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add the rest of the code.

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Nathan TeBlunthuis
2024-03-12 09:39:12 -07:00
parent 29abd26b97
commit 2c733a8788
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SHELL:=/bin/bash
data/20200301_article_labelings.json_SUCCESS:
./run_aql_jobs.sh
data/20200301_article_labelings_sample.json:sample_training_labels.py
source ./bin/activate; \
./sample_training_labels.py
data/article_sample.csv:sample_articles.py
source ./bin/activate; \
start_spark_and_run.sh 1 sample_articles.py
data/scored_article_sample.feather:data/article_sample_set.csv ores_scores_sample.py
source ./bin/activate; \
python3 ores_scores_sample.py data/article_sample_set.parquet data/scored_article_sample.feather
# run this step on kibo
data/20200301_al_sample_revisions.w_text.json:data/20200301_article_labelings_sample.json
source ./bin/activate; \
python3 articlequality/utility fetch_text \
--api-host=https://en.wikipedia.org \
--labelings=data/20200301_article_labelings_sample.json \
--output=data/20200301_al_sample_revisions.w_text.json \
# run this step on kibo
data/20200301_al_sample_revisions.w_scores.json:data/20200301_al_sample_revisions.w_text.json
python3 score_sample_labels.py
models/ordinal_quality.RDS:data/20200301_al_sample_revisions.w_text.json ordinal_quality_models.R
Rscript ordinal_quality_models.R
PHONY: data/20200301_article_labelings.json

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#!/usr/bin/env Rscript
library(arrow)
library(brms)
library(data.table)
library(ggplot2)
library(parallel)
options(mc.cores=26)
registerDoParallel(cores=26)
dataset <- as.data.table(read_feather("data/scored_article_sample.feather"))
dataset <- dataset[order(articleid,time_session_end)]
quality_model <- readRDS("models/ordinal_quality_noFa.RDS")
posterior_coefs <- as.data.table(quality_model)
f <- function(cols){
post_qual <- as.matrix(posterior_coefs[,.(b_Stub, b_Start, b_C, b_B, b_GA)]) %*% as.numeric(cols)
list(med_quality = median(post_qual),
mean_quality = mean(post_qual),
sd_quality = sd(post_qual)
)
}
cl <- makeForkCluster(nnodes=26)
res <- rbindlist(parApply(cl,dataset[,.(Stub,Start,C,B,GA)],1,f))
dataset[,names(res):=res]
f2 <- function(revscores){
posterior_quality <- as.matrix(posterior_coefs[,.(b_Stub,b_Start,b_C,b_B,b_GA)]) %*% t(as.matrix(revscores))
posterior_quality_diff <- apply(posterior_quality, 1, function(x) diff(x,1,1))
posterior_quality_diff2 <- apply(posterior_quality, 1, function(x) diff(x,1,2))
list(
mean_quality_diff1 = c(NA,apply(posterior_quality_diff,1,mean)),
sd_quality_diff1 = c(NA,apply(posterior_quality_diff,1,sd)),
median_quality_diff1 = c(NA,apply(posterior_quality_diff,1,median)),
mean_quality_diff2 = c(c(NA,NA),apply(posterior_quality_diff2,1,mean)),
sd_quality_diff2 = c(c(NA,NA),apply(posterior_quality_diff2,1,sd)),
median_quality_diff2 = c(c(NA,NA),apply(posterior_quality_diff2,1,median)))
}
dataset[,c("mean_qual_diff1","sd_qual_diff1","median_qual_diff1","mean_qual_diff2","sd_qual_diff2","median_qual_diff2"):=f2(.SD),by=.(articleid),.SDcols=c("Stub","Start","C","B","GA")]
write_feather(dataset,'data/ordinal_scored_article_sample.feather')
## in an earlier version I computed the full posterior of quality for the dataset, but it took too much memory.
## Lines below checked (and confirmed) that posteriors were approximately normal.
## we can check that the means and the medians are close as a clue that normality is a good assumptoin
## mean(med_quality/mean_quality)
## mean(med_quality - mean_quality)
## mean((med_quality - mean_quality)^2)
## ## plot some of the posteriors to check.
## quality_post <- dataset[1:8]
## quality_post <- melt(quality_post)
## p <- ggplot(quality_post, aes(x=value,group=variable)) + geom_histogram(bins=50) + facet_wrap(.~variable)
## ggsave("plots/quality_posterior_normality.pdf",device='pdf')

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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.RDS")
#model.1 <- readRDS("models/ordinal_quality_intercept.RDS")
model.main.pca <- readRDS("models/ordinal_quality_pca.RDS")
model.main.pca.cumulative <- readRDS("models/ordinal_quality_pca.cumulative.RDS")
model.qe6 <- readRDS("models/ordinal_quality_qe6.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 <- readRDS("data/ores_pca_features.RDS")
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)
}
new_pca_features <- wpca_transform(pca_features, test.df[,.(Stub, Start, C, B, GA, FA)])
test.df<-test.df[,":="(pca1 = new_pca_features[,1],
pca2 = new_pca_features[,2],
pca3 = new_pca_features[,3],
pca4 = new_pca_features[,4],
pca5 = new_pca_features[,5])]
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$Estimate
remember(ordinal.fitted,'ordinal.fitted')
ordinal.quality <- ordinal.pred
quality.even6 <- apply(test.df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(0,1,2,3,4,5))
quality.even5 <- apply(test.df[,.(Stub,Start,B,GA,FA)],1,function(r) r %*% c(1,2,3,4,5))
test.df <- test.df[,quality.ordinal := ordinal.quality]
test.df <- test.df[,quality.even6 := quality.even6]
(spearcor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='spearman', weights=test.df$article_weight))
remember(spearcor, 'spearman.corr')
(pearsoncor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='pearson', weights=test.df$article_weight))
remember(pearsoncor, 'pearson.corr')
ordinal.preds <- data.table(predict(best.model, test.df, robust=F))
#names(ordinal.preds) <- c("Stub","Start","C","B","A","GA","FA")
names(ordinal.preds) <- c("Stub","Start","C","B","GA","FA")
ordinal.preds <- ordinal.preds[,idx.max:=.(apply(ordinal.preds[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
pred.qe6 <- data.table(predict(model.qe6,test.df))
names(pred.qe6) <- c("Stub","Start","C","B","GA","FA")
pred.qe6 <- pred.qe6[,idx.max:=.(apply(pred.qe6[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
test.df <- test.df[,ordinal.pred := ordinal.preds$predicted]
test.df <- test.df[,pred.qe6 := pred.qe6$predicted]
test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
(top.pred.accuracy <- weighted.mean(test.df[,.(MPQC)] == test.df[,.(wp10)],test.df$article_weight))
remember(top.pred.accuracy, "top.pred.accuracy")
(ordinal.pred.accuracy <- weighted.mean(test.df[,.(ordinal.pred)] == test.df[,.(wp10)], test.df$article_weight))
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
quality.even6 <- apply(df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(1,2,3,4,5,6))
(pred.qe6.accuracy <- weighted.mean(test.df[,.(pred.qe6)] == test.df[,.(wp10)], test.df$article_weight))
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
remember(best.model, "best.model")
remember(test.df,'test.df')
ordinal.preds[,wp10:=test.df$wp10]
ordinal.preds[,weight:=test.df$article_weight]
total.weight <- sum(ordinal.preds$weight)
library(modi)
print("Calibration stats 1")
calibration.stats.1 <- ordinal.preds[,.(prob.predicted=apply(.SD,2,function(c) weighted.mean(c,weight)),
var.predicted=apply(.SD,2,function(c) weighted.var(c,weight))),.SDcols=c("Stub","Start","C","B","GA","FA")]
