[Breaking] Require format to be specified in input URI. (#9077)

Previously, we use `libsvm` as default when format is not specified. However, the dmlc
data parser is not particularly robust against errors, and the most common type of error
is undefined format.

Along with which, we will recommend users to use other data loader instead. We will
continue the maintenance of the parsers as it's currently used for many internal tests
including federated learning.

doc/tutorials/external_memory.rst

@@ -77,7 +77,7 @@ The external memory version takes in the following `URI <https://en.wikipedia.or
 
 .. code-block:: none
 
-  filename#cacheprefix
+  filename?format=libsvm#cacheprefix
 
 The ``filename`` is the normal path to LIBSVM format file you want to load in, and
 ``cacheprefix`` is a path to a cache file that XGBoost will use for caching preprocessed
@@ -97,13 +97,13 @@ you have a dataset stored in a file similar to ``agaricus.txt.train`` with LIBSV
 
 .. code-block:: python
 
-  dtrain = DMatrix('../data/agaricus.txt.train#dtrain.cache')
+  dtrain = DMatrix('../data/agaricus.txt.train?format=libsvm#dtrain.cache')
 
 XGBoost will first load ``agaricus.txt.train`` in, preprocess it, then write to a new file named
 ``dtrain.cache`` as an on disk cache for storing preprocessed data in an internal binary format.  For
 more notes about text input formats, see :doc:`/tutorials/input_format`.
 
-For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train#dtrain.cache"``.
+For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train?format=libsvm#dtrain.cache"``.
 
 
 **********************************

doc/tutorials/input_format.rst

@@ -2,10 +2,15 @@
 Text Input Format of DMatrix
 ############################
 
+.. _basic_input_format:
+
+Here we will briefly describe the text input formats for XGBoost. However, for users with access to a supported language environment like Python or R, it's recommended to use data parsers from that ecosystem instead. For instance, :py:func:`sklearn.datasets.load_svmlight_file`.
+
 ******************
 Basic Input Format
 ******************
-XGBoost currently supports two text formats for ingesting data: LIBSVM and CSV. The rest of this document will describe the LIBSVM format. (See `this Wikipedia article <https://en.wikipedia.org/wiki/Comma-separated_values>`_ for a description of the CSV format.).  Please be careful that, XGBoost does **not** understand file extensions, nor try to guess the file format, as there is no universal agreement upon file extension of LIBSVM or CSV.  Instead it employs `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ format for specifying the precise input file type.  For example if you provide a `csv` file ``./data.train.csv`` as input, XGBoost will blindly use the default LIBSVM parser to digest it and generate a parser error.  Instead, users need to provide an URI in the form of ``train.csv?format=csv``.  For external memory input, the URI should of a form similar to ``train.csv?format=csv#dtrain.cache``.  See :ref:`python_data_interface` and :doc:`/tutorials/external_memory` also.
+
+XGBoost currently supports two text formats for ingesting data: LIBSVM and CSV. The rest of this document will describe the LIBSVM format. (See `this Wikipedia article <https://en.wikipedia.org/wiki/Comma-separated_values>`_ for a description of the CSV format.).  Please be careful that, XGBoost does **not** understand file extensions, nor try to guess the file format, as there is no universal agreement upon file extension of LIBSVM or CSV.  Instead it employs `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ format for specifying the precise input file type.  For example if you provide a `csv` file ``./data.train.csv`` as input, XGBoost will blindly use the default LIBSVM parser to digest it and generate a parser error.  Instead, users need to provide an URI in the form of ``train.csv?format=csv`` or ``train.csv?format=libsvm``.  For external memory input, the URI should of a form similar to ``train.csv?format=csv#dtrain.cache``.  See :ref:`python_data_interface` and :doc:`/tutorials/external_memory` also.
 
 For training or predicting, XGBoost takes an instance file with the format as below: