gemseo / mlearning / classification

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svm module

The Support Vector Machine algorithm for classification.

This module implements the SVMClassifier class. A support vector machine (SVM) passes the data through a kernel in order to increase its dimension and thereby make the classes linearly separable.

Dependence

The classifier relies on the SVC class of the scikit-learn library.

class gemseo.mlearning.classification.svm.SVMClassifier(data, transformer=mappingproxy({}), input_names=None, output_names=None, C=1.0, kernel='rbf', probability=False, random_state=0, **parameters)

Bases: BaseMLClassificationAlgo

The Support Vector Machine algorithm for classification.

Parameters:

• data (IODataset) – The learning dataset.

• transformer (TransformerType) –

  The strategies to transform the variables. The values are instances of BaseTransformer 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 BaseTransformer 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.

• C (float) –

  The inverse L2 regularization parameter. Higher values give less regularization.

  By default it is set to 1.0.

• kernel (str | Callable | None) –

  The name of the kernel or a callable for the SVM. Examples: “linear”, “poly”, “rbf”, “sigmoid”, “precomputed” or a callable.

  By default it is set to “rbf”.

• probability (bool) –

  Whether to enable the probability estimates. The algorithm is faster if set to False.

  By default it is set to False.

• random_state (int | None) –

  The random state passed to the random number generator. Use an integer for reproducible results.

  By default it is set to 0.

• **parameters (int | float | bool | str | None) – The parameters of the machine learning algorithm.

Raises:

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

LIBRARY: ClassVar[str] = 'scikit-learn'

The name of the library of the wrapped machine learning algorithm.

SHORT_ALGO_NAME: ClassVar[str] = 'SVM'

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 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[BaseResampler, list[BaseMLAlgo], 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 BaseResampler, 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, BaseTransformer]

The strategies to transform the variables, if any.

The values are instances of BaseTransformer 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 BaseTransformer will be applied to all the variables of this group.