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半監督式分類法 Semi-Supervised Classification
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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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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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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n_total_samples = len(y)
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n_labeled_points = 40
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indices = np.arange(n_total_samples)
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unlabeled_set = indices[n_labeled_points:]
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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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cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)
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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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print(classification_report(true_labels, predicted_labels))
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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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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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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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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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# Pick the top 10 most uncertain labels
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uncertainty_index = np.argsort(pred_entropies)[-10:]
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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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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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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
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print(__doc__)
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# Authors: Clay Woolam <[email protected]>
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# License: BSD
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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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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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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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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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n_total_samples = len(y)
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n_labeled_points = 40
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indices = np.arange(n_total_samples)
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unlabeled_set = indices[n_labeled_points:]
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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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# 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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cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)
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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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print(classification_report(true_labels, predicted_labels))
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print("Confusion matrix")
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print(cm)
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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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# 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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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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f.suptitle('Learning with small amount of labeled data')
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plt.show()
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