.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/gaussian_process/plot_gpr_noisy.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_gaussian_process_plot_gpr_noisy.py: ========================================================================= Ability of Gaussian process regression (GPR) to estimate data noise-level ========================================================================= This example shows the ability of the :class:`~sklearn.gaussian_process.kernels.WhiteKernel` to estimate the noise level in the data. Moreover, we show the importance of kernel hyperparameters initialization. .. GENERATED FROM PYTHON SOURCE LINES 11-16 .. code-block:: Python # Authors: Jan Hendrik Metzen # Guillaume Lemaitre # License: BSD 3 clause .. GENERATED FROM PYTHON SOURCE LINES 17-23 Data generation --------------- We will work in a setting where `X` will contain a single feature. We create a function that will generate the target to be predicted. We will add an option to add some noise to the generated target. .. GENERATED FROM PYTHON SOURCE LINES 23-34 .. code-block:: Python import numpy as np def target_generator(X, add_noise=False): target = 0.5 + np.sin(3 * X) if add_noise: rng = np.random.RandomState(1) target += rng.normal(0, 0.3, size=target.shape) return target.squeeze() .. GENERATED FROM PYTHON SOURCE LINES 35-37 Let's have a look to the target generator where we will not add any noise to observe the signal that we would like to predict. .. GENERATED FROM PYTHON SOURCE LINES 37-40 .. code-block:: Python X = np.linspace(0, 5, num=30).reshape(-1, 1) y = target_generator(X, add_noise=False) .. GENERATED FROM PYTHON SOURCE LINES 41-48 .. code-block:: Python import matplotlib.pyplot as plt plt.plot(X, y, label="Expected signal") plt.legend() plt.xlabel("X") _ = plt.ylabel("y") .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_001.png :alt: plot gpr noisy :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 49-52 The target is transforming the input `X` using a sine function. Now, we will generate few noisy training samples. To illustrate the noise level, we will plot the true signal together with the noisy training samples. .. GENERATED FROM PYTHON SOURCE LINES 52-56 .. code-block:: Python rng = np.random.RandomState(0) X_train = rng.uniform(0, 5, size=20).reshape(-1, 1) y_train = target_generator(X_train, add_noise=True) .. GENERATED FROM PYTHON SOURCE LINES 57-69 .. code-block:: Python plt.plot(X, y, label="Expected signal") plt.scatter( x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations", ) plt.legend() plt.xlabel("X") _ = plt.ylabel("y") .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_002.png :alt: plot gpr noisy :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_002.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 70-88 Optimisation of kernel hyperparameters in GPR --------------------------------------------- Now, we will create a :class:`~sklearn.gaussian_process.GaussianProcessRegressor` using an additive kernel adding a :class:`~sklearn.gaussian_process.kernels.RBF` and :class:`~sklearn.gaussian_process.kernels.WhiteKernel` kernels. The :class:`~sklearn.gaussian_process.kernels.WhiteKernel` is a kernel that will able to estimate the amount of noise present in the data while the :class:`~sklearn.gaussian_process.kernels.RBF` will serve at fitting the non-linearity between the data and the target. However, we will show that the hyperparameter space contains several local minima. It will highlights the importance of initial hyperparameter values. We will create a model using a kernel with a high noise level and a large length scale, which will explain all variations in the data by noise. .. GENERATED FROM PYTHON SOURCE LINES 88-98 .. code-block:: Python from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, WhiteKernel kernel = 1.0 * RBF(length_scale=1e1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel( noise_level=1, noise_level_bounds=(1e-5, 1e1) ) gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0) gpr.fit(X_train, y_train) y_mean, y_std = gpr.predict(X, return_std=True) .. rst-class:: sphx-glr-script-out .. code-block:: none /home/circleci/project/sklearn/gaussian_process/kernels.py:455: ConvergenceWarning: The optimal value found for dimension 0 of parameter k1__k2__length_scale is close to the specified upper bound 1000.0. Increasing the bound and calling fit again may find a better value. .. GENERATED FROM PYTHON SOURCE LINES 99-112 .. code-block:: Python plt.plot(X, y, label="Expected signal") plt.scatter(x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations") plt.errorbar(X, y_mean, y_std) plt.legend() plt.xlabel("X") plt.ylabel("y") _ = plt.title( ( f"Initial: {kernel}\nOptimum: {gpr.kernel_}\nLog-Marginal-Likelihood: " f"{gpr.log_marginal_likelihood(gpr.kernel_.theta)}" ), fontsize=8, ) .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_003.png :alt: Initial: 1**2 * RBF(length_scale=10) + WhiteKernel(noise_level=1) Optimum: 0.763**2 * RBF(length_scale=1e+03) + WhiteKernel(noise_level=0.525) Log-Marginal-Likelihood: -23.499266455424188 :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 113-122 We see that the optimum kernel found still have a high noise level and an even larger length scale. Furthermore, we observe that the model does not provide faithful predictions. Now, we will initialize the :class:`~sklearn.gaussian_process.kernels.RBF` with a larger `length_scale` and the :class:`~sklearn.gaussian_process.kernels.WhiteKernel` with a smaller noise level lower bound. .. GENERATED FROM PYTHON SOURCE LINES 122-129 .. code-block:: Python kernel = 1.0 * RBF(length_scale=1e-1, length_scale_bounds=(1e-2, 1e3)) + WhiteKernel( noise_level=1e-2, noise_level_bounds=(1e-10, 1e1) ) gpr = GaussianProcessRegressor(kernel=kernel, alpha=0.0) gpr.fit(X_train, y_train) y_mean, y_std = gpr.predict(X, return_std=True) .. GENERATED FROM PYTHON SOURCE LINES 130-144 .. code-block:: Python plt.plot(X, y, label="Expected signal") plt.scatter(x=X_train[:, 0], y=y_train, color="black", alpha=0.4, label="Observations") plt.errorbar(X, y_mean, y_std) plt.legend() plt.xlabel("X") plt.ylabel("y") _ = plt.title( ( f"Initial: {kernel}\nOptimum: {gpr.kernel_}\nLog-Marginal-Likelihood: " f"{gpr.log_marginal_likelihood(gpr.kernel_.theta)}" ), fontsize=8, ) .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_004.png :alt: Initial: 1**2 * RBF(length_scale=0.1) + WhiteKernel(noise_level=0.01) Optimum: 1.05**2 * RBF(length_scale=0.569) + WhiteKernel(noise_level=0.134) Log-Marginal-Likelihood: -18.429732528984054 :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 145-155 First, we see that the model's predictions are more precise than the previous model's: this new model is able to estimate the noise-free functional relationship. Looking at the kernel hyperparameters, we see that the best combination found has a smaller noise level and shorter length scale than the first model. We can inspect the Log-Marginal-Likelihood (LML) of :class:`~sklearn.gaussian_process.GaussianProcessRegressor` for different hyperparameters to get a sense of the local minima. .. GENERATED FROM PYTHON SOURCE LINES 155-169 .. code-block:: Python from matplotlib.colors import LogNorm length_scale = np.logspace(-2, 4, num=50) noise_level = np.logspace(-2, 1, num=50) length_scale_grid, noise_level_grid = np.meshgrid(length_scale, noise_level) log_marginal_likelihood = [ gpr.log_marginal_likelihood(theta=np.log([0.36, scale, noise])) for scale, noise in zip(length_scale_grid.ravel(), noise_level_grid.ravel()) ] log_marginal_likelihood = np.reshape( log_marginal_likelihood, newshape=noise_level_grid.shape ) .. GENERATED FROM PYTHON SOURCE LINES 170-187 .. code-block:: Python vmin, vmax = (-log_marginal_likelihood).min(), 50 level = np.around(np.logspace(np.log10(vmin), np.log10(vmax), num=50), decimals=1) plt.contour( length_scale_grid, noise_level_grid, -log_marginal_likelihood, levels=level, norm=LogNorm(vmin=vmin, vmax=vmax), ) plt.colorbar() plt.xscale("log") plt.yscale("log") plt.xlabel("Length-scale") plt.ylabel("Noise-level") plt.title("Log-marginal-likelihood") plt.show() .. image-sg:: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_005.png :alt: Log-marginal-likelihood :srcset: /auto_examples/gaussian_process/images/sphx_glr_plot_gpr_noisy_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 188-193 We see that there are two local minima that correspond to the combination of hyperparameters previously found. Depending on the initial values for the hyperparameters, the gradient-based optimization might converge whether or not to the best model. It is thus important to repeat the optimization several times for different initializations. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.712 seconds) .. _sphx_glr_download_auto_examples_gaussian_process_plot_gpr_noisy.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/1.4.X?urlpath=lab/tree/notebooks/auto_examples/gaussian_process/plot_gpr_noisy.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/gaussian_process/plot_gpr_noisy.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gpr_noisy.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gpr_noisy.py ` .. include:: plot_gpr_noisy.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_