check in some old simulation updates and a dv examples with real data
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civil_comments/01_dv_example.R
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civil_comments/01_dv_example.R
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@ -0,0 +1,54 @@
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source('load_perspective_data.R')
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source("../simulations/measerr_methods.R")
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source("../simulations/RemembR/R/RemembeR.R")
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change.remember.file("dv_perspective_example.RDS")
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# for reproducibility
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set.seed(1111)
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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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compare_dv_models <-function(pred_formula, outcome_formula, proxy_formula, df, sample.prop, remember_prefix){
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pred_model <- glm(pred_formula, df, family=binomial(link='logit'))
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remember(coef(pred_model), paste0(remember_prefix, "coef_pred_model"))
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remember(diag(vcov((pred_model))), paste0(remember_prefix, "se_pred_model"))
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coder_model <- glm(outcome_formula, df, family=binomial(link='logit'))
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remember(coef(coder_model), paste0(remember_prefix, "coef_coder_model"))
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remember(diag(vcov((coder_model))), paste0(remember_prefix, "se_coder_model"))
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df_measerr_method <- copy(df)[sample(1:.N, sample.prop * .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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sample_model <- glm(outcome_formula, df_measerr_method, family=binomial(link='logit'))
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remember(coef(sample_model), paste0(remember_prefix, "coef_sample_model"))
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remember(diag(vcov((sample_model))), paste0(remember_prefix, "se_sample_model"))
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measerr_model <- measerr_mle_dv(df_measerr_method, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula=proxy_formula, proxy_family=binomial(link='logit'))
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inv_hessian = solve(measerr_model$hessian)
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stderr = diag(inv_hessian)
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remember(stderr, paste0(remember_prefix, "measerr_model_stderr"))
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remember(measerr_model$par, paste0(remember_prefix, "measerr_model_par"))
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}
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print("running first example")
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compare_dv_models(pred_formula = toxicity_pred ~ funny*white,
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outcome_formula = toxicity_coded ~ funny*white, proxy_formula,
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proxy_formula = toxicity_pred ~ toxicity_coded*funny*white,
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df=df,
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sample.prop=0.01,
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remember_prefix='cc_ex_tox.funny.white')
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print("running second example")
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compare_dv_models(pred_formula = toxicity_pred ~ likes+race_disclosed,
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outcome_formula = toxicity_coded ~ likes + race_disclosed, proxy_formula,
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proxy_formula = toxicity_pred ~ toxicity_coded*likes*race_disclosed,
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df=df,
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sample.prop=0.01,
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remember_prefix='cc_ex_tox.funny.race_disclosed')
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@ -1,18 +1,5 @@
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library(data.table)
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library(MASS)
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scores <- fread("perspective_scores.csv")
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scores <- scores[,id:=as.character(id)]
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df <- fread("all_data.csv")
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# only use the data that has identity annotations
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df <- df[identity_annotator_count > 0]
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(df[!(df$id %in% scores$id)])
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df <- df[scores,on='id',nomatch=NULL]
