Ex 3: Label Propagation digits: Demonstrating performance

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半監督式分類法/範例3 : Label Propagation digits: Demonstrating performance
本範例目的：
利用少量標籤的手寫數字資料集進行模型訓練，展現半監督式學習的能力
一、半監督式學習
在實際的應用上，大部分的資料沒有標籤且數量會遠多於有標籤的資料，而將這些沒有標籤的資料一一標籤是非常耗時的，相對而言，蒐集無標籤的資料更容易，因此可以利用半監督式學習(Semi-supervised learning)對少部分的資料進行標籤，透過這些有標籤的資料擷取特徵，然後再對其他資料進行分類。
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 confusion_matrix, classification_report
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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340筆進行訓練，其中40筆為labeled，其餘為unlabeled
複製一組340筆的target (y_train)作為訓練集，並將第40筆之後的label都設為-1
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digits = datasets.load_digits()
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rng = np.random.RandomState(2)
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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[:340]]
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y = digits.target[indices[:340]]
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images = digits.images[indices[:340]]
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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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​
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indices = np.arange(n_total_samples)
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​
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unlabeled_set = indices[n_labeled_points:]
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​
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y_train = np.copy(y)
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y_train[unlabeled_set] = -1
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四、模型訓練與預測
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
列出classification report
support為每個標籤出現的次數
precision(精確度)為true positives/(true positivies + false positivies)
recall(召回率)為true positivies/(true positivies + false negatives)
f1值為精確度與召回率的調和均值，為2 x precision x recall/(precision + recall)
micro avg為所有數據中，正確預測的比率
macro avg為每個評估項目未加權的平均值
weighted avg為每個評估項目加權平均值
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lp_model = LabelSpreading(gamma=.25, max_iter=20)
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lp_model.fit(X, y_train)
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predicted_labels = lp_model.transduction_[unlabeled_set]
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true_labels = y[unlabeled_set]
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​
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cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)
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​
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print("Label Spreading model: %d labeled & %d unlabeled points (%d total)" %
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      (n_labeled_points, n_total_samples - n_labeled_points, 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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Out:
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Label Spreading model: 40 labeled & 300 unlabeled points (340 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        27
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             1       0.82      1.00      0.90        37
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             2       1.00      0.86      0.92        28
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             3       1.00      0.80      0.89        35
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             4       0.92      1.00      0.96        24
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             5       0.74      0.94      0.83        34
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             6       0.89      0.96      0.92        25
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             7       0.94      0.89      0.91        35
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             8       1.00      0.68      0.81        31
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             9       0.81      0.88      0.84        24
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​
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     micro avg       0.90      0.90      0.90       300
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     macro avg       0.91      0.90      0.90       300
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  weighted avg       0.91      0.90      0.90       300
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​
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  Confusion matrix
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  [[27  0  0  0  0  0  0  0  0  0]
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   [ 0 37  0  0  0  0  0  0  0  0]
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   [ 0  1 24  0  0  0  2  1  0  0]
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   [ 0  0  0 28  0  5  0  1  0  1]
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   [ 0  0  0  0 24  0  0  0  0  0]
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   [ 0  0  0  0  0 32  0  0  0  2]
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   [ 0  0  0  0  0  1 24  0  0  0]
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   [ 0  0  0  0  1  3  0 31  0  0]
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   [ 0  7  0  0  0  0  1  0 21  2]
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   [ 0  0  0  0  1  2  0  0  0 21]]
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五、結果觀察與分析
利用stats進行數據的統計，並找出前10筆預測結果最不佳的結果
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# Calculate uncertainty values for each transduced distribution
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pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)
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​
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# Pick the top 10 most uncertain labels
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uncertainty_index = np.argsort(pred_entropies)[-10:]
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​
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# Plot
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f = plt.figure(figsize=(7, 5))
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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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    sub = f.add_subplot(2, 5, index + 1)
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    sub.imshow(image, cmap=plt.cm.gray_r)
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    plt.xticks([])
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    plt.yticks([])
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    sub.set_title('predict: %i\ntrue: %i' % (
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        lp_model.transduction_[image_index], y[image_index]))
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​
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f.suptitle('Learning with small amount of labeled data')
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plt.show()
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png
六、原始碼列表
Python source code: plot_label_propagation_digits.py
​https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.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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​
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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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​
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from sklearn.metrics import confusion_matrix, classification_report
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​
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digits = datasets.load_digits()
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rng = np.random.RandomState(2)
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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[:340]]
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y = digits.target[indices[:340]]
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images = digits.images[indices[:340]]
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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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​
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indices = np.arange(n_total_samples)
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​
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unlabeled_set = indices[n_labeled_points:]
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​
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# #############################################################################
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# Shuffle everything around
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y_train = np.copy(y)
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y_train[unlabeled_set] = -1
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​
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# #############################################################################
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# Learn with LabelSpreading
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lp_model = LabelSpreading(gamma=.25, max_iter=20)
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lp_model.fit(X, y_train)
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predicted_labels = lp_model.transduction_[unlabeled_set]
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true_labels = y[unlabeled_set]
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​
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cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)
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​
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print("Label Spreading model: %d labeled & %d unlabeled points (%d total)" %
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      (n_labeled_points, n_total_samples - n_labeled_points, 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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# #############################################################################
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# Calculate uncertainty values for each transduced distribution
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pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)
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​
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# #############################################################################
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# Pick the top 10 most uncertain labels
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uncertainty_index = np.argsort(pred_entropies)[-10:]
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​
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# #############################################################################
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# Plot
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f = plt.figure(figsize=(7, 5))
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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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    sub = f.add_subplot(2, 5, index + 1)
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    sub.imshow(image, cmap=plt.cm.gray_r)
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    plt.xticks([])
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    plt.yticks([])
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    sub.set_title('predict: %i\ntrue: %i' % (
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        lp_model.transduction_[image_index], y[image_index]))
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​
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f.suptitle('Learning with small amount of labeled data')
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plt.show()
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半監督式分類法 Semi-Supervised Classification
Ex 4: Label Propagation digits active learning
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半監督式分類法/範例3 : Label Propagation digits: Demonstrating performance