# Extending auto-sklearn¶

auto-sklearn can be easily extended with new classification, regression and feature preprocessing methods. In order to do so, a user has to implement a wrapper class and register it to auto-sklearn. This manual will walk you through the process.

## Writing a component¶

Depending on the purpose, the component has to be a subclass of one of the following base classes:

In general, these classes are wrappers around existing machine learning models and only add the functionality auto-sklearn needs. Of course you can also implement a machine learning algorithm directly inside a component.

Each component has to implement a method which returns its configuration space, a method for querying properties of the component and methods like fit(), predict() or transform() based on the task of the component. These are described in the subsections get_hyperparameter_search_space() and get_properties()

After writing a component class, you have to tell auto-sklearn about its existence. You have to add it with the following function calls, depending on the type of component:

autosklearn.pipeline.components.classification.add_classifier(classifier)[source]
autosklearn.pipeline.components.regression.add_regressor(regressor)[source]
autosklearn.pipeline.components.feature_preprocessing.add_preprocessor(preprocessor)[source]

### get_hyperparameter_search_space()¶

Return an instance of HPOlibConfigSpace.configuration_space .ConfigurationSpace.

See also the abstract definitions: AutoSklearnClassificationAlgorithm.get_hyperparameter_search_space() AutoSklearnRegressionAlgorithm.get_hyperparameter_search_space() AutoSklearnPreprocessingAlgorithm.get_hyperparameter_search_space()

To find out about how to create a ConfigurationSpace-object, please look at the source code on github.com.

### get_properties()¶

Return a dictionary which defines how the component can be used when constructing a machine learning pipeline. The following fields must be specified:

• shortname : str
an abbreviation of the component
• name : str
the full name of the component
• handles_regression : bool
whether the component can handle regression data
• handles_classification : bool
whether the component can handle classification data
• handles_multiclass : bool
whether the component can handle multiclass classification data
• handles_multilabel : bool
whether the component can multilabel classification data
• is_deterministic : bool
whether the component gives the same result when using several times, but with the same random seed
• input : tuple
type of input data the component can handle, can have multiple values:
• autosklearn.constants.DENSE
dense data arrays, mutually exclusive with autosklearn.constants.SPARSE
• autosklearn.constants.SPARSE
sparse data matrices, mutually exclusive with autosklearn.constants.DENSE
• autosklearn.constants.UNSIGNED_DATA
unsigned data array, meaning only positive input, mutually exclusive with autosklearn.constants.SIGNED_DATA
• autosklearn.constants.SIGNED_DATA
signed data array, meaning both positive and negative input values, mutually exclusive with autosklearn.constants.UNSIGNED_DATA
• output : tuple
type of output data the component produces
• autosklearn.constants.PREDICTIONS
predictions, for example by a classifier
• autosklearn.constants.INPUT
data in the same form as the input
• autosklearn.constants.DENSE
dense data arrays, mutually exclusive with autosklearn.constants.SPARSE. This implies that sparse data will be converted into a dense representation.
• autosklearn.constants.SPARSE
sparse data matrices, mutually exclusive with autosklearn.constants.DENSE. This implies that dense data will be converted into a sparse representation
• autosklearn.constants.UNSIGNED_DATA
unsigned data array, meaning only positive input, mutually exclusive with autosklearn.constants.SIGNED_DATA. This allows for algorithms which can only work on positive data.
• autosklearn.constants.SIGNED_DATA
signed data array, meaning both positive and negative input values, mutually exclusive with autosklearn.constants.UNSIGNED_DATA

## Classification¶

In addition two get_properties() and get_hyperparameter_search_space() you have to implement AutoSklearnClassificationAlgorithm.fit() and AutoSklearnClassificationAlgorithm.predict() . These are an implementation of the scikit-learn predictor API.

## Regression¶

In addition two get_properties() and get_hyperparameter_search_space() you have to implement AutoSklearnRegressionAlgorithm.fit() and AutoSklearnRegressionAlgorithm.predict() . These are an implementation of the scikit-learn predictor API.

## Feature Preprocessing¶

In addition two get_properties() and get_hyperparameter_search_space() you have to implement AutoSklearnPreprocessingAlgorithm.fit() and AutoSklearnPreprocessingAlgorithm.transform() . These are an implementation of the scikit-learn predictor API.