GaussianProcessRegressor
Gaussian process regression (GPR).
The implementation is based on Algorithm 2.1 of [RW2006].
In addition to standard scikit-learn estimator API, GaussianProcessRegressor:
Python Reference (opens in a new tab)
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.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
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:25 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:23 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:22 (opens in a new tab)
_py
PythonBridge
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:21 (opens in a new tab)
id
string
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:18 (opens in a new tab)
opts
any
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:19 (opens in a new tab)
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:497 (opens in a new tab)
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:416 (opens in a new tab)
alpha_
Dual coefficients of training data points in kernel space.
Signature
alpha_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:524 (opens in a new tab)
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:605 (opens in a new tab)
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:470 (opens in a new tab)
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:551 (opens in a new tab)
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:578 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:75 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/gaussian_process/GaussianProcessRegressor.ts:79 (opens in a new tab)
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:443 (opens in a new tab)
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:142 (opens in a new tab)
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:159 (opens in a new tab)
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:88 (opens in a new tab)
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:201 (opens in a new tab)
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:257 (opens in a new tab)
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:310 (opens in a new tab)
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:365 (opens in a new tab)