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 func 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 func 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:

algo (MLSupervisedAlgo) – The supervised learning algorithm.
input_data (DataType) – The input data.
*args (Any) – The positional arguments of the function func.
**kwargs (Any) – The keyword arguments of the function func.

Returns:

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

Return type:

DataType

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: Dataset: 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.

resampling_results: dict[str, tuple[Resampler, list[MLAlgo], list[ndarray] | ndarray]]

The resampler class names bound to the resampling results.

A resampling result is formatted as (resampler, ml_algos, predictions) where resampler is a Resampler, ml_algos is the list of the associated machine learning algorithms built during the resampling stage and predictions are the predictions obtained with the latter.

resampling_results stores only one resampling result per resampler type (e.g., "CrossValidation", "LeaveOneOut" and "Boostrap").

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.