.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.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_semi_supervised_plot_label_propagation_digits_active_learning.py: 标签传播数字主动学习 ======================== 演示了一种使用标签传播学习手写数字的主动学习技术。 我们首先用仅有的10个标记点训练一个标签传播模型，然后选择最不确定的前五个点进行标记。接下来，我们用15个标记点（原来的10个加上新标记的5个）进行训练。我们重复这个过程四次，以获得一个用30个标记样本训练的模型。请注意，通过更改 `max_iterations` ，可以将标记数量增加到超过30个。标记超过30个可能有助于了解这种主动学习技术的收敛速度。 将会出现一个图表，显示每次训练迭代中最不确定的前5个数字。这些数字可能包含错误，但我们将使用它们的真实标签来训练下一个模型。 .. GENERATED FROM PYTHON SOURCE LINES 12-116 .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_digits_active_learning_001.png :alt: Active learning with Label Propagation. Rows show 5 most uncertain labels to learn with the next model., predict: 1 true: 1, predict: 2 true: 1, predict: 1 true: 1, predict: 1 true: 1, predict: 3 true: 3, predict: 4 true: 4, predict: 4 true: 4, predict: 4 true: 4, predict: 8 true: 2, predict: 8 true: 7, predict: 2 true: 2, predict: 9 true: 5, predict: 9 true: 5, predict: 5 true: 9, predict: 7 true: 7, predict: 8 true: 8, predict: 1 true: 8, predict: 3 true: 3, predict: 4 true: 4, predict: 8 true: 8, predict: 1 true: 1, predict: 1 true: 1, predict: 7 true: 7, predict: 7 true: 7, predict: 1 true: 1 :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_digits_active_learning_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none Iteration 0 ______________________________________________________________________ Label Spreading model: 40 labeled & 290 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 22 1 0.78 0.69 0.73 26 2 0.93 0.93 0.93 29 3 1.00 0.89 0.94 27 4 0.92 0.96 0.94 23 5 0.96 0.70 0.81 33 6 0.97 0.97 0.97 35 7 0.94 0.91 0.92 33 8 0.62 0.89 0.74 28 9 0.73 0.79 0.76 34 accuracy 0.87 290 macro avg 0.89 0.87 0.87 290 weighted avg 0.88 0.87 0.87 290 Confusion matrix [[22 0 0 0 0 0 0 0 0 0] [ 0 18 2 0 0 0 1 0 5 0] [ 0 0 27 0 0 0 0 0 2 0] [ 0 0 0 24 0 0 0 0 3 0] [ 0 1 0 0 22 0 0 0 0 0] [ 0 0 0 0 0 23 0 0 0 10] [ 0 1 0 0 0 0 34 0 0 0] [ 0 0 0 0 0 0 0 30 3 0] [ 0 3 0 0 0 0 0 0 25 0] [ 0 0 0 0 2 1 0 2 2 27]] Iteration 1 ______________________________________________________________________ Label Spreading model: 45 labeled & 285 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 22 1 0.79 1.00 0.88 22 2 1.00 0.93 0.96 29 3 1.00 1.00 1.00 26 4 0.92 0.96 0.94 23 5 0.96 0.70 0.81 33 6 1.00 0.97 0.99 35 7 0.94 0.91 0.92 33 8 0.77 0.86 0.81 28 9 0.73 0.79 0.76 34 accuracy 0.90 285 macro avg 0.91 0.91 0.91 285 weighted avg 0.91 0.90 0.90 285 Confusion matrix [[22 0 0 0 0 0 0 0 0 0] [ 0 22 0 0 0 0 0 0 0 0] [ 0 0 27 0 0 0 0 0 2 0] [ 0 0 0 26 0 0 0 0 0 0] [ 0 1 0 0 22 0 0 0 0 0] [ 0 0 0 0 0 23 0 0 0 10] [ 0 1 0 0 0 0 34 0 0 0] [ 0 0 0 0 0 0 0 30 3 0] [ 0 4 0 0 0 0 0 0 24 0] [ 0 0 0 0 2 1 0 2 2 27]] Iteration 2 ______________________________________________________________________ Label Spreading model: 50 labeled & 280 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 22 1 0.85 1.00 0.92 22 2 1.00 1.00 1.00 28 3 1.00 1.00 1.00 26 4 0.87 1.00 0.93 20 5 0.96 0.70 0.81 33 6 1.00 0.97 0.99 35 7 0.94 1.00 0.97 32 8 0.92 0.86 0.89 28 9 0.73 0.79 0.76 34 accuracy 0.92 280 macro avg 0.93 0.93 0.93 280 weighted avg 0.93 0.92 0.92 280 Confusion matrix [[22 0 0 0 0 0 0 0 0 0] [ 0 22 0 0 0 0 0 0 0 0] [ 0 0 28 0 0 0 0 0 0 0] [ 0 0 0 26 0 0 0 0 0 0] [ 0 0 0 0 20 0 0 0 0 0] [ 0 0 0 0 0 23 0 0 0 10] [ 0 1 0 0 0 0 34 0 0 0] [ 0 0 0 0 0 0 0 32 0 0] [ 0 3 0 0 1 0 0 0 24 0] [ 0 0 0 0 2 1 0 2 2 27]] Iteration 3 ______________________________________________________________________ Label Spreading model: 55 labeled & 275 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 22 1 0.85 1.00 0.92 22 2 1.00 1.00 1.00 27 3 1.00 1.00 1.00 26 4 0.87 1.00 0.93 20 5 0.96 0.87 0.92 31 6 1.00 0.97 0.99 35 7 1.00 1.00 1.00 31 8 0.92 0.86 0.89 28 9 0.88 0.85 0.86 33 accuracy 0.95 275 macro avg 0.95 0.95 0.95 275 weighted avg 0.95 0.95 0.95 275 Confusion matrix [[22 0 0 0 0 0 0 0 0 0] [ 0 22 0 0 0 0 0 0 0 0] [ 0 0 27 0 0 0 0 0 0 0] [ 0 0 0 26 0 0 0 