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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())
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