GitLab

* allow usage and compilation of 32-bit library

* added docs

* reworked pandas dtypes mapper

* added tests

* added sparsity support for new version of pandas

* fixed tests for old pandas

* check pd.Series for bad dtypes as well

* enhanced tests

* fixed pylint

Otherwise, it would print `basic.py:762: UserWarning: categorical_feature in param dict is overridden.`. Because when updating the params for a validation test, the updated params for the train test was used which contains `'categorical_column'`.

* 📝 FAQ overhaul for Issue #2268

Reformat "Contents" to use the `.. contents::` directive
Reword "Critical" into "Critical Issues"
Reformat "Critical" section to define "critical issues"
Reformat FAQ sections to follow a new format
Reformat FAQ sections so individual questions have links

All sections now follow a new format (below).
A "frequently asked question" may also include a possible
cause and a solution (if the two are not obvious from
the context):

```rst
Section Title
=============

.. contents::
  :local:
  :backlinks: none

1. Question 1
-------------

**Possible Cause**: This is likely due to...

**Solution**: Fix with...
```

* ✏️ Correcting typos and links

Add period to `2. Error messages: ....`
Fix links to FAQ in Installation-Guide.rst

* ✏️ Removing FAQ link and correcting `python-package` README

Drop general FAQ link in `Installation-Guide.rst`
Add FAQ question links to `python-package/README.rst`

* add weight in tree model output

* fix bug

* updated Python plotting part to handle weights

* 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 "download" badge

* Update README.rst

* Update README.md

* replaced issue badge with releases downloads badge

* Update README.md

* fixed class_weight

* fixed lint

* added test

* hotfix

* Update sklearn.py

* Update parameter_generator.py

Replace "Traninig" with "Training"

[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

* PR #1879

* Update docs with parameter_generator.py

* Update wrapper doc for sklearn

* added ability to pass first_metric_only in params

* simplified tests

* fixed test

* fixed punctuation

* fix travis badge

* updated GitHub Microsoft URL

* added plot_split_value_histogram function

* updated init module

* added plot split value histogram example

* added plot_split_value_histogram to notebook

* added test

* fixed pylint

* updated API docs

* fixed grammar

* set y ticks to int value in more sufficient way

* updated installation guide

* updated Python installation guide

* added note about opencl path to Windows section

* added space before path in message

* minor correction for option description in Python installation guide

* set platform via A option

* style hotfix

* updated R installation script

* updated Python installation script

* updated CI test script

* provide VS version-ingependent link for redistributables download

* added link to VS 2019 redistributables

* added VS 2019 match for Boost binaries

* disable default pandas cat features if cat features were explicitly provided

* added assertion for cat features

* ignore pandas ordered categorical columns by default

* fix tests

* fix tests

* added comments

* Update doc string for pred_contrib

See comments at the end of #1969

* Update basic.py

* Update basic.py

* update doc strings

* update equals sign in doc string

* strip whitespace and gen rst

* strip whitespace

* [python] displaying train loss during training with lgb.cv

* modifying only display running type when disp_train_loss==True

* Add test for display train loss

* del .idea files

* Rename disp_train_loss to show_train_loss and revise comment.

* Change aug name show_train_loss -> eval_train_metric , and add a test item.

* Modifying comment of eval_train_metric.

* updated HDFS guide

* updated guide

* no info about Clang

* pass paths in quotes

* Update README.rst

* fixed Python intro

* fixed typos

* scikit-learn added support of https

* Use first_metric_only flag for early_stopping function.

In order to apply early stopping with only first metric, applying first_metric_only flag for early_stopping function.

* upcate comment

* Revert "upcate comment"

This reverts commit 1e75a1a415cc16cfbe795181e148ebfe91469be4.

* added test

* fixed docstring

* cut comment and save one line

* document new feature

* disabled split value histogram for categorical features

* updated test for cat. feature

* updated docs

* added get_split_value_histogram method

* added param for ordinary return value

* Initial attempt to implement appending features in-memory to another data set

The intent is for this to enable munging files together easily, without needing to round-trip via numpy or write multiple copies to disk.
In turn, that enables working more efficiently with data sets that were written separately.

* Implement Dataset.dump_text, and fix small bug in appending of group bin boundaries.

Dumping to text enables us to compare results, without having to worry about issues like features being reordered.

* Add basic tests for validation logic for add_features_from.

* Remove various internal mapping items from dataset text dumps

These are too sensitive to the exact feature order chosen, which is not visible to the user.
Including them in tests appears unnecessary, as the data dumping code should provide enough coverage.

* Add test that add_features_from results in identical data sets according to dump_text.

* Add test that booster behaviour after using add_features_from matches that of training on the full data

This checks:
- That training after add_features_from works at all
- That add_features_from does not cause training to misbehave

* Expose feature_penalty and monotone_types/constraints via get_field

These getters allow us to check that add_features_from does the right thing with these vectors.

* Add tests that add_features correctly handles feature_penalty and monotone_constraints.

* Ensure add_features_from properly frees the added dataset and add unit test for this

Since add_features_from moves the feature group pointers from the added dataset to the dataset being added to, the added dataset is invalid after the call.
We must ensure we do not try and access this handle.

* Remove some obsolete TODOs

* Tidy up DumpTextFile by using a single iterator for each feature

This iterators were also passed around as raw pointers without being freed, which is now fixed.

* Factor out offsetting logic in AddFeaturesFrom

* Remove obsolete TODO

* Remove another TODO

This one is debatable, test code can be a bit messy and duplicate-heavy, factoring it out tends to end badly.
Leaving this for now, will revisit if adding more tests later on becomes a mess.

* Add documentation for newly-added methods.

* Fix whitespace issues identified by pylint.

* Fix a few more whitespace issues.

* Fix doc comments

* Implement deep copying for feature groups.

* Replace awkward std::move usage by emplace_back, and reduce vector size to num_features rather than num_total_features.

* Copy feature groups in addFeaturesFrom, rather than moving them.

* Fix bugs in FeatureGroup copy constructor and ensure source dataset remains usable

* Add reserve to PushVector and PushOffset

* Move definition of Clone into class body

* Fix PR review issues

* Fix for loop increment style.

* Fix test failure

* Some more docstring fixes.

* Remove blank line

* convert datatable to numpy directly

* fix according to comments

* updated more docstrings

* simplified isinstance check

* Update compat.py