Merge branch 'master' into r/example-warnings

.github/CODEOWNERS

@@ -33,8 +33,8 @@ R-package/    @Laurae2 @jameslamb
 python-package/    @StrikerRUS @chivee @wxchan @henry0312 @shiyu1994 @jameslamb
 
 # Dask integration
-python-package/lightgbm/dask.py    @jameslamb
-tests/python_package_test/test_dask.py    @jameslamb
+python-package/lightgbm/dask.py    @jameslamb @jmoralez
+tests/python_package_test/test_dask.py    @jameslamb @jmoralez
 
 # helpers
 helpers/    @StrikerRUS @guolinke

R-package/tests/testthat.R

@@ -5,4 +5,5 @@ test_check(
     package = "lightgbm"
     , stop_on_failure = TRUE
     , stop_on_warning = FALSE
+    , reporter = testthat::SummaryReporter$new()
 )

README.md

@@ -91,7 +91,7 @@ Shapash (model visualization and interpretation): https://github.com/MAIF/shapas
 
 dtreeviz (decision tree visualization and model interpretation): https://github.com/parrt/dtreeviz
 
-MMLSpark (LightGBM on Spark): https://github.com/Azure/mmlspark
+SynapseML (LightGBM on Spark): https://github.com/microsoft/SynapseML
 
 Kubeflow Fairing (LightGBM on Kubernetes): https://github.com/kubeflow/fairing
 

docs/FAQ.rst

@@ -26,6 +26,7 @@ You may also ping a member of the core team according to the relevant area of ex
 -  `@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 / Dask-package)
+-  `@jmoralez <https://github.com/jmoralez>`__ **José Morales** (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

@@ -47,13 +47,13 @@ This section describes how to run distributed LightGBM training in various progr
 Apache Spark
 ^^^^^^^^^^^^
 
-Apache Spark users can use `MMLSpark`_ for machine learning workflows with LightGBM. This project is not maintained by LightGBM's maintainers.
+Apache Spark users can use `SynapseML`_ for machine learning workflows with LightGBM. This project is not maintained by LightGBM's maintainers.
 
-See `this MMLSpark example`_ and `the MMLSpark documentation`_ for additional information on using LightGBM on Spark.
+See `this SynapseML example`_ and `the SynapseML documentation`_ for additional information on using LightGBM on Spark.
 
 .. note::
 
-  ``MMLSpark`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/Azure/mmlspark/issues.
+  ``SynapseML`` is not maintained by LightGBM's maintainers. Bug reports or feature requests should be directed to https://github.com/microsoft/SynapseML/issues.
 
 Dask
 ^^^^
@@ -453,9 +453,9 @@ Example
 
 .. _Dask: https://docs.dask.org/en/latest/
 
-.. _MMLSpark: https://aka.ms/spark
+.. _SynapseML: https://aka.ms/spark
 
-.. _this MMLSpark example: https://github.com/Azure/mmlspark/blob/master/notebooks/LightGBM%20-%20Overview.ipynb
+.. _this SynapseML example: https://github.com/microsoft/SynapseML/blob/master/notebooks/LightGBM%20-%20Overview.ipynb
 
 .. _the Dask Array documentation: https://docs.dask.org/en/latest/array.html
 
@@ -467,7 +467,7 @@ Example
 
 .. _the metrics functions from dask-ml: https://ml.dask.org/modules/api.html#dask-ml-metrics-metrics
 
-.. _the MMLSpark Documentation: https://github.com/Azure/mmlspark/blob/master/docs/lightgbm.md
+.. _the SynapseML Documentation: https://github.com/microsoft/SynapseML/blob/master/docs/lightgbm.md
 
 .. _these Dask examples: https://github.com/microsoft/lightgbm/tree/master/examples/python-guide/dask
 

python-package/lightgbm/dask.py

@@ -443,8 +443,8 @@ def _train(
         Initial model score for each validation set in eval_set.
     eval_group : list of Dask Arrays, Dask Series or None, optional (default=None)
         Group/query for each validation set in eval_set.
-    eval_metric : string, callable, list or None, optional (default=None)
-        If string, it should be a built-in evaluation metric to use.
+    eval_metric : str, callable, list or None, optional (default=None)
+        If str, it should be a built-in evaluation metric to use.
         If callable, it should be a custom evaluation metric, see note below for more details.
         If list, it can be a list of built-in metrics, a list of custom evaluation metrics, or a mix of both.
         In either case, the ``metric`` from the Dask model parameters (or inferred from the objective) will be evaluated and used as well.