semvec/pypi-packages huggingface dataset:
tno.quantum.ml.clustering.bkmeans 2.0.3
Quantum Balanced K-Means Clustering module
License: Apache License, Version 2.0
Requires Python: >=3.10
Keywords: TNO, Quantum, Balanced K-Means, Machine Learning

# 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](https://scikit-learn.org/stable/developers/develop.html), which means that the clustering algorithm can be used as any other scikit-learn
clustering algorithm and combined with transforms through
[Pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html).


*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](https://tno-quantum.github.io/documentation/).


## Install

Easily install the `tno.quantum.ml.clustering.bkmeans` package using pip:

```console
$ 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:

  ```console
  $ python -m pip install tno.quantum.ml.clustering.bkmeans[example]
  ```

```python
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()
```