gpb.save {gpboost}

Save GPBoost model

Description

Save GPBoost model

Usage

gpb.save(booster, filename, start_iteration = NULL, num_iteration = NULL,
  save_raw_data = FALSE, ...)

Arguments

booster

Object of class gpb.Booster

filename

saved filename

start_iteration

int or NULL, optional (default=NULL) Start index of the iteration to predict. If NULL or <= 0, starts from the first iteration.

num_iteration

int or NULL, optional (default=NULL) Limit number of iterations in the prediction. If NULL, if the best iteration exists and start_iteration is NULL or <= 0, the best iteration is used; otherwise, all iterations from start_iteration are used. If <= 0, all iterations from start_iteration are used (no limits).

save_raw_data

If TRUE, the raw data (predictor / covariate data) for the Booster is also saved. Enable this option if you want to change start_iteration or num_iteration at prediction time after loading.

...

Additional named arguments passed to the predict() method of the gpb.Booster object passed to object. This is only used when there is a gp_model and when save_raw_data=FALSE

Value

gpb.Booster

Author(s)

Fabio Sigrist, authors of the LightGBM R package

Examples

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

# Train model and make prediction
gp_model <- GPModel(group_data = group_data[,1], likelihood = "gaussian")
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)
pred <- predict(bst, data = X_test, group_data_pred = group_data_test[,1],
                predict_var= TRUE, pred_latent = TRUE)
# Save model to file
filename <- tempfile(fileext = ".json")
gpb.save(bst,filename = filename)
# Load from file and make predictions again
bst_loaded <- gpb.load(filename = filename)
pred_loaded <- predict(bst_loaded, data = X_test, group_data_pred = group_data_test[,1],
                       predict_var= TRUE, pred_latent = TRUE)
# Check equality
pred$fixed_effect - pred_loaded$fixed_effect
pred$random_effect_mean - pred_loaded$random_effect_mean
pred$random_effect_cov - pred_loaded$random_effect_cov