9 source("RemembR/R/RemembeR.R")
11 change.remember.file("ordinal.quality.analysis_revisions.RDS")
13 #model.1 <- readRDS("models/ordinal_quality_intercept.RDS")
14 model.main.pca <- readRDS("models/ordinal_quality_pca_revision.RDS")
15 model.main.pca.cumulative <- readRDS("models/ordinal_quality_pca_revision.cumulative.RDS")
16 model.qe6 <- readRDS("models/ordinal_quality_qe6_revision.RDS")
17 df <- readRDS("data/training_quality_labels.RDS")
19 # then compare them with loo
20 comparison.loo <- loo_compare(model.main.pca,model.qe6,model.main.pca.cumulative)
21 #comparison.waic <- loo_compare(model.main.noC,model.main.noB,model.main.noFa,model.main.noGa,model.main.noStart,model.main.noStub,criterion='waic')
22 print(comparison.loo,simplify=F,digits=2)
23 remember(comparison.loo,"comparison.loo")
26 best.model <- model.main.pca.cumulative
28 pca_features <- readRDS("data/ores_pca_features_revisions.RDS")
29 pca_features_unweighted <- readRDS("data/ores_pca_features.noweights.RDS")
31 test.df <- readRDS("data/holdout_quality_labels.RDS")
33 wpca_transform <- function(wpca, x){
35 centered <- as.matrix(t(t(x) - wpca$means))
36 return(centered %*% wpca$basis)
39 new_pca_features <- wpca_transform(pca_features, test.df[,.(Stub, Start, C, B, GA, FA)])
41 test.df<-test.df[,":="(pca1.revision = new_pca_features[,1],
42 pca2.revision = new_pca_features[,2],
43 pca3.revision = new_pca_features[,3],
44 pca4.revision = new_pca_features[,4],
45 pca5.revision = new_pca_features[,5])]
47 draws <- as.data.table(best.model)
49 test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
50 test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
51 top.preds <- test.df[,MPQC]
53 #ordinal.fitted.1 <- fitted(best.model, test.df, scale='response')
54 ordinal.fitted <- data.table(fitted(best.model, test.df, scale='linear'))
55 remember(ordinal.fitted,'ordinal.fitted')
56 ordinal.pred <- ordinal.fitted$Estimate
58 quality.ordinal <- ordinal.pred
59 quality.even6 <- apply(test.df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(0,1,2,3,4,5))
60 quality.even5 <- apply(test.df[,.(Stub,Start,B,GA,FA)],1,function(r) r %*% c(1,2,3,4,5))
62 test.df <- test.df[,quality.ordinal := quality.ordinal]
63 test.df <- test.df[,quality.even6 := quality.even6]
65 (spearcor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='spearman', weights=test.df$revision_weight))
66 remember(spearcor, 'spearman.corr')
67 (pearsoncor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='pearson', weights=test.df$revision_weight))
68 remember(pearsoncor, 'pearson.corr')
70 ordinal.preds <- data.table(predict(best.model, test.df, robust=F))
71 #names(ordinal.preds) <- c("Stub","Start","C","B","A","GA","FA")
72 names(ordinal.preds) <- c("Stub","Start","C","B","GA","FA")
73 ordinal.preds <- ordinal.preds[,idx.max:=.(apply(ordinal.preds[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
74 #ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
75 ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
76 pred.qe6 <- data.table(predict(model.qe6,test.df))
77 names(pred.qe6) <- c("Stub","Start","C","B","GA","FA")
78 pred.qe6 <- pred.qe6[,idx.max:=.(apply(pred.qe6[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
79 #pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
80 pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
82 test.df <- test.df[,ordinal.pred := ordinal.preds$predicted]
83 test.df <- test.df[,pred.qe6 := pred.qe6$predicted]
84 test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
85 test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
87 (top.pred.accuracy <- weighted.mean(test.df[,.(MPQC)] == test.df[,.(wp10)],test.df$revision_weight))
88 remember(top.pred.accuracy, "top.pred.accuracy")
89 (ordinal.pred.accuracy <- weighted.mean(test.df[,.(ordinal.pred)] == test.df[,.(wp10)], test.df$revision_weight))
90 remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
91 quality.even6 <- apply(df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(1,2,3,4,5,6))
92 (pred.qe6.accuracy <- weighted.mean(test.df[,.(pred.qe6)] == test.df[,.(wp10)], test.df$revision_weight))
93 remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
94 remember(best.model, "best.model")
96 remember(test.df,'test.df')
99 ordinal.preds[,wp10:=test.df$wp10]
100 ordinal.preds[,weight:=test.df$article_weight]
101 total.weight <- sum(ordinal.preds$weight)
103 calibration.stats.1 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
104 var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
106 calibration.stats.1[,wp10:=c("stub","start","c","b","ga","fa")]
107 calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
108 var.data=var(weight)/total.weight),by=.(wp10)]
110 calibration.stats.1 <- calibration.stats.1[calip.data,on=.(wp10)]
112 calibration.stats.1$weighttype <- 'Article weight'
114 ordinal.preds[,weight:=test.df$revision_weight]
115 total.weight <- sum(ordinal.preds$weight)
117 calibration.stats.2 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
118 var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
121 calibration.stats.2[,wp10:=c("stub","start","c","b","ga","fa")]
122 calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
123 var.data=var(weight)/total.weight),by=.(wp10)]
125 calibration.stats.2 <- calibration.stats.2[calip.data,on=.(wp10)]
127 calibration.stats.2$weighttype <- 'Revision weight'
130 ordinal.preds[,weight:=rep(1,nrow(ordinal.preds))]
131 total.weight <- sum(ordinal.preds$weight)
133 calibration.stats.3 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
134 var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
137 calibration.stats.3[,wp10:=c("stub","start","c","b","ga","fa")]
138 calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
139 var.data=var(weight)/total.weight),by=.(wp10)]
141 calibration.stats.3 <- calibration.stats.3[calip.data,on=.(wp10)]
143 calibration.stats.3$weighttype <- 'No weight'
145 calibration.stats <- rbind(calibration.stats.1,calibration.stats.2,calibration.stats.3)
147 calibration.stats[,'calibration':=prob.data - prob.predicted]
149 remember(calibration.stats, "calibration.stats")
152 ## p <- ggplot(data.frame(quality.ordinal, quality.even6, quality.even5))
153 ## p <- p + geom_point(aes(x=quality.even6,y=quality.ordinal)) + geom_smooth(aes(x=quality.even6,y=quality.ordinal))
158 ## post.pred <- posterior_predict(model.main)
159 ## preds <- as.character(predict(polrmodel))
160 ## polrmodel.accuracy <- weighted.mean(preds==df$wp10,df$weight)