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* Fix bug where small values of max_bin cause crash.

* Revert "Fix bug where small values of max_bin cause crash."

This reverts commit fe5c8e2547057c1fa5750bcddd359dd7708fab4b.

* Add auc-mu multiclass metric.

* Fix bug where scores are equal.

* Merge.

* Change name to auc_mu everywhere (instead of auc-mu).

* Fix comparison between signed and unsigned int.

* Change name to AUC-mu in docs and output messages.

* Improve test.

* Use prefix increment.

* Update R package.

* Fix style issues.

* Tidy up test code.

* Read all lines first then process.

* Allow passing AUC-mu weights directly as a list in parameters.

* Remove unused code, improve example and docs.

* LightGBM would hang on MPI run if only some nodes have a fatal error. The issue is that the destructor of Application calls MPI_Finalize(), which will cause the program to hand and prevent from exiting. So we move the network finalization out of the destructor and call MPI_Finalize() or MPI_Abort() based on whether there was an unhandled exception.

* Minor updates: Remove excess debug logging, whitespaces.

* Add comments for new functions.

* fix compile warning for HDFS version

* cast to size_t

* Fix build error when HDFS is enabled.

* Replace string with char array to eliminate cpplint.

* added <algorithm> to parser header

* reordered inputs per style guide

Fix #2564

* Update sparse_bin.hpp

* check sorted in c_api

* fix python package

* fix tests

* fix test

* std::is_sorted

* Update basic.py

* reduce the buffer when using high dimensional data in distributed mode.

* Update dataset_loader.cpp

* refix

* typo

* fix number of bin accumulation.

* avoid overflow

* fix warning

* efficient solution.

* Update dataset.h

* fix bin count output

* fix warning

* bug in dist number of feature check

* fix possible edge case

* Update dataset.cpp

* possible bug fix

* fix

* fixed miscellaneous typos in documentation

* fix typo introduced in typo-fixing PR

* check the shape for mat, csr and csc

* guess from csr

* support file checking

* better error msg

* grammar

* clean code

* code clean

* check range for CSR

* Update test_.py

* Update test_.py

* added tests

* Fix bug where small values of max_bin cause crash.

* Revert "Fix bug where small values of max_bin cause crash."

This reverts commit fe5c8e2547057c1fa5750bcddd359dd7708fab4b.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Change binning behavior to be same as PR #2342.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Change binning behavior to be same as PR #2342.

* Add functionality to force bin thresholds.

* Fix style issues.

* Minor style and doc fixes.

* Add functionality to force bin thresholds.

* Fix style issues.

* Minor style and doc fixes.

* Change binning behavior to be same as PR #2342.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Add functionality to force bin thresholds.

* Fix style issues.

* Use stable sort.

* Minor style and doc fixes.

* Change binning behavior to be same as PR #2342.

* Use different bin finding function for predefined bounds.

* Fix style issues.

* Minor refactoring, overload FindBinWithZeroAsOneBin.

* Fix style issues.

* Fix bug and add new test.

* Add warning when using categorical features with forced bins.

* Pass forced_upper_bounds by reference.

* Pass container types by const reference.

* Get categorical features using FeatureBinMapper.

* Fix bug for small max_bin.

* Move GetForcedBins to DatasetLoader.

* Find forced bins in dataset_loader.

* Minor fixes.

* Fix integer overflow https://github.com/microsoft/LightGBM/issues/2357

* Use 2 spaces not 4

* Move constant the config.h
Move check outside the max_depth check

* Move the max leaves check to config.h

* Remove unnecessary check

* Update feature_histogram.hpp

* Update feature_histogram.hpp

* refactoring

* fix style

* fix style

* Update cost_effective_gradient_boosting.hpp

* Update serial_tree_learner.cpp

* Update serial_tree_learner.h

* fix style

* update vc project

* Update cost_effective_gradient_boosting.hpp

* fix

* fix the bug

* refined order of includes

* hotfix

* fix many cpp lint errors

* indent

* fix bug

* fix more

* fix gpu

* more fixes

* at least 2 features are chosen in subcolumn

* Update serial_tree_learner.cpp

* rounding

* fix bug

* fix include

* update

* fix a bug

* Update config.h

* Update Parameters.rst

* Some basic changes to the plot of the trees to make them readable.

* Squeezed the information in the nodes.

* Added colouring when a dictionnary mapping the features to the constraints is passed.

* Fix spaces.

* Added data percentage as an option in the nodes.

* Squeezed the information in the leaves.

* Important information is now in bold.

* Added a legend for the color of monotone splits.

* Changed "split_gain" to "gain" and "internal_value" to "value".

* Sqeezed leaves a bit more.

* Changed description in the legend.

* Revert "Sqeezed leaves a bit more."

This reverts commit dd8bf14a3ba604b0dfae3b7bb1c64b6784d15e03.

* Increased the readability for the gain.

* Tidied up the legend.

* Added the data percentage in the leaves.

* Added the monotone constraints to the dumped model.

* Monotone constraints are now specified automatically when plotting trees.

* Raise an exception instead of the bug that was here before.

* Removed operators on the branches for a clearer design.

* Small cleaning of the code.

* Setting a monotone constraint on a categorical feature now returns an exception instead of doing nothing.

* Fix bug when monotone constraints are empty.

* Fix another bug when monotone constraints are empty.

* Variable name change.

* Added is / isn't on every edge of the trees.

* Fix test "tree_create_digraph".

* Add new test for plotting trees with monotone constraints.

* Typo.

* Update documentation of categorical features.

* Typo.

* Information in nodes more explicit.

* Used regular strings instead of raw strings.

* Small refactoring.

* Some cleaning.

* Added future statement.

* Changed output for consistency.

* Updated documentation.

* Added comments for colors.

* Changed text on edges for more clarity.

* Small refactoring.

* Modified text in leaves for consistency with nodes.

* Updated default values and documentaton for consistency.

* Replaced CHECK with Log::Fatal for user-friendliness.

* Updated tests.

* Typo.

* Simplify imports.

* Swapped count and weight to improve readibility of the leaves in the plotted trees.

* Thresholds in bold.

* Made information in nodes written in a specific order.

* Added information to clarify legend.

* Code cleaning.

* avoid nan and inf in weight/label/init_score

* use prefix increment

* add parameter

* implement

* fix bug

* fix bug

* fix according comment

* add test

* Update test_engine.py

* Update test_engine.py

* Update test_engine.py

* [mmlspark] Fix cached predictor causing bad values for predicted probabilities

* updated based on comments

* removed tabs

* change network retry delay strategy

* refine

* norm the lambda scores

* change default to false

* update doc

* typo

* Update Parameters.rst

* Update config.h

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update rank_objective.hpp

* Update Parameters.rst

* Update config.h

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_sklearn.py

* fix the bug in bin with small values

* Update bin.cpp

* Update test_engine.py

* Lambdas and hessians need to factor sigmoid_ into the computation. Additionally, the sigmoid function has an arbitrary factor of 2 in the exponent; it is not just non-standard but the gradients are not computed correctly anyway.

* Update unit test

* Also remove a heuristic that normalizes the gradient by the difference in scores.

* Also fix unit test after removing the heuristic