lgbm.predict: LightGBM Prediction
In Laurae2/Laurae: Advanced High Performance Data Science Toolbox for R

Description Usage Arguments Details Value Examples

This function allows to run predictions on provided data.

lgbm.predict(model, y_pred = NA, x_pred = NA, SVMLight = is(x_pred,
  "dgCMatrix"), data_has_label = FALSE, lgbm_path = ifelse(is.list(model),
  model[["lgbm"]], getwd()), workingdir = ifelse(is.list(model),
  model[["Path"]], getwd()), input_model = ifelse(is.list(model),
  model[["Name"]], "lgbm_model.txt"), pred_conf = "lgbm_pred.conf",
  predict_leaf_index = FALSE, verbose = TRUE,
  data_name = ifelse(is.list(model) & is.null(dim(x_pred)), model[["Valid"]],
  paste0("lgbm_test", ifelse(SVMLight, ".svm", ".csv"))), files_exist = TRUE,
  output_preds = "lgbm_predict_result.txt",
  data.table = exists("data.table"))

`model`	Type: list. The model file. If a character vector is provided, it is considered to be the model which is going to be saved as `input_model`. If a list is provided, it is used to setup to fetch the correct variables, which you can override by setting the arguments manually. If a single value is provided (like `NA`), then it is ignored and uses the other arguments to fetch the model locally.
`y_pred`	Type: vector. The validation labels. Leave it alone unless you know what you are doing. Defaults to `NA`.
`x_pred`	Type: data.table (preferred), data.frame, or dgCMatrix (with `SVMLight = TRUE`). The validation features. Defaults to `NA`.
`SVMLight`	Type: boolean. Whether the input is a dgCMatrix to be output to SVMLight format. Setting this to `TRUE` enforces you must provide labels separately (in `y_train`) and headers will be ignored. This is default behavior of SVMLight format. Defaults to `is(x_pred, "dgCMatrix")`.
`data_has_label`	Type: boolean. Whether the data has labels or not. Do not modify this. Defaults to `FALSE`.
`lgbm_path`	Type: character. Where is stored LightGBM? Include only the folder to it. Defaults to `ifelse(is.list(model), model[["File"]], getwd())`, which means "take the model LightGBM path if provided the model list, else take the default working directory".
`workingdir`	Type: character. The working directory used for LightGBM. Defaults to `ifelse(is.list(model), model[["Path"]], getwd())`, which means "take the model working directory if provided the model list, else take the default working directory".
`input_model`	Type: character. The file name of the model. Defaults to `ifelse(is.list(model), model[["Name"]], 'lgbm_model.txt')`, which means "take the input model name if provided the model list, else take "lgbm_model.txt".
`pred_conf`	Type: character. The name of the pred_conf file for the model. Defaults to `'lgbm_pred.conf'`.
`predict_leaf_index`	Type: boolean. Should LightGBM predict leaf indexes instead of pure predictions? Defaults to `FALSE`.
`verbose`	Type: boolean. Whether to print to console verbose information. When FALSE, the printing is diverted to `"diverted_verbose.txt"`. Defaults to `TRUE`. Might not work when your lgbm_path has a space.
`data_name`	Type: character. The file output name for the vaildation file. Defaults to `ifelse(is.list(model) & is.null(dim(x_pred)), model[["Valid"]], paste0('lgbm_test', ifelse(SVMLight, '.svm', '.csv')))`, which means "take the validation file name if provided the model list and x_pred is left as is, else take "lgbm_test.csv". Original name is `val_name`.
`files_exist`	Type: boolean. Whether to NOT create CSV files for the prediction data, if already created. Defaults to `TRUE`.
`output_preds`	Type: character. The output prediction file. Defaults to `'lgbm_predict_result.txt'`. Original name is `output_result`.
`data.table`	Type: boolean. Whether to use data.table to read data (returns a data.table). Defaults to `exists("data.table")`.