File size: 4,231 Bytes
1be89f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 |
import logging
import pathlib as Path
import random
import click
import numpy as np
import polars as pl
import torch
from model import SASRecEncoder
from torch.utils.data import DataLoader
from data import Data, EvalDataset, collate_fn, preprocess
from yambda.evaluation.metrics import calc_metrics
from yambda.evaluation.ranking import Embeddings, Targets, rank_items
logging.basicConfig(
level=logging.DEBUG, format='[%(asctime)s] [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S'
)
logger = logging.getLogger(__name__)
def infer_users(eval_dataloader: DataLoader, model: torch.nn.Module, device: str):
user_ids = []
user_embeddings = []
model.eval()
for batch in eval_dataloader:
for key in batch.keys():
batch[key] = batch[key].to(device)
user_ids.append(batch['user.ids']) # (batch_size)
user_embeddings.append(model(batch)) # (batch_size, embedding_dim)
return torch.cat(user_ids, dim=0), torch.cat(user_embeddings, dim=0)
def infer_items(model: SASRecEncoder):
return model.item_embeddings.weight.data
@click.command()
@click.option('--exp_name', required=True, type=str)
@click.option('--data_dir', required=True, type=str, default='../../data/', show_default=True)
@click.option(
'--size',
required=True,
type=click.Choice(['50m', '500m', '5b']),
default='50m',
show_default=True,
)
@click.option(
'--interaction',
required=True,
type=click.Choice(['likes', 'listens']),
default='likes',
show_default=True,
)
@click.option('--batch_size', required=True, type=int, default=256, show_default=True)
@click.option('--max_seq_len', required=False, type=int, default=200, show_default=True)
@click.option('--seed', required=False, type=int, default=42, show_default=True)
@click.option('--device', required=True, type=str, default='cuda:0', show_default=True)
def main(
exp_name: str,
data_dir: str,
size: str,
interaction: str,
batch_size: int,
max_seq_len: int,
seed: int,
device: str,
):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.set_float32_matmul_precision('high')
path = Path.Path(data_dir) / 'sequential' / size / interaction
df = pl.scan_parquet(path.with_suffix('.parquet'))
logger.debug('Preprocessing data...')
data: Data = preprocess(df, interaction, val_size=0, max_seq_len=max_seq_len)
train_df = data.train.collect(engine="streaming")
eval_df = data.test.collect(engine="streaming")
logger.debug('Preprocessing data has finished!')
eval_df = train_df.join(eval_df, on='uid', how='inner', suffix='_valid').select(
pl.col('uid'), pl.col('item_id').alias('item_id_train'), pl.col('item_id_valid')
)
eval_dataset = EvalDataset(dataset=eval_df, max_seq_len=max_seq_len)
eval_dataloader = DataLoader(
dataset=eval_dataset,
batch_size=batch_size,
collate_fn=collate_fn,
drop_last=False,
shuffle=True,
)
model = torch.load(f'./checkpoints/{exp_name}_best_state.pth', weights_only=False).to(device)
model.eval()
with torch.inference_mode():
user_ids, user_embeddings = infer_users(eval_dataloader=eval_dataloader, model=model, device=device)
item_embeddings = infer_items(model=model)
item_embeddings = Embeddings(
ids=torch.arange(start=0, end=item_embeddings.shape[0], device=device), embeddings=item_embeddings
)
user_embeddings = Embeddings(ids=user_ids, embeddings=user_embeddings)
df_user_ids = torch.tensor(eval_df['uid'].to_list(), dtype=torch.long, device=device)
df_target_ids = [
torch.tensor(item_ids, dtype=torch.long, device=device) for item_ids in eval_df['item_id_valid'].to_list()
]
targets = Targets(user_ids=df_user_ids, item_ids=df_target_ids)
with torch.no_grad():
ranked = rank_items(users=user_embeddings, items=item_embeddings, num_items=100)
metric_names = [f'{name}@{k}' for name in ["recall", "ndcg", "coverage"] for k in [10, 50, 100]]
metrics = calc_metrics(ranked, targets, metrics=metric_names)
print(metrics)
if __name__ == '__main__':
main()
|