Rework the precision metric. (#9222)

- Rework the precision metric for both CPU and GPU.
- Mention it in the document.
- Cleanup old support code for GPU ranking metric.
- Deterministic GPU implementation.

* Drop support for classification.

* type.

* use batch shape.

* lint.

* cpu build.

* cpu build.

* lint.

* Tests.

* Fix.

* Cleanup error message.

doc/parameter.rst

@@ -424,6 +424,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
       After XGBoost 1.6, both of the requirements and restrictions for using ``aucpr`` in classification problem are similar to ``auc``.  For ranking task, only binary relevance label :math:`y \in [0, 1]` is supported.  Different from ``map (mean average precision)``, ``aucpr`` calculates the *interpolated* area under precision recall curve using continuous interpolation.
 
+    - ``pre``: Precision at :math:`k`. Supports only learning to rank task.
     - ``ndcg``: `Normalized Discounted Cumulative Gain <http://en.wikipedia.org/wiki/NDCG>`_
     - ``map``: `Mean Average Precision <http://en.wikipedia.org/wiki/Mean_average_precision#Mean_average_precision>`_
 
@@ -435,7 +436,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
       where :math:`I_{(k)}` is an indicator function that equals to :math:`1` when the document at :math:`k` is relevant and :math:`0` otherwise. The :math:`P@k` is the precision at :math:`k`, and :math:`N` is the total number of relevant documents. Lastly, the `mean average precision` is defined as the weighted average across all queries.
 
-    - ``ndcg@n``, ``map@n``: :math:`n` can be assigned as an integer to cut off the top positions in the lists for evaluation.
+    - ``ndcg@n``, ``map@n``, ``pre@n``: :math:`n` can be assigned as an integer to cut off the top positions in the lists for evaluation.
     - ``ndcg-``, ``map-``, ``ndcg@n-``, ``map@n-``: In XGBoost, the NDCG and MAP evaluate the score of a list without any positive samples as :math:`1`. By appending "-" to the evaluation metric name, we can ask XGBoost to evaluate these scores as :math:`0` to be consistent under some conditions.
     - ``poisson-nloglik``: negative log-likelihood for Poisson regression
     - ``gamma-nloglik``: negative log-likelihood for gamma regression