X-Git-Url: https://code.communitydata.science/ml_measurement_error_public.git/blobdiff_plain/3d1964b806106d76f13301f0cf6dccf35cd7d66c..c45ea9dfebca86dfddc1e9237aa74866c5166519:/civil_comments/design_example.R diff --git a/civil_comments/design_example.R b/civil_comments/design_example.R index 5991334..4907688 100644 --- a/civil_comments/design_example.R +++ b/civil_comments/design_example.R @@ -1,126 +1,10 @@ -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. @@ -134,14 +18,46 @@ cortab['toxicity_coded',] 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)