.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/developing_estimators/sklearn_is_fitted.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_developing_estimators_sklearn_is_fitted.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_developing_estimators_sklearn_is_fitted.py:


========================================
`__sklearn_is_fitted__` as Developer API
========================================

The `__sklearn_is_fitted__` method is a convention used in scikit-learn for
checking whether an estimator object has been fitted or not. This method is
typically implemented in custom estimator classes that are built on top of
scikit-learn's base classes like `BaseEstimator` or its subclasses.

Developers should use :func:`~sklearn.utils.validation.check_is_fitted`
at the beginning of all methods except `fit`. If they need to customize or
speed-up the check, they can implement the `__sklearn_is_fitted__` method as
shown below.

In this example the custom estimator showcases the usage of the
`__sklearn_is_fitted__` method and the `check_is_fitted` utility function
as developer APIs. The `__sklearn_is_fitted__` method checks fitted status
by verifying the presence of the `_is_fitted` attribute.

.. GENERATED FROM PYTHON SOURCE LINES 23-29

An example custom estimator implementing a simple classifier
------------------------------------------------------------
This code snippet defines a custom estimator class called `CustomEstimator`
that extends both the `BaseEstimator` and `ClassifierMixin` classes from
scikit-learn and showcases the usage of the `__sklearn_is_fitted__` method
and the `check_is_fitted` utility function.

.. GENERATED FROM PYTHON SOURCE LINES 29-77

.. code-block:: Python


    # Author: Kushan <kushansharma1@gmail.com>
    #
    # License: BSD 3 clause

    from sklearn.base import BaseEstimator, ClassifierMixin
    from sklearn.utils.validation import check_is_fitted


    class CustomEstimator(BaseEstimator, ClassifierMixin):
        def __init__(self, parameter=1):
            self.parameter = parameter

        def fit(self, X, y):
            """
            Fit the estimator to the training data.
            """
            self.classes_ = sorted(set(y))
            # Custom attribute to track if the estimator is fitted
            self._is_fitted = True
            return self

        def predict(self, X):
            """
            Perform Predictions

            If the estimator is not fitted, then raise NotFittedError
            """
            check_is_fitted(self)
            # Perform prediction logic
            predictions = [self.classes_[0]] * len(X)
            return predictions

        def score(self, X, y):
            """
            Calculate Score

            If the estimator is not fitted, then raise NotFittedError
            """
            check_is_fitted(self)
            # Perform scoring logic
            return 0.5

        def __sklearn_is_fitted__(self):
            """
            Check fitted status and return a Boolean value.
            """
            return hasattr(self, "_is_fitted") and self._is_fitted


.. _sphx_glr_download_auto_examples_developing_estimators_sklearn_is_fitted.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/developing_estimators/sklearn_is_fitted.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/developing_estimators/sklearn_is_fitted.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: sklearn_is_fitted.ipynb <sklearn_is_fitted.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: sklearn_is_fitted.py <sklearn_is_fitted.py>`


.. include:: sklearn_is_fitted.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

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