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eval.py
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eval.py
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import os
import argparse
import torch
import config
from main import init
from src.data import ReviewDataset
from src.evaluate import test_rate_mse, test_rate_ndcg, load_ndcg
DIR_PATH = os.path.dirname(__file__)
def load_test_dataset():
testfile = os.path.join(DIR_PATH, config.TEST_CORPUS)
print("Reading Testing data from %s..." % testfile)
test_dataset = ReviewDataset(testfile)
print(f'Read {len(test_dataset)} testing reviews')
return test_dataset
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model', help='model name to save/load checkpoints')
parser.add_argument('-c', '--checkpoint')
parser.add_argument('evals', nargs='+')
args = parser.parse_args()
torch.no_grad()
model, misc = init(args.model, args.checkpoint)
model.eval()
test_dataset = load_test_dataset()
for ev in args.evals:
if ev == 'rmse':
mse = test_rate_mse(test_dataset, model)
print('Rate RMSE: ', mse)
elif ev == 'ndcg':
ndcg_path = os.path.join(DIR_PATH, 'data/ndcg_150.ls')
ndcg_user_items = load_ndcg(ndcg_path)
print('User size:', len(ndcg_user_items))
vals = next(iter(ndcg_user_items.values()))
size = len(vals)
avg_ndcg, ndcg = test_rate_ndcg(model, test_dataset, ndcg_user_items)
print(f'Rate NDCG({size}):', avg_ndcg, ndcg)
if __name__ == '__main__':
main()