0 0 0] [ 0 0 0 0 20 0 0 0 0 0] [ 0 0 0 0 0 27 0 0 0 4] [ 0 1 0 0 0 0 34 0 0 0] [ 0 0 0 0 0 0 0 31 0 0] [ 0 3 0 0 1 0 0 0 24 0] [ 0 0 0 0 2 1 0 0 2 28]] Iteration 4 ______________________________________________________________________ Label Spreading model: 60 labeled & 270 unlabeled (330 total) precision recall f1-score support 0 1.00 1.00 1.00 22 1 0.96 1.00 0.98 22 2 1.00 0.96 0.98 27 3 0.96 1.00 0.98 25 4 0.86 1.00 0.93 19 5 0.96 0.87 0.92 31 6 1.00 0.97 0.99 35 7 1.00 1.00 1.00 31 8 0.92 0.96 0.94 25 9 0.88 0.85 0.86 33 accuracy 0.96 270 macro avg 0.95 0.96 0.96 270 weighted avg 0.96 0.96 0.96 270 Confusion matrix [[22 0 0 0 0 0 0 0 0 0] [ 0 22 0 0 0 0 0 0 0 0] [ 0 0 26 1 0 0 0 0 0 0] [ 0 0 0 25 0 0 0 0 0 0] [ 0 0 0 0 19 0 0 0 0 0] [ 0 0 0 0 0 27 0 0 0 4] [ 0 1 0 0 0 0 34 0 0 0] [ 0 0 0 0 0 0 0 31 0 0] [ 0 0 0 0 1 0 0 0 24 0] [ 0 0 0 0 2 1 0 0 2 28]] | .. code-block:: Python # 作者：scikit-learn 开发者 # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from scipy import stats from sklearn import datasets from sklearn.metrics import classification_report, confusion_matrix from sklearn.semi_supervised import LabelSpreading digits = datasets.load_digits() rng = np.random.RandomState(0) indices = np.arange(len(digits.data)) rng.shuffle(indices) X = digits.data[indices[:330]] y = digits.target[indices[:330]] images = digits.images[indices[:330]] n_total_samples = len(y) n_labeled_points = 40 max_iterations = 5 unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:] f = plt.figure() for i in range(max_iterations): if len(unlabeled_indices) == 0: print("No unlabeled items left to label.") break y_train = np.copy(y) y_train[unlabeled_indices] = -1 lp_model = LabelSpreading(gamma=0.25, max_iter=20) lp_model.fit(X, y_train) predicted_labels = lp_model.transduction_[unlabeled_indices] true_labels = y[unlabeled_indices] cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_) print("Iteration %i %s" % (i, 70 * "_")) print( "Label Spreading model: %d labeled & %d unlabeled (%d total)" % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples) ) print(classification_report(true_labels, predicted_labels)) print("Confusion matrix") print(cm) # 计算转导标签分布的熵 pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T) # 选择分类器最不确定的最多5个数字示例 uncertainty_index = np.argsort(pred_entropies)[::-1] uncertainty_index = uncertainty_index[ np.isin(uncertainty_index, unlabeled_indices) ][:5] # 记录我们获得标签的索引 delete_indices = np.array([], dtype=int) # 对于超过5次迭代的情况，仅在前5次迭代中可视化增益 if i < 5: f.text( 0.05, (1 - (i + 1) * 0.183), "model %d\n\nfit with\n%d labels" % ((i + 1), i * 5 + 10), size=10, ) for index, image_index in enumerate(uncertainty_index): image = images[image_index] # 对于超过5次迭代的情况，仅在前5次迭代中可视化增益 if i < 5: sub = f.add_subplot(5, 5, index + 1 + (5 * i)) sub.imshow(image, cmap=plt.cm.gray_r, interpolation="none") sub.set_title( "predict: %i\ntrue: %i" % (lp_model.transduction_[image_index], y[image_index]), size=10, ) sub.axis("off") # 标记5个点，远离标记集 (delete_index,) = np.where(unlabeled_indices == image_index) delete_indices = np.concatenate((delete_indices, delete_index)) unlabeled_indices = np.delete(unlabeled_indices, delete_indices) n_labeled_points += len(uncertainty_index) f.suptitle( ( "Active learning with Label Propagation.\nRows show 5 most " "uncertain labels to learn with the next model." ), y=1.15, ) plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.230 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_digits_active_learning.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/semi_supervised/plot_label_propagation_digits_active_learning.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_label_propagation_digits_active_learning.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_label_propagation_digits_active_learning.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_label_propagation_digits_active_learning.zip ` .. include:: plot_label_propagation_digits_active_learning.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_