gpboost: Train a GPBoost model

In gpboost: Combining Tree-Boosting with Gaussian Process and Mixed Effects Models

Train a GPBoost model

Description

Simple interface for training a GPBoost model.

Usage

gpboost(data, label = NULL, weight = NULL, params = list(),
  nrounds = 100L, gp_model = NULL, use_gp_model_for_validation = TRUE,
  train_gp_model_cov_pars = TRUE, valids = list(), obj = NULL,
  eval = NULL, verbose = 1L, record = TRUE, eval_freq = 1L,
  early_stopping_rounds = NULL, init_model = NULL, colnames = NULL,
  categorical_feature = NULL, callbacks = list(), ...)

Arguments

data	a gpb.Dataset object, used for training. Some functions, such as gpb.cv, may allow you to pass other types of data like matrix and then separately supply label as a keyword argument.
label	Vector of response values / labels, used if data is not an gpb.Dataset
weight	Vector of weights. The GPBoost algorithm currently does not support weights
params	list of "tuning" parameters. See the parameter documentation for more information. A few key parameters: learning_rate: The learning rate, also called shrinkage or damping parameter (default = 0.1). An important tuning parameter for boosting. Lower values usually lead to higher predictive accuracy but more boosting iterations are needed num_leaves: Number of leaves in a tree. Tuning parameter for tree-boosting (default = 31) max_depth: Maximal depth of a tree. Tuning parameter for tree-boosting (default = no limit) min_data_in_leaf: Minimal number of samples per leaf. Tuning parameter for tree-boosting (default = 20) lambda_l2: L2 regularization (default = 0) lambda_l1: L1 regularization (default = 0) max_bin: Maximal number of bins that feature values will be bucketed in (default = 255) train_gp_model_cov_pars: If TRUE, the covariance parameters of the Gaussian process are stimated in every boosting iterations, otherwise the gp_model parameters are not estimated. In the latter case, you need to either esimate them beforehand or provide the values via the 'init_cov_pars' parameter when creating the gp_model (default = TRUE). use_gp_model_for_validation: If TRUE, the Gaussian process is also used (in addition to the tree model) for calculating predictions on the validation data (default = TRUE) leaves_newton_update: Set this to TRUE to do a Newton update step for the tree leaves after the gradient step. Applies only to Gaussian process boosting (GPBoost algorithm) num_threads: Number of threads. For the best speed, set this to the number of real CPU cores(parallel::detectCores(logical = FALSE)), not the number of threads (most CPU using hyper-threading to generate 2 threads per CPU core).
nrounds	number of boosting iterations (= number of trees). This is the most important tuning parameter for boosting
gp_model	A GPModel object that contains the random effects (Gaussian process and / or grouped random effects) model
use_gp_model_for_validation	Boolean. If TRUE, the gp_model (Gaussian process and/or random effects) is also used (in addition to the tree model) for calculating predictions on the validation data. If FALSE, the gp_model (random effects part) is ignored for making predictions and only the tree ensemble is used for making predictions for calculating the validation / test error.
train_gp_model_cov_pars	Boolean. If TRUE, the covariance parameters of the gp_model (Gaussian process and/or random effects) are estimated in every boosting iterations, otherwise the gp_model parameters are not estimated. In the latter case, you need to either estimate them beforehand or provide the values via the init_cov_pars parameter when creating the gp_model
valids	a list of gpb.Dataset objects, used for validation
obj	(character) The distribution of the response variable (=label) conditional on fixed and random effects. This only needs to be set when doing independent boosting without random effects / Gaussian processes.
eval	evaluation function(s). This can be a character vector, function, or list with a mixture of strings and functions. a. character vector: If you provide a character vector to this argument, it should contain strings with valid evaluation metrics. See the "metric" section of the parameter documentation for a list of valid metrics. b. function: You can provide a custom evaluation function. This should accept the keyword arguments preds and dtrain and should return a named list with three elements: name: A string with the name of the metric, used for printing and storing results. value: A single number indicating the value of the metric for the given predictions and true values higher_better: A boolean indicating whether higher values indicate a better fit. For example, this would be FALSE for metrics like MAE or RMSE. c. list: If a list is given, it should only contain character vectors and functions. These should follow the requirements from the descriptions above.
verbose	verbosity for output, if <= 0, also will disable the print of evaluation during training
record	Boolean, TRUE will record iteration message to booster$record_evals
eval_freq	evaluation output frequency, only effect when verbose > 0
early_stopping_rounds	int. Activates early stopping. Requires at least one validation data and one metric. When this parameter is non-null, training will stop if the evaluation of any metric on any validation set fails to improve for early_stopping_rounds consecutive boosting rounds. If training stops early, the returned model will have attribute best_iter set to the iteration number of the best iteration.
init_model	path of model file of gpb.Booster object, will continue training from this model
colnames	feature names, if not null, will use this to overwrite the names in dataset
categorical_feature	categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. c(1L, 10L) to say "the first and tenth columns").
callbacks	List of callback functions that are applied at each iteration.
...	Additional arguments passed to gpb.train. For example valids: a list of gpb.Dataset objects, used for validation eval: evaluation function, can be (a list of) character or custom eval function record: Boolean, TRUE will record iteration message to booster$record_evals colnames: feature names, if not null, will use this to overwrite the names in dataset categorical_feature: categorical features. This can either be a character vector of feature names or an integer vector with the indices of the features (e.g. c(1L, 10L) to say "the first and tenth columns"). reset_data: Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets

