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04_Performance_and_variable_importance.R
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04_Performance_and_variable_importance.R
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###Script that calculates performance and variable importance of modelled umap maps##
##########
require(vegan)
require(phyloseq)
require(ggplot2)
require(fossil)
require(naturalsort)
require(reshape2)
require(rgdal)
require(plyr)
require(raster)
load('Themes/Vanessa_theme.RData')
#####
goof <- function(observed,
predicted,
coefficient=c('R2','concordance','MSE','RMSE','bias','MSEc','RMSEc','RPD','RPIQ'),
plot=TRUE,...){
if(any(!coefficient%in%c('R2','concordance','MSE','RMSE','bias','MSEc','RMSEc','RPD','RPIQ'))) stop('Please choose a valid coefficient')
# Coefficient of determination
rLM <- lm(predicted ~ observed)
R2 <- as.matrix(summary(rLM)$adj.r.squared)
# Standard error of prediction ^2
SEP2 <- mean((observed - predicted)^2)
# Standard error of prediction
SEP <- sqrt(SEP2)
#Bias
bias <- mean(predicted) - mean(observed)
# residual variance
SEP2c <- sum(((predicted - bias - observed)^2) / length(observed))
SEPc <- sqrt(SEP2c)
# ratio of performance to deviation
RPD <- sd(observed) / SEP
# Ratio of performance to interquartile distance
IQ <- c(quantile(observed))[3] - c(quantile(observed))[2]
RPIQ <- IQ / SEP
# Concordance
mx <- mean(observed)
my <- mean(predicted)
s2x <- var(observed)
s2y <- var(predicted)
sxy <- mean((observed-mx) * (predicted-my))
ccc <- 2 * sxy / (s2x + s2y + (mx - my)^2)
if(plot){
plot(observed,
predicted,
ylim=c(min(c(observed,predicted)),max(c(observed,predicted))),
xlim = c(min(c(observed,predicted)),max(c(observed,predicted))),
asp=1,
...)
abline(a = 0, b = 1, col = "brown4")
}
coefs_tmp <- data.frame(R2=R2, concordance=ccc, MSE=SEP2, RMSE=SEP, bias=bias,
MSEc=SEP2c,RMSEc=SEPc, RPD=RPD, RPIQ=RPIQ, row.names=NULL)
gf <- data.frame(coefs_tmp[,coefficient])
gf
}
#####
type <- 'Bacteria'
index <- 'UMAP1'
folder <- 'UMAP1'
getUncertainty <- function(type,index){
readRDS(paste0('PredictionsARTEMIS/',type,'/',folder,'/Uncertainty_models.RDS'))
}
selectPreds <- function(Uncertainty){
UP <- quantile(Uncertainty$R2,probs=c(0.75),names = FALSE,na.rm=T)
DOWN <- quantile(Uncertainty$R2,probs=c(0.25),names = FALSE,na.rm=T)
return(which(Uncertainty$R2>DOWN&Uncertainty$R2<UP))
}
getObs <- function(type,index){
tmp <- readRDS(paste0('PredictionsARTEMIS/',type,'/',folder,'/obs.RDS'))
tmp[,index]
}
getMeanPred <- function(type,index,selectModel){
tmp <- readRDS(paste0('PredictionsARTEMIS/',type,'/',folder,'/preds.RDS'))
tmp <- tmp[selectModel,]
colMeans(tmp)
}
getMeanPredTraining <- function(type,index,selectModel){
require(Cubist)
tmp <- naturalsort(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'Model',full.names = T))
tmp <- tmp[selectModel]
samplesData <- read.csv(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'Extract',full.names = T))
Inputvariables <- c('clay','cec','soc','ph','Rainfall_sum','pha','amp','ET','EVI',
'aspect','hillshade','slope',
'B1','B2','B3','B4','B5','B6','K','Th','U')
samplesData <- samplesData[,Inputvariables]
Preds_all_models <- do.call(cbind,lapply(tmp,function(x){
tmp_model <- readRDS(x)
tmp_pred <- predict(tmp_model,samplesData)
}))
return(rowMeans(Preds_all_models))
}
getObsTraining <- function(type,index){
samplesData <- read.csv(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'Extract',full.names = T))
return(samplesData[,index])
}
createStack <- function(type,index,selectModel){
tmp_stack <- stack(naturalsort(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'cubistMap',full.names = T)))
tmp_stack <- tmp_stack[[selectModel]]
return(tmp_stack)
}
#This functions run on Calc() function together with the Stack
getUpPred <- function(x) quantile(x,probs=0.95,type=7,names = FALSE,na.rm = TRUE)
getLoPred <- function(x) quantile(x,probs=0.05,type=7,names = FALSE,na.rm = TRUE)
