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, hard=True)[source]

Predict the probability of belonging to each cluster from input data.

The user can specify these input data either as a numpy array, e.g. array([1., 2., 3.]) or as a dictionary, e.g. {'a': array([1.]), 'b': array([2., 3.])}.

If the numpy arrays are of dimension 2, their i-th rows represent the input data of the i-th sample; while if the numpy arrays are of dimension 1, there is a single sample.

The type of the output data and the dimension of the output arrays will be consistent with the type of the input data and the size of the input arrays.

Parameters:

input_data (DataType) – The input data.
hard (bool) –
Whether clustering should be hard (True) or soft (False).

By default it is set to True.

Returns:

The probability of belonging to each cluster.

Return type:

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: 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.