tno.quantum.ml.clustering.bkmeans 2.0.3

Quantum Balanced K-Means Clustering module

TNO Quantum: ML - Clustering - Balanced K-Means

TNO Quantum provides generic software components aimed at facilitating the development of quantum applications.

This package implements a QUBO based Balanced K-Means clustering algorithm. The implementation has been done in accordance with the scikit-learn estimator API, which means that the clustering algorithm can be used as any other scikit-learn clustering algorithm and combined with transforms through Pipelines.

Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.

Documentation

Documentation of the tno.quantum.ml.clustering.bkmeans package can be found here.

Install

Easily install the tno.quantum.ml.clustering.bkmeans package using pip:

$ python -m pip install tno.quantum.ml.clustering.bkmeans

Example

The Balanced K-Means clustering can be used as shown in the following example.

• Note: This example requires tno.quantum.optimization.solvers[dwave] and tno.quantum.ml.datasets which can be installed along the package using:

  $ python -m pip install tno.quantum.ml.clustering.bkmeans[example]
  

import matplotlib.pyplot as plt
import numpy as np

from tno.quantum.ml.clustering.bkmeans import QBKMeans
from tno.quantum.ml.datasets import get_blobs_clustering_dataset

# Generate sample data
n_centers = 4
X, true_labels = get_blobs_clustering_dataset(
    n_samples=20, n_features=2, n_centers=n_centers
)

# Create QBKMeans object and fit
cluster_algo = QBKMeans(
    n_clusters=n_centers,
    solver_config={
        "name": "simulated_annealing_solver",
        "options": {"number_of_reads": 100},
    },
)
pred_labels = cluster_algo.fit_predict(X)

# Plot results
fig, ax = plt.subplots(nrows=1, ncols=1)
unique_labels = np.unique(pred_labels)
colors = plt.cm.Spectral(np.linspace(0, 1, len(unique_labels)))
for k, col in zip(unique_labels, colors):
    class_member_mask = cluster_algo.labels_ == k
    xy = X[class_member_mask]
    x, y = np.split(xy, 2, axis=1)
    ax.plot(x, y, "o", mfc=tuple(col), mec="k", ms=6)

ax.set_title("Quantum BKMeans clustering")
plt.show()