This class implements the classification task. It implements a pipeline, which includes one preprocessing step and one classification algorit

This class implements the classification task.

This class implements the regression task. It implements a pipeline, which includes one preprocessing step and one regression algorithm. It c

Checks the input data to Auto-Sklearn. It also determines what columns are categorical and which ones are numerical, so that the pre-pro

A class to pre-process targets. It validates the data provided during fit to make sure it matches Sklearn expectation as well as encoding the

This component is used to apply distinct transformations to categorical, numerical and text features of a dataset. It is built on top of skle

Makes sure the input data complies with Auto-sklearn requirements. Categorical inputs are encoded via a Label Encoder, if the input is a

This class implements a pipeline for data preprocessing of categorical features. It assumes that the data to be transformed is made only of catego

This class implements the regression task.

This class implements a pipeline for data preprocessing of numerical features. It assumes that the data to be transformed is made only of numerica

Class to collect timing tasks.

Substitute missing values by constant: When strategy == “constant”, fill_value is used to replace all occurrences of missing_valu

Add 3 to every category.

Group together categories which occurence is less than a specified minimum fraction. Coalesced categories get index of one.

Encode categorical integer features using a one-hot aka one-of-K scheme. The input to this transformer should be a sparse matrix of integers, den

Base class for handling the AutoML procedure

Add 3 to every category. Down in the pipeline, category 2 will be attribute to missing values, category 1 will be assigned to low occurence ca

Splitter that deals with classes with too few samples

Some kind of re-used fixture for losses calculated

A task to start

Builds ensembles out of runs that exist in the Backend This is used by EnsembleBuilderManager and created in a dask-client every time a run f

Group categories whose occurence is less than a specified minimum fraction.

Class for storing information about a run used during ensemble building. Note ---- This is for internal use by the EnsembleBuilder and no

Ensemble consisting of a single model. This class is used by the :class:`MultiObjectiveDummyEnsemble` to represent ensembles consisting of a

A class to Mock the Distributed Client class, in case Auto-Sklearn is meant to run in the current Thread.

A dask instance created by a user

Simple model that returns incorrect shape and issues warning

Stratified K-Folds cross-validator that ensures that there is always at least 1 sample per class in the training set.

A class that mimics a distributed Future, the outcome of performing submit on a distributed client.

This class implement a entity that receives tcp messages on a given address For further information, please check https://docs.python.org

Should not be used for testing the actual loggers functionality Overwrites all methods with mock objects that can be queries * All logging me