Type: Package
Title: Dividing Local Gaussian Processes for Online Learning Regression
Version: 1.0.1
Maintainer: Timo Braun <gptreeo.timo.braun@gmail.com>
Description: We implement and extend the Dividing Local Gaussian Process algorithm by Lederer et al. (2020) <doi:10.48550/arXiv.2006.09446>. Its main use case is in online learning where it is used to train a network of local GPs (referred to as tree) by cleverly partitioning the input space. In contrast to a single GP, 'GPTreeO' is able to deal with larger amounts of data. The package includes methods to create the tree and set its parameter, incorporating data points from a data stream as well as making joint predictions based on all relevant local GPs.
License: MIT + file LICENSE
Imports: R6, hash, DiceKriging, mlegp
Suggests: knitr, rmarkdown, spelling, testthat
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.3.1
Language: en-US
NeedsCompilation: no
Packaged: 2024-10-16 15:06:08 UTC; traschpanda
Author: Timo Braun ORCID iD [aut, cre], Anders Kvellestad ORCID iD [aut], Riccardo De Bin ORCID iD [ctb]
Repository: CRAN
Date/Publication: 2024-10-16 15:20:02 UTC

Factory function called by GPNode to create the wrapper for a specified GP package

Description

Factory function called by GPNode to create the wrapper for a specified GP package

Usage

CreateWrappedGP(
  wrapper,
  X,
  y,
  y_var,
  gp_control,
  init_covpars,
  retrain_buffer_length,
  add_buffer_in_prediction
)

Arguments

wrapper

A string specifying what GP implementation is used

X

Input data matrix with x_dim columns and at maximum Nbar rows. Is used to create the first iteration of the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

Initial covariance parameters of the local GP

retrain_buffer_length

Only retrain when the number of buffer points or collected points exceeds this value

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

Details

A detailed list of expected functions from GPTree and GPNode can be found in the comments of this file. Currently, GPs from the DiceKriging package (WrappedDiceKrigingGP) and mlegp package (WrappedmlegpGP) are implemented. The user can create their own wrapper using WrappedGP.

Value

The wrapper of the chosen GP package, containing the respective GP and information on the shared points and those stored in the buffer.

R6 Class for the nodes / leaves in the GPTree tree

Description

The nodes contain the local GP if they are leaves (at the end of a branch). Nodes that are just nodes contain information on how the input space was split. They are responsible for computing and updating the splitting probabilities. Also, the tree interacts with the local GPs through the nodes.

Currently, GPs from the DiceKriging package (WrappedDiceKrigingGP) and mlegp package (WrappedmlegpGP) are implemented. The user can create their own wrapper using WrappedGP.

Public fields

key

A string like "0110100" to identify the node in the binary tree

x_dim

Dimensionality of input points. It is set once the first point is received through the GPTree method update. It needs to be specified if min_ranges should be different from default.

theta

Overlap ratio between two leafs in the split direction. The default value is 0.

split_direction_criterion

A string that indicates which spitting criterion to use. The options are:

  • "max_spread": Split along the direction which has the largest data spread.

  • "min_lengthscale": split along the direction with the smallest length-scale hyperparameter from the local GP.

  • "max_spread_per_lengthscale": Split along the direction with the largest data spread relative to the corresponding GP length-scale hyperparameter.

  • "max_corr": Split along the direction where the input data is most strongly correlated with the target variable.

  • "principal_component": Split along the first principal component.

The default value is "max_spread_per_lengthscale".

split_position_criterion

A string indicating how the split position along the split direction should be set. Possible values are ("mean" and "median"). The default is "mean".

shape_decay

A string specifying how the probability function for a point to be assigned to the left leaf should fall off in the overlap region. The available options are a linear shape ("linear"), an exponential shape ("exponential") or a Gaussian shape ("gaussian"). Another option is to select no overlap region. This can be achieved by selecting "deterministic" or to set theta to 0. The default is "linear".

prob_min_theta

Minimum probability after which the overlap shape gets truncated (either towards 0 or 1). The default value is 0.01.

Nbar

Maximum number of data points for each GP in a leaf before it is split. The default value is 1000.

min_ranges

Smallest allowed input data spread (per dimension) before node splitting stops. It is set to its default min_ranges = rep(0.0, x_dim) once the first point is received through the update method. x_dim needs to be specified by the user if it should be different from the default.

is_leaf

If TRUE, this node a leaf, i.e the last node on its branch

wrapped_gp

An instance of the WrappedGP type

can_split

If TRUE for a given dimension, the leaf can be split along that dimension

rotation_matrix

A rotation matrix, used for transforming the data

shift

A shift, used for transforming the data

use_pc_transform

TRUE if principal components transformation is used for node splitting

x_spread

Vector of data spread for each dimension

split_index

Index for the split dimension

position_split

Position of the split along dimension split_index

width_overlap

Width of overlap region along dimension split_index

point_ids

IDs of the points assigned to this node

residuals

Vector of residuals

pred_errs

Vector of prediction uncertainties

error_scaler

Scaling factor for the prediction error to ensure desired coverage

use_n_residuals

Number of past residuals to use in calibrating the error_scaler

Methods

Public methods

Method new()

