tno.quantum.ml.clustering.kmedoids 2.0.2

Quantum K-medoid clustering

TNO Quantum: ML - Classification - KMedoid

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

This package implements a scikit-learn compatible kmedoid clustering.

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.kmedoids package can be found here.

Install

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

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

Example

The K-medoids 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.kmedoids[example]
  

import matplotlib.pyplot as plt
import numpy as np
from tno.quantum.ml.datasets import get_blobs_clustering_dataset

from tno.quantum.ml.clustering.kmedoids import QKMedoids

# Generate sample data
n_centers = 6
X, true_labels = get_blobs_clustering_dataset(
    n_samples=120, n_features=2, n_centers=n_centers
)

# Create QKMedoids object and fit
cobj = QKMedoids(
    n_clusters=n_centers,
    solver_config={
        "name": "simulated_annealing_solver",
        "options": {"random_state": 42},
    },
)
pred_labels = cobj.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 = cobj.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)

x_centers, y_centers = np.split(cobj.cluster_centers_, 2, axis=1)
ax.plot(x_centers, y_centers, "o", mfc="cyan", mec="k", ms=6)

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

We refer to the documentation for more information regarding possible parameters.