Made the off line reports shine even more

.gitignore

@@ -14,9 +14,8 @@ examples/**/*.html
 r/*.csv
 r/tests/testthat/*.html
 r/tests/testthat/*.pdf
-/*.ipynb
-
-# Python
+**/*.ipynb
+**/*.gz
 *.pyc
 build/
 *.egg-info/

examples/datamart/offlinereports.R

@@ -1,17 +1,140 @@
-# Example preprocessing to create off-line model reports for
-# CDH Sample, for CreditCards offers in Outbound channels
+# Example R script to create off-line model reports. There is a similar
+# but much less complex bash script to do the same. Any language can really
+# be used here - the fact that the off-line model reports are in an R notebook
+# does not mean the preprocessing and batch processing needs to be in R as well.
+
+# You can run this script from R, from R Studio, VS Code or really any editor
+# of your choice. You will need to change some of the paths defined here.
 
 library(pdstools)
 library(data.table)
+library(rmarkdown)
+library(arrow)
+
+# Pandoc is needed by RMarkdown and part of RStudio. If you run this
+# script outside of RStudio you'll need to make sure pandoc is installed
+# and known to R / markdown. For now this is the best I could think of. To
+# make it slightly more generic, dir can be a character vector of paths:
+if (!rmarkdown::pandoc_available()) {
+  rmarkdown::find_pandoc(dir = c("/opt/anaconda3/bin"))
+  cat("Pandoc:", rmarkdown::pandoc_available(), fill = T)
+}
+
+customer <- "SampleCustomer" # just for titles, change to your customer name
+datamart_datasets_folder <- "~/Downloads" # will pick the latest from there
+
+# Path to the checked out versions of the notebooks. You'll need them locally
+# so make sure to to a "clone" of the PDS Tools GitHub repository at
+# https://github.com/pegasystems/pega-datascientist-tools. Update the path
+# below to reflect the folder where you cloned the repo.
+
+pdstools_repo_folder <- "~/Documents/pega/pega-datascientist-tools"
+
+healthcheck_notebook_R <- file.path(pdstools_repo_folder, "examples/datamart/healthcheck.Rmd")
+offlinemodelreport_notebook_R <- file.path(pdstools_repo_folder, "examples/datamart/modelreport.Rmd")
 
-# Read the latest ADM Model export files from the Downloads folder
-dm <- ADMDatamart("~/Downloads", 
+working_folder <- tempdir(TRUE)
+output_folder <- file.path(getwd(), "reports")
+if (!dir.exists(output_folder)) dir.create(output_folder)
+
+# Read ADM Datamart from the folder specified above. You can also give
+# explicit paths to both dataset. See help on ADMDatamart, in R Studio
+# with ?pdstools::ADMDatamart or online at
+# https://pegasystems.github.io/pega-datascientist-tools/R/reference/ADMDatamart.html
+
+# Example of a function you can implement to highlight certain 
+# types of predictors based on their names. By default the system will
+# highlight IH.* and Param.* predictors - simply splitting on the first dot,
+# but you can customize this as shown below:
+
+myPredictorCategorization <- function(name)
+{
+  if (startsWith(name, "Param.ExtGroup")) return("External Model")
+  if (endsWith(name, "Score")) return("External Model")
+  if (endsWith(name, "RiskCode")) return("External Model")
+
+  return(defaultPredictorCategorization(name))
+}
+
+dm <- ADMDatamart(datamart_datasets_folder, 
+                  # optional predictor categorization, see above
+                  predictorCategorization = myPredictorCategorization,
+
+                  # filtering the data to be used
                   filterModelData = function(mdls) {
-                    return(mdls[ConfigurationName == "OmniAdaptiveModel" & Group == "CreditCards" & Direction == "Outbound"])
+                    return(mdls[ConfigurationName %in% c("OmniAdaptiveModel") & 
+                                  Group == "CreditCards" & 
+                                  Direction == "Outbound"])
                   })
 
