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translate an h2o model into sql
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| {r} | |
| translate <- function(fit){ | |
| ### return the sql translation of a h2o.glm model ### | |
| # safety | |
| stopifnot(class(fit) %in% c("H2OBinomialModel","H2ORegressionModel")) | |
| stopifnot(.hasSlot(fit, "algorithm")) | |
| stopifnot(.hasSlot(fit, "model")) | |
| stopifnot(.hasSlot(fit, "parameters")) | |
| stopifnot(fit@algorithm == "glm") | |
| stopifnot(fit@parameters$family %in% c("binomial","gaussian")) | |
| stopifnot("coefficients" %in% names(fit@model)) | |
| # extract coefficients | |
| df <- data.frame( | |
| term = names(fit@model$coefficients), | |
| beta = fit@model$coefficients, | |
| sql = character(length(fit@model$coefficients)), | |
| stringsAsFactors = FALSE, | |
| row.names = NULL | |
| ) | |
| # pick out the intercept | |
| intercept <- grepl("intercept",tolower(df$term)) | |
| # translate each row into sql term * beta | |
| df$sql[intercept] <- paste0('(',df$beta[intercept],')') # no multiplication | |
| df$sql[!intercept] <- paste0('(',df$term[!intercept],'',' * ',df$beta[!intercept],')') | |
| # a classification model | |
| if(class(fit) == "H2OBinomialModel"){ | |
| # logistic function | |
| sql <- paste0( | |
| '1.0 - 1.0 / (1.0 + EXP(', | |
| paste(df$sql,collapse = " + "), | |
| '))' | |
| ) | |
| # otherwise a regression model | |
| } else { | |
| sql <- paste(df$sql,collapse = " + ") | |
| } | |
| return(sql) | |
| } |
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