Value

a trained gpb.Booster

Early Stopping

"early stopping" refers to stopping the training process if the model's performance on a given validation set does not improve for several consecutive iterations.

If multiple arguments are given to eval, their order will be preserved. If you enable early stopping by setting early_stopping_rounds in params, by default all metrics will be considered for early stopping.

If you want to only consider the first metric for early stopping, pass first_metric_only = TRUE in params. Note that if you also specify metric in params, that metric will be considered the "first" one. If you omit metric, a default metric will be used based on your choice for the parameter obj (keyword argument) or objective (passed into params).

Author(s)

Fabio Sigrist, authors of the LightGBM R package

Examples

# See https://github.com/fabsig/GPBoost/tree/master/R-package for more examples


library(gpboost)
data(GPBoost_data, package = "gpboost")

#--------------------Combine tree-boosting and grouped random effects model----------------
# Create random effects model
gp_model <- GPModel(group_data = group_data[,1], likelihood = "gaussian")
# The default optimizer for covariance parameters (hyperparameters) is 
# Nesterov-accelerated gradient descent.
# This can be changed to, e.g., Nelder-Mead as follows:
# re_params <- list(optimizer_cov = "nelder_mead")
# gp_model$set_optim_params(params=re_params)
# Use trace = TRUE to monitor convergence:
# re_params <- list(trace = TRUE)
# gp_model$set_optim_params(params=re_params)

# Train model
bst <- gpboost(data = X, label = y, gp_model = gp_model, nrounds = 16,
               learning_rate = 0.05, max_depth = 6, min_data_in_leaf = 5,
               verbose = 0)
# Estimated random effects model
summary(gp_model)

# Make predictions
# Predict latent variables
pred <- predict(bst, data = X_test, group_data_pred = group_data_test[,1],
                predict_var = TRUE, pred_latent = TRUE)
pred$random_effect_mean # Predicted latent random effects mean
pred$random_effect_cov # Predicted random effects variances
pred$fixed_effect # Predicted fixed effects from tree ensemble
# Predict response variable
pred_resp <- predict(bst, data = X_test, group_data_pred = group_data_test[,1],
                     predict_var = TRUE, pred_latent = FALSE)
pred_resp$response_mean # Predicted response mean
# For Gaussian data: pred$random_effect_mean + pred$fixed_effect = pred_resp$response_mean
pred$random_effect_mean + pred$fixed_effect - pred_resp$response_mean

#--------------------Combine tree-boosting and Gaussian process model----------------
# Create Gaussian process model
gp_model <- GPModel(gp_coords = coords, cov_function = "exponential",
                    likelihood = "gaussian")
# Train model
bst <- gpboost(data = X, label = y, gp_model = gp_model, nrounds = 8,
               learning_rate = 0.1, max_depth = 6, min_data_in_leaf = 5,
               verbose = 0)
# Estimated random effects model
summary(gp_model)
# Make predictions
pred <- predict(bst, data = X_test, gp_coords_pred = coords_test,
                predict_var = TRUE, pred_latent = TRUE)
pred$random_effect_mean # Predicted latent random effects mean
pred$random_effect_cov # Predicted random effects variances
pred$fixed_effect # Predicted fixed effects from tree ensemble
# Predict response variable
pred_resp <- predict(bst, data = X_test, gp_coords_pred = coords_test,
                     predict_var = TRUE, pred_latent = FALSE)
pred_resp$response_mean # Predicted response mean

gpboost documentation built on Oct. 24, 2023, 9:09 a.m.