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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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@ -27,21 +14,6 @@ F1 <- function(y, predictions){
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return (2 * precision * recall ) / (precision + recall)
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}
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df[, ":="(identity_attack_pred = identity_attack_prob >=0.5,
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insult_pred = insult_prob >= 0.5,
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profanity_pred = profanity_prob >= 0.5,
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severe_toxicity_pred = severe_toxicity_prob >= 0.5,
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threat_pred = threat_prob >= 0.5,
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toxicity_pred = toxicity_prob >= 0.5,
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identity_attack_coded = identity_attack >= 0.5,
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insult_coded = insult >= 0.5,
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profanity_coded = obscene >= 0.5,
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severe_toxicity_coded = severe_toxicity >= 0.5,
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threat_coded = threat >= 0.5,
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toxicity_coded = toxicity >= 0.5
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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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@ -88,6 +60,7 @@ df <- df[,":="(identity_error = identity_attack_coded - identity_attack_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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@ -134,14 +107,62 @@ cortab['toxicity_coded',]
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cortab['identity_error',]
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cortab['white',]
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glm(white ~ toxicity_coded + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
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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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glm(white ~ toxicity_pred + psychiatric_or_mental_illness, data = df, family=binomial(link='logit'))
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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)
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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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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)
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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$hessian)
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se <- diag(inv_hessian)
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lm2 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity_pred, data = df)
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m3 <- glm.nb(funny ~ (male + female + transgender + other_gender + heterosexual + bisexual + other_sexual_orientation + christian + jewish + hindu + buddhist + atheist + other_religion + asian + latino + other_race_or_ethnicity + physical_disability + intellectual_or_learning_disability + white + black + psychiatric_or_mental_illness)*toxicity, data = df)
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41
civil_comments/load_perspective_data.R
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civil_comments/load_perspective_data.R
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library(data.table)
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library(MASS)
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set.seed(1111)
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scores <- fread("perspective_scores.csv")
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scores <- scores[,id:=as.character(id)]
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df <- fread("all_data.csv")
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# only use the data that has identity annotations
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df <- df[identity_annotator_count > 0]
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(df[!(df$id %in% scores$id)])
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df <- df[scores,on='id',nomatch=NULL]
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df[, ":="(identity_attack_pred = identity_attack_prob >=0.5,
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insult_pred = insult_prob >= 0.5,
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profanity_pred = profanity_prob >= 0.5,
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severe_toxicity_pred = severe_toxicity_prob >= 0.5,
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threat_pred = threat_prob >= 0.5,
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toxicity_pred = toxicity_prob >= 0.5,
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identity_attack_coded = identity_attack >= 0.5,
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insult_coded = insult >= 0.5,
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profanity_coded = obscene >= 0.5,
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severe_toxicity_coded = severe_toxicity >= 0.5,
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threat_coded = threat >= 0.5,
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toxicity_coded = toxicity >= 0.5
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)]
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gt.0.5 <- function(v) { v >= 0.5 }
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dt.apply.any <- function(fun, ...){apply(apply(cbind(...), 2, fun),1,any)}
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df <- df[,":="(gender_disclosed = dt.apply.any(gt.0.5, male, female, transgender, other_gender),
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sexuality_disclosed = dt.apply.any(gt.0.5, heterosexual, bisexual, other_sexual_orientation),
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religion_disclosed = dt.apply.any(gt.0.5, christian, jewish, hindu, buddhist, atheist, muslim, other_religion),
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race_disclosed = dt.apply.any(gt.0.5, white, black, asian, latino, other_race_or_ethnicity),
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disability_disclosed = dt.apply.any(gt.0.5,physical_disability, intellectual_or_learning_disability, psychiatric_or_mental_illness, other_disability))]
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df <- df[,white:=gt.0.5(white)]
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@ -159,7 +159,7 @@ if(args$m < args$N){
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## pc.df <- pc(suffStat=list(C=cor(df.pc),n=nrow(df.pc)),indepTest=gaussCItest,labels=names(df.pc),alpha=0.05)
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## plot(pc.df)
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result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=args$Bzx, 'Bzy'=Bzy, 'Px'=Px, .'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='')
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result <- list('N'=args$N,'m'=args$m,'B0'=B0,'Bxy'=Bxy, 'Bzx'=args$Bzx, 'Bzy'=Bzy, 'Px'=Px, 'seed'=args$seed, 'y_explained_variance'=args$y_explained_variance, 'prediction_accuracy'=args$prediction_accuracy, 'accuracy_imbalance_difference'=args$accuracy_imbalance_difference, 'y_bias'=args$y_bias,'outcome_formula'=args$outcome_formula, 'proxy_formula'=args$proxy_formula,truth_formula=args$truth_formula, error='')
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outline <- run_simulation(df, result, outcome_formula=as.formula(args$outcome_formula), proxy_formula=as.formula(args$proxy_formula), truth_formula=as.formula(args$truth_formula))
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@ -1,4 +1,4 @@
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.ONESHELL:
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SHELL=bash
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Ns=[1000, 5000, 10000]
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@ -6,8 +6,9 @@ ms=[100, 200, 400]
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seeds=[$(shell seq -s, 1 500)]
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explained_variances=[0.1]
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all:remembr.RDS remember_irr.RDS
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supplement: remember_robustness_misspec.RDS
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all:main supplement
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main:remembr.RDS
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supplement:robustness_1.RDS robustness_1_dv.RDS robustness_2.RDS robustness_2_dv.RDS robustness_3.RDS robustness_3_dv.RDS robustness_4.RDS robustness_4_dv.RDS
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srun=sbatch --wait --verbose run_job.sbatch
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@ -24,7 +25,7 @@ joblists:example_1_jobs example_2_jobs example_3_jobs
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example_1_jobs: 01_two_covariates.R simulation_base.R grid_sweep.py pl_methods.R
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs
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${srun} grid_sweep.py --command "Rscript 01_two_covariates.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["example_1.feather"], "y_explained_variance":${explained_variances}, "Bzx":[1]}' --outfile example_1_jobs
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example_1.feather: example_1_jobs
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rm -f example_1.feather
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@ -124,7 +125,14 @@ robustness_1_jobs: 02_indep_differential.R simulation_base.R grid_sweep.py
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sbatch --wait --verbose run_job.sbatch grid_sweep.py --command "Rscript 02_indep_differential.R" --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_1.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~1"]}' --outfile robustness_1_jobs
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START=0
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STEP=1000
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ONE=1
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robustness_1.feather: robustness_1_jobs
