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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_compose_plot_compare_reduction.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_compose_plot_compare_reduction.py:


=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================

This example constructs a pipeline that does dimensionality
reduction followed by prediction with a support vector
classifier. It demonstrates the use of ``GridSearchCV`` and
``Pipeline`` to optimize over different classes of estimators in a
single CV run -- unsupervised ``PCA`` and ``NMF`` dimensionality
reductions are compared to univariate feature selection during
the grid search.

Additionally, ``Pipeline`` can be instantiated with the ``memory``
argument to memoize the transformers within the pipeline, avoiding to fit
again the same transformers over and over.

Note that the use of ``memory`` to enable caching becomes interesting when the
fitting of a transformer is costly.

.. GENERATED FROM PYTHON SOURCE LINES 22-27

.. code-block:: default


    # Authors: Robert McGibbon
    #          Joel Nothman
    #          Guillaume Lemaitre








.. GENERATED FROM PYTHON SOURCE LINES 28-30

Illustration of ``Pipeline`` and ``GridSearchCV``
##############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 30-72

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_digits
    from sklearn.decomposition import NMF, PCA
    from sklearn.feature_selection import SelectKBest, mutual_info_classif
    from sklearn.model_selection import GridSearchCV
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.svm import LinearSVC

    X, y = load_digits(return_X_y=True)

    pipe = Pipeline(
        [
            ("scaling", MinMaxScaler()),
            # the reduce_dim stage is populated by the param_grid
            ("reduce_dim", "passthrough"),
            ("classify", LinearSVC(dual=False, max_iter=10000)),
        ]
    )

    N_FEATURES_OPTIONS = [2, 4, 8]
    C_OPTIONS = [1, 10, 100, 1000]
    param_grid = [
        {
            "reduce_dim": [PCA(iterated_power=7), NMF(max_iter=1_000)],
            "reduce_dim__n_components": N_FEATURES_OPTIONS,
            "classify__C": C_OPTIONS,
        },
        {
            "reduce_dim": [SelectKBest(mutual_info_classif)],
            "reduce_dim__k": N_FEATURES_OPTIONS,
            "classify__C": C_OPTIONS,
        },
    ]
    reducer_labels = ["PCA", "NMF", "KBest(mutual_info_classif)"]

    grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
    grid.fit(X, y)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-37 {
      /* Definition of color scheme common for light and dark mode */
      --sklearn-color-text: black;
      --sklearn-color-line: gray;
      /* Definition of color scheme for unfitted estimators */
      --sklearn-color-unfitted-level-0: #fff5e6;
      --sklearn-color-unfitted-level-1: #f6e4d2;
      --sklearn-color-unfitted-level-2: #ffe0b3;
      --sklearn-color-unfitted-level-3: chocolate;
      /* Definition of color scheme for fitted estimators */
      --sklearn-color-fitted-level-0: #f0f8ff;
      --sklearn-color-fitted-level-1: #d4ebff;
      --sklearn-color-fitted-level-2: #b3dbfd;
      --sklearn-color-fitted-level-3: cornflowerblue;

      /* Specific color for light theme */
      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
      --sklearn-color-icon: #696969;

      @media (prefers-color-scheme: dark) {
        /* Redefinition of color scheme for dark theme */
        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
        --sklearn-color-icon: #878787;
      }
    }

    #sk-container-id-37 {
      color: var(--sklearn-color-text);
    }

    #sk-container-id-37 pre {
      padding: 0;
    }

    #sk-container-id-37 input.sk-hidden--visually {
      border: 0;
      clip: rect(1px 1px 1px 1px);
      clip: rect(1px, 1px, 1px, 1px);
      height: 1px;
      margin: -1px;
      overflow: hidden;
      padding: 0;
      position: absolute;
      width: 1px;
    }

    #sk-container-id-37 div.sk-dashed-wrapped {
      border: 1px dashed var(--sklearn-color-line);
      margin: 0 0.4em 0.5em 0.4em;
      box-sizing: border-box;
      padding-bottom: 0.4em;
      background-color: var(--sklearn-color-background);
    }

