.. 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 :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. 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 22-26 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 27-29 Illustration of ``Pipeline`` and ``GridSearchCV`` ############################################################################## .. GENERATED FROM PYTHON SOURCE LINES 29-71 .. code-block:: Python 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

GridSearchCV(estimator=Pipeline(steps=[('scaling', MinMaxScaler()),
                                           ('reduce_dim', 'passthrough'),
                                           ('classify',
                                            LinearSVC(dual=False,
                                                      max_iter=10000))]),
                 n_jobs=1,
                 param_grid=[{'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [PCA(iterated_power=7),
                                             NMF(max_iter=1000)],
                              'reduce_dim__n_components': [2, 4, 8]},
                             {'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [SelectKBest(score_func=<function mutual_info_classif at 0x7f128041cee0>)],
                              'reduce_dim__k': [2, 4, 8]}])

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.. GENERATED FROM PYTHON SOURCE LINES 72-93 .. code-block:: Python 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 94-105 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 105-125 .. code-block:: Python 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 126-132 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 48.736 seconds) .. _sphx_glr_download_auto_examples_compose_plot_compare_reduction.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/compose/plot_compare_reduction.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_compare_reduction.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_compare_reduction.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_compare_reduction.zip ` .. include:: plot_compare_reduction.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_