-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]
-
+set.seed(1111)
+source('load_perspective_data.R')
## 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.
cortab['identity_error',]
cortab['white',]
-glm(white ~ toxicity_coded + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
+## 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?
+summary(glm(white ~ toxicity_coded*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
+
+summary(glm(white ~ toxicity_pred*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
-glm(white ~ toxicity_pred + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
+summary(glm(white ~ toxicity_coded*male, 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)
+summary(glm(white ~ toxicity_pred*male, data = df, family=binomial(link='logit')))
+
+summary(glm(toxicity_coded ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
+
+summary(glm(toxicity_pred ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
+
+
+## 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?
+
+summary(glm(toxicity_pred ~ funny*white, data=df, family=binomial(link='logit')))
+summary(glm(toxicity_coded ~ funny*white, data=df, family=binomial(link='logit')))
+
+source("../simulations/measerr_methods.R")
+
+saved_model_file <- "measerr_model_tox.eq.funny.cross.white.RDS"
+overwrite_model <- TRUE
+
+# 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.
+df_measerr_method <- copy(df)[sample(1:.N, 0.05 * .N), toxicity_coded_1 := toxicity_coded]
+df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
+summary(glm(toxicity_coded ~ funny*white, data=df_measerr_method[!is.na(toxicity_coded)], family=binomial(link='logit')))
+
+if(!file.exists(saved_model_file) || (overwrite_model == TRUE)){
+ 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)
+saveRDS(measerr_model, saved_model_file)
+} else {
+ measerr_model <- readRDS(saved_model_file)
+}
-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)
+inv_hessian <- solve(measerr_model$hessian)
+se <- diag(inv_hessian)
+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)
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)
projects / ml_measurement_error_public.git / blobdiff