    #sk-container-id-37 div.sk-container {
      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
         but bootstrap.min.css set `[hidden] { display: none !important; }`
         so we also need the `!important` here to be able to override the
         default hidden behavior on the sphinx rendered scikit-learn.org.
         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
      display: inline-block !important;
      position: relative;
    }

    #sk-container-id-37 div.sk-text-repr-fallback {
      display: none;
    }

    div.sk-parallel-item,
    div.sk-serial,
    div.sk-item {
      /* draw centered vertical line to link estimators */
      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
      background-size: 2px 100%;
      background-repeat: no-repeat;
      background-position: center center;
    }

    /* Parallel-specific style estimator block */

    #sk-container-id-37 div.sk-parallel-item::after {
      content: "";
      width: 100%;
      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
      flex-grow: 1;
    }

    #sk-container-id-37 div.sk-parallel {
      display: flex;
      align-items: stretch;
      justify-content: center;
      background-color: var(--sklearn-color-background);
      position: relative;
    }

    #sk-container-id-37 div.sk-parallel-item {
      display: flex;
      flex-direction: column;
    }

    #sk-container-id-37 div.sk-parallel-item:first-child::after {
      align-self: flex-end;
      width: 50%;
    }

    #sk-container-id-37 div.sk-parallel-item:last-child::after {
      align-self: flex-start;
      width: 50%;
    }

    #sk-container-id-37 div.sk-parallel-item:only-child::after {
      width: 0;
    }

    /* Serial-specific style estimator block */

    #sk-container-id-37 div.sk-serial {
      display: flex;
      flex-direction: column;
      align-items: center;
      background-color: var(--sklearn-color-background);
      padding-right: 1em;
      padding-left: 1em;
    }


    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
    clickable and can be expanded/collapsed.
    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-37 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
      background-color: var(--sklearn-color-background);
    }

    /* Toggleable label */
    #sk-container-id-37 label.sk-toggleable__label {
      cursor: pointer;
      display: block;
      width: 100%;
      margin-bottom: 0;
      padding: 0.5em;
      box-sizing: border-box;
      text-align: center;
    }

    #sk-container-id-37 label.sk-toggleable__label-arrow:before {
      /* Arrow on the left of the label */
      content: "▸";
      float: left;
      margin-right: 0.25em;
      color: var(--sklearn-color-icon);
    }

    #sk-container-id-37 label.sk-toggleable__label-arrow:hover:before {
      color: var(--sklearn-color-text);
    }

    /* Toggleable content - dropdown */

    #sk-container-id-37 div.sk-toggleable__content {
      max-height: 0;
      max-width: 0;
      overflow: hidden;
      text-align: left;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-37 div.sk-toggleable__content.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-37 div.sk-toggleable__content pre {
      margin: 0.2em;
      border-radius: 0.25em;
      color: var(--sklearn-color-text);
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-37 div.sk-toggleable__content.fitted pre {
      /* unfitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    #sk-container-id-37 input.sk-toggleable__control:checked~div.sk-toggleable__content {
      /* Expand drop-down */
      max-height: 200px;
      max-width: 100%;
      overflow: auto;
    }

    #sk-container-id-37 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
      content: "▾";
    }

    /* Pipeline/ColumnTransformer-specific style */

    #sk-container-id-37 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-37 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator-specific style */

    /* Colorize estimator box */
    #sk-container-id-37 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-37 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    #sk-container-id-37 div.sk-label label.sk-toggleable__label,
    #sk-container-id-37 div.sk-label label {
      /* The background is the default theme color */
      color: var(--sklearn-color-text-on-default-background);
    }

    /* On hover, darken the color of the background */
    #sk-container-id-37 div.sk-label:hover label.sk-toggleable__label {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    /* Label box, darken color on hover, fitted */
    #sk-container-id-37 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
      color: var(--sklearn-color-text);
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Estimator label */

