def run(classifier1, classifier2):
parser = LC_QaudParser()
query_builder = Orchestrator(None, classifier1, classifier2, parser, auto_train=False)
print "train_question_classifier"
scores = query_builder.train_question_classifier(file_path="../data/LC-QUAD/data_v8.json", test_size=0.5)
print scores
y_pred = query_builder.question_classifier.predict(query_builder.X_test)
print(classification_report(query_builder.y_test, y_pred))
print "double_relation_classifer"
scores = query_builder.train_double_relation_classifier(file_path="../data/LC-QUAD/data_v8.json", test_size=0.5)
print scores
y_pred = query_builder.double_relation_classifer.predict(query_builder.X_test)
print(classification_report(query_builder.y_test, y_pred))
if __name__ == "__main__":
args = Struct()
base_path = "./learning/treelstm/"
args.save = os.path.join(base_path, "checkpoints/")
args.expname = "lc_quad"
args.mem_dim = 150
args.hidden_dim = 50
args.num_classes = 2
args.input_dim = 300
args.sparse = ""
args.lr = 0.01
args.wd = 1e-4
args.data = os.path.join(base_path, "data/lc_quad/")
args.cuda = False
parser = LC_QaudParser()
kb = parser.kb
base_dir = "./output"
question_type_classifier_path = os.path.join(base_dir,
"question_type_classifier")
utility.makedirs(question_type_classifier_path)
question_type_classifier = SVMClassifier(
os.path.join(question_type_classifier_path, "svm.model"))
o = Orchestrator(None, question_type_classifier, None, parser, True)
raw_entities = [{
"surface":
"",
"uris": [{
"confidence": 1,