gpb.grid.search.tune.parameters: Function for choosing tuning parameters

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

Function for choosing tuning parameters

Description

Function that allows for choosing tuning parameters from a grid in a determinstic or random way using cross validation or validation data sets.

Usage

gpb.grid.search.tune.parameters(param_grid, data, params = list(),
  num_try_random = NULL, nrounds = 100L, gp_model = NULL,
  use_gp_model_for_validation = TRUE, train_gp_model_cov_pars = TRUE,
  folds = NULL, nfold = 4L, label = NULL, weight = NULL, obj = NULL,
  eval = NULL, verbose_eval = 1L, stratified = TRUE, init_model = NULL,
  colnames = NULL, categorical_feature = NULL,
  early_stopping_rounds = NULL, callbacks = list(), ...)

Arguments

param_grid	list with candidate parameters defining the grid over which a search is done
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.
params	list with other parameters not included in param_grid
num_try_random	integer with number of random trial on parameter grid. If NULL, a deterministic search is done
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
folds	list provides a possibility to use a list of pre-defined CV folds (each element must be a vector of test fold's indices). When folds are supplied, the nfold and stratified parameters are ignored.
nfold	the original dataset is randomly partitioned into nfold equal size subsamples.
label	Vector of labels, used if data is not an gpb.Dataset
weight	vector of response values. If not NULL, will set to dataset
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_eval	integer. Whether to display information on the progress of tuning parameter choice. If None or 0, verbose is of. If = 1, summary progress information is displayed for every parameter combination. If >= 2, detailed progress is displayed at every boosting stage for every parameter combination.
stratified	a boolean indicating whether sampling of folds should be stratified by the values of outcome labels.
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").
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.
callbacks	List of callback functions that are applied at each iteration.
...	other parameters, see Parameters.rst for more information.

Value

A list with the best parameter combination and score The list has the following format: list("best_params" = best_params, "best_iter" = best_iter, "best_score" = best_score)

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

Examples

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

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

# Create random effects model, dataset, and define parameter grid
gp_model <- GPModel(group_data = group_data[,1], likelihood="gaussian")
dataset <- gpb.Dataset(X, label = y)
param_grid = list("learning_rate" = c(1,0.1,0.01), 
                  "min_data_in_leaf" = c(10,100,1000),
                  "max_depth" = c(1,2,3,5,10),
                  "lambda_l2" = c(0,1,10))
other_params <- list(num_leaves = 2^10)
# Note: here we try different values for 'max_depth' and thus set 'num_leaves' to a large value.
#       An alternative strategy is to impose no limit on 'max_depth', 
#       and try different values for 'num_leaves' as follows:
# param_grid = list("learning_rate" = c(1,0.1,0.01), 
#                   "min_data_in_leaf" = c(10,100,1000),
#                   "num_leaves" = 2^(1:10),
#                   "lambda_l2" = c(0,1,10))
# other_params <- list(max_depth = -1)
set.seed(1)
opt_params <- gpb.grid.search.tune.parameters(param_grid = param_grid, params = other_params,
                                              num_try_random = NULL, nfold = 4,
                                              data = dataset, gp_model = gp_model,
                                              use_gp_model_for_validation=TRUE, verbose_eval = 1,
                                              nrounds = 1000, early_stopping_rounds = 10)
print(paste0("Best parameters: ",
             paste0(unlist(lapply(seq_along(opt_params$best_params), 
                                  function(y, n, i) { paste0(n[[i]],": ", y[[i]]) }, 
                                  y=opt_params$best_params, 
                                  n=names(opt_params$best_params))), collapse=", ")))
print(paste0("Best number of iterations: ", opt_params$best_iter))
print(paste0("Best score: ", round(opt_params$best_score, digits=3)))
# Note: other scoring / evaluation metrics can be chosen using the 
#       'metric' argument, e.g., metric = "l1"

# Using manually defined validation data instead of cross-validation
valid_tune_idx <- sample.int(length(y), as.integer(0.2*length(y)))
folds = list(valid_tune_idx)
opt_params <- gpb.grid.search.tune.parameters(param_grid = param_grid, params = other_params,
                                              num_try_random = NULL, folds = folds,
                                              data = dataset, gp_model = gp_model,
                                              use_gp_model_for_validation=TRUE, verbose_eval = 1,
                                              nrounds = 1000, early_stopping_rounds = 10)

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