.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/99_plot_semi_supervised_ssekmeans.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_99_plot_semi_supervised_ssekmeans.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_99_plot_semi_supervised_ssekmeans.py:


Semi-supervised SSEKM on a toy dataset
======================================

This example shows how to use SSEKM with partial labels provided via matrix F.
We label a few samples and vary theta to control the influence of supervision.

.. GENERATED FROM PYTHON SOURCE LINES 8-90



.. image-sg:: /auto_examples/images/sphx_glr_99_plot_semi_supervised_ssekmeans_001.png
   :alt: Semi-supervised SSEKM with partial labels F, EKMeans 415 ms, SSEKM theta=0.0 539 ms, SSEKM theta=0.5 287 ms, SSEKM theta=1.0 549 ms, SSEKM theta=auto 748 ms
   :srcset: /auto_examples/images/sphx_glr_99_plot_semi_supervised_ssekmeans_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import time

    import matplotlib.pyplot as plt
    import numpy as np
    from matplotlib.colors import ListedColormap

    from sklekmeans import SSEKM, EKMeans


    def make_toy(seed=0):
        rng = np.random.RandomState(seed)
        X1 = rng.normal(loc=[0, 0], scale=0.35, size=(4000, 2))
        X2 = rng.normal(loc=[2.0, 0.2], scale=0.35, size=(50, 2))
        X3 = rng.normal(loc=[1.7, 2.6], scale=0.35, size=(100, 2))
        X = np.vstack([X1, X2, X3])
        return X


    X = make_toy(0)
    K = 3

    # Create partial labels: mark first 300 points as class 0, next 20 as class 1, next 20 as class 2
    F = np.zeros((X.shape[0], K), dtype=float)
    F[:300, 0] = 1.0
    F[4000:4020, 1] = 1.0
    F[4050:4070, 2] = 1.0

    # Baseline EKMeans
    start = time.time()
    ekm = EKMeans(n_clusters=K, random_state=0, n_init=3)
    ekm.fit(X)
    t_ekm = time.time() - start

    # SSEKM with different thetas
    results = []
    for theta in [0.0, 0.5, 1.0, "auto"]:
        start = time.time()
        sse = SSEKM(n_clusters=K, random_state=0, n_init=3, theta=theta)
        sse.fit(X, prior_matrix=F)
        t = time.time() - start
        results.append((theta, sse, t))

    # Plot
    cmap = ListedColormap(["#1f77b4", "#ff7f0e", "#2ca02c"])
    fig, axes = plt.subplots(1, 5, figsize=(14, 3), sharex=True, sharey=True)
    axes = axes.ravel()


    def scatter_with_centers(ax, X, labels, centers, title):
        ax.scatter(
            X[:, 0], X[:, 1], c=labels, s=15, cmap=cmap, alpha=0.8, edgecolors="none"
        )
        ax.scatter(
            centers[:, 0],
            centers[:, 1],
            c=np.arange(K),
            s=180,
            marker="X",
            cmap=cmap,
            edgecolors="k",
            linewidths=1.0,
        )
        ax.set_title(title)
        ax.set_xlabel("x1")


    # Plot EKMeans
    labels_ekm = ekm.predict(X)
    scatter_with_centers(
        axes[0], X, labels_ekm, ekm.cluster_centers_, f"EKMeans\n{t_ekm*1e3:.0f} ms"
    )

    # Plot SSEKM results
    for ax, (theta, sse, t) in zip(axes[1:], results):
        labels = sse.predict(X)
        title = f"SSEKM theta={theta}\n{t*1e3:.0f} ms"
        scatter_with_centers(ax, X, labels, sse.cluster_centers_, title)

    fig.suptitle("Semi-supervised SSEKM with partial labels F")
    fig.tight_layout()
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 2.945 seconds)


.. _sphx_glr_download_auto_examples_99_plot_semi_supervised_ssekmeans.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: 99_plot_semi_supervised_ssekmeans.ipynb <99_plot_semi_supervised_ssekmeans.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: 99_plot_semi_supervised_ssekmeans.py <99_plot_semi_supervised_ssekmeans.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: 99_plot_semi_supervised_ssekmeans.zip <99_plot_semi_supervised_ssekmeans.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_