--- /dev/null
+library(data.table)
+library(MASS)
+
+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]
+
+## how accurate are the classifiers?
+
+## the API claims that these scores are "probabilities"
+## say we care about the model of the classification, not the probability
+
+F1 <- function(y, predictions){
+ tp <- sum( (predictions == y) & (predictions==1))
+ fn <- sum( (predictions != y) & (predictions!=1))
+ fp <- sum( (predictions != y) & (predictions==1))
+ precision <- tp / (tp + fp)
+ recall <- tp / (tp + fn)
+ return (2 * precision * recall ) / (precision + recall)
+}
+
+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
+ )]
+
+
+
+## toxicity is about 93% accurate, with an f1 of 0.8
+## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
+## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
+accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
+ insult_pred_acc = mean(insult_pred == insult_coded),
+ profanity_acc = mean(profanity_pred == profanity_coded),
+ severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
+ theat_acc = mean(threat_pred == threat_coded),
+ toxicity_acc = mean(toxicity_pred == toxicity_coded))]
+
+f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
+ insult_f1 = F1(insult_coded,insult_pred),
+ profanity_f1 = F1(profanity_coded,profanity_pred),
+ severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
+ theat_f1 = F1(threat_coded,threat_pred),
+ toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
+
+positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
+ insults = sum(insult_coded),
+ profanities = sum(profanity_coded),
+ severe_toxic_comments = sum(severe_toxicity_coded),
+ threats = sum(threat_coded),
+ toxic_comments = sum(toxicity_coded))]
+
+## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
+
+proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
+ prop_insults = mean(insult_coded),
+ prop_profanity = mean(profanity_coded),
+ prop_severe = mean(severe_toxicity_coded),
+ prop_threats = mean(threat_coded),
+ prop_toxic = mean(toxicity_coded))]
+
+## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
+
+## now look for an example where differential error affects an identity, or a reaction.
+df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
+ insult_error = insult_coded - insult_pred,
+ profanity_error = profanity_coded - profanity_pred,
+ severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
+ threat_error = threat_coded - threat_pred,
+ toxicity_error = toxicity_coded - toxicity_pred)]
+
+## what's correlated with toxicity_error ?
+df <- df[,approved := rating == "approved"]
+
+cortab <- cor(df[,.(toxicity_error,
+ identity_error,
+ toxicity_coded,
+ funny,
+ approved,
+ sad,
+ wow,
+ likes,
+ disagree,
+ male,
+ female,
+ transgender,
+ other_gender,
+ heterosexual,
+ bisexual,
+ other_sexual_orientation,
+ christian,
+ jewish,
+ hindu,
+ buddhist,
+ atheist,
+ other_religion,
+ black,
+ white,
+ asian,
+ latino,
+ other_race_or_ethnicity,
+ physical_disability,
+ intellectual_or_learning_disability,
+ psychiatric_or_mental_illness,
+ other_disability)])
+
+## toxicity error is weakly correlated pearson's R = 0.1 with both "white" and "black".
+
+## compare regressions with "white" or "black" as the outcome and "toxicity_coded" or "toxicity_pred" as a predictor.
+## here's a great example with presumambly non-differential error: about what identities is toxicity found humorous?
+## a bunch of stars reappear when you used the ground-truth data instead of the predictions.
+## pro/con of this example: will have to implement family='poisson'.
+## shouldn't be that bad though haha.
+cortab['toxicity_error',]
+cortab['toxicity_error','funny']
+cortab['toxicity_coded',]
+cortab['identity_error',]
+cortab['white',]
+
+glm(white ~ toxicity_coded + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
+
+glm(white ~ toxicity_pred + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
+
+m1 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_coded, data = df)
+
+m2 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_pred, data = df)
+
+m3 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity, data = df)
+
+
+glm(white ~ disagree, data = df, family=binomial(link='logit'))
+
+## example with differential error
+
+glm(white ~ toxicity_coded + toxicity_error, data=df,family=binomial(link='logit'))
+
+glm(toxicity_coded ~ white, data = df, family=binomial(link='logit'))
+
+glm(toxicity_pred ~ white, data = df, family=binomial(link='logit'))
projects / ml_measurement_error_public.git / commitdiff