Class: GaussianProcessClassifier
Gaussian process classification (GPC) based on Laplace approximation.
The implementation is based on Algorithm 3.1, 3.2, and 5.1 from [RW2006].
Internally, the Laplace approximation is used for approximating the non-Gaussian posterior by a Gaussian.
Currently, the implementation is restricted to using the logistic link function. For multi-class classification, several binary one-versus rest classifiers are fitted. Note that this class thus does not implement a true multi-class Laplace approximation.
Read more in the User Guide.
Constructors
new GaussianProcessClassifier()
new GaussianProcessClassifier(
opts?):GaussianProcessClassifier
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.copy_X_train? | boolean | 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. |
opts.kernel? | any | The kernel specifying the covariance function of the GP. If undefined is passed, the kernel “1.0 * RBF(1.0)” is used as default. Note that the kernel’s hyperparameters are optimized during fitting. Also kernel cannot be a CompoundKernel. |
opts.max_iter_predict? | number | 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. |
opts.multi_class? | "one_vs_rest" | "one_vs_one" | Specifies how multi-class 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 multi-class predictions. Note that ‘one_vs_one’ does not support predicting probability estimates. |
opts.n_jobs? | number | The number of jobs to use for the computation: the specified multiclass problems are computed in parallel. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.n_restarts_optimizer? | number | The number of restarts of the optimizer for finding the kernel’s parameters which maximize the log-marginal likelihood. The first run of the optimizer is performed from the kernel’s initial parameters, the remaining ones (if any) from thetas sampled log-uniform randomly from the space of allowed theta-values. If greater than 0, all bounds must be finite. Note that n_restarts_optimizer=0 implies that one run is performed. |
opts.optimizer? | "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: |
opts.random_state? | number | Determines random number generation used to initialize the centers. Pass an int for reproducible results across multiple function calls. See Glossary. |
opts.warm_start? | boolean | If warm-starts 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. |
Returns GaussianProcessClassifier
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:29
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/gaussian_process/GaussianProcessClassifier.ts:27 |
_isInitialized | boolean | false | generated/gaussian_process/GaussianProcessClassifier.ts:26 |
_py | PythonBridge | undefined | generated/gaussian_process/GaussianProcessClassifier.ts:25 |
id | string | undefined | generated/gaussian_process/GaussianProcessClassifier.ts:22 |
opts | any | undefined | generated/gaussian_process/GaussianProcessClassifier.ts:23 |
Accessors
base_estimator_
Get Signature
get base_estimator_():
Promise<any>
The estimator instance that defines the likelihood function using the observed data.
Returns Promise<any>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:453
classes_
Get Signature
get classes_():
Promise<ArrayLike>
Unique class labels.
Returns Promise<ArrayLike>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:507
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:588
log_marginal_likelihood_value_
Get Signature
get log_marginal_likelihood_value_():
Promise<number>
The log-marginal-likelihood of self.kernel_.theta
Returns Promise<number>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:480
n_classes_
Get Signature
get n_classes_():
Promise<number>
The number of classes in the training data
Returns Promise<number>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:534
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:561
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:91
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:147
fit()
fit(
opts):Promise<any>
Fit Gaussian process classification model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Feature vectors or other representations of training data. |
opts.y? | ArrayLike | Target values, must be binary. |
Returns Promise<any>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:164
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:205
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:104
log_marginal_likelihood()
log_marginal_likelihood(
opts):Promise<number>
Return log-marginal likelihood of theta for training data.
In the case of multi-class classification, the mean log-marginal likelihood of the one-versus-rest classifiers are returned.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.clone_kernel? | boolean | If true, the kernel attribute is copied. If false, the kernel attribute is modified, but may result in a performance improvement. |
opts.eval_gradient? | boolean | If true, the gradient of the log-marginal likelihood with respect to the kernel hyperparameters at position theta is returned additionally. Note that gradient computation is not supported for non-binary classification. If true, theta must not be undefined. |
opts.theta? | ArrayLike | Kernel hyperparameters for which the log-marginal likelihood is evaluated. In the case of multi-class 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 undefined, the precomputed log_marginal_likelihood of self.kernel_.theta is returned. |
Returns Promise<number>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:243
predict()
predict(
opts):Promise<ArrayLike>
Perform classification on an array of test vectors X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Query points where the GP is evaluated for classification. |
Returns Promise<ArrayLike>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:293
predict_proba()
predict_proba(
opts):Promise<ArrayLike[]>
Return probability estimates for the test vector X.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Query points where the GP is evaluated for classification. |
Returns Promise<ArrayLike[]>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:329
score()
score(
opts):Promise<number>
Return the mean accuracy on the given test data and labels.
In multi-label 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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike | True labels for X. |
Returns Promise<number>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:367
set_score_request()
set_score_request(
opts):Promise<any>
Request metadata passed to the score method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/gaussian_process/GaussianProcessClassifier.ts:417