.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/ml/plot_clustering.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_ml_plot_clustering.py: Clustering ========== In this example, the use of the clustering plot methods is shown applied to the Canadian Weather dataset. K-Means and Fuzzy K-Means algorithms are employed to calculate the results plotted. .. GENERATED FROM PYTHON SOURCE LINES 9-15 .. code-block:: Python # Author: Amanda Hernando Bernabé # License: MIT # sphinx_gallery_thumbnail_number = 6 .. GENERATED FROM PYTHON SOURCE LINES 16-20 First, the Canadian Weather dataset is downloaded from the package 'fda' in CRAN. It contains a FDataGrid with daily temperatures and precipitations, that is, it has a 2-dimensional image. We are interested only in the daily average temperatures, so we select the first coordinate function. .. GENERATED FROM PYTHON SOURCE LINES 20-36 .. code-block:: Python import numpy as np from skfda.datasets import fetch_weather X, y = fetch_weather(return_X_y=True, as_frame=True) fd = X.iloc[:, 0].array target = y.array fd_temperatures = fd.coordinates[0] # The desired FDataGrid only contains 10 random samples, so that the example # provides clearer plots. indices_samples = np.array([1, 3, 5, 10, 14, 17, 21, 25, 27, 30]) fd = fd_temperatures[indices_samples] .. GENERATED FROM PYTHON SOURCE LINES 45-48 The data is plotted to show the curves we are working with. They are divided according to the target. In this case, it includes the different climates to which the weather stations belong to. .. GENERATED FROM PYTHON SOURCE LINES 48-60 .. code-block:: Python import matplotlib.pyplot as plt climates = target[indices_samples].remove_unused_categories() fd.plot( group=climates.codes, group_names=climates.categories, ) plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_001.png :alt: Canadian Weather :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 61-64 The number of clusters is set with the number of climates, in order to see the performance of the clustering methods, and the seed is set to one in order to obatain always the same result for the example. .. GENERATED FROM PYTHON SOURCE LINES 64-68 .. code-block:: Python n_clusters = len(climates.categories) seed = 2 .. GENERATED FROM PYTHON SOURCE LINES 69-75 First, the class :class:`~skfda.ml.clustering.KMeans` is instantiated with the desired. parameters. Its :func:`~skfda.ml.clustering.KMeans.fit` method is called, resulting in the calculation of several attributes which include among others, the the number of cluster each sample belongs to (labels), and the centroids of each cluster. The labels are obtaiined calling the method :func:`~skfda.ml.clustering.KMeans.predict`. .. GENERATED FROM PYTHON SOURCE LINES 75-83 .. code-block:: Python from skfda.ml.clustering import KMeans kmeans = KMeans(n_clusters=n_clusters, random_state=seed) kmeans.fit(fd) print(kmeans.predict(fd)) .. rst-class:: sphx-glr-script-out .. code-block:: none [0 1 0 0 0 2 2 1 0 2] .. GENERATED FROM PYTHON SOURCE LINES 87-89 To see the information in a graphic way, we can use the class :class:`~skfda.exploratory.visualization.clustering.ClusterPlot`. .. GENERATED FROM PYTHON SOURCE LINES 89-106 .. code-block:: Python from skfda.exploratory.visualization.clustering import ClusterPlot # Customization of cluster colors and labels in order to match the first image # of raw data. cluster_colors = ["C0", "C2", "C1"] cluster_labels = climates.categories[np.array([0, 2, 1])] ClusterPlot( kmeans, fd, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_002.png :alt: Canadian Weather :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 107-117 Other clustering algorithm implemented is the Fuzzy K-Means found in the class :class:`~skfda.ml.clustering.FuzzyCMeans`. Following the above procedure, an object of this type is instantiated with the desired data and then, the :func:`~skfda.ml.clustering.FuzzyCMeans.fit` method is called. Internally, the attribute ``membership_degree_`` is calculated, which contains ``n_clusters`` elements for each sample and dimension, denoting the degree of membership of each sample to each cluster. They are obtained calling the method :func:`~skfda.ml.clustering.FuzzyCMeans.predict_proba`. Also, the centroids of each cluster are obtained. .. GENERATED FROM PYTHON SOURCE LINES 117-124 .. code-block:: Python from skfda.ml.clustering import FuzzyCMeans fuzzy_kmeans = FuzzyCMeans(n_clusters=n_clusters, random_state=seed) fuzzy_kmeans.fit(fd) print(fuzzy_kmeans.predict_proba(fd)) .. rst-class:: sphx-glr-script-out .. code-block:: none [[0.8721254 0.11189295 0.01598165] [0.4615364 0.51285956 0.02560405] [0.97428363 0.01882257 0.0068938 ] [0.91184323 0.05369029 0.03446648] [0.79072268 0.18411219 0.02516513] [0.178624 0.05881132 0.76256468] [0.01099498 0.00492593 0.98407909] [0.03156897 0.96349997 0.00493106] [0.8084018 0.13418057 0.05741763] [0.03767122 0.01891178 0.943417 ]] .. GENERATED FROM PYTHON SOURCE LINES 128-132 To see the information in a graphic way, the class :class:`~skfda.exploratory.visualization.clustering.ClusterPlot` can be used. It assigns each sample to the cluster whose membership value is the greatest. .. GENERATED FROM PYTHON SOURCE LINES 132-142 .. code-block:: Python ClusterPlot( fuzzy_kmeans, fd, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_003.png :alt: Canadian Weather :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 143-150 Another plot implemented to show the results in the class :class:`~skfda.ml.clustering.FuzzyCMeans` is :class:`~skfda.exploratory.visualization.clustering.\ ClusterMembershipLinesPlot`. which is similar to parallel coordinates. It is recommended to assign colors to each of the samples in order to identify them. In this example, the colors are the ones of the first plot, dividing the samples by climate. .. GENERATED FROM PYTHON SOURCE LINES 150-166 .. code-block:: Python from skfda.exploratory.visualization.clustering import ( ClusterMembershipLinesPlot, ) colors_by_climate = np.array(["C0", "C1", "C2"])[climates.codes] ClusterMembershipLinesPlot( fuzzy_kmeans, fd, cluster_labels=cluster_labels, sample_colors=colors_by_climate, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_004.png :alt: Degrees of membership of the samples to each cluster :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 167-172 Finally, the class :class:`~skfda.exploratory.visualization.clustering.ClusterMembershipPlot` has a plot method which returns a barplot. Each sample is designated with a bar which is filled proportionally to the membership values with the color of each cluster. .. GENERATED FROM PYTHON SOURCE LINES 172-184 .. code-block:: Python from skfda.exploratory.visualization.clustering import ClusterMembershipPlot ClusterMembershipPlot( fuzzy_kmeans, fd, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_005.png :alt: Degrees of membership of the samples to each cluster :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 185-190 The possibility of sorting the bars according to a cluster is given specifying the number of cluster, which belongs to the interval [0, n_clusters). We can order the data using the first cluster: .. GENERATED FROM PYTHON SOURCE LINES 190-200 .. code-block:: Python ClusterMembershipPlot( fuzzy_kmeans, fd, sort=0, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_006.png :alt: Degrees of membership of the samples to each cluster :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_006.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 201-202 Using the second cluster: .. GENERATED FROM PYTHON SOURCE LINES 202-212 .. code-block:: Python ClusterMembershipPlot( fuzzy_kmeans, fd, sort=1, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_007.png :alt: Degrees of membership of the samples to each cluster :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_007.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 213-214 And using the third cluster: .. GENERATED FROM PYTHON SOURCE LINES 214-223 .. code-block:: Python ClusterMembershipPlot( fuzzy_kmeans, fd, sort=2, cluster_colors=cluster_colors, cluster_labels=cluster_labels, ).plot() plt.show() .. image-sg:: /auto_examples/ml/images/sphx_glr_plot_clustering_008.png :alt: Degrees of membership of the samples to each cluster :srcset: /auto_examples/ml/images/sphx_glr_plot_clustering_008.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.569 seconds) .. _sphx_glr_download_auto_examples_ml_plot_clustering.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/GAA-UAM/scikit-fda/develop?filepath=examples/ml/plot_clustering.py :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_clustering.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_clustering.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_clustering.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_