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Guide: Audit Existing Data

Already have a training dataset? Audit it for temporal leakage without rebuilding.

Step 1: Inspect your data

timefence inspect data/train.parquet

This shows column types and suggests which columns are likely keys and timestamps.

Step 2: Run the audit

import timefence

transactions = timefence.Source(
    path="data/transactions.parquet",
    keys=["user_id"],
    timestamp="created_at",
)

rolling_spend = timefence.Feature(
    source=transactions,
    columns=["amount"],
    embargo="1d",
)

report = timefence.audit(
    data="data/train.parquet",
    features=[rolling_spend],
    keys=["user_id"],
    label_time="label_time",
)

print(report)

timefence audit data/train.parquet \
  --features features.py \
  --keys user_id \
  --label-time label_time

Step 3: Inspect results

import timefence

# (assuming `report` from step 2)

# Check overall status
report.has_leakage        # True/False
report.leaky_features     # ["rolling_spend_30d"]
report.clean_features     # ["user_country"]

# Per-feature details
detail = report["rolling_spend_30d"]
detail.leaky_row_count    # 1520
detail.leaky_row_pct      # 0.304
detail.severity           # "HIGH"
detail.max_leakage        # timedelta(days=15)

# Export
report.to_html("audit_report.html")
report.to_json("audit_report.json")

Step 4: Assert in tests

report.assert_clean()  # Raises TimefenceLeakageError if leakage found

See the audit() API reference for full parameter documentation.