126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
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"""
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========================================
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Label Propagation digits active learning
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========================================
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Demonstrates an active learning technique to learn handwritten digits
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using label propagation.
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We start by training a label propagation model with only 10 labeled points,
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then we select the top five most uncertain points to label. Next, we train
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with 15 labeled points (original 10 + 5 new ones). We repeat this process
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four times to have a model trained with 30 labeled examples. Note you can
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increase this to label more than 30 by changing `max_iterations`. Labeling
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more than 30 can be useful to get a sense for the speed of convergence of
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this active learning technique.
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A plot will appear showing the top 5 most uncertain digits for each iteration
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of training. These may or may not contain mistakes, but we will train the next
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model with their true labels.
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"""
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# Authors: Clay Woolam <clay@woolam.org>
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# License: BSD
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import matplotlib.pyplot as plt
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import numpy as np
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from scipy import stats
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from sklearn import datasets
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from sklearn.metrics import classification_report, confusion_matrix
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from sklearn.semi_supervised import LabelSpreading
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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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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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n_total_samples = len(y)
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n_labeled_points = 40
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max_iterations = 5
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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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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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lp_model = LabelSpreading(gamma=0.25, max_iter=20)
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lp_model.fit(X, y_train)
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predicted_labels = lp_model.transduction_[unlabeled_indices]
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true_labels = y[unlabeled_indices]
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cm = confusion_matrix(true_labels, predicted_labels, labels=lp_model.classes_)
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print("Iteration %i %s" % (i, 70 * "_"))
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print(
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"Label Spreading model: %d labeled & %d unlabeled (%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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print("Confusion matrix")
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print(cm)
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# compute the entropies of transduced label distributions
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pred_entropies = stats.distributions.entropy(lp_model.label_distributions_.T)
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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.isin(uncertainty_index, unlabeled_indices)
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][:5]
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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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# 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(
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0.05,
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(1 - (i + 1) * 0.183),
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"model %d\n\nfit with\n%d labels" % ((i + 1), i * 5 + 10),
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size=10,
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)
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for index, image_index in enumerate(uncertainty_index):
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image = images[image_index]
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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(
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"predict: %i\ntrue: %i"
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% (lp_model.transduction_[image_index], y[image_index]),
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size=10,
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)
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sub.axis("off")
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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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unlabeled_indices = np.delete(unlabeled_indices, delete_indices)
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n_labeled_points += len(uncertainty_index)
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f.suptitle(
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(
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"Active learning with Label Propagation.\nRows show 5 most "
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"uncertain labels to learn with the next model."
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),
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y=1.15,
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)
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plt.subplots_adjust(left=0.2, bottom=0.03, right=0.9, top=0.9, wspace=0.2, hspace=0.85)
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plt.show()
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