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[ml_measurement_error_public.git] / civil_comments / design_example.R
1 set.seed(1111)
2 source('load_perspective_data.R')
3 ## how accurate are the classifiers?
4
5 ## the API claims that these scores are "probabilities"
6 ## say we care about the model of the classification, not the probability
7
8 F1 <- function(y, predictions){
9     tp <- sum( (predictions == y) & (predictions==1))
10     fn <- sum( (predictions != y) & (predictions!=1))
11     fp <- sum( (predictions != y) & (predictions==1))
12     precision <- tp / (tp + fp)
13     recall <- tp / (tp + fn)
14     return (2 * precision * recall ) / (precision + recall)
15 }
16
17
18 ## toxicity is about 93% accurate, with an f1 of 0.8
19 ## identity_attack has high accuracy 97%, but an unfortunant f1 of 0.5.
20 ## threat has high accuracy 99%, but a really bad looking f1 of 0.48.
21 accuracies <- df[,.(identity_attack_acc = mean(identity_attack_pred == identity_attack_coded),
22                     insult_pred_acc = mean(insult_pred == insult_coded),
23                     profanity_acc = mean(profanity_pred == profanity_coded),
24                     severe_toxicity_acc = mean(severe_toxicity_pred == severe_toxicity_coded),
25                     theat_acc = mean(threat_pred == threat_coded),
26                     toxicity_acc = mean(toxicity_pred == toxicity_coded))]
27
28 f1s <- df[,.(identity_attack_f1 = F1(identity_attack_coded,identity_attack_pred),
29                     insult_f1 = F1(insult_coded,insult_pred),
30                     profanity_f1 = F1(profanity_coded,profanity_pred),
31                     severe_toxicity_f1 = F1(severe_toxicity_coded,severe_toxicity_pred),
32                     theat_f1 = F1(threat_coded,threat_pred),
33                     toxicity_f1 = F1(toxicity_coded,toxicity_pred))]
34
35 positive_cases <- df[,.(identity_attacks = sum(identity_attack_coded),
36                         insults = sum(insult_coded),
37                         profanities = sum(profanity_coded),
38                         severe_toxic_comments = sum(severe_toxicity_coded),
39                         threats = sum(threat_coded),
40                         toxic_comments = sum(toxicity_coded))]
41
42 ## there are 50,000 toxic comments, 13000 identity attacks, 30000 insults, 3000 profanities, 8 severe toxic, and 1000 threats.
43
44 proportions_cases <- df[,.(prop_identity = mean(identity_attack_coded),
45                            prop_insults = mean(insult_coded),
46                            prop_profanity = mean(profanity_coded),
47                            prop_severe = mean(severe_toxicity_coded),
48                            prop_threats = mean(threat_coded),
49                            prop_toxic = mean(toxicity_coded))]
50
51 ## at 11% of comments, "toxicity" seems not so badly skewed. Try toxicity first, and if it doesn't work out try insults.
52
53 ## now look for an example where differential error affects an identity, or a reaction.
54 df <- df[,":="(identity_error = identity_attack_coded - identity_attack_pred,
55                insult_error = insult_coded - insult_pred,
56                profanity_error = profanity_coded - profanity_pred,
57                severe_toxic_error = severe_toxicity_coded - severe_toxicity_pred,
58                threat_error = threat_coded - threat_pred,
59                toxicity_error = toxicity_coded - toxicity_pred)]
60
61 ## what's correlated with toxicity_error ?
62 df <- df[,approved := rating == "approved"]
63 df <- df[,white := white > 0.5]
64
65 cortab <- cor(df[,.(toxicity_error,
66                     identity_error,
67                     toxicity_coded,
68                     funny,
69                     approved,
70                     sad,
71                     wow,
72                     likes,
73                     disagree,
74                     male,
75                     female,
76                     transgender,
77                     other_gender,
78                     heterosexual,
79                     bisexual,
80                     other_sexual_orientation,
81                     christian,
82                     jewish,
83                     hindu,
84                     buddhist,
85                     atheist,
86                     other_religion,
87                     black,
88                     white,
89                     asian,
90                     latino,
91                     other_race_or_ethnicity,
92                     physical_disability,
93                     intellectual_or_learning_disability,
94                     psychiatric_or_mental_illness,
95                     other_disability)])
96
97 ## toxicity error is weakly correlated pearson's R = 0.1 with both "white" and "black".
98
99 ## compare regressions with "white" or "black" as the outcome and "toxicity_coded" or "toxicity_pred" as a predictor.
100 ## here's a great example with presumambly non-differential error: about what identities is toxicity found humorous?
101 ## a bunch of stars reappear when you used the ground-truth data instead of the predictions.
102 ## pro/con of this example: will have to implement family='poisson'.
103 ## shouldn't be that bad though haha.
104 cortab['toxicity_error',]
105 cortab['toxicity_error','funny']
106 cortab['toxicity_coded',]
107 cortab['identity_error',]
108 cortab['white',]
109
110 cortab <- cor(df[,.(toxicity_error,
111                     identity_error,
112                     toxicity_coded,
113                     funny,
114                     approved,
115                     sad,
116                     wow,
117                     likes,
118                     disagree,
119                     gender_disclosed,
120                     sexuality_disclosed,
121                     religion_disclosed,
122                     race_disclosed,
123                     disability_disclosed)])
124
125
126 ## 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? 
127 summary(glm(white ~ toxicity_coded*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
128
129 summary(glm(white ~ toxicity_pred*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
130
131 summary(glm(white ~ toxicity_coded*male, data = df, family=binomial(link='logit')))
132
133 summary(glm(white ~ toxicity_pred*male, data = df, family=binomial(link='logit')))
134
135 summary(glm(toxicity_coded ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
136
137 summary(glm(toxicity_pred ~ white*psychiatric_or_mental_illness, data = df, family=binomial(link='logit')))
138
139
140 ## 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?
141
142 summary(glm(toxicity_pred ~ funny*white, data=df, family=binomial(link='logit')))
143 summary(glm(toxicity_coded ~ funny*white, data=df, family=binomial(link='logit')))
144
145 source("../simulations/measerr_methods.R")
146                                                                                        
147 saved_model_file <- "measerr_model_tox.eq.funny.cross.white.RDS"
148 overwrite_model <- TRUE 
149
150 # 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.
151 df_measerr_method <- copy(df)[sample(1:.N, 0.05 * .N), toxicity_coded_1 := toxicity_coded]
152 df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
153 summary(glm(toxicity_coded ~ funny*white, data=df_measerr_method[!is.na(toxicity_coded)], family=binomial(link='logit')))
154
155 if(!file.exists(saved_model_file) || (overwrite_model == TRUE)){
156     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)
157 saveRDS(measerr_model, saved_model_file)
158 } else {
159     measerr_model <- readRDS(saved_model_file)
160 }
161
162 inv_hessian <- solve(measerr_model$hessian)
163 se <- diag(inv_hessian)
164
165 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)
166 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)
167
168
169 glm(white ~ disagree, data = df, family=binomial(link='logit'))
170
171 ## example with differential error
172
173 glm(white ~ toxicity_coded + toxicity_error, data=df,family=binomial(link='logit'))
174
175 glm(toxicity_coded ~ white, data = df, family=binomial(link='logit'))
176
177 glm(toxicity_pred ~ white, data = df, family=binomial(link='logit'))

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