[doc] Some notes for external memory. (#5065)

doc/python/python_intro.rst

@@ -54,7 +54,7 @@ The data is stored in a :py:class:`DMatrix <xgboost.DMatrix>` object.
   .. note:: Categorical features not supported
 
     Note that XGBoost does not provide specialization for categorical features; if your data contains
-    categorical features, load it as a NumPy array first and then perform
+    categorical features, load it as a NumPy array first and then perform corresponding preprocessing steps like
     `one-hot encoding <http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html>`_.
 
   .. note:: Use Pandas to load CSV files with headers

doc/tutorials/external_memory.rst

@@ -4,24 +4,34 @@ Using XGBoost External Memory Version (beta)
 There is no big difference between using external memory version and in-memory version.
 The only difference is the filename format.
 
-The external memory version takes in the following filename format:
+The external memory version takes in the following `URI <https://en.wikipedia.org/wiki/Uniform_Resource_Identifier>`_ format:
 
 .. code-block:: none
 
   filename#cacheprefix
 
-The ``filename`` is the normal path to libsvm file you want to load in, and ``cacheprefix`` is a
-path to a cache file that XGBoost will use for external memory cache.
+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
+data in binary form.
 
 .. note:: External memory is also available with GPU algorithms (i.e. when ``tree_method`` is set to ``gpu_hist``)
 
-The following code was extracted from `demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_:
+To provide a simple example for illustration, extracting the code from
+`demo/guide-python/external_memory.py <https://github.com/dmlc/xgboost/blob/master/demo/guide-python/external_memory.py>`_. If
+you have a dataset stored in a file similar to ``agaricus.txt.train`` with libSVM format, the external memory support can be enabled by:
+
+.. code-block:: python
+
+  dtrain = DMatrix('../data/agaricus.txt.train#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 a internal binary format.  For
+more notes about text input formats, see :doc:`/tutorials/input_format`.
 
 .. code-block:: python
 
   dtrain = xgb.DMatrix('../data/agaricus.txt.train#dtrain.cache')
 
-You can find that there is additional ``#dtrain.cache`` following the libsvm file, this is the name of cache file.
 For CLI version, simply add the cache suffix, e.g. ``"../data/agaricus.txt.train#dtrain.cache"``.
 
 ****************
@@ -47,7 +57,7 @@ so that you can directly use ``dtrain.cache`` to cache to current folder.
 **********
 Usage Note
 **********
-* This is a experimental version
+* This is an experimental version
 * Currently only importing from libsvm format is supported
 * OSX is not tested.
 

doc/tutorials/input_format.rst

@@ -5,10 +5,7 @@ Text Input Format of DMatrix
 ******************
 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.)
-
-.. note::
-   * XGBoost does **not** understand file extensions nor try to guess the file format.  Instead it employs uri format for specifying input file type.  For example if you provide a `csv` file ``./data.train.csv`` as input, XGBoost will 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` 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``.  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: