178 lines
8.6 KiB
R
178 lines
8.6 KiB
R
set.seed(1111)
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source('load_perspective_data.R')
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## how accurate are the classifiers?
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## the API claims that these scores are "probabilities"
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## say we care about the model of the classification, not the probability
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F1 <- function(y, predictions){
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tp <- sum( (predictions == y) & (predictions==1))
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fn <- sum( (predictions != y) & (predictions!=1))
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fp <- sum( (predictions != y) & (predictions==1))
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precision <- tp / (tp + fp)
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recall <- tp / (tp + fn)
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return (2 * precision * recall ) / (precision + recall)
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}
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## toxicity is about 93% accurate, with an f1 of 0.8
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## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
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## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
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accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
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insult_pred_acc = mean(insult_pred == insult_coded),
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profanity_acc = mean(profanity_pred == profanity_coded),
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severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
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theat_acc = mean(threat_pred == threat_coded),
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toxicity_acc = mean(toxicity_pred == toxicity_coded))]
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f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
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insult_f1 = F1(insult_coded,insult_pred),
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profanity_f1 = F1(profanity_coded,profanity_pred),
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severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
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theat_f1 = F1(threat_coded,threat_pred),
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toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
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positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
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insults = sum(insult_coded),
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profanities = sum(profanity_coded),
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severe_toxic_comments = sum(severe_toxicity_coded),
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threats = sum(threat_coded),
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toxic_comments = sum(toxicity_coded))]
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## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
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proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
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prop_insults = mean(insult_coded),
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prop_profanity = mean(profanity_coded),
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prop_severe = mean(severe_toxicity_coded),
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prop_threats = mean(threat_coded),
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prop_toxic = mean(toxicity_coded))]
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## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
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## now look for an example where differential error affects an identity, or a reaction.
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df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
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insult_error = insult_coded - insult_pred,
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profanity_error = profanity_coded - profanity_pred,
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severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
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threat_error = threat_coded - threat_pred,
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toxicity_error = toxicity_coded - toxicity_pred)]
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## what's correlated with toxicity_error ?
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df <- df[,approved := rating == "approved"]
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df <- df[,white := white > 0.5]
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cortab <- cor(df[,.(toxicity_error,
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identity_error,
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toxicity_coded,
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funny,
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approved,
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sad,
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wow,
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likes,
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disagree,
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male,
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female,
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transgender,
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other_gender,
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heterosexual,
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bisexual,
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other_sexual_orientation,
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christian,
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jewish,
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hindu,
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buddhist,
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atheist,
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other_religion,
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black,
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white,
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asian,
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latino,
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other_race_or_ethnicity,
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physical_disability,
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intellectual_or_learning_disability,
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psychiatric_or_mental_illness,
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other_disability)])
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## toxicity error is weakly correlated pearson's R = 0.1 with both "white" and "black".
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## compare regressions with "white" or "black" as the outcome and "toxicity_coded" or "toxicity_pred" as a predictor.
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## here's a great example with presumambly non-differential error: about what identities is toxicity found humorous?
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## a bunch of stars reappear when you used the ground-truth data instead of the predictions.
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## pro/con of this example: will have to implement family='poisson'.
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## shouldn't be that bad though haha.
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cortab['toxicity_error',]
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cortab['toxicity_error','funny']
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cortab['toxicity_coded',]
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cortab['identity_error',]
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cortab['white',]
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cortab <- cor(df[,.(toxicity_error,
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identity_error,
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toxicity_coded,
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funny,
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approved,
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sad,
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wow,
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likes,
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disagree,
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gender_disclosed,
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sexuality_disclosed,
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religion_disclosed,
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race_disclosed,
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disability_disclosed)])
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## here's a simple example, is P(white | toxic and mentally ill) > P(white | toxic or mentally ill). Are people who discuss their mental illness in a toxic way more likely to be white compared to those who just talk about their mental illness or are toxic?
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summary(glm(white ~ toxicity_coded*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
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summary(glm(white ~ toxicity_pred*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
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summary(glm(white ~ toxicity_coded*male, data = df, family=binomial(link='logit')))
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summary(glm(white ~ toxicity_pred*male, data = df, family=binomial(link='logit')))
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summary(glm(toxicity_coded ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
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summary(glm(toxicity_pred ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
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## another simple enough example: is P(toxic | funny and white) > P(toxic | funny nand white)? Or, are funny comments more toxic when people disclose that they are white?
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summary(glm(toxicity_pred ~ funny*white, data=df, family=binomial(link='logit')))
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summary(glm(toxicity_coded ~ funny*white, data=df, family=binomial(link='logit')))
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source("../simulations/measerr_methods.R")
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saved_model_file <- "measerr_model_tox.eq.funny.cross.white.RDS"
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overwrite_model <- TRUE
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# it works so far with a 20% and 15% sample. Smaller is better. let's try a 10% sample again. It didn't work out. We'll go forward with a 15% sample.
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df_measerr_method <- copy(df)[sample(1:.N, 0.05 * .N), toxicity_coded_1 := toxicity_coded]
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df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
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summary(glm(toxicity_coded ~ funny*white, data=df_measerr_method[!is.na(toxicity_coded)], family=binomial(link='logit')))
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if(!file.exists(saved_model_file) || (overwrite_model == TRUE)){
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measerr_model <- measerr_mle_dv(df_measerr_method,toxicity_coded ~ funny*white,outcome_family=binomial(link='logit'), proxy_formula=toxicity_pred ~ toxicity_coded*funny*white)
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saveRDS(measerr_model, saved_model_file)
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} else {
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measerr_model <- readRDS(saved_model_file)
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}
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inv_hessian <- solve(measerr_model$hessian)
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se <- diag(inv_hessian)
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lm2 <- 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)
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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)
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glm(white ~ disagree, data = df, family=binomial(link='logit'))
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## example with differential error
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glm(white ~ toxicity_coded + toxicity_error, data=df,family=binomial(link='logit'))
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glm(toxicity_coded ~ white, data = df, family=binomial(link='logit'))
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glm(toxicity_pred ~ white, data = df, family=binomial(link='logit'))
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