8000 [MRG] Reduce hard-coding in "Gaussian Mixture Model Selection" example by yunesj · Pull Request #15401 · scikit-learn/scikit-learn · GitHub
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[MRG] Reduce hard-coding in "Gaussian Mixture Model Selection" example #15401

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[MRG] Reduce hard-coding in "Gaussian Mixture Model Selection" example #15401

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51 changes: 40 additions & 11 deletions examples/mixture/plot_gmm_selection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,7 @@
but BIC is better suited if the problem is to identify the right model.
Unlike Bayesian procedures, such inferences are prior-free.

In that case, the model with 2 components and full covariance
(which corresponds to the true generative model) is selected.
In this case, the model with 3 components and full covariance is selected.
"""

import numpy as np
Expand All @@ -23,26 +22,34 @@ 8000
import matplotlib as mpl

from sklearn import mixture
from sklearn.datasets import make_blobs

print(__doc__)

# Number of samples per component
n_samples = 500
skew_points = True
n_components = 3

# Generate random sample, two components
np.random.seed(0)
C = np.array([[0., -0.1], [1.7, .4]])
X = np.r_[np.dot(np.random.randn(n_samples, 2), C),
.7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]
np.random.seed(1)
n_features = 2
cluster_std = np.random.random(n_components) + .5
X = make_blobs(n_samples=n_samples,
n_features=n_features,
centers=n_components,
cluster_std=cluster_std)[0]
if skew_points:
transformation = [[1, .8], [-.2, 1]]
X = np.dot(X, transformation)

lowest_bic = np.infty
bic = []
n_components_range = range(1, 7)
cv_types = ['spherical', 'tied', 'diag', 'full']
for cv_type in cv_types:
for n_components in n_components_range:
for n_components_trial in n_components_range:
# Fit a Gaussian mixture with EM
gmm = mixture.GaussianMixture(n_components=n_components,
gmm = mixture.GaussianMixture(n_components=n_components_trial,
covariance_type=cv_type)
gmm.fit(X)
bic.append(gmm.bic(X))
Expand Down Expand Up @@ -76,7 +83,27 @@
# Plot the winner
splot = plt.subplot(2, 1, 2)
Y_ = clf.predict(X)
for i, (mean, cov, color) in enumerate(zip(clf.means_, clf.covariances_,

# convert covariances_ attribute to shape:
# (n_components, n_features, n_features)

if clf.covariance_type == 'spherical':
identity_mx = np.identity(n_features)
covariances = identity_mx[:, :, np.newaxis] * clf.covariances_
covariances = covariances.T
elif clf.covariance_type == 'tied':
covariances = clf.covariances_[np.newaxis, :, :]
covariances = np.repeat(covariances, clf.n_components, axis=0)
elif clf.covariance_type == 'diag':
identity_mx = np.identity(n_features)
covariances = np.repeat(identity_mx[:, :, np.newaxis],
clf.n_components,
axis=2)
covariances = covariances.T * clf.covariances_[:, :, np.newaxis]
elif clf.covariance_type == 'full':
covariances = clf.covariances_

for i, (mean, cov, color) in enumerate(zip(clf.means_, covariances,
color_iter)):
v, w = linalg.eigh(cov)
if not np.any(Y_ == i):
Expand All @@ -94,6 +121,8 @@

plt.xticks(())
plt.yticks(())
plt.title('Selected GMM: full model, 2 components')
plt.title('Selected GMM: {cv_type} model, {n_components} components'.format(
cv_type=clf.covariance_type,
n_components=clf.n_components))
plt.subplots_adjust(hspace=.35, bottom=.02)
plt.show()
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