.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/preprocessing/plot_discretization_classification.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_preprocessing_plot_discretization_classification.py: ====================== 特征离散化 ====================== 在合成分类数据集上展示特征离散化。特征离散化将每个特征分解为一组等宽分布的区间。然后将离散值进行独热编码，并交给线性分类器。这种预处理使得即使分类器是线性的，也能表现出非线性行为。 在这个例子中，前两行代表线性不可分的数据集（新月形和同心圆），而第三行大致是线性可分的。在两个线性不可分的数据集中，特征离散化大大提高了线性分类器的性能。在线性可分的数据集中，特征离散化降低了线性分类器的性能。还展示了两个非线性分类器以作比较。 这个例子应谨慎对待，因为所传达的直觉不一定适用于真实数据集。特别是在高维空间中，数据更容易线性分离。此外，使用特征离散化和独热编码会增加特征的数量，当样本数量较少时，这很容易导致过拟合。 图中显示了用实色表示的训练点和半透明的测试点。右下角显示了测试集上的分类准确率。 .. GENERATED FROM PYTHON SOURCE LINES 15-214 .. image-sg:: /auto_examples/preprocessing/images/sphx_glr_plot_discretization_classification_001.png :alt: Input data, LogisticRegression, LinearSVC, KBinsDiscretizer LogisticRegression, KBinsDiscretizer LinearSVC, GradientBoostingClassifier, SVC :srcset: /auto_examples/preprocessing/images/sphx_glr_plot_discretization_classification_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none dataset 0 --------- LogisticRegression: 0.86 LinearSVC: 0.86 KBinsDiscretizer + LogisticRegression: 0.86 KBinsDiscretizer + LinearSVC: 0.94 GradientBoostingClassifier: 0.90 SVC: 0.94 dataset 1 --------- LogisticRegression: 0.40 LinearSVC: 0.40 KBinsDiscretizer + LogisticRegression: 0.78 KBinsDiscretizer + LinearSVC: 0.80 GradientBoostingClassifier: 0.84 SVC: 0.84 dataset 2 --------- LogisticRegression: 0.98 LinearSVC: 0.96 KBinsDiscretizer + LogisticRegression: 0.94 KBinsDiscretizer + LinearSVC: 0.94 GradientBoostingClassifier: 0.94 SVC: 0.98 | .. code-block:: Python # 代码来源：Tom Dupré la Tour # 改编自Gaël Varoquaux和Andreas Müller的plot_classifier_comparison # # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from matplotlib.colors import ListedColormap from sklearn.datasets import make_circles, make_classification, make_moons from sklearn.ensemble import GradientBoostingClassifier from sklearn.exceptions import ConvergenceWarning from sklearn.linear_model import LogisticRegression from sklearn.model_selection import GridSearchCV, train_test_split from sklearn.pipeline import make_pipeline from sklearn.preprocessing import KBinsDiscretizer, StandardScaler from sklearn.svm import SVC, LinearSVC from sklearn.utils._testing import ignore_warnings h = 0.02 # step size in the mesh def get_name(estimator): name = estimator.__class__.__name__ if name == "Pipeline": name = [get_name(est[1]) for est in estimator.steps] name = " + ".join(name) return name # （估计器，参数网格）的列表，其中参数网格在 GridSearchCV 中使用。 # 在此示例中，参数空间被限制在一个狭窄的范围内以减少运行时间。在实际使用中，应该为算法使用更广泛的搜索空间。 classifiers = [ ( make_pipeline(StandardScaler(), LogisticRegression(random_state=0)), {"logisticregression__C": np.logspace(-1, 1, 3)}, ), ( make_pipeline(StandardScaler(), LinearSVC(random_state=0)), {"linearsvc__C": np.logspace(-1, 1, 3)}, ), ( make_pipeline( StandardScaler(), KBinsDiscretizer(encode="onehot", random_state=0), LogisticRegression(random_state=0), ), { "kbinsdiscretizer__n_bins": np.arange(5, 8), "logisticregression__C": np.logspace(-1, 1, 3), }, ), ( make_pipeline( StandardScaler(), KBinsDiscretizer(encode="onehot", random_state=0), LinearSVC(random_state=0), ), { "kbinsdiscretizer__n_bins": np.arange(5, 8), "linearsvc__C": np.logspace(-1, 1, 