calibration.stats.1[,wp10:=c("stub","start","c","b","ga","fa")]
calip.data = ordinal.preds[order(wp10),.(prob.data=sum(weight)/total.weight,
var.data=var(weight)/total.weight),by=.(wp10)]
calibration.stats.1 <- calibration.stats.1[calip.data,on=.(wp10)]
calibration.stats.1$weighttype <- 'Article weight'
ordinal.preds[,weight:=test.df$revision_weight]
total.weight <- sum(ordinal.preds$weight)
print("Calibration stats 2")
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)
print("Calibration stats 3")
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)

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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_revisions.RDS")
#model.1 <- readRDS("models/ordinal_quality_intercept.RDS")
model.main.pca <- readRDS("models/ordinal_quality_pca_revision.RDS")
model.main.pca.cumulative <- readRDS("models/ordinal_quality_pca_revision.cumulative.RDS")
model.qe6 <- readRDS("models/ordinal_quality_qe6_revision.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 <- readRDS("data/ores_pca_features_revisions.RDS")
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)
}
new_pca_features <- wpca_transform(pca_features, test.df[,.(Stub, Start, C, B, GA, FA)])
test.df<-test.df[,":="(pca1.revision = new_pca_features[,1],
pca2.revision = new_pca_features[,2],
pca3.revision = new_pca_features[,3],
pca4.revision = new_pca_features[,4],
pca5.revision = new_pca_features[,5])]
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'))
remember(ordinal.fitted,'ordinal.fitted')
ordinal.pred <- ordinal.fitted$Estimate
quality.ordinal <- ordinal.pred
quality.even6 <- apply(test.df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(0,1,2,3,4,5))
quality.even5 <- apply(test.df[,.(Stub,Start,B,GA,FA)],1,function(r) r %*% c(1,2,3,4,5))
test.df <- test.df[,quality.ordinal := quality.ordinal]
test.df <- test.df[,quality.even6 := quality.even6]
(spearcor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='spearman', weights=test.df$revision_weight))
remember(spearcor, 'spearman.corr')
(pearsoncor <- weightedCorr(test.df$quality.ordinal, test.df$quality.even6, method='pearson', weights=test.df$revision_weight))
remember(pearsoncor, 'pearson.corr')
ordinal.preds <- data.table(predict(best.model, test.df, robust=F))
#names(ordinal.preds) <- c("Stub","Start","C","B","A","GA","FA")
names(ordinal.preds) <- c("Stub","Start","C","B","GA","FA")
ordinal.preds <- ordinal.preds[,idx.max:=.(apply(ordinal.preds[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
ordinal.preds <- ordinal.preds[,predicted:=.(apply(ordinal.preds[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
pred.qe6 <- data.table(predict(model.qe6,test.df))
names(pred.qe6) <- c("Stub","Start","C","B","GA","FA")
pred.qe6 <- pred.qe6[,idx.max:=.(apply(pred.qe6[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
#pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b",'a',"ga","fa")[idx]))]
pred.qe6 <- pred.qe6[,predicted:=.(apply(pred.qe6[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
test.df <- test.df[,ordinal.pred := ordinal.preds$predicted]
test.df <- test.df[,pred.qe6 := pred.qe6$predicted]
test.df <- test.df[,idx.max:=.(apply(test.df[,.(Stub,Start,C,B,GA,FA)],1,which.max))]
test.df <- test.df[,MPQC:=.(apply(test.df[,.(idx.max)],1,function(idx) c("stub","start","c","b","ga","fa")[idx]))]
(top.pred.accuracy <- weighted.mean(test.df[,.(MPQC)] == test.df[,.(wp10)],test.df$revision_weight))
remember(top.pred.accuracy, "top.pred.accuracy")
(ordinal.pred.accuracy <- weighted.mean(test.df[,.(ordinal.pred)] == test.df[,.(wp10)], test.df$revision_weight))
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
quality.even6 <- apply(df[,.(Stub,Start,B,C,GA,FA)],1,function(r) r %*% c(1,2,3,4,5,6))
(pred.qe6.accuracy <- weighted.mean(test.df[,.(pred.qe6)] == test.df[,.(wp10)], test.df$revision_weight))
remember(ordinal.pred.accuracy, "ordinal.pred.accuracy")
remember(best.model, "best.model")
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)

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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)

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load_data.R Normal file
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library(MASS)
library(brms)
options(mc.cores=28)
library(data.table)
library(arrow)
sample.params <- readRDS("remember_sample_quality_labels.RDS")
df <- data.table(read_feather("data/scored_article_sample.feather"))
wp10dict <- list('start','stub','c','b','a','ga','fa')
df[,wp10:=wp10dict[wp10]]
df <- df[,wp10:=factor(wp10,levels=c('stub','start','c','b','a','ga','fa'),ordered=TRUE)]
## remove 'a' class articles for a fair comparison.
df <- df[wp10!='a']
df <- df[,datetime := as.POSIXct(timestamp,format="%Y%m%d%H%M%S")]
df <- df[,datetime.numeric := as.numeric(timestamp)]
df <- df[,datetime.numeric := (datetime.numeric - min(datetime.numeric))]
df <- df[,datetime.numeric := datetime.numeric/max(datetime.numeric)]
data.counts <- data.table(sample.params$label_sample_counts)
#data.counts <- data.counts[,wp10:=factor(wp10,levels=c('stub','start','c','b','a','ga','fa'),ordered=TRUE)]
data.counts <- data.counts[,wp10:=factor(wp10,levels=c('stub','start','c','b','a','ga','fa'),ordered=TRUE)]
sample.counts <- df[,.(.N),by=.(wp10)][order(wp10)]
#sample.counts <- sample.counts[,wp10:=factor(wp10,levels=c('stub','start','c','b','a','ga','fa'),ordered=TRUE)]
sample.counts <- sample.counts[,wp10:=factor(wp10,levels=c('stub','start','c','b','ga','fa'),ordered=TRUE)]
weights <- data.counts[sample.counts,on=.(wp10)]
weights <- weights[,article_weight:=(n_articles/sum(weights$n_articles))/(N/sum(weights$N))]
weights <- weights[,revision_weight:=(n_revisions/sum(weights$n_revisions))/(N/sum(weights$N))]
df <- df[weights,on=.(wp10)]
df[,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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ordinal_quality_models.R Normal file
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source("RemembR/R/RemembeR.R")
source("load_data.R")
change.remember.file("ordinal.quality.model.RDS")
test <- F
remember(weights, "sample.weights")
n.holdout <- 4000
remember(n.holdout,"n.holdout")
holdout <- df[sample(.N,n.holdout)]
saveRDS(holdout,'data/holdout_quality_labels.RDS')
df <- df[!(revid %in% holdout$revid)]
saveRDS(df,'data/training_quality_labels.RDS')
if(test == TRUE){
df <- df[sample(.N,2000)]
}
## So it turns out that the 6 predictors we have are highly correlated creating problems for sampling so use QR decomposition
df <- df[!is.na(wp10)]
df[, start.p.stub := Start + Stub]
saveRDS(df,"data/training_quality_labels.RDS")
## So it turns out that the 6 predictors we have are highly correlated creating problems for sampling so use QR decomposition
df <- df[!is.na(wp10)]
df[, start.p.stub := Start + Stub]
fam.cloglog <- sratio(link='cloglog', threshold='flexible')
#formula.1 <- brmsformula(wp10 | weights(weight) ~ 1,decomp='QR',center=TRUE)
fam <- sratio(link='logit', threshold='flexible')
fam.cumulative <- sratio(link='logit', threshold='flexible')
