Error: BoxCox error during preprocess imputation R language

Question

我正在查看应用预测建模一书 Max Kuhn 中第 6 章练习 3 问题的答案，我在插补预测步骤中遇到错误（尽管完全遵循他们的答案）。可重现的代码和问题如下：

    library(AppliedPredictiveModeling)
    library(caret)
    library(RANN)

    data(ChemicalManufacturingProcess)
    predictors <- subset(ChemicalManufacturingProcess,select= -Yield)
    yield <- subset(ChemicalManufacturingProcess,select="Yield")
    # Impute
    #Split data into training and test sets
    set.seed(517)
    trainingRows <- createDataPartition(yield$Yield,
                                          p = 0.7,
                                          list = FALSE)
    
    trainPredictors <- predictors[trainingRows,]
    trainYield <- yield[trainingRows,]
    testPredictors <- predictors[-trainingRows,]
    testYield <- yield[-trainingRows,]
    
    #Pre-process trainPredictors and apply to trainPredictors and testPredictors
    pp <- preProcess(trainPredictors,method=c("BoxCox","center","scale","knnImpute"))
    ppTrainPredictors <- predict(pp,newdata=trainPredictors)
    ppTestPredictors <- predict(pp,newdata=testPredictors) # This results in an error

它给出的错误是：Error in RANN::nn2(old[, non_missing_cols, drop = FALSE], new[, non_missing_cols, : NA/NaN/Inf in foreign function call (arg 2)

当我改用 YeoJohnson 转换时，它似乎有效（我读到它能够处理非正数）

但是，我不明白为什么它不能处理测试数据，因为它只是训练数据的一个不同子集？它只是用于问题的插补步骤？

我找不到任何答案，这看起来很奇怪，因为肯定其他看过这本书的人会注意到吗？还是我太厚了？

谢谢

Answer 1

您收到该错误是因为 boxcox 转换不接受零。如果您查看 BoxCoxTrans 的帮助页面，它会写道：

If any(y <= 0) or if length(unique(y)) < numUnique, lambda is not estimated and no transformation is applied.

因此，如果您的 preProcess() 在列中没有零的训练集上是运行，则会应用 boxcox 变换，但它不适用于包含零的测试集。

在上面的书籍示例中，很可能种子是使用较旧的 R 版本设置的，所以它可以工作。如果您使用的是较新版本的 R，则它不起作用。所以如果我检查你的例子：

cbind(colSums(trainPredictors==0,na.rm=TRUE),colSums(testPredictors==0,na.rm=TRUE)) 
                       [,1] [,2]
BiologicalMaterial01      0    0
BiologicalMaterial02      0    0
BiologicalMaterial03      0    0
BiologicalMaterial04      0    0
BiologicalMaterial05      0    0
BiologicalMaterial06      0    0
BiologicalMaterial07      0    0
BiologicalMaterial08      0    0
BiologicalMaterial09      0    0
BiologicalMaterial10      0    0
BiologicalMaterial11      0    0
BiologicalMaterial12      0    0
ManufacturingProcess01    1    2
ManufacturingProcess02   29    6
ManufacturingProcess03    0    0
ManufacturingProcess04    0    0
ManufacturingProcess05    0    0
ManufacturingProcess06    0    0
ManufacturingProcess07    0    0
ManufacturingProcess08    0    0
ManufacturingProcess09    0    0
ManufacturingProcess10    0    0
ManufacturingProcess11    0    0
ManufacturingProcess12  104   38
ManufacturingProcess13    0    0
ManufacturingProcess14    0    0
ManufacturingProcess15    0    0
ManufacturingProcess16    1    0
ManufacturingProcess17    0    0
ManufacturingProcess18    1    0

你看ManufacturingProcess16,ManufacturingProcess18会给你出问题

Yeo-Johnson 变换可以处理零或负值，所以这不是问题。

如果您想继续工作示例，您可以尝试使用另一个种子：

set.seed(517)
trainingRows <- createDataPartition(yield$Yield,
                                          p = 0.7,
                                          list = FALSE)
    
trainPredictors <- predictors[trainingRows,]
trainYield <- yield[trainingRows,]
testPredictors <- predictors[-trainingRows,]
testYield <- yield[-trainingRows,]

Error: BoxCox error during preprocess imputation R language

Error: BoxCox error during preprocess imputation R language

r

transformation

r-caret

predict

imputation