.. 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: ================================================================= 使用Pipeline和GridSearchCV选择降维方法 ================================================================= 本示例构建了一个管道，先进行降维，然后使用支持向量分类器进行预测。它展示了如何使用 ``GridSearchCV`` 和 ``Pipeline`` 在一次交叉验证运行中优化不同类别的估计器——在网格搜索期间，无监督的 ``PCA`` 和 ``NMF`` 降维方法与单变量特征选择进行了比较。此外，可以使用 ``memory`` 参数实例化 ``Pipeline`` 以对管道中的转换器进行缓存，避免重复拟合相同的转换器。请注意，当转换器的拟合成本较高时，使用 ``memory`` 启用缓存会变得很有意义。 .. GENERATED FROM PYTHON SOURCE LINES 13-18 .. code-block:: Python # 作者：罗伯特·麦吉本 # 乔尔·诺斯曼 # 纪尧姆·勒梅特 .. GENERATED FROM PYTHON SOURCE LINES 19-20 ``Pipeline`` 和 ``GridSearchCV`` 的示例 .. GENERATED FROM PYTHON SOURCE LINES 22-64 .. 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()), # reduce_dim阶段由param_grid填充 ("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 0xffff7dcd2160>)],
                              'reduce_dim__k': [2, 4, 8]}])

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.. GENERATED FROM PYTHON SOURCE LINES 65-86 .. code-block:: Python import pandas as pd mean_scores = np.array(grid.cv_results_["mean_test_score"]) # 分数按param_grid迭代的顺序排列，即按字母顺序排列 mean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS)) # 选择最佳C的分数 mean_scores = mean_scores.max(axis=0) # 创建一个数据框以简化绘图 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 87-88 在 ``Pipeline`` 中缓存转换器 .. GENERATED FROM PYTHON SOURCE LINES 90-94 有时存储特定转换器的状态是值得的，因为它可能会再次使用。在 ``GridSearchCV`` 中使用管道会触发这种情况。因此，我们使用参数 ``memory`` 来启用缓存。 .. warning:: 请注意，这个例子只是一个说明，因为在这种特定情况下，拟合PCA不一定比加载缓存慢。因此，当变换器的拟合代价较高时，请使用 ``memory`` 构造参数。 .. GENERATED FROM PYTHON SOURCE LINES 94-114 .. code-block:: Python from shutil import rmtree from joblib import Memory # 创建一个临时文件夹来存储流水线的转换器 location = "cachedir" memory = Memory(location=location, verbose=10) cached_pipe = Pipeline( [("reduce_dim", PCA()), ("classify", LinearSVC(dual=False, max_iter=10000))], memory=memory, ) # 这次将在网格搜索中使用缓存的管道 # 退出前删除临时缓存 memory.clear(warn=False) rmtree(location) .. GENERATED FROM PYTHON SOURCE LINES 115-116 ``PCA`` 拟合仅在评估 ``LinearSVC`` 分类器的第一个 ``C`` 参数配置时计算。其他 ``C`` 参数配置将触发加载缓存的 ``PCA`` 估计器数据，从而节省处理时间。因此，当拟合变换器成本较高时，使用 ``memory`` 缓存管道非常有利。 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 40.968 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 `_