[docs] Clarify the fact that predict() on a file does not support saved Datasets (fixes #4034)

R-package/R/lgb.Booster.R

@@ -682,7 +682,8 @@ Booster <- R6::R6Class(
 #' @title Predict method for LightGBM model
 #' @description Predicted values based on class \code{lgb.Booster}
 #' @param object Object of class \code{lgb.Booster}
-#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
+#' @param data a \code{matrix} object, a \code{dgCMatrix} object or
+#'             a character representing a path to a text file (CSV, TSV, or LibSVM)
 #' @param start_iteration int or None, optional (default=None)
 #'                        Start index of the iteration to predict.
 #'                        If None or <= 0, starts from the first iteration.

R-package/R/lgb.Dataset.R

@@ -710,7 +710,9 @@ Dataset <- R6::R6Class(
 #' @title Construct \code{lgb.Dataset} object
 #' @description Construct \code{lgb.Dataset} object from dense matrix, sparse matrix
 #'              or local file (that was created previously by saving an \code{lgb.Dataset}).
-#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
+#' @param data a \code{matrix} object, a \code{dgCMatrix} object,
+#'             a character representing a path to a text file (CSV, TSV, or LibSVM)
+#'             or a LightGBM Dataset binary file
 #' @param params a list of parameters. See
 #'               \href{https://lightgbm.readthedocs.io/en/latest/Parameters.html#dataset-parameters}{
 #'               The "Dataset Parameters" section of the documentation} for a list of parameters
@@ -774,7 +776,9 @@ lgb.Dataset <- function(data,
 #' @title Construct validation data
 #' @description Construct validation data according to training data
 #' @param dataset \code{lgb.Dataset} object, training data
-#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
+#' @param data a \code{matrix} object, a \code{dgCMatrix} object,
+#'             a character representing a path to a text file (CSV, TSV, or LibSVM),
+#'             or a character representing a path to a binary \code{Dataset} file
 #' @param info a list of information of the \code{lgb.Dataset} object
 #' @param ... other information to pass to \code{info}.
 #'

R-package/tests/testthat/test_lgb.Booster.R

@@ -1,5 +1,7 @@
 context("Booster")
 
+TOLERANCE <- 1e-6
+
 test_that("Booster$finalize() should not fail", {
     X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L)
     y <- iris[["Sepal.Length"]]
@@ -419,6 +421,54 @@ test_that("Creating a Booster from a Dataset with an existing predictor should w
     expect_equal(bst_from_ds$current_iter(), nrounds)
 })
 
+test_that("Booster$eval() should work on a Dataset stored in a binary file", {
+    set.seed(708L)
+    data(agaricus.train, package = "lightgbm")
+    train <- agaricus.train
+    dtrain <- lgb.Dataset(train$data, label = train$label)
+
+    bst <- lgb.train(
+        params = list(
+            objective = "regression"
+            , metric = "l2"
+            , num_leaves = 4L
+        )
+        , data = dtrain
+        , nrounds = 2L
+    )
+
+    data(agaricus.test, package = "lightgbm")
+    test <- agaricus.test
+    dtest <- lgb.Dataset.create.valid(
+        dataset = dtrain
+        , data = test$data
+        , label = test$label
+    )
+    dtest$construct()
+
+    eval_in_mem <- bst$eval(
+        data = dtest
+        , name = "test"
+    )
+
+    test_file <- tempfile(pattern = "lgb.Dataset_")
+    lgb.Dataset.save(
+        dataset = dtest
+        , fname = test_file
+    )
+    rm(dtest)
+
+    eval_from_file <- bst$eval(
+        data = lgb.Dataset(
+            data = test_file
+        )$construct()
+        , name = "test"
+    )
+
+    expect_true(abs(eval_in_mem[[1L]][["value"]] - 0.1744423) < TOLERANCE)
+    expect_identical(eval_in_mem, eval_from_file)
+})
+
 test_that("Booster$rollback_one_iter() should work as expected", {
     set.seed(708L)
     data(agaricus.train, package = "lightgbm")

docs/Python-Intro.rst

@@ -33,7 +33,7 @@ Data Interface
 
 The LightGBM Python module can load data from:
 
--  LibSVM (zero-based) / TSV / CSV / TXT format file
+-  LibSVM (zero-based) / TSV / CSV format text file
 
 -  NumPy 2D array(s), pandas DataFrame, H2O DataTable's Frame, SciPy sparse matrix
 

python-package/lightgbm/basic.py

@@ -744,7 +744,7 @@ def predict(self, data, start_iteration=0, num_iteration=-1,
         ----------
         data : string, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
             Data source for prediction.
-            When data type is string or pathlib.Path, it represents the path of txt file.
+            When data type is string or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         start_iteration : int, optional (default=0)
             Start index of the iteration to predict.
         num_iteration : int, optional (default=-1)
@@ -1132,7 +1132,7 @@ def __init__(self, data, label=None, reference=None,
         ----------
         data : string, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequences or list of numpy arrays
             Data source of Dataset.
-            If string or pathlib.Path, it represents the path to txt file.
+            If string or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.
         label : list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None)
             Label of the data.
         reference : Dataset or None, optional (default=None)
@@ -1776,7 +1776,7 @@ def create_valid(self, data, label=None, weight=None, group=None,
         ----------
         data : string, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse, Sequence, list of Sequences or list of numpy arrays
             Data source of Dataset.
-            If string or pathlib.Path, it represents the path to txt file.
+            If string or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM) or a LightGBM Dataset binary file.
         label : list, numpy 1-D array, pandas Series / one-column DataFrame or None, optional (default=None)
             Label of the data.
         weight : list, numpy 1-D array, pandas Series or None, optional (default=None)
@@ -3405,7 +3405,7 @@ def predict(self, data, start_iteration=0, num_iteration=None,
         ----------
         data : string, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
             Data source for prediction.
-            If string or pathlib.Path, it represents the path to txt file.
+            If string or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         start_iteration : int, optional (default=0)
             Start index of the iteration to predict.
             If <= 0, starts from the first iteration.
@@ -3460,7 +3460,7 @@ def refit(self, data, label, decay_rate=0.9, **kwargs):
         ----------
         data : string, pathlib.Path, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
             Data source for refit.
-            If string or pathlib.Path, it represents the path to txt file.
+            If string or pathlib.Path, it represents the path to a text file (CSV, TSV, or LibSVM).
         label : list, numpy 1-D array or pandas Series / one-column DataFrame
             Label for refit.
         decay_rate : float, optional (default=0.9)

src/io/parser.cpp

@@ -236,7 +236,7 @@ Parser* Parser::CreateParser(const char* filename, bool header, int num_features
   int num_col = 0;
   DataType type = GetDataType(filename, header, lines, &num_col);
   if (type == DataType::INVALID) {
-    Log::Fatal("Unknown format of training data.");
+    Log::Fatal("Unknown format of training data. Only CSV, TSV, and LibSVM (zero-based) formatted text files are supported.");
   }
   std::unique_ptr<Parser> ret;
   int output_label_index = -1;