Remove unrequired functions

src/mlpack/methods/xgboost/loss_functions/sse_loss.hpp

@@ -51,49 +51,6 @@ class SSELoss
     return arma::accu(values) / (typename VecType::elem_type) values.n_elem;
   }
 
-  /**
-   * Returns the first order gradient of the loss function with respect to the
-   * values.
-   *
-   * This is primarily used in calculating the residuals and split gain for the
-   * gradient boosted trees.
-   *
-   * @tparam T The type of input data. This can be both a vector or a scalar.
-   * @param observed The true observed values.
-   * @param values The values with respect to which the gradient will be
-   *     calculated.
-   */
-  template<typename T>
-  T Gradients(const T& observed, const T& values)
-  {
-    return values - observed;
-  }
-
-  /**
-   * Returns the second order gradient of the loss function with respect to the
-   * values.
-   */
-  template<typename VecType>
-  VecType Hessians(const VecType& /* observed */, const VecType& values)
-  {
-    VecType h(values.n_elem, arma::fill::ones);
-    return h;
-  }
-
-  /**
-   * Returns the pseudo residuals of the predictions.
-   * This is equal to the negative gradient of the loss function with respect
-   * to the predicted values f.
-   *
-   * @param observed The true observed values.
-   * @param f The prediction at the current step of boosting.
-   */
-  template<typename VecType>
-  VecType Residuals(const VecType& observed, const VecType& f)
-  {
-    return observed - f;
-  }
-
   /**
    * Returns the output value for the leaf in the tree.
    */
@@ -105,17 +62,10 @@ class SSELoss
   /**
    * Calculates the similarity score for evaluating the splits.
    */
-  template<typename VecType>
-  double SimilarityScore(const VecType& observed, const VecType& residuals,
-      const size_t begin, const size_t end)
+  double SimilarityScore(const size_t begin, const size_t end)
   {
-    VecType gradients = Gradients(observed.subvec(begin, end),
-        residuals.subvec(begin, end));
-    VecType hessians = Hessians(observed.subvec(begin, end),
-        residuals.subvec(begin, end));
-
-    return std::pow(ApplyL1(arma::accu(gradients)), 2) /
-        (arma::accu(hessians) + lambda);
+    return std::pow(ApplyL1(arma::accu(gradients.subvec(begin, end))), 2) /
+        (arma::accu(hessians.subvec(begin, end)) + lambda);
   }
 
   /**

src/mlpack/tests/xgboost_test.cpp

@@ -31,78 +31,20 @@ TEST_CASE("SSEInitialPredictionTest", "[XGBTest]")
   REQUIRE(Loss.InitialPrediction(values) == initPred);
 }
 
-/**
- * Test that gradients are calculated correctly for SSE Loss.
- */
-TEST_CASE("SSEGradientsTest", "[XGBTest]")
-{
-  arma::vec observed = {1, 3, 2, 2, 5, 6, 9, 11, 8, 8};
-  arma::vec predicted = {0.5, 1, 2.5, 1.5, 5, 8, 8, 10.75, 9, 9.5};
-
-  // Actual gradients.
-  arma::vec gradients = {-0.5, -2, 0.5, -0.5, 0, 2, -1, -0.25, 1, 1.5};
-
-  SSELoss Loss;
-  // Calculated gradients.
-  arma::vec calculatedGradients = Loss.Gradients(observed, predicted);
-
-  for (int i = 0; i < 10; i++)
-    REQUIRE(calculatedGradients[i] == gradients[i]);
-}
-
-/**
- * Test that hessians are calculated correctly for SSE Loss.
- */
-TEST_CASE("SSEHessiansTest", "[XGBTest]")
-{
-  arma::vec observed = {1, 3, 2, 2, 5, 6, 9, 11, 8, 8};
-  arma::vec predicted = {0.5, 1, 2.5, 1.5, 5, 8, 8, 10.75, 9, 9.5};
-
-  // Actual hessians.
-  arma::vec hessians = {1, 1, 1, 1, 1, 1, 1, 1, 1, 1};
-
-  SSELoss Loss;
-  // Calculated hessians.
-  arma::vec calculatedHessians = Loss.Hessians(observed, predicted);
-
-  for (int i = 0; i < 10; i++)
-    REQUIRE(calculatedHessians[i] == hessians[i]);
-}
-
-/**
- * Test that residuals are calculated correctly for SSE Loss.
- */
-TEST_CASE("SSEResidualsTest", "[XGBTest]")
-{
-  arma::vec observed = {1, 3, 2, 2, 5, 6, 9, 11, 8, 8};
-  arma::vec predicted = {0.5, 1, 2.5, 1.5, 5, 8, 8, 10.75, 9, 9.5};
-
-  // Actual residuals.
-  arma::vec residuals = {0.5, 2, -0.5, 0.5, 0, -2, 1, 0.25, -1, -1.5};
-
-  SSELoss Loss;
-  // Calculated residuals.
-  arma::vec calculatedResiduals = Loss.Residuals(observed, predicted);
-
-  for (int i = 0; i < 10; i++)
-    REQUIRE(calculatedResiduals[i] == residuals[i]);
-}
-
 /**
  * Test that output leaf value is calculated correctly for SSE Loss.
  */
 TEST_CASE("SSELeafValueTest", "[XGBTest]")
 {
-  arma::vec observed = {1, 3, 2, 2, 5, 6, 9, 11, 8, 8};
-  arma::vec predicted = {0.5, 1, 2.5, 1.5, 5, 8, 8, 10.75, 9, 9.5};
+  arma::mat input = { { 1,   3,   2,   2, 5, 6, 9,    11, 8,   8 },
+                      { 0.5, 1, 2.5, 1.5, 5, 8, 8, 10.75, 9, 9.5 } };
+  arma::vec weights; // dummy weights not used.
 
   // Actual output leaf value.
   double leafValue = -0.075;
 
   SSELoss Loss;
-  // Calculating gradients and hessians for input to OutputLeafValue().
-  arma::vec gradients = Loss.Gradients(observed, predicted);
-  arma::vec hessians = Loss.Hessians(observed, predicted);
+  double gain = Loss.Evaluate<false>(input, weights);
 
-  REQUIRE(Loss.OutputLeafValue(gradients, hessians) == leafValue);
+  REQUIRE(Loss.OutputLeafValue() == leafValue);
 }