Create a new node object

Usage
GPNode$new(
  key,
  x_dim,
  theta,
  split_direction_criterion,
  split_position_criterion,
  shape_decay,
  prob_min_theta,
  Nbar,
  wrapper,
  gp_control,
  retrain_buffer_length,
  add_buffer_in_prediction,
  min_ranges = NULL,
  is_leaf = TRUE
)
Arguments
key

A string like "0110100" to identify the node in the binary tree

x_dim

Dimensionality of input points. It is set once the first point is received through the GPTree method update. It needs to be specified if min_ranges should be different from default.

theta

Overlap ratio between two leafs in the split direction. The default value is 0.

split_direction_criterion

A string that indicates which spitting criterion to use. The options are:

  • "max_spread": Split along the direction which has the largest data spread.

  • "min_lengthscale": split along the direction with the smallest length-scale hyperparameter from the local GP.

  • "max_spread_per_lengthscale": Split along the direction with the largest data spread relative to the corresponding GP length-scale hyperparameter.

  • "max_corr": Split along the direction where the input data is most strongly correlated with the target variable.

  • "principal_component": Split along the first principal component.

The default value is "max_spread_per_lengthscale".

split_position_criterion

A string indicating how the split position along the split direction should be set. Possible values are ("mean" and "median"). The default is "mean".

shape_decay

A string specifying how the probability function for a point to be assigned to the left leaf should fall off in the overlap region. The available options are a linear shape ("linear"), an exponential shape ("exponential") or a Gaussian shape ("gaussian"). Another option is to select no overlap region. This can be achieved by selecting "deterministic" or to set theta to 0. The default is "linear".

prob_min_theta

Minimum probability after which the overlap shape gets truncated (either towards 0 or 1). The default value is 0.01.

Nbar

Maximum number of data points for each GP in a leaf before it is split. The default value is 1000.

wrapper

A string that indicates which GP implementation should be used. The current version includes wrappers for the packages "DiceKriging" and "mlegp". The default setting is "DiceKriging".

gp_control

A list of control parameter that is forwarded to the wrapper. Here, the covariance function is specified. DiceKriging allows for the following kernels, passed as string: "gauss", "matern5_2", "matern3_2", "exp", "powexp" where "matern3_2" is set as default.

retrain_buffer_length

Size of the retrain buffer. The buffer for a each node collects data points and holds them until the buffer length is reached. Then the GP in the node is updated with the data in the buffer. For a fixed Nbar, higher values for retrain_buffer_length lead to faster run time (less frequent retraining), but the trade-off is a temporary reduced prediction accuracy. We advise that the choice for retrain_buffer_length should depend on the chosen Nbar. By default retrain_buffer_length is set equal to Nbar.

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated. The default is FALSE.

min_ranges

Smallest allowed input data spread (per dimension) before node splitting stops. It is set to its default min_ranges = rep(0.0, x_dim) once the first point is received through the GPTree method update. x_dim needs to be specified by the user if it should be different from the default.

is_leaf

If TRUE, this node a leaf, i.e the last node on its branch.

n_points_train_limit

Number of points at which a GP is created in the leaf

Returns

A new GPNode object. Contains the local GP in the field wrapped_gp, and information used for and related to splitting the node. If the node has been split, the local GP is removed.

Method transform()

Method to transform input data through a shift and a rotation. IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPNode$transform(X)
Arguments
X

Matrix with x points

Returns

The transformed X matrix

Method update_prob_pars()

Method to update the probability parameters (x_spread, can_split, split_index, position_split, width_overlap). IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPNode$update_prob_pars()

Method get_prob_child_1()

Method to compute the probability that a point x should go to child 1. IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPNode$get_prob_child_1(x)
Arguments
x

Single data point for which probability is computed; has to be a vector with length equal to x_dim

Returns

The probability that a point x should go to child 1

Method register_residual()

Method to register prediction performance

Usage
GPNode$register_residual(x, y)
Arguments
x

Most recent single input data point from the data stream; has to be a vector with length equal to x_dim

y

Target variable which has to be a one-dimensional matrix or a vector; any further columns will be ignored

Method update_empirical_error_pars()

Method for updating the empirical error parameters

Usage
GPNode$update_empirical_error_pars()

Method delete_gp()

Method to delete the GP. IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPNode$delete_gp()

Method clone()

The objects of this class are cloneable with this method.

Usage
GPNode$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

GPTree() for the main methods

Tree structure storing all nodes containing local GPs

Description

The base class which contains and where all parameters are set. Here, all information on how and when the splitting is carried out is stored. wrapper and gp_control specify the Gaussian process (GP) implementation and its parameters. Moreover, minimum errors and calibration of the predictions are specified here, too.

Essential methods

The following three methods are essential for the package. The remaining ones are mostly not expected to be called by the user.

Brief package functionality overview

The tree collects the information from all GPNodes which in turn contain the local GP. Currently, GPs from the DiceKriging package (WrappedDiceKrigingGP) and mlegp package (WrappedmlegpGP) are implemented. The user can create their own wrapper using WrappedGP.

