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name: sklearn-pipelines-and-operators description: "Decide supported sklearn operator, pipeline, data-layout, and tree-strategy conversion tasks." disable-model-invocation: true metadata: disco-role: operating license: MIT

Sklearn pipelines and operators

Use this sub-skill when the task is to decide whether a scikit-learn estimator, transformer, or composite pipeline can be converted by Hummingbird, how to keep its input layout valid, or how to choose a tree conversion strategy.

Natural triggers include: supported sklearn operators, MissingConverter for a sklearn model, Pipeline, ColumnTransformer, FeatureUnion, pandas DataFrames, multiple inputs, OneHotEncoder strings, tree_implementation, tree_op_precision_dtype, and pipeline conversion parity.

Route first

• Basic hummingbird.ml.convert(...) / convert_batch(...) call construction and ordinary CPU parity checks: ../core-conversion/SKILL.md.
• LightGBM, XGBoost, SparkML, Prophet, or optional source-package dependency questions: ../optional-source-models/SKILL.md.
• TorchScript/ONNX/TVM/CUDA backend choice, threading, batching performance, and benchmark work: ../advanced-backends-and-performance/SKILL.md.

Use this sub-skill for

1. Checking whether every sklearn operator in a model or pipeline is in a supported family.
2. Understanding parser-level composite wrappers such as Pipeline, ColumnTransformer, FeatureUnion, model-selection wrappers, multi-output wrappers, bagging, and stacking.
3. Choosing the output method family to validate after conversion: transform, predict, predict_proba, decision_function, or score_samples.
4. Keeping pandas, tuple/multiple-input, column-name, and string-feature layouts aligned between conversion and inference.
5. Selecting and validating tree strategies: "gemm", "perf_tree_trav", or "tree_trav", plus "float32"/"float64" tree precision.

Operating path

1. Identify the concrete fitted sklearn classes and any wrappers around them.
2. Check operator coverage. If a wrapper is supported but a child operator is not, the whole pipeline is not convertible without changing that child.
3. For Pipeline, ColumnTransformer, FeatureUnion, pandas, tuple inputs, or strings, use pipeline and data formats before writing conversion code.
4. For tree models, decide whether to rely on Hummingbird's depth heuristic or set extra_config explicitly, then validate the selected inference method on representative data.
5. If conversion or parity fails, use troubleshooting and route backend-only issues to the sibling backend sub-skills.

Bundled references

• Operator coverage lists supported sklearn families, parser-level wrappers, output method families, and tree strategy knobs.
• Pipeline and data formats covers ColumnTransformer/FeatureUnion behavior, pandas and tuple inputs, names, and string handling.
• Troubleshooting maps common sklearn operator, pipeline, string, and tree-strategy failures to corrective actions.

No sub-skill-specific scripts are bundled for this area; use the references above and route generic conversion smokes to the core conversion sub-skill.