1.7. Gaussian Processes

Gaussian Processes (GP) are a nonparametric supervised learning method used to solve regression and probabilistic classification problems.

The advantages of Gaussian processes are:

• The prediction interpolates the observations (at least for regular kernels).

• The prediction is probabilistic (Gaussian) so that one can compute empirical confidence intervals and decide based on those if one should refit (online fitting, adaptive fitting) the prediction in some region of interest.

• Versatile: different kernels can be specified. Common kernels are provided, but it is also possible to specify custom kernels.

The disadvantages of Gaussian processes include:

• Our implementation is not sparse, i.e., they use the whole samples/features information to perform the prediction.

• They lose efficiency in high dimensional spaces – namely when the number of features exceeds a few dozens.

1.7.1. Gaussian Process Regression (GPR)

The GaussianProcessRegressor implements Gaussian processes (GP) for regression purposes. For this, the prior of the GP needs to be specified. GP will combine this prior and the likelihood function based on training samples. It allows to give a probabilistic approach to prediction by giving the mean and standard deviation as output when predicting.

The prior mean is assumed to be constant and zero (for normalize_y=False) or the training data’s mean (for normalize_y=True). The prior’s covariance is specified by passing a kernel object. The hyperparameters of the kernel are optimized when fitting the GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. The first run is always conducted starting from the initial hyperparameter values of the kernel; subsequent runs are conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters should be kept fixed, None can be passed as optimizer.

The noise level in the targets can be specified by passing it via the parameter alpha, either globally as a scalar or per datapoint. Note that a moderate noise level can also be helpful for dealing with numeric instabilities during fitting as it is effectively implemented as Tikhonov regularization, i.e., by adding it to the diagonal of the kernel matrix. An alternative to specifying the noise level explicitly is to include a WhiteKernel component into the kernel, which can estimate the global noise level from the data (see example below). The figure below shows the effect of noisy target handled by setting the parameter alpha.

The implementation is based on Algorithm 2.1 of [RW2006]. In addition to the API of standard scikit-learn estimators, GaussianProcessRegressor:

• allows prediction without prior fitting (based on the GP prior)

• provides an additional method sample_y(X), which evaluates samples drawn from the GPR (prior or posterior) at given inputs

• exposes a method log_marginal_likelihood(theta), which can be used externally for other ways of selecting hyperparameters, e.g., via Markov chain Monte Carlo.

Examples

• Gaussian Processes regression: basic introductory example

• Ability of Gaussian process regression (GPR) to estimate data noise-level

• Comparison of kernel ridge and Gaussian process regression

• Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)

1.7.2. Gaussian Process Classification (GPC)

The GaussianProcessClassifier implements Gaussian processes (GP) for classification purposes, more specifically for probabilistic classification, where test predictions take the form of class probabilities. GaussianProcessClassifier places a GP prior on a latent function \(f\), which is then squashed through a link function to obtain the probabilistic classification. The latent function \(f\) is a so-called nuisance function, whose values are not observed and are not relevant by themselves. Its purpose is to allow a convenient formulation of the model, and \(f\) is removed (integrated out) during prediction. GaussianProcessClassifier implements the logistic link function, for which the integral cannot be computed analytically but is easily approximated in the binary case.

In contrast to the regression setting, the posterior of the latent function \(f\) is not Gaussian even for a GP prior since a Gaussian likelihood is inappropriate for discrete class labels. Rather, a non-Gaussian likelihood corresponding to the logistic link function (logit) is used. GaussianProcessClassifier approximates the non-Gaussian posterior with a Gaussian based on the Laplace approximation. More details can be found in Chapter 3 of [RW2006].

The GP prior mean is assumed to be zero. The prior’s covariance is specified by passing a kernel object. The hyperparameters of the kernel are optimized during fitting of GaussianProcessRegressor by maximizing the log-marginal-likelihood (LML) based on the passed optimizer. As the LML may have multiple local optima, the optimizer can be started repeatedly by specifying n_restarts_optimizer. The first run is always conducted starting from the initial hyperparameter values of the kernel; subsequent runs are conducted from hyperparameter values that have been chosen randomly from the range of allowed values. If the initial hyperparameters should be kept fixed, None can be passed as optimizer.

GaussianProcessClassifier supports multi-class classification by performing either one-versus-rest or one-versus-one based training and prediction. In one-versus-rest, one binary Gaussian process classifier is fitted for each class, which is trained to separate this class from the rest. In “one_vs_one”, one binary Gaussian process classifier is fitted for each pair of classes, which is trained to separate these two classes. The predictions of these binary predictors are combined into multi-class predictions. See the section on multi-class classification for more details.

In the case of Gaussian process classification, “one_vs_one” might be computationally cheaper since it has to solve many problems involving only a subset of the whole training set rather than fewer problems on the whole dataset. Since Gaussian process classification scales cubically with the size of the dataset, this might be considerably faster. However, note that “one_vs_one” does not support predicting probability estimates but only plain predictions. Moreover, note that GaussianProcessClassifier does not (yet) implement a true multi-class Laplace approximation internally, but as discussed above is based on solving several binary classification tasks internally, which are combined using one-versus-rest or one-versus-one.

1.7.3. GPC examples

1.7.3.1. Probabilistic predictions with GPC

This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters. The first figure shows the predicted probability of GPC with arbitrarily chosen hyperparameters and with the hyperparameters corresponding to the maximum log-marginal-likelihood (LML).

While the hyperparameters chosen by optimizing LML have a considerably larger LML, they perform slightly worse according to the log-loss on test data. The figure shows that this is because they exhibit a steep change of the class probabilities at the class boundaries (which is good) but have predicted probabilities close to 0.5 far away from the class boundaries (which is bad) This undesirable effect is caused by the Laplace approximation used internally by GPC.

The second figure shows the log-marginal-likelihood for different choices of the kernel’s hyperparameters, highlighting the two choices of the hyperparameters used in the first figure by black dots.

1.7.3.2. Illustration of GPC on the XOR dataset

This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel (DotProduct). On this particular dataset, the DotProduct kernel obtains considerably better results because the class-boundaries are linear and coincide with the coordinate axes. In practice, however, stationary kernels such as RBF often obtain better results.

1.7.3.3. Gaussian process classification (GPC) on iris dataset

This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the iris-dataset. This illustrates the applicability of GPC to non-binary classification. The anisotropic RBF kernel obtains slightly higher log-marginal-likelihood by assigning different length-scales to the two feature dimensions.

1.7.4. Kernels for Gaussian Processes

Kernels (also called “covariance functions” in the context of GPs) are a crucial ingredient of GPs which determine the shape of prior and posterior of the GP. They encode the assumptions on the function being learned by defining the “similarity” of two datapoints combined with the assumption that similar datapoints should have similar target values. Two categories of kernels can be distinguished: stationary kernels depend only on the distance of two datapoints and not on their absolute values \(k(x_i, x_j)= k(d(x_i, x_j))\) and are thus invariant to translations in the input space, while non-stationary kernels depend also on the specific values of the datapoints. Stationary kernels can further be subdivided into isotropic and anisotropic kernels, where isotropic kernels are also invariant to rotations in the input space. For more details, we refer to Chapter 4 of [RW2006]. For guidance on how to best combine different kernels, we refer to [Duv2014].

Gaussian Process Kernel API Click for more details

The main usage of a Kernel is to compute the GP’s covariance between datapoints. For this, the method __call__ of the kernel can be called. This method can either be used to compute the “auto-covariance” of all pairs of datapoints in a 2d array X, or the “cross-covariance” of all combinations of datapoints of a 2d array X with datapoints in a 2d array Y. The following identity holds true for all kernels k (except for the WhiteKernel): k(X) == K(X, Y=X)

If only the diagonal of the auto-covariance is being used, the method diag() of a kernel can be called, which is more computationally efficient than the equivalent call to __call__: np.diag(k(X, X)) == k.diag(X)

Kernels are parameterized by a vector \(\theta\) of hyperparameters. These hyperparameters can for instance control length-scales or periodicity of a kernel (see below). All kernels support computing analytic gradients of the kernel’s auto-covariance with respect to \(log(\theta)\) via setting eval_gradient=True in the __call__ method. That is, a (len(X), len(X), len(theta)) array is returned where the entry [i, j, l] contains \(\frac{\partial k_\theta(x_i, x_j)}{\partial log(\theta_l)}\). This gradient is used by the Gaussian process (both regressor and classifier) in computing the gradient of the log-marginal-likelihood, which in turn is used to determine the value of \(\theta\), which maximizes the log-marginal-likelihood, via gradient ascent. For each hyperparameter, the initial value and the bounds need to be specified when creating an instance of the kernel. The current value of \(\theta\) can be get and set via the property theta of the kernel object. Moreover, the bounds of the hyperparameters can be accessed by the property bounds of the kernel. Note that both properties (theta and bounds) return log-transformed values of the internally used values since those are typically more amenable to gradient-based optimization. The specification of each hyperparameter is stored in the form of an instance of Hyperparameter in the respective kernel. Note that a kernel using a hyperparameter with name “x” must have the attributes self.x and self.x_bounds.

