---

name: river description: "Use River for online machine learning with streaming estimators, pipelines, datasets, progressive evaluation, drift, anomaly, clustering, forecasting, bandits, and recommender workflows." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

River repo skill

Use this skill when a task involves River, online machine learning, one-sample-at-a-time estimators, dictionary feature streams, progressive validation, concept drift, or River package maintenance.

First checks

• Normal package install: pip install river.
• Mini-batch/DataFrame workflows: pip install "river[pandas]".
• Source checkout installs build a Rust extension through maturin; prefer released wheels unless the task is repository maintenance.
• Minimal import check:

import river
from river import compose, linear_model, metrics, preprocessing
model = preprocessing.StandardScaler() | linear_model.LogisticRegression()
metric = metrics.Accuracy()

Run scripts/check_river_environment.py when installation, Rust-extension import, or quickstart behavior is uncertain.

Route map

Task	Read
Estimator lifecycle, learn_one, predict_one, transform_one, base classes, tags, cloning, mutation, generic checks, new-estimator compatibility.	sub-skills/online-core-api/SKILL.md
Pipelines, `	, +, *, selectors, feature unions, preprocessing, feature extraction, online statistics, target aggregation, learn_during_predict`, pipeline debugging.
Built-in datasets, stream adapters, CSV/array/dataframe ingestion, progressive validation, delayed labels, sample weights, metrics, metric/model compatibility.	sub-skills/streaming-evaluation/SKILL.md
Supervised classification/regression model families, linear models, optimizers/losses, trees, forests, ensembles, naive Bayes, neighbors, multiclass/multioutput/model-selection/compat wrappers.	sub-skills/supervised-models/SKILL.md
Concept drift, anomaly detection, clustering, forecasting/time series, bandits, recommenders, factorization machines in sparse interaction workflows, imbalanced learning, probability utilities.	sub-skills/specialized-workflows/SKILL.md

Shared references

• Read references/api-map.md for public module-family routing and task-to-sub-skill mapping.
• Read references/troubleshooting.md for installation, import, Rust extension, optional dependency, and cross-cutting runtime failures.
• Read references/development-and-verification.md when editing a River checkout or choosing focused tests.
• Read references/repo-provenance.md before deciding whether this generated skill is stale for a checkout.

Common workflow skeleton

from river import compose, linear_model, metrics, preprocessing

model = preprocessing.StandardScaler() | linear_model.LogisticRegression()
metric = metrics.Accuracy()

for x, y in stream:
    y_pred = model.predict_one(x)
    if y_pred is not None:
        metric.update(y, y_pred)
    model.learn_one(x, y)

Use evaluate.progressive_val_score instead of a manual loop when the task is standard online evaluation or delayed progressive validation.

Important constraints

• River's core *_one API is dictionary-based and does not require pandas.
• Optional pandas support is needed for mini-batch *_many APIs and some dataframe workflows.
• River generally avoids heavy input validation; bad feature/target types may fail inside the model.
• Predict before learning when evaluating a stream, including clustering and anomaly workflows when a score/assignment is compared before state update.
• No primary package CLI exists; use Python APIs and bundled smoke scripts.
• Do not use original River notebooks or tests as runtime dependencies for this skill; the bundled scripts provide safe reusable checks.

Smoke scripts

From this skill directory, run only the scripts that match the task:

python scripts/check_river_environment.py
python sub-skills/online-core-api/scripts/estimator_contract_smoke.py
python sub-skills/pipelines-and-features/scripts/pipeline_feature_smoke.py
python sub-skills/streaming-evaluation/scripts/stream_evaluation_smoke.py
python sub-skills/supervised-models/scripts/supervised_model_smoke.py
python sub-skills/specialized-workflows/scripts/specialized_workflows_smoke.py