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name: model-operations description: "Operate PyOD model persistence, thresholding, score combination, optional extras, and validation safely." disable-model-invocation: true metadata: disco-role: operating license: BSD 2-Clause

PyOD Model Operations

Use this sub-skill when a PyOD task is about operating fitted detectors after or around training: saving and loading models, validating recovered artifacts, choosing non-contamination thresholders, combining multiple detector scores, enabling optional operational extras, and diagnosing model-operation failures.

Route detector selection, baseline fit/predict recipes, and tabular model-family choice to classic-detectors or automated-lifecycle. Route graph, time-series, embedding, audio, and deep-backend constraints to specialized-modalities before applying persistence or operational validation guidance here.

Operating Rules

1. Treat every pickle/joblib artifact as executable code. Use pyod.utils.persistence.load(..., trusted=True) or compat_load(..., trusted=True) only for artifacts from a trusted training pipeline, registry, or owner. trusted=True is an acknowledgement, not a scan or sandbox.
2. Prefer pyod.utils.persistence.save / load for new artifacts. Raw joblib.dump / joblib.load is a fallback only when you intentionally need the raw format and accept the missing envelope metadata.
3. Validate a loaded model before use: check the class, fitted attributes, score shape, finite scores, and at least one known probe batch if available.
4. Install optional extras explicitly and exactly. Base PyOD does not install combo, pythresh, suod, or xgboost; failures in those areas are usually dependency-scope failures, not detector logic failures.
5. Standardize detector-score matrices before using score-combination functions unless the scores are already on a deliberately comparable scale.

Bundled References and Scripts

• references/persistence.md: use for save, load, compat_load, trust boundaries, sklearn Tree dtype compatibility, metadata, strict mode, and raw joblib caveats.
• references/thresholding-and-combination.md: use for pyod.models.thresholds, PyThresh-backed contamination objects, pyod.models.combination, required score shapes, and validation checks.
• references/optional-extras.md: use when imports fail or a task asks for SUOD acceleration, XGBOD supervised operation, combo score combination, or pythresh thresholding.
• references/troubleshooting.md: use to map operational symptoms to recovery actions for persistence, thresholding, combination, optional extras, and post-load validation.
• scripts/persistence_smoke.py: safe deterministic smoke check that fits a tiny detector, saves and loads it through PyOD persistence, verifies the trust guard, compares predictions, and writes only a temporary file.

Quick Safe Checks

Run the bundled script from any working directory in an environment with PyOD installed:

python scripts/persistence_smoke.py --help
python scripts/persistence_smoke.py --json

A passing run proves the local PyOD install can perform a trusted temporary persistence round-trip for a classic detector. It does not prove that an arbitrary external artifact is safe, compatible, or semantically valid.