    #sk-container-id-37 div.sk-label label {
      font-family: monospace;
      font-weight: bold;
      display: inline-block;
      line-height: 1.2em;
    }

    #sk-container-id-37 div.sk-label-container {
      text-align: center;
    }

    /* Estimator-specific */
    #sk-container-id-37 div.sk-estimator {
      font-family: monospace;
      border: 1px dotted var(--sklearn-color-border-box);
      border-radius: 0.25em;
      box-sizing: border-box;
      margin-bottom: 0.5em;
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-0);
    }

    #sk-container-id-37 div.sk-estimator.fitted {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-0);
    }

    /* on hover */
    #sk-container-id-37 div.sk-estimator:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-2);
    }

    #sk-container-id-37 div.sk-estimator.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-2);
    }

    /* Specification for estimator info (e.g. "i" and "?") */

    /* Common style for "i" and "?" */

    .sk-estimator-doc-link,
    a:link.sk-estimator-doc-link,
    a:visited.sk-estimator-doc-link {
      float: right;
      font-size: smaller;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1em;
      height: 1em;
      width: 1em;
      text-decoration: none !important;
      margin-left: 1ex;
      /* unfitted */
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
      color: var(--sklearn-color-unfitted-level-1);
    }

    .sk-estimator-doc-link.fitted,
    a:link.sk-estimator-doc-link.fitted,
    a:visited.sk-estimator-doc-link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    div.sk-estimator:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover,
    div.sk-label-container:hover .sk-estimator-doc-link:hover,
    .sk-estimator-doc-link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover,
    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
    .sk-estimator-doc-link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
    .sk-estimator-doc-link span {
      display: none;
      z-index: 9999;
      position: relative;
      font-weight: normal;
      right: .2ex;
      padding: .5ex;
      margin: .5ex;
      width: min-content;
      min-width: 20ex;
      max-width: 50ex;
      color: var(--sklearn-color-text);
      box-shadow: 2pt 2pt 4pt #999;
      /* unfitted */
      background: var(--sklearn-color-unfitted-level-0);
      border: .5pt solid var(--sklearn-color-unfitted-level-3);
    }

    .sk-estimator-doc-link.fitted span {
      /* fitted */
      background: var(--sklearn-color-fitted-level-0);
      border: var(--sklearn-color-fitted-level-3);
    }

    .sk-estimator-doc-link:hover span {
      display: block;
    }

    /* "?"-specific style due to the `<a>` HTML tag */

    #sk-container-id-37 a.estimator_doc_link {
      float: right;
      font-size: 1rem;
      line-height: 1em;
      font-family: monospace;
      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
      /* unfitted */
      color: var(--sklearn-color-unfitted-level-1);
      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
    }

    #sk-container-id-37 a.estimator_doc_link.fitted {
      /* fitted */
      border: var(--sklearn-color-fitted-level-1) 1pt solid;
      color: var(--sklearn-color-fitted-level-1);
    }

