rxTransform: Dynamic Variable Transformation Functions

發行項
07/15/2019

Description

Description of the recommended method for creating on the fly variables.

Details

Analytical functions within the RevoScaleR package use a formal transformation function framework for generating on the fly variables as opposed to the traditional R method of including transformations directly in a formula statement. The RevoScaleR approach is to use the following analytical function arguments to create new named variables that can be referenced by name within a formula statement:

transformFunc: R function whose first argument and return value are named R lists with equal length vector elements. The output list can contain modifications to the input elements as well as newly named elements. R lists, instead of R data frames, are used to minimize function execution timings, implying that row name information will not be available during calculations.
transformVars: character vector selecting the names of the variables to be represented as list elements for the input to transformFunc.

In application, the transformFunc argument is analogous to the FUN argument in R's apply, lapply, etc. functions. Just as with the *apply functions, the transformFunc function does not receive results from previous chunks as input and so an external mechanism is required to carry over results from one chunk to the next. Additional information variables are available for use within a transformation function when working in a local compute context:

.rxStartRow: the row number from the original data set of the first row in the chunk of data that is being processed.
.rxChunkNum: the chunk number of the data being processed, set to 0 if a test sample of data is being processed. For example, if there are 10 blocks of data in an .xdf file, and blocksPerRead is set to 2, there will be 5 chunks of data.
.rxNumRows: the number of rows in the current chunk.
.rxReadFileName: character string containing the name of the .xdf file being processed.
.rxIsTestChunk: logical set to TRUE if the chunk of data being processed is a test sample of data.
.rxIsPrediction: logical set to TRUE if the chunk of data being processed being used in a prediction rather than a model estimation.
.rxTransformEnvir: environment containing the data specified in transformObjects, which is also the parent environment to the transformation functions.

Three functions are also available for use within transformation functions that facilitate the interaction with transformObjects objects packed into transformation function environment (.rxTransformEnvir):

.rxGet(objName): Get the value of an object in the transform environment, e.g., .rxGet("myObject").
.rxSet(objName, objValue): Set the value of an object in the transform environment, e.g., .rxSet("myObject", pi).
.rxModify(objName, ..., FUN = "+"): Modify the value of an object in the transform environment by applying to the current value a specified function, FUN. The first argument passed (behind the scenes) to FUN is always the current value of "objName". The ellipsis argument ( ...) denotes additional arguments passed to FUN. By default, FUN = "+", the addition operator. e.g.,
- Increment the current value of "myObject" by 2:.rxModify("myObject", 2)
- Decrement the current value of "myObject" by 4:.rxModify("myObject", 4, FUN = "-")
- Convert "myObject", assumed to be a vector, to a factor with specified levels:.rxModify("myObject", levels = 1:5, exclude = 6:10, FUN = "factor")

Examples


 # Develop a data.frame to serve as a local data source
 set.seed(100)
 N <- 100
 feet <- round(runif(N, 4, 6))
 inches <- floor(runif(N, 1, 12))
 sportLevels <- c("tennis", "golf", "cycling", "running")
 myDF <- data.frame(sex = sample(c("Male", "Female"), N, replace = TRUE),
                    age = round(runif(N, 15, 40)),
                    height = paste(feet, "ft", inches, "in", sep = ""),
                    pounds = rnorm(N, 140, 20),
                    sport = factor(sample(sportLevels, N, replace = TRUE),
                                   levels = sportLevels))

 # Take a peek at the data source
 head(myDF)

 # Develop a transformation function
 xform <- function(dataList)
 {
     # Create a new continuous variable from an existing continuous variable: 
     # convert weight in units of pounds to kilograms.
     dataList$kilos <- dataList$pounds * 0.45359237

     # Remove an existing variable: 'pounds'
     dataList$pounds <- NULL

     # Create a new continuous variable from an existing factor variable:
     # convert 'height' categories (character strings) to a continuous 
     # variable named 'heightInches' containing the height in inches.
     heightStrings <- as.character(dataList$height)
     feet <- as.numeric(sub("ft.*", "", heightStrings))
     inches <- as.numeric(sub("in", "", sub(".*ft", "", heightStrings)))
     dataList$heightInches <- feet * 12 + inches

     # Alter an existing factor variable: for plotting purposes, 
     # coerce the 'sport' factor levels to be in alphabetical order.
     dataList$sport <- factor(dataList$sport,
                              levels = sort(levels(dataList$sport)))

     # Return the adapted variable list
   dataList
 }

 # Perform a cube. Note the use of the 'kilos' variable in the formula, which
 # is created in the variable transformation function.
 cubeWeight <- rxCube(kilos ~ sport : sex, data = myDF, transformFunc = xform,
                      transformVars = c("pounds", "height", "sport"))

 # Analyze the cube results
 cubeWeight
 if ("lattice" %in% .packages())
 {
   barchart(kilos ~ sport | sex, data = cubeWeight, xlab = "Sport",
          ylab = "Mean Weight (kg)")
 }

 # Perform a cube with multiple dependent variables using a formula string
 cubeHW <- rxCube(cbind(kilos, heightInches) ~ sport : sex, data = myDF,
                  transformFunc = xform,
                  transformVars = c("pounds", "height", "sport"))
 cubeHW
 if ("lattice" %in% .packages())
 {
   barchart(heightInches + kilos ~ sex | sport, data = cubeHW, xlab = "Sex",
          ylab = "Mean Height (in) | Mean Weight (kg)")
 }

