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similar to yardstick::conf_mat() but can handle weights
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library(dplyr) | |
#' Confusion Matrix With Observation Weights | |
#' | |
#' @param df dataframe | |
#' @param truth Column that represents 'truth' | |
#' @param extimate Columns that rrepresents class prediction | |
#' @param wt Column with observation weights. | |
#' @param scale_weights_one Whether observations in confusion matrix should equal number of observations. | |
#' @param dnn Character vector of dimnames for the table | |
#' | |
#' @return a confusion matrix | |
conf_mat_weighted <- | |
function(df, | |
truth, | |
estimate, | |
wt = NULL, | |
scale_weights_one = !rlang::quo_is_null(enquo(wt)), | |
dnn = c("Prediction", "Truth")) { | |
freq_df <- count(df, {{ truth }}, {{ estimate }}, wt = {{ wt }}, .drop = FALSE) | |
pred_vals <- unique(freq_df[[1]]) | |
truth_vals <- unique(freq_df[[2]]) | |
output <- matrix(freq_df$n, | |
nrow = length(pred_vals), | |
byrow = FALSE, | |
dimnames = | |
list(pred_vals, truth_vals) %>% | |
purrr::set_names(dnn) | |
) | |
if(scale_weights_one){ | |
scale_factor <- summarise(df, | |
sum = sum({{ wt }}), | |
n = n(), | |
scale_factor = n / sum) %>% | |
pull(scale_factor) | |
output <- output * scale_factor | |
} | |
yardstick:::conf_mat.table(output) | |
} |
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