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$(eval END_1!=cat robustness_1_jobs | wc -l)
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$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
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rm -f robustness_1.feather
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sbatch --wait --verbose --array=1-1000 run_simulation.sbatch 0 robustness_1_jobs
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sbatch --wait --verbose --array=1001-2000 run_simulation.sbatch 0 robustness_1_jobs
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@ -132,22 +140,25 @@ robustness_1.feather: robustness_1_jobs
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sbatch --wait --verbose --array=3001-4000 run_simulation.sbatch 0 robustness_1_jobs
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sbatch --wait --verbose --array=4001-$(shell cat robustness_1_jobs | wc -l) run_simulation.sbatch 0 robustness_1_jobs
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$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_jobs;)
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robustness_1.RDS: robustness_1.feather
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rm -f robustness_1.RDS
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${srun} Rscript plot_example.R --infile $< --name "robustness_1" --remember-file $@
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robustness_1_dv_jobs: simulation_base.R 04_depvar_differential.R grid_sweep.py
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${srun} bash -c "source ~/.bashrc && grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict \"{'N':${Ns},'m':${ms}, 'seed':${seeds}, 'outfile':['robustness_1_dv.feather'], 'y_explained_variance':${explained_variances}, 'proxy_formula':['w_pred~y']}\" --outfile robustness_1_dv_jobs"
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${srun} grid_sweep.py --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["robustness_1_dv.feather"], "proxy_formula":["w_pred~y"],"z_bias":[0.5]}' --outfile robustness_1_dv_jobs
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robustness_1_dv.feather: robustness_1_dv_jobs
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rm -f robustness_1_dv.feather
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sbatch --wait --verbose --array=1-$(shell cat example_3_jobs | wc -l) run_simulation.sbatch 0 robustness_1_dv_jobs
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$(eval END_1!=cat robustness_1_dv_jobs | wc -l)
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$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
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$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_1_dv_jobs;)
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robustness_1_dv.RDS: robustness_1_dv.feather
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rm -f $@
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${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --outfile $@
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${srun} Rscript plot_dv_example.R --infile $< --name "robustness_1_dv" --remember-file $@
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robustness_2_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
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@ -166,59 +177,59 @@ robustness_2_jobs_p4: grid_sweep.py 01_two_covariates.R simulation_base.R grid_s
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rm -f $@
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${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
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START=0
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END_1=$(shell cat robustness_2_jobs_p1 | wc -l)
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END_2=$(shell cat robustness_2_jobs_p2 | wc -l)
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END_3=$(shell cat robustness_2_jobs_p3 | wc -l)
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END_4=$(shell cat robustness_2_jobs_p4 | wc -l)
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STEP=1000
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ONE=1
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ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
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ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
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ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
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ITEMS_4=$(shell seq $(START) $(STEP) $(END_4))
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robustness_2.feather: robustness_2_jobs_p1 robustness_2_jobs_p2 robustness_2_jobs_p3 robustness_2_jobs_p4
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p1)
|
||||
$(eval END_1!=cat robustness_2_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_2_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_2_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_2_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_jobs_p4;)
|
||||
|
||||
robustness_2.RDS: plot_example.R robustness_2.feather
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2" --remember-file $@
|
||||