 # Use a transformation function to match external group data to
 # individual observations

 censusWorkers <- file.path(rxGetOption("sampleDataDir"), "CensusWorkers.xdf")

 # Create a function that creates a transformation function
 makeTransformFunc <- function()
 {
   # Put average per capita educational expenditures into a named vector
   educExp <- c(Connecticut=1795.57, Washington=1170.46, Indiana = 1289.66)

   function( dataList )
   {
       dataList$stateEducExpPC <- educExp[match(dataList$state, names(educExp))]
       return( dataList)
   }
 }

 linModObj <- rxLinMod(incwage~sex + age + stateEducExpPC, data=censusWorkers, 
   transformFun=makeTransformFunc(), transformVars="state")
 summary(linModObj)         


 ###
 # Illustrate the use of .rxGet, .rxSet and .rxModify
 ###

 # This file has five blocks and so our data step will loop through the
 # transformation function (defined below) six times: once as a test
 # check performed by rxDataStep and once per block in the file.
 # The test step is skipped if returnTransformObjects is TRUE

 inFile <- file.path(rxGetOption("unitTestDataDir"), "test100r5b.xdf") 

 # Basic example
 "myTestFun1" <- function(dataList) 
 { 
     # If returnTransformObjects is TRUE, we won't get a test chunk (0)
     print(paste("Processing chunk", .rxChunkNum))

     numRows <- length(dataList[[1]])
     # Increment numRows by the number of rows in this chunk
     .rxSet("numRows", .rxGet("numRows") + numRows)

     # Increment numChunks by 1
     .rxModify("numChunks", 1L, FUN = "+")

     # Multiply current count3 by twice its value
     sumxnum1 <- sum(dataList$xnum1)
     print(paste("Sum of chunk xnum1:", sumxnum1))
     .rxModify("sumxnum1", sumxnum1, FUN = "+")

     # Don't return any data, since were just want transformObjects
     return( NULL )  
 }

 newValues <- rxDataStep( inData = inFile, transformFunc = myTestFun1,
     transformObjects = list(numRows = 0L, numChunks = 0L, sumxnum1 = 0L),
     returnTransformObjects = TRUE)
 newValues

 # More complicated example using transformEnv
 "myFunction" <- function() 
 { 
     count1 <- 100L

     "myTestFun" <- function(dataList) 
     { 
         if (!.rxIsTestChunk)
         {
             # Ladder update: this only updates local count1: better to use .rxSet or .rxModify
             # You should get a warning about two variables with the name 'count1'
             # Chunk 0: 100
             # Chunk 1: 101
             # Chunk 2: 102
             # Chunk 3: 103
             # Chunk 4: 104
             # Chunk 5: 105         
             count1 <<- count1 + 1L
             print(count1)

             # Targeted update: increment count2
             .rxSet("count2", .rxGet("count2") + 1L)

             # Targeted update with .rxModify: increment count3
             .rxModify("count3", 1L, FUN = "+")

             # Targeted update with .rxModify: multiply current count4 by twice its value
             .rxModify("count4", 2L, FUN = "*")

             # Targeted update with .rxModify: permute second term of series, add nothing
             # Chunk 0: 1 2 3 4 5
             # Chunk 1: 1 3 4 5 2
             # Chunk 2: 1 4 5 2 3
             # Chunk 3: 1 5 2 3 4
             # Chunk 4: 1 2 3 4 5
             # Chunk 5: 1 3 4 5 2
             .rxModify("series", n = 2L, bias = 0L, FUN = "permute")
         }

         return(dataList) 
     }

     myEnv <- new.env(hash = TRUE, parent = baseenv())
     assign("permute", function(x, n = 1L, bias = 10) c(x[-n], x[n]) + bias, envir = myEnv)
     rxDataStep( inData = inFile, transformFunc = myTestFun, 
                 transformObjects = list(count1 = 0L, count2 = 0L, count3 = 0L, count4 = 1L, series = 1:5),
                 transformEnv = myEnv)

     myEnv
 }

 # Obtain the transform environment containing the results
 transformEnvir <- myFunction()

 # Check results
 all.equal(transformEnvir[["count1"]], 0L)
 all.equal(transformEnvir[["count2"]], 5L)
 all.equal(transformEnvir[["count3"]], 5L)
 all.equal(transformEnvir[["count4"]], 32L)
 all.equal(transformEnvir[["series"]], c(1L, 3L, 4L, 5L, 2L))

 #############################################################################################
 # Exclude computations for dependent variable when using rxPredict

 myTransform <- function(dataList)
 {
     if (!.rxIsPrediction)
     {
         dataList$SepalLengthLog <- log(dataList$Sepal.Length)
     }
     dataList$SepalWidthLog <- log(dataList$Sepal.Width)
     return( dataList )
 }
 linModOut <- rxLinMod(SepalLengthLog~SepalWidthLog, data = iris,
    transformFunc = myTransform, transformVars = c("Sepal.Length", "Sepal.Width"))

 # Copy the iris data and remove Sepal.Length - used for the dependent variable
 iris1 <- iris
 iris1$Sepal.Length <- NULL
 # Run a prediction using the smaller data set
 predOut <- rxPredict(linModOut, data = iris1)

共用方式為

rxTransform: Dynamic Variable Transformation Functions

Description

Details

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Examples

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