3), }, ), ( make_pipeline( StandardScaler(), GradientBoostingClassifier(n_estimators=5, random_state=0) ), {"gradientboostingclassifier__learning_rate": np.logspace(-2, 0, 5)}, ), ( make_pipeline(StandardScaler(), SVC(random_state=0)), {"svc__C": np.logspace(-1, 1, 3)}, ), ] names = [get_name(e).replace("StandardScaler + ", "") for e, _ in classifiers] n_samples = 100 datasets = [ make_moons(n_samples=n_samples, noise=0.2, random_state=0), make_circles(n_samples=n_samples, noise=0.2, factor=0.5, random_state=1), make_classification( n_samples=n_samples, n_features=2, n_redundant=0, n_informative=2, random_state=2, n_clusters_per_class=1, ), ] fig, axes = plt.subplots( nrows=len(datasets), ncols=len(classifiers) + 1, figsize=(21, 9) ) cm_piyg = plt.cm.PiYG cm_bright = ListedColormap(["#b30065", "#178000"]) # 遍历数据集 for ds_cnt, (X, y) in enumerate(datasets): print(f"\ndataset {ds_cnt}\n---------") # 划分为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.5, random_state=42 ) # 创建背景颜色网格 x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # 绘制数据集首先 ax = axes[ds_cnt, 0] if ds_cnt == 0: ax.set_title("Input data") # 绘制训练点 ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k") # 和测试点 ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k" ) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) # 遍历分类器 for est_idx, (name, (estimator, param_grid)) in enumerate(zip(names, classifiers)): ax = axes[ds_cnt, est_idx + 1] clf = GridSearchCV(estimator=estimator, param_grid=param_grid) with ignore_warnings(category=ConvergenceWarning): clf.fit(X_train, y_train) score = clf.score(X_test, y_test) print(f"{name}: {score:.2f}") # 绘制决策边界。为此，我们将为网格 [x_min, x_max]*[y_min, y_max] 中的每个点分配一个颜色。 if hasattr(clf, "decision_function"): Z = clf.decision_function(np.column_stack([xx.ravel(), yy.ravel()])) else: Z = clf.predict_proba(np.column_stack([xx.ravel(), yy.ravel()]))[:, 1] # 将结果放入彩色图中 Z = Z.reshape(xx.shape) ax.contourf(xx, yy, Z, cmap=cm_piyg, alpha=0.8) # 绘制训练点 ax.scatter( X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k" ) # 和测试点 ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6, ) ax.set_xlim(xx.min(), xx.max()) ax.set_ylim(yy.min(), yy.max()) ax.set_xticks(()) ax.set_yticks(()) if ds_cnt == 0: ax.set_title(name.replace(" + ", "\n")) ax.text( 0.95, 0.06, (f"{score:.2f}").lstrip("0"), size=15, bbox=dict(boxstyle="round", alpha=0.8, facecolor="white"), transform=ax.transAxes, horizontalalignment="right", ) plt.tight_layout() # 在图形上方添加字幕 plt.subplots_adjust(top=0.90) suptitles = [ "Linear classifiers", "Feature discretization and linear classifiers", "Non-linear classifiers", ] for i, suptitle in zip([1, 3, 5], suptitles): ax = axes[0, i] ax.text( 1.05, 1.25, suptitle, transform=ax.transAxes, horizontalalignment="center", size="x-large", ) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.506 seconds) .. _sphx_glr_download_auto_examples_preprocessing_plot_discretization_classification.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/preprocessing/plot_discretization_classification.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_discretization_classification.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_discretization_classification.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_discretization_classification.zip ` .. include:: plot_discretization_classification.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_