The abstract base class for all kernels is Kernel. Kernel implements a similar interface as BaseEstimator, providing the methods get_params(), set_params(), and clone(). This allows setting kernel values also via meta-estimators such as Pipeline or GridSearchCV. Note that due to the nested structure of kernels (by applying kernel operators, see below), the names of kernel parameters might become relatively complicated. In general, for a binary kernel operator, parameters of the left operand are prefixed with k1__ and parameters of the right operand with k2__. An additional convenience method is clone_with_theta(theta), which returns a cloned version of the kernel but with the hyperparameters set to theta. An illustrative example:

>>> from sklearn.gaussian_process.kernels import ConstantKernel, RBF
>>> kernel = ConstantKernel(constant_value=1.0, constant_value_bounds=(0.0, 10.0)) * RBF(length_scale=0.5, length_scale_bounds=(0.0, 10.0)) + RBF(length_scale=2.0, length_scale_bounds=(0.0, 10.0))
>>> for hyperparameter in kernel.hyperparameters: print(hyperparameter)
Hyperparameter(name='k1__k1__constant_value', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
Hyperparameter(name='k1__k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
Hyperparameter(name='k2__length_scale', value_type='numeric', bounds=array([[ 0., 10.]]), n_elements=1, fixed=False)
>>> params = kernel.get_params()
>>> for key in sorted(params): print("%s : %s" % (key, params[key]))
k1 : 1**2 * RBF(length_scale=0.5)
k1__k1 : 1**2
k1__k1__constant_value : 1.0
k1__k1__constant_value_bounds : (0.0, 10.0)
k1__k2 : RBF(length_scale=0.5)
k1__k2__length_scale : 0.5
k1__k2__length_scale_bounds : (0.0, 10.0)
k2 : RBF(length_scale=2)
k2__length_scale : 2.0
k2__length_scale_bounds : (0.0, 10.0)
>>> print(kernel.theta)  # Note: log-transformed
[ 0.         -0.69314718  0.69314718]
>>> print(kernel.bounds)  # Note: log-transformed
[[      -inf 2.30258509]
 [      -inf 2.30258509]
 [      -inf 2.30258509]]

All Gaussian process kernels are interoperable with sklearn.metrics.pairwise and vice versa: instances of subclasses of Kernel can be passed as metric to pairwise_kernels from sklearn.metrics.pairwise. Moreover, kernel functions from pairwise can be used as GP kernels by using the wrapper class PairwiseKernel. The only caveat is that the gradient of the hyperparameters is not analytic but numeric and all those kernels support only isotropic distances. The parameter gamma is considered to be a hyperparameter and may be optimized. The other kernel parameters are set directly at initialization and are kept fixed.

1.7.4.1. Basic kernels

The ConstantKernel kernel can be used as part of a Product kernel where it scales the magnitude of the other factor (kernel) or as part of a Sum kernel, where it modifies the mean of the Gaussian process. It depends on a parameter \(constant\_value\). It is defined as:

\[k(x_i, x_j) = constant\_value \;\forall\; x_1, x_2\]

The main use-case of the WhiteKernel kernel is as part of a sum-kernel where it explains the noise-component of the signal. Tuning its parameter \(noise\_level\) corresponds to estimating the noise-level. It is defined as:

\[k(x_i, x_j) = noise\_level \text{ if } x_i == x_j \text{ else } 0\]

1.7.4.2. Kernel operators

Kernel operators take one or two base kernels and combine them into a new kernel. The Sum kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \(k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)\). The Product kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \(k_{product}(X, Y) = k_1(X, Y) * k_2(X, Y)\). The Exponentiation kernel takes one base kernel and a scalar parameter \(p\) and combines them via \(k_{exp}(X, Y) = k(X, Y)^p\). Note that magic methods __add__, __mul___ and __pow__ are overridden on the Kernel objects, so one can use e.g. RBF() + RBF() as a shortcut for Sum(RBF(), RBF()).