|
||||
robustness_2_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.60,0.65]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.70,0.75]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.80,0.85]}' --outfile $@
|
||||
|
||||
robustness_2_dv_jobs_p4: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
|
||||
START=0
|
||||
END_1=$(shell cat robustness_2_dv_jobs_p1 | wc -l)
|
||||
END_2=$(shell cat robustness_2_dv_jobs_p2 | wc -l)
|
||||
END_3=$(shell cat robustness_2_dv_jobs_p3 | wc -l)
|
||||
END_4=$(shell cat robustness_2_dv_jobs_p4 | wc -l)
|
||||
STEP=1000
|
||||
ONE=1
|
||||
ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
|
||||
ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
|
||||
ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
|
||||
ITEMS_4=$(shell seq $(START) $(STEP) $(END_4))
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_2_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.90,0.95]}' --outfile $@
|
||||
|
||||
robustness_2_dv.feather: robustness_2_dv_jobs_p1 robustness_2_dv_jobs_p2 robustness_2_dv_jobs_p3 robustness_2_dv_jobs_p4
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p1)
|
||||
$(eval END_1!=cat robustness_2_dv_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_2_dv_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_2_dv_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
$(eval END_4!=cat robustness_2_dv_jobs_p4 | wc -l)
|
||||
$(eval ITEMS_4!=seq $(START) $(STEP) $(END_4))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p3;)
|
||||
$(foreach item,$(ITEMS_4),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_2_dv_jobs_p4;)
|
||||
|
||||
robustness_2_dv.RDS: plot_example.R robustness_2_dv.feather
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_2_dv" --remember-file $@
|
||||
|
||||
|
||||
robustness_3_jobs_p1: grid_sweep.py 01_two_covariates.R simulation_base.R grid_sweep.py
|
||||
@ -233,125 +244,131 @@ robustness_3_jobs_p3: grid_sweep.py 01_two_covariates.R simulation_base.R grid_s
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 01_two_covariates.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"Px":[0.9,0.95], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
START=0
|
||||
END_1=$(shell cat robustness_3_jobs_p1 | wc -l)
|
||||
END_2=$(shell cat robustness_3_jobs_p2 | wc -l)
|
||||
END_3=$(shell cat robustness_3_jobs_p3 | wc -l)
|
||||
|
||||
STEP=1000
|
||||
ONE=1
|
||||
ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
|
||||
ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
|
||||
ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
|
||||
|
||||
robustness_3.feather: robustness_3_jobs_p1 robustness_3_jobs_p2 robustness_3_jobs_p3
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p1)
|
||||
$(eval END_1!=cat robustness_3_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_3_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_3_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_jobs_p3;)
|
||||
|
||||
robustness_3.RDS: plot_example.R robustness_3.feather
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3" --remember-file $@
|
||||
|
||||
robustness_3_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5,0.6], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5,0.6], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
|
||||
|
||||
robustness_3_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.7,0.8], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.7,0.8], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
|
||||
robustness_3_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.9,0.95], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
START=0
|
||||
END_1=$(shell cat robustness_3_dv_jobs_p1 | wc -l)
|
||||
END_2=$(shell cat robustness_3_dv_jobs_p2 | wc -l)
|
||||
END_3=$(shell cat robustness_3_dv_jobs_p3 | wc -l)
|
||||
|
||||
STEP=1000
|
||||
ONE=1
|
||||
ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
|
||||
ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
|
||||
ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_3_dv.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.9,0.95], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85]}' --outfile $@
|
||||
|
||||
robustness_3_dv.feather: robustness_3_dv_jobs_p1 robustness_3_dv_jobs_p2 robustness_3_dv_jobs_p3
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p1)
|
||||
$(eval END_1!=cat robustness_3_dv_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_3_dv_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_3_dv_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_3_dv_jobs_p3;)
|
||||
|
||||
|
||||
robustness_3_dv.RDS: plot_dv_example.R robustness_3_dv.feather
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_3_dv" --remember-file $@
|
||||
|
||||
|
||||
robustness_4_jobs_p1: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],y_bias=[-1,-0.85]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],"y_bias":[-1,-0.85]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p2: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], y_bias=[-0.70,-0.55]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85], "y_bias":[-0.70,-0.55]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p3: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],y_bias=[-0.4,-0.25]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],"y_bias":[-0.4,-0.25]}' --outfile $@
