GitLab

* 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.

* 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'`.

* 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

* 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

* 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.

* 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

* always save the score of the first round in early stopping

fix #1971

* avoid using std::log on non-positive numbers

* remove unnecessary changes

* add tests

* Update test_sklearn.py

* enhanced tests

[tests][python] added tests for metrics' behavior and fixed case for multiclass task with custom objective (#1954)

* added metrics test for standard interface

* simplified code

* less trees

* less trees

* use dummy custom objective and metric

* added tests for multiclass metrics aliases

* fixed bug in case of custom obj and num_class > 1

* added metric test for sklearn wrapper

* add warnings for override parameters of Dataset

* fix pep8

* add feature_penalty

* refactor

* add R's code

* Update basic.py

* Update basic.py

* fix parameter bug

* Update lgb.Dataset.R

* fix a bug