.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/neighbors/plot_kde_1d.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 JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_neighbors_plot_kde_1d.py: =================================== Simple 1D Kernel Density Estimation =================================== This example uses the :class:`~sklearn.neighbors.KernelDensity` class to demonstrate the principles of Kernel Density Estimation in one dimension. The first plot shows one of the problems with using histograms to visualize the density of points in 1D. Intuitively, a histogram can be thought of as a scheme in which a unit "block" is stacked above each point on a regular grid. As the top two panels show, however, the choice of gridding for these blocks can lead to wildly divergent ideas about the underlying shape of the density distribution. If we instead center each block on the point it represents, we get the estimate shown in the bottom left panel. This is a kernel density estimation with a "top hat" kernel. This idea can be generalized to other kernel shapes: the bottom-right panel of the first figure shows a Gaussian kernel density estimate over the same distribution. Scikit-learn implements efficient kernel density estimation using either a Ball Tree or KD Tree structure, through the :class:`~sklearn.neighbors.KernelDensity` estimator. The available kernels are shown in the second figure of this example. The third figure compares kernel density estimates for a distribution of 100 samples in 1 dimension. Though this example uses 1D distributions, kernel density estimation is easily and efficiently extensible to higher dimensions as well. .. GENERATED FROM PYTHON SOURCE LINES 30-158 .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_001.png :alt: plot kde 1d :srcset: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_002.png :alt: Available Kernels :srcset: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_002.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_003.png :alt: plot kde 1d :srcset: /auto_examples/neighbors/images/sphx_glr_plot_kde_1d_003.png :class: sphx-glr-multi-img .. code-block:: Python # Author: Jake Vanderplas # import matplotlib.pyplot as plt import numpy as np from scipy.stats import norm from sklearn.neighbors import KernelDensity # ---------------------------------------------------------------------- # Plot the progression of histograms to kernels np.random.seed(1) N = 20 X = np.concatenate( (np.random.normal(0, 1, int(0.3 * N)), np.random.normal(5, 1, int(0.7 * N))) )[:, np.newaxis] X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis] bins = np.linspace(-5, 10, 10) fig, ax = plt.subplots(2, 2, sharex=True, sharey=True) fig.subplots_adjust(hspace=0.05, wspace=0.05) # histogram 1 ax[0, 0].hist(X[:, 0], bins=bins, fc="#AAAAFF", density=True) ax[0, 0].text(-3.5, 0.31, "Histogram") # histogram 2 ax[0, 1].hist(X[:, 0], bins=bins + 0.75, fc="#AAAAFF", density=True) ax[0, 1].text(-3.5, 0.31, "Histogram, bins shifted") # tophat KDE kde = KernelDensity(kernel="tophat", bandwidth=0.75).fit(X) log_dens = kde.score_samples(X_plot) ax[1, 0].fill(X_plot[:, 0], np.exp(log_dens), fc="#AAAAFF") ax[1, 0].text(-3.5, 0.31, "Tophat Kernel Density") # Gaussian KDE kde = KernelDensity(kernel="gaussian", bandwidth=0.75).fit(X) log_dens = kde.score_samples(X_plot) ax[1, 1].fill(X_plot[:, 0], np.exp(log_dens), fc="#AAAAFF") ax[1, 1].text(-3.5, 0.31, "Gaussian Kernel Density") for axi in ax.ravel(): axi.plot(X[:, 0], np.full(X.shape[0], -0.01), "+k") axi.set_xlim(-4, 9) axi.set_ylim(-0.02, 0.34) for axi in ax[:, 0]: axi.set_ylabel("Normalized Density") for axi in ax[1, :]: axi.set_xlabel("x") # ---------------------------------------------------------------------- # Plot all available kernels X_plot = np.linspace(-6, 6, 1000)[:, None] X_src = np.zeros((1, 1)) fig, ax = plt.subplots(2, 3, sharex=True, sharey=True) fig.subplots_adjust(left=0.05, right=0.95, hspace=0.05, wspace=0.05) def format_func(x, loc): if x == 0: return "0" elif x == 1: return "h" elif x == -1: return "-h" else: return "%ih" % x for i, kernel in enumerate( ["gaussian", "tophat", "epanechnikov", "exponential", "linear", "cosine"] ): axi = ax.ravel()[i] log_dens = KernelDensity(kernel=kernel).fit(X_src).score_samples(X_plot) axi.fill(X_plot[:, 0], np.exp(log_dens), "-k", fc="#AAAAFF") axi.text(-2.6, 0.95, kernel) axi.xaxis.set_major_formatter(plt.FuncFormatter(format_func)) axi.xaxis.set_major_locator(plt.MultipleLocator(1)) axi.yaxis.set_major_locator(plt.NullLocator()) axi.set_ylim(0, 1.05) axi.set_xlim(-2.9, 2.9) ax[0, 1].set_title("Available Kernels") # ---------------------------------------------------------------------- # Plot a 1D density example N = 100 np.random.seed(1) X = np.concatenate( (np.random.normal(0, 1, int(0.3 * N)), np.random.normal(5, 1, int(0.7 * N))) )[:, np.newaxis] X_plot = np.linspace(-5, 10, 1000)[:, np.newaxis] true_dens = 0.3 * norm(0, 1).pdf(X_plot[:, 0]) + 0.7 * norm(5, 1).pdf(X_plot[:, 0]) fig, ax = plt.subplots() ax.fill(X_plot[:, 0], true_dens, fc="black", alpha=0.2, label="input distribution") colors = ["navy", "cornflowerblue", "darkorange"] kernels = ["gaussian", "tophat", "epanechnikov"] lw = 2 for color, kernel in zip(colors, kernels): kde = KernelDensity(kernel=kernel, bandwidth=0.5).fit(X) log_dens = kde.score_samples(X_plot) ax.plot( X_plot[:, 0], np.exp(log_dens), color=color, lw=lw, linestyle="-", label="kernel = '{0}'".format(kernel), ) ax.text(6, 0.38, "N={0} points".format(N)) ax.legend(loc="upper left") ax.plot(X[:, 0], -0.005 - 0.01 * np.random.random(X.shape[0]), "+k") ax.set_xlim(-4, 9) ax.set_ylim(-0.02, 0.4) plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.578 seconds) .. _sphx_glr_download_auto_examples_neighbors_plot_kde_1d.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/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/neighbors/plot_kde_1d.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/neighbors/plot_kde_1d.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_kde_1d.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_kde_1d.py ` .. include:: plot_kde_1d.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_