sklearn.gaussian_process
.GaussianProcessClassifier¶

class
sklearn.gaussian_process.
GaussianProcessClassifier
(kernel=None, *, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=None)[source]¶ Gaussian process classification (GPC) based on Laplace approximation.
The implementation is based on Algorithm 3.1, 3.2, and 5.1 of Gaussian Processes for Machine Learning (GPML) by Rasmussen and Williams.
Internally, the Laplace approximation is used for approximating the nonGaussian posterior by a Gaussian.
Currently, the implementation is restricted to using the logistic link function. For multiclass classification, several binary oneversus rest classifiers are fitted. Note that this class thus does not implement a true multiclass Laplace approximation.
Read more in the User Guide.
 Parameters
 kernelkernel instance, default=None
The kernel specifying the covariance function of the GP. If None is passed, the kernel “1.0 * RBF(1.0)” is used as default. Note that the kernel’s hyperparameters are optimized during fitting.
 optimizer‘fmin_l_bfgs_b’ or callable, default=’fmin_l_bfgs_b’
Can either be one of the internally supported optimizers for optimizing the kernel’s parameters, specified by a string, or an externally defined optimizer passed as a callable. If a callable is passed, it must have the signature:
def optimizer(obj_func, initial_theta, bounds): # * 'obj_func' is the objective function to be maximized, which # takes the hyperparameters theta as parameter and an # optional flag eval_gradient, which determines if the # gradient is returned additionally to the function value # * 'initial_theta': the initial value for theta, which can be # used by local optimizers # * 'bounds': the bounds on the values of theta .... # Returned are the best found hyperparameters theta and # the corresponding value of the target function. return theta_opt, func_min
Per default, the ‘LBFGSB’ algorithm from scipy.optimize.minimize is used. If None is passed, the kernel’s parameters are kept fixed. Available internal optimizers are:
'fmin_l_bfgs_b'
 n_restarts_optimizerint, default=0
The number of restarts of the optimizer for finding the kernel’s parameters which maximize the logmarginal likelihood. The first run of the optimizer is performed from the kernel’s initial parameters, the remaining ones (if any) from thetas sampled loguniform randomly from the space of allowed thetavalues. If greater than 0, all bounds must be finite. Note that n_restarts_optimizer=0 implies that one run is performed.
 max_iter_predictint, default=100
The maximum number of iterations in Newton’s method for approximating the posterior during predict. Smaller values will reduce computation time at the cost of worse results.
 warm_startbool, default=False
If warmstarts are enabled, the solution of the last Newton iteration on the Laplace approximation of the posterior mode is used as initialization for the next call of _posterior_mode(). This can speed up convergence when _posterior_mode is called several times on similar problems as in hyperparameter optimization. See the Glossary.
 copy_X_trainbool, default=True
If True, a persistent copy of the training data is stored in the object. Otherwise, just a reference to the training data is stored, which might cause predictions to change if the data is modified externally.
 random_stateint, RandomState instance or None, default=None
Determines random number generation used to initialize the centers. Pass an int for reproducible results across multiple function calls. See :term:
Glossary <random_state>
. multi_class{‘one_vs_rest’, ‘one_vs_one’}, default=’one_vs_rest’
Specifies how multiclass classification problems are handled. Supported are ‘one_vs_rest’ and ‘one_vs_one’. In ‘one_vs_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 multiclass predictions. Note that ‘one_vs_one’ does not support predicting probability estimates.
 n_jobsint, default=None
The number of jobs to use for the computation: the specified multiclass problems are computed in parallel.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
 Attributes
 base_estimator_
Estimator
instance The estimator instance that defines the likelihood function using the observed data.
 kernel_kernel instance
The kernel used for prediction. In case of binary classification, the structure of the kernel is the same as the one passed as parameter but with optimized hyperparameters. In case of multiclass classification, a CompoundKernel is returned which consists of the different kernels used in the oneversusrest classifiers.
 log_marginal_likelihood_value_float
The logmarginallikelihood of
self.kernel_.theta
 classes_arraylike of shape (n_classes,)
Unique class labels.
 n_classes_int
The number of classes in the training data
 base_estimator_
Examples
>>> from sklearn.datasets import load_iris >>> from sklearn.gaussian_process import GaussianProcessClassifier >>> from sklearn.gaussian_process.kernels import RBF >>> X, y = load_iris(return_X_y=True) >>> kernel = 1.0 * RBF(1.0) >>> gpc = GaussianProcessClassifier(kernel=kernel, ... random_state=0).fit(X, y) >>> gpc.score(X, y) 0.9866... >>> gpc.predict_proba(X[:2,:]) array([[0.83548752, 0.03228706, 0.13222543], [0.79064206, 0.06525643, 0.14410151]])
New in version 0.18.
Methods
fit
(X, y)Fit Gaussian process classification model
get_params
([deep])Get parameters for this estimator.
log_marginal_likelihood
([theta, …])Returns logmarginal likelihood of theta for training data.
predict
(X)Perform classification on an array of test vectors X.
Return probability estimates for the test vector X.
score
(X, y[, sample_weight])Return the mean accuracy on the given test data and labels.
set_params
(**params)Set the parameters of this estimator.

