Plot feature contribution as a bar graph

Plot previously calculated feature contribution as a bar graph.

lgb.plot.interpretation(
  tree_interpretation_dt,
  top_n = 10L,
  cols = 1L,
  left_margin = 10L,
  cex = NULL
)

Arguments

tree_interpretation_dt

a data.table returned by lgb.interprete.

top_n

maximal number of top features to include into the plot.

cols

the column numbers of layout, will be used only for multiclass classification feature contribution.

left_margin

(base R barplot) allows to adjust the left margin size to fit feature names.

cex

(base R barplot) passed as cex.names parameter to barplot.

Value

The lgb.plot.interpretation function creates a barplot.

Details

The graph represents each feature as a horizontal bar of length proportional to the defined contribution of a feature. Features are shown ranked in a decreasing contribution order.

Examples

# \donttest{
Logit <- function(x) {
  log(x / (1.0 - x))
}
data(agaricus.train, package = "lightgbm")
labels <- agaricus.train$label
dtrain <- lgb.Dataset(
  agaricus.train$data
  , label = labels
)
set_field(
  dataset = dtrain
  , field_name = "init_score"
  , data = rep(Logit(mean(labels)), length(labels))
)

data(agaricus.test, package = "lightgbm")

params <- list(
  objective = "binary"
  , learning_rate = 0.1
  , max_depth = -1L
  , min_data_in_leaf = 1L
  , min_sum_hessian_in_leaf = 1.0
  , num_threads = 2L
)
model <- lgb.train(
  params = params
  , data = dtrain
  , nrounds = 5L
)
#> [LightGBM] [Info] Number of positive: 3140, number of negative: 3373
#> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000739 seconds.
#> You can set `force_row_wise=true` to remove the overhead.
#> And if memory is not enough, you can set `force_col_wise=true`.
#> [LightGBM] [Info] Total Bins 232
#> [LightGBM] [Info] Number of data points in the train set: 6513, number of used features: 116
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf
#> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf

tree_interpretation <- lgb.interprete(
  model = model
  , data = agaricus.test$data
  , idxset = 1L:5L
)
lgb.plot.interpretation(
  tree_interpretation_dt = tree_interpretation[[1L]]
  , top_n = 3L
)

# }