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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,55 @@ | ||
| #' Compute feature importance in a model | ||
| #' | ||
| #' Creates a \code{data.table} of feature importances in a model. | ||
| #' | ||
| #' @param model object of class \code{lgb.Booster}. | ||
| #' @param percentage whether to show importance in relative percentage. | ||
| #' | ||
| #' @return | ||
| #' | ||
| #' For a tree model, a \code{data.table} with the following columns: | ||
| #' \itemize{ | ||
| #' \item \code{Feature} Feature names in the model. | ||
| #' \item \code{Gain} The total gain of this feature's splits. | ||
| #' \item \code{Cover} The number of observation related to this feature. | ||
| #' \item \code{Frequency} The number of times a feature splited in trees. | ||
| #' } | ||
| #' | ||
| #' @examples | ||
| #' | ||
| #' data(agaricus.train, package = 'lightgbm') | ||
| #' train <- agaricus.train | ||
| #' dtrain <- lgb.Dataset(train$data, label = train$label) | ||
| #' | ||
| #' params = list(objective = "binary", | ||
| #' learning_rate = 0.01, num_leaves = 63, max_depth = -1, | ||
| #' min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1) | ||
| #' model <- lgb.train(params, dtrain, 20) | ||
| #' model <- lgb.train(params, dtrain, 20) | ||
| #' | ||
| #' tree_imp1 <- lgb.importance(model, percentage = TRUE) | ||
| #' tree_imp2 <- lgb.importance(model, percentage = FALSE) | ||
| #' | ||
| #' @importFrom magrittr %>% %T>% | ||
| #' @importFrom data.table := | ||
| #' @export | ||
|
|
||
| lgb.importance <- function(model, percentage = TRUE) { | ||
| if (!any(class(model) == "lgb.Booster")) { | ||
| stop("'model' has to be an object of class lgb.Booster") | ||
| } | ||
| tree_dt <- lgb.model.dt.tree(model) | ||
| tree_imp <- tree_dt %>% | ||
| magrittr::extract(., | ||
| i = is.na(split_index) == FALSE, | ||
| j = .(Gain = sum(split_gain), Cover = sum(internal_count), Frequency = .N), | ||
| by = "split_feature") %T>% | ||
| data.table::setnames(., old = "split_feature", new = "Feature") %>% | ||
| magrittr::extract(., i = order(Gain, decreasing = TRUE)) | ||
| if (percentage) { | ||
| tree_imp[, ":="(Gain = Gain / sum(Gain), | ||
| Cover = Cover / sum(Cover), | ||
| Frequency = Frequency / sum(Frequency))] | ||
| } | ||
| return(tree_imp) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,95 @@ | ||
| #' Parse a LightGBM model json dump | ||
| #' | ||
| #' Parse a LightGBM model json dump into a \code{data.table} structure. | ||
| #' | ||
| #' @param model object of class \code{lgb.Booster} | ||
| #' | ||
| #' @return | ||
| #' A \code{data.table} with detailed information about model trees' nodes and leafs. | ||
| #' | ||
| #' The columns of the \code{data.table} are: | ||
| #' | ||
| #' \itemize{ | ||
| #' \item \code{tree_index}: ID of a tree in a model (integer) | ||
| #' \item \code{split_index}: ID of a node in a tree (integer) | ||
| #' \item \code{split_feature}: for a node, it's a feature name (character); | ||
| #' for a leaf, it simply labels it as \code{'NA'} | ||
| #' \item \code{node_parent}: ID of the parent node for current node (integer) | ||
| #' \item \code{leaf_index}: ID of a leaf in a tree (integer) | ||
| #' \item \code{leaf_parent}: ID of the parent node for current leaf (integer) | ||
| #' \item \code{split_gain}: Split gain of a node | ||
| #' \item \code{threshold}: Spliting threshold value of a node | ||
