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name: specialized-workflows description: "River workflows for drift detection, anomaly scoring, clustering, forecasting, bandits, recommendation, imbalanced learning, factorization, and probability distributions." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Specialized Workflows

Use this sub-skill when a River task goes beyond ordinary supervised prediction and needs a specialized online workflow: drift detection and retraining, anomaly scoring and filtering, clustering, forecasting, bandits, recommendation/ranking, imbalanced streaming wrappers, factorization machines for recommendation or CTR-style data, or streaming probability distributions.

Route here

• Monitor drift detectors, interpret drift/warning state, or wrap an existing classifier with drift.DriftRetrainingClassifier.
• Score anomalies, threshold or quantile-filter anomaly scores, or decide whether anomalies should update the detector.
• Train clustering models, keep cluster centers or micro-clusters current, and choose internal or external clustering metrics.
• Forecast time series with time_series.Forecaster APIs and horizon-aware evaluation metrics.
• Run bandit policy loops with pull and update, or replay logged bandit history without requiring a Gym environment.
• Rank candidate items with reco models or model user/item/context interactions with facto factorization machines.
• Add imblearn sampling wrappers around classifiers or regressors and decide where the sampling boundary belongs.
• Use proba distributions as online state, reward objects, density estimates, or rolling uncertainty summaries.

Reroute

• Shared stream adapters, built-in datasets, delayed labels, progressive validation, and generic metric compatibility: use ../streaming-evaluation/SKILL.md.
• Ordinary supervised classification/regression model family selection, optimizer/loss choice, ensembles, multiclass, multioutput, and model-selection wrappers: use ../supervised-models/SKILL.md.
• Feature construction, pipeline operators, selectors, unions, rolling feature extraction, parameter routing, and pipeline debugging: use ../pipelines-and-features/SKILL.md.

Start fast

1. Drift detectors are state machines updated one observation at a time. Read drift_detected and, when supported, warning_detected immediately after each update.
2. DriftRetrainingClassifier wraps the classifier that should be reset or swapped. With background training enabled, use a warning-capable drift detector such as the binary DDM-family detectors.
3. Anomaly detectors return scores, not class labels. Use ThresholdFilter.classify(score) or QuantileFilter.classify(score) when a boolean anomaly decision is required.
4. Clustering models learn from x only. Internal metrics such as Silhouette need x, y_pred, and current centers; external metrics compare predicted clusters with known labels.
5. Forecasters learn with learn_one(y, x=None) and return one forecast per requested horizon step. Use evaluate.evaluate or evaluate.iter_evaluate for horizon metrics.
6. Bandit policies can be controlled directly with pull/update; use bandit.evaluate_offline for replay-style evaluation when no interactive environment is available.
7. Recommenders use hashable user/item IDs and rank a candidate item set. Factorization machines treat string features as categorical variables and are useful for CTR-style interaction features.

Run the bundled synthetic smoke check in an environment where import river succeeds:

python scripts/specialized_workflows_smoke.py

Select a subset when validating one workflow:

python scripts/specialized_workflows_smoke.py --section drift --section anomaly --json

References

• references/drift-anomaly.md covers detector update patterns, retraining wrapper placement, anomaly score semantics, filters, and anomaly evaluation caveats.
• references/clustering-forecasting-bandits-reco.md covers clustering loops and metrics, forecasting horizon metrics, bandit online/offline evaluation, recommendation/ranking, factorization machines, imbalanced learning, and probability distributions.
• references/troubleshooting.md maps common state, threshold, metric, horizon, bandit history, recommender ID, sampler placement, and optional dependency failures to checks and fixes.
• scripts/specialized_workflows_smoke.py runs tiny deterministic checks with synthetic data and skips optional pieces gracefully when unsupported.