def eval(self, test_dataset: GowallaTopNDataset):
users = []
ground_truth = []
for user in test_dataset.get_all_users():
user_positive_items = test_dataset.get_user_positives(user)
if user_positive_items:
users.append(user)
ground_truth.append(user_positive_items)
preds = self.recommend(users)
max_length = max(map(len, metrics.metric_dict.keys())) + max(
map(lambda x: len(str(x)), config['METRICS_REPORT']))
for metric_name, metric_func in metrics.metric_dict.items():
for k in config['METRICS_REPORT']:
metric_name_total = f'{metric_name}@{k}'
metric_value = metric_func(preds, ground_truth, k).mean()
logger.info(f'{metric_name_total: >{max_length + 1}} = {metric_value}')
from pathlib import Path
from config import config
from model import LightGCN, TopNModel, TopNPersonalized, TopNNearestModel
from dataloader import GowallaLightGCNDataset, GowallaTopNDataset, GowallaALSDataset
if __name__ == '__main__':
dataset_path = Path('dataset') / config['DATASET'] / config['DATASET']
if config['MODEL'] == 'LightGCN':
train_dataset = GowallaLightGCNDataset(f'{dataset_path}_custom.train')
test_dataset = GowallaLightGCNDataset(f'{dataset_path}_custom.test',
train=False)
model = LightGCN(train_dataset)
model.fit(config['TRAIN_EPOCHS'], test_dataset)
elif config['MODEL'] == 'TopNModel':
train_dataset = GowallaTopNDataset(f'{dataset_path}.train')
test_dataset = GowallaTopNDataset(f'{dataset_path}.test', train=False)
model = TopNModel(config['TOP_N'])
model.fit(train_dataset)
model.eval(test_dataset)
elif config['MODEL'] == 'TopNPersonalized':
train_dataset = GowallaTopNDataset(f'{dataset_path}.train')
test_dataset = GowallaTopNDataset(f'{dataset_path}.test', train=False)
model = TopNPersonalized(config['TOP_N'])
model.fit(train_dataset)
model.eval(test_dataset)
elif config['MODEL'] == 'TopNNearestModel':
train_dataset = GowallaTopNDataset(f'{dataset_path}.train')
test_dataset = GowallaTopNDataset(f'{dataset_path}.test', train=False)
import pandas as pd
from config import config
from model import LightGCN, TopNModel, TopNPersonalized, TopNNearestModel
from dataloader import GowallaLightGCNDataset, GowallaTopNDataset, GowallaALSDataset
if __name__ == '__main__':
dataset = config['DATASET']
if config['MODEL'] == 'LightGCN':
train_dataset = GowallaLightGCNDataset(f'dataset/{dataset}.train')
test_dataset = GowallaLightGCNDataset(f'dataset/{dataset}.test',
train=False)
model = LightGCN(train_dataset)
model.fit(config['TRAIN_EPOCHS'], test_dataset)
elif config['MODEL'] == 'TopNModel':
train_dataset = GowallaTopNDataset(f'dataset/{dataset}.train')
test_dataset = GowallaTopNDataset(f'dataset/{dataset}.test',
train=False)
model = TopNModel(config['TOP_N'])
model.fit(train_dataset)
model.eval(test_dataset)
elif config['MODEL'] == 'TopNPersonalized':
train_dataset = GowallaTopNDataset(f'dataset/{dataset}.train')
test_dataset = GowallaTopNDataset(f'dataset/{dataset}.test',
train=False)
model = TopNPersonalized(config['TOP_N'])
model.fit(train_dataset)
model.eval(test_dataset)
elif config['MODEL'] == 'TopNNearestModel':