fixing doc

Readme.md

@@ -1,17 +1,17 @@
 # TUG Benchmark
 
-**This Readme is under construction**
+**Refer to the =Description.pdf= in the =doc= folder**
 
-This repository contains input data from POET simulations used in the NAAICE
+This repository contains input data from POET simulations used in the
-project and is simulated with the latest version of tug with heterogeneous
+NAAICE project and is simulated with the latest version of tug with
-alphas.
+heterogeneous alphas.
 
-The alpha values are randomly generated from a uniform distribution between 1e-7
+Benchmarks (grids, timestep, file paths, etc) are defined in the
-and 1e-6 and stored in according .csv files. 
+header file `eval/bench_defs.hpp.in`. The data used by the three
+benchmarks `barite_200`, `barite_large` and `surfex` are in the
+corresponding subdirectories of `eval`, as `tar.gz`. Remember to
+unpack them before running the executable!
 
-You can find the three benchmarks in the `eval` directory. There is also a
+To write the results to file, set the `BENCH_OUTPUT` environmental
-header file, which describes the benchmarks with its grid shape etc. 
+variable, e.g. `BENCH_OUTPUT=1 ./bench`. The resulting fields are
-
+written to according .csv files *in the `build` directory*.
-To write the resulting fields to a file, you can set the `BENCH_OUTPUT`
-variable, e.g. `BENCH_OUTPUT=1 ./bench`. The resulting fields are written to
-according .csv files.

doc/Description.tex

@@ -1,4 +1,4 @@
-%% Time-stamp: "Last modified 2024-04-10 23:08:54 delucia"
+%% Time-stamp: "Last modified 2024-04-10 23:33:46 delucia"
 \documentclass[a4paper,10pt]{article}
 
 \usepackage{listings}
@@ -360,8 +360,9 @@ Absolute Percentage Error (\textbf{MAPE}) and Relative RMSE
 \end{equation}
 
 These relative measures account for discrepancies across all
-magnitudes of the $y$ and $\hat{y}$ variables and preserve the
+magnitudes of the $y$ and $\hat{y}$ variables while preserving the
-physical meaning of 0.
+physical meaning of 0. An implementation of all these metrics in R is
+given in the \texttt{Metrics.R} file in this same directory.
 