1.7.4.3. Radial basis function (RBF) kernel

The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel. It is parameterized by a length-scale parameter \(l>0\), which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs \(x\) (anisotropic variant of the kernel). The kernel is given by:

\[k(x_i, x_j) = \text{exp}\left(- \frac{d(x_i, x_j)^2}{2l^2} \right)\]

where \(d(\cdot, \cdot)\) is the Euclidean distance. This kernel is infinitely differentiable, which implies that GPs with this kernel as covariance function have mean square derivatives of all orders, and are thus very smooth. The prior and posterior of a GP resulting from an RBF kernel are shown in the following figure:

1.7.4.4. Matérn kernel

The Matern kernel is a stationary kernel and a generalization of the RBF kernel. It has an additional parameter \(\nu\) which controls the smoothness of the resulting function. It is parameterized by a length-scale parameter \(l>0\), which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs \(x\) (anisotropic variant of the kernel).

Mathematical implementation of Matérn kernel Click for more details

The kernel is given by:

\[k(x_i, x_j) = \frac{1}{\Gamma(\nu)2^{\nu-1}}\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg)^\nu K_\nu\Bigg(\frac{\sqrt{2\nu}}{l} d(x_i , x_j )\Bigg),\]

where \(d(\cdot,\cdot)\) is the Euclidean distance, \(K_\nu(\cdot)\) is a modified Bessel function and \(\Gamma(\cdot)\) is the gamma function. As \(\nu\rightarrow\infty\), the Matérn kernel converges to the RBF kernel. When \(\nu = 1/2\), the Matérn kernel becomes identical to the absolute exponential kernel, i.e.,

\[k(x_i, x_j) = \exp \Bigg(- \frac{1}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{1}{2}\]

In particular, \(\nu = 3/2\):

\[k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{3}}{l} d(x_i , x_j )\Bigg) \exp \Bigg(-\frac{\sqrt{3}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{3}{2}\]

and \(\nu = 5/2\):

\[k(x_i, x_j) = \Bigg(1 + \frac{\sqrt{5}}{l} d(x_i , x_j ) +\frac{5}{3l} d(x_i , x_j )^2 \Bigg) \exp \Bigg(-\frac{\sqrt{5}}{l} d(x_i , x_j ) \Bigg) \quad \quad \nu= \tfrac{5}{2}\]

are popular choices for learning functions that are not infinitely differentiable (as assumed by the RBF kernel) but at least once (\(\nu = 3/2\)) or twice differentiable (\(\nu = 5/2\)).

The flexibility of controlling the smoothness of the learned function via \(\nu\) allows adapting to the properties of the true underlying functional relation.

The prior and posterior of a GP resulting from a Matérn kernel are shown in the following figure:

See [RW2006], pp84 for further details regarding the different variants of the Matérn kernel.

1.7.4.5. Rational quadratic kernel

The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum) of RBF kernels with different characteristic length-scales. It is parameterized by a length-scale parameter \(l>0\) and a scale mixture parameter \(\alpha>0\) Only the isotropic variant where \(l\) is a scalar is supported at the moment. The kernel is given by:

\[k(x_i, x_j) = \left(1 + \frac{d(x_i, x_j)^2}{2\alpha l^2}\right)^{-\alpha}\]

The prior and posterior of a GP resulting from a RationalQuadratic kernel are shown in the following figure:

1.7.4.6. Exp-Sine-Squared kernel

The ExpSineSquared kernel allows modeling periodic functions. It is parameterized by a length-scale parameter \(l>0\) and a periodicity parameter \(p>0\). Only the isotropic variant where \(l\) is a scalar is supported at the moment. The kernel is given by:

\[k(x_i, x_j) = \text{exp}\left(- \frac{ 2\sin^2(\pi d(x_i, x_j) / p) }{ l^ 2} \right)\]

The prior and posterior of a GP resulting from an ExpSineSquared kernel are shown in the following figure:

1.7.4.7. Dot-Product kernel

The DotProduct kernel is non-stationary and can be obtained from linear regression by putting \(N(0, 1)\) priors on the coefficients of \(x_d (d = 1, . . . , D)\) and a prior of \(N(0, \sigma_0^2)\) on the bias. The DotProduct kernel is invariant to a rotation of the coordinates about the origin, but not translations. It is parameterized by a parameter \(\sigma_0^2\). For \(\sigma_0^2 = 0\), the kernel is called the homogeneous linear kernel, otherwise it is inhomogeneous. The kernel is given by

\[k(x_i, x_j) = \sigma_0 ^ 2 + x_i \cdot x_j\]

The DotProduct kernel is commonly combined with exponentiation. An example with exponent 2 is shown in the following figure:

1.7.4.8. References

[RW2006] (1,2,3,4)

Carl E. Rasmussen and Christopher K.I. Williams, “Gaussian Processes for Machine Learning”, MIT Press 2006

[Duv2014]

David Duvenaud, “The Kernel Cookbook: Advice on Covariance functions”, 2014