## It turns out that the matrix is singular if we include all the predictors.
## C is the most correlated with the other variables so for now let's remove it.
## it turns out that we don't need to do model selection at all since we don't care about the coefficients.
## we can just do the csv!
x <- df[,.(Stub,Start,C,B,GA,FA)]
wpca <- function(x, weight){
name <- names(x)
x <- as.matrix(x)
means <- unlist(lapply(1:dim(x)[2], function(i) weighted.mean(x[,i], weight)))
names(means) <- name
centered <- as.matrix(t(t(x) - means))
weightmat <- diag(weight)
covmat <- t(centered) %*% weightmat %*% centered / (sum(weight) - 1)
factors <- eigen(covmat)
basis <- factors$vectors
result <- centered %*% basis
# return a list with the info we need to do the transformation
return(list(means=means, basis=basis, x=result))
}
#unweighted.pca <- wpca(df[,.(Stub,Start,C,B,GA,FA)],rep(1,nrow(df)))
upca <- prcomp(df[,.(Stub,Start,C,B,GA,FA)])
unweighted.pca <- list(means = upca$center, basis=upca$rotation, x=upca$x)
saveRDS(unweighted.pca,"data/ores_pca_features.noweights.RDS")
weighted.pca <- wpca(df[,.(Stub,Start,C,B,GA,FA)],df$article_weight)
saveRDS(weighted.pca, "data/ores_pca_features.RDS")
revision.pca <- wpca(df[,.(Stub,Start,C,B,GA,FA)],df$revision_weight)
saveRDS(revision.pca, "data/ores_pca_features_revisions.RDS")
df <- df[,":="(pca1 = weighted.pca$x[,1],
pca2 = weighted.pca$x[,2],
pca3 = weighted.pca$x[,3],
pca4 = weighted.pca$x[,4],
pca5 = weighted.pca$x[,5],
pca6 = weighted.pca$x[,6])]
df <- df[,":="(pca1.revision = revision.pca$x[,1],
pca2.revision = revision.pca$x[,2],
pca3.revision = revision.pca$x[,3],
pca4.revision = revision.pca$x[,4],
pca5.revision = revision.pca$x[,5],
pca6.revision = revision.pca$x[,6])]
df <- df[,":="(pca1.noweights = unweighted.pca$x[,1],
pca2.noweights = unweighted.pca$x[,2],
pca3.noweights = unweighted.pca$x[,3],
pca4.noweights = unweighted.pca$x[,4],
pca5.noweights = unweighted.pca$x[,5],
pca6.noweights = unweighted.pca$x[,6])]
qformula.main.pca.cs <- brmsformula(wp10 | weights(article_weight) ~ cs(pca1) + cs(pca2) + cs(pca3) + cs(pca4) + cs(pca5))
formula.main.pca.noweights.cs <- brmsformula(wp10 ~ cs(pca1.noweights) + cs(pca2.noweights) + cs(pca3.noweights) + cs(pca4.noweights) + cs(pca5.noweights))
formula.revision.pca.cs <- brmsformula(wp10 | weights(revision_weight) ~ cs(pca1.revision) + cs(pca2.revision) + cs(pca3.revision) + cs(pca4.revision) + cs(pca5.revision))
formula.qe6.cs <- brmsformula(wp10 | weights(article_weight) ~ cs(quality.even6))
formula.qe6.revision.cs <- brmsformula(wp10 | weights(revision_weight) ~ cs(quality.even6))
formula.qe6.noweights.cs <- brmsformula(wp10 ~ cs(quality.even6))
formula.main.pca <- brmsformula(wp10 | weights(article_weight) ~ pca1 + pca2 + pca3 + pca4 + pca5)
formula.main.pca.noweights <- brmsformula(wp10 ~ pca1.noweights + pca2.noweights + pca3.noweights + pca4.noweights + pca5.noweights)
formula.revision.pca <- brmsformula(wp10 | weights(revision_weight) ~ pca1.revision + pca2.revision + pca3.revision + pca4.revision + pca5.revision)
formula.qe6 <- brmsformula(wp10 | weights(article_weight) ~ quality.even6)
formula.qe6.revision <- brmsformula(wp10 | weights(revision_weight) ~ quality.even6)
formula.qe6.noweights <- brmsformula(wp10 ~ quality.even6)
formula.scores.noweights <- brmsformula(wp10 ~ Start + Stub + GA + FA + B)
library(future)
library(parallel)
options(mc.cores = parallel::detectCores())
plan(lapply(1:7,function(x) tweak(multisession, workers=4)))
model.main.pca %<-% brm(formula=formula.main.pca, data=df, family=fam, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
model.qe6 %<-% brm(formula.qe6, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.main.revision %<-% brm(formula=formula.revision.pca, data=df, family=fam, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
model.qe6.revision %<-% brm(formula.qe6.revision, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.qe6.noweights %<-% brm(formula.qe6.noweights, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.main.pca.noweights %<-% brm(formula=formula.main.pca.noweights, data=df, family=fam, control=list(max_treedepth=15), future=TRUE,save_pars=save_pars(all=TRUE))
## model.main.pca.cs %<-% brm(formula=formula.main.pca.cs, data=df, family=fam, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
## model.qe6.cs %<-% brm(formula.qe6.cs, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
## model.main.revision.cs %<-% brm(formula=formula.revision.pca.cs, data=df, family=fam, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
## model.qe6.revision.cs %<-% brm(formula.qe6.revision.cs, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
## model.qe6.noweights.cs %<-% brm(formula.qe6.noweights.cs, data=df, family=fam, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
## model.main.pca.noweights.cs %<-% brm(formula=formula.main.pca.noweights.cs, data=df, family=fam, control=list(max_treedepth=15), future=TRUE,save_pars=save_pars(all=TRUE))
model.main.pca.cumulative %<-% brm(formula=formula.main.pca, data=df, family=fam.cumulative, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
model.qe6.cumulative %<-% brm(formula.qe6, data=df, family=fam.cumulative, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.main.revision.cumulative %<-% brm(formula=formula.revision.pca, data=df, family=fam.cumulative, control=list(max_treedepth=15), future=TRUE, save_pars=save_pars(all=TRUE))
model.qe6.revision.cumulative %<-% brm(formula.qe6.revision, data=df, family=fam.cumulative, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.qe6.noweights.cumulative %<-% brm(formula.qe6.noweights, data=df, family=fam.cumulative, control=list(max_treedepth=15),future=TRUE,save_pars=save_pars(all=TRUE))
model.main.pca.noweights.cumulative %<-% brm(formula=formula.main.pca.noweights, data=df, family=fam.cumulative, control=list(max_treedepth=15), future=TRUE,save_pars=save_pars(all=TRUE))
#model.scores.noweights <- brm(formula=formula.scores.noweights, data=df, family=fam, control=list(max_treedepth=15), future=TRUE,save_pars=save_pars(all=TRUE))
models <- resolve(globalenv(),result=F)
print("adding loo")
model.main.revision <- add_criterion(model.main.revision,'loo',moment_match=T)
model.main.pca <- add_criterion(model.main.pca,'loo',moment_match=T)
model.qe6.revision <- add_criterion(model.qe6.revision,'loo')
model.qe6 <- add_criterion(model.qe6,'loo')
model.main.pca.noweights <- add_criterion(model.main.pca.noweights,'loo',moment_match=T)
model.qe6.noweights <- add_criterion(model.qe6.noweights,'loo')
model.main.revision.cumulative <- add_criterion(model.main.revision.cumulative,'loo',moment_match=T)
model.main.pca.cumulative <- add_criterion(model.main.pca.cumulative,'loo',moment_match=T)
