Python eval_output_file Exemples

Exemple #1

Fichier : main_tools.py Projet : zhuyuying/Semantic-Texual-Similarity-Toolkits

def test_sts(model):
    test_file = './data/stsbenchmark/sts-test.csv'
    test_instances = stst.load_parse_data(test_file, nlp)
    model.test(test_instances, test_file)
    test_pearsonr = stst.eval_output_file(model.output_file)
    print('Test:', test_pearsonr)
    return test_pearsonr

Exemple #2

Fichier : main_tools.py Projet : zhuyuying/Semantic-Texual-Similarity-Toolkits

def dev_sts(model):
    dev_file = './data/stsbenchmark/sts-dev.csv'
    dev_instances = stst.load_parse_data(dev_file, nlp)
    model.test(dev_instances, dev_file)
    dev_pearsonr = stst.eval_output_file(model.output_file)
    print('Dev:', dev_pearsonr)
    return dev_pearsonr

Exemple #3

Fichier : example.py Projet : zhuyuying/Semantic-Texual-Similarity-Toolkits

model.add(stst.NegativeFeature())

# train and test
train_file = './data/stsbenchmark/sts-train.csv'
dev_file = './data/stsbenchmark/sts-dev.csv'
test_file = './data/stsbenchmark/sts-test.csv'

# init the server and input the address
nlp = stst.StanfordNLP('http://localhost:9000')

# parse data
train_instances = stst.load_parse_data(train_file, nlp)
dev_instances = stst.load_parse_data(dev_file, nlp)

# train and test
model.train(train_instances, train_file)
model.test(dev_instances, dev_file)

# evaluation
dev_pearsonr = stst.eval_output_file(model.output_file)
print('Dev:', dev_pearsonr)

# test on new data set
test_instances = stst.load_parse_data(test_file, nlp)
model.test(test_instances, test_file)
test_pearsonr = stst.eval_output_file(model.output_file)
print('Test:', test_pearsonr)

recod_file = './data/records.csv'
stst.record(recod_file, dev_pearsonr, test_pearsonr, model)