Public fields

Nbar

Maximum number of data points for each GP in a leaf before it is split. The default value is 1000.

retrain_buffer_length

Size of the retrain buffer. The buffer for a each node collects data points and holds them until the buffer length is reached. Then the GP in the node is updated with the data in the buffer. For a fixed Nbar, higher values for retrain_buffer_length lead to faster run time (less frequent retraining), but the trade-off is a temporary reduced prediction accuracy. We advise that the choice for retrain_buffer_length should depend on the chosen Nbar. By default retrain_buffer_length is set equal to Nbar.

gradual_split

If TRUE, gradual splitting is used for splitting. The default value is TRUE.

theta

Overlap ratio between two leafs in the split direction. The default value is 0.

wrapper

A string that indicates which GP implementation should be used. The current version includes wrappers for the packages "DiceKriging" and "mlegp". The default setting is "DiceKriging".

gp_control

A list of control parameter that is forwarded to the wrapper. Here, the covariance function is specified. DiceKriging allows for the following kernels, passed as string: "gauss", "matern5_2", "matern3_2", "exp", "powexp" where "matern3_2" is set as default.

split_direction_criterion

A string that indicates which spitting criterion to use. The options are:

  • "max_spread": Split along the direction which has the largest data spread.

  • "min_lengthscale": split along the direction with the smallest length-scale hyperparameter from the local GP.

  • "max_spread_per_lengthscale": Split along the direction with the largest data spread relative to the corresponding GP length-scale hyperparameter.

  • "max_corr": Split along the direction where the input data is most strongly correlated with the target variable.

  • "principal_component": Split along the first principal component.

The default value is "max_spread_per_lengthscale".

split_position_criterion

A string indicating how the split position along the split direction should be set. Possible values are ("median" and "mean"). The default is "median".

shape_decay

A string specifying how the probability function for a point to be assigned to the left leaf should fall off in the overlap region. The available options are a linear shape ("linear"), an exponential shape ("exponential") or a Gaussian shape ("gaussian"). Another option is to select no overlap region. This can be achieved by selecting "deterministic" or to set theta to 0. The default is "linear".

use_empirical_error

If TRUE, the uncertainty is calibrated using recent data points. The default value is TRUE.

The most recent 25 observations are used to ensure that the prediction uncertainty yields approximately 68 % coverage. This coverage is only achieved if theta = 0 (also together with gradual_split = TRUE) is used. Nevertheless, the coverage will be closer to 68 % than it would be without calibration. The prediction uncertainties at the beginning are conservative and become less conservative with increasing number of input points.

use_reference_gp

If TRUE, the covariance parameters determined for the GP in node 0 will be used for all subsequent GPs. The default is FALSE.

min_abs_y_err

Minimum absolute error assumed for y data. The default value is 0.

min_rel_y_err

Minimum relative error assumed for y data. The default value is 100 * .Machine$double.eps.

min_abs_node_pred_err

Minimum absolute error on the prediction from a single node. The default value is 0.

min_rel_node_pred_err

Minimum relative error on the prediction from a single node. The default value is 100 * .Machine$double.eps.

prob_min_theta

Minimum probability after which the overlap shape gets truncated (either towards 0 or 1). The default value is 0.01.

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated. The default is FALSE.

x_dim

Dimensionality of input points. It is set once the first point is received through the update() or joint_prediction() method. It needs to be specified if min_ranges should be different from default.

min_ranges

Smallest allowed input data spread (per dimension) before node splitting stops. It is set to its default min_ranges = rep(0.0, x_dim) once the first point is received through the update() method. x_dim needs to be specified by the user if it should be different from the default.

max_cond_num

Add additional noise if the covariance matrix condition number exceeds this value. The default is NULL.

max_points

The maximum number of points the tree is allowed to store. The default value is Inf.

End of the user-defined input fields.

nodes

A hash to hold the GP tree, using string keys to identify nodes and their position in the tree ("0", "00", "01", "000", "001", "010", "011", etc.)

leaf_keys

Stores the keys ("0", "00", "01", "000", "001", "010", "011", etc.) for the leaves

n_points

Number of points in the tree

n_fed

Number of points fed to the tree

Methods

Public methods

Method new()

Usage
GPTree$new(
  Nbar = 1000,
  retrain_buffer_length = Nbar,
  gradual_split = TRUE,
  theta = 0,
  wrapper = "DiceKriging",
  gp_control = list(covtype = "matern3_2"),
  split_direction_criterion = "max_spread_per_lengthscale",
  split_position_criterion = "median",
  shape_decay = "linear",
  use_empirical_error = TRUE,
  use_reference_gp = FALSE,
  min_abs_y_err = 0,
  min_rel_y_err = 100 * .Machine$double.eps,
  min_abs_node_pred_err = 0,
  min_rel_node_pred_err = 100 * .Machine$double.eps,
  prob_min_theta = 0.01,
  add_buffer_in_prediction = FALSE,
  x_dim = 0,
  min_ranges = NULL,
  max_cond_num = NULL,
  max_points = Inf
)
Arguments
Nbar

Maximum number of data points for each GP in a leaf before it is split. The default value is 1000.

