#' Step 3: Get prediction breakdown and waterfall chart for a single row of data
#'
#' This function prints the feature impact breakdown for a single data row, and plots an accompanying waterfall chart.
#' @param lgb.model A trained lightgbm model
#' @param explainer The output from the buildExplainer function, for this model
#' @param lgb.dtrain The lgb.dtrain in which the row to be predicted is stored
#' @param lgb.train.data The matrix of data from which the lgb.dtrain was built
#' @param idx The row number of the data to be explained
#' @param type The objective function of the model - either "binary" (for binary:logistic) or "regression" (for reg:linear)
#' @param threshold Default = 0.0001. The waterfall chart will group all variables with absolute impact less than the threshold into a variable called 'Other'
#' @return None
#' @export
#' @import data.table
#' @import lightgbm
#' @import waterfalls
#' @import scales
#' @import ggplot2
showWaterfall = function(lgb.model, explainer, lgb.dtrain, lgb.train.data, id, type = "binary", threshold = 0.0001){
breakdown = explainPredictions(lgb.model, explainer, lgb.train.data[id,,drop=FALSE])
weight = rowSums(breakdown)
if (type == 'regression'){
pred = weight
}else{
pred = 1/(1+exp(-weight))
}
breakdown_summary = as.matrix(breakdown)[1,]
data_for_label = lgb.train.data[id,]
idx = order(abs(breakdown_summary),decreasing=TRUE)
breakdown_summary = breakdown_summary[idx]
data_for_label = data_for_label[idx]
intercept = breakdown_summary[names(breakdown_summary)=='intercept']
data_for_label = data_for_label[names(breakdown_summary)!='intercept']
breakdown_summary = breakdown_summary[names(breakdown_summary)!='intercept']
idx_other =which(abs(breakdown_summary)<threshold)
other_impact = 0
if (length(idx_other > 0)){
other_impact = sum(breakdown_summary[idx_other])
names(other_impact) = 'other'
breakdown_summary = breakdown_summary[-idx_other]
data_for_label = data_for_label[-idx_other]
}
if (abs(other_impact) > 0){
breakdown_summary = c(intercept, breakdown_summary, other_impact)
data_for_label = c("", data_for_label,"")
labels = paste0(names(breakdown_summary)," = ", data_for_label)
labels[1] = 'intercept'
labels[length(labels)] = 'other'
}else{
breakdown_summary = c(intercept, breakdown_summary)
data_for_label = c("", data_for_label)
labels = paste0(names(breakdown_summary)," = ", data_for_label)
labels[1] = 'intercept'
}
if (!is.null(lgb.dtrain)){
if (!is.null(lgb.dtrain$getinfo("label")[id])){
cat("\nActual: ", lgb.dtrain$getinfo("label")[id])
}
}
cat("\nPrediction: ", pred)
cat("\nWeight: ", weight)
cat("\nBreakdown")
cat('\n')
print(breakdown_summary)
if (type == 'regression'){
waterfall(values = round(breakdown_summary,2), labels = labels
, calc_total = TRUE
, total_axis_text = "Prediction") + theme(axis.text.x = element_text(angle = 45, hjust = 1))
}else{
inverse_logit_trans <- trans_new("inverse logit",
transform = plogis,
inverse = qlogis)
inverse_logit_labels = function(x){return (1/(1+exp(-x)))}
logit = function(x){return(log(x/(1-x)))}
ybreaks<-logit(seq(2,98,2)/100)
waterfall(values = round(breakdown_summary,2), labels = labels
, calc_total = TRUE
, total_axis_text = "Prediction") + scale_y_continuous(labels = inverse_logit_labels, breaks = ybreaks) + theme(axis.text.x = element_text(angle = 45, hjust = 1))
}
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.