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import logging |
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import pathlib as Path |
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import random |
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import click |
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import numpy as np |
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import polars as pl |
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import torch |
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from model import SASRecEncoder |
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from torch.utils.data import DataLoader |
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from data import Data, EvalDataset, collate_fn, preprocess |
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from yambda.evaluation.metrics import calc_metrics |
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from yambda.evaluation.ranking import Embeddings, Targets, rank_items |
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logging.basicConfig( |
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level=logging.DEBUG, format='[%(asctime)s] [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S' |
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) |
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logger = logging.getLogger(__name__) |
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def infer_users(eval_dataloader: DataLoader, model: torch.nn.Module, device: str): |
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user_ids = [] |
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user_embeddings = [] |
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model.eval() |
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for batch in eval_dataloader: |
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for key in batch.keys(): |
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batch[key] = batch[key].to(device) |
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user_ids.append(batch['user.ids']) |
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user_embeddings.append(model(batch)) |
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return torch.cat(user_ids, dim=0), torch.cat(user_embeddings, dim=0) |
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def infer_items(model: SASRecEncoder): |
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return model.item_embeddings.weight.data |
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@click.command() |
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@click.option('--exp_name', required=True, type=str) |
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@click.option('--data_dir', required=True, type=str, default='../../data/', show_default=True) |
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@click.option( |
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'--size', |
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required=True, |
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type=click.Choice(['50m', '500m', '5b']), |
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default='50m', |
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show_default=True, |
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) |
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@click.option( |
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'--interaction', |
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required=True, |
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type=click.Choice(['likes', 'listens']), |
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default='likes', |
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show_default=True, |
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) |
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@click.option('--batch_size', required=True, type=int, default=256, show_default=True) |
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@click.option('--max_seq_len', required=False, type=int, default=200, show_default=True) |
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@click.option('--seed', required=False, type=int, default=42, show_default=True) |
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@click.option('--device', required=True, type=str, default='cuda:0', show_default=True) |
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def main( |
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exp_name: str, |
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data_dir: str, |
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size: str, |
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interaction: str, |
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batch_size: int, |
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max_seq_len: int, |
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seed: int, |
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device: str, |
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): |
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random.seed(seed) |
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np.random.seed(seed) |
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torch.manual_seed(seed) |
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torch.cuda.manual_seed_all(seed) |
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torch.set_float32_matmul_precision('high') |
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path = Path.Path(data_dir) / 'sequential' / size / interaction |
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df = pl.scan_parquet(path.with_suffix('.parquet')) |
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logger.debug('Preprocessing data...') |
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data: Data = preprocess(df, interaction, val_size=0, max_seq_len=max_seq_len) |
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train_df = data.train.collect(engine="streaming") |
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eval_df = data.test.collect(engine="streaming") |
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logger.debug('Preprocessing data has finished!') |
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eval_df = train_df.join(eval_df, on='uid', how='inner', suffix='_valid').select( |
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pl.col('uid'), pl.col('item_id').alias('item_id_train'), pl.col('item_id_valid') |
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) |
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eval_dataset = EvalDataset(dataset=eval_df, max_seq_len=max_seq_len) |
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eval_dataloader = DataLoader( |
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dataset=eval_dataset, |
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batch_size=batch_size, |
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collate_fn=collate_fn, |
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drop_last=False, |
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shuffle=True, |
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) |
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model = torch.load(f'./checkpoints/{exp_name}_best_state.pth', weights_only=False).to(device) |
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model.eval() |
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with torch.inference_mode(): |
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user_ids, user_embeddings = infer_users(eval_dataloader=eval_dataloader, model=model, device=device) |
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item_embeddings = infer_items(model=model) |
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item_embeddings = Embeddings( |
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ids=torch.arange(start=0, end=item_embeddings.shape[0], device=device), embeddings=item_embeddings |
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) |
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user_embeddings = Embeddings(ids=user_ids, embeddings=user_embeddings) |
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df_user_ids = torch.tensor(eval_df['uid'].to_list(), dtype=torch.long, device=device) |
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df_target_ids = [ |
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torch.tensor(item_ids, dtype=torch.long, device=device) for item_ids in eval_df['item_id_valid'].to_list() |
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] |
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targets = Targets(user_ids=df_user_ids, item_ids=df_target_ids) |
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with torch.no_grad(): |
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ranked = rank_items(users=user_embeddings, items=item_embeddings, num_items=100) |
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metric_names = [f'{name}@{k}' for name in ["recall", "ndcg", "coverage"] for k in [10, 50, 100]] |
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metrics = calc_metrics(ranked, targets, metrics=metric_names) |
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print(metrics) |
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if __name__ == '__main__': |
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main() |
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