model.qe6.revision.cumulative <- add_criterion(model.qe6.revision.cumulative,'loo')
model.qe6.cumulative <- add_criterion(model.qe6.cumulative,'loo')
model.main.pca.noweights.cumulative <- add_criterion(model.main.pca.noweights.cumulative,'loo',moment_match=T)
model.qe6.noweights.cumulative <- add_criterion(model.qe6.noweights.cumulative,'loo')
## model.main.revision.cs <- add_criterion(model.main.revision.cs,'loo',moment_match=T)
## model.main.pca.cs <- add_criterion(model.main.pca.cs,'loo',moment_match=T)
## model.qe6.revision.cs <- add_criterion(model.qe6.revision.cs,'loo')
## model.qe6.cs <- add_criterion(model.qe6.cs,'loo')
## model.main.pca.noweights.cs <- add_criterion(model.main.pca.noweights.cs,'loo',moment_match=T)
## model.qe6.noweights.cs <- add_criterion(model.qe6.noweights.cs,'loo')
saveRDS(model.qe6.revision,"models/ordinal_quality_qe6_revision.RDS")
saveRDS(model.qe6,"models/ordinal_quality_qe6.RDS")
saveRDS(model.main.pca.noweights,"models/ordinal_quality_pca.noweights.RDS")
saveRDS(model.qe6.noweights,"models/ordinal_quality_qe6.noweights.RDS")
saveRDS(model.main.pca,"models/ordinal_quality_pca.RDS")
saveRDS(model.main.revision,"models/ordinal_quality_pca_revision.RDS")
saveRDS(model.qe6.revision.cumulative,"models/ordinal_quality_qe6_revision.cumulative.RDS")
saveRDS(model.qe6.cumulative,"models/ordinal_quality_qe6.cumulative.RDS")
saveRDS(model.main.pca.noweights.cumulative,"models/ordinal_quality_pca.noweights.cumulative.RDS")
saveRDS(model.qe6.noweights.cumulative,"models/ordinal_quality_qe6.noweights.cumulative.RDS")
saveRDS(model.main.pca.cumulative,"models/ordinal_quality_pca.cumulative.RDS")
saveRDS(model.main.revision.cumulative,"models/ordinal_quality_pca_revision.cumulative.RDS")
## saveRDS(model.qe6.revision.cs,"models/ordinal_quality_qe6_revision.RDS")
## saveRDS(model.qe6.cs,"models/ordinal_quality_qe6.RDS")
## saveRDS(model.main.pca.noweights.cs,"models/ordinal_quality_pca.noweights.RDS")
## saveRDS(model.qe6.noweights.cs,"models/ordinal_quality_qe6.noweights.RDS")
## saveRDS(model.main.pca.cs,"models/ordinal_quality_pca.RDS")
## saveRDS(model.main.revision.cs,"models/ordinal_quality_pca_revision.RDS")
models <- resolve(globalenv(),result=F)

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ores_score_sample.sh Executable file
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#!/bin/bash
python3 ores_scores_sample.py --sample_file="/data/nti9383home/production_functions/data/20200301_article_labelings_sample.feather" --output=/data/nti9383home/production_functions/data/scored_article_sample.feather

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ores_scores_sample.py Normal file
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import mwapi
from revscoring import Model
import articlequality
import pyarrow
import pandas as pd
import scoring_utils
from itertools import chain, zip_longest
from multiprocessing import Pool
from functools import partial
from pyRemembeR import Remember
import fire
from pathlib import Path
import tqdm
remember = Remember("score_sample_articles.RDS")
def get_revision_text(revid_batch, api):
revid_batch = filter(lambda rid: rid is not None, revid_batch)
doc = api.get(action='query',
prop='revisions',
revids=revid_batch,
rvprop=['ids','content'],
rvslots=['main'])
pages = doc.get('query',{}).get('pages',{})
for pageid, doc in pages.items():
revisions = doc.get('revisions',[])
for revision in revisions:
text = revision.get('slots',{}).get('main',{}).get('*',{})
yield {'revid':revision.get('revid',{}), 'text':text}
def grouper(n, iterable, fillvalue=None):
"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
args = [iter(iterable)] * n
return zip_longest(fillvalue=fillvalue, *args)
def pull_revision_texts(revids, api, api_batch_size):
batches = grouper(api_batch_size,revids)
get_revision_text_2 = partial(get_revision_text,api=api)
revs = chain(* map(get_revision_text_2, batches))
yield from revs
def score_revisions(revids, api, api_batch_size=50, parallel=True):
revs = pull_revision_texts(revids, api, api_batch_size)
ncores = 28
pool = Pool(ncores)
scorer_model = Model.load(open('articlequality/models/enwiki.nettrom_wp10.gradient_boosting.model', 'rb'))
add_score = partial(scoring_utils.add_score, scorer_model=scorer_model)
if parallel:
ncores = 48
pool = Pool(ncores)
revs = pool.imap_unordered(add_score, revs, chunksize = api_batch_size*4)
else:
revs = map(add_score,revs)
to_pddict = partial(scoring_utils.to_pddict,kept_keys=['revid'])
revs = map(to_pddict, revs)
yield from revs
#sample_file_parquet = "data/article_sample_set.parquet"; output_feather="data/scored_article_sample.feather";
sample_file="/data/nti9383home/production_functions/data/20200301_article_labelings_sample.feather";output="/data/nti9383home/production_functions/data/scored_article_sample.feather"
def score_sample(sample_file = "data/article_sample_set.feather", output="data/scored_article_sample.feather"):
sample = pd.read_feather(sample_file)
revids = set(sample.revid)
user_agent = "Nate TeBlunthuis <nathante@uw.edu>. What's the relationship between contributors and article quality?"
api = mwapi.Session("https://en.wikipedia.org",user_agent=user_agent)
scores = tqdm.tqdm(score_revisions(revids, api, 50, True),total=len(revids),miniters=100,smoothing=0.2)
p = Path(output)
output_csv = Path(str(p).replace("".join(p.suffixes), ".csv"))
output_json = Path(str(p).replace("".join(p.suffixes), ".json"))
output_feather = Path(str(p).replace("".join(p.suffixes), ".feather"))
saved_scores = list()
with open(output_json,'w') as of:
for score in scores:
of.write(str(score) + '\n')
saved_scores.append(score)
scored_revids = pd.DataFrame(saved_scores)
sample_1 = sample.merge(scored_revids,left_on="revid",right_on="revid")
remember(sample_1.shape[0],"sample_size_unscored")
remember(sample_1.shape[0],"sample_size_scored")
sample_1.to_feather(output_feather)
sample_1.to_csv(output_csv)
if __name__ == "__main__":
fire.Fire(score_sample)

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#!/bin/bash
Rscript ordinal_quality_models.R && \
Rscript analyze_quality_models.R && \
Rscript analyze_quality_models_unweighted.R
Rscript analyze_quality_models_revisions.R

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sample_training_labels.py Executable file
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#!/usr/bin/env python3
'''
Take a stratified sample of article quality labels.
For now we just stratify by label type.
Later we might add date.
Later we might stratify by wikiproject too.
A key limitation of this approach is that we can sample on the level of the page.
We'd really like to be able to sample on the level of edit session.
But that isn't possible because of how article assessments work.
