Add MAPE metric

R-package/R/xgb.cv.R

@@ -36,6 +36,8 @@
 #'   \item \code{error} binary classification error rate
 #'   \item \code{rmse} Rooted mean square error
 #'   \item \code{logloss} negative log-likelihood function
+#'   \item \code{mae} Mean absolute error
+#'   \item \code{mape} Mean absolute percentage error
 #'   \item \code{auc} Area under curve
 #'   \item \code{aucpr} Area under PR curve
 #'   \item \code{merror} Exact matching error, used to evaluate multi-class classification

R-package/R/xgb.train.R

@@ -137,6 +137,8 @@
 #'            By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
 #'            Different threshold (e.g., 0.) could be specified as "error@0."
 #'      \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
+#'      \item \code{mae} Mean absolute error
+#'      \item \code{mape} Mean absolute percentage error
 #'      \item \code{auc} Area under the curve. \url{https://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
 #'      \item \code{aucpr} Area under the PR curve. \url{https://en.wikipedia.org/wiki/Precision_and_recall} for ranking evaluation.
 #'      \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{https://en.wikipedia.org/wiki/NDCG}

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.

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala

@@ -56,8 +56,8 @@ private[spark] trait LearningTaskParams extends Params {
   /**
    * evaluation metrics for validation data, a default metric will be assigned according to
    * objective(rmse for regression, and error for classification, mean average precision for
-   * ranking). options: rmse, rmsle, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map,
-   * gamma-deviance
+   * ranking). options: rmse, rmsle, mae, mape, logloss, error, merror, mlogloss, auc, aucpr, ndcg,
+   * map, gamma-deviance
    */
   final val evalMetric = new Param[String](this, "evalMetric", "evaluation metrics for " +
     "validation data, a default metric will be assigned according to objective " +
@@ -121,6 +121,6 @@ private[spark] object LearningTaskParams {
 
   val evalMetricsToMaximize = HashSet("auc", "aucpr", "ndcg", "map")
 
-  val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "logloss", "error", "merror",
+  val evalMetricsToMinimize = HashSet("rmse", "rmsle", "mae", "mape", "logloss", "error", "merror",
     "mlogloss", "gamma-deviance")
 }

src/metric/elementwise_metric.cu

@@ -168,6 +168,18 @@ struct EvalRowMAE {
   }
 };
 
+struct EvalRowMAPE {
+  const char *Name() const {
+    return "mape";
+  }
+  XGBOOST_DEVICE bst_float EvalRow(bst_float label, bst_float pred) const {
+    return std::abs((label - pred) / label);
+  }
+  static bst_float GetFinal(bst_float esum, bst_float wsum) {
+    return wsum == 0 ? esum : esum / wsum;
+  }
+};
+
 struct EvalRowLogLoss {
   const char *Name() const {
     return "logloss";
@@ -369,6 +381,10 @@ XGBOOST_REGISTER_METRIC(MAE, "mae")
 .describe("Mean absolute error.")
 .set_body([](const char* param) { return new EvalEWiseBase<EvalRowMAE>(); });
 
+XGBOOST_REGISTER_METRIC(MAPE, "mape")
+    .describe("Mean absolute percentage error.")
+    .set_body([](const char* param) { return new EvalEWiseBase<EvalRowMAPE>(); });
+
 XGBOOST_REGISTER_METRIC(MPHE, "mphe")
 .describe("Mean Pseudo Huber error.")
 .set_body([](const char* param) { return new EvalEWiseBase<EvalRowMPHE>(); });

tests/cpp/metric/test_elementwise_metric.cc

@@ -44,6 +44,17 @@ TEST(Metric, DeclareUnifiedTest(MAE)) {
   delete metric;
 }
 
+TEST(Metric, DeclareUnifiedTest(MAPE)) {
+  auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX);
+  xgboost::Metric * metric = xgboost::Metric::Create("mape", &lparam);
+  metric->Configure({});
+  ASSERT_STREQ(metric->Name(), "mape");
+  EXPECT_NEAR(GetMetricEval(metric, {150, 300}, {100, 200}), 0.5f, 1e-10);
+  EXPECT_NEAR(GetMetricEval(metric, {50, 400, 500, 4000}, {100, 200, 500, 1000}),
+              1.125f, 0.001f);
+  delete metric;
+}
+
 TEST(Metric, DeclareUnifiedTest(MPHE)) {
   auto lparam = xgboost::CreateEmptyGenericParam(GPUIDX);
   xgboost::Metric * metric = xgboost::Metric::Create("mphe", &lparam);