..
   Copyright 2021 IRT Saint-Exupéry, https://www.irt-saintexupery.com

   This work is licensed under the Creative Commons Attribution-ShareAlike 4.0
   International License. To view a copy of this license, visit
   http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative
   Commons, PO Box 1866, Mountain View, CA 94042, USA.

.. _gen_classification_algos:

Classification algorithms
=========================

.. warning::
   Some capabilities may require the :ref:`installation <installation>` of |g| with :ref:`all its features <dependencies>`
   and some others may depend on :ref:`plugins <plugins>`.

.. warning::
   All the features of the wrapped libraries may not be exposed through |g|.


.. note::
   The algorithm settings can be passed to a function of the form

   .. code-block:: python

      function(..., settings_model: AlgorithmSettings | None = None, **settings: Any)

   either one by one:

   .. code-block:: python

      function(..., setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)

   or using the argument name ``"settings_model"`` and the Pydantic model associated with the algorithm:

   .. code-block:: python

      settings_model = AlgorithmSettings(setting_name_1=setting_name_1, setting_name_2=setting_name_2, ...)
      function(..., settings_model=settings_model)








.. _KNNClassifier_options:

KNNClassifier
-------------



Module: :class:`gemseo.mlearning.classification.algos.knn`




.. code-block:: python
   :caption: Pydantic model of the settings for KNNClassifier

   from gemseo.settings.mlearning import KNNClassifier_Settings
















































.. raw:: html

    <dl class="field-list simple">
    <dt class="field-odd">Optional settings</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**input_names** : *collections.abc.Sequence[str], optional*

The names of the input variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**n_neighbors** : *<class 'int'>, optional*

The number of neighbors.


By default it is set to 5.


.. raw:: html

    </li>

    <li>

**output_names** : *collections.abc.Sequence[str], optional*

The names of the output variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**parameters** : *collections.abc.Mapping[str, typing.Any], optional*

Other parameters.


By default it is set to {}.


.. raw:: html

    </li>

    <li>

**transformer** : *collections.abc.Mapping[str, typing.Any], optional*

The strategies to transform the variables.

The values are instances of :class:`.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 :class:`.BaseTransformer` will be applied
to all the variables of this group.
If :attr:`.IDENTITY`, do not transform the variables.


By default it is set to {}.


.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



.. _RandomForestClassifier_options:

RandomForestClassifier
----------------------



Module: :class:`gemseo.mlearning.classification.algos.random_forest`




.. code-block:: python
   :caption: Pydantic model of the settings for RandomForestClassifier

   from gemseo.settings.mlearning import RandomForestClassifier_Settings






















































.. raw:: html

    <dl class="field-list simple">
    <dt class="field-odd">Optional settings</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**input_names** : *collections.abc.Sequence[str], optional*

The names of the input variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**n_estimators** : *<class 'int'>, optional*

The number of trees in the forest.


By default it is set to 100.


.. raw:: html

    </li>

    <li>

**output_names** : *collections.abc.Sequence[str], optional*

The names of the output variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**parameters** : *collections.abc.Mapping[str, typing.Any], optional*

Other parameters.


By default it is set to {}.


.. raw:: html

    </li>

    <li>

**random_state** : *typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional*

The random state parameter.

If ``None``, use the global random state instance from ``numpy.random``.
Creating the model multiple times will produce different results.
If ``int``, use a new random number generator seeded by this integer.
This will produce the same results.


By default it is set to 0.


.. raw:: html

    </li>

    <li>

**transformer** : *collections.abc.Mapping[str, typing.Any], optional*

The strategies to transform the variables.

The values are instances of :class:`.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 :class:`.BaseTransformer` will be applied
to all the variables of this group.
If :attr:`.IDENTITY`, do not transform the variables.


By default it is set to {}.


.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>



.. _SVMClassifier_options:

SVMClassifier
-------------



Module: :class:`gemseo.mlearning.classification.algos.svm`




.. code-block:: python
   :caption: Pydantic model of the settings for SVMClassifier

   from gemseo.settings.mlearning import SVMClassifier_Settings


































































.. raw:: html

    <dl class="field-list simple">
    <dt class="field-odd">Optional settings</dt>
    <dd class="field-odd"><ul class="simple">


    <li>

**C** : *<class 'float'>, optional*

The inverse L2 regularization parameter.


By default it is set to 1.0.


.. raw:: html

    </li>

    <li>

**input_names** : *collections.abc.Sequence[str], optional*

The names of the input variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**kernel** : *typing.Union[str, typing.Annotated[collections.abc.Callable, WithJsonSchema(json_schema={}, mode=None)]], optional*

The name of the kernel or a callable for the SVM.

Examples of names: "linear", "poly", "rbf", "sigmoid", "precomputed".


By default it is set to rbf.


.. raw:: html

    </li>

    <li>

**output_names** : *collections.abc.Sequence[str], optional*

The names of the output variables


By default it is set to ().


.. raw:: html

    </li>

    <li>

**parameters** : *collections.abc.Mapping[str, typing.Any], optional*

Other parameters.


By default it is set to {}.


.. raw:: html

    </li>

    <li>

**probability** : *<class 'bool'>, optional*

Whether to enable the probability estimates.


By default it is set to False.


.. raw:: html

    </li>

    <li>

**random_state** : *typing.Optional[typing.Annotated[int, Ge(ge=0)]], optional*

The random state parameter.

If ``None``, use the global random state instance from ``numpy.random``.
Creating the model multiple times will produce different results.
If ``int``, use a new random number generator seeded by this integer.
This will produce the same results.


By default it is set to 0.


.. raw:: html

    </li>

    <li>

**transformer** : *collections.abc.Mapping[str, typing.Any], optional*

The strategies to transform the variables.

The values are instances of :class:`.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 :class:`.BaseTransformer` will be applied
to all the variables of this group.
If :attr:`.IDENTITY`, do not transform the variables.


By default it is set to {}.


.. raw:: html

    </li>

    </ul>
    </dd>
    </dl>