'''
from itertools import islice, chain
from pathlib import Path
import pandas as pd
import numpy as np
random = np.random.RandomState(1968)
import json
import pyarrow.feather as feather
import fire
from collections import Counter
from pyRemembeR import Remember
from enum import IntEnum, unique
from datetime import datetime
from dataclasses import dataclass, asdict
from multiprocessing import Pool
from urllib.parse import unquote
from pyspark.sql import functions as f
from pyspark.sql import SparkSession, Window
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
from numpy import dtype
import csv
def wikiq_to_parquet():
path = Path("/gscratch/comdata/users/nathante/wikiqRunning/wikiq_output/")
outpath = Path("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante_parquet/")
files = list(map(Path,path.glob("*.tsv")))
dumpfile = files[0]
def wikiq_tsv_to_parquet(dumpfile, outpath = Path("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante.parquet/")):
outfile = outpath / (dumpfile.name + ".parquet")
outpath.mkdir(parents=True, exist_ok=True)
_wikiq_tsv_to_parquet(dumpfile,outfile)
dumpfile = Path("/gscratch/comdata/users/nathante/wikiqRunning/wikiq_output/enwiki-20200301-pages-meta-history12-p4980874p5038451.tsv")
def _wikiq_tsv_to_parquet(dumpfile, outfile):
dtypes = {'anon': dtype('O'), 'articleid': dtype('int64'), 'deleted': dtype('bool'), 'editor': dtype('O'), 'editor_id': dtype('float64'), 'minor': dtype('bool'), 'namespace': dtype('int64'), 'revert': dtype('O'), 'reverteds': dtype('O'), 'revid': dtype('int64'), 'sha1': dtype('O'), 'text_chars': dtype('float64'), 'title': dtype('O')}
print(dumpfile)
df = pd.read_csv(dumpfile,sep='\t',quoting=csv.QUOTE_NONE,error_bad_lines=False, warn_bad_lines=True,parse_dates=['date_time'],dtype=dtypes)
df.to_parquet(outfile)
with Pool(28) as pool:
jobs = pool.imap_unordered(wikiq_tsv_to_parquet, files)
list(jobs)
spark = SparkSession.builder.getOrCreate()
@udf(StringType())
def decode_strip_udf(val):
if val is None:
return ""
else:
return unquote(val).strip('\"')
df = spark.read.parquet('/gscratch/comdata/output/wikiq_enwiki_20200301_nathante.parquet')
df = df.withColumnRenamed("anon","anonRaw")
df = df.withColumn("anon",f.when(f.col("anonRaw")=="TRUE",True).otherwise(False))
df = df.drop("anonRaw")
df = df.withColumnRenamed("text_chars","text_chars_raw")
df = df.withColumn("text_chars",f.col("text_chars_raw").cast('int'))
df = df.drop("text_chars_raw")
df = df.withColumnRenamed("editor_id",'editor_id_raw')
df = df.withColumn("editor_id",f.col("editor_id_raw").cast("int"))
df = df.drop("editor_id_raw")
df = df.withColumnRenamed("revert","revert_raw")
df = df.withColumn("revert",f.when(f.col("revert_raw")=="TRUE",True).otherwise(False))
df = df.drop("revert_raw")
df = df.withColumnRenamed("title","title_raw")
df = df.withColumn("title", decode_strip_udf(f.col("title_raw")))
df = df.drop("title_raw")
df = df.withColumnRenamed("editor","editor_raw")
df = df.withColumn("editor", decode_strip_udf(f.col("editor_raw")))
df = df.drop("editor_raw")
df = df.repartition(400,'articleid')
df.write.parquet("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante_partitioned.parquet",mode='overwrite')
@unique
class WP10(IntEnum):
start = 1
stub = 2
c = 3
b = 4
a = 5
ga = 6
fa = 7
@staticmethod
def from_string(s):
return {'start':WP10.start,
'stub':WP10.stub,
'c':WP10.c,
'b':WP10.b,
'a':WP10.a,
'ga':WP10.ga,
'fa':WP10.fa}.get(s,None)
def to_string(self):
return {WP10.start:'start',
WP10.stub:'stub',
WP10.c:'c',
WP10.b:'b',
WP10.a:'a',
WP10.ga:'ga',
WP10.fa:'fa'}[self]
@dataclass
class PageLabel:
timestamp:datetime
wp10:WP10
@staticmethod
def from_json(obj):
timestamp = obj.get('timestamp',None)
if timestamp is not None:
timestamp = datetime.strptime(obj['timestamp'],'%Y%m%d%H%M%S')
else:
timestamp = None
return PageLabel(timestamp=timestamp,
wp10=WP10.from_string(obj.get('wp10')))
@staticmethod
def from_row(row):
return PageLabel(timestamp = row.timestamp,
wp10 = WP10(row.wp10))
def to_json(self):
d = asdict(self)
if self.timestamp is not None:
d['timestamp'] = self.timestamp.strftime('%Y%m%d%H%M%S')
if self.wp10 is not None:
d['wp10'] = self.wp10.to_string()
return json.dumps(d)
@dataclass
class TalkPageLabel(PageLabel):
dump_talk_page_title:str
talk_page_id:int
project:str
@staticmethod
def from_json(obj):
res = PageLabel.from_json(obj)
return TalkPageLabel(dump_talk_page_title=obj.get('dump_talk_page_title',None),
talk_page_id=obj.get('talk_page_id',None),
project=obj.get("project",None),
**asdict(res)
)
@staticmethod
def from_row(row):
res = PageLabel.from_row(row)
return TalkPageLabel(dump_talk_page_title = row.dump_talk_page_title,
talk_page_id = row.talk_page_id,
project = row.project
**asdict(res))
@dataclass
class ArticlePageLabel(PageLabel):
'''class representing labels to a page'''
title: str
articleid: int
revid:int
@staticmethod
def from_json(obj):
res = PageLabel.from_json(obj)
return ArticlePageLabel(title=obj.get('title',None),
articleid=obj.get('articleid',None),
**asdict(res)
)
@staticmethod
def from_row(row):
res = PageLabel.from_row(row)
return ArticlePageLabel(title = row.title,
articleid = row.articleid,
revid = row.revid,
**asdict(res))
infiles="enwiki-20200301-pages-meta-history*.xml-p*.7z_article_labelings.json"; samplesize=5000*7
def main(infiles="enwiki-20200301-pages-meta-history*.xml-p*.7z_article_labelings.json", samplesize=5000*7):
path = Path('data')
infiles = path.glob(infiles)
pool = Pool(28)
lines = chain(* map(lambda f: open(f,'r'), infiles))
parsed = pool.imap_unordered(json.loads, lines, chunksize=int(1e3))
formatted = pool.imap_unordered(TalkPageLabel.from_json, parsed, chunksize=int(1e3))
dicted = pool.imap_unordered(asdict,formatted, chunksize=int(1e3))
# data frame of the the latest labels.
df = pd.DataFrame(dicted)
df = df.loc[df.timestamp <= datetime(2019,1,1)]
groups = df.groupby(["talk_page_id"])
max_labels = groups.wp10.max().reset_index()
df2 = pd.merge(df,max_labels,on=['talk_page_id','wp10'],how='right')
last_timestamp = df2.groupby(['talk_page_id']).timestamp.max().reset_index()
df2 = pd.merge(df2, last_timestamp, on=['talk_page_id','timestamp'], how='right')
first_project = df2.groupby(['talk_page_id']).project.first()
df2 = pd.merge(df2, first_project,on=['talk_page_id','project'], how='right')
tpid = df2
#.wp10.max().reset_index()
tpid = tpid.loc[~tpid.dump_talk_page_title.isna()]
# pick out just the samples we want.
spark = SparkSession.builder.getOrCreate()
sparkdf = spark.read.parquet("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante_partitioned.parquet")
tpid['timestamp'] = tpid['timestamp'].dt.tz_localize('utc')
labels = spark.createDataFrame(tpid)
talks = sparkdf.filter(sparkdf.namespace==1)
articles = sparkdf.filter(sparkdf.namespace==0)
# labels = labels.join(talks,on=[labels.talk_page_id == talks.articleid],how='left_outer')
talks = talks.join(labels,on=[labels.talk_page_id == talks.articleid])
#talks.filter(talks.wp10==7).select('talk_page_id').distinct().count()
talks = talks.withColumn('timediff', f.datediff(talks.timestamp, talks.date_time))
talks = talks.filter(talks.timediff <= 0)
win = Window.partitionBy("talk_page_id")
talks = talks.withColumn('best_timediff', f.max('timediff').over(win))
talks = talks.filter(talks.timediff == talks.best_timediff)
talks = talks.withColumn('article_title',f.substring_index(f.col("title"),':',-1))
talks = talks.select(['article_title','wp10',f.col('timestamp').alias('timestamp'),'talk_page_id']).distinct()
articles = articles.join(talks,on=[talks.article_title == articles.title])
articles = articles.withColumn('timediff', f.datediff(articles.timestamp, articles.date_time))
articles = articles.filter(articles.timediff <= 0)
win2 = Window.partitionBy("articleid")
articles = articles.filter(f.col("revert")==False)
articles = articles.withColumn('best_timediff', f.max('timediff').over(win2))
articles = articles.filter(articles.timediff == articles.best_timediff)
articles = articles.select(['revid','timestamp','wp10','articleid','title'])
articles = articles.groupby(['timestamp','wp10','articleid','title']).agg(f.first(f.col("revid")).alias("revid"))
articles.write.parquet("data/article_quality_data.parquet",mode='overwrite')
tpid = pd.read_parquet("data/article_quality_data.parquet")
