Applies to version 1.3.0 of package MicrosoftML.
Reports per-instance scoring results in a data frame or RevoScaleR data source using a trained Microsoft R Machine Learning model with a RevoScaleR data source.
## S3 method for class `mlModel': rxPredict (modelObject, data, outData = NULL, writeModelVars = FALSE, extraVarsToWrite = NULL, suffix = NULL, overwrite = FALSE, dataThreads = NULL, blocksPerRead = rxGetOption("blocksPerRead"), reportProgress = rxGetOption("reportProgress"), verbose = 1, computeContext = rxGetOption("computeContext"), ...)
A RevoScaleR data source object, a data frame, or the path to a
Output text or xdf file name or an
RxDataSource with write capabilities in which to store predictions. If
NULL, a data frame is returned. The default value is
TRUE, variables in the model are written to the output data set in addition to the scoring variables. If variables from the input data set are transformed in the model, the transformed variables are also included. The default value is
NULL or character vector of additional variables names from the input data to include in the
TRUE, model variables are included as well. The default value is
A character string specifying suffix to append to the created scoring variable(s) or
NULL in there is no suffix. The default value is
TRUE, an existing
outData is overwritten; if
FALSE an existing
outData is not overwritten. The default value is
An integer specifying the desired degree of parallelism in the data pipeline. If
NULL, the number of threads used is determined internally. The default value is
Specifies the number of blocks to read for each chunk of data read from the data source.
An integer value that specifies the level of reporting on the row processing progress:
0: no progress is reported.
1: the number of processed rows is printed and updated.
2: rows processed and timings are reported.
3: rows processed and all timings are reported.
The default value is
An integer value that specifies the amount of output wanted. If
0, no verbose output is printed during calculations. Integer values from
4 provide increasing amounts of information. The default value is
Sets the context in which computations are executed, specified with a valid RxComputeContext. Currently local and RxInSqlServer compute contexts are supported.
Additional arguments to be passed directly to the Microsoft Compute Engine.
The following items are reported in the output by default: scoring on three variables for the binary classifiers: PredictedLabel, Score, and Probability; the Score for oneClassSvm and regression classifiers; PredictedLabel for Multi-class classifiers, plus a variable for each category prepended by the Score.
A data frame or an RxDataSource object
representing the created output data. By default, output from scoring binary
classifiers include three variables:
rxOneClassSvm and regression
include one variable:
Score; and multi-class classifiers include
PredictedLabel plus a variable for each category prepended by
Score. If a
suffix is provided, it is added to the end
of these output variable names.
# Estimate a logistic regression model infert1 <- infert infert1$isCase <- (infert1$case == 1) myModelInfo <- rxLogisticRegression(formula = isCase ~ age + parity + education + spontaneous + induced, data = infert1) # Create an xdf file with per-instance results using rxPredict xdfOut <- tempfile(pattern = "scoreOut", fileext = ".xdf") scoreDS <- rxPredict(myModelInfo, data = infert1, outData = xdfOut, overwrite = TRUE, extraVarsToWrite = c("isCase", "Probability")) # Summarize results with an ROC curve rxRocCurve(actualVarName = "isCase", predVarNames = "Probability", data = scoreDS) # Use the built-in data set 'airquality' to create test and train data DF <- airquality[!is.na(airquality$Ozone), ] DF$Ozone <- as.numeric(DF$Ozone) set.seed(12) randomSplit <- rnorm(nrow(DF)) trainAir <- DF[randomSplit >= 0,] testAir <- DF[randomSplit < 0,] airFormula <- Ozone ~ Solar.R + Wind + Temp # Regression Fast Tree for train data fastTreeReg <- rxFastTrees(airFormula, type = "regression", data = trainAir) # Put score and model variables in data frame, including the model variables # Add the suffix "Pred" to the new variable fastTreeScoreDF <- rxPredict(fastTreeReg, data = testAir, writeModelVars = TRUE, suffix = "Pred") rxGetVarInfo(fastTreeScoreDF) # Clean-up file.remove(xdfOut)