Non parametric clustering

This example illustrates how we can run nonparametric clustering using GEMINI. The specificity of this model is that the decision of model is not dependent on the position of the inputs, but only on the parameters associated to the input. Consequently, this model cannot be used for unseen samples as it will always return the same predictions.

import numpy as np
from matplotlib import pyplot as plt
from sklearn import metrics

from gemclus import data
from gemclus.nonparametric import CategoricalMMD

Draw samples from a GMM

# Generate samples on that are simple to separate
N = 100  # Number of nodes in the graph
# GMM parameters
means = np.array([[1, -1], [1, 1], [-1, -1], [-1, 1]])*2
covariances = [np.eye(2)*0.5]*4
X, y = data.draw_gmm(N, means, covariances, np.ones(4) / 4, random_state=1789)

Train the model

Create the Non parametric GEMINI clustering model and call the .fit method to optimise the cluster assignment of the nodes

model = CategoricalMMD(n_clusters=4, ovo=True, random_state=0, learning_rate=1e-2)
y_pred = model.fit_predict(X)

Final Clustering

plt.scatter(X[:, 0], X[:, 1], c=y_pred)
plt.show()

ari_score = metrics.adjusted_rand_score(y, y_pred)
gemini_score = model.score(X)
print(f"Final ARI score: {ari_score:.3f}")
print(f"GEMINI score is {gemini_score:.3f}")

Final ARI score: 0.975
GEMINI score is 3.328