Given a lgb.Booster, return evaluation results for a
particular metric on a particular dataset.
lgb.get.eval.result( booster, data_name, eval_name, iters = NULL, is_err = FALSE )
| booster | Object of class |
|---|---|
| data_name | Name of the dataset to return evaluation results for. |
| eval_name | Name of the evaluation metric to return results for. |
| iters | An integer vector of iterations you want to get evaluation results for. If NULL (the default), evaluation results for all iterations will be returned. |
| is_err | TRUE will return evaluation error instead |
numeric vector of evaluation result
# \donttest{ # train a regression model data(agaricus.train, package = "lightgbm") train <- agaricus.train dtrain <- lgb.Dataset(train$data, label = train$label) data(agaricus.test, package = "lightgbm") test <- agaricus.test dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label) params <- list( objective = "regression" , metric = "l2" , min_data = 1L , learning_rate = 1.0 , num_threads = 2L ) valids <- list(test = dtest) model <- lgb.train( params = params , data = dtrain , nrounds = 5L , valids = valids ) #> [LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000927 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] [Info] Start training from score 0.482113 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [1]: test's l2:6.44165e-17 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [2]: test's l2:1.97215e-31 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [3]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [4]: test's l2:0 #> [LightGBM] [Warning] No further splits with positive gain, best gain: -inf #> [LightGBM] [Warning] Stopped training because there are no more leaves that meet the split requirements #> [5]: test's l2:0 # Examine valid data_name values print(setdiff(names(model$record_evals), "start_iter")) #> [1] "test" # Examine valid eval_name values for dataset "test" print(names(model$record_evals[["test"]])) #> [1] "l2" # Get L2 values for "test" dataset lgb.get.eval.result(model, "test", "l2") #> [1] 6.441652e-17 1.972152e-31 0.000000e+00 0.000000e+00 0.000000e+00 # }