You are an agent, your current working directory is /app.
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Repair the evaluation metric boundary
The workspace contains a frozen slice of a Hugging Face Evaluate repository
under /app/vendor/project. Its precision, recall, and F1 metric scripts call
scikit-learn and then normalize the returned score for downstream JSON
serialization. With current scikit-learn, binary averages can return a native
Python scalar while per-label (average=None) calls return an array. The
existing normalization assumes every result has the same array API.
Repair the existing metric modules so the integration runner works for every
case in /app/cases.json and for unseen case manifests supplied by the
verifier. Keep sklearn's metric values and options intact: binary and other
aggregate averages must serialize as a Python float, while average=None
must remain an ordered per-label array. Apply the compatibility boundary
consistently to metrics/f1/f1.py, metrics/precision/precision.py, and
metrics/recall/recall.py.
Run the integration command:
python3 /app/run_metrics.py --input /app/cases.json --output /app/output.json
The output must be exactly one JSON object with schema_version
ml-eval-output.v1 and a results array in input order. Each row contains its
case id and f1, precision, and recall values (a number or an array).
Do not add dependencies, access the network, or replace the vendored metric
implementations with a new evaluator.