Changeset 12871 for branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions

Timestamp:

08/17/15 18:31:39 (9 years ago)

Author:

gkronber

Message:

#2434 derived ILossFunction from IItem to allow execution on hive without privileged flag (made an "after deserialization"-hook necessary to convert the parameter type)

Location:

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions

Files:

• AbsoluteErrorLoss.cs (modified) (2 diffs)
• ILossFunction.cs (modified) (2 diffs)
• LogisticRegressionLoss.cs (modified) (2 diffs)
• RelativeErrorLoss.cs (modified) (2 diffs)
• SquaredErrorLoss.cs (modified) (2 diffs)

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/AbsoluteErrorLoss.cs

@@ -23 +23 @@
 using System;
 using System.Collections.Generic;
-using System.Diagnostics;
-using System.Linq;
 using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // loss function for the weighted absolute error
-  public class AbsoluteErrorLoss : ILossFunction {
+  [StorableClass]
+  [Item("Absolute error loss", "")]
+  public class AbsoluteErrorLoss : Item, ILossFunction {
+    public AbsoluteErrorLoss() { }
+
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
@@ -77 +81 @@
     }
 
-    public override string ToString() {
-      return "Absolute error loss";
+    #region item implementation
+    private AbsoluteErrorLoss(AbsoluteErrorLoss original, Cloner cloner) : base(original, cloner) { }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new AbsoluteErrorLoss(this, cloner);
     }
+    #endregion
   }
 }

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/ILossFunction.cs

@@ -22 +22 @@
 
 using System.Collections.Generic;
+using HeuristicLab.Core;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
@@ -27 +28 @@
   // target represents the target vector  (original targets from the problem data, never changed)
   // pred   represents the current vector of predictions (a weighted combination of models learned so far, this vector is updated after each step)
-  public interface ILossFunction {
+  public interface ILossFunction : IItem {
     // returns the loss of the current prediction vector 
     double GetLoss(IEnumerable<double> target, IEnumerable<double> pred);

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/LogisticRegressionLoss.cs

@@ -26 +26 @@
 using System.Linq;
 using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // Greedy Function Approximation: A Gradient Boosting Machine (page 9) 
-  public class LogisticRegressionLoss : ILossFunction {
+  [StorableClass]
+  [Item("Logistic regression loss", "")]
+  public class LogisticRegressionLoss : Item, ILossFunction {
+    public LogisticRegressionLoss() { }
+
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
@@ -83 +89 @@
     }
 
-    public override string ToString() {
-      return "Logistic regression loss";
+    #region item implementation
+    private LogisticRegressionLoss(LogisticRegressionLoss original, Cloner cloner) : base(original, cloner) { }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new LogisticRegressionLoss(this, cloner);
     }
+    #endregion
+
   }
 }

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/RelativeErrorLoss.cs

@@ -26 +26 @@
 using System.Linq;
 using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
   // relative error loss is a special case of weighted absolute error loss with weights = (1/target)
-  public class RelativeErrorLoss : ILossFunction {
+  [StorableClass]
+  [Item("Relative error loss", "")]
+  public class RelativeErrorLoss : Item, ILossFunction {
+    public RelativeErrorLoss() { }
+
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
@@ -105 +111 @@
     }
 
-    public override string ToString() {
-      return "Relative error loss";
+    #region item implementation
+    private RelativeErrorLoss(RelativeErrorLoss original, Cloner cloner) : base(original, cloner) { }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new RelativeErrorLoss(this, cloner);
     }
+    #endregion
   }
 }

branches/crossvalidation-2434/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/LossFunctions/SquaredErrorLoss.cs

@@ -24 +24 @@
 using System.Collections.Generic;
 using System.Linq;
+using HeuristicLab.Common;
+using HeuristicLab.Core;
+using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
-  public class SquaredErrorLoss : ILossFunction {
+  [StorableClass]
+  [Item("Squared error loss", "")]
+  public class SquaredErrorLoss : Item, ILossFunction {
+    public SquaredErrorLoss() { }
+
     public double GetLoss(IEnumerable<double> target, IEnumerable<double> pred) {
       var targetEnum = target.GetEnumerator();
@@ -70 +77 @@
     }
 
-    public override string ToString() {
-      return "Squared error loss";
+    #region item implementation
+    private SquaredErrorLoss(SquaredErrorLoss original, Cloner cloner) : base(original, cloner) { }
+
+    public override IDeepCloneable Clone(Cloner cloner) {
+      return new SquaredErrorLoss(this, cloner);
     }
+    #endregion
   }
 }