Ex 4: Label Propagation digits active learning

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半監督式分類法/範例4 : Label Propagation digits active learning
本範例目的：
展示active learning(主動學習)進行以label propagation(標籤傳播法)學習辨識手寫數字
一、Active Learning 主動學習
在實際應用上，通常我們獲得到的數據，有一大部分是未標籤的，如果要套用在常用的分類法上，最直接的想法是標籤所有的數據，但一一標籤所有數據是非常耗時耗工的，因此，在面對未標籤的數據遠多於有標籤的數據之情況下，可以透過active learning，主動的挑選一些數據進行標籤。 Active learning分成兩部分：
從已標籤的數據中隨機抽取一小部分作為訓練集，訓練出一個分類模型
透過迭代，將分類器預測出來的結果再進行訓練。
二、引入函式與模型
stats用來進行統計與分析
LabelSpreading為半監督式學習的模型
confusion_matrix為混淆矩陣
classification_report用於觀察預測和實際數值的差異，包含precision、recall、f1-score及support
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import numpy as np
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import matplotlib.pyplot as plt
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​
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from scipy import stats
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from sklearn import datasets
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from sklearn.semi_supervised import LabelSpreading
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from sklearn.metrics import classification_report, confusion_matrix
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三、建立dataset
Dataset取自sklearn.datasets.load_digits，內容為0~9的手寫數字，共有1797筆
使用其中的330筆進行訓練(y_train)，其中40筆為labeled，其餘290筆為unlabeled(標為-1)
迭代的次數設定為5次
scikit learn網站中的範例程式敘述為10筆labeled，但原始程式碼為40筆，因此在這邊以原始碼為主
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digits = datasets.load_digits()
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rng = np.random.RandomState(0)
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indices = np.arange(len(digits.data))
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rng.shuffle(indices)
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​
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X = digits.data[indices[:330]]
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y = digits.target[indices[:330]]
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images = digits.images[indices[:330]]
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​
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n_total_samples = len(y)
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n_labeled_points = 40
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max_iterations = 5
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​
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unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]
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f = plt.figure()
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四、利用Active learning進行模型訓練與預測
以下程式為每一次迭代所做的過程(for迴圈的內容)
每一次迭代都利用訓練過後的模型進行預測，得到predicted_labels，並與true_labels計算混淆矩陣與classification report
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if len(unlabeled_indices) == 0:
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    print("No unlabeled items left to label.")
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    break
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y_train = np.copy(y)
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y_train[unlabeled_indices] = -1
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​
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lp_model = LabelSpreading(gamma=0.25, max_iter=20)
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lp_model.fit(X, y_train)
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​
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predicted_labels = lp_model.transduction_[unlabeled_indices]
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true_labels = y[unlabeled_indices]
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​
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cm = confusion_matrix(true_labels, predicted_labels,
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                      labels=lp_model.classes_)
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​
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print("Iteration %i %s" % (i, 70 * "_"))
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print("Label Spreading model: %d labeled & %d unlabeled (%d total)"
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      % (n_labeled_points, n_total_samples - n_labeled_points,
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         n_total_samples))
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​
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print(classification_report(true_labels, predicted_labels))
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​
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print("Confusion matrix")
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print(cm)
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利用stats進行數據的統計，找出前5筆預測最不佳的結果，將其預測的label與true label和圖像顯示出來
每一次迭代的最後挑出上述的5筆預測最不佳的結果，進行下一次的迭代時，把相對應的true label替換給y_train測試集裡面，其餘(第40筆之後的數據)的label依然給予-1表示unlabeled
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# compute the entropies of transduced label distributions
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pred_entropies = stats.distributions.entropy(
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    lp_model.label_distributions_.T)
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​
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# select up to 5 digit examples that the classifier is most uncertain about
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uncertainty_index = np.argsort(pred_entropies)[::-1]
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uncertainty_index = uncertainty_index[
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    np.in1d(uncertainty_index, unlabeled_indices)][:5]
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​
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# keep track of indices that we get labels for
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delete_indices = np.array([], dtype=int)
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​
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# for more than 5 iterations, visualize the gain only on the first 5
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if i < 5:
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    f.text(.05, (1 - (i + 1) * .183),
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           "model %d\n\nfit with\n%d labels" %
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           ((i + 1), i * 5 + 40), size=10)
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for index, image_index in enumerate(uncertainty_index):
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    image = images[image_index]
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​
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    # for more than 5 iterations, visualize the gain only on the first 5
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    if i < 5:
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        sub = f.add_subplot(5, 5, index + 1 + (5 * i))
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        sub.imshow(image, cmap=plt.cm.gray_r, interpolation='none')
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        sub.set_title("predict: %i\ntrue: %i" % (
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            lp_model.transduction_[image_index], y[image_index]), size=10)
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        sub.axis('off')
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​
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    # labeling 5 points, remote from labeled set
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    delete_index, = np.where(unlabeled_indices == image_index)
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    delete_indices = np.concatenate((delete_indices, delete_index))
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​
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unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