|
||||
|
||||
robustness_4_jobs_p4: grid_sweep.py 02_indep_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],y_bias=[-0.1,0]}' --outfile $@
|
||||
|
||||
START=0
|
||||
END_1=$(shell cat robustness_4_jobs_p1 | wc -l)
|
||||
END_2=$(shell cat robustness_4_jobs_p2 | wc -l)
|
||||
END_3=$(shell cat robustness_4_jobs_p3 | wc -l)
|
||||
END_4=$(shell cat robustness_4_jobs_p3 | wc -l)
|
||||
|
||||
STEP=1000
|
||||
ONE=1
|
||||
ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
|
||||
ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
|
||||
ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
|
||||
ITEMS_4=$(shell seq $(START) $(STEP) $(END_4))
|
||||
${srun} $< --command 'Rscript 02_indep_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "outcome_formula":["y~x+z"], "proxy_formula":["w_pred~y+x"], "truth_formula":["x~z"], "prediction_accuracy":[0.85],"y_bias":[-0.1,0]}' --outfile $@
|
||||
|
||||
robustness_4.feather: robustness_4_jobs_p1 robustness_4_jobs_p2 robustness_4_jobs_p3
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p1)
|
||||
$(eval END_1!=cat robustness_4_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_4_jobs_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_4_jobs_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_jobs_p3;)
|
||||
|
||||
|
||||
robustness_4_dv_jobs_p1: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_4.RDS: plot_example.R robustness_4.feather
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5] "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0,0.1]}' --outfile $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@
|
||||
|
||||
robustness_4_dv_jobs_p2: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
|
||||
# '{"N":${Ns},"Bxy":[0.7],"Bzy":[-0.7],"m":${ms}, "seed":${seeds}, "outfile":["example_4.feather"], "z_bias":[0.5]}' --outfile example_4_jobs
|
||||
|
||||
robustness_4_dv_jobs_p1: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3],"B0":[0.5] "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.25,0.4]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0,0.1]}' --outfile $@
|
||||
|
||||
robustness_4_dv_jobs_p3: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
robustness_4_dv_jobs_p2: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.5], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.55,0.7]}' --outfile $@
|
||||
robustness_4_dv_jobs_p4: grid_sweep.py 03_depvar.R simulation_base.R grid_sweep.py
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.25,0.4]}' --outfile $@
|
||||
|
||||
robustness_4_dv_jobs_p3: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 03_depvar.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4.feather"],"y_explained_variance":${explained_variances}, "Bzy":[-0.3],"Bxy":[0.3],"Bzx":[0.3], "B0":[0.5], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],z_bias=[0.85,1]}' --outfile $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"], "Bzy":[-0.7],"Bxy":[0.7],"outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.55,0.7]}' --outfile $@
|
||||
|
||||
|
||||
START=0
|
||||
END_1=$(shell cat robustness_4_dv_jobs_p1 | wc -l)
|
||||
END_2=$(shell cat robustness_4_dv_jobs_p2 | wc -l)
|
||||
END_3=$(shell cat robustness_4_dv_jobs_p3 | wc -l)
|
||||
|
||||
STEP=1000
|
||||
ONE=1
|
||||
ITEMS_1=$(shell seq $(START) $(STEP) $(END_1))
|
||||
ITEMS_2=$(shell seq $(START) $(STEP) $(END_2))
|
||||
ITEMS_3=$(shell seq $(START) $(STEP) $(END_3))
|
||||
robustness_4_dv_jobs_p4: grid_sweep.py 04_depvar_differential.R simulation_base.R grid_sweep.py
|
||||
rm -f $@
|
||||
${srun} $< --command 'Rscript 04_depvar_differential.R' --arg_dict '{"N":${Ns},"m":${ms}, "seed":${seeds}, "outfile":["robustness_4_dv.feather"],"Bzy":[-0.7],"Bxy":[0.7], "outcome_formula":["y~x+z"], "prediction_accuracy":[0.85],"z_bias":[0.85,1]}' --outfile $@
|
||||
|
||||
robustness_4_dv.feather: robustness_4_dv_jobs_p1 robustness_4_dv_jobs_p2 robustness_4_dv_jobs_p3
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p1)
|
||||
$(eval END_1!=cat robustness_4_dv_jobs_p1 | wc -l)
|
||||
$(eval ITEMS_1!=seq $(START) $(STEP) $(END_1))
|
||||
$(eval END_2!=cat robustness_4_dv_p2 | wc -l)
|
||||
$(eval ITEMS_2!=seq $(START) $(STEP) $(END_2))
|
||||
$(eval END_3!=cat robustness_4_dv_p3 | wc -l)
|
||||
$(eval ITEMS_3!=seq $(START) $(STEP) $(END_3))
|
||||
|
||||
$(foreach item,$(ITEMS_1),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p1;)
|
||||
$(foreach item,$(ITEMS_2),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p2;)
|
||||
$(foreach item,$(ITEMS_3),sbatch --wait --verbose --array=$(shell expr $(item) + $(ONE))-$(shell expr $(item) + $(STEP)) run_simulation.sbatch 0 robustness_4_dv_jobs_p3;)
|
||||
|
||||
|
||||
robustness_4_dv.RDS: plot_dv_example.R robustness_4_dv.feather
|
||||
rm -f $@
|
||||
${srun} Rscript $< --infile $(word 2, $^) --name "robustness_4" --remember-file $@
|
||||
|
||||
#
|
||||
clean:
|
||||
rm *.feather
|
||||
rm -f remembr.RDS
|
||||
rm -f remembr*.RDS
|
||||
rm -f robustness*.RDS
|
||||
rm -f example_*_jobs
|
||||
rm -f robustness_*_jobs_*
|
||||
# sbatch --wait --verbose --array=3001-6001 run_simulation.sbatch 0 example_2_B_jobs
|
||||
|
||||
# example_2_B_mecor_jobs:
|
||||
|
||||
@ -5,6 +5,7 @@ from itertools import product
|
||||
import pyRemembeR