# we want to sample /papges/ not /labels/.
# so we need to do a /full/ groupby pages.
# this is why we have a lot of RAM!
# we need the number of
label_counts = {}
sample_page_ids = {}
label_max_samplesize = int(samplesize / len(WP10))
sample_chunks = []
for lab in WP10:
print(lab)
page_ids = tpid.loc[tpid.wp10==lab].articleid
label_counts[lab] = len(page_ids)
print(lab,label_counts)
if(label_counts[lab] <= label_max_samplesize):
sample_page_ids[lab] = page_ids
else:
sample_page_ids[lab] = random.choice(page_ids,label_max_samplesize,replace=False)
# get the labels for each sampled article
sample_data_lab = tpid.loc[(tpid.articleid.isin(sample_page_ids[lab]))]
sample_chunks.append(sample_data_lab)
remember = Remember(f='remember_sample_quality_labels.RDS')
remember(label_max_samplesize, 'label_max_samplesize')
# Note that different wikiprojects can have different labels
sample = pd.concat(sample_chunks,ignore_index=True)
revisions_per_article = sparkdf.filter(sparkdf.namespace==0).select(['revid','articleid','date_time','title'])
revisions_per_article = revisions_per_article.filter(f.col("date_time") >= datetime(2019,1,1))
revisions_per_article = revisions_per_article.filter(f.col("date_time") <= datetime(2019,12,31))
revisions_per_article = revisions_per_article.groupby(["articleid",'title']).count().toPandas()
revisions_per_article['title'] = revisions_per_article.title.apply(lambda s: unquote(s).strip('\"'))
revisions_per_article = pd.merge(revisions_per_article,tpid,left_on='articleid',right_on='articleid')
revisions_per_class = revisions_per_article.groupby('wp10').agg({'count':'sum'}).reset_index()
revisions_per_class['wp10'] = revisions_per_class.wp10.apply(lambda s: WP10(s).to_string())
label_counts = pd.DataFrame({'wp10':map(lambda x: x.to_string(),label_counts.keys()),'n_articles':label_counts.values()})
label_counts = pd.merge(label_counts,revisions_per_class,left_on='wp10',right_on='wp10')
label_counts = label_counts.rename(columns={'count':'n_revisions'})
remember(label_counts, 'label_sample_counts')
sample.to_feather("data/20200301_article_labelings_sample.feather")
sample = pd.read_feather("data/20200301_article_labelings_sample.feather")
sample_counts = sample.articleid.groupby(sample.wp10).count().reset_index()
remember(sample_counts,'sample_counts')
sample_labels = sample.apply(ArticlePageLabel.from_row,axis=1)
sample_labels = map(PageLabel.to_json, sample_labels)
with open("data/20200301_article_labelings_sample.json",'w') as of:
of.writelines((l + '\n' for l in sample_labels))
pool.close()
if __name__ == "__main__":
fire.Fire(main)

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score_sample_articles.RDS Symbolic link
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.git/annex/objects/J8/FP/SHA256E-s123--ca8ebf4d8748b52e9edeca96f14f4132042c8039a4d6376ffa87033adc36d8cb.RDS/SHA256E-s123--ca8ebf4d8748b52e9edeca96f14f4132042c8039a4d6376ffa87033adc36d8cb.RDS

573
wikiq Executable file
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#!/usr/bin/env python3
# original wikiq headers are: title articleid revid date_time anon
# editor editor_id minor text_size text_entropy text_md5 reversion
# additions_size deletions_size
import argparse
import sys
import os, os.path
import re
from subprocess import Popen, PIPE
from collections import deque
from hashlib import sha1
from mwxml import Dump
from deltas.tokenizers import wikitext_split
import mwpersistence
import mwreverts
from urllib.parse import quote
TO_ENCODE = ('title', 'editor')
PERSISTENCE_RADIUS=7
from deltas import SequenceMatcher
from deltas import SegmentMatcher
class PersistMethod:
none = 0
sequence = 1
segment = 2
legacy = 3
def calculate_persistence(tokens_added):
return(sum([(len(x.revisions)-1) for x in tokens_added]),
len(tokens_added))
class WikiqIterator():
def __init__(self, fh, collapse_user=False):
self.fh = fh
self.collapse_user = collapse_user
self.mwiterator = Dump.from_file(self.fh)
self.namespace_map = { ns.id : ns.name for ns in
self.mwiterator.site_info.namespaces }
self.__pages = self.load_pages()
def load_pages(self):
for page in self.mwiterator:
yield WikiqPage(page,
namespace_map = self.namespace_map,
collapse_user=self.collapse_user)
def __iter__(self):
return self.__pages
def __next__(self):
return next(self._pages)
class WikiqPage():
__slots__ = ('id', 'title', 'namespace', 'redirect',
'restrictions', 'mwpage', '__revisions',
'collapse_user')
def __init__(self, page, namespace_map, collapse_user=False):
self.id = page.id
self.namespace = page.namespace
# following mwxml, we assume namespace 0 in cases where
# page.namespace is inconsistent with namespace_map
if page.namespace not in namespace_map:
self.title = page.title
page.namespace = 0
if page.namespace != 0:
self.title = ':'.join([namespace_map[page.namespace], page.title])
else:
self.title = page.title
self.restrictions = page.restrictions
self.collapse_user = collapse_user
self.mwpage = page
self.__revisions = self.rev_list()
def rev_list(self):
# Outline for how we want to handle collapse_user=True
# iteration rev.user prev_rev.user add prev_rev?
# 0 A None Never
# 1 A A False
# 2 B A True
# 3 A B True
# 4 A A False
# Post-loop A Always
for i, rev in enumerate(self.mwpage):
# never yield the first time
if i == 0:
if self.collapse_user:
collapsed_revs = 1
rev.collapsed_revs = collapsed_revs
else:
if self.collapse_user:
# yield if this is the last edit in a seq by a user and reset
# also yield if we do know who the user is
if rev.deleted.user or prev_rev.deleted.user:
yield prev_rev
collapsed_revs = 1
rev.collapsed_revs = collapsed_revs
elif not rev.user.text == prev_rev.user.text:
yield prev_rev
collapsed_revs = 1
rev.collapsed_revs = collapsed_revs
# otherwise, add one to the counter
else:
collapsed_revs += 1
rev.collapsed_revs = collapsed_revs
# if collapse_user is false, we always yield
else:
yield prev_rev
prev_rev = rev
# also yield the final time
yield prev_rev
def __iter__(self):
return self.__revisions
def __next__(self):
return next(self.__revisions)
class RegexPair(object):
def __init__(self, pattern, label):
self.pattern = re.compile(pattern)
self.label = label
self.has_groups = bool(self.pattern.groupindex)
if self.has_groups:
self.capture_groups = list(self.pattern.groupindex.keys())
def _make_key(self, cap_group):
return ("{}_{}".format(self.label, cap_group))
def matchmake(self, content, rev_data):
temp_dict = {}
# if there are named capture groups in the regex
if self.has_groups:
# if there are matches of some sort in this revision content, fill the lists for each cap_group
if self.pattern.search(content) is not None:
m = self.pattern.finditer(content)
matchobjects = list(m)
for cap_group in self.capture_groups:
key = self._make_key(cap_group)
temp_list = []
for match in matchobjects:
# we only want to add the match for the capture group if the match is not None
if match.group(cap_group) != None:
temp_list.append(match.group(cap_group))
# if temp_list of matches is empty just make that column None
if len(temp_list)==0:
temp_dict[key] = None
# else we put in the list we made in the for-loop above
else:
temp_dict[key] = ', '.join(temp_list)
# there are no matches at all in this revision content, we default values to None
else:
for cap_group in self.capture_groups:
key = self._make_key(cap_group)
temp_dict[key] = None
# there are no capture groups, we just search for all the matches of the regex
else:
#given that there are matches to be made
if self.pattern.search(content) is not None:
m = self.pattern.findall(content)
temp_dict[self.label] = ', '.join(m)
else:
temp_dict[self.label] = None
# update rev_data with our new columns
rev_data.update(temp_dict)
return rev_data
class WikiqParser():
def __init__(self, input_file, output_file, regex_match_revision, regex_match_comment, regex_revision_label, regex_comment_label, collapse_user=False, persist=None, urlencode=False, namespaces = None, revert_radius=15):
"""
Parameters:
persist : what persistence method to use. Takes a PersistMethod value
"""
self.input_file = input_file
self.output_file = output_file
self.collapse_user = collapse_user
self.persist = persist
self.printed_header = False
self.namespaces = []
self.urlencode = urlencode
self.revert_radius = revert_radius
if namespaces is not None:
self.namespace_filter = set(namespaces)
else:
self.namespace_filter = None
self.regex_revision_pairs = self.make_matchmake_pairs(regex_match_revision, regex_revision_label)
self.regex_comment_pairs = self.make_matchmake_pairs(regex_match_comment, regex_comment_label)
def make_matchmake_pairs(self, patterns, labels):
if (patterns is not None and labels is not None) and \
(len(patterns) == len(labels)):
return [RegexPair(pattern, label) for pattern, label in zip(patterns, labels)]
elif (patterns is None and labels is None):
return []
else:
sys.exit('Each regular expression *must* come with a corresponding label and vice versa.')
