Changeset 3452 for trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleRSquaredEvaluator.cs

Timestamp:

04/20/10 20:31:23 (14 years ago)

Author:

gkronber

Message:

Included tracking of best of run solution (based on validation set) and calculation of MSE, R² and rel. Error on training and test sets. #938 (Data types and operators for regression problems)

File:

• trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleRSquaredEvaluator.cs (copied) (copied from trunk/sources/HeuristicLab.Modeling/3.2/SimpleStableCorrelationCoefficientEvaluator.cs) (1 diff)

trunk/sources/HeuristicLab.Problems.DataAnalysis/3.3/Evaluators/SimpleRSquaredEvaluator.cs

@@ -1 @@
-using System;
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2010 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
+ *
+ * This file is part of HeuristicLab.
+ *
+ * HeuristicLab is free software: you can redistribute it and/or modify
+ * it under the terms of the GNU General Public License as published by
+ * the Free Software Foundation, either version 3 of the License, or
+ * (at your option) any later version.
+ *
+ * HeuristicLab is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU General Public License for more details.
+ *
+ * You should have received a copy of the GNU General Public License
+ * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
+ */
+#endregion
+
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
 using HeuristicLab.Core;
+using HeuristicLab.Common;
 using HeuristicLab.Data;
-using HeuristicLab.DataAnalysis;
+using HeuristicLab.Parameters;
 
-namespace HeuristicLab.Modeling {
-  public class SimpleStableCorrelationCoefficientEvaluator : SimpleEvaluatorBase {
-
-    public override string OutputVariableName {
-      get {
-        return "R2";
-      }
+namespace HeuristicLab.Problems.DataAnalysis.Evaluators {
+  public class SimpleRSquaredEvaluator : SimpleEvaluator {
+    public ILookupParameter<DoubleValue> RSquaredParameter {
+      get { return (ILookupParameter<DoubleValue>)Parameters["RSquared"]; }
     }
 
-    public override double Evaluate(double[,] values) {
-      try {
-        return Calculate(values);
-      }
-      catch (ArgumentException) {
-        return double.NegativeInfinity;
-      }
+    public SimpleRSquaredEvaluator() {
+      Parameters.Add(new LookupParameter<DoubleValue>("RSquared", "The squared Pearson's Product Moment Correlation (R²) of estimated values and original values."));
     }
 
-    public static double Calculate(double[,] values) {
+    protected override void Apply(DoubleMatrix values) {
+      var original = from i in Enumerable.Range(0, values.Rows)
+                     select values[i, ORIGINAL_INDEX];
+      var estimated = from i in Enumerable.Range(0, values.Rows)
+                      select values[i, ESTIMATION_INDEX];
+      RSquaredParameter.ActualValue = new DoubleValue(Calculate(original, estimated));
+    }
+
+
+    public static double Calculate(IEnumerable<double> original, IEnumerable<double> estimated) {
+      var originalEnumerator = original.GetEnumerator();
+      var estimatedEnumerator = estimated.GetEnumerator();
+      originalEnumerator.MoveNext();
+      estimatedEnumerator.MoveNext();
+      double e = estimatedEnumerator.Current;
+      double o = originalEnumerator.Current;
+
+      // stable and iterative calculation of R² in one pass over original and estimated
       double sum_sq_x = 0.0;
       double sum_sq_y = 0.0;
       double sum_coproduct = 0.0;
-      if (IsInvalidValue(values[0, ORIGINAL_INDEX]) || IsInvalidValue(values[0, ESTIMATION_INDEX])) {
-        throw new ArgumentException("Correlation coefficient is not defined for variables with NaN or infinity values.");
+      if (IsInvalidValue(o) || IsInvalidValue(e)) {
+        throw new ArgumentException("R² is not defined for variables with NaN or infinity values.");
       }
-      double mean_x = values[0, ORIGINAL_INDEX];
-      double mean_y = values[0, ESTIMATION_INDEX];
-      for (int i = 1; i < values.GetLength(0); i++) {
-        double sweep = (i - 1.0) / i;
-        if (IsInvalidValue(values[i, ORIGINAL_INDEX]) || IsInvalidValue(values[i, ESTIMATION_INDEX])) {
+      double mean_x = o;
+      double mean_y = e;
+      int n = 1;
+      while (originalEnumerator.MoveNext() & estimatedEnumerator.MoveNext()) {
+        e = estimatedEnumerator.Current;
+        o = originalEnumerator.Current;
+        double sweep = (n - 1.0) / n;
+        if (IsInvalidValue(o) || IsInvalidValue(e)) {
           throw new ArgumentException("Correlation coefficient is not defined for variables with NaN or infinity values.");
         }
-        double delta_x = values[i, ORIGINAL_INDEX] - mean_x;
-        double delta_y = values[i, ESTIMATION_INDEX] - mean_y;
+        double delta_x = o - mean_x;
+        double delta_y = e - mean_y;
         sum_sq_x += delta_x * delta_x * sweep;
         sum_sq_y += delta_y * delta_y * sweep;
         sum_coproduct += delta_x * delta_y * sweep;
-        mean_x += delta_x / i;
-        mean_y += delta_y / i;
+        mean_x += delta_x / n;
+        mean_y += delta_y / n;
+        n++;
       }
-      double pop_sd_x = Math.Sqrt(sum_sq_x / values.GetLength(0));
-      double pop_sd_y = Math.Sqrt(sum_sq_y / values.GetLength(0));
-      double cov_x_y = sum_coproduct / values.GetLength(0);
+      if (estimatedEnumerator.MoveNext() || originalEnumerator.MoveNext()) {
+        throw new ArgumentException("Number of elements in original and estimated enumeration doesn't match.");
+      } else {
+        double pop_sd_x = Math.Sqrt(sum_sq_x / n);
+        double pop_sd_y = Math.Sqrt(sum_sq_y / n);
+        double cov_x_y = sum_coproduct / n;
 
-      if (pop_sd_x == 0.0 || pop_sd_y == 0.0)
-        return 0.0;
-      else {
-        double r = cov_x_y / (pop_sd_x * pop_sd_y);
-        return r * r;
+        if (pop_sd_x.IsAlmost(0.0) || pop_sd_y.IsAlmost(0.0))
+          return 0.0;
+        else {
+          double r = cov_x_y / (pop_sd_x * pop_sd_y);
+          return r * r;
+        }
       }
     }