def rte_classifier(algorithm, sample_N=None):
from nltk.corpus import rte as rte_corpus
train_set = rte_corpus.pairs(
["rte1_dev.xml", "rte2_dev.xml", "rte3_dev.xml"])
test_set = rte_corpus.pairs(
["rte1_test.xml", "rte2_test.xml", "rte3_test.xml"])
if sample_N is not None:
train_set = train_set[:sample_N]
test_set = test_set[:sample_N]
featurized_train_set = rte_featurize(train_set)
featurized_test_set = rte_featurize(test_set)
# Train the classifier
print("Training classifier...")
if algorithm in ["megam", "BFGS"]: # MEGAM based algorithms.
# Ensure that MEGAM is configured first.
check_megam_config()
clf = lambda x: MaxentClassifier.train(featurized_train_set, algorithm)
elif algorithm in ["GIS", "IIS"]: # Use default GIS/IIS MaxEnt algorithm
clf = MaxentClassifier.train(featurized_train_set, algorithm)
else:
err_msg = str("RTEClassifier only supports these algorithms:\n "
"'megam', 'BFGS', 'GIS', 'IIS'.\n")
raise Exception(err_msg)
print("Testing classifier...")
acc = accuracy(clf, featurized_test_set)
print("Accuracy: %6.4f" % acc)
return clf
def rte_classifier(algorithm):
from nltk.corpus import rte as rte_corpus
train_set = rte_corpus.pairs(
['rte1_dev.xml', 'rte2_dev.xml', 'rte3_dev.xml'])
test_set = rte_corpus.pairs(
['rte1_test.xml', 'rte2_test.xml', 'rte3_test.xml'])
featurized_train_set = rte_featurize(train_set)
featurized_test_set = rte_featurize(test_set)
# Train the classifier
print('Training classifier...')
if algorithm in ['megam', 'BFGS']: # MEGAM based algorithms.
# Ensure that MEGAM is configured first.
check_megam_config()
clf = lambda x: MaxentClassifier.train(featurized_train_set, algorithm)
elif algorithm in ['GIS', 'IIS']: # Use default GIS/IIS MaxEnt algorithm
clf = MaxentClassifier.train(featurized_train_set, algorithm)
else:
err_msg = str("RTEClassifier only supports these algorithms:\n "
"'megam', 'BFGS', 'GIS', 'IIS'.\n")
raise Exception(err_msg)
print('Testing classifier...')
acc = accuracy(clf, featurized_test_set)
print('Accuracy: %6.4f' % acc)
return clf
def rte_classifier(algorithm):
from nltk.corpus import rte as rte_corpus
train_set = rte_corpus.pairs(['rte1_dev.xml', 'rte2_dev.xml', 'rte3_dev.xml'])
test_set = rte_corpus.pairs(['rte1_test.xml', 'rte2_test.xml', 'rte3_test.xml'])
featurized_train_set = rte_featurize(train_set)
featurized_test_set = rte_featurize(test_set)
# Train the classifier
print('Training classifier...')
if algorithm in ['megam', 'BFGS']: # MEGAM based algorithms.
# Ensure that MEGAM is configured first.
check_megam_config()
clf = lambda x: MaxentClassifier.train(featurized_train_set, algorithm)
elif algorithm in ['GIS', 'IIS']: # Use default GIS/IIS MaxEnt algorithm
clf = MaxentClassifier.train(featurized_train_set, algorithm)
else:
err_msg = str(
"RTEClassifier only supports these algorithms:\n "
"'megam', 'BFGS', 'GIS', 'IIS'.\n"
)
raise Exception(err_msg)
print('Testing classifier...')
acc = accuracy(clf, featurized_test_set)
print('Accuracy: %6.4f' % acc)
return clf