def matchmake(self, rev, rev_data):
rev_data = self.matchmake_revision(rev.text, rev_data)
rev_data = self.matchmake_comment(rev.comment, rev_data)
return rev_data
def matchmake_revision(self, text, rev_data):
return self.matchmake_pairs(text, rev_data, self.regex_revision_pairs)
def matchmake_comment(self, comment, rev_data):
return self.matchmake_pairs(comment, rev_data, self.regex_comment_pairs)
def matchmake_pairs(self, text, rev_data, pairs):
for pair in pairs:
rev_data = pair.matchmake(text, rev_data)
return rev_data
def __get_namespace_from_title(self, title):
default_ns = None
for ns in self.namespaces:
# skip if the namespace is not defined
if ns == None:
default_ns = self.namespaces[ns]
continue
if title.startswith(ns + ":"):
return self.namespaces[ns]
# if we've made it this far with no matches, we return the default namespace
return default_ns
def process(self):
# create a regex that creates the output filename
# output_filename = re.sub(r'^.*/(enwiki\-\d+)\-.*p(\d+)p.*$',
# r'output/wikiq-\1-\2.tsv',
# input_filename)
# Construct dump file iterator
dump = WikiqIterator(self.input_file, collapse_user=self.collapse_user)
# extract list of namspaces
self.namespaces = {ns.name : ns.id for ns in dump.mwiterator.site_info.namespaces}
page_count = 0
rev_count = 0
# Iterate through pages
for page in dump:
namespace = page.namespace if page.namespace is not None else self.__get_namespace_from_title(page.title)
# skip namespaces not in the filter
if self.namespace_filter is not None:
if namespace not in self.namespace_filter:
continue
rev_detector = mwreverts.Detector(radius = self.revert_radius)
if self.persist != PersistMethod.none:
window = deque(maxlen=PERSISTENCE_RADIUS)
if self.persist == PersistMethod.sequence:
state = mwpersistence.DiffState(SequenceMatcher(tokenizer = wikitext_split),
revert_radius=PERSISTENCE_RADIUS)
elif self.persist == PersistMethod.segment:
state = mwpersistence.DiffState(SegmentMatcher(tokenizer = wikitext_split),
revert_radius=PERSISTENCE_RADIUS)
# self.persist == PersistMethod.legacy
else:
from mw.lib import persistence
state = persistence.State()
# Iterate through a page's revisions
for rev in page:
# initialize rev_data
rev_data = {
'revid':rev.id,
'date_time' : rev.timestamp.strftime('%Y-%m-%d %H:%M:%S'),
'articleid' : page.id,
'editor_id' : "" if rev.deleted.user == True or rev.user.id is None else rev.user.id,
'title' : '"' + page.title + '"',
'namespace' : namespace,
'deleted' : "TRUE" if rev.deleted.text else "FALSE"
}
rev_data = self.matchmake(rev, rev_data)
# if revisions are deleted, /many/ things will be missing
if rev.deleted.text:
rev_data['text_chars'] = ""
rev_data['sha1'] = ""
rev_data['revert'] = ""
rev_data['reverteds'] = ""
else:
# rev.text can be None if the page has no text
if not rev.text:
rev.text = ""
# if text exists, we'll check for a sha1 and generate one otherwise
if rev.sha1:
text_sha1 = rev.sha1
else:
text_sha1 = sha1(bytes(rev.text, "utf8")).hexdigest()
rev_data['sha1'] = text_sha1
# TODO rev.bytes doesn't work.. looks like a bug
rev_data['text_chars'] = len(rev.text)
# generate revert data
revert = rev_detector.process(text_sha1, rev.id)
if revert:
rev_data['revert'] = "TRUE"
rev_data['reverteds'] = '"' + ",".join([str(x) for x in revert.reverteds]) + '"'
else:
rev_data['revert'] = "FALSE"
rev_data['reverteds'] = ""
# if the fact that the edit was minor can be hidden, this might be an issue
rev_data['minor'] = "TRUE" if rev.minor else "FALSE"
if not rev.deleted.user:
# wrap user-defined editors in quotes for fread
rev_data['editor'] = '"' + rev.user.text + '"'
rev_data['anon'] = "TRUE" if rev.user.id == None else "FALSE"
else:
rev_data['anon'] = ""
rev_data['editor'] = ""
#if re.match(r'^#redirect \[\[.*\]\]', rev.text, re.I):
# redirect = True
#else:
# redirect = False
#TODO missing: additions_size deletions_size
# if collapse user was on, lets run that
if self.collapse_user:
rev_data['collapsed_revs'] = rev.collapsed_revs
if self.persist != PersistMethod.none:
if rev.deleted.text:
for k in ["token_revs", "tokens_added", "tokens_removed", "tokens_window"]:
old_rev_data[k] = None
else:
if self.persist != PersistMethod.legacy:
_, tokens_added, tokens_removed = state.update(rev.text, rev.id)
else:
_, tokens_added, tokens_removed = state.process(rev.text, rev.id, text_sha1)
window.append((rev.id, rev_data, tokens_added, tokens_removed))
if len(window) == PERSISTENCE_RADIUS:
old_rev_id, old_rev_data, old_tokens_added, old_tokens_removed = window[0]
num_token_revs, num_tokens = calculate_persistence(old_tokens_added)
old_rev_data["token_revs"] = num_token_revs
old_rev_data["tokens_added"] = num_tokens
old_rev_data["tokens_removed"] = len(old_tokens_removed)
old_rev_data["tokens_window"] = PERSISTENCE_RADIUS-1
self.print_rev_data(old_rev_data)
else:
self.print_rev_data(rev_data)
rev_count += 1
if self.persist != PersistMethod.none:
# print out metadata for the last RADIUS revisions
for i, item in enumerate(window):
# if the window was full, we've already printed item 0
if len(window) == PERSISTENCE_RADIUS and i == 0:
continue
rev_id, rev_data, tokens_added, tokens_removed = item
num_token_revs, num_tokens = calculate_persistence(tokens_added)
rev_data["token_revs"] = num_token_revs
rev_data["tokens_added"] = num_tokens
rev_data["tokens_removed"] = len(tokens_removed)
rev_data["tokens_window"] = len(window)-(i+1)
self.print_rev_data(rev_data)
page_count += 1
print("Done: %s revisions and %s pages." % (rev_count, page_count),
file=sys.stderr)
def print_rev_data(self, rev_data):
# if it's the first time through, print the header
if self.urlencode:
for field in TO_ENCODE:
rev_data[field] = quote(str(rev_data[field]))
if not self.printed_header:
print("\t".join([str(k) for k in sorted(rev_data.keys())]), file=self.output_file)
self.printed_header = True
print("\t".join([str(v) for k, v in sorted(rev_data.items())]), file=self.output_file)
def open_input_file(input_filename):
if re.match(r'.*\.7z$', input_filename):
cmd = ["7za", "x", "-so", input_filename, '*']
elif re.match(r'.*\.gz$', input_filename):
cmd = ["zcat", input_filename]
elif re.match(r'.*\.bz2$', input_filename):
cmd = ["bzcat", "-dk", input_filename]
try:
input_file = Popen(cmd, stdout=PIPE).stdout
except NameError:
input_file = open(input_filename, 'r')
return input_file
def open_output_file(input_filename):
# create a regex that creates the output filename
output_filename = re.sub(r'\.(7z|gz|bz2)?$', '', input_filename)
output_filename = re.sub(r'\.xml', '', output_filename)
output_filename = output_filename + ".tsv"
output_file = open(output_filename, "w")
return output_file
parser = argparse.ArgumentParser(description='Parse MediaWiki XML database dumps into tab delimitted data.')
