---

name: custom-components description: "Extend auto-sklearn with custom classifiers, regressors, feature preprocessors, data preprocessors, ConfigSpace search spaces, registration, and component IDs." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

custom-components

Use this sub-skill when a task needs to author, repair, register, or restrict auto-sklearn components rather than merely run an existing estimator. The package imports as autosklearn; the inspected package version was 0.16.0dev / 0.16.0.dev0.

Route here for

• Creating or fixing subclasses of:
  • AutoSklearnClassificationAlgorithm
  • AutoSklearnRegressionAlgorithm
  • AutoSklearnPreprocessingAlgorithm
• Implementing get_properties(), get_hyperparameter_search_space(), fit(), predict(), predict_proba(), and transform() for custom components.
• Calling add_classifier(), add_regressor(), or the feature/data add_preprocessor() registry functions.
• Choosing the correct custom component ID for include / exclude after registration.
• Replacing a broad built-in component search space with a custom wrapper that exposes fewer hyperparameters or narrower ranges.

Route elsewhere

• Basic AutoSklearnClassifier, AutoSklearnRegressor, or AutoSklearn2Classifier usage: use ../estimators/SKILL.md.
• Ordinary built-in include / exclude, Dask, SMAC, budget, or ensemble configuration with no custom component code: use ../search-and-parallelism/SKILL.md.
• Pandas dtypes, feat_type, target validation, custom metrics, resampling, or dataset compression: use ../data-metrics-validation/SKILL.md.
• Long AutoML training runs for a new component: do not start them from this sub-skill. Validate the class, registry entry, and configuration space first.

Operating procedure

1. Identify the component kind and registry:
   • classifier: base class AutoSklearnClassificationAlgorithm, registry autosklearn.pipeline.components.classification.add_classifier, include key "classifier".
   • regressor: base class AutoSklearnRegressionAlgorithm, registry autosklearn.pipeline.components.regression.add_regressor, include key "regressor".
   • feature preprocessor: base class AutoSklearnPreprocessingAlgorithm, registry autosklearn.pipeline.components.feature_preprocessing.add_preprocessor, include key "feature_preprocessor".
   • data preprocessor: base class AutoSklearnPreprocessingAlgorithm, registry autosklearn.pipeline.components.data_preprocessing.add_preprocessor, include key "data_preprocessor".
2. Read references/component-contracts.md before editing code. It contains the required properties, constants, built-in IDs, registry rules, and ConfigSpace rules.
3. If a safe starting file is useful, generate a no-training template: python scripts/component_skeleton.py --kind classifier --class-name MyClassifier or write it with --output my_components.py.
4. Keep the registration call in code that is imported before constructing the auto-sklearn estimator. For custom components, the ID used in include / exclude is the class name, for example {"classifier": ["MyClassifier"]}.
5. Validate without long training: import the module, call get_properties(), call get_hyperparameter_search_space(), register the class, and confirm the choice appears in the relevant component catalog or estimator configuration space.
6. For complete recipes, use references/workflows.md. For errors, use references/troubleshooting.md.

Quick validation snippets

Classifier registry check after importing the module that defines the class:

from autosklearn.pipeline.components.classification import ClassifierChoice, add_classifier
from my_components import MyClassifier

add_classifier(MyClassifier)  # harmless if your module did not self-register
props = MyClassifier.get_properties()
required = {
    "shortname", "name", "handles_regression", "handles_classification",
    "handles_multiclass", "handles_multilabel", "handles_multioutput",
    "is_deterministic", "input", "output",
}
assert set(props) == required
cs = MyClassifier.get_hyperparameter_search_space()
assert "MyClassifier" in ClassifierChoice.get_components()

Estimator restriction check without starting a long run:

import autosklearn.classification

automl = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=30,
    per_run_time_limit=10,
    include={"classifier": ["MyClassifier"]},
    initial_configurations_via_metalearning=0,
    smac_scenario_args={"runcount_limit": 1},
)
# Build or inspect the configuration space with tiny in-memory X/y before any
# expensive fit. If the ID is wrong, this should fail before training.

Native evidence anchors for later verification

Relevant native candidates are the component registration unit checks, component base-contract checks, and the extension examples. The extension examples are fit-heavy and should remain skip-expensive unless a verifier deliberately bounds them; prefer the bundled skeleton --help check and static registry/config-space checks first.