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Hi all!


Based on the title, here's some background information: SHAPLEY Values


Currently, one way of doing so is to utilize the Python tool to write out the script and install the package. However, this will require running Alteryx as an administrator in order to successfully load, test, and run the script. The problem is, a substantial number of companies do not grant such privileges to their Alteryx teams to run as administrator fully as it will always require admin credentials to log in to even open Alteryx after closing it.


I am aware that there is a macro covering SHAP but I've recently tested it and it did not work as intended, plus it covers non-categorical values as determinants only, thereby requiring a conversion of categorical variables into numeric categories or binary categories. 


It will be nice to have a built in Alteryx ML tool that does this analysis and produces a graph akin to a heat map that showcases the values like below:



By doing so, it adds more value to the ML suite and actually helps convince companies to get it.


Otherwise teams will just use Python and be done with it, leaving only Alteryx as the clean-up ETL tool. It leaves much to be desired, and can leave some teams hanging.


I hope for some consideration on this - thank you.


Credit to @pgdelafuente in his post Export Variables from Assisted Modelling Feature I... - Alteryx Community


This came up in a call with a large client - basically there's no easy way to output the feature importance plot, the accuracy metrics of the selected model (i.e. root mean squared error, correlation, max error, etc.). I would assume this is an easy addition into the Assisted Modeling tools, and perhaps useful for some of the Predictive tools!

Alteryx hosting CRAN


Installing R packages in Alteryx has been a tricky issue with many posts over the years and it fundamentally boils down to the way the install.packages() function is used; I've made a detailed post on the subject. There is a way that Alteryx can help remedy the compatibility challenge between their updates of Predictive Tools and the ever-changing landscape that is open-source development. That way is for Alteryx to host their own CRAN!


The current version of Alteryx runs R 4.1.3, which is considered an 'old release', and there are over 18,000 packages on CRAN for this version of R. By the time you read this post, there is likely a newer version of one of these packages that the package author has submitted to the R Foundation's CRAN. There is also a good chance that package isn't compatible with any Alteryx tool that uses R. What if you need that package for a macro you've downloaded? How do you get the old version, the one that is compatible? This is where Alteryx hosting CRAN comes into full fruition.


Alteryx can host their own CRAN, one that is not updated by one of many package authors throughout its history, and the packages will remain unchanged and compatible with the version of Predictive Tools that is released. All we need to do as Alteryx users is direct install.packages() to the Alteryx CRAN to get our new packages, like so,



install.packages(pkg_name, repo = "")




There is a R package to create a CRAN directory, so Alteryx can get R to do the legwork for them. Here is a way of using the miniCRAN package,



path2CRAN <- "/local/path/to/CRAN"
ver <- paste(R.version$major, strsplit(R.version$minor, "\\.")[[1]][1], sep = ".") # ver = 4.1
repo <- "" # R Foundation's CRAN
m <- available.packages() # a matrix of all packages and their meta data from repo
pkgs4CRAN <- m[,"Package"] # character vector of all packages from repo
makeRepo(pkgs = pkgs4CRAN, path = path2CRAN, type = c("win.binary", "source"), repos = repo) # makes the local repo
write_PACKAGES(paste(path2CRAN, "bin/windows/contrib", ver, sep = "/"), type = "win.binary") # creates the PACKAGES file for package binaries
write_PACKAGES(paste(path2CRAN, "src/contrib", sep = "/"), type = "source") # creates the PACKAGES files for package sources



It will create a directory structure that replicates R Foundation's CRAN, but just for the version that Alteryx uses, 4.1/. 


Alteryx can create the CRAN, host it to somewhere meaningful (like, update Predictive Tools to use the packages downloaded with the script above and then release the new version of Predictive Tools and announce the CRAN. Users like me and you just need to tell the R Tool (for example) to install from the Alteryx repo rather than any others, which may have package dependency conflicts.


This is future-proof too. Let's say Alteryx decide to release a new version of Designer and Predictive Tools based on R 4.2.2. What do they do? Download R 4.2.2, run the above script, it'll create a new directory called 4.2/, update Predictive Tools to work with R 4.2.2 and the packages in their CRAN, host the 4.2/ directory to their CRAN and then release the new version of Designer and Predictive Tools.



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