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n_labeled_points += len(uncertainty_index)
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下列程式屬於for迴圈外圍
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f.suptitle("Active learning with Label Propagation.\nRows show 5 most "
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           "uncertain labels to learn with the next model.", y=1.15)
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plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2,
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                    hspace=0.85)
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plt.show()
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以下即為每一次迭代的結果，可以看到每一次迭代之後，micro avg逐漸上升
Out:
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  Iteration 0 ______________________________________________________________________
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  Label Spreading model: 40 labeled & 290 unlabeled (330 total)
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                precision    recall  f1-score   support
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​
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             0       1.00      1.00      1.00        22
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             1       0.78      0.69      0.73        26
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             2       0.93      0.93      0.93        29
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             3       1.00      0.89      0.94        27
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             4       0.92      0.96      0.94        23
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             5       0.96      0.70      0.81        33
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             6       0.97      0.97      0.97        35
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             7       0.94      0.91      0.92        33
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             8       0.62      0.89      0.74        28
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             9       0.73      0.79      0.76        34
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​
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     micro avg       0.87      0.87      0.87       290
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     macro avg       0.89      0.87      0.87       290
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  weighted avg       0.88      0.87      0.87       290
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​
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  Confusion matrix
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  [[22  0  0  0  0  0  0  0  0  0]
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   [ 0 18  2  0  0  0  1  0  5  0]
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   [ 0  0 27  0  0  0  0  0  2  0]
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   [ 0  0  0 24  0  0  0  0  3  0]
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   [ 0  1  0  0 22  0  0  0  0  0]
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   [ 0  0  0  0  0 23  0  0  0 10]
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   [ 0  1  0  0  0  0 34  0  0  0]
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   [ 0  0  0  0  0  0  0 30  3  0]
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   [ 0  3  0  0  0  0  0  0 25  0]
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   [ 0  0  0  0  2  1  0  2  2 27]]
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  Iteration 1 ______________________________________________________________________
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  Label Spreading model: 45 labeled & 285 unlabeled (330 total)
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                precision    recall  f1-score   support
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​
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             0       1.00      1.00      1.00        22
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             1       0.79      1.00      0.88        22
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             2       1.00      0.93      0.96        29
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             3       1.00      1.00      1.00        26
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             4       0.92      0.96      0.94        23
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             5       0.96      0.70      0.81        33
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             6       1.00      0.97      0.99        35
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             7       0.94      0.91      0.92        33
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             8       0.77      0.86      0.81        28
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             9       0.73      0.79      0.76        34
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​
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     micro avg       0.90      0.90      0.90       285
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     macro avg       0.91      0.91      0.91       285
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  weighted avg       0.91      0.90      0.90       285
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​
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  Confusion matrix
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  [[22  0  0  0  0  0  0  0  0  0]
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   [ 0 22  0  0  0  0  0  0  0  0]
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   [ 0  0 27  0  0  0  0  0  2  0]
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   [ 0  0  0 26  0  0  0  0  0  0]
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   [ 0  1  0  0 22  0  0  0  0  0]
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   [ 0  0  0  0  0 23  0  0  0 10]
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   [ 0  1  0  0  0  0 34  0  0  0]
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   [ 0  0  0  0  0  0  0 30  3  0]
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   [ 0  4  0  0  0  0  0  0 24  0]
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   [ 0  0  0  0  2  1  0  2  2 27]]
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  Iteration 2 ______________________________________________________________________
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  Label Spreading model: 50 labeled & 280 unlabeled (330 total)
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                precision    recall  f1-score   support
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​
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             0       1.00      1.00      1.00        22
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             1       0.85      1.00      0.92        22
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             2       1.00      1.00      1.00        28
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             3       1.00      1.00      1.00        26
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             4       0.87      1.00      0.93        20
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             5       0.96      0.70      0.81        33
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             6       1.00      0.97      0.99        35
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             7       0.94      1.00      0.97        32
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             8       0.92      0.86      0.89        28
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             9       0.73      0.79      0.76        34
