source('load_perspective_data.R')
source("../simulations/measerr_methods.R")
source("../simulations/RemembR/R/RemembeR.R")

change.remember.file("dv_perspective_example.RDS")
remember(accuracies, "civil_comments_accuracies")
remember(f1s, "civil_comments_f1s")
remember(positive_cases, "civil_comments_positive_cases")
remember(proportions_cases, "civil_comments_proportions_cases")
remember(cortab, "civil_comments_cortab")
remember(nrow(df), 'n.annotated.comments')
# for reproducibility
set.seed(111)

## 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?

compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, sample.size, remember_prefix){
    if(is.null(sample.prop)){
        sample.prop <- sample.size / nrow(df)
    }
    if(is.null(sample.size)){
        sample.size <- nrow(df) * sample.prop
    }

    pred_model <- glm(pred_formula, df, family=binomial(link='logit'))

    remember(sample.size, paste0(remember_prefix, "sample.size"))
    remember(sample.prop, paste0(remember_prefix, "sample.prop"))
    remember(pred_formula, paste0(remember_prefix, "pred_formula"))
    remember(outcome_formula, paste0(remember_prefix, 'outcome_formula'))
    remember(proxy_formula, paste0(remember_prefix, 'proxy_formula'))

    remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model"))
    remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model"))

    coder_model <- glm(outcome_formula, df, family=binomial(link='logit'))
    remember(coef(coder_model), paste0(remember_prefix, "coef_coder_model"))
    remember(diag(vcov((coder_model))), paste0(remember_prefix, "se_coder_model"))

    df_measerr_method <- copy(df)[sample(1:.N, sample.size), toxicity_coded_1 := toxicity_coded]
    df_measerr_method <- df_measerr_method[,toxicity_coded := toxicity_coded_1]
    sample_model <- glm(outcome_formula, df_measerr_method, family=binomial(link='logit'))
    remember(coef(sample_model), paste0(remember_prefix, "coef_sample_model"))
    remember(diag(vcov((sample_model))), paste0(remember_prefix, "se_sample_model"))

    measerr_model <- measerr_mle_dv(df_measerr_method, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula=proxy_formula, proxy_family=binomial(link='logit'))

    inv_hessian = solve(measerr_model$hessian)
    stderr = diag(inv_hessian)
    remember(stderr, paste0(remember_prefix, "measerr_model_stderr"))
    remember(measerr_model$par, paste0(remember_prefix, "measerr_model_par"))
}

print("running first example")

pred_formula = toxicity_pred ~ likes + race_disclosed
outcome_formula = toxicity_coded ~ likes + race_disclosed
proxy_formula = toxicity_pred ~ toxicity_coded*race_disclosed*likes

compare_dv_models(pred_formula = pred_formula,
                  outcome_formula = outcome_formula,
                  proxy_formula = proxy_formula,
                  df=df,
                  sample.prop=0.01,
                  sample.size=NULL,
                  remember_prefix='cc_ex_tox.likes.race_disclosed')


print("running second example")

compare_dv_models(pred_formula = pred_formula,
                  outcome_formula = outcome_formula,
                  proxy_formula = proxy_formula,
                  df=df,
                  sample.size=10000,
                  sample.prop=NULL,
                  remember_prefix='cc_ex_tox.likes.race_disclosed.medsamp')


print("running third example")

compare_dv_models(pred_formula = pred_formula,
                  outcome_formula = outcome_formula,
                  proxy_formula = proxy_formula,
                  df=df,
                  sample.prop=0.05,
                  sample.size=NULL,
                  remember_prefix='cc_ex_tox.likes.race_disclosed.largesamp')