|
||||
|
||||
def main(command, arg_dict, outfile, remember_file='remember_grid_sweep.RDS'):
|
||||
print(remember_file)
|
||||
remember = pyRemembeR.remember.Remember()
|
||||
remember.set_file(remember_file)
|
||||
remember[outfile] = arg_dict
|
||||
|
||||
@ -19,15 +19,30 @@ library(bbmle)
|
||||
|
||||
## outcome_formula <- y ~ x + z; proxy_formula <- w_pred ~ y + x + z + x:z + x:y + z:y
|
||||
measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), proxy_formula, proxy_family=binomial(link='logit'),method='optim'){
|
||||
|
||||
nll <- function(params){
|
||||
df.obs <- model.frame(outcome_formula, df)
|
||||
proxy.variable <- all.vars(proxy_formula)[1]
|
||||
proxy.model.matrix <- model.matrix(proxy_formula, df)
|
||||
proxy.variable <- all.vars(proxy_formula)[1]
|
||||
|
||||
df.proxy.obs <- model.frame(proxy_formula,df)
|
||||
proxy.obs <- with(df.proxy.obs, eval(parse(text=proxy.variable)))
|
||||
|
||||
response.var <- all.vars(outcome_formula)[1]
|
||||
y.obs <- with(df.obs,eval(parse(text=response.var)))
|
||||
outcome.model.matrix <- model.matrix(outcome_formula, df.obs)
|
||||
|
||||
df.unobs <- df[is.na(df[[response.var]])]
|
||||
df.unobs.y1 <- copy(df.unobs)
|
||||
df.unobs.y1[[response.var]] <- 1
|
||||
df.unobs.y0 <- copy(df.unobs)
|
||||
df.unobs.y0[[response.var]] <- 0
|
||||
|
||||
outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1)
|
||||
proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1)
|
||||
proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0)
|
||||
proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable)))
|
||||
|
||||
nll <- function(params){
|
||||
|
||||
param.idx <- 1
|
||||
n.outcome.model.covars <- dim(outcome.model.matrix)[2]
|
||||
outcome.params <- params[param.idx:n.outcome.model.covars]
|
||||
@ -39,12 +54,9 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
|
||||
ll.y.obs[y.obs==0] <- plogis(outcome.params %*% t(outcome.model.matrix[y.obs==0,]),log=TRUE,lower.tail=FALSE)
|
||||
}
|
||||
|
||||
df.obs <- model.frame(proxy_formula,df)
|
||||
n.proxy.model.covars <- dim(proxy.model.matrix)[2]
|
||||
proxy.params <- params[param.idx:(n.proxy.model.covars+param.idx-1)]
|
||||
|
||||
param.idx <- param.idx + n.proxy.model.covars
|
||||
proxy.obs <- with(df.obs, eval(parse(text=proxy.variable)))
|
||||
|
||||
if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
|
||||
ll.w.obs <- vector(mode='numeric',length=dim(proxy.model.matrix)[1])
|
||||
@ -54,14 +66,7 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
|
||||
|
||||
ll.obs <- sum(ll.y.obs + ll.w.obs)
|
||||
|
||||
df.unobs <- df[is.na(df[[response.var]])]
|
||||
df.unobs.y1 <- copy(df.unobs)
|
||||
df.unobs.y1[[response.var]] <- 1
|
||||
df.unobs.y0 <- copy(df.unobs)
|
||||
df.unobs.y0[[response.var]] <- 0
|
||||
|
||||
## integrate out y
|
||||
outcome.model.matrix.y1 <- model.matrix(outcome_formula, df.unobs.y1)
|
||||
|
||||
if((outcome_family$family == "binomial") & (outcome_family$link == 'logit')){
|
||||
ll.y.unobs.1 <- vector(mode='numeric', length=dim(outcome.model.matrix.y1)[1])
|
||||
@ -70,10 +75,6 @@ measerr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='lo
|
||||
ll.y.unobs.0 <- plogis(outcome.params %*% t(outcome.model.matrix.y1),log=TRUE,lower.tail=FALSE)
|
||||
}
|
||||
|
||||
proxy.model.matrix.y1 <- model.matrix(proxy_formula, df.unobs.y1)
|
||||
proxy.model.matrix.y0 <- model.matrix(proxy_formula, df.unobs.y0)
|
||||
proxy.unobs <- with(df.unobs, eval(parse(text=proxy.variable)))
|
||||
|
||||
if( (proxy_family$family=="binomial") & (proxy_family$link=='logit')){
|
||||
ll.w.unobs.1 <- vector(mode='numeric',length=dim(proxy.model.matrix.y1)[1])
|
||||
ll.w.unobs.0 <- vector(mode='numeric',length=dim(proxy.model.matrix.y0)[1])
|
||||
@ -431,7 +432,7 @@ measerr_irr_mle <- function(df, outcome_formula, outcome_family=gaussian(), code
|
||||
## Experimental, and does not work.
|
||||
measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link='logit'), coder_formulas=list(y.obs.0~y+w_pred+y.obs.1,y.obs.1~y+w_pred+y.obs.0), proxy_formula=w_pred~y, proxy_family=binomial(link='logit'),method='optim'){
|
||||
integrate.grid <- expand.grid(replicate(1 + length(coder_formulas), c(0,1), simplify=FALSE))
|
||||
print(integrate.grid)
|
||||
# print(integrate.grid)
|
||||
|
||||
|
||||
outcome.model.matrix <- model.matrix(outcome_formula, df)
|
||||
@ -527,8 +528,8 @@ measerr_irr_mle_dv <- function(df, outcome_formula, outcome_family=binomial(link
|
||||
|
||||
## likelihood of observed data
|
||||
target <- -1 * sum(lls)
|
||||
print(target)
|
||||
print(params)
|
||||
# print(target)
|
||||
# print(params)
|
||||
return(target)
|
||||
}
|
||||
}
|
||||
|
||||
@ -31,8 +31,8 @@ zhang.mle.dv <- function(df){
|
||||
(1-w_pred) * (log(1-fpr) - exp(log(1-fnr-fpr)+pi.y.1)))))
|
||||
|
||||
ll <- ll + sum(lls)
|
||||
print(paste0(B0,Bxy,Bzy))
|
||||
print(ll)
|
||||
# print(paste0(B0,Bxy,Bzy))
|
||||
# print(ll)
|
||||
return(-ll)
|
||||
}
|
||||
mlefit <- mle2(minuslogl = nll, control=list(maxit=1e6),method='L-BFGS-B',lower=c(B0=-Inf, Bxy=-Inf, Bzy=-Inf),
|
||||
|
||||
@ -10,11 +10,11 @@ Like `robustness\_1.RDS` but with a less precise model for $w_pred$. In the mai
|
||||
|
||||
# robustness_2.RDS
|
||||
|
||||
This is just example 1 with varying levels of classifier accuracy.