    /* On hover */
    #sk-container-id-37 a.estimator_doc_link:hover {
      /* unfitted */
      background-color: var(--sklearn-color-unfitted-level-3);
      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    #sk-container-id-37 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-37" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                           (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                           (&#x27;classify&#x27;,
                                            LinearSVC(dual=False,
                                                      max_iter=10000))]),
                 n_jobs=1,
                 param_grid=[{&#x27;classify__C&#x27;: [1, 10, 100, 1000],
                              &#x27;reduce_dim&#x27;: [PCA(iterated_power=7),
                                             NMF(max_iter=1000)],
                              &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                             {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7f009bd05260&gt;)],
                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-117" type="checkbox" ><label for="sk-estimator-id-117" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;GridSearchCV<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html">?<span>Documentation for GridSearchCV</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()),
                                           (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                                           (&#x27;classify&#x27;,
                                            LinearSVC(dual=False,
                                                      max_iter=10000))]),
                 n_jobs=1,
                 param_grid=[{&#x27;classify__C&#x27;: [1, 10, 100, 1000],
                              &#x27;reduce_dim&#x27;: [PCA(iterated_power=7),
                                             NMF(max_iter=1000)],
                              &#x27;reduce_dim__n_components&#x27;: [2, 4, 8]},
                             {&#x27;classify__C&#x27;: [1, 10, 100, 1000],
                              &#x27;reduce_dim&#x27;: [SelectKBest(score_func=&lt;function mutual_info_classif at 0x7f009bd05260&gt;)],
                              &#x27;reduce_dim__k&#x27;: [2, 4, 8]}])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-118" type="checkbox" ><label for="sk-estimator-id-118" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">estimator: Pipeline</label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaling&#x27;, MinMaxScaler()), (&#x27;reduce_dim&#x27;, &#x27;passthrough&#x27;),
                    (&#x27;classify&#x27;, LinearSVC(dual=False, max_iter=10000))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-119" type="checkbox" ><label for="sk-estimator-id-119" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;MinMaxScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-120" type="checkbox" ><label for="sk-estimator-id-120" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">passthrough</label><div class="sk-toggleable__content fitted"><pre>passthrough</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-121" type="checkbox" ><label for="sk-estimator-id-121" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;LinearSVC<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.svm.LinearSVC.html">?<span>Documentation for LinearSVC</span></a></label><div class="sk-toggleable__content fitted"><pre>LinearSVC(dual=False, max_iter=10000)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 73-94

.. code-block:: default

    import pandas as pd

    mean_scores = np.array(grid.cv_results_["mean_test_score"])
    # scores are in the order of param_grid iteration, which is alphabetical
    mean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS))
    # select score for best C
    mean_scores = mean_scores.max(axis=0)
    # create a dataframe to ease plotting
    mean_scores = pd.DataFrame(
        mean_scores.T, index=N_FEATURES_OPTIONS, columns=reducer_labels
    )

    ax = mean_scores.plot.bar()
    ax.set_title("Comparing feature reduction techniques")
    ax.set_xlabel("Reduced number of features")
    ax.set_ylabel("Digit classification accuracy")
    ax.set_ylim((0, 1))
    ax.legend(loc="upper left")

    plt.show()




.. image-sg:: /auto_examples/compose/images/sphx_glr_plot_compare_reduction_001.png
   :alt: Comparing feature reduction techniques
   :srcset: /auto_examples/compose/images/sphx_glr_plot_compare_reduction_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 95-106

Caching transformers within a ``Pipeline``
##############################################################################
 It is sometimes worthwhile storing the state of a specific transformer
 since it could be used again. Using a pipeline in ``GridSearchCV`` triggers
 such situations. Therefore, we use the argument ``memory`` to enable caching.

 .. warning::
     Note that this example is, however, only an illustration since for this
     specific case fitting PCA is not necessarily slower than loading the
     cache. Hence, use the ``memory`` constructor parameter when the fitting
     of a transformer is costly.

.. GENERATED FROM PYTHON SOURCE LINES 106-126

.. code-block:: default


    from shutil import rmtree

    from joblib import Memory

    # Create a temporary folder to store the transformers of the pipeline
    location = "cachedir"
    memory = Memory(location=location, verbose=10)
    cached_pipe = Pipeline(
        [("reduce_dim", PCA()), ("classify", LinearSVC(dual=False, max_iter=10000))],
        memory=memory,
    )

    # This time, a cached pipeline will be used within the grid search


    # Delete the temporary cache before exiting
    memory.clear(warn=False)
    rmtree(location)








.. GENERATED FROM PYTHON SOURCE LINES 127-133

The ``PCA`` fitting is only computed at the evaluation of the first
configuration of the ``C`` parameter of the ``LinearSVC`` classifier. The
other configurations of ``C`` will trigger the loading of the cached ``PCA``
estimator data, leading to save processing time. Therefore, the use of
caching the pipeline using ``memory`` is highly beneficial when fitting
a transformer is costly.


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

   **Total running time of the script:** ( 0 minutes  52.312 seconds)


.. _sphx_glr_download_auto_examples_compose_plot_compare_reduction.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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