GitLab

* 🎨 `sphinx.ext.autosummary` for generating Python-API summaries

Add `docs/.gitignore` to not track autosummary stubs
Add `sphinx.ext.autosummary` in `docs/conf.py`
  Add 'members' and 'inherited-members' as default parameters
  Add 'autosummary = True' for setting output with `:toctree:`
Add `.. autosummary::` tags to replace `.. autoclass::`

Previously the `Python-API.rst` dumped all of the Python API onto
a single page.

This replaces the Python-API documentation with an index listing
all modules, and paginates all functions and classes onto
separate pages.

* ✏️ Corrections following feedback

Drop `docs/.gitignore` to use the general `.gitignore`
Add `show-inheritance` to `autodoc_default_flags` in `docs/conf.py`
Fix `both` to `class` in `autoclass_content` in `docs/conf.py`

* ✏️ Replacing deprecated Sphinx parameter

Fix deprecated `autodoc_default_flags` to `autodoc_default_options`

* ✏️ Adding `autodoc_default_flags` in to support early Sphinx versions

Add `autodoc_default_flags` with parameters from
  `autodoc_default_options`

* fix metric alias

* fix format

* updated docs

* simplify alias in objective function

* move the alias parsing to config.cpp

* updated docs

* fix multi-class aliases

* updated regression aliases in docs

* fixed trailing space

* removed temp fix for conda

* fixed conda activation before any conda command

* add weight in tree model output

* fix bug

* updated Python plotting part to handle weights

* throw error when meet non ascii

* check ascii for config strings.

* added CODEOWNERS (fixes #2194)

* revisions to CODEOWNERS

* more general wildcard in CODEOWNERS

* fix init_model with subset

* Update basic.py

* added test

* fix predictor naming issue

* Update basic.py

* fix bug

* fix pylint

* fix comments

* Update basic.py

* Update basic.py

* updated test

* fixed bug

* fixed lint

* fix warning

* add get_data before initial prediction

* refine the warning in get_data

* refine warning

* Update basic.py

* add test

* fix a index bug

* Add parameter max_bin_by_feature.

* Fix minor bug.

* Fix minor bug.

* Fix calculation of header size for writing binary file.

* Fix style issues.

* Fix python style issue.

* Fix test and python style issue.

* Update .appveyor.yml

* Update .vsts-ci.yml

* Update setup.sh

* Update test.py

* Update test_consistency.py

* Update test_basic.py

* Update test_sklearn.py

* Update test_sklearn.py

* Update test_engine.py

* more replacements

* add balanced bagging

* refine code

* fix format

* clarify usage only for binary application

* add "download" badge

* Update README.rst

* Update README.md

* replaced issue badge with releases downloads badge

* Update README.md

* mojave install r openmp fix

* if not gcc/g++ env

* add comment

* fix

* consider max_depth for histogram_pool_size

* Update serial_tree_learner.cpp

* Update config.cpp

* Update serial_tree_learner.cpp

* fixed class_weight

* fixed lint

* added test

* hotfix

* Update sklearn.py

* Update parameter_generator.py

Replace "Traninig" with "Training"

* fix unsorted eval_at

* Update config.cpp

* fixed and enhanced format of C API

* fixed description of dataset creation functions

* Callback for NA handling

* lgb.Booster default score => NA

* lgb.cv default best score => NA

* Fix back callback

* lgb.train with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

* lgb.cv with booster check at the end

manual tests done: 
* With early stopping + with validation set
* With early stopping + without validation set
* Without early stopping + with validation set
* Without early stopping + without validation set

And with multiple metrics / validation sets.

[python] fixed picklability of sklearn models with custom obj and updated docstings for custom obj (#2191)

* refactored joblib test

* fixed picklability of sklearn models with custom obj and updated docstings for custom obj

* pickled model should be able to predict without refitting

* Implement top-k multiclass error metric. Add new parameter top_k_threshold.

* Add test for multiclass metrics

* Make test less sensitive to avoid floating-point issues.

* Change tabs to spaces.

* Fix problem with test in Python 2. Refactor to use np.testing. Decrease number of training rounds so loss is larger and easier to compare.

* Move multiclass tests into test_engine.py

* Change parameter name from top_k_threshold to multi_error_top_k.

* Fix top-k error metric to handle case where scores are equal. Update tests and docs.

* Change name of top-k metric to multi_error@k.

* Change tabs to spaces.

* Fix formatting.

* Fix minor issues in docs.

* [ci] for autogenerating docs

* resolved comments

* resolved comments 2

* update to 36c89134

`num_round` is redundant here because it will be overrideen by `num_trees` in the `param` dictionary.