def with_svm():
training_sentences, training_target_tags = return_tags_training_set()
instance = SVMWithGridSearch(TAGS_TRAINING_SENTENCES, TAG_TARGETS)
classifier = instance.classifier()
print "Accuracy with 200 samples with SVM grid search %.3f" % (
classifier.score(TEST_SENTENCES, TEST_TARGET))
classifier = GridSearchCV(pipeline, tuned_parameters, verbose=1)
classifier.fit(TAGS_TRAINING_SENTENCES, TAG_TARGETS)
print "Accuracy with 200 samples with LDA %.3f" % (classifier.score(
TEST_SENTENCES, TEST_TARGET))
print "Best score: %0.3f" % classifier.best_score_
print "Best parameters set:"
best_parameters = classifier.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print "\t%s: %r" % (param_name, best_parameters[param_name])
def with_svm():
training_sentences, training_target_tags = return_tags_training_set()
instance = SVMWithGridSearch(TAGS_TRAINING_SENTENCES, TAG_TARGETS)
classifier = instance.classifier()
print "Accuracy with 200 samples with SVM grid search %.3f"%(classifier.score(TEST_SENTENCES, TEST_TARGET))
classifier= GridSearchCV(pipeline, tuned_parameters, verbose=1)
classifier.fit(TAGS_TRAINING_SENTENCES, TAG_TARGETS)
print "Accuracy with 200 samples with LDA %.3f"%(classifier.score(TEST_SENTENCES, TEST_TARGET))
print "Best score: %0.3f" % classifier.best_score_
print "Best parameters set:"
best_parameters = classifier.best_estimator_.get_params()
for param_name in sorted(parameters.keys()):
print "\t%s: %r" % (param_name, best_parameters[param_name])
def svm_grid_search_classifier(self):
print "\n Running {0} \n".format(inspect.stack()[0][3])
instance = SVMWithGridSearch(self.training_sentences, self.training_target_tags)
classifier = instance.classifier()
return classifier