Ex 4: Label Propagation digits active learning
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
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 classification_report, confusion_matrix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
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()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
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)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
# compute the entropies of transduced label distributions
pred_entropies = stats.distributions.entropy(
lp_model.label_distributions_.T)
# select up to 5 digit examples that the classifier is most uncertain about
uncertainty_index = np.argsort(pred_entropies)[::-1]
uncertainty_index = uncertainty_index[
np.in1d(uncertainty_index, unlabeled_indices)][:5]
# keep track of indices that we get labels for
delete_indices = np.array([], dtype=int)
# for more than 5 iterations, visualize the gain only on the first 5
if i < 5:
f.text(.05, (1 - (i + 1) * .183),
"model %d\n\nfit with\n%d labels" %
((i + 1), i * 5 + 40), size=10)
for index, image_index in enumerate(uncertainty_index):
image = images[image_index]
# for more than 5 iterations, visualize the gain only on the first 5
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')
# labeling 5 points, remote from labeled set
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)下列程式屬於for迴圈外圍
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()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:
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
micro avg 0.87 0.87 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
micro avg 0.90 0.90 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
micro avg 0.92 0.92 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
micro avg 0.95 0.95 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
micro avg 0.96 0.96 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]]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
print(__doc__)
# Authors: Clay Woolam <clay@woolam.org>
# 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 classification_report, confusion_matrix
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)
# compute the entropies of transduced label distributions
pred_entropies = stats.distributions.entropy(
lp_model.label_distributions_.T)
# select up to 5 digit examples that the classifier is most uncertain about
uncertainty_index = np.argsort(pred_entropies)[::-1]
uncertainty_index = uncertainty_index[
np.in1d(uncertainty_index, unlabeled_indices)][:5]
# keep track of indices that we get labels for
delete_indices = np.array([], dtype=int)
# for more than 5 iterations, visualize the gain only on the first 5
if i < 5:
f.text(.05, (1 - (i + 1) * .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]
# for more than 5 iterations, visualize the gain only on the first 5
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')
# labeling 5 points, remote from labeled set
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()Previous半監督式分類法 Semi-Supervised ClassificationNextDecision boundary of label propagation versus SVM on the Iris dataset
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