def test_predict_proba_distance_kernel(self):
"""Test the 'predict_proba' class method using 'distance' kernel."""
training_dataset = gs.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0],
[3.0, 0.0]])
labels = [0, 0, 1, 1]
kde = KernelDensityEstimationClassifier(kernel="distance",
distance=self.distance)
kde.fit(training_dataset, labels)
result = kde.predict_proba(gs.array([[1.0, 0.0]]))
expected = gs.array([[1, 0]])
self.assertAllClose(expected, result, atol=gs.atol)
def test_predict_proba_triangular_kernel_callable_distance(self):
"""Test the 'predict_proba' class method using a triangular kernel."""
training_dataset = gs.array([[0.0, 0.0], [1.0, 0.0], [2.0, 0.0],
[3.0, 0.0]])
labels = [0, 0, 1, 1]
kde = KernelDensityEstimationClassifier(
kernel=triangular_radial_kernel,
bandwidth=2.0,
distance=self.distance)
kde.fit(training_dataset, labels)
result = kde.predict_proba(gs.array([[1.0, 0.0]]))
expected = gs.array([[3 / 4, 1 / 4]])
self.assertAllClose(expected, result, atol=gs.atol)
def test_predict_proba_uniform_kernel_one_dimensional_data(self):
"""Test the 'predict_proba' class method using the 'uniform' kernel.
Test the 'predict_proba' class method using the 'uniform' kernel on
one-dimensional date of shape [n_samples,].
"""
training_dataset = gs.array([0, 1, 2, 3])
labels = [0, 0, 1, 1]
kde = KernelDensityEstimationClassifier(kernel="uniform",
distance=self.distance)
kde.fit(training_dataset, labels)
result = kde.predict_proba(gs.array([0.9]))
expected = gs.array([[1 / 2, 1 / 2]])
self.assertAllClose(expected, result, atol=gs.atol)
def main():
"""Plot a Kernel Density Estimation Classification on the sphere."""
sphere = Hypersphere(dim=2)
sphere_distance = sphere.metric.dist
n_labels = 2
n_samples_per_dataset = 10
n_targets = 200
radius = np.inf
kernel = triangular_radial_kernel
bandwidth = 3
n_training_samples = n_labels * n_samples_per_dataset
dataset_1 = sphere.random_von_mises_fisher(
kappa=10,
n_samples=n_samples_per_dataset)
dataset_2 = - sphere.random_von_mises_fisher(
kappa=10,
n_samples=n_samples_per_dataset)
training_dataset = gs.concatenate((dataset_1, dataset_2), axis=0)
labels_dataset_1 = gs.zeros([n_samples_per_dataset], dtype=gs.int64)
labels_dataset_2 = gs.ones([n_samples_per_dataset], dtype=gs.int64)
labels = gs.concatenate((labels_dataset_1, labels_dataset_2))
target = sphere.random_uniform(n_samples=n_targets)
labels_colors = gs.zeros([n_labels, 3])
labels_colors[0, :] = gs.array([0, 0, 1])
labels_colors[1, :] = gs.array([1, 0, 0])
kde = KernelDensityEstimationClassifier(
radius=radius,
distance=sphere_distance,
kernel=kernel,
bandwidth=bandwidth,
outlier_label='most_frequent')
kde.fit(training_dataset, labels)
target_labels = kde.predict(target)
target_labels_proba = kde.predict_proba(target)
plt.figure(0)
ax = plt.subplot(111, projection='3d')
plt.title('Training set')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = gs.zeros([n_training_samples, 3])
for i_sample in range(n_training_samples):
colors[i_sample, :] = labels_colors[labels[i_sample], :]
sphere_plot.draw_points(ax=ax, points=training_dataset, c=colors)
plt.figure(1)
ax = plt.subplot(111, projection='3d')
plt.title('Classification')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = gs.zeros([n_targets, 3])
for i_target in range(n_targets):
colors[i_target, :] = labels_colors[target_labels[i_target], :]
sphere_plot.draw_points(ax=ax, points=target, c=colors)
plt.figure(2)
ax = plt.subplot(111, projection='3d')
plt.title('Probabilistic classification')
sphere_plot = visualization.Sphere()
sphere_plot.draw(ax=ax)
colors = target_labels_proba @ labels_colors
sphere_plot.draw_points(ax=ax, points=target, c=colors)
plt.show()