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# Label Propagation digits: Demonstrating performance

This example demonstrates the power of semisupervised learning by training a Label Spreading model to classify handwritten digits with sets of very few labels.

The handwritten digit dataset has 1797 total points. The model will be trained using all points, but only 30 will be labeled. Results in the form of a confusion matrix and a series of metrics over each class will be very good.

At the end, the top 10 most uncertain predictions will be shown.

    # Authors: Clay Woolam <clay@woolam.org>
    # License: BSD

## Data generation

We use the digits dataset. We only use a subset of randomly selected samples.

    import numpy as np

    from sklearn import datasets

    digits = datasets.load_digits()
    rng = np.random.RandomState(2)
    indices = np.arange(len(digits.data))
    rng.shuffle(indices)

We selected 340 samples of which only 40 will be associated with a known label. Therefore, we store the indices of the 300 other samples for which we are not supposed to know their labels.

    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

## Semi-supervised learning

We fit a `LabelSpreading` and use it to predict the unknown labels.

    from sklearn.metrics import classification_report
    from sklearn.semi_supervised import LabelSpreading

    lp_model = LabelSpreading(gamma=0.25, max_iter=20)
    lp_model.fit(X, y_train)
    predicted_labels = lp_model.transduction_[unlabeled_set]
    true_labels = y[unlabeled_set]

    print(
        "Label Spreading model: %d labeled & %d unlabeled points (%d total)"
        % (n_labeled_points, n_total_samples - n_labeled_points, n_total_samples)
    )

    Label Spreading model: 40 labeled & 300 unlabeled points (340 total)

Classification report

    print(classification_report(true_labels, predicted_labels))

                  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

        accuracy                           0.90       300
       macro avg       0.91      0.90      0.90       300
    weighted avg       0.91      0.90      0.90       300

Confusion matrix

    from sklearn.metrics import ConfusionMatrixDisplay

    ConfusionMatrixDisplay.from_predictions(
        true_labels, predicted_labels, labels=lp_model.classes_
    )

    <sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7fcadd758410>

## Plot the most uncertain predictions

Here, we will pick and show the 10 most uncertain predictions.

    from scipy import stats

    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

    import matplotlib.pyplot as plt

    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()

Total running time of the script: (0 minutes 0.227 seconds)

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