getSD <- function(x) sd(x,na.rm = TRUE)
getModelsConditionsCount <- function(type,index,selectModel){
tmp <- naturalsort(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'Model',full.names = T))
tmp <- tmp[selectModel]
variables_all_models <- do.call(cbind,lapply(tmp,function(x){
tmp_model <- readRDS(x)$usage
}))
COUNTS <- count(unlist(sapply(1:nrow(variables_all_models),function(x) {
var_names <- as.character(variables_all_models[x,c(3,6,3:length(tmp)*3)])
count_per_var <- as.numeric(variables_all_models[x,c(3,6,3:length(tmp)*3)-2])
vector_freq_vars <- rep(var_names,count_per_var)
return(vector_freq_vars)
})
)
)
}
getModelsTipsCount <- function(type,index,selectModel){
tmp <- naturalsort(dir(paste0('PredictionsARTEMIS/',type,'/',folder),pattern = 'Model',full.names = T))
tmp <- tmp[selectModel]
variables_all_models <- do.call(cbind,lapply(tmp,function(x){
tmp_model <- readRDS(x)$usage
}))
COUNTS <- count(unlist(sapply(1:nrow(variables_all_models),function(x) {
var_names <- as.character(variables_all_models[x,c(3,6,3:length(tmp)*3)])
count_per_var <- as.numeric(variables_all_models[x,c(3,6,3:length(tmp)*3)-1])
vector_freq_vars <- rep(var_names,count_per_var)
return(vector_freq_vars)
})
)
)
}
createPerformancePlot <- function(type,index,obsTrain,predTrain,obsVal,predVal,coef='concordance'){
dataVal <- data.frame(Observed=obsVal,PredsValidation=predVal)
dataTrain <- data.frame(Observed=obsTrain,PredsTraining=predTrain)
min_value <- range(c(dataVal$PredsValidation,dataVal$Observed,dataTrain$Observed,dataTrain$PredsTraining))[1]
max_value <- range(c(dataVal$PredsValidation,dataVal$Observed,dataTrain$Observed,dataTrain$PredsTraining))[2]
perf_val <- round(goof(dataVal$Observed,dataVal$PredsValidation,coefficient = coef),2)
perf_train <- round(goof(dataTrain$Observed,dataTrain$PredsTraining,coefficient = coef),2)
ggplot(dataTrain,aes(Observed,PredsTraining))+
geom_point(colour='blue',size=2)+
geom_point(data = dataVal,aes(Observed,PredsValidation),colour='red',size=3)+
coord_equal()+
geom_abline(slope=1,aes(linetype=2))+
lims(x = c(min_value, max_value), y = c(min_value, max_value))+
ylab('Predicted')+
ggtitle(paste0(type,' - ',index,' Model performace; Train ',coef,' = ', perf_train,'; Val ',coef,' = ', perf_val))
}
#Start#
#Get the uncertainty to choose the right models
Uncertainty <- getUncertainty(type,index)
# View(Uncertainty)
# View(Uncertainty[selectPreds(Uncertainty),])
# length(selectPreds(Uncertainty))
# goof(getObs(type,index),getMeanPred(type,index,selectPreds(Uncertainty)))
# goof(getObsTraining(type,index),getMeanPredTraining(type,index,selectPreds(Uncertainty)))
#made a plot of the performance
plot_performance <- createPerformancePlot(type,
index,
obsTrain = getObsTraining(type,index),
predTrain = getMeanPredTraining(type,index,selectPreds(Uncertainty)),
obsVal=getObs(type,index),
predVal = getMeanPred(type,index,selectPreds(Uncertainty)))
#create a stack of rasters from the selected models (based again on their respective undertainty)
Stack <- createStack(type,index,selectPreds(Uncertainty))
meanPreds <- mean(Stack)
# plot(meanPreds)
UncertPreds <- calc(Stack,getSD)
# plot(UncertPreds)
#write rasters
writeRaster(meanPreds,
filename = paste0('PredictionsARTEMIS/',type,'/FromR/Preds_',folder,'_',index,'.tif'),
format="GTiff",
overwrite=TRUE)
writeRaster(UncertPreds,
filename = paste0('PredictionsARTEMIS/',type,'/FromR/Uncert_',folder,'_',index,'.tif'),
format="GTiff",
overwrite=TRUE)
a <- getModelsConditionsCount(type,index,selectPreds(Uncertainty))
b <- getModelsTipsCount(type,index,selectPreds(Uncertainty))
#write model details
write.csv(a,file =paste0('PredictionsARTEMIS/',type,'/FromR/Splits_',type,'_',folder,'.csv'))
write.csv(b,file =paste0('PredictionsARTEMIS/',type,'/FromR/Tips_',type,'_',folder,'.csv'))
#save performance plots
ggsave(plot_performance,filename = paste0('PredictionsARTEMIS/',type,'/FromR/Performance_',type,'_',folder,'.png'),height = 7,width = 10)