gemseo / mlearning / core

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unsupervised module

This module contains the base class for the unsupervised machine learning algorithms.

The unsupervised module implements the concept of unsupervised machine learning models, where the data has no notion of input or output.

This concept is implemented through the MLUnsupervisedAlgo class, which inherits from the MLAlgo class.

class gemseo.mlearning.core.unsupervised.MLUnsupervisedAlgo(data, transformer=mappingproxy({}), var_names=None, **parameters)

Bases: MLAlgo

Unsupervised machine learning algorithm.

Inheriting classes shall overload the MLUnsupervisedAlgo._fit() method.

Parameters:

• data (Dataset) – The learning dataset.

• transformer (TransformerType) –

  The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

  By default it is set to {}.

• var_names (Iterable[str] | None) – The names of the variables. If None, consider all variables mentioned in the learning dataset.

• **parameters (MLAlgoParameterType) – The parameters of the machine learning algorithm.

Raises:

ValueError – When both the variable and the group it belongs to have a transformer.

SHORT_ALGO_NAME: ClassVar[str] = 'MLUnsupervisedAlgo'

The short name of the machine learning algorithm, often an acronym.

Typically used for composite names, e.g. f"{algo.SHORT_ALGO_NAME}_{dataset.name}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the variables.

learning_set: Dataset

The learning dataset.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

transformer: dict[str, Transformer]

The strategies to transform the variables, if any.

The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group.