Dyncov: Rename predicted.CLV -> predicted.period.CLV

R/clv_template_controlflow_predict.R

@@ -81,7 +81,9 @@ clv.controlflow.predict.add.uncertainty.estimates <- function(clv.fitted, dt.pre
   dt.boots[, Id := sub("_BOOTSTRAP_ID_[0-9]+$", "", Id)]
 
   # quantiles for each predicted quantity: select only the existing ones
-  cols.predictions <- c("PAlive", "CET", "DERT", "DECT", "predicted.mean.spending", "predicted.period.spending", "predicted.CLV")
+  cols.predictions <- c("PAlive", "CET", "DERT", "DECT",
+                        "predicted.mean.spending", "predicted.period.spending",
+                        "predicted.CLV", "predicted.period.CLV")
   cols.predictions <- cols.predictions[cols.predictions %in% colnames(dt.boots)]
 
   # Long-format for easier handling of different prediction columns

R/f_generics_clvfittedtransactions.R

@@ -206,7 +206,7 @@ setMethod("clv.controlflow.predict.add.actuals", signature(clv.fitted="clv.fitte
 # .clv.controlflow.predict.post.process.prediction.table ------------------------------------------------------------------------------
 setMethod("clv.controlflow.predict.post.process.prediction.table", signature = signature(clv.fitted="clv.fitted.transactions"), function(clv.fitted, dt.predictions, has.actuals, verbose, predict.spending, ...){
   predicted.mean.spending <- i.predicted.mean.spending <- actual.period.spending <- i.actual.period.spending <- predicted.period.spending <- NULL
-  predicted.CLV <- CET <- DECT <- DERT <- NULL
+  predicted.CLV <- predicted.period.CLV <- CET <- DECT <- DERT <- NULL
 
   # Predict spending ---------------------------------------------------------------------------------------
   # depends on content of predict.spending:
@@ -289,7 +289,7 @@ setMethod("clv.controlflow.predict.post.process.prediction.table", signature = s
     if("DERT" %in% colnames(dt.predictions))
       dt.predictions[, predicted.CLV := DERT * predicted.mean.spending]
     if("DECT" %in% colnames(dt.predictions))
-      dt.predictions[, predicted.CLV := DECT * predicted.mean.spending]
+      dt.predictions[, predicted.period.CLV := DECT * predicted.mean.spending]
 
     # If spending is predicted, also add predicted.period.spending
     dt.predictions[, predicted.period.spending := predicted.mean.spending * CET]
@@ -316,6 +316,8 @@ setMethod("clv.controlflow.predict.post.process.prediction.table", signature = s
 
   if("predicted.CLV" %in% colnames(dt.predictions))
     cols <- c(cols, "predicted.CLV")
+  if("predicted.period.CLV" %in% colnames(dt.predictions))
+    cols <- c(cols, "predicted.period.CLV")
 
   setcolorder(dt.predictions, cols)
 

R/f_interface_predict_clvfittedtransactions.R

@@ -100,7 +100,8 @@
 #' \item{actual.period.spending}{Actual total spending until prediction.end. Only if there is a holdout period and the prediction ends in it, otherwise not reported.}
 #' \item{predicted.mean.spending}{The mean spending per transactions as predicted by the spending model.}
 #' \item{predicted.period.spending}{The predicted total spending until prediction.end (\code{CET*predicted.mean.spending}).}
-#' \item{predicted.CLV}{Customer Lifetime Value based on \code{DERT/DECT} and \code{predicted.mean.spending}.}
+#' \item{predicted.CLV}{Customer Lifetime Value based on \code{DERT*predicted.mean.spending}.}
+#' \item{predicted.period.CLV}{Customer Lifetime Value until prediction.end based on \code{DECT*predicted.mean.spending}.}
 #'
 #' If predicting for new customers (using \code{newcustomer()}), a numeric scalar indicating the expected
 #' number of transactions is returned instead.

tests/testthat/helper_testthat_consistency.R

@@ -72,7 +72,10 @@ fct.testthat.consistency.cov.params.0.predict.same <- function(fitted.nocov, fit
     fct.compare.prediction.result <- function(dt.pred.nocov, dt.pred.cov){
       if(is.dyncov == TRUE){
         # DERT unequal to DECT because only predict short period!
-        expect_silent(data.table::setnames(dt.pred.cov, old="DECT",new = "DERT"))
+        expect_silent(data.table::setnames(
+          dt.pred.cov,
+          old=c("DECT", "predicted.period.CLV"),
+          new = c("DERT", "predicted.CLV")))
         expect_true(isTRUE(all.equal(dt.pred.nocov[,  !c("DERT", "predicted.CLV")],
                                      dt.pred.cov[,    !c("DERT", "predicted.CLV")])))
       }else{

tests/testthat/helper_testthat_inputchecks_nocov.R

@@ -215,7 +215,7 @@ fct.testthat.inputchecks.cannot.predict.without.spending <- function(method, is.
                    regexp = "there is no spending data")
       # but works without spending
       expect_silent(dt.pred <- predict(clv.spending, newdata=clv.cdnow.nospending, predict.spending=FALSE, verbose=FALSE))
-      expect_false(any(c("predicted.mean.spending","predicted.CLV") %in% colnames(dt.pred)))
+      expect_false(any(c("predicted.mean.spending","predicted.CLV", "predicted.period.CLV") %in% colnames(dt.pred)))
     }
   })
 }