* Add a new API function for predicting on `DMatrix`. This function aligns
with rest of the `XGBoosterPredictFrom*` functions on semantic of function
arguments.
* Purge `ntree_limit` from libxgboost, use iteration instead.
* [dask] Use `inplace_predict` by default for dask sklearn models.
* [dask] Run prediction shape inference on worker instead of client.
The breaking change is in the Python sklearn `apply` function, I made it to be
consistent with other prediction functions where `best_iteration` is used by
default.
* Accept array interface for csr and array.
* Accept an optional proxy dmatrix for metainfo.
This constructs an explicit `_ProxyDMatrix` type in Python.
* Remove unused doc.
* Add strict output.
This PR changes predict and inplace_predict to accept a Future of model, to avoid sending models to workers repeatably.
* Document is updated to reflect functionality additions in recent changes.
* [dask] Use a 1 line sample to infer output shape.
This is for inferring shape with direct prediction (without DaskDMatrix).
There are a few things that requires known output shape before carrying out
actual prediction, including dask meta data, output dataframe columns.
* Infer output shape based on local prediction.
* Remove set param in predict function as it's not thread safe nor necessary as
we now let dask to decide the parallelism.
* Simplify prediction on `DaskDMatrix`.
The old (before fix) best_ntree_limit ignores the num_class parameters, which is incorrect. In before we workarounded it in c++ layer to avoid possible breaking changes on other language bindings. But the Python interpretation stayed incorrect. The PR fixed that in Python to consider num_class, but didn't remove the old workaround, so tree calculation in predictor is incorrect, see PredictBatch in CPUPredictor.
* Initial support for distributed LTR using dask.
* Support `qid` in libxgboost.
* Refactor `predict` and `n_features_in_`, `best_[score/iteration/ntree_limit]`
to avoid duplicated code.
* Define `DaskXGBRanker`.
The dask ranker doesn't support group structure, instead it uses query id and
convert to group ptr internally.
* For sklearn:
- Handles user defined objective function.
- Handles `softmax`.
* For dask:
- Use the implementation from sklearn, the previous implementation doesn't perform any extra handling.
* Implement early stopping with training continuation.
* Add new C API for obtaining boosted rounds.
* Fix off by 1 in `save_best`.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
* Enable loading model from <1.0.0 trained with objective='binary:logitraw'
* Add binary:logitraw in model compatibility testing suite
* Feedback from @trivialfis: Override ProbToMargin() for LogisticRaw
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Add management functions for global configuration: XGBSetGlobalConfig(), XGBGetGlobalConfig().
* Add Python interface: set_config(), get_config(), and config_context().
* Add unit tests for Python
* Add R interface: xgb.set.config(), xgb.get.config()
* Add unit tests for R
Co-authored-by: Jiaming Yuan <jm.yuan@outlook.com>
* Do not derive from unittest.TestCase (not needed for pytest)
* assertRaises -> pytest.raises
* Simplify test_empty_dmatrix with test parametrization
* setUpClass -> setup_class, tearDownClass -> teardown_class
* Don't import unittest; import pytest
* Use plain assert
* Use parametrized tests in more places
* Fix test_gpu_with_sklearn.py
* Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls
* Fix test_eta_decay_gpu_hist
* Add parametrized tests for monotone constraints
* Fix test names
* Remove test parametrization
* Revise test_slice to be not flaky
Deprecate positional arguments in following functions:
- `__init__` for all classes in sklearn module.
- `fit` method for all classes in sklearn module.
- dask interface.
- `set_info` for `DMatrix` class.
Refactor the evaluation matrices handling.
This PR is meant the end the confusion around best_ntree_limit and unify model slicing. We have multi-class and random forests, asking users to understand how to set ntree_limit is difficult and error prone.
* Implement the save_best option in early stopping.
Co-authored-by: Philip Hyunsu Cho <chohyu01@cs.washington.edu>
