label_propagation.py

# Authors: Clay Woolam <clay@woolam.org>
#          Andreas Mueller <amueller@ais.uni-bonn.de>
# License: BSD

import numpy as np
import matplotlib.pyplot as plt
from sklearn.semi_supervised import LabelSpreading
from sklearn.datasets import make_circles

# generate ring with inner box
n_samples = 200
X, y = make_circles(n_samples=n_samples, shuffle=False)
outer, inner = 0, 1
labels = np.full(n_samples, -1.0)
labels[0] = outer
labels[-1] = inner

# #############################################################################
kernels = ["rbf", "knn"]
alphas = [0.2, 0.4, 0.6, 0.8]

for kernel in kernels:
    for alpha in alphas:
        # Learn with LabelSpreading
        label_spread = LabelSpreading(kernel=kernel, alpha=alpha)
        label_spread.fit(X, labels)

        # #############################################################################
        # Plot output labels
        output_labels = label_spread.transduction_
        plt.figure(figsize=(8.5, 4))
        plt.subplot(1, 2, 1)
        plt.scatter(
            X[labels == outer, 0],
            X[labels == outer, 1],
            color="navy",
            marker="s",
            lw=0,
            label="outer labeled",
            s=10,
        )
        plt.scatter(
            X[labels == inner, 0],
            X[labels == inner, 1],
            color="c",
            marker="s",
            lw=0,
            label="inner labeled",
            s=10,
        )
        plt.scatter(
            X[labels == -1, 0],
            X[labels == -1, 1],
            color="darkorange",
            marker=".",
            label="unlabeled",
        )
        plt.legend(scatterpoints=1, shadow=False, loc="upper right")
        plt.title("Raw data (2 classes=outer and inner)")

        plt.subplot(1, 2, 2)
        output_label_array = np.asarray(output_labels)
        outer_numbers = np.where(output_label_array == outer)[0]
        inner_numbers = np.where(output_label_array == inner)[0]
        plt.scatter(
            X[outer_numbers, 0],
            X[outer_numbers, 1],
            color="navy",
            marker="s",
            lw=0,
            s=10,
            label="outer learned",
        )
        plt.scatter(
            X[inner_numbers, 0],
            X[inner_numbers, 1],
            color="c",
            marker="s",
            lw=0,
            s=10,
            label="inner learned",
        )
        plt.legend(scatterpoints=1, shadow=False, loc="upper right")
        plt.title("Labels learned with Label Spreading {0}, alpha={1}".format(kernel,alpha))

        plt.subplots_adjust(left=0.07, bottom=0.07, right=0.93, top=0.92)
        plt.show()