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)
