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Support optimal partitioning for GPU hist. (#7652)

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* Implement `MaxCategory` in quantile.
* Implement partition-based split for GPU evaluation.  Currently, it's based on the existing evaluation function.
* Extract an evaluator from GPU Hist to store the needed states.
* Added some CUDA stream/event utilities.
* Update document with references.
* Fixed a bug in approx evaluator where the number of data points is less than the number of categories.
This commit is contained in:
Jiaming Yuan
2022-02-15 03:03:12 +08:00
committed by GitHub
parent 2369d55e9a
commit 0d0abe1845
26 changed files with 1088 additions and 528 deletions
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17
demo/guide-python/categorical.py
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@@ -3,15 +3,15 @@ Getting started with categorical data
=====================================
Experimental support for categorical data. After 1.5 XGBoost `gpu_hist` tree method has
experimental support for one-hot encoding based tree split, and in 1.6 `approx` supported
experimental support for one-hot encoding based tree split, and in 1.6 `approx` support
was added.
In before, users need to run an encoder themselves before passing the data into XGBoost,
which creates a sparse matrix and potentially increase memory usage. This demo showcases
the experimental categorical data support, more advanced features are planned.
Also, see :doc:`the tutorial </tutorials/categorical>` for using XGBoost with categorical data
which creates a sparse matrix and potentially increase memory usage. This demo
showcases the experimental categorical data support, more advanced features are planned.
Also, see :doc:`the tutorial </tutorials/categorical>` for using XGBoost with
categorical data.
.. versionadded:: 1.5.0
@@ -55,8 +55,11 @@ def main() -> None:
# For scikit-learn interface, the input data must be pandas DataFrame or cudf
# DataFrame with categorical features
X, y = make_categorical(100, 10, 4, False)
# Specify `enable_categorical` to True.
reg = xgb.XGBRegressor(tree_method="gpu_hist", enable_categorical=True)
# Specify `enable_categorical` to True, also we use onehot encoding based split
# here for demonstration. For details see the document of `max_cat_to_onehot`.
reg = xgb.XGBRegressor(
tree_method="gpu_hist", enable_categorical=True, max_cat_to_onehot=5
)
reg.fit(X, y, eval_set=[(X, y)])
# Pass in already encoded data