Add MAPE metric (#6119)

doc/gpu/index.rst

@@ -153,6 +153,8 @@ Following table shows current support status for evaluation metrics on the GPU.
 +------------------------------+-------------+
 | mae                          | |tick|      |
 +------------------------------+-------------+
+| mape                         | |tick|      |
++------------------------------+-------------+
 | mphe                         | |tick|      |
 +------------------------------+-------------+
 | logloss                      | |tick|      |

doc/parameter.rst

@@ -383,6 +383,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
     - ``rmse``: `root mean square error <http://en.wikipedia.org/wiki/Root_mean_square_error>`_
     - ``rmsle``: root mean square log error: :math:`\sqrt{\frac{1}{N}[log(pred + 1) - log(label + 1)]^2}`. Default metric of ``reg:squaredlogerror`` objective. This metric reduces errors generated by outliers in dataset.  But because ``log`` function is employed, ``rmsle`` might output ``nan`` when prediction value is less than -1.  See ``reg:squaredlogerror`` for other requirements.
     - ``mae``: `mean absolute error <https://en.wikipedia.org/wiki/Mean_absolute_error>`_
+    - ``mape``: `mean absolute percentage error <https://en.wikipedia.org/wiki/Mean_absolute_percentage_error>`_
     - ``mphe``: `mean Pseudo Huber error <https://en.wikipedia.org/wiki/Huber_loss>`_. Default metric of ``reg:pseudohubererror`` objective.
     - ``logloss``: `negative log-likelihood <http://en.wikipedia.org/wiki/Log-likelihood>`_
     - ``error``: Binary classification error rate. It is calculated as ``#(wrong cases)/#(all cases)``. For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others as negative instances.