retrain_buffer_length

Size of the retrain buffer. The buffer for a each node collects data points and holds them until the buffer length is reached. Then the GP in the node is updated with the data in the buffer. For a fixed Nbar, higher values for retrain_buffer_length lead to faster run time (less frequent retraining), but the trade-off is a temporary reduced prediction accuracy. We advise that the choice for retrain_buffer_length should depend on the chosen Nbar. By default retrain_buffer_length is set equal to Nbar.

gradual_split

If TRUE, gradual splitting is used for splitting. The default value is TRUE.

theta

Overlap ratio between two leafs in the split direction. The default value is 0.

wrapper

A string that indicates which GP implementation should be used. The current version includes wrappers for the packages "DiceKriging" and "mlegp". The default setting is "DiceKriging".

gp_control

A list of control parameter that is forwarded to the wrapper. Here, the covariance function is specified. DiceKriging allows for the following kernels, passed as string: "gauss", "matern5_2", "matern3_2", "exp", "powexp" where "matern3_2" is set as default.

split_direction_criterion

A string that indicates which spitting criterion to use. The options are:

  • "max_spread": Split along the direction which has the largest data spread.

  • "min_lengthscale": split along the direction with the smallest length-scale hyperparameter from the local GP.

  • "max_spread_per_lengthscale": Split along the direction with the largest data spread relative to the corresponding GP length-scale hyperparameter.

  • "max_corr": Split along the direction where the input data is most strongly correlated with the target variable.

  • "principal_component": Split along the first principal component.

The default value is "max_spread_per_lengthscale".

split_position_criterion

A string indicating how the split position along the split direction should be set. Possible values are ("median" and "mean"). The default is "median".

shape_decay

A string specifying how the probability function for a point to be assigned to the left leaf should fall off in the overlap region. The available options are a linear shape ("linear"), an exponential shape ("exponential") or a Gaussian shape ("gaussian"). Another option is to select no overlap region. This can be achieved by selecting "deterministic" or to set theta to 0. The default is "linear".

use_empirical_error

If TRUE, the uncertainty is calibrated using recent data points. The default value is TRUE.

The most recent 25 observations are used to ensure that the prediction uncertainty yields approximately 68 % coverage. This coverage is only achieved if theta = 0 (also together with gradual_split = TRUE) is used. Nevertheless, the coverage will be closer to 68 % than it would be without calibration. The prediction uncertainties at the beginning are conservative and become less conservative with increasing number of input points.

use_reference_gp

If TRUE, the covariance parameters determined for the GP in node 0 will be used for all subsequent GPs. The default is FALSE.

min_abs_y_err

Minimum absolute error assumed for y data. The default value is 0.

min_rel_y_err

Minimum relative error assumed for y data. The default value is 100 * .Machine$double.eps.

min_abs_node_pred_err

Minimum absolute error on the prediction from a single node. The default value is 0.

min_rel_node_pred_err

Minimum relative error on the prediction from a single node. The default value is 100 * .Machine$double.eps.

prob_min_theta

Minimum probability after which the overlap shape gets truncated (either towards 0 or 1). The default value is 0.01.

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated. The default is FALSE.

x_dim

Dimensionality of input points. It is set once the first point is received through the update method. It needs to be specified if min_ranges should be different from default.

min_ranges

Smallest allowed input data spread (per dimension) before node splitting stops. It is set to its default min_ranges = rep(0.0, x_dim) once the first point is received through the update method. x_dim needs to be specified by the user if it should be different from the default.

max_cond_num

Add additional noise if the covariance matrix condition number exceeds this value. The default is NULL.

max_points

The maximum number of points the tree is allowed to store. The default value is Inf.

Returns

A new GPTree object. Tree-specific parameters are listed in this object. The field nodes contains a hash with all GPNodes and information related to nodes. The nodes in turn contain the local GPs. Nodes that have been split no longer contain a GP.

Examples
set.seed(42)
## Use the 1d toy data set from Higdon (2002)
X <- as.matrix(sample(seq(0, 10, length.out = 31)))
y <- sin(2 * pi * X / 10) + 0.2 * sin(2 * pi * X / 2.5)
y_variance <- rep(0.1**2, 31)

## Initialize a tree with Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE,
## and default parameters otherwise
gptree <- GPTree$new(Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE)

## For the purpose of this example, we simulate the data stream through a simple for loop.
## In actual applications, the input stream comes from e.g. a differential evolutionary scanner.
## We follow the procedure in the associated paper, thus letting the tree make a prediction
## first before we update the tree with the point.
for (i in 1:nrow(X)) {
y_pred_with_err = gptree$joint_prediction(X[i,], return_std = TRUE)
## Update the tree with the true (X,y) pair
gptree$update(X[i,], y[i], y_variance[i])
}

## In the following, we go over different initializations of the tree
## 1. The same tree as before, but using the package mlegp:
## Note: since the default for gp_control is gp_control = list(covtype = "matern3_2"),
## we set gp_control to an empty list when using mlegp.
gptree <- GPTree$new(Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE,
wrapper = "mlegp", gp_control = list())

