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[Doc] Document new objectives and metrics available on GPUs (#5909)

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Philip Hyunsu Cho 2020-07-21 02:10:59 -07:00 committed by GitHub
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80
doc/gpu/index.rst
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@ -97,39 +97,43 @@ Objective functions
===================
Most of the objective functions implemented in XGBoost can be run on GPU. Following table shows current support status.
+--------------------+-------------+
+----------------------+-------------+
| Objectives | GPU support |
+--------------------+-------------+
+----------------------+-------------+
| reg:squarederror | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| reg:squaredlogerror | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| reg:logistic | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| reg:pseudohubererror | |tick| |
+----------------------+-------------+
| binary:logistic | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| binary:logitraw | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| binary:hinge | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| count:poisson | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| reg:gamma | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| reg:tweedie | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| multi:softmax | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| multi:softprob | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| survival:cox | |cross| |
+--------------------+-------------+
+----------------------+-------------+
| survival:aft | |tick| |
+----------------------+-------------+
| rank:pairwise | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| rank:ndcg | |tick| |
+--------------------+-------------+
+----------------------+-------------+
| rank:map | |tick| |
+--------------------+-------------+
+----------------------+-------------+
Objective will run on GPU if GPU updater (``gpu_hist``), otherwise they will run on CPU by
default. For unsupported objectives XGBoost will fall back to using CPU implementation by
@ -140,41 +144,47 @@ Metric functions
===================
Following table shows current support status for evaluation metrics on the GPU.
+-----------------+-------------+
+------------------------------+-------------+
| Metric | GPU Support |
+=================+=============+
+==============================+=============+
| rmse | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| rmsle | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| mae | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| mphe | |tick| |
+------------------------------+-------------+
| logloss | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| error | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| merror | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| mlogloss | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| auc | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| aucpr | |cross| |
+-----------------+-------------+
+------------------------------+-------------+
| ndcg | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| map | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| poisson-nloglik | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| gamma-nloglik | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| cox-nloglik | |cross| |
+-----------------+-------------+
+------------------------------+-------------+
| aft-nloglik | |tick| |
+------------------------------+-------------+
| interval-regression-accuracy | |tick| |
+------------------------------+-------------+
| gamma-deviance | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
| tweedie-nloglik | |tick| |
+-----------------+-------------+
+------------------------------+-------------+
Similar to objective functions, default device for metrics is selected based on tree
updater and predictor (which is selected based on tree updater).

4
doc/parameter.rst
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@ -357,7 +357,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
Note that predictions are returned on the hazard ratio scale (i.e., as HR = exp(marginal_prediction) in the proportional hazard function ``h(t) = h0(t) * HR``).
- ``survival:aft``: Accelerated failure time model for censored survival time data.
See :doc:`/tutorials/aft_survival_analysis` for details.
- ``aft_loss_distribution``: Probabilty Density Function used by ``survival:aft`` and ``aft-nloglik`` metric.
- ``aft_loss_distribution``: Probabilty Density Function used by ``survival:aft`` objective and ``aft-nloglik`` metric.
- ``multi:softmax``: set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes)
- ``multi:softprob``: same as softmax, but output a vector of ``ndata * nclass``, which can be further reshaped to ``ndata * nclass`` matrix. The result contains predicted probability of each data point belonging to each class.
- ``rank:pairwise``: Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
@ -399,6 +399,8 @@ Specify the learning task and the corresponding learning objective. The objectiv
- ``tweedie-nloglik``: negative log-likelihood for Tweedie regression (at a specified value of the ``tweedie_variance_power`` parameter)
- ``aft-nloglik``: Negative log likelihood of Accelerated Failure Time model.
See :doc:`/tutorials/aft_survival_analysis` for details.
- ``interval-regression-accuracy``: Fraction of data points whose predicted labels fall in the interval-censored labels.
Only applicable for interval-censored data. See :doc:`/tutorials/aft_survival_analysis` for details.
* ``seed`` [default=0]