GaussianProcessRegressor

Gaussian process regression (GPR).

The implementation is based on Algorithm 2.1 of [RW2006].

In addition to standard scikit-learn estimator API, GaussianProcessRegressor:

Constructors

constructor()

Signature

new GaussianProcessRegressor(opts?: object): GaussianProcessRegressor;

Parameters

Name	Type	Description
`opts?`	`object`	-
`opts.alpha?`	`number` \| `ArrayLike`	Value added to the diagonal of the kernel matrix during fitting. This can prevent a potential numerical issue during fitting, by ensuring that the calculated values form a positive definite matrix. It can also be interpreted as the variance of additional Gaussian measurement noise on the training observations. Note that this is different from using a `WhiteKernel`. If an array is passed, it must have the same number of entries as the data used for fitting and is used as datapoint-dependent noise level. Allowing to specify the noise level directly as a parameter is mainly for convenience and for consistency with `Ridge`. `Default Value` `1e-10`
`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. `Default Value` `true`
`opts.kernel?`	`any`	The kernel specifying the covariance function of the GP. If `undefined` is passed, the kernel `ConstantKernel(1.0, constant\_value\_bounds="fixed") \* RBF(1.0, length\_scale\_bounds="fixed")` is used as default. Note that the kernel hyperparameters are optimized during fitting unless the bounds are marked as “fixed”.
`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. `Default Value` `0`
`opts.n_targets?`	`number`	The number of dimensions of the target values. Used to decide the number of outputs when sampling from the prior distributions (i.e. calling `sample\_y` before `fit`). This parameter is ignored once `fit` has been called.
`opts.normalize_y?`	`boolean`	Whether or not to normalize the target values `y` by removing the mean and scaling to unit-variance. This is recommended for cases where zero-mean, unit-variance priors are used. Note that, in this implementation, the normalisation is reversed before the GP predictions are reported. `Default Value` `false`
`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: `Default Value` `'fmin_l_bfgs_b'`
`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.

Returns

GaussianProcessRegressor

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:25

Methods

dispose()

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Signature

dispose(): Promise<void>;

Returns

Promise<void>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:149

fit()

Fit Gaussian process regression model.

Signature

fit(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Feature vectors or other representations of training data.
`opts.y?`	`ArrayLike`	Target values.

Returns

Promise<any>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:166

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Signature

get_metadata_routing(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.routing?`	`any`	A `MetadataRequest` encapsulating routing information.

Returns

Promise<any>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:210

init()

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Signature

init(py: PythonBridge): Promise<void>;

Parameters

Name	Type
`py`	`PythonBridge`

Returns

Promise<void>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:93

log_marginal_likelihood()

Return log-marginal likelihood of theta for training data.

Signature

log_marginal_likelihood(opts: object): Promise<number>;

Parameters

Name	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. `Default Value` `true`
`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. If `true`, theta must not be `undefined`. `Default Value` `false`
`opts.theta?`	`any`	Kernel hyperparameters for which the log-marginal likelihood is evaluated. If `undefined`, the precomputed log_marginal_likelihood of `self.kernel\_.theta` is returned.

Returns

Promise<number>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:248

predict()

Predict using the Gaussian process regression model.

We can also predict based on an unfitted model by using the GP prior. In addition to the mean of the predictive distribution, optionally also returns its standard deviation (return\_std=True) or covariance (return\_cov=True). Note that at most one of the two can be requested.

Signature

predict(opts: object): Promise<ArrayLike>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Query points where the GP is evaluated.
`opts.return_cov?`	`boolean`	If `true`, the covariance of the joint predictive distribution at the query points is returned along with the mean. `Default Value` `false`
`opts.return_std?`	`boolean`	If `true`, the standard-deviation of the predictive distribution at the query points is returned along with the mean. `Default Value` `false`

Returns

Promise<ArrayLike>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:304

sample_y()

Draw samples from Gaussian process and evaluate at X.

Signature

sample_y(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Query points where the GP is evaluated.
`opts.n_samples?`	`number`	Number of samples drawn from the Gaussian process per query point. `Default Value` `1`
`opts.random_state?`	`number`	Determines random number generation to randomly draw samples. Pass an int for reproducible results across multiple function calls. See Glossary. `Default Value` `0`

Returns

Promise<any>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:357

score()

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Signature

score(opts: object): Promise<number>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.X?`	`ArrayLike`[]	Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape `(n\_samples, n\_samples\_fitted)`, where `n\_samples\_fitted` is the number of samples used in the fitting for the estimator.
`opts.sample_weight?`	`ArrayLike`	Sample weights.
`opts.y?`	`ArrayLike`	True values for `X`.

Returns

Promise<number>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:412

set_predict_request()

Request metadata passed to the predict 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:

Signature

set_predict_request(opts: object): Promise<any>;

Parameters

Name	Type	Description
`opts`	`object`	-
`opts.return_cov?`	`string` \| `boolean`	Metadata routing for `return\_cov` parameter in `predict`.
`opts.return_std?`	`string` \| `boolean`	Metadata routing for `return\_std` parameter in `predict`.

Returns

Promise<any>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:467

set_score_request()

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:

Signature

set_score_request(opts: object): Promise<any>;

Parameters

Name	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/GaussianProcessRegressor.ts:514

Properties

_isDisposed

boolean = false

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:23

_isInitialized

boolean = false

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:22

_py

PythonBridge

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:21

id

string

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:18

opts

any

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:19

Accessors

L_

Lower-triangular Cholesky decomposition of the kernel in X\_train\_.

Signature

L_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:633

X_train_

Feature vectors or other representations of training data (also required for prediction).

Signature

X_train_(): Promise<ArrayLike[]>;

Returns

Promise<ArrayLike[]>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:552

alpha_

Dual coefficients of training data points in kernel space.

Signature

alpha_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:660

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Signature

feature_names_in_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:741

kernel_

The kernel used for prediction. The structure of the kernel is the same as the one passed as parameter but with optimized hyperparameters.

Signature

kernel_(): Promise<any>;

Returns

Promise<any>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:606

log_marginal_likelihood_value_

The log-marginal-likelihood of self.kernel\_.theta.

Signature

log_marginal_likelihood_value_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:687

n_features_in_

Number of features seen during fit.

Signature

n_features_in_(): Promise<number>;

Returns

Promise<number>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:714

py

Signature

py(): PythonBridge;

Returns

PythonBridge

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:80

Signature

py(pythonBridge: PythonBridge): void;

Parameters

Name	Type
`pythonBridge`	`PythonBridge`

Returns

void

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:84

y_train_

Target values in training data (also required for prediction).

Signature

y_train_(): Promise<ArrayLike>;

Returns

Promise<ArrayLike>

Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:579

Files

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GaussianProcessRegressor

Constructors

constructor()

Signature

Parameters

Returns

Methods

dispose()

Signature

Returns

fit()

Signature

Parameters

Returns

get_metadata_routing()

Signature

Parameters

Returns

init()

Signature

Parameters

Returns

log_marginal_likelihood()

Signature

Parameters

Returns

predict()

Signature

Parameters

Returns

sample_y()

Signature

Parameters

Returns

score()

Signature

Parameters

Returns

set_predict_request()

Signature

Parameters

Returns

set_score_request()

Signature

Parameters

Returns

Properties

_isDisposed

_isInitialized

_py

id

opts

Accessors

L_

Signature

Returns

X_train_

Signature

Returns

alpha_

Signature

Returns

feature_names_in_

Signature

Returns

kernel_

Signature

Returns

log_marginal_likelihood_value_

Signature

Returns

n_features_in_

Signature

Returns