|
||||
This is just example 1 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable..
|
||||
|
||||
# robustness_2_dv.RDS
|
||||
|
||||
Example 3 with varying levels of classifier accuracy
|
||||
Example 3 with varying levels of classifier accuracy indicated by the `prediction_accuracy` variable.
|
||||
|
||||
# robustness_3.RDS
|
||||
|
||||
|
||||
17
simulations/run_job.sbatch
Normal file
17
simulations/run_job.sbatch
Normal file
@ -0,0 +1,17 @@
|
||||
#!/bin/bash
|
||||
#SBATCH --job-name="simulate measurement error models"
|
||||
## Allocation Definition
|
||||
#SBATCH --account=comdata
|
||||
#SBATCH --partition=compute-bigmem,compute-hugemem
|
||||
## Resources
|
||||
#SBATCH --nodes=1
|
||||
## Walltime (4 hours)
|
||||
#SBATCH --time=4:00:00
|
||||
## Memory per node
|
||||
#SBATCH --mem=4G
|
||||
#SBATCH --cpus-per-task=1
|
||||
#SBATCH --ntasks-per-node=1
|
||||
#SBATCH --chdir /gscratch/comdata/users/nathante/ml_measurement_error_public/simulations
|
||||
#SBATCH --output=simulation_jobs/%A_%a.out
|
||||
#SBATCH --error=simulation_jobs/%A_%a.err
|
||||
"$@"
|
||||
@ -180,26 +180,35 @@ run_simulation_depvar <- function(df, result, outcome_formula=y~x+z, proxy_formu
|
||||
|
||||
|
||||
# amelia says use normal distribution for binary variables.
|
||||
amelia_result <- list(Bxy.est.amelia.full = NA,
|
||||
Bxy.ci.upper.amelia.full = NA,
|
||||
Bxy.ci.lower.amelia.full = NA,
|
||||
Bzy.est.amelia.full = NA,
|
||||
Bzy.ci.upper.amelia.full = NA,
|
||||
Bzy.ci.lower.amelia.full = NA
|
||||
)
|
||||
|
||||
tryCatch({
|
||||
amelia.out.k <- amelia(df, m=200, p2s=0, idvars=c('y','ystar','w'))
|
||||
mod.amelia.k <- zelig(y.obs~x+z, model='ls', data=amelia.out.k$imputations, cite=FALSE)
|
||||
(coefse <- combine_coef_se(mod.amelia.k, messages=FALSE))
|
||||
est.x.mi <- coefse['x','Estimate']
|
||||
est.x.se <- coefse['x','Std.Error']
|
||||
result <- append(result,
|
||||
list(Bxy.est.amelia.full = est.x.mi,
|
||||
Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
|
||||
Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se
|
||||
))
|
||||
|
||||
est.z.mi <- coefse['z','Estimate']
|
||||
est.z.se <- coefse['z','Std.Error']
|
||||
|
||||
result <- append(result,
|
||||
list(Bzy.est.amelia.full = est.z.mi,
|
||||
amelia_result <- list(Bxy.est.amelia.full = est.x.mi,
|
||||
Bxy.ci.upper.amelia.full = est.x.mi + 1.96 * est.x.se,
|
||||
Bxy.ci.lower.amelia.full = est.x.mi - 1.96 * est.x.se,
|
||||
Bzy.est.amelia.full = est.z.mi,
|
||||
Bzy.ci.upper.amelia.full = est.z.mi + 1.96 * est.z.se,
|
||||
Bzy.ci.lower.amelia.full = est.z.mi - 1.96 * est.z.se
|
||||
))
|
||||
)
|
||||
},
|
||||
error = function(e){
|
||||
result[['error']] <- e}
|
||||
)
|
||||
result <- append(result,amelia_result)
|
||||
|
||||
return(result)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user