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1f78dbc | 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 | """Time-based splits that mirror the official evaluation.
Evaluation = each user's FIRST event at/after the cutoff. So for a cutoff C:
fit events = all train events with ts < C (labels usable for aggregates)
target/val = for each user, the first train event with C <= ts < C+W (W=None -> until end of train)
Whole events always stay together (split is by event, rows follow their event).
"""
import polars as pl
J20 = 1737331200 # 2025-01-20 00:00 UTC (start of train window; earlier clicks = warm-up)
DAY = 86400
C_MAIN = J20 + 5 * DAY # 2025-01-25
C_SECOND = J20 + 6 * DAY # 2025-01-26
C_FINAL = J20 + 7 * DAY # 2025-01-27 (official cutoff)
def fit_events(TE: pl.DataFrame, C: int) -> pl.DataFrame:
return TE.filter(pl.col('ts') < C)
def first_events(TE: pl.DataFrame, C: int, W: int | None = None) -> pl.DataFrame:
"""First event per user in [C, C+W). Ties on ts broken by eid (deterministic)."""
m = pl.col('ts') >= C
if W is not None:
m = m & (pl.col('ts') < C + W)
return TE.filter(m).sort('ts', 'eid').group_by('u', maintain_order=True).first()
def split(TE: pl.DataFrame, TR: pl.DataFrame, C: int, W: int | None = None):
"""Return (fit_ev, fit_rows, val_ev, val_rows)."""
fe = fit_events(TE, C)
ve = first_events(TE, C, W)
fr = TR.join(fe.select('eid'), on='eid', how='semi')
vr = TR.join(ve.select('eid'), on='eid', how='semi')
return fe, fr, ve, vr
def train_fold_cutoffs(C_val: int):
"""Daily cutoffs strictly before C_val used to build model-training rows (each fold: first event/user in [Ck, Ck+1d))."""
return [J20 + k * DAY for k in range(1, (C_val - J20) // DAY)]
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