* Don't set_params at the end of set_state.
* Also fix another issue found in dask prediction.
* Add note about prediction.
Don't support other prediction modes at the moment.
* Initial support for cudf integration.
* Add two C APIs for consuming data and metainfo.
* Add CopyFrom for SimpleCSRSource as a generic function to consume the data.
* Add FromDeviceColumnar for consuming device data.
* Add new MetaInfo::SetInfo for consuming label, weight etc.
* _maybe_pandas_xxx should return their arguments unchanged if no pandas installed
* Tests should not assume pandas is installed
* Mark tests which require pandas as such
* Implement tree model dump with a code generator.
* Split up generators.
* Implement graphviz generator.
* Use pattern matching.
* [Breaking] Return a Source in `to_graphviz` instead of Digraph in Python package.
Co-Authored-By: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* adding support for matrix slicing with query ID for cross-validation
* hail mary test of unrar installation for windows tests
* trying to modify tests to run in Github CI
* Remove dependency on wget and unrar
* Save error log from R test
* Relax assertion in test_training
* Use int instead of bool in C function interface
* Revise R interface
* Add XGDMatrixSliceDMatrixEx and keep old XGDMatrixSliceDMatrix for API compatibility
* Ensure lists cannot be passed into DMatrix
The documentation does not include lists as an allowed type for the data inputted into DMatrix. Despite this, a list can be passed in without an error. This change would prevent a list form being passed in directly.
* Add scikit-learn tests
Goal is to pass scikit-learn's check_estimator() for XGBClassifier,
XGBRegressor, and XGBRanker. It is actually not possible to do so
entirely, since check_estimator() assumes that NaN is disallowed,
but XGBoost allows for NaN as missing values. However, it is always
good ideas to add some checks inspired by check_estimator().
* Fix lint
* Fix lint
* Add XGBRanker to Python API doc
* Show inherited members of XGBRegressor in API doc, since XGBRegressor uses default methods from XGBModel
* Add table of contents to Python API doc
* Skip JVM doc download if not available
* Show inherited members for XGBRegressor and XGBRanker
* Expose XGBRanker to Python XGBoost module directory
* Add docstring to XGBRegressor.predict() and XGBRanker.predict()
* Fix rendering errors in Python docstrings
* Fix lint
* Revert "Fix #3485, #3540: Don't use dropout for predicting test sets (#3556)"
This reverts commit 44811f233071c5805d70c287abd22b155b732727.
* Document behavior of predict() for DART booster
* Add notice to parameter.rst
* Fix bug of using list(x) function when x is string
list('abcdcba') = ['a', 'b', 'c', 'd', 'c', 'b', 'a']
* Allow feature_names/feature_types to be of any type
If feature_names/feature_types is iterable, e.g. tuple, list, then convert the value to list, except for string; otherwise construct a list with a single value
* Delete excess whitespace
* Fix whitespace to pass lint
Add `'total_gain'` and `'total_cover'` as possible `importance_type`
arguments to `Booster.get_score` in the Python package.
`get_score` already accepts a `'gain'` argument, which returns each
feature's average gain over all of its splits. `'total_gain'` does the
same, but returns a total rather than an average. This seems more
intuitively meaningful, and also matches the behavior of the R package's
`xgb.importance` function.
I also added an analogous `'total_cover'` command for consistency.
This should resolve#3484.
* Improved library loading a bit
* Fixed indentation.
* Fixes according to the discussion
* Moved the comment to a separate line.
* specified exception type
* Support CSV file in DMatrix
We'd just need to expose the CSV parser in dmlc-core to the Python wrapper
* Revert extra code; document existing CSV support
CSV support is already there but undocumented
* Add notice about categorical features
