Usage
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"))Arguments
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").