fit
(X, y)[source]¶ Fit Gaussian process classification model
 Parameters
 Xarraylike of shape (n_samples, n_features) or list of object
Feature vectors or other representations of training data.
 yarraylike of shape (n_samples,)
Target values, must be binary
 Returns
 selfreturns an instance of self.

get_params
(deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsdict
Parameter names mapped to their values.

log_marginal_likelihood
(theta=None, eval_gradient=False, clone_kernel=True)[source]¶ Returns logmarginal likelihood of theta for training data.
In the case of multiclass classification, the mean logmarginal likelihood of the oneversusrest classifiers are returned.
 Parameters
 thetaarraylike of shape (n_kernel_params,), default=None
Kernel hyperparameters for which the logmarginal likelihood is evaluated. In the case of multiclass classification, theta may be the hyperparameters of the compound kernel or of an individual kernel. In the latter case, all individual kernel get assigned the same theta values. If None, the precomputed log_marginal_likelihood of
self.kernel_.theta
is returned. eval_gradientbool, default=False
If True, the gradient of the logmarginal likelihood with respect to the kernel hyperparameters at position theta is returned additionally. Note that gradient computation is not supported for nonbinary classification. If True, theta must not be None.
 clone_kernelbool, default=True
If True, the kernel attribute is copied. If False, the kernel attribute is modified, but may result in a performance improvement.
 Returns
 log_likelihoodfloat
Logmarginal likelihood of theta for training data.
 log_likelihood_gradientndarray of shape (n_kernel_params,), optional
Gradient of the logmarginal likelihood with respect to the kernel hyperparameters at position theta. Only returned when
eval_gradient
is True.

predict
(X)[source]¶ Perform classification on an array of test vectors X.
 Parameters
 Xarraylike of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification.
 Returns
 Cndarray of shape (n_samples,)
Predicted target values for X, values are from
classes_

predict_proba
(X)[source]¶ Return probability estimates for the test vector X.
 Parameters
 Xarraylike of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification.
 Returns
 Carraylike of shape (n_samples, n_classes)
Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

score
(X, y, sample_weight=None)[source]¶ Return the mean accuracy on the given test data and labels.
In multilabel classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
 Parameters
 Xarraylike of shape (n_samples, n_features)
Test samples.
 yarraylike of shape (n_samples,) or (n_samples, n_outputs)
True labels for
X
. sample_weightarraylike of shape (n_samples,), default=None
Sample weights.
 Returns
 scorefloat
Mean accuracy of
self.predict(X)
wrt.y
.

set_params
(**params)[source]¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object. Parameters
 **paramsdict
Estimator parameters.
 Returns
 selfestimator instance
Estimator instance.