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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_clustering_plot_dtw_boss.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_clustering_plot_dtw_boss.py:


=========================================
Time Series Clustering with DTW and BOSS
=========================================

This example shows the differences between various metrics
related to time series clustering. Besides the Euclidean distance,
:func:`pyts.metrics.dtw` and :func:`pyts.metrics.boss` are considered to
analyze the :func:`pyts.datasets.make_cylinder_bell_funnel` dataset.
In contrast to reference [1], DTW-based clustering shows the best results
here. The reason for this could be differences in the experimental setup,
e.g., the hyperparameters of the methods or the number of considered samples.
Depending on the time series structure, a suitable metric should be chosen:
While the Euclidean distance and DTW are sensitive to time-dependent events,
clustering with BOSS does not consider the temporal component outside the
sliding window.

References
----------
[1] Patrick Schäfer, "The BOSS is concerned with time series classification
    in the presence of noise". Data Mining and Knowledge Discovery,
    29(6), 1505-1530 (2015).

.. GENERATED FROM PYTHON SOURCE LINES 24-103



.. image-sg:: /auto_examples/clustering/images/sphx_glr_plot_dtw_boss_001.png
   :alt: Euclidean, DTW, BOSS
   :srcset: /auto_examples/clustering/images/sphx_glr_plot_dtw_boss_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Author: Lucas Plagwitz <lucas.plagwitz@uni-muenster.de>
    # License: BSD-3-Clause

    import itertools
    import numpy as np
    import matplotlib.pyplot as plt
    from scipy.spatial.distance import euclidean
    from scipy.cluster.hierarchy import dendrogram
    from sklearn.metrics import homogeneity_score
    from sklearn.cluster import AgglomerativeClustering

    from pyts.metrics import dtw, boss
    from pyts.transformation import BOSS
    from pyts.datasets import make_cylinder_bell_funnel


    def create_dist_matrix(dataset, dist_func, **kwargs):
        distance_mat = np.zeros((len(dataset), len(dataset)))
        for i, j in itertools.product(range(len(dataset)),
                                      range(len(dataset))):
            distance_mat[i, j] = dist_func(dataset[i], dataset[j], **kwargs)
        return distance_mat


    def plot_dendrogram(model, **kwargs):
        # function copied from sklearn:
        # plot_agglomerative_dendrogram.html
        #
        # Create linkage matrix and then plot the dendrogram

        # create the counts of samples under each node
        counts = np.zeros(model.children_.shape[0])
        n_samples = len(model.labels_)
        for i, merge in enumerate(model.children_):
            current_count = 0
            for child_idx in merge:
                if child_idx < n_samples:
                    current_count += 1  # leaf node
                else:
                    current_count += counts[child_idx - n_samples]
            counts[i] = current_count
        linkage_matrix = np.column_stack(
            [model.children_, model.distances_, counts]
        ).astype(float)

        # Plot the corresponding dendrogram
        dendrogram(linkage_matrix,
                   color_threshold=sorted(model.distances_)[-2], **kwargs)


    n_samples = 14
    fig, axes = plt.subplots(1, 3, figsize=(16, 8))

    X, y = make_cylinder_bell_funnel(n_samples=n_samples, random_state=42,
                                     shuffle=False)
    for k_axis, metric in enumerate(["Euclidean", "DTW", "BOSS"]):
        if metric == "DTW":
            dist_mat = create_dist_matrix(X, dtw)
        elif metric == "BOSS":
            dist_mat = create_dist_matrix(BOSS(sparse=False, n_bins=3,
                                               word_size=3).fit_transform(X),
                                          boss)
        else:
            dist_mat = create_dist_matrix(X, euclidean)

        model = AgglomerativeClustering(compute_full_tree=True,
                                        compute_distances=True,
                                        n_clusters=3, metric="precomputed",
                                        linkage="complete")
        cluster = model.fit_predict(dist_mat)
        score = round(homogeneity_score(labels_true=y, labels_pred=cluster), 2)

        plot_dendrogram(model, orientation='left', ax=axes[k_axis], labels=y)
        axes[k_axis].set_xticks([], [])
        axes[k_axis].set_title(metric, size='xx-large')
        axes[k_axis].set_xlabel(f"homogeneity score: {score}", size='xx-large')

    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.267 seconds)


.. _sphx_glr_download_auto_examples_clustering_plot_dtw_boss.py:

.. only:: html

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


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

      :download:`Download Python source code: plot_dtw_boss.py <plot_dtw_boss.py>`

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

      :download:`Download Jupyter notebook: plot_dtw_boss.ipynb <plot_dtw_boss.ipynb>`


.. only:: html

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

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