# 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 ```python import numpy as np import matplotlib.pyplot as plt from scipy import stats from sklearn import datasets from sklearn.semi_supervised import LabelSpreading from sklearn.metrics import confusion_matrix, classification_report ``` ## 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 ```python digits = datasets.load_digits() rng = np.random.RandomState(2) indices = np.arange(len(digits.data)) rng.shuffle(indices) X = digits.data[indices[:340]] y = digits.target[indices[:340]] images = digits.images[indices[:340]] n_total_samples = len(y) n_labeled_points = 40 indices = np.arange(n_total_samples) unlabeled_set = indices[n_labeled_points:] y_train = np.copy(y) y_train[unlabeled_set] = -1 ``` ## 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 ```python lp_model = LabelSpreading(gamma=.25, max_iter=20) lp_model.fit(X, y_train) predicted_labels = lp_model.transduction_[unlabeled_set] true_labels = y[unlabeled_set] cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_) print("Label Spreading model: %d labeled & %d unlabeled points (%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) ``` Out: ``` Label Spreading model: 40 labeled & 300 unlabeled points (340 total) precision recall f1-score support 0 1.00 1.00 1.00 27 1 0.82 1.00 0.90 37 2 1.00 0.86 0.92 28 3 1.00 0.80 0.89 35 4 0.92 1.00 0.96 24 5 0.74 0.94 0.83 34 6 0.89 0.96 0.92 25 7 0.94 0.89 0.91 35 8 1.00 0.68 0.81 31 9 0.81 0.88 0.84 24 micro avg 0.90 0.90 0.90 300 macro avg 0.91 0.90 0.90 300 weighted avg 0.91 0.90 0.90 300 Confusion matrix [[27 0 0 0 0 0 0 0 0 0] [ 0 37 0 0 0 0 0 0 0 0] [ 0 1 24 0 0 0 2 1 0 0] [ 0 0 0 28 0 5 0 1 0 1] [ 0 0 0 0 24 0 0 0 0 0] [ 0 0 0 0 0 32 0 0 0 2] [ 0 0 0 0 0 1 24 0 0 0] [ 0 0 0 0 1 3 0 31 0 0] [ 0 7 0 0 0 0 1 0 21 2] [ 0 0 0 0 1 2 0 0 0 21]] ``` ## 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 ```python # Calculate uncertainty values for each transduced distribution pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T) # Pick the top 10 most uncertain labels uncertainty_index = np.argsort(pred_entropies)[-10:] # Plot f = plt.figure(figsize=(7, 5)) for index, image_index in enumerate(uncertainty_index): image = images[image_index] sub = f.add_subplot(2, 5, index + 1) sub.imshow(image, cmap=plt.cm.gray_r) plt.xticks([]) plt.yticks([]) sub.set_title('predict: %i\ntrue: %i' % ( lp_model.transduction_[image_index], y[image_index])) f.suptitle('Learning with small amount of labeled data') plt.show() ``` ![png](/files/-M-n7kdq_RL8p3oC8sE8) ## 六、原始碼列表 Python source code: plot\_label\_propagation\_digits.py ```python print(__doc__) # Authors: Clay Woolam # License: BSD import numpy as np import matplotlib.pyplot as plt from scipy import stats from sklearn import datasets from sklearn.semi_supervised import LabelSpreading from sklearn.metrics import confusion_matrix, classification_report digits = datasets.load_digits() rng = np.random.RandomState(2) indices = np.arange(len(digits.data)) rng.shuffle(indices) X = digits.data[indices[:340]] y = digits.target[indices[:340]] images = digits.images[indices[:340]] n_total_samples = len(y) n_labeled_points = 40 indices = np.arange(n_total_samples) unlabeled_set = indices[n_labeled_points:] # ############################################################################# # Shuffle everything around y_train = np.copy(y) y_train[unlabeled_set] = -1 # ############################################################################# # Learn with LabelSpreading lp_model = LabelSpreading(gamma=.25, max_iter=20) lp_model.fit(X, y_train) predicted_labels = lp_model.transduction_[unlabeled_set] true_labels = y[unlabeled_set] cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_) print("Label Spreading model: %d labeled & %d unlabeled points (%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) # ############################################################################# # Calculate uncertainty values for each transduced distribution pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T) # ############################################################################# # Pick the top 10 most uncertain labels uncertainty_index = np.argsort(pred_entropies)[-10:] # ############################################################################# # Plot f = plt.figure(figsize=(7, 5)) for index, image_index in enumerate(uncertainty_index): image = images[image_index] sub = f.add_subplot(2, 5, index + 1) sub.imshow(image, cmap=plt.cm.gray_r) plt.xticks([]) plt.yticks([]) sub.set_title('predict: %i\ntrue: %i' % ( lp_model.transduction_[image_index], y[image_index])) f.suptitle('Learning with small amount of labeled data') plt.show() ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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