extractJuice: extractJuice

In sjoshistrats/JuiceBox: Caret Extensions

Description

Returns cross validation predictions from folds (i.e. if 2 folds, then aggregate the predictions on fold 2 after training only on fold 1 & the predictions on fold 1 after training only on fold 2). This is helps tremendously in ensembling schemes such as stacked generalization.

Usage

extractJuice(X_train, Y_train, numFolds, parCV, numCores, seedNum, verbose_p,
  fn, fn_params)

Arguments

X_train	Training Data (excludes the response/target we wish to predict ) that will be fed into the pipeline function.
Y_train	Training Response/Target - The response/target that will be fed into the pipeline function.
numFolds	Integer indicating the number of folds to use to extract predictions
parCV	Boolean indicating whether to parallelize the extraction prodcedure.
numCores	Integer indicating the number of cores to use when generating predictions.
seedNum	Integer indicating the seed number. Using the same seed will generate the same folds.
verbose_p	Boolean indicating if prediction extraction details should be printed out the screen.
fn	The pipeline function. The pipeline function must take parameters training data, training response, validation data, validation response. See examples for details.
fn_params	Additional parameters to supply to the pipeline function. See examples for details.

Value

CV Fold Predictions

Examples

library(JuiceBox)
library(Metrics)
library(xgboost)

# Toy data set for classification
irisAllMat <- iris
irisTrainMat <- irisAllMat[,c(1:4)]
irisTrainResponse <- irisAllMat[,c(ncol(irisAllMat))]
irisTrainResponse <- factor(ifelse(irisTrainResponse == "setosa", "Yes", "No"))

# Toy data set for regression
mtcarsMat <- mtcars
mtcarsResponse <-mtcarsMat[,1]
mtcarsMat <- mtcarsMat[,c(2:ncol(mtcarsMat))]

# Pipelines
xgbPipeline_regression <- function(X_train, Y_train, X_test, Y_test, params)
{
  Y_train <- as.numeric(Y_train) - 1
  xgbFit <- xgboost(data = as.matrix(X_train), label = Y_train, max.depth = params[1],
                    eta = params[2], nround = params[3], objective = "reg:linear", verbose = 1)
  predictions <- predict(xgbFit, newdata = as.matrix(X_test))
  actual <- as.numeric(Y_test) - 1
  rmseValue <- rmse(predictions, actual)
  return(-rmseValue)
}

xgbPipeline_classification <- function(X_train, Y_train, X_test, Y_test, params)
{
  Y_train <- as.numeric(Y_train) - 1
  xgbFit <- xgboost(data = as.matrix(X_train), label = Y_train, max.depth = params[1],
                    eta = params[2], nround = params[3], objective = "binary:logistic", verbose = 1)
  predictions <- predict(xgbFit, newdata = as.matrix(X_test))
  actual <- as.numeric(Y_test) - 1
  logLossValue <- logLoss(actual, predictions)
  return(-logLossValue)
}

xgbPipeline_regression_extraction <- function(X_train, Y_train, X_test, Y_test, params)
{
  Y_train <- as.numeric(Y_train) - 1
  xgbFit <- xgboost(data = as.matrix(X_train), label = Y_train, max.depth = params[1],
                    eta = params[2], nround = params[3], objective = "reg:linear", verbose = 1)
  predictions <- predict(xgbFit, newdata = as.matrix(X_test))
  return(predictions)
}

xgbPipeline_classification_extraction <- function(X_train, Y_train, X_test, Y_test, params)
{
  Y_train <- as.numeric(Y_train) - 1
  xgbFit <- xgboost(data = as.matrix(X_train), label = Y_train, max.depth = params[1],
                    eta = params[2], nround = params[3], objective = "binary:logistic", verbose = 1)
  predictions <- predict(xgbFit, newdata = as.matrix(X_test))
  return(predictions)
}

# Testing extractJuice
extractJuice(X_train = mtcarsMat, Y_train = mtcarsResponse, numFolds = 2,
             parCV = FALSE, numCores = 8, seedNum = 101, verbose_p = TRUE,
             fn = xgbPipeline_regression_extraction, fn_params = c(3, 2, 10))

extractJuice(X_train = irisTrainMat, Y_train = irisTrainResponse, numFolds = 2,
             parCV = FALSE, numCores = 8, seedNum = 101, verbose_p = TRUE,
             fn = xgbPipeline_classification_extraction, fn_params = c(3, 2, 10))

sjoshistrats/JuiceBox documentation built on May 30, 2019, 12:05 a.m.