tasks/candidate-1669-operations-marketing/instruction.md

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Repair campaign-outcome validation in uplift evaluation

Work in /app/vendor/scikit-uplift, a frozen source tree for a real marketing uplift library. Its treatment-aware metrics correctly require two binary campaign arms, but malformed non-binary response outcomes currently flow into incrementality calculations and plots.

Diagnose and repair the existing implementation. The repair must be made in both sklift/metrics/metrics.py and sklift/viz/base.py; do not replace the library with a separate evaluator, coerce response values, or special-case the supplied fixture.

The public behavior is:

• Every metric entry point that accepts y_true must reject response arrays containing values outside binary 0 and 1 with ValueError before computing cohort statistics.
• Every plotting entry point that accepts y_true must perform the same validation locally, before delegating to metric helpers or constructing misleading diagnostics.
• Existing consistent-length validation and binary treatment validation must remain in force.
• Valid binary campaigns must retain the existing model, uplift/Qini curve, AUC, percentile, and plotting behavior.
• Keep the frozen source and existing tests intact except for the two designated production modules.

After repairing the source, run the supplied end-to-end workflow:

python3 /app/tools/run_campaign_evaluation.py --output /app/campaign_evaluation.json

The command must complete offline. /app/campaign_evaluation.json must use output_schema_version sklift.campaign-evaluation.v1, record the three workflow stages, contain the valid campaign's model predictions, curves, scores, percentile table and plot titles, record each malformed campaign case as rejected with ValueError, and list the five source modules exercised by the workflow.