Mastering Machine Learning Algorithms

Example of label spreading

We can test this algorithm using the Scikit-Learn implementation. Let's start by creating a very dense dataset:

from sklearn.datasets import make_classificationnb_samples = 5000nb_unlabeled = 1000X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0, random_state=100)Y[nb_samples - nb_unlabeled:nb_samples] = -1

We can train a LabelSpreading instance with a clamping factor alpha=0.2. We want to preserve 80% of the original labels but, at the same time, we need a smooth solution:

from sklearn.semi_supervised import LabelSpreadingls = LabelSpreading(kernel='rbf', gamma=10.0, alpha=0.2)ls.fit(X, Y)Y_final = ls.predict(X)

The result is shown, as usual, together with the original ...