wehub-resource-sync
118 lines
3.3 KiB
R
118 lines
3.3 KiB
R
library(lightgbm)
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# We load the default iris dataset shipped with R
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data(iris)
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# We must convert factors to numeric
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# They must be starting from number 0 to use multiclass
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# For instance: 0, 1, 2, 3, 4, 5...
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iris$Species <- as.numeric(as.factor(iris$Species)) - 1L
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# Create imbalanced training data (20, 30, 40 examples for classes 0, 1, 2)
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train <- as.matrix(iris[c(1L:20L, 51L:80L, 101L:140L), ])
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# The 10 last samples of each class are for validation
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test <- as.matrix(iris[c(41L:50L, 91L:100L, 141L:150L), ])
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dtrain <- lgb.Dataset(data = train[, 1L:4L], label = train[, 5L])
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dtest <- lgb.Dataset.create.valid(dtrain, data = test[, 1L:4L], label = test[, 5L])
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valids <- list(train = dtrain, test = dtest)
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# Method 1 of training with built-in multiclass objective
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# Note: need to turn off boost from average to match custom objective
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# (https://github.com/lightgbm-org/LightGBM/issues/1846)
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params <- list(
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min_data = 1L
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, learning_rate = 1.0
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, num_class = 3L
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, boost_from_average = FALSE
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, metric = "multi_logloss"
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)
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model_builtin <- lgb.train(
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params
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, dtrain
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, 100L
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, valids
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, early_stopping_rounds = 10L
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, obj = "multiclass"
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)
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preds_builtin <- predict(model_builtin, test[, 1L:4L], type = "raw")
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probs_builtin <- exp(preds_builtin) / rowSums(exp(preds_builtin))
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# Method 2 of training with custom objective function
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# User defined objective function, given prediction, return gradient and second order gradient
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custom_multiclass_obj <- function(preds, dtrain) {
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labels <- get_field(dtrain, "label")
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# preds is a matrix with rows corresponding to samples and columns corresponding to choices
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preds <- matrix(preds, nrow = length(labels))
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# to prevent overflow, normalize preds by row
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preds <- preds - apply(preds, MARGIN = 1L, max)
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prob <- exp(preds) / rowSums(exp(preds))
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# compute gradient
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grad <- prob
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subset_index <- as.matrix(
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data.frame(
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seq_along(labels)
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, labels + 1L
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, fix.empty.names = FALSE
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)
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, nrow = length(labels)
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, dimnames = NULL
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)
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grad[subset_index] <- grad[subset_index] - 1L
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# compute hessian (approximation)
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hess <- 2.0 * prob * (1.0 - prob)
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return(list(grad = grad, hess = hess))
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}
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# define custom metric
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custom_multiclass_metric <- function(preds, dtrain) {
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labels <- get_field(dtrain, "label")
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preds <- matrix(preds, nrow = length(labels))
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preds <- preds - apply(preds, 1L, max)
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prob <- exp(preds) / rowSums(exp(preds))
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subset_index <- as.matrix(
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data.frame(
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seq_along(labels)
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, labels + 1L
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, fix.empty.names = FALSE
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)
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, nrow = length(labels)
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, dimnames = NULL
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)
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return(list(
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name = "error"
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, value = -mean(log(prob[subset_index]))
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, higher_better = FALSE
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))
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}
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params <- list(
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min_data = 1L
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, learning_rate = 1.0
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, num_class = 3L
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)
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model_custom <- lgb.train(
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params
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, dtrain
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, 100L
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, valids
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, early_stopping_rounds = 10L
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, obj = custom_multiclass_obj
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, eval = custom_multiclass_metric
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)
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preds_custom <- predict(model_custom, test[, 1L:4L], type = "raw")
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probs_custom <- exp(preds_custom) / rowSums(exp(preds_custom))
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# compare predictions
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stopifnot(identical(probs_builtin, probs_custom))
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stopifnot(identical(preds_builtin, preds_custom))
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