import numpy as np import polars as pl from yambda.processing.timesplit import flat_split_train_val_test, sequential_split_train_val_test def create_dataframe(n: int = 1000) -> pl.DataFrame: uids = np.random.randint(1, int(n * 0.05), size=n) item_ids = np.random.randint(100, 200, size=n) timestamps = np.random.randint(0, 100_000, size=n) is_organic = np.random.choice([True, False], size=n) df = pl.DataFrame( {"uid": uids, "item_id": item_ids, "timestamp": timestamps, "is_organic": is_organic}, schema={"uid": pl.UInt32, "item_id": pl.UInt32, "timestamp": pl.UInt32, "is_organic": pl.UInt8}, ) df = df.sort(["uid", "timestamp"]) return df def test_cross_check(): df = create_dataframe(10000) q75_timestamp = int(df["timestamp"].quantile(0.75)) print(q75_timestamp) flat_train, flat_val, flat_test = flat_split_train_val_test( df.lazy(), test_timestamp=q75_timestamp, gap_size=1000, val_size=1000 ) assert flat_val is not None df.group_by("uid", maintain_order=True).agg(pl.all().exclude("uid")).lazy() seq_train, seq_val, seq_test = sequential_split_train_val_test( df.group_by("uid", maintain_order=True).agg(pl.all().exclude("uid")).lazy(), test_timestamp=q75_timestamp, gap_size=1000, val_size=1000, ) assert seq_val is not None assert seq_train.explode(pl.all().exclude("uid")).collect().equals(flat_train.collect()) assert seq_val.explode(pl.all().exclude("uid")).collect().equals(flat_val.collect()) assert seq_test.explode(pl.all().exclude("uid")).collect().equals(flat_test.collect())