# arguments for the input direction
parser.add_argument('dumpfiles', metavar="DUMPFILE", nargs="*", type=str,
help="Filename of the compressed or uncompressed XML database dump. If absent, we'll look for content on stdin and output on stdout.")
parser.add_argument('-o', '--output-dir', metavar='DIR', dest='output_dir', type=str, nargs=1,
help="Directory for output files.")
parser.add_argument('-s', '--stdout', dest="stdout", action="store_true",
help="Write output to standard out (do not create dump file)")
parser.add_argument('--collapse-user', dest="collapse_user", action="store_true",
help="Operate only on the final revision made by user a user within all sequences of consecutive edits made by a user. This can be useful for addressing issues with text persistence measures.")
parser.add_argument('-p', '--persistence', dest="persist", default=None, const='', type=str, choices = ['','segment','sequence','legacy'], nargs='?',
help="Compute and report measures of content persistent: (1) persistent token revisions, (2) tokens added, and (3) number of revision used in computing the first measure. This may by slow. The defualt is -p=sequence, which uses the same algorithm as in the past, but with improvements to wikitext parsing. Use -p=legacy for old behavior used in older research projects. Use -p=segment for advanced persistence calculation method that is robust to content moves, but prone to bugs, and slower.")
parser.add_argument('-u', '--url-encode', dest="urlencode", action="store_true",
help="Output url encoded text strings. This works around some data issues like newlines in editor names. In the future it may be used to output other text data.")
parser.add_argument('-n', '--namespace-include', dest="namespace_filter", type=int, action='append',
help="Id number of namspace to include. Can be specified more than once.")
parser.add_argument('-rr',
'--revert-radius',
dest="revert_radius",
type=int,
action='store',
default=15,
help="Number of edits to check when looking for reverts (default: 15)")
parser.add_argument('-RP', '--revision-pattern', dest="regex_match_revision", default=None, type=str, action='append',
help="The regular expression to search for in revision text. The regex must be surrounded by quotes.")
parser.add_argument('-RPl', '--revision-pattern-label', dest="regex_revision_label", default=None, type=str, action='append',
help="The label for the outputted column based on matching the regex in revision text.")
parser.add_argument('-CP', '--comment-pattern', dest="regex_match_comment", default=None, type=str, action='append',
help="The regular expression to search for in comments of revisions.")
parser.add_argument('-CPl', '--comment-pattern-label', dest="regex_comment_label", default=None, type=str, action='append',
help="The label for the outputted column based on matching the regex in comments.")
args = parser.parse_args()
# set persistence method
if args.persist is None:
persist = PersistMethod.none
elif args.persist == "segment":
persist = PersistMethod.segment
elif args.persist == "legacy":
persist = PersistMethod.legacy
else:
persist = PersistMethod.sequence
if args.namespace_filter is not None:
namespaces = args.namespace_filter
else:
namespaces = None
if len(args.dumpfiles) > 0:
for filename in args.dumpfiles:
input_file = open_input_file(filename)
# open directory for output
if args.output_dir:
output_dir = args.output_dir[0]
else:
output_dir = "."
print("Processing file: %s" % filename, file=sys.stderr)
if args.stdout:
output_file = sys.stdout
else:
filename = os.path.join(output_dir, os.path.basename(filename))
output_file = open_output_file(filename)
wikiq = WikiqParser(input_file,
output_file,
collapse_user=args.collapse_user,
persist=persist,
urlencode=args.urlencode,
namespaces=namespaces,
revert_radius=args.revert_radius,
regex_match_revision = args.regex_match_revision,
regex_revision_label = args.regex_revision_label,
regex_match_comment = args.regex_match_comment,
regex_comment_label = args.regex_comment_label)
wikiq.process()
# close things
input_file.close()
output_file.close()
else:
wikiq = WikiqParser(sys.stdin,
sys.stdout,
collapse_user=args.collapse_user,
persist=persist,
#persist_legacy=args.persist_legacy,
urlencode=args.urlencode,
namespaces=namespaces,
revert_radius=args.revert_radius,
regex_match_revision = args.regex_match_revision,
regex_revision_label = args.regex_revision_label,
regex_match_comment = args.regex_match_comment,
regex_comment_label = args.regex_comment_label)
wikiq.process()
# stop_words = "a,able,about,across,after,all,almost,also,am,among,an,and,any,are,as,at,be,because,been,but,by,can,cannot,could,dear,did,do,does,either,else,ever,every,for,from,get,got,had,has,have,he,her,hers,him,his,how,however,i,if,in,into,is,it,its,just,least,let,like,likely,may,me,might,most,must,my,neither,no,nor,not,of,off,often,on,only,or,other,our,own,rather,said,say,says,she,should,since,so,some,than,that,the,their,them,then,there,these,they,this,tis,to,too,twas,us,wants,was,we,were,what,when,where,which,while,who,whom,why,will,with,would,yet,you,your"
# stop_words = stop_words.split(",")

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wikiq_to_parquet.py Normal file
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from pathlib import Path
import pandas as pd
from multiprocessing import Pool
from pyspark.sql import functions as f
from pyspark.sql import SparkSession, Window
from pyspark.sql.functions import udf
from pyspark.sql.types import StringType
import csv
path = Path("/gscratch/comdata/users/nathante/wikiqRunning/wikiq_output/")
outpath = Path("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante_parquet/")
files = list(map(Path,path.glob("*.tsv")))
dumpfile = files[0]
def wikiq_tsv_to_parquet(dumpfile, outpath = Path("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante.parquet/")):
outfile = outpath / (dumpfile.name + ".parquet")
outpath.mkdir(parents=True, exist_ok=True)
_wikiq_tsv_to_parquet(dumpfile,outfile)
def _wikiq_tsv_to_parquet(dumpfile, outfile):
dtypes = {'anon': dtype('O'), 'articleid': dtype('int64'), 'deleted': dtype('bool'), 'editor': dtype('O'), 'editor_id': dtype('float64'), 'minor': dtype('bool'), 'namespace': dtype('int64'), 'revert': dtype('O'), 'reverteds': dtype('O'), 'revid': dtype('int64'), 'sha1': dtype('O'), 'text_chars': dtype('float64'), 'title': dtype('O')}
print(dumpfile)
df = pd.read_csv(dumpfile,sep='\t',quoting=csv.QUOTE_NONE,error_bad_lines=False, warn_bad_lines=True,parse_dates=['date_time'],dtype=dtypes)
df.to_parquet(outfile)
with Pool(28) as pool:
jobs = pool.imap_unordered(wikiq_tsv_to_parquet, files)
list(jobs)
spark = SparkSession.builder.getOrCreate()
@udf(StringType())
def decode_strip_udf(val):
if val is None:
return ""
else:
return unquote(val).strip('\"')
df = spark.read.parquet('/gscratch/comdata/output/wikiq_enwiki_20200301_nathante.parquet')
df = df.withColumnRenamed("anon","anonRaw")
df = df.withColumn("anon",f.when(f.col("anonRaw")=="TRUE",True).otherwise(False))
df = df.drop("anonRaw")
df = df.withColumnRenamed("text_chars","text_chars_raw")
df = df.withColumn("text_chars",f.col("text_chars_raw").cast('int'))
df = df.drop("text_chars_raw")
df = df.withColumnRenamed("editor_id",'editor_id_raw')
df = df.withColumn("editor_id",f.col("editor_id_raw").cast("int"))
df = df.drop("editor_id_raw")
df = df.withColumnRenamed("revert","revert_raw")
df = df.withColumn("revert",f.when(f.col("revert_raw")=="TRUE",True).otherwise(False))
df = df.drop("revert_raw")
df = df.withColumnRenamed("title","title_raw")
df = df.withColumn("title", decode_strip_udf(f.col("title_raw")))
df = df.drop("title_raw")
df = df.withColumnRenamed("editor","editor_raw")
df = df.withColumn("editor", decode_strip_udf(f.col("editor_raw")))
df = df.drop("editor_raw")
df = df.repartition(400,'articleid')
df.write.parquet("/gscratch/comdata/output/wikiq_enwiki_20200301_nathante_partitioned.parquet",mode='overwrite')