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​
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     micro avg       0.92      0.92      0.92       280
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     macro avg       0.93      0.93      0.93       280
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  weighted avg       0.93      0.92      0.92       280
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​
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  Confusion matrix
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  [[22  0  0  0  0  0  0  0  0  0]
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   [ 0 22  0  0  0  0  0  0  0  0]
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   [ 0  0 28  0  0  0  0  0  0  0]
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   [ 0  0  0 26  0  0  0  0  0  0]
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   [ 0  0  0  0 20  0  0  0  0  0]
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   [ 0  0  0  0  0 23  0  0  0 10]
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   [ 0  1  0  0  0  0 34  0  0  0]
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   [ 0  0  0  0  0  0  0 32  0  0]
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   [ 0  3  0  0  1  0  0  0 24  0]
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   [ 0  0  0  0  2  1  0  2  2 27]]
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  Iteration 3 ______________________________________________________________________
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  Label Spreading model: 55 labeled & 275 unlabeled (330 total)
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                precision    recall  f1-score   support
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​
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             0       1.00      1.00      1.00        22
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             1       0.85      1.00      0.92        22
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             2       1.00      1.00      1.00        27
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             3       1.00      1.00      1.00        26
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             4       0.87      1.00      0.93        20
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             5       0.96      0.87      0.92        31
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             6       1.00      0.97      0.99        35
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             7       1.00      1.00      1.00        31
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             8       0.92      0.86      0.89        28
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             9       0.88      0.85      0.86        33
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​
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     micro avg       0.95      0.95      0.95       275
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     macro avg       0.95      0.95      0.95       275
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  weighted avg       0.95      0.95      0.95       275
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​
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  Confusion matrix
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  [[22  0  0  0  0  0  0  0  0  0]
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   [ 0 22  0  0  0  0  0  0  0  0]
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   [ 0  0 27  0  0  0  0  0  0  0]
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   [ 0  0  0 26  0  0  0  0  0  0]
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   [ 0  0  0  0 20  0  0  0  0  0]
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   [ 0  0  0  0  0 27  0  0  0  4]
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   [ 0  1  0  0  0  0 34  0  0  0]
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   [ 0  0  0  0  0  0  0 31  0  0]
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   [ 0  3  0  0  1  0  0  0 24  0]
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   [ 0  0  0  0  2  1  0  0  2 28]]
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  Iteration 4 ______________________________________________________________________
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  Label Spreading model: 60 labeled & 270 unlabeled (330 total)
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                precision    recall  f1-score   support
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​
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             0       1.00      1.00      1.00        22
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             1       0.96      1.00      0.98        22
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             2       1.00      0.96      0.98        27
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             3       0.96      1.00      0.98        25
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             4       0.86      1.00      0.93        19
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             5       0.96      0.87      0.92        31
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             6       1.00      0.97      0.99        35
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             7       1.00      1.00      1.00        31
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             8       0.92      0.96      0.94        25
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             9       0.88      0.85      0.86        33
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​
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     micro avg       0.96      0.96      0.96       270
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     macro avg       0.95      0.96      0.96       270
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  weighted avg       0.96      0.96      0.96       270
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​
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  Confusion matrix
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  [[22  0  0  0  0  0  0  0  0  0]
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   [ 0 22  0  0  0  0  0  0  0  0]
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   [ 0  0 26  1  0  0  0  0  0  0]
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   [ 0  0  0 25  0  0  0  0  0  0]
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   [ 0  0  0  0 19  0  0  0  0  0]
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   [ 0  0  0  0  0 27  0  0  0  4]
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   [ 0  1  0  0  0  0 34  0  0  0]
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   [ 0  0  0  0  0  0  0 31  0  0]
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   [ 0  0  0  0  1  0  0  0 24  0]
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   [ 0  0  0  0  2  1  0  0  2 28]]
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png
上圖的結果即為Active Learning訓練過程的結果，第一次迭代以330筆的資料進行訓練，其中包含40筆labeled的資料與290 unlabeled的資料，再對unlabeled的資料做預測，將預測出來的結果中，5個預測最不佳的結果顯示出來，即第一列的5張圖，將這5筆資料的從測試集中強制變為true label的結果，再下一次迭代中，labeled的資料就變成45筆，unlabeled的資料為285筆，總和為330筆的資料進行第二次的訓練，以此類推，因此可以看到，每一次訓練，labeled的資料會5筆、5筆的增加。
五、原始碼列表
Python source code: plot_label_propagation_digits_active_learning.py
​https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html​
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print(__doc__)
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​
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# Authors: Clay Woolam <[email protected]>
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# License: BSD
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​
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy import stats
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​
