make.configs: Helper Functions for Manipulating Base Learner Configurations

Description

Helper Functions for Manipulating Base Learner Configurations

Usage

make.configs(baselearner=c("nnet","rf","svm","gbm","knn","penreg")
  , config.df, type = "regression")
make.configs.knn.regression(df=expand.grid(
  kernel=c("rectangular","epanechnikov","triweight","gaussian")
  , k=c(5,10,20,40)))
make.configs.gbm.regression(df=expand.grid(
  n.trees=c(1000,2000)
  , interaction.depth=c(3,4)
  , shrinkage=c(0.001,0.01,0.1,0.5)
  , bag.fraction=0.5))
make.configs.svm.regression(df=expand.grid(
  cost=c(0.1,0.5,1.0,5.0,10,50,75,100)
  , epsilon=c(0.1,0.25)
  , kernel="radial"))
make.configs.rf.regression(df=expand.grid(
  ntree=c(100,500)
  , mtry.mult=c(1,2)
  , nodesize=c(2,5,25,100)))
make.configs.nnet.regression(df=expand.grid(
  decay=c(1e-4,1e-2,1,100)
  , size=c(5,10,20,40)
  , maxit=2000))
make.configs.penreg.regression(df = expand.grid(
  alpha = 0.0
  , lambda = 10^(-8:+7)))
make.configs.bart.regression(df = rbind(cbind(expand.grid(
  num_trees = c(50, 100), k = c(2,3,4,5)), q = 0.9, nu = 3)
  , cbind(expand.grid(
  num_trees = c(50, 100), k = c(2,3,4,5)), q = 0.75, nu = 10)
  ))
make.instances(baselearner.configs, partitions)
extract.baselearner.name(config, type="regression")

Arguments

baselearner

Name of base learner algorithm. Currently, seven base learners are included: 1) Neural Network (nnet using package nnet), 2) Random Forest (rf using package randomForest), 3) Support Vector Machine (

df,config.df

Data frame, with columns named after tuning parameters belonging to the base learner, and each row indicating a tuning-parameter combination to include in the configuration list.

type

Type of base learner. Currently, only "regression" is supported.

baselearner.configs

Base learner configuration list to use in generating instances.

partitions

A matrix whose columns define data partitions, usually the output of generate.partitions.

config

Base learner configuration object.

Value

The make.configs family of functions return a list of objects of various base learner config classes, such as KNN.Regression.Config. Function make.instances returns an object of class Instance.List. Function extract.baselearner.name returns a character object representing the name of the base learner associated with the passed-in config object. For example, for a KNN.Regression.Config object, we get back "KNN". This utility function can be used in printing base learner names based on class of a config object.