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import json |
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from pathlib import Path |
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import click |
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import polars as pl |
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import torch |
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from yambda.constants import Constants |
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from yambda.evaluation.metrics import calc_metrics |
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from yambda.evaluation.ranking import Ranked, Targets |
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from yambda.processing import timesplit |
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from yambda.utils import mean_dicts |
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@click.command() |
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@click.option( |
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'--data_dir', |
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required=True, |
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type=str, |
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default="../../data/flat", |
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show_default=True, |
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help="Expects flat data", |
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) |
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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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multiple=True, |
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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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multiple=True, |
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show_default=True, |
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) |
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@click.option('--device', required=True, type=str, default="cuda:0", show_default=True) |
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@click.option('--num_repeats', required=True, type=int, default=2, show_default=True) |
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def main( |
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data_dir: str, |
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size: list[str], |
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interaction: list[str], |
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device: str, |
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num_repeats: int, |
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): |
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print(f"calc metrics: {Constants.METRICS}") |
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for s in size: |
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for i in interaction: |
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print(f"SIZE {s}, INTERACTION {i}") |
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result = random_rec(data_dir, s, i, num_repeats, device) |
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print(json.dumps(result, indent=2)) |
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def scan(path: str, dataset_size: str, dataset_name: str) -> pl.LazyFrame: |
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path: Path = Path(path) / dataset_size / dataset_name |
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return pl.scan_parquet(path.with_suffix(".parquet")) |
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def preprocess( |
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df: pl.LazyFrame, interaction: str, val_size: int |
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) -> tuple[pl.LazyFrame, pl.LazyFrame | None, pl.LazyFrame]: |
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if interaction == "listens": |
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df = df.filter(pl.col("played_ratio_pct") >= Constants.TRACK_LISTEN_THRESHOLD) |
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train, val, test = timesplit.flat_split_train_val_test( |
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df, val_size=val_size, test_timestamp=Constants.TEST_TIMESTAMP |
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) |
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return ( |
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train, |
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val.collect(engine="streaming").lazy() if val is not None else None, |
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test.collect(engine="streaming").lazy(), |
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) |
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def random_rec( |
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data_dir: str, |
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size: str, |
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interaction: str, |
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num_repeats: int, |
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device: str, |
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) -> dict[str, dict[int, float]]: |
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df = scan(data_dir, size, interaction) |
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train, _, test = preprocess(df, interaction, val_size=0) |
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unique_user_ids = train.select("uid").unique().sort("uid").collect(engine="streaming")["uid"].to_torch().to(device) |
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unique_item_ids = ( |
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train.select("item_id").unique().sort("item_id").collect(engine="streaming")["item_id"].to_torch().to(device) |
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) |
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print(f"NUM_USERS {unique_user_ids.shape[0]}, NUM_ITEMS {unique_item_ids.shape[0]}") |
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targets = Targets.from_sequential( |
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test.group_by('uid', maintain_order=True).agg("item_id"), |
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device, |
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) |
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metrics_list = [] |
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for _ in range(num_repeats): |
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ranked = Ranked( |
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user_ids=unique_user_ids, |
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item_ids=unique_item_ids[ |
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torch.randint( |
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0, unique_item_ids.shape[0] - 1, size=(unique_user_ids.shape[0], Constants.NUM_RANKED_ITEMS) |
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) |
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], |
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num_item_ids=unique_item_ids.shape[0], |
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) |
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metrics_list.append( |
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calc_metrics( |
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ranked, |
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targets, |
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metrics=Constants.METRICS, |
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) |
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) |
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return mean_dicts(metrics_list) |
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if __name__ == "__main__": |
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main() |
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