[dask][docs] initial setup for Dask docs

README.md

@@ -87,8 +87,6 @@ ML.NET (.NET/C#-package): https://github.com/dotnet/machinelearning
 
 LightGBM.NET (.NET/C#-package): https://github.com/rca22/LightGBM.Net
 
-Dask-LightGBM (distributed and parallel Python-package): https://github.com/dask/dask-lightgbm
-
 Ruby gem: https://github.com/ankane/lightgbm
 
 LightGBM4j (Java high-level binding): https://github.com/metarank/lightgbm4j

docs/FAQ.rst

@@ -24,7 +24,7 @@ You may also ping a member of the core team according to the relevant area of ex
 -  `@chivee <https://github.com/chivee>`__ **Qiwei Ye** (C++ code / Python-package)
 -  `@btrotta <https://github.com/btrotta>`__ **Belinda Trotta** (C++ code)
 -  `@Laurae2 <https://github.com/Laurae2>`__ **Damien Soukhavong** (R-package)
--  `@jameslamb <https://github.com/jameslamb>`__ **James Lamb** (R-package)
+-  `@jameslamb <https://github.com/jameslamb>`__ **James Lamb** (R-package / Dask-package)
 -  `@wxchan <https://github.com/wxchan>`__ **Wenxuan Chen** (Python-package)
 -  `@henry0312 <https://github.com/henry0312>`__ **Tsukasa Omoto** (Python-package)
 -  `@StrikerRUS <https://github.com/StrikerRUS>`__ **Nikita Titov** (Python-package)

docs/Parallel-Learning-Guide.rst

@@ -7,7 +7,7 @@ Follow the `Quick Start <./Quick-Start.rst>`__ to know how to use LightGBM first
 
 **List of external libraries in which LightGBM can be used in a distributed fashion**
 
--  `Dask-LightGBM`_ allows to create ML workflow on Dask distributed data structures.
+-  `Dask API of LightGBM <./Python-API.rst#dask-api>`__ (formerly it was a separate package) allows to create ML workflow on Dask distributed data structures.
 
 -  `MMLSpark`_ integrates LightGBM into Apache Spark ecosystem.
    `The following example`_ demonstrates how easy it's possible to utilize the great power of Spark.
@@ -134,8 +134,6 @@ Example
 
 -  `A simple parallel example`_
 
-.. _Dask-LightGBM: https://github.com/dask/dask-lightgbm
-
 .. _MMLSpark: https://aka.ms/spark
 
 .. _The following example: https://github.com/Azure/mmlspark/blob/master/notebooks/samples/LightGBM%20-%20Quantile%20Regression%20for%20Drug%20Discovery.ipynb

docs/Python-API.rst

@@ -33,6 +33,16 @@ Scikit-learn API
     LGBMRegressor
     LGBMRanker
 
+Dask API
+--------
+
+.. autosummary::
+    :toctree: pythonapi/
+
+    DaskLGBMClassifier
+    DaskLGBMRegressor
+    DaskLGBMRanker
+
 Callbacks
 ---------
 

docs/conf.py

@@ -39,7 +39,7 @@
 
 # -- mock out modules
 MOCK_MODULES = ['numpy', 'scipy', 'scipy.sparse',
-                'sklearn', 'matplotlib', 'pandas', 'graphviz']
+                'sklearn', 'matplotlib', 'pandas', 'graphviz', 'dask', 'dask.distributed']
 for mod_name in MOCK_MODULES:
     sys.modules[mod_name] = Mock()
 

python-package/README.rst

@@ -183,12 +183,22 @@ Run ``python setup.py install --bit32``, if you want to use 32-bit version. All
 
 If you get any errors during installation or due to any other reasons, you may want to build dynamic library from sources by any method you prefer (see `Installation Guide <https://github.com/microsoft/LightGBM/blob/master/docs/Installation-Guide.rst>`__) and then just run ``python setup.py install --precompile``.
 
-
 Build Wheel File
 ****************
 
 You can use ``python setup.py bdist_wheel`` instead of ``python setup.py install`` to build wheel file and use it for installation later. This might be useful for systems with restricted or completely without network access.
 
+Install Dask-package
+''''''''''''''''''''
+
+To install all additional dependencies required for Dask-package, you can append ``[dask]`` to LightGBM package name:
+
+.. code:: sh
+
+    pip install lightgbm[dask]
+
+Or replace ``python setup.py install`` with ``pip install -e .[dask]`` if you are installing the package from source files.
+
 Troubleshooting
 ---------------
 

python-package/lightgbm/__init__.py

@@ -19,6 +19,10 @@
                            plot_tree, create_tree_digraph)
 except ImportError:
     pass
+try:
+    from .dask import DaskLGBMRegressor, DaskLGBMClassifier, DaskLGBMRanker
+except ImportError:
+    pass
 
