.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/decomposition/plot_incremental_pca.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_decomposition_plot_incremental_pca.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_decomposition_plot_incremental_pca.py:


===============
Incremental PCA
===============

Incremental principal component analysis (IPCA) is typically used as a
replacement for principal component analysis (PCA) when the dataset to be
decomposed is too large to fit in memory. IPCA builds a low-rank approximation
for the input data using an amount of memory which is independent of the
number of input data samples. It is still dependent on the input data features,
but changing the batch size allows for control of memory usage.

This example serves as a visual check that IPCA is able to find a similar
projection of the data to PCA (to a sign flip), while only processing a
few samples at a time. This can be considered a "toy example", as IPCA is
intended for large datasets which do not fit in main memory, requiring
incremental approaches.

.. GENERATED FROM PYTHON SOURCE LINES 21-64



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_001.png
         :alt: Incremental PCA of iris dataset Mean absolute unsigned error 0.002201
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_002.png
         :alt: PCA of iris dataset
         :srcset: /auto_examples/decomposition/images/sphx_glr_plot_incremental_pca_002.png
         :class: sphx-glr-multi-img





.. code-block:: Python


    # Authors: Kyle Kastner
    # License: BSD 3 clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_iris
    from sklearn.decomposition import PCA, IncrementalPCA

    iris = load_iris()
    X = iris.data
    y = iris.target

    n_components = 2
    ipca = IncrementalPCA(n_components=n_components, batch_size=10)
    X_ipca = ipca.fit_transform(X)

    pca = PCA(n_components=n_components)
    X_pca = pca.fit_transform(X)

    colors = ["navy", "turquoise", "darkorange"]

    for X_transformed, title in [(X_ipca, "Incremental PCA"), (X_pca, "PCA")]:
        plt.figure(figsize=(8, 8))
        for color, i, target_name in zip(colors, [0, 1, 2], iris.target_names):
            plt.scatter(
                X_transformed[y == i, 0],
                X_transformed[y == i, 1],
                color=color,
                lw=2,
                label=target_name,
            )

        if "Incremental" in title:
            err = np.abs(np.abs(X_pca) - np.abs(X_ipca)).mean()
            plt.title(title + " of iris dataset\nMean absolute unsigned error %.6f" % err)
        else:
            plt.title(title + " of iris dataset")
        plt.legend(loc="best", shadow=False, scatterpoints=1)
        plt.axis([-4, 4, -1.5, 1.5])

    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.244 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_incremental_pca.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_incremental_pca.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

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

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

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

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

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


.. include:: plot_incremental_pca.recommendations


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_