def test_wordsim_evaluation():
from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
wordsim_eval = WordSimEvaluation()
wns = WordNetSimilarity()
#define similarity metrics
lin = lambda x, y: wns.word_similarity(x, y, 'lin')
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
#evaluate similarity metrics
assert wordsim_eval.evaluate_multiple_metrics({'lin':lin, 'wpath':wpath}, 'noun_simlex') is not None
#performa Steiger's Z significance Test
assert wordsim_eval.statistical_test('wpath', 'lin', 'noun_simlex') is not None
def test_wordsim_evaluation():
from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
wordsim_eval = WordSimEvaluation()
wns = WordNetSimilarity()
#define similarity metrics
lin = lambda x, y: wns.word_similarity(x, y, 'lin')
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
#evaluate similarity metrics
assert wordsim_eval.evaluate_multiple_metrics({
'lin': lin,
'wpath': wpath
}, 'noun_simlex') is not None
#performa Steiger's Z significance Test
assert wordsim_eval.statistical_test('wpath', 'lin',
'noun_simlex') is not None
def test_wordsim_evaluation():
from sematch.evaluation import WordSimEvaluation
from sematch.semantic.similarity import WordNetSimilarity
evaluation = WordSimEvaluation()
print evaluation.dataset_names()
wns = WordNetSimilarity()
# define similarity metrics
wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.8)
# evaluate similarity metrics
print evaluation.evaluate_metric('wpath', wpath, 'noun_simlex')
# performa Steiger's Z significance Test
print evaluation.statistical_test('wpath', 'path', 'noun_simlex')
wpath_es = lambda x, y: wns.monol_word_similarity(x, y, 'spa', 'path')
wpath_en_es = lambda x, y: wns.crossl_word_similarity(
x, y, 'eng', 'spa', 'wpath')
print evaluation.evaluate_metric('wpath_es', wpath_es, 'rg65_spanish')
print evaluation.evaluate_metric('wpath_en_es', wpath_en_es, 'rg65_EN-ES')
def test_simcat_classifier():
from sematch.classification import SimCatClassifier
from sematch.evaluation import ABSAEvaluation
from sematch.semantic.similarity import WordNetSimilarity
# defining similarity metric
wns = WordNetSimilarity()
sim_metric_jcn = lambda x, y: wns.word_similarity(x, y, 'jcn')
sim_metric_wpath = lambda x, y: wns.word_similarity_wpath(x, y, 0.9)
# loadding dataset
absa_eval = ABSAEvaluation()
X_train_16, y_train_16 = absa_eval.load_dataset('eval/aspect/ABSA16_Restaurants_Train_SB1_v2.xml')
X_test_16, y_test_16 = absa_eval.load_dataset('eval/aspect/ABSA16_Restaurants_Train_SB1_v2.xml')
# train the classifiers
sim_jcn_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_jcn)
sim_wpath_classifier = SimCatClassifier.train(zip(X_train_16, y_train_16), sim_metric_wpath)
# evaluate the classifiers
#absa_eval.evaluate(X_test_16, y_test_16, sim_jcn_classifier)
#absa_eval.evaluate(X_test_16, y_test_16, sim_wpath_classifier)
assert sim_jcn_classifier is not None
assert sim_wpath_classifier is not None