## 2. Minimum working example:
gptree <- GPTree$new()

## 3. Fully specified example corresponding to the default settings
## Here, we choose to specify x_dim and min_ranges so that they correspond to the default values.
## If we do not specifiy them here, they will be automatically specified once
## the update or predict method is called.
gptree <- GPTree$new(Nbar = 1000, retrain_buffer_length = 1000,
gradual_split = TRUE, theta = 0, wrapper = "DiceKriging",
gp_control = list(covtype = "matern3_2"),
split_direction_criterion = "max_spread_per_lengthscale", split_position_criterion = "mean",
shape_decay = "linear", use_empirical_error = TRUE, 
use_reference_gp = FALSE, min_abs_y_err = 0, min_rel_y_err = 100 * .Machine$double.eps,
min_abs_node_pred_err = 0, min_rel_node_pred_err = 100 * .Machine$double.eps,
prob_min_theta = 0.01, add_buffer_in_prediction = FALSE, x_dim = ncol(X),
min_ranges = rep(0.0, ncol(X)), max_cond_num = NULL, max_points = Inf)

Method add_node()

Add a new GPNode to the tree. IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPTree$add_node(key)
Arguments
key

Key of the new leaf

Method get_marginal_point_prob()

Marginal probability for point x to belong to node with given key. IS EXPECTED TO NOT BE CALLED BY THE USER

Usage
GPTree$get_marginal_point_prob(x, key)
Arguments
x

Single input data point from the data stream; has to be a vector with length equal to x_dim

key

Key of the node

Returns

Returns the marginal probability for point x to belong to node with given key

Method update()

Assigns the given input point x with target variable y and associated variance y_var to a node and updates the tree accordingly

Usage
GPTree$update(x, y, y_var = 0, retrain_node = TRUE)
Arguments
x

Most recent single input data point from the data stream; has to be a vector with length equal to x_dim

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

retrain_node

If TRUE, the GP node will be retrained after the point is added.

Details

The methods takes care of both updating an existing node and splitting the parent node into two child nodes. It ensures that the each child node has at least n_points_train_limit in each GP. Further handling of duplicate points is also done here.

Method get_data_split_table()

Generates a table used to distribute data points from a node to two child nodes

Usage
GPTree$get_data_split_table(current_node)
Arguments
current_node

The GPNode whose data should be distributed

Returns

A matrix object

Method joint_prediction()

Compute the joint prediction from all relevant leaves for an input point x

Usage
GPTree$joint_prediction(x, return_std = TRUE)
Arguments
x

Single data point for which the predicted joint mean (and standard deviation) is computed; has to be a vector with length equal to x_dim

return_std

If TRUE, the standard error of the prediction is returned

Details

We follow Eqs. (5) and (6) in this paper

Returns

The prediction (and its standard error) for input point x from this tree

Method clone()

The objects of this class are cloneable with this method.

Usage
GPTree$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


## ------------------------------------------------
## Method `GPTree$new`
## ------------------------------------------------

set.seed(42)
## Use the 1d toy data set from Higdon (2002)
X <- as.matrix(sample(seq(0, 10, length.out = 31)))
y <- sin(2 * pi * X / 10) + 0.2 * sin(2 * pi * X / 2.5)
y_variance <- rep(0.1**2, 31)

## Initialize a tree with Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE,
## and default parameters otherwise
gptree <- GPTree$new(Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE)

## For the purpose of this example, we simulate the data stream through a simple for loop.
## In actual applications, the input stream comes from e.g. a differential evolutionary scanner.
## We follow the procedure in the associated paper, thus letting the tree make a prediction
## first before we update the tree with the point.
for (i in 1:nrow(X)) {
y_pred_with_err = gptree$joint_prediction(X[i,], return_std = TRUE)
## Update the tree with the true (X,y) pair
gptree$update(X[i,], y[i], y_variance[i])
}

## In the following, we go over different initializations of the tree
## 1. The same tree as before, but using the package mlegp:
## Note: since the default for gp_control is gp_control = list(covtype = "matern3_2"),
## we set gp_control to an empty list when using mlegp.
gptree <- GPTree$new(Nbar = 15, retrain_buffer_length = 15, use_empirical_error = FALSE,
wrapper = "mlegp", gp_control = list())

## 2. Minimum working example:
gptree <- GPTree$new()

## 3. Fully specified example corresponding to the default settings
## Here, we choose to specify x_dim and min_ranges so that they correspond to the default values.
## If we do not specifiy them here, they will be automatically specified once
## the update or predict method is called.
gptree <- GPTree$new(Nbar = 1000, retrain_buffer_length = 1000,
gradual_split = TRUE, theta = 0, wrapper = "DiceKriging",
gp_control = list(covtype = "matern3_2"),
split_direction_criterion = "max_spread_per_lengthscale", split_position_criterion = "mean",
shape_decay = "linear", use_empirical_error = TRUE, 
use_reference_gp = FALSE, min_abs_y_err = 0, min_rel_y_err = 100 * .Machine$double.eps,
min_abs_node_pred_err = 0, min_rel_node_pred_err = 100 * .Machine$double.eps,
prob_min_theta = 0.01, add_buffer_in_prediction = FALSE, x_dim = ncol(X),
min_ranges = rep(0.0, ncol(X)), max_cond_num = NULL, max_points = Inf)

