library(MASS)
library(brms)
options(mc.cores=28)
library(ggplot2)
library(data.table)
library(arrow)
library(wCorr)

source("RemembR/R/RemembeR.R")

change.remember.file("ordinal.quality.analysis.noweights.RDS")

#model.1 <- readRDS("models/ordinal_quality_intercept.RDS")
model.main.pca <- readRDS("models/ordinal_quality_pca.noweights.RDS")
model.main.pca.cumulative <- readRDS("models/ordinal_quality_pca.noweights.cumulative.RDS")
model.qe6 <- readRDS("models/ordinal_quality_qe6.noweights.RDS")
df <- readRDS("data/training_quality_labels.RDS")

# then compare them with loo
comparison.loo <- loo_compare(model.main.pca,model.qe6,model.main.pca.cumulative)
#comparison.waic <- loo_compare(model.main.noC,model.main.noB,model.main.noFa,model.main.noGa,model.main.noStart,model.main.noStub,criterion='waic')
print(comparison.loo,simplify=F,digits=2)
remember(comparison.loo,"comparison.loo")

# LOO Chooses NoC
best.model <- model.main.pca.cumulative

pca_features_unweighted <- readRDS("data/ores_pca_features.noweights.RDS")

test.df <- readRDS("data/holdout_quality_labels.RDS")

wpca_transform <- function(wpca, x){
    x <- as.matrix(x)
    centered <- as.matrix(t(t(x) - wpca$means))
    return(centered %*% wpca$basis)
}

unweighted.pca <- wpca_transform(pca_features_unweighted, test.df[,.(Stub, Start, C, B, GA, FA)])

test.df <- test.df[,":="(pca1.noweights = unweighted.pca[,1],
                         pca2.noweights = unweighted.pca[,2],
                         pca3.noweights = unweighted.pca[,3],
                         pca4.noweights = unweighted.pca[,4],
                         pca5.noweights = unweighted.pca[,5],
                         pca6.noweights = unweighted.pca[,6])]

draws <- as.data.table(best.model)

test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
top.preds <- test.df[,MPQC]

#ordinal.fitted.1 <- fitted(best.model, test.df, scale='response')
ordinal.fitted <- data.table(fitted(best.model, test.df, scale='linear'))
ordinal.pred <- ordinal.fitted$Estimate
remember(ordinal.fitted,'ordinal.fitted')

quality.ordinal <- ordinal.pred
quality.even6 <- apply(test.df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(0,1,2,3,4,5))
quality.even5 <- apply(test.df[,.(Stub,Start,B,GA,FA)],1,function(r) r %*% c(1,2,3,4,5))

test.df <- test.df[,quality.ordinal := quality.ordinal]
test.df <- test.df[,quality.even6 := quality.even6]

(spearcor <- cor(test.df$quality.ordinal, test.df$quality.even6, method='spearman'))
remember(spearcor, 'spearman.corr')
(pearsoncor <- cor(test.df$quality.ordinal, test.df$quality.even6, method='pearson'))
remember(pearsoncor, 'pearson.corr')

ordinal.preds <- data.table(predict(best.model, test.df, robust=T))
#names(ordinal.preds) <- c("Stub","Start","C","B","A","GA","FA")
names(ordinal.preds) <- c("Stub","Start","C","B","GA","FA")
ordinal.preds <- ordinal.preds[,idx.max:=.(apply(ordinal.preds[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
pred.qe6 <- data.table(predict(model.qe6,test.df))
names(pred.qe6) <- c("Stub","Start","C","B","GA","FA")
pred.qe6 <- pred.qe6[,idx.max:=.(apply(pred.qe6[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]

test.df <- test.df[,ordinal.pred := ordinal.preds$predicted]
test.df <- test.df[,pred.qe6 := pred.qe6$predicted]
test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]

(top.pred.accuracy <- test.df[,mean(MPQC==wp10)])
remember(top.pred.accuracy, "top.pred.accuracy")
(ordinal.pred.accuracy <- test.df[,mean(ordinal.pred == wp10)])
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
quality.even6 <- apply(df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(1,2,3,4,5,6))
(pred.qe6.accuracy <- mean(test.df[,.(pred.qe6)] == test.df[,.(wp10)]))
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
remember(best.model, "best.model")

(accuracy.macro <- test.df[,.(top.pred.accuracy = mean(MPQC==wp10),
                              ordinal.pred.accuracy = mean(ordinal.pred==wp10),
                              pred.qe6.accuracy = mean(pred.qe6==wp10)),by=.(wp10)])

accuracy.macro[,sapply(.SD,mean), .SDcols=c("top.pred.accuracy","ordinal.pred.accuracy","pred.qe6.accuracy")]
    

remember(test.df,'test.df')


ordinal.preds[,wp10:=test.df$wp10]
ordinal.preds[,weight:=test.df$article_weight]
total.weight <- sum(ordinal.preds$weight)
library(modi)
calibration.stats.1 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
                                      var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]

calibration.stats.1[,wp10:=c("stub","start","c","b","ga","fa")]
calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
                                         var.data=var(weight)/total.weight),by=.(wp10)]

calibration.stats.1 <- calibration.stats.1[calip.data,on=.(wp10)]

calibration.stats.1$weighttype <- 'Article weight'

ordinal.preds[,weight:=test.df$revision_weight]
total.weight <- sum(ordinal.preds$weight)

calibration.stats.2 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
                                        var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]


calibration.stats.2[,wp10:=c("stub","start","c","b","ga","fa")]
calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
                                         var.data=var(weight)/total.weight),by=.(wp10)]

calibration.stats.2 <- calibration.stats.2[calip.data,on=.(wp10)]

calibration.stats.2$weighttype <- 'Revision weight'


ordinal.preds[,weight:=rep(1,nrow(ordinal.preds))]
total.weight <- sum(ordinal.preds$weight)

calibration.stats.3 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
                                        var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]


calibration.stats.3[,wp10:=c("stub","start","c","b","ga","fa")]
calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
                                         var.data=var(weight)/total.weight),by=.(wp10)]

calibration.stats.3 <- calibration.stats.3[calip.data,on=.(wp10)]

calibration.stats.3$weighttype <- 'No weight'

calibration.stats <- rbind(calibration.stats.1,calibration.stats.2,calibration.stats.3)

calibration.stats[,'calibration':=prob.data - prob.predicted]

remember(calibration.stats, "calibration.stats")

## p <- ggplot(data.frame(quality.ordinal, quality.even6, quality.even5))
## p <- p + geom_point(aes(x=quality.even6,y=quality.ordinal)) + geom_smooth(aes(x=quality.even6,y=quality.ordinal))

## print(p)
## dev.off()

## post.pred <- posterior_predict(model.main)
## preds <- as.character(predict(polrmodel))
## polrmodel.accuracy <- weighted.mean(preds==df$wp10,df$weight)