import argparse import logging from datetime import datetime from models.neural_nets.cnn_only_classifier import CnnOnlyClassifier from run import run from utils.other_utils import get_dataset_name if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--csv_path', type=str, help='Path to .csv file') args = parser.parse_args() logging.basicConfig(filename='outputs/logs/{}-{}-cnn_only.log'.format( datetime.now().strftime('%Y-%m-%dT%H:%M:%S'), get_dataset_name(args.csv_path)), level=logging.INFO) classifier = CnnOnlyClassifier(question_body_words_count=300, answer_body_words_count=500, filters_count=32, kernel_sizes=[2, 3, 5, 7], mode='aesd') run(classifier, args.csv_path, epochs=20)
import argparse import logging from datetime import datetime from sklearn.linear_model import SGDClassifier from models.sklearn.sklearn_classifier import SKLearnClassifier from models.sklearn.tfidf_vectorizer_adapter import TfIdfVectorizerAdapter from run import run from utils.other_utils import get_dataset_name if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--csv_path', type=str, help='Path to .csv file') args = parser.parse_args() logging.basicConfig(filename='outputs/logs/{}-{}-sgd_tfidf_vectorizer.log'.format( datetime.now().strftime('%Y-%m-%dT%H:%M:%S'), get_dataset_name(args.csv_path)), level=logging.INFO) logging.info('SGD classifier') classifier = SKLearnClassifier(SGDClassifier(), TfIdfVectorizerAdapter()) run(classifier, args.csv_path)