gmmfcovariance.py

import matplotlib as mpl
import matplotlib.pyplot as plt

import numpy as np

from sklearn import datasets
from sklearn.mixture import GaussianMixture
from sklearn.model_selection import StratifiedKFold

print(__doc__)

colors = ['navy', 'turquoise', 'darkorange']


def make_ellipses(gmm, ax):
	for n, color in enumerate(colors):
		if gmm.covariance_type == 'full':
			covariances = gmm.covariances_[n][:2, :2]
		elif gmm.covariance_type == 'tied':
			covariances = gmm.covariances_[:2, :2]
		elif gmm.covariance_type == 'diag':
			covariances = np.diag(gmm.covariances_[n][:2])
		elif gmm.covariance_type == 'spherical':
			covariances = np.eye(gmm.means_.shape[1]) * gmm.covariances_[n]
		v, w = np.linalg.eigh(covariances)
		u = w[0] / np.linalg.norm(w[0])
		angle = np.arctan2(u[1], u[0])
		angle = 180 * angle / np.pi  # convert to degrees
		v = 2. * np.sqrt(2.) * np.sqrt(v)
		ell = mpl.patches.Ellipse(gmm.means_[n, :2], v[0], v[1],180 + angle, color=color)
		ell.set_clip_box(ax.bbox)
		ell.set_alpha(0.5)
		ax.add_artist(ell)

iris = datasets.load_iris()

# Break up the dataset into non-overlapping training (75%) and testing
# (25%) sets.
skf = StratifiedKFold(n_splits=4)
# Only take the first fold.
train_index, test_index = next(iter(skf.split(iris.data, iris.target)))


X_train = iris.data[train_index]
y_train = iris.target[train_index]
X_test = iris.data[test_index]
y_test = iris.target[test_index]

n_classes = len(np.unique(y_train))

# Try GMMs using different types of covariances.
estimators = dict((cov_type, GaussianMixture(n_components=n_classes,covariance_type=cov_type, max_iter=20, random_state=0))for cov_type in ['spherical', 'diag', 'tied', 'full'])

n_estimators = len(estimators)

plt.figure(figsize=(3 * n_estimators // 2, 6))
plt.subplots_adjust(bottom=.01, top=0.95, hspace=.15, wspace=.05,left=.01, right=.99)


for index, (name, estimator) in enumerate(estimators.items()):
	# Since we have class labels for the training data, we can
	# initialize the GMM parameters in a supervised manner.
	estimator.means_init = np.array([X_train[y_train == i].mean(axis=0)for i in range(n_classes)])

	# Train the other parameters using the EM algorithm.
	estimator.fit(X_train)

	h = plt.subplot(2, n_estimators // 2, index + 1)
	make_ellipses(estimator, h)

	for n, color in enumerate(colors):
		data = iris.data[iris.target == n]
		plt.scatter(data[:, 0], data[:, 1], s=0.8, color=color,label=iris.target_names[n])
	# Plot the test data with crosses
	for n, color in enumerate(colors):
		data = X_test[y_test == n]
		plt.scatter(data[:, 0], data[:, 1], marker='x', color=color)

	y_train_pred = estimator.predict(X_train)
	train_accuracy = np.mean(y_train_pred.ravel() == y_train.ravel()) * 100
	plt.text(0.05, 0.9, 'Train accuracy: %.1f' % train_accuracy,transform=h.transAxes)

	y_test_pred = estimator.predict(X_test)
	test_accuracy = np.mean(y_test_pred.ravel() == y_test.ravel()) * 100
	plt.text(0.05, 0.8, 'Test accuracy: %.1f' % test_accuracy,transform=h.transAxes)

	plt.xticks(())
	plt.yticks(())
	plt.title(name)

plt.legend(scatterpoints=1, loc='lower right', prop=dict(size=12))


plt.show()