-# Write back a CSV with only the model data of interest
-write.csv(dm$modeldata, "~/Downloads/models.csv", row.names = F)
+# Write back temp files with the filtered data - not strictly necessary, you
+# can also refer to the full files in the call to the notebooks.
+
+tempModelFile <- tempfile(fileext = "_mdls.arrow", tmpdir = working_folder)
+arrow::write_ipc_file(dm$modeldata, sink = tempModelFile)
+tempPredictorFile <- tempfile(fileext = "_preds.arrow", tmpdir = working_folder)
+arrow::write_ipc_file(dm$predictordata, sink = tempPredictorFile)
+
+# Create Health Check (legacy R version - now superseded by the new Python version)
+
+rmarkdown::render(healthcheck_notebook_R,
+                  params = list(
+                    modelfile = tempModelFile,
+                    predictordatafile = tempPredictorFile,
+                    title = paste("ADM Health Check", customer, sep = " - "),
+                    subtitle = "legacy R version"
+                  ),
+                  output_dir = working_folder,
+                  output_file = paste("ADM Health Check ", customer, ".html", sep = ""),
+                  quiet = FALSE, intermediates_dir = working_folder
+)
+
+# Individual Model reports
+
+# In real life situations you probably want to select a subset of the 
+# models, not run a model report for every possible ADM instance, which
+# would typically be in the 100's or 1000's.
+
+# Below we select 5 of the models from every channel with the largest
+# response counts. This is just a simple example that can easily be
+# extended.
+
+recentModels <- filterLatestSnapshotOnly(dm$modeldata)[Positives > 10]
+recentModels[, PosRank := frank(-Positives, ties.method="random"), by=c("Direction", "Channel", "ConfigurationName")]
+ids <- recentModels[PosRank <= 5, "ModelID"]
+
+# Associate a name with the model IDs
+modelNames <- sapply(ids, function(id) {
+  make.names(paste(
+    sapply(unique(dm$modeldata[
+      ModelID == id,
+      c("ConfigurationName", "Channel", "Direction", "Issue", "Group", "Name", "Treatment")
+    ]), as.character),
+    collapse = "_"
+  ))
+})
+
+# Create a report for every of these models
+for (n in seq_along(ids)) {
+  id <- ids[order(modelNames)][n]
+  modelName <- modelNames[id]
+
+  cat("Model:", modelName, n, "of", length(ids), fill = T)
+
+  localModelReportHTMLFile <- paste0(customer, "_", modelName, ".html")
+
+  rmarkdown::render(offlinemodelreport_notebook_R,
+                    params = list(
+                      predictordatafile = tempPredictorFile,
+                      modeldescription = modelName,
+                      modelid = id
+                    ),
+                    output_dir = output_folder,
+                    output_file = localModelReportHTMLFile,
+                    quiet = F, intermediates_dir = working_folder
+  )
+}
+
+cat("Done. Output is in", output_folder, fill=T)
+
 
-# Write back a CSV with only the predictor data for the models of interest
-write.csv(dm$predictordata, "~/Downloads/predictors.csv", row.names = F)

r/NAMESPACE

@@ -24,6 +24,7 @@ export(getScoringModelFromJSONFactoryString)
 export(hasMultipleSnapshots)
 export(lift)
 export(plotBinning)
+export(plotBinningLift)
 export(plotCumulativeGains)
 export(plotCumulativeLift)
 export(plotPerformanceOverTime)

r/R/adm.R

@@ -741,6 +741,12 @@ admVarImp <- function(datamart, facets = NULL, filter = function(x) {filterClass
 
 getActiveRanges <- function(dm)
 {
+  is_AUC_activerange <- is_AUC_fullrange <- nClassifierBins <- is_full_indexrange <- active_index_max <- NULL # Trick to silence warnings from R CMD Check
+  active_index_min <- activeRangeAUC <- fullRangeAUC <- Performance <- reportedAUC <- NULL
+  ModelID <- sumMaxLogOdds <- score_max <- NULL
+  EntryType <- BinPositives <- BinNegatives <- totalPos <- totalNeg <- NULL
+  logOddsMin <- logOddsMax <- score_min <- classifierLogOffset <- sumMinLogOdds <- nActivePredictors <- NULL
+
   # log odds contribution of the bins, including Laplace smoothing
   binLogOdds <- function(binpos, binneg) {
     nbins = length(binpos)