.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/classification/plot_digits_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_classification_plot_digits_classification.py: ========== 识别手写数字 ========== 这个例子展示了如何使用scikit-learn来识别0-9的手写数字图像。 .. GENERATED FROM PYTHON SOURCE LINES 9-20 .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier: BSD-3-Clause # 标准科学Python导入 import matplotlib.pyplot as plt # 导入数据集、分类器和性能指标 from sklearn import datasets, metrics, svm from sklearn.model_selection import train_test_split .. GENERATED FROM PYTHON SOURCE LINES 21-27 数字数据集 -------------- 数字数据集由8x8像素的数字图像组成。数据集的 ``images`` 属性存储每个图像的8x8灰度值数组。我们将使用这些数组来可视化前4个图像。数据集的 ``target`` 属性存储每个图像所代表的数字，并将其包含在下面4个图的标题中。 注意：如果我们处理的是图像文件（例如 'png' 文件），我们会使用 :func:`matplotlib.pyplot.imread` 加载它们。 .. GENERATED FROM PYTHON SOURCE LINES 27-36 .. code-block:: Python digits = datasets.load_digits() _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3)) for ax, image, label in zip(axes, digits.images, digits.target): ax.set_axis_off() ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest") ax.set_title("Training: %i" % label) .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png :alt: Training: 0, Training: 1, Training: 2, Training: 3 :srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 37-43 分类 ------ 要在这些数据上应用分类器，我们需要将图像展平，将每个二维的灰度值数组从形状 ``(8, 8)`` 变为形状 ``(64,)`` 。随后，整个数据集的形状将变为 ``(n_samples, n_features)`` , 其中 ``n_samples`` 是图像的数量， ``n_features`` 是每个图像中的像素总数。 我们可以将数据分为训练集和测试集，并在训练样本上拟合支持向量分类器。随后，拟合的分类器可以用于预测测试集样本的数字值。 .. GENERATED FROM PYTHON SOURCE LINES 43-62 .. code-block:: Python # 扁平化图像 n_samples = len(digits.images) data = digits.images.reshape((n_samples, -1)) # 创建一个分类器：支持向量分类器 clf = svm.SVC(gamma=0.001) # 将数据分为50%的训练子集和50%的测试子集 X_train, X_test, y_train, y_test = train_test_split( data, digits.target, test_size=0.5, shuffle=False ) # 学习训练子集上的数字 clf.fit(X_train, y_train) # 预测测试子集上的数字值 predicted = clf.predict(X_test) .. GENERATED FROM PYTHON SOURCE LINES 63-64 下面我们将可视化前四个测试样本，并在标题中显示它们的预测数字值。 .. GENERATED FROM PYTHON SOURCE LINES 64-72 .. code-block:: Python _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3)) for ax, image, prediction in zip(axes, X_test, predicted): ax.set_axis_off() image = image.reshape(8, 8) ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest") ax.set_title(f"Prediction: {prediction}") .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png :alt: Prediction: 8, Prediction: 8, Prediction: 4, Prediction: 9 :srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 73-74 :func:`~sklearn.metrics.classification_report` 构建一个文本报告，显示主要的分类指标。 .. GENERATED FROM PYTHON SOURCE LINES 74-80 .. code-block:: Python print( f"Classification report for classifier {clf}:\n" f"{metrics.classification_report(y_test, predicted)}\n" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Classification report for classifier SVC(gamma=0.001): precision recall f1-score support 0 1.00 0.99 0.99 88 1 0.99 0.97 0.98 91 2 0.99 0.99 0.99 86 3 0.98 0.87 0.92 91 4 0.99 0.96 0.97 92 5 0.95 0.97 0.96 91 6 0.99 0.99 0.99 91 7 0.96 0.99 0.97 89 8 0.94 1.00 0.97 88 9 0.93 0.98 0.95 92 accuracy 0.97 899 macro avg 0.97 0.97 0.97 899 weighted avg 0.97 0.97 0.97 899 .. GENERATED FROM PYTHON SOURCE LINES 81-82 我们还可以绘制一个真实数字值和预测数字值的: ref:`混淆矩阵 ` 。 .. GENERATED FROM PYTHON SOURCE LINES 82-89 .. code-block:: Python disp = metrics.ConfusionMatrixDisplay.from_predictions(y_test, predicted) disp.figure_.suptitle("Confusion Matrix") print(f"Confusion matrix:\n{disp.confusion_matrix}") plt.show() .. image-sg:: /auto_examples/classification/images/sphx_glr_plot_digits_classification_003.png :alt: Confusion Matrix :srcset: /auto_examples/classification/images/sphx_glr_plot_digits_classification_003.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Confusion matrix: [[87 0 0 0 1 0 0 0 0 0] [ 0 88 1 0 0 0 0 0 1 1] [ 0 0 85 1 0 0 0 0 0 0] [ 0 0 0 79 0 3 0 4 5 0] [ 0 0 0 0 88 0 0 0 0 4] [ 0 0 0 0 0 88 1 0 0 2] [ 0 1 0 0 0 0 90 0 0 0] [ 0 0 0 0 0 1 0 88 0 0] [ 0 0 0 0 0 0 0 0 88 0] [ 0 0 0 1 0 1 0 0 0 90]] .. GENERATED FROM PYTHON SOURCE LINES 90-91 如果评估分类器的结果是以 :ref:`混淆矩阵 ` 的形式存储的，而不是以 `y_true` 和 `y_pred` 的形式存储的，仍然可以按如下方式构建 :func:`~sklearn.metrics.classification_report` ： .. GENERATED FROM PYTHON SOURCE LINES 91-110 .. code-block:: Python # The ground truth and predicted lists # # y_true = [] y_pred = [] cm = disp.confusion_matrix # 对于混淆矩阵中的每个单元格，将相应的真实值和预测值添加到列表中。 for gt in range(len(cm)): for pred in range(len(cm)): y_true += [gt] * cm[gt][pred] y_pred += [pred] * cm[gt][pred] print( "Classification report rebuilt from confusion matrix:\n" f"{metrics.classification_report(y_true, y_pred)}\n" ) .. rst-class:: sphx-glr-script-out .. code-block:: none Classification report rebuilt from confusion matrix: precision recall f1-score support 0 1.00 0.99 0.99 88 1 0.99 0.97 0.98 91 2 0.99 0.99 0.99 86 3 0.98 0.87 0.92 91 4 0.99 0.96 0.97 92 5 0.95 0.97 0.96 91 6 0.99 0.99 0.99 91 7 0.96 0.99 0.97 89 8 0.94 1.00 0.97 88 9 0.93 0.98 0.95 92 accuracy 0.97 899 macro avg 0.97 0.97 0.97 899 weighted avg 0.97 0.97 0.97 899 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.189 seconds) .. _sphx_glr_download_auto_examples_classification_plot_digits_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/classification/plot_digits_classification.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_digits_classification.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_digits_classification.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_digits_classification.zip ` .. include:: plot_digits_classification.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_