Implement slope for Pseduo-Huber. (#7727)

* Add objective and metric.
* Some refactoring for CPU/GPU dispatching using linalg module.

doc/model.schema

@@ -204,6 +204,14 @@
         }
       }
     },
+    "pseduo_huber_param": {
+      "type": "object",
+      "properties": {
+        "huber_slope": {
+          "type": "string"
+        }
+      }
+    },
     "aft_loss_param": {
       "type": "object",
       "properties": {

doc/parameter.rst

@@ -338,15 +338,6 @@ Parameters for Linear Booster (``booster=gblinear``)
 
   - The number of top features to select in ``greedy`` and ``thrifty`` feature selector. The value of 0 means using all the features.
 
-Parameters for Tweedie Regression (``objective=reg:tweedie``)
-=============================================================
-* ``tweedie_variance_power`` [default=1.5]
-
-  - Parameter that controls the variance of the Tweedie distribution ``var(y) ~ E(y)^tweedie_variance_power``
-  - range: (1,2)
-  - Set closer to 2 to shift towards a gamma distribution
-  - Set closer to 1 to shift towards a Poisson distribution.
-
 ************************
 Learning Task Parameters
 ************************
@@ -356,14 +347,14 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
   - ``reg:squarederror``: regression with squared loss.
   - ``reg:squaredlogerror``: regression with squared log loss :math:`\frac{1}{2}[log(pred + 1) - log(label + 1)]^2`.  All input labels are required to be greater than -1.  Also, see metric ``rmsle`` for possible issue  with this objective.
-  - ``reg:logistic``: logistic regression
+  - ``reg:logistic``: logistic regression.
   - ``reg:pseudohubererror``: regression with Pseudo Huber loss, a twice differentiable alternative to absolute loss.
   - ``binary:logistic``: logistic regression for binary classification, output probability
   - ``binary:logitraw``: logistic regression for binary classification, output score before logistic transformation
   - ``binary:hinge``: hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.
   - ``count:poisson``: Poisson regression for count data, output mean of Poisson distribution.
 
-    - ``max_delta_step`` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
+    + ``max_delta_step`` is set to 0.7 by default in Poisson regression (used to safeguard optimization)
 
   - ``survival:cox``: Cox regression for right censored survival time data (negative values are considered right censored).
     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``).
@@ -435,6 +426,20 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
   - Seed PRNG determnisticly via iterator number.
 
+Parameters for Tweedie Regression (``objective=reg:tweedie``)
+=============================================================
+* ``tweedie_variance_power`` [default=1.5]
+
+  - Parameter that controls the variance of the Tweedie distribution ``var(y) ~ E(y)^tweedie_variance_power``
+  - range: (1,2)
+  - Set closer to 2 to shift towards a gamma distribution
+  - Set closer to 1 to shift towards a Poisson distribution.
+
+Parameter for using Pseudo-Huber (``reg:pseudohubererror``)
+===========================================================
+
+* ``huber_slope`` : A parameter used for Pseudo-Huber loss to define the :math:`\delta` term. [default = 1.0]
+
 ***********************
 Command Line Parameters
 ***********************