Added Adjusted R2

src/mlpack/core/cv/metrics/r2_score.hpp

@@ -43,7 +43,10 @@ namespace cv {
  * For example, a model having R2Score = 0.85, explains 85 \% variability of
  * the response data around its mean.
  */
-class R2Score
+
+template<bool adjustedR2> class R2Score;
+
+template<> class R2Score<false>
 {
  public:
   /**
@@ -67,6 +70,30 @@ class R2Score
   static const bool NeedsMinimization = false;
 };
 
+template<> class R2Score<true>
+{
+ public:
+  /**
+   * Run prediction and calculate the Adjusted R squared error.
+   *
+   * @param model A regression model.
+   * @param data Column-major data containing test items.
+   * @param responses Ground truth (correct) target values for the test items,
+   *     should be either a row vector or a column-major matrix.
+   * @return calculated Ajusted R2 Score.
+   */
+  template<typename MLAlgorithm, typename DataType, typename ResponsesType>
+  static double Evaluate(MLAlgorithm& model,
+                         const DataType& data,
+                         const ResponsesType& responses);
+
+  /**
+   * Information for hyper-parameter tuning code. It indicates that we want
+   * to maximize the measurement.
+   */
+  static const bool NeedsMinimization = false;
+};
+
 } // namespace cv
 } // namespace mlpack
 

src/mlpack/core/cv/metrics/r2_score_impl.hpp

@@ -16,7 +16,7 @@ namespace mlpack {
 namespace cv {
 
 template<typename MLAlgorithm, typename DataType, typename ResponsesType>
-double R2Score::Evaluate(MLAlgorithm& model,
+double R2Score<false>::Evaluate(MLAlgorithm& model,
                          const DataType& data,
                          const ResponsesType& responses)
 {
@@ -45,10 +45,46 @@ double R2Score::Evaluate(MLAlgorithm& model,
   // Handling undefined R2 Score when both denominator and numerator is 0.0.
   if (residualSumSquared == 0.0)
     return totalSumSquared ? 1.0 : DBL_MIN;
-
+
+  // Returning R-squared
   return 1 - residualSumSquared / totalSumSquared;
 }
 
+template<typename MLAlgorithm, typename DataType, typename ResponsesType>
+  double R2Score<true>::Evaluate(MLAlgorithm& model,
+                           const DataType& data,
+                           const ResponsesType& responses)
+  {
+    if (data.n_cols != responses.n_cols)
+    {
+      std::ostringstream oss;
+      oss << "R2Score::Evaluate(): number of points (" << data.n_cols << ") "
+          << "does not match number of responses (" << responses.n_cols << ")!"
+          << std::endl;
+      throw std::invalid_argument(oss.str());
+    }
+
+    ResponsesType predictedResponses;
+    // Taking Predicted Output from the model.
+    model.Predict(data, predictedResponses);
+    // Mean value of response.
+    double meanResponses = arma::mean(responses);
+
+    // Calculate the numerator i.e. residual sum of squares.
+    double residualSumSquared = arma::accu(arma::square(responses -
+        predictedResponses));
+
+    // Calculate the denominator i.e.total sum of squares.
+    double totalSumSquared = arma::accu(arma::square(responses - meanResponses));
+
+    // Handling undefined R2 Score when both denominator and numerator is 0.0.
+    if (residualSumSquared == 0.0)
+      return totalSumSquared ? 1.0 : DBL_MIN;
+    // Returning adjusted R-squared.
+    double rsq = 1 - (residualSumSquared / totalSumSquared);
+    return (1 - ((1 - rsq) * ((data.n_cols - 1) / (data.n_cols - data.n_rows - 1))));
+  }
+
 } // namespace cv
 } // namespace mlpack
 

src/mlpack/tests/cv_test.cpp

@@ -187,10 +187,32 @@ TEST_CASE("R2ScoreTest", "[CVTest]")
 
   double expectedR2 = 0.99999779;
 
-  REQUIRE(R2Score::Evaluate(lr, data, responses)
+  REQUIRE(R2Score<false>::Evaluate(lr, data, responses)
           == Approx(expectedR2).epsilon(1e-7));
 }
 
+/**
+ * Test the Adjusted R squared metric
+ */
+TEST_CASE("AdjR2ScoreTest", "[CVTest]")
+{
+  // Making two variables that define the linear function is
+  // f(x1, x2) = x1 + x2
+  arma::mat X;
+  X << 1 << 2 << 3 << 4 << 5 << 6 << arma::endr
+    << 2 << 3 << 4 << 5 << 6 << 7 << arma::endr;
+  arma::rowvec Y;
+  Y << 3 << 5 << 7 << 9 << 11 << 13;
+
+  LinearRegression lr(X, Y);
+
+  //Theoretically Adjusted R squared should be equal 1
+  double expAdjR2 = 1;
+  REQUIRE(std::abs(R2Score<true>::Evaluate(lr, X, Y) - expAdjR2)
+          <= 1e-7);
+}
+
+
 /**
  * Test the mean squared error with matrix responses.
  */