167 lines
7.5 KiB
R
167 lines
7.5 KiB
R
library(MASS)
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library(brms)
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options(mc.cores=28)
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library(ggplot2)
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library(data.table)
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library(arrow)
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library(wCorr)
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source("RemembR/R/RemembeR.R")
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change.remember.file("ordinal.quality.analysis.noweights.RDS")
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#model.1 <- readRDS("models/ordinal_quality_intercept.RDS")
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model.main.pca <- readRDS("models/ordinal_quality_pca.noweights.RDS")
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model.main.pca.cumulative <- readRDS("models/ordinal_quality_pca.noweights.cumulative.RDS")
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model.qe6 <- readRDS("models/ordinal_quality_qe6.noweights.RDS")
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df <- readRDS("data/training_quality_labels.RDS")
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# then compare them with loo
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comparison.loo <- loo_compare(model.main.pca,model.qe6,model.main.pca.cumulative)
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#comparison.waic <- loo_compare(model.main.noC,model.main.noB,model.main.noFa,model.main.noGa,model.main.noStart,model.main.noStub,criterion='waic')
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print(comparison.loo,simplify=F,digits=2)
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remember(comparison.loo,"comparison.loo")
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# LOO Chooses NoC
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best.model <- model.main.pca.cumulative
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pca_features_unweighted <- readRDS("data/ores_pca_features.noweights.RDS")
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test.df <- readRDS("data/holdout_quality_labels.RDS")
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wpca_transform <- function(wpca, x){
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x <- as.matrix(x)
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centered <- as.matrix(t(t(x) - wpca$means))
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return(centered %*% wpca$basis)
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}
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unweighted.pca <- wpca_transform(pca_features_unweighted, test.df[,.(Stub, Start, C, B, GA, FA)])
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test.df <- test.df[,":="(pca1.noweights = unweighted.pca[,1],
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pca2.noweights = unweighted.pca[,2],
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pca3.noweights = unweighted.pca[,3],
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pca4.noweights = unweighted.pca[,4],
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pca5.noweights = unweighted.pca[,5],
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pca6.noweights = unweighted.pca[,6])]
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draws <- as.data.table(best.model)
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test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
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test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
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top.preds <- test.df[,MPQC]
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#ordinal.fitted.1 <- fitted(best.model, test.df, scale='response')
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ordinal.fitted <- data.table(fitted(best.model, test.df, scale='linear'))
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ordinal.pred <- ordinal.fitted$Estimate
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remember(ordinal.fitted,'ordinal.fitted')
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quality.ordinal <- ordinal.pred
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quality.even6 <- apply(test.df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(0,1,2,3,4,5))
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quality.even5 <- apply(test.df[,.(Stub,Start,B,GA,FA)],1,function(r) r %*% c(1,2,3,4,5))
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test.df <- test.df[,quality.ordinal := quality.ordinal]
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test.df <- test.df[,quality.even6 := quality.even6]
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(spearcor <- cor(test.df$quality.ordinal, test.df$quality.even6, method='spearman'))
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remember(spearcor, 'spearman.corr')
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(pearsoncor <- cor(test.df$quality.ordinal, test.df$quality.even6, method='pearson'))
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remember(pearsoncor, 'pearson.corr')
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ordinal.preds <- data.table(predict(best.model, test.df, robust=T))
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#names(ordinal.preds) <- c("Stub","Start","C","B","A","GA","FA")
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names(ordinal.preds) <- c("Stub","Start","C","B","GA","FA")
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ordinal.preds <- ordinal.preds[,idx.max:=.(apply(ordinal.preds[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
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#ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
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ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
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pred.qe6 <- data.table(predict(model.qe6,test.df))
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names(pred.qe6) <- c("Stub","Start","C","B","GA","FA")
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pred.qe6 <- pred.qe6[,idx.max:=.(apply(pred.qe6[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
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#pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
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pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
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test.df <- test.df[,ordinal.pred := ordinal.preds$predicted]
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test.df <- test.df[,pred.qe6 := pred.qe6$predicted]
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test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
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test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
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(top.pred.accuracy <- test.df[,mean(MPQC==wp10)])
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remember(top.pred.accuracy, "top.pred.accuracy")
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(ordinal.pred.accuracy <- test.df[,mean(ordinal.pred == wp10)])
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remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
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quality.even6 <- apply(df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(1,2,3,4,5,6))
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(pred.qe6.accuracy <- mean(test.df[,.(pred.qe6)] == test.df[,.(wp10)]))
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remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
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remember(best.model, "best.model")
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(accuracy.macro <- test.df[,.(top.pred.accuracy = mean(MPQC==wp10),
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ordinal.pred.accuracy = mean(ordinal.pred==wp10),
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pred.qe6.accuracy = mean(pred.qe6==wp10)),by=.(wp10)])
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accuracy.macro[,sapply(.SD,mean), .SDcols=c("top.pred.accuracy","ordinal.pred.accuracy","pred.qe6.accuracy")]
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remember(test.df,'test.df')
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ordinal.preds[,wp10:=test.df$wp10]
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ordinal.preds[,weight:=test.df$article_weight]
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total.weight <- sum(ordinal.preds$weight)
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library(modi)
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calibration.stats.1 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
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var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
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calibration.stats.1[,wp10:=c("stub","start","c","b","ga","fa")]
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calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
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var.data=var(weight)/total.weight),by=.(wp10)]
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calibration.stats.1 <- calibration.stats.1[calip.data,on=.(wp10)]
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calibration.stats.1$weighttype <- 'Article weight'
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ordinal.preds[,weight:=test.df$revision_weight]
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total.weight <- sum(ordinal.preds$weight)
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calibration.stats.2 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
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var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
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calibration.stats.2[,wp10:=c("stub","start","c","b","ga","fa")]
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calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
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var.data=var(weight)/total.weight),by=.(wp10)]
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calibration.stats.2 <- calibration.stats.2[calip.data,on=.(wp10)]
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calibration.stats.2$weighttype <- 'Revision weight'
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ordinal.preds[,weight:=rep(1,nrow(ordinal.preds))]
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total.weight <- sum(ordinal.preds$weight)
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calibration.stats.3 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
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var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
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calibration.stats.3[,wp10:=c("stub","start","c","b","ga","fa")]
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calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
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var.data=var(weight)/total.weight),by=.(wp10)]
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calibration.stats.3 <- calibration.stats.3[calip.data,on=.(wp10)]
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calibration.stats.3$weighttype <- 'No weight'
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calibration.stats <- rbind(calibration.stats.1,calibration.stats.2,calibration.stats.3)
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calibration.stats[,'calibration':=prob.data - prob.predicted]
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remember(calibration.stats, "calibration.stats")
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## p <- ggplot(data.frame(quality.ordinal, quality.even6, quality.even5))
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## p <- p + geom_point(aes(x=quality.even6,y=quality.ordinal)) + geom_smooth(aes(x=quality.even6,y=quality.ordinal))
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## print(p)
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## dev.off()
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## post.pred <- posterior_predict(model.main)
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## preds <- as.character(predict(polrmodel))
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## polrmodel.accuracy <- weighted.mean(preds==df$wp10,df$weight)
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