.

        try:
            tp = TransitionParser(Transition, FeatureExtractor)
            tp.train(subdata)   # train with 200 randomly selected dependency graphs(sentences).
            tp.save(model)  # save the trained model.

            tp = TransitionParser.load(model)   # load the trained model for parsing.

            parsed = tp.parse(testdata) # parse the test data

            with open('test.conll', 'w') as f:
                for p in parsed:
                    f.write(p.to_conll(10).encode('utf-8'))
                    f.write('\n')

            # evaluate the test parse result here...
            ev = DependencyEvaluator(testdata, parsed)
            print 'Model: {}'.format(model_type)
            # LAS: labeled attachment score - percentage of scoring tokens for which the parsing system has predicted the
            #    correct head and dependency label.
            # UAS:
            print "LAS: {} \nUAS: {}".format(*ev.eval())

            # parsing arbitrary sentences (english):