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ml_measurement_error_public/civil_comments/load_perspective_data.R

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1.6 KiB
R

library(data.table)
library(MASS)
set.seed(1111)
scores <- fread("perspective_scores.csv")
scores <- scores[,id:=as.character(id)]
df <- fread("all_data.csv")
# only use the data that has identity annotations
df <- df[identity_annotator_count > 0]
(df[!(df$id %in% scores$id)])
df <- df[scores,on='id',nomatch=NULL]
df[, ":="(identity_attack_pred = identity_attack_prob >=0.5,
insult_pred = insult_prob >= 0.5,
profanity_pred = profanity_prob >= 0.5,
severe_toxicity_pred = severe_toxicity_prob >= 0.5,
threat_pred = threat_prob >= 0.5,
toxicity_pred = toxicity_prob >= 0.5,
identity_attack_coded = identity_attack >= 0.5,
insult_coded = insult >= 0.5,
profanity_coded = obscene >= 0.5,
severe_toxicity_coded = severe_toxicity >= 0.5,
threat_coded = threat >= 0.5,
toxicity_coded = toxicity >= 0.5
)]
gt.0.5 <- function(v) { v >= 0.5 }
dt.apply.any <- function(fun, ...){apply(apply(cbind(...), 2, fun),1,any)}
df <- df[,":="(gender_disclosed = dt.apply.any(gt.0.5, male, female, transgender, other_gender),
sexuality_disclosed = dt.apply.any(gt.0.5, heterosexual, bisexual, other_sexual_orientation),
religion_disclosed = dt.apply.any(gt.0.5, christian, jewish, hindu, buddhist, atheist, muslim, other_religion),
race_disclosed = dt.apply.any(gt.0.5, white, black, asian, latino, other_race_or_ethnicity),
disability_disclosed = dt.apply.any(gt.0.5,physical_disability, intellectual_or_learning_disability, psychiatric_or_mental_illness, other_disability))]
df <- df[,white:=gt.0.5(white)]