---

name: pipelines-and-features description: "Build, debug, and validate River pipelines and online feature engineering." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Pipelines and Features

Use this sub-skill when you need to assemble River feature pipelines, feature branches, rolling aggregates, or inspect feature flow.

Scope

Covers:

• compose.Pipeline, |, +, *, TransformerUnion, TransformerProduct, Grouper
• compose.Select, Discard, SelectType, FuncTransformer
• feature_extraction.Agg, TargetAgg, BagOfWords, TFIDF, PolynomialExtender
• preprocessing transforms for scaling, encoding, hashing, and imputation
• feature_selection, stats, sketch, and utils.Rolling / TimeRolling
• compose.learn_during_predict, debug_one, and utils.log_method_calls

Route elsewhere

• Base estimator and online interface rules: online-core-api
• Model-family choice and estimator selection: supervised-models
• Evaluation loops and streaming scoring: streaming-evaluation

How to work

1. Read references/pipeline-workflows.md for composition, learning order, routing, and debugging.
2. Read references/feature-engineering.md for branch design, rolling stats, sketches, and text/numeric patterns.
3. Read references/troubleshooting.md when names, keys, or timestamps behave unexpectedly.
4. Run scripts/pipeline_feature_smoke.py to sanity-check a local River setup.

Practical rules

• Keep feature dictionaries pure; return fresh dicts from custom transformers.
• Split mixed text and numeric data into separate branches, then merge.
• Prefix or rename branch outputs when two branches can emit the same key.
• Pass t= only for steps that accept timestamps, and pass w= only where sample weights are supported.
• Use mini-batch methods only when the involved steps support them or when a row-by-row fallback is acceptable.