R6 class WrappedDiceKrigingGP

Description

Contains the GP created by DiceKriging::km from the DiceKriging package

Public fields

gp

The DiceKriging GP object (DiceKriging::km in the DiceKriging manual)

X_buffer

Buffer matrix to collect x points until first GP can be trained

y_buffer

Buffer vector to collect y points until first GP can be trained

y_var_buffer

Buffer vector to collect variance of y points until first GP can be trained

add_y_var

Small additional variance used to keep the covariance matrix condition number under control

n_points_train_limit

Number of points needed before we can create the GP

n_points

The number of collected points belonging to this GP

x_dim

Dimensionality of input points

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

The initial covariance parameters when training the DiceKriging GP object in self@gp

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

retrain_buffer_length

Only retrain after this many new points have been added to the buffer

retrain_buffer_counter

Counter for the number of new points added since last retraining

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

n_shared_points

The number of own points shared with the GP in the sibling node

Methods

Public methods

Method new()

Create a new WrappedDiceKrigingGP object

Usage
WrappedDiceKrigingGP$new(
  X,
  y,
  y_var,
  gp_control,
  init_covpars,
  retrain_buffer_length,
  add_buffer_in_prediction,
  estimate_covpars = TRUE,
  X_shared = NULL,
  y_shared = NULL,
  y_var_shared = NULL
)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows. Is used to create the first iteration of the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

Initial covariance parameters of the local GP

retrain_buffer_length

Only retrain when the number of buffer points or collected points exceeds this value

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

Returns

A new WrappedDiceKrigingGP object. Besides the local GP, information on the shared points and those stored in the buffer are collected. For more information on the GP, consult the method DiceKriging::km in the DiceKriging package.

Method update_init_covpars()

Stores the initial covariance parameters (length-scales, standard deviation and trend coefficients) of the GP in the field init_covpars

Usage
WrappedDiceKrigingGP$update_init_covpars()

Method get_lengthscales()

Retrieves the length-scales of the kernel of the local GP

Usage
WrappedDiceKrigingGP$get_lengthscales()

Method get_X_data()

Retrieves the design matrix X

Usage
WrappedDiceKrigingGP$get_X_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_data()

Retrieves the response

Usage
WrappedDiceKrigingGP$get_y_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_var_data()

Retrieves the individual variances from the response

Usage
WrappedDiceKrigingGP$get_y_var_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_cov_mat()

Retrieves the covariance matrix

Usage
WrappedDiceKrigingGP$get_cov_mat()
Returns

the covariance matrix

Method update_add_y_var()

Method for updating add_y_var based on a bound for the covariance matrix condition number, based on this paper, Section 5.4

Usage
WrappedDiceKrigingGP$update_add_y_var(max_cond_num)
Arguments
max_cond_num

Max allowed condition number

Method store_point()

Stores a new point into the respective buffer method

Usage
WrappedDiceKrigingGP$store_point(
  x,
  y,
  y_var,
  shared = FALSE,
  remove_shared = TRUE
)
Arguments
x

Single input data point from the data stream; has to be a vector or row matrix with length equal to x_dim

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

shared

If TRUE, this point is shared between this GP and its sibling GP

remove_shared

If TRUE, the last of the shared points is removed

Method delete_buffers()

Method for clearing the buffers

Usage
WrappedDiceKrigingGP$delete_buffers()

Method train()

Method for (re)creating / (re)training the GP

Usage
WrappedDiceKrigingGP$train(do_buffer_check = TRUE)
Arguments
do_buffer_check

If TRUE, only train the GP if the number of stored points is larger than retrain_buffer_length

Returns

TRUE if training was performed, otherwise FALSE

Method predict()

Method for prediction

Usage
WrappedDiceKrigingGP$predict(x, return_std = TRUE)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector or row matrix with length equal to x_dim

return_std

If TRUE, the standard error is returned in addition to the prediction

Returns

Prediction for input point x

Method delete_gp()

Method to delete the GP object in self$gp

Usage
WrappedDiceKrigingGP$delete_gp()

Method create_DiceKriging_gp()

Method for calling the 'km' function in DiceKriging to create a GP object, stored in self$gp

Usage
WrappedDiceKrigingGP$create_DiceKriging_gp(X, y, y_var)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows for the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

Returns

TRUE

Method call_DiceKriging_predict()

Method for calling the 'predict' function in DiceKriging

Usage
WrappedDiceKrigingGP$call_DiceKriging_predict(x, use_gp = NULL)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector with length equal to x_dim

use_gp

optional user-defined GP which is evaluated instead of the local GP

Returns

The predictions for x from the specified GP, by default the local GP

Method clone()

The objects of this class are cloneable with this method.

Usage
WrappedDiceKrigingGP$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

R6 class WrappedGP

Description

Contains the GP created by a user-defined GP package

Details

This is effectively a dummy wrapper based on the wrapper for the mlegp package (see WrappedmlegpGP). It contains a basic implementation of the wrapper. The vignette offers a tutorial on how to change this wrapper for the new GP package.

