@guolinke I looked through the code and I found that predicting form lgb.Dataset hasn't been supported yet. Could you support that when you got time? Otherwise we can not use all cv models to predict on each fold.

Below is a simple function that generating cv predictions from original dataset, @JackStat you can use that for your problem, though I think you had figured it out by yourself.

LGB_CV_Predict <- function(lgb_cv, data, num_iteration = NULL, folds) {
  if (is.null(num_iteration)) {
    num_iteration <- lgb_cv$best_iter
  }
  cv_pred_mat <- foreach::foreach(i = seq_along(lgb_cv$boosters), .combine = "rbind") %do% {
    lgb_tree <- lgb_cv$boosters[[i]][[1]]
    predict(lgb_tree, 
            data[folds[[i]],], 
            num_iteration = num_iteration, 
            rawscore = FALSE, predleaf = FALSE, header = FALSE, reshape = TRUE)
  }
  if (ncol(cv_pred_mat) == 1) {
    as.double(cv_pred_mat)[order(unlist(folds))]
  } else {
    cv_pred_mat[order(unlist(folds)), , drop = FALSE]
  }
}

Add option to keep cv predicted values #283

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JackStatopenedon Feb 5, 2017

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JackStat
openedon Feb 5, 2017