gemseo / mlearning / classification

Show inherited members

classification module

This module contains the base class for classification algorithms.

The classification module implements classification algorithms, whose goal is to assess the membership of input data to classes.

Classification algorithms provide methods for predicting classes of new input data, as well as predicting the probabilities of belonging to each of the classes wherever possible.

This concept is implemented through the MLClassificationAlgo class which inherits from the MLSupervisedAlgo class.

class gemseo.mlearning.classification.classification.MLClassificationAlgo(data, transformer=mappingproxy({}), input_names=None, output_names=None, **parameters)[source]

Bases: MLSupervisedAlgo

Classification Algorithm.

Inheriting classes shall implement the MLSupervisedAlgo._fit() and MLClassificationAlgo._predict() methods, and MLClassificationAlgo._predict_proba_soft() method if possible.

Parameters:

  • data (IODataset) – 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 {}.

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

  • output_names (Iterable[str] | None) – The names of the output variables. If None, consider all the output variables of 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.

predict_proba(input_data, *args, **kwargs)

Evaluate ‘predict’ with either array or dictionary-based input data.

Firstly, the pre-processing stage converts the input data to a NumPy data array, if these data are expressed as a dictionary of NumPy data arrays.

Then, the processing evaluates the function ‘predict’ from this NumPy input data array.

Lastly, the post-processing transforms the output data to a dictionary of output NumPy data array if the input data were passed as a dictionary of NumPy data arrays.

Parameters:

  • input_data (ndarray | Mapping[str, ndarray]) – The input data.

  • *args – The positional arguments of the function ‘predict’.

  • **kwargs – The keyword arguments of the function ‘predict’.

Returns:

The output data with the same type as the input one.

Return type:

ndarray | Mapping[str, ndarray]

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the input variables.

input_space_center: dict[str, ndarray]

The center of the input space.

learning_set: IODataset

The learning dataset.

n_classes: int

The number of classes.

output_names: list[str]

The names of the output variables.

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.