sklearn/examples/semi_supervised/plot_label_propagation_digits.py

"""
===================================================
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 :class:`~sklearn.semi_supervised.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)
)

# %%
# Classification report
print(classification_report(true_labels, predicted_labels))

# %%
# Confusion matrix
from sklearn.metrics import ConfusionMatrixDisplay

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

# %%
# 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()