gpboost (version 0.7.9)

gpboost: 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(), ...)

Value

a trained gpb.Booster

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 labels, used if data is not an gpb.Dataset

weight

vector of response values. If not NULL, will set to dataset

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)

• min_data_in_leaf Minimal number of samples per leaf. Tuning parameter for tree-boosting (default = 20)

• max_depth Maximal depth of a tree. Tuning parameter for tree-boosting (default = no limit)

• 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)

• 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)

• use_nesterov_acc Set this to TRUE to do boosting with Nesterov acceleration (default = FALSE). Can currently only be used for tree_learner = "serial" (default option)

• nesterov_acc_rate Acceleration rate for momentum step in case Nesterov accelerated boosting is used (default = 0.5)

• oosting Boosting type. "gbdt", "rf", "dart" or "goss". Only "gbdt" allows for doing Gaussian process boosting.

• 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. Default = 100

gp_model

A GPModel object that contains the random effects (Gaussian process and / or grouped random effects) model

use_gp_model_for_validation

Boolean (default = TRUE). 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 (default = TRUE). 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

objective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary, lambdarank, multiclass, multiclass

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

• obj: objective function, can be character or custom objective function. Examples include regression, regression_l1, huber, binary, lambdarank, multiclass, multiclass

• 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

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

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,
               objective = "regression_l2", 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

# \donttest{
#--------------------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,
               objective = "regression_l2", 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
# }