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from sklearn import datasets
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from sklearn.semi_supervised import LabelSpreading
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from sklearn.metrics import classification_report, confusion_matrix
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​
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digits = datasets.load_digits()
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rng = np.random.RandomState(0)
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indices = np.arange(len(digits.data))
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rng.shuffle(indices)
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​
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X = digits.data[indices[:330]]
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y = digits.target[indices[:330]]
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images = digits.images[indices[:330]]
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​
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n_total_samples = len(y)
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n_labeled_points = 40
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max_iterations = 5
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​
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unlabeled_indices = np.arange(n_total_samples)[n_labeled_points:]
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f = plt.figure()
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​
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for i in range(max_iterations):
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    if len(unlabeled_indices) == 0:
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        print("No unlabeled items left to label.")
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        break
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    y_train = np.copy(y)
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    y_train[unlabeled_indices] = -1
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​
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    lp_model = LabelSpreading(gamma=0.25, max_iter=20)
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    lp_model.fit(X, y_train)
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​
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    predicted_labels = lp_model.transduction_[unlabeled_indices]
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    true_labels = y[unlabeled_indices]
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​
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    cm = confusion_matrix(true_labels, predicted_labels,
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                          labels=lp_model.classes_)
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​
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    print("Iteration %i %s" % (i, 70 * "_"))
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    print("Label Spreading model: %d labeled & %d unlabeled (%d total)"
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          % (n_labeled_points, n_total_samples - n_labeled_points,
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             n_total_samples))
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​
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    print(classification_report(true_labels, predicted_labels))
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​
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    print("Confusion matrix")
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    print(cm)
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​
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    # compute the entropies of transduced label distributions
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    pred_entropies = stats.distributions.entropy(
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        lp_model.label_distributions_.T)
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​
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    # select up to 5 digit examples that the classifier is most uncertain about
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    uncertainty_index = np.argsort(pred_entropies)[::-1]
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    uncertainty_index = uncertainty_index[
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        np.in1d(uncertainty_index, unlabeled_indices)][:5]
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​
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    # keep track of indices that we get labels for
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    delete_indices = np.array([], dtype=int)
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​
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    # for more than 5 iterations, visualize the gain only on the first 5
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    if i < 5:
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        f.text(.05, (1 - (i + 1) * .183),
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               "model %d\n\nfit with\n%d labels" %
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               ((i + 1), i * 5 + 10), size=10)
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    for index, image_index in enumerate(uncertainty_index):
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        image = images[image_index]
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​
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        # for more than 5 iterations, visualize the gain only on the first 5
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        if i < 5:
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            sub = f.add_subplot(5, 5, index + 1 + (5 * i))
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            sub.imshow(image, cmap=plt.cm.gray_r, interpolation='none')
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            sub.set_title("predict: %i\ntrue: %i" % (
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                lp_model.transduction_[image_index], y[image_index]), size=10)
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            sub.axis('off')
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​
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        # labeling 5 points, remote from labeled set
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        delete_index, = np.where(unlabeled_indices == image_index)
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        delete_indices = np.concatenate((delete_indices, delete_index))
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​
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    unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
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    n_labeled_points += len(uncertainty_index)
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​
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f.suptitle("Active learning with Label Propagation.\nRows show 5 most "
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           "uncertain labels to learn with the next model.", y=1.15)
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plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2,
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                    hspace=0.85)
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plt.show()
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Ex 3: Label Propagation digits: Demonstrating performance
Decision boundary of label propagation versus SVM on the Iris dataset
Last modified 1yr ago
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Contents
半監督式分類法/範例4 : Label Propagation digits active learning
一、Active Learning 主動學習
二、引入函式與模型
三、建立dataset
四、利用Active learning進行模型訓練與預測
五、原始碼列表