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
@@ -31,5 +35,6 @@
            'register_logger',
            'train', 'cv',
            'LGBMModel', 'LGBMRegressor', 'LGBMClassifier', 'LGBMRanker',
+           'DaskLGBMRegressor', 'DaskLGBMClassifier', 'DaskLGBMRanker',
            'print_evaluation', 'record_evaluation', 'reset_parameter', 'early_stopping',
            'plot_importance', 'plot_split_value_histogram', 'plot_metric', 'plot_tree', 'create_tree_digraph']

python-package/lightgbm/compat.py

@@ -105,3 +105,12 @@ def _check_sample_weight(sample_weight, X, dtype=None):
     _LGBMAssertAllFinite = None
     _LGBMCheckClassificationTargets = None
     _LGBMComputeSampleWeight = None
+
+"""dask"""
+try:
+    from dask import array
+    from dask import dataframe
+    from dask.distributed import Client
+    DASK_INSTALLED = True
+except ImportError:
+    DASK_INSTALLED = False

python-package/lightgbm/dask.py

@@ -21,7 +21,8 @@
 from dask import delayed
 from dask.distributed import Client, default_client, get_worker, wait
 
-from .basic import _ConfigAliases, _LIB, _log_warning, _safe_call
+from .basic import _ConfigAliases, _LIB, _log_warning, _safe_call, LightGBMError
+from .compat import DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED
 from .sklearn import LGBMClassifier, LGBMRegressor, LGBMRanker
 
 
@@ -393,6 +394,9 @@ def _predict(model, data, raw_score=False, pred_proba=False, pred_leaf=False, pr
 
 
 class _LGBMModel:
+    def __init__(self):
+        if not all((DASK_INSTALLED, PANDAS_INSTALLED, SKLEARN_INSTALLED)):
+            raise LightGBMError('dask, pandas and scikit-learn are required for lightgbm.dask')
 
     def _fit(self, model_factory, X, y=None, sample_weight=None, group=None, client=None, **kwargs):
         """Docstring is inherited from the LGBMModel."""
@@ -431,7 +435,7 @@ def _copy_extra_params(source, dest):
             setattr(dest, name, attributes[name])
 
 
-class DaskLGBMClassifier(_LGBMModel, LGBMClassifier):
+class DaskLGBMClassifier(LGBMClassifier, _LGBMModel):
     """Distributed version of lightgbm.LGBMClassifier."""
 
     def fit(self, X, y=None, sample_weight=None, client=None, **kwargs):
@@ -479,7 +483,7 @@ def to_local(self):
         return self._to_local(LGBMClassifier)
 
 
-class DaskLGBMRegressor(_LGBMModel, LGBMRegressor):
+class DaskLGBMRegressor(LGBMRegressor, _LGBMModel):
     """Docstring is inherited from the lightgbm.LGBMRegressor."""
 
     def fit(self, X, y=None, sample_weight=None, client=None, **kwargs):
@@ -515,7 +519,7 @@ def to_local(self):
         return self._to_local(LGBMRegressor)
 
 
-class DaskLGBMRanker(_LGBMModel, LGBMRanker):
+class DaskLGBMRanker(LGBMRanker, _LGBMModel):
     """Docstring is inherited from the lightgbm.LGBMRanker."""
 
     def fit(self, X, y=None, sample_weight=None, init_score=None, group=None, client=None, **kwargs):

python-package/lightgbm/engine.py

@@ -334,15 +334,15 @@ def _make_n_folds(full_data, folds, nfold, params, seed, fpreproc=None, stratifi
                                              "xe_ndcg", "xe_ndcg_mart", "xendcg_mart"}
                for obj_alias in _ConfigAliases.get("objective")):
             if not SKLEARN_INSTALLED:
-                raise LightGBMError('Scikit-learn is required for ranking cv.')
+                raise LightGBMError('scikit-learn is required for ranking cv')
             # ranking task, split according to groups
             group_info = np.array(full_data.get_group(), dtype=np.int32, copy=False)
             flatted_group = np.repeat(range(len(group_info)), repeats=group_info)
             group_kfold = _LGBMGroupKFold(n_splits=nfold)
             folds = group_kfold.split(X=np.zeros(num_data), groups=flatted_group)
         elif stratified:
             if not SKLEARN_INSTALLED:
-                raise LightGBMError('Scikit-learn is required for stratified cv.')
+                raise LightGBMError('scikit-learn is required for stratified cv')
             skf = _LGBMStratifiedKFold(n_splits=nfold, shuffle=shuffle, random_state=seed)
             folds = skf.split(X=np.zeros(num_data), y=full_data.get_label())
         else:

python-package/lightgbm/sklearn.py

@@ -289,7 +289,7 @@ def __init__(self, boosting_type='gbdt', num_leaves=31, max_depth=-1,
         and you should group grad and hess in this way as well.
         """
         if not SKLEARN_INSTALLED:
-            raise LightGBMError('Scikit-learn is required for this module')
+            raise LightGBMError('scikit-learn is required for lightgbm.sklearn')
 
         self.boosting_type = boosting_type
         self.objective = objective

python-package/setup.py

@@ -344,7 +344,7 @@ def run(self):
           extras_require={
               'dask': [
                   'dask[array]>=2.0.0',
-                  'dask[dataframe]>=2.0.0'
+                  'dask[dataframe]>=2.0.0',
                   'dask[distributed]>=2.0.0',
                   'pandas',
               ],