Public fields

gp

The mlegp GP object (mlegp::mlegp in the mlegp manual)

X_buffer

Buffer matrix to collect x points until first GP can be trained

y_buffer

Buffer vector to collect y points until first GP can be trained

y_var_buffer

Buffer vector to collect variance of y points until first GP can be trained

add_y_var

Small additional variance used to keep the covariance matrix condition number under control

n_points_train_limit

Number of points needed before we can create the GP

n_points

The number of collected points belonging to this GP

x_dim

Dimensionality of input points

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

The initial covariance parameters when training the mlegp GP object in self@gp

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

retrain_buffer_length

Only retrain after this many new points have been added to the buffer

retrain_buffer_counter

Counter for the number of new points added since last retraining

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

n_shared_points

The number of own points shared with the GP in the sibling node

Methods

Public methods

Method new()

Create a new WrappedmlegpGP object

Usage
WrappedGP$new(
  X,
  y,
  y_var,
  gp_control,
  init_covpars,
  retrain_buffer_length,
  add_buffer_in_prediction,
  estimate_covpars = TRUE,
  X_shared = NULL,
  y_shared = NULL,
  y_var_shared = NULL
)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows. Is used to create the first iteration of the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

Initial covariance parameters of the local GP

retrain_buffer_length

Only retrain when the number of buffer points or collected points exceeds this value

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

Returns

A new WrappedGP object. Besides the local GP, information on the shared points and those stored in the buffer are collected. For more information on the GP, consult the respective met in the GP package.

Method update_init_covpars()

Stores the initial covariance parameters (length-scales, standard deviation and trend coefficients) of the GP in the field init_covpars

Usage
WrappedGP$update_init_covpars()

Method get_lengthscales()

Retrieves the length-scales of the kernel of the local GP

Usage
WrappedGP$get_lengthscales()

Method get_X_data()

Retrieves the design matrix X

Usage
WrappedGP$get_X_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_data()

Retrieves the response

Usage
WrappedGP$get_y_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_var_data()

Retrieves the individual variances from the response

Usage
WrappedGP$get_y_var_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_cov_mat()

Retrieves the covariance matrix

Usage
WrappedGP$get_cov_mat()
Returns

the covariance matrix

Method update_add_y_var()

Method for updating add_y_var based on a bound for the covariance matrix condition number, based on this paper, Section 5.4

Usage
WrappedGP$update_add_y_var(max_cond_num)
Arguments
max_cond_num

Max allowed condition number

Method store_point()

Stores a new point into the respective buffer method

Usage
WrappedGP$store_point(x, y, y_var, shared = FALSE, remove_shared = TRUE)
Arguments
x

Single input data point from the data stream; has to be a vector or row matrix with length equal to x_dim

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

shared

If TRUE, this point is shared between this GP and its sibling GP

remove_shared

If TRUE, the last of the shared points is removed

Method delete_buffers()

Method for clearing the buffers

Usage
WrappedGP$delete_buffers()

Method delete_gp()

Method to delete the GP object in self$gp

Usage
WrappedGP$delete_gp()

Method call_create_gp()

Method for calling the 'mlegp' function in mlegp to create a GP object, stored in self$gp

Usage
WrappedGP$call_create_gp(X, y, y_var)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows for the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

Returns

TRUE

Method call_predict()

Method for calling the 'predict' function in mlegp

Usage
WrappedGP$call_predict(x, use_gp = NULL)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector with length equal to x_dim

use_gp

Optional user-defined GP which is evaluated instead of the local GP

Returns

The predictions for x from the specified GP, by default the local GP. The output needs to be a list with fields mean and sd for the prediction and prediction error, respectively.

Method train()

Method for (re)creating / (re)training the GP

Usage
WrappedGP$train(do_buffer_check = TRUE)
Arguments
do_buffer_check

If TRUE, only train the GP if the number of stored points is larger than retrain_buffer_length

Returns

TRUE if training was performed, otherwise FALSE

Method predict()

Method for prediction

Usage
WrappedGP$predict(x, return_std = TRUE)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector or row matrix with length equal to x_dim

return_std

If TRUE, the standard error is returned in addition to the prediction

Returns

Prediction for input point x

Method clone()

The objects of this class are cloneable with this method.

Usage
WrappedGP$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

R6 class WrappedmlegpGP

Description

Contains the GP created by mlegp::mlegp from the mlegp package

Details

This package is by default not able to include individual uncertainties for input points. For this reason, all fields related to y_var are not used when updating the GP. No covariance kernel can be specified either. This implementation also assumes a vector for y (and not a matrix with multiple columns). Moreover, since no parameters can be specified for the GP, we will only update the GP parameters due to internal dependencies, but not use init_covpars.

Public fields

gp

The mlegp GP object (mlegp::mlegp in the mlegp manual)

X_buffer

Buffer matrix to collect x points until first GP can be trained

y_buffer

Buffer vector to collect y points until first GP can be trained

y_var_buffer

Buffer vector to collect variance of y points until first GP can be trained

add_y_var

Small additional variance used to keep the covariance matrix condition number under control

n_points_train_limit

Number of points needed before we can create the GP

n_points

The number of collected points belonging to this GP

x_dim

Dimensionality of input points

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

The initial covariance parameters when training the mlegp GP object in self@gp

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

retrain_buffer_length

Only retrain after this many new points have been added to the buffer

retrain_buffer_counter

Counter for the number of new points added since last retraining

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

n_shared_points

The number of own points shared with the GP in the sibling node

Methods

Public methods

Method new()

Create a new WrappedmlegpGP object

Usage
WrappedmlegpGP$new(
  X,
  y,
  y_var,
  gp_control,
  init_covpars,
  retrain_buffer_length,
  add_buffer_in_prediction,
  estimate_covpars = TRUE,
  X_shared = NULL,
  y_shared = NULL,
  y_var_shared = NULL
)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows. Is used to create the first iteration of the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

gp_control

A list of GP implementation-specific options, passed directly to the wrapped GP implementation

init_covpars

Initial covariance parameters of the local GP

retrain_buffer_length

Only retrain when the number of buffer points or collected points exceeds this value

add_buffer_in_prediction

If TRUE, points in the data buffers are added to the GP before prediction. They are added into a temporarily created GP which contains the not yet included points. The GP in the node is not yet updated.

estimate_covpars

If TRUE, the parameters are estimated by the package. Otherwise, the parameters from init_covpars are taken

X_shared

Matrix with x points that this GP shares with the GP in the sibling node

y_shared

Vector of y points that this GP shares with the GP in the sibling node

y_var_shared

Vector of y_var points that this GP shares with the GP in the sibling node

Returns

A new WrappedmlegpGP object. Besides the local GP, information on the shared points and those stored in the buffer are collected. For more information on the GP, consult the method mlegp::mlegp in the mlegp package.

Method update_init_covpars()

Stores the initial covariance parameters (length-scales, standard deviation and trend coefficients) of the GP in the field init_covpars

Usage
WrappedmlegpGP$update_init_covpars()

Method get_lengthscales()

Retrieves the length-scales of the kernel of the local GP

Usage
WrappedmlegpGP$get_lengthscales()

Method get_X_data()

Retrieves the design matrix X

Usage
WrappedmlegpGP$get_X_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_data()

Retrieves the response

Usage
WrappedmlegpGP$get_y_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_y_var_data()

Retrieves the individual variances from the response

Usage
WrappedmlegpGP$get_y_var_data(include_shared = FALSE)
Arguments
include_shared

If TRUE, shared points between this GP and its sibling GP are included

Method get_cov_mat()

Retrieves the covariance matrix

Usage
WrappedmlegpGP$get_cov_mat()
Returns

the covariance matrix

Method update_add_y_var()

Method for updating add_y_var based on a bound for the covariance matrix condition number, based on this paper, Section 5.4

Usage
WrappedmlegpGP$update_add_y_var(max_cond_num)
Arguments
max_cond_num

Max allowed condition number

Method store_point()

Stores a new point into the respective buffer method

Usage
WrappedmlegpGP$store_point(x, y, y_var, shared = FALSE, remove_shared = TRUE)
Arguments
x

Single input data point from the data stream; has to be a vector or row matrix with length equal to x_dim

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

shared

If TRUE, this point is shared between this GP and its sibling GP

remove_shared

If TRUE, the last of the shared points is removed

Method delete_buffers()

Method for clearing the buffers

Usage
WrappedmlegpGP$delete_buffers()

Method train()

Method for (re)creating / (re)training the GP

Usage
WrappedmlegpGP$train(do_buffer_check = TRUE)
Arguments
do_buffer_check

If TRUE, only train the GP if the number of stored points is larger than retrain_buffer_length

Returns

TRUE if training was performed, otherwise FALSE

Method predict()

Method for prediction

Usage
WrappedmlegpGP$predict(x, return_std = TRUE)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector or row matrix with length equal to x_dim

return_std

If TRUE, the standard error is returned in addition to the prediction

Returns

Prediction for input point x

Method delete_gp()

Method to delete the GP object in self$gp

Usage
WrappedmlegpGP$delete_gp()

Method create_mlegp_gp()

Method for calling the 'mlegp' function in mlegp to create a GP object, stored in self$gp

Usage
WrappedmlegpGP$create_mlegp_gp(X, y, y_var)
Arguments
X

Input data matrix with x_dim columns and at maximum Nbar rows for the local GP.

y

Value of target variable at input point x; has to be a one-dimensional matrix or a vector; any further columns will be ignored

y_var

Variance of the target variable; has to be a one-dimensional matrix or vector

Returns

TRUE

Method call_mlegp_predict()

Method for calling the 'predict' function in mlegp

Usage
WrappedmlegpGP$call_mlegp_predict(x, use_gp = NULL)
Arguments
x

Single data point for which the predicted mean (and standard deviation) is computed; has to be a vector with length equal to x_dim

use_gp

Optional user-defined GP which is evaluated instead of the local GP

Returns

The predictions for x from the specified GP, by default the local GP. The output needs to be a list with fields mean and sd for the prediction and prediction error, respectively.

Method clone()

The objects of this class are cloneable with this method.

Usage
WrappedmlegpGP$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

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