| #' \item \code{decision_type}: Decision type of a node | ||
| #' \item \code{internal_value}: Node value | ||
| #' \item \code{internal_count}: The number of observation collected by a node | ||
| #' \item \code{leaf_value}: Leaf value | ||
| #' \item \code{leaf_count}: The number of observation collected by a leaf | ||
| #' } | ||
| #' | ||
| #' @examples | ||
| #' | ||
| #' data(agaricus.train, package = 'lightgbm') | ||
| #' train <- agaricus.train | ||
| #' dtrain <- lgb.Dataset(train$data, label = train$label) | ||
| #' | ||
| #' params = list(objective = "binary", | ||
| #' learning_rate = 0.01, num_leaves = 63, max_depth = -1, | ||
| #' min_data_in_leaf = 1, min_sum_hessian_in_leaf = 1) | ||
| #' model <- lgb.train(params, dtrain, 20) | ||
| #' model <- lgb.train(params, dtrain, 20) | ||
| #' | ||
| #' tree_dt <- lgb.model.dt.tree(model) | ||
| #' | ||
| #' @importFrom magrittr %>% | ||
| #' @importFrom data.table := | ||
| #' @export | ||
|
|
||
| lgb.model.dt.tree <- function(model, num_iteration = NULL) { | ||
| json_model <- lgb.dump(model, num_iteration = num_iteration) | ||
| parsed_json_model <- jsonlite::fromJSON(json_model, | ||
| simplifyVector = TRUE, | ||
| simplifyDataFrame = FALSE, | ||
| simplifyMatrix = FALSE, | ||
| flatten = FALSE) | ||
| tree_list <- lapply(parsed_json_model$tree_info, single.tree.parse) | ||
| tree_dt <- data.table::rbindlist(tree_list, use.names = TRUE) | ||
| tree_dt[, split_feature := Lookup(split_feature, | ||
| seq(0, parsed_json_model$max_feature_idx, by = 1), | ||
| parsed_json_model$feature_names)] | ||
| return(tree_dt) | ||
| } | ||
|
|
||
| single.tree.parse <- function(lgb_tree) { | ||
| single_tree_dt <- data.table::data.table(tree_index = integer(0), | ||
| split_index = integer(0), split_feature = integer(0), node_parent = integer(0), | ||
| leaf_index = integer(0), leaf_parent = integer(0), | ||
| split_gain = numeric(0), threshold = numeric(0), decision_type = character(0), | ||
| internal_value = integer(0), internal_count = integer(0), | ||
| leaf_value = integer(0), leaf_count = integer(0)) | ||
| pre_order_traversal <- function(tree_node_leaf, parent_index = NA) { | ||
| if (!is.null(tree_node_leaf$split_index)) { | ||
| single_tree_dt <<- data.table::rbindlist(l = list(single_tree_dt, | ||
| c(tree_node_leaf[c("split_index", "split_feature", | ||
| "split_gain", "threshold", "decision_type", | ||
| "internal_value", "internal_count")], | ||
| "node_parent" = parent_index)), | ||
| use.names = TRUE, fill = TRUE) | ||
| pre_order_traversal(tree_node_leaf$left_child, parent_index = tree_node_leaf$split_index) | ||
| pre_order_traversal(tree_node_leaf$right_child, parent_index = tree_node_leaf$split_index) | ||
| } else if (!is.null(tree_node_leaf$leaf_index)) { | ||
| single_tree_dt <<- data.table::rbindlist(l = list(single_tree_dt, | ||
| tree_node_leaf[c("leaf_index", "leaf_parent", | ||
| "leaf_value", "leaf_count")]), | ||
| use.names = TRUE, fill = TRUE) | ||
| } | ||
| } | ||
| pre_order_traversal(lgb_tree$tree_structure) | ||
| single_tree_dt[, tree_index := lgb_tree$tree_index] | ||
| return(single_tree_dt) | ||
| } | ||
|
|
||
| Lookup <- function(key, key_lookup, value_lookup, missing = NA) { | ||
| match(key, key_lookup) %>% | ||
| magrittr::extract(value_lookup, .) %>% | ||
| magrittr::inset(. , is.na(.), missing) | ||
| } |
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