 \end{document}
 

doc/Metrics.R

@@ -0,0 +1,189 @@
+## R code to compute the different metrics for GeoML Benchmarks, GFZ
+## implementation
+##
+## M. De Lucia, delucia@gfz-potsdam.de
+##
+## Time-stamp: "Last modified 2024-04-10 23:40:08 delucia"
+
+
+## Hopefully self-evident naming of the functions
+
+
+## Convenience function for output of messages to stdout prepending
+## name of function which called it
+msg_ <- function(...) {
+    ## check if we are called from another function or interactively
+    fname <- sys.call(-1)
+    if (is.null(fname)) {
+        cat(paste(..., "\n"))
+    } else {
+        prefix <- paste0("::", as.list(fname)[[1]], "::")
+        cat(paste(prefix, ..., "\n"))
+    }
+    invisible()
+}
+
+MAElog <- function(actual, pred) {
+    pred[is.na(pred)] <- 0
+    ind <- which(actual == 0 | pred <= 0)
+    if (length(ind) > 0) {
+        msg_("Removed ", length(ind))
+        mean(abs(log(pred[-ind]/actual[-ind])), na.rm=TRUE)
+    } else {
+        mean(abs(log(pred/actual)), na.rm=TRUE)
+    }
+}
+
+RMSElog <- function(actual, pred, eps=FALSE) {
+    pred[is.na(pred)] <- 0
+    if (length(ind <- which(actual==0 | pred<=0)) > 0) {
+        msg_("Removed ", length(ind))
+        sqrt(mean(log(pred[-ind]/actual[-ind])^2, na.rm=TRUE))
+    } else {
+        sqrt(mean(log(pred/actual)^2, na.rm=TRUE))
+    }
+}
+
+GMAQabs  <- function(actual, pred) {
+    pred[is.na(pred)] <- 0
+    ind <- which(actual==0 | pred<=0)
+    if (length(ind) > 0) {
+        msg_("Removed ", length(ind))
+        abs(1-exp(mean(log(abs(pred[-ind]/actual[-ind])), na.rm=TRUE)))
+    } else {
+        abs(1-exp(mean(log(abs(pred/actual)), na.rm=TRUE)))
+    }
+}
+
+## Workhorse function computing the "alphas" (cfr workflows/metrics)
+## for relative error measures. We first compute the alphas, then take
+## care of the +Inf, -Inf, NaN cases scanning the input vectors and
+## substituting 0 and 1. NAs in the predictions are preserved
+Alphas <- function(actual, pred) {
+    alphas <- 1-pred/actual
+    alphas[actual==0 & pred==0]  <- 0
+    alphas[actual==0 & pred > 0] <- 1
+    alphas[actual==0 & pred < 0] <- -1
+    alphas
+}
+
+MAPE <- function(actual, pred) {
+    100*mean(abs(Alphas(actual, pred)), na.rm=TRUE)
+}
+
+RRMSE <- function(actual, pred) {
+    sqrt(mean(Alphas(actual, pred)^2, na.rm=TRUE))
+}
+
+RMSLE <- function(actual, pred) {
+    sqrt(mean((log((1+pred)/(1+actual))^2), na.rm=TRUE))
+}
+
+MAE <- function(actual, pred) {
+    mean(abs(actual-pred), na.rm=TRUE)
+}
+
+RMSE <- function(actual, pred) {
+    sqrt(mean((actual-pred)^2, na.rm=TRUE))
+}
+
+MSE <- function(actual, pred) {
+    mean((actual-pred)^2, na.rm=TRUE)
+}
+
+## Function is called "R2" but it actually computes "1-R2" (note that
+## "1-rss/tss" is missing)
+R2 <-  function(actual, pred) {
+    rss <- sum((actual - pred)^2, na.rm=TRUE)
+    tss <- sum((pred - mean(pred, na.rm=TRUE))^2, na.rm=TRUE)
+    rss/tss
+}
+
+Range <- function(x) {
+    diff(range(x, na.rm=TRUE))
+}
+
+## 2024-03-08: fixed Range of actual instead of pred, thanks @Mary
+Normsupnorm<- function(actual, pred) {
+    max(abs(actual - pred), na.rm=TRUE)/Range(actual)
+}
+
+
+
+## This function computes all the measures at once in the (hopefully)
+## expected right order. You need to loop this function over all
+## variables.
+Metrics <- function(actual, pred) {
+    ## MAE MSE RMSE 1-R2 NMAE NRMSE Normsupnorm MAElog RMSElog RMSLE GMAQabs MAPE RRMSE NNEG Range
+    alphas <- Alphas(actual, pred)
+    ran_pred <- Range(pred) 
+    ran_actu <- Range(actual) ## 2024-03-08: same fix as in Normsupnorm
+    ret <- c(
+        MAE         = MAE(actual, pred),
+        MSE         = MSE(actual, pred),
+        RMSE        = RMSE(actual, pred),
+        '1-R2'      = R2(actual, pred),
+        NMAE        = MAE(actual, pred)/ran_actu,
+        NRMSE       = RMSE(actual, pred)/ran_actu,
+        Normsupnorm = Normsupnorm(actual, pred),
+        MAElog      = MAElog(actual,pred),
+        RMSElog     = RMSElog(actual,pred),
+        RMSLE       = RMSLE(actual, pred),
+        GMAQabs     = GMAQabs(actual, pred),
+        MAPE        = MAPE(actual, pred),
+        RRMSE       = RRMSE(actual, pred),
+        NNEG        = NNEG(pred), 
+        Range       = ran_pred
+    )
+    ret
+}
+
+## Function applying Metrics() on all columns of two data.frames
+## having the same name
+DoAllMetrics <- function(vals, preds) {
+    ## Match column names (=variables) in both data.frames
+    vars <- intersect(colnames(vals), colnames(preds))
+
+    msg_("Going to compute metrics for: ", paste(vars, collapse=', '))
+    ## Some lambda calculus combined with R ugliness
+    as.data.frame(t(sapply(vars, function(x) Metrics(actual = vals[[x]], pred = preds[[x]]))))
+}
+
+
+### Store the computed metrics into a .csv file. First format all
+### columns with "formatC" specifying 7 digits, and then use function
+### "data.table::fwrite" with option "scipen=1" to try and use the
+### scientific/exponential notation as much as possible. The empty
+### spaces resulting from padding are removed too.
+WriteMetrics <- function(tab, fileout) {
+    require(data.table)
+    tocsv <- data.frame(Output=rownames(tab), gsub(" ", "", sapply(tab, formatC, digits = 7)), check.names = FALSE)
+    data.table::fwrite(tocsv, file=fileout, scipen=1)
+}
+
+##################################################################
+###                                                            ###
+###                     Usage Example                          ###
+###                                                            ###
+##################################################################
+
+### We start by sourcing these functions
+## source("./Metrics.R")
+
+### NOTE: I use the extension package data.table to read and write the
+### .csv files.
+
+## require(data.table)
+
+### NOTE2: we expect named columns in the .csv - with header!
+
+### Example to compute all metrics at once for from a file - check colnames!
+## Trues  <- data.table::fread("./Trues.csv", header=TRUE, data.table = FALSE)
+
+### Load the simulation results dataset
+## Preds <- data.table::fread("./MyPreds.csv", header=TRUE, data.table = FALSE)
+
+## res <- DoAllMetrics(Val, Preds)
+
+### Write out the metrics
+## WriteMetrcs(res, file="MyMetrics.csv")