Object used to get / set parameters and other model configuration options for a forest model in the "low-level" stochtree interface
Source:R/config.R
ForestModelConfig.RdThe "low-level" stochtree interface enables a high degreee of sampler customization, in which users employ R wrappers around C++ objects like ForestDataset, Outcome, CppRng, and ForestModel to run the Gibbs sampler of a BART model with custom modifications. ForestModelConfig allows users to specify / query the parameters of a forest model they wish to run.
Value
Vector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
Vector specifying sampling probability for all p covariates in ForestDataset
Number of trees in a forest
Number of features in a forest model training set
Number of observations in a forest model training set
Root node split probability in tree prior
Depth prior penalty in tree prior
Minimum number of samples in a tree leaf
Maximum depth of any tree in the ensemble in the model
Integer coded leaf model type
Scale parameter used in Gaussian leaf models
Shape parameter for IG leaf models
Scale parameter for IG leaf models
Number of unique cutpoints to consider
Public fields
feature_typesVector of integer-coded feature types (integers where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
leaf_dimensionDimension of the leaf model
alphaRoot node split probability in tree prior
betaDepth prior penalty in tree prior
min_samples_leafMinimum number of samples in a tree leaf
max_depthMaximum depth of any tree in the ensemble in the model. Setting to
-1does not enforce any depth limits on trees.leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression)
leaf_model_scaleScale parameter used in Gaussian leaf models
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
variance_forest_shapeShape parameter for IG leaf models (applicable when
leaf_model_type = 3)variance_forest_scaleScale parameter for IG leaf models (applicable when
leaf_model_type = 3)cutpoint_grid_sizeNumber of unique cutpoints to consider Create a new ForestModelConfig object.
Methods
Method new()
Usage
ForestModelConfig$new(
feature_types = NULL,
num_trees = NULL,
num_features = NULL,
num_observations = NULL,
variable_weights = NULL,
leaf_dimension = 1,
alpha = 0.95,
beta = 2,
min_samples_leaf = 5,
max_depth = -1,
leaf_model_type = 1,
leaf_model_scale = NULL,
variance_forest_shape = 1,
variance_forest_scale = 1,
cutpoint_grid_size = 100
)Arguments
feature_typesVector of integer-coded feature types (where 0 = numeric, 1 = ordered categorical, 2 = unordered categorical)
num_treesNumber of trees in the forest being sampled
num_featuresNumber of features in training dataset
num_observationsNumber of observations in training dataset
variable_weightsVector specifying sampling probability for all p covariates in ForestDataset
leaf_dimensionDimension of the leaf model (default:
1)alphaRoot node split probability in tree prior (default:
0.95)betaDepth prior penalty in tree prior (default:
2.0)min_samples_leafMinimum number of samples in a tree leaf (default:
5)max_depthMaximum depth of any tree in the ensemble in the model. Setting to
-1does not enforce any depth limits on trees. Default:-1.leaf_model_typeInteger specifying the leaf model type (0 = constant leaf, 1 = univariate leaf regression, 2 = multivariate leaf regression). Default:
0.leaf_model_scaleScale parameter used in Gaussian leaf models (can either be a scalar or a q x q matrix, where q is the dimensionality of the basis and is only >1 when
leaf_model_int = 2). Calibrated internally as1/num_trees, propagated along diagonal if needed for multivariate leaf models.variance_forest_shapeShape parameter for IG leaf models (applicable when
leaf_model_type = 3). Default:1.variance_forest_scaleScale parameter for IG leaf models (applicable when
leaf_model_type = 3). Default:1.cutpoint_grid_sizeNumber of unique cutpoints to consider (default:
100)
Method get_alpha()
Query root node split probability in tree prior for this ForestModelConfig object
Method get_min_samples_leaf()
Query root node split probability in tree prior for this ForestModelConfig object
Method get_max_depth()
Query root node split probability in tree prior for this ForestModelConfig object
Method get_leaf_model_scale()
Query scale parameter used in Gaussian leaf models for this ForestModelConfig object
Method get_variance_forest_shape()
Query shape parameter for IG leaf models for this ForestModelConfig object
Method get_variance_forest_scale()
Query scale parameter for IG leaf models for this ForestModelConfig object