Changeset 16847 for branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/M5Regression.cs

Timestamp:

04/19/19 13:06:11 (5 years ago)

Author:

gkronber

Message:

#2847: made some minor changes while reviewing

File:

• branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/M5Regression.cs (modified) (9 diffs)

branches/2847_M5Regression/HeuristicLab.Algorithms.DataAnalysis/3.4/M5Regression/M5Regression.cs

@@ -1 @@
-using System;
+#region License Information
+/* HeuristicLab
+ * Copyright (C) 2002-2019 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;
@@ -9 +30 @@
 using HeuristicLab.Optimization;
 using HeuristicLab.Parameters;
-using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
 using HeuristicLab.PluginInfrastructure;
 using HeuristicLab.Problems.DataAnalysis;
 using HeuristicLab.Random;
+using HEAL.Attic;
 
 namespace HeuristicLab.Algorithms.DataAnalysis {
-  [StorableClass]
+  [StorableType("FC8D8E5A-D16D-41BB-91CF-B2B35D17ADD7")]
   [Creatable(CreatableAttribute.Categories.DataAnalysisRegression, Priority = 95)]
   [Item("M5RegressionTree", "A M5 regression tree / rule set")]
@@ -28 +49 @@
     public const string TrainingSetVariableName = "TrainingSet";
 
-    #region Parametername
+    #region Parameter names
     private const string GenerateRulesParameterName = "GenerateRules";
     private const string HoldoutSizeParameterName = "HoldoutSize";
-    private const string SpliterParameterName = "Splitter";
+    private const string SplitterParameterName = "Splitter";
     private const string MinimalNodeSizeParameterName = "MinimalNodeSize";
     private const string LeafModelParameterName = "LeafModel";
@@ -47 +68 @@
       get { return (IFixedValueParameter<PercentValue>)Parameters[HoldoutSizeParameterName]; }
     }
-    public IConstrainedValueParameter<ISplitter> ImpurityParameter {
-      get { return (IConstrainedValueParameter<ISplitter>)Parameters[SpliterParameterName]; }
+    public IConstrainedValueParameter<ISplitter> SplitterParameter {
+      get { return (IConstrainedValueParameter<ISplitter>)Parameters[SplitterParameterName]; }
     }
     public IFixedValueParameter<IntValue> MinimalNodeSizeParameter {
@@ -73 +94 @@
     public bool GenerateRules {
       get { return GenerateRulesParameter.Value.Value; }
+      set { GenerateRulesParameter.Value.Value = value; }
     }
     public double HoldoutSize {
       get { return HoldoutSizeParameter.Value.Value; }
+      set { HoldoutSizeParameter.Value.Value = value; }
     }
     public ISplitter Splitter {
-      get { return ImpurityParameter.Value; }
+      get { return SplitterParameter.Value; }
+      // no setter because this is a constrained parameter
     }
     public int MinimalNodeSize {
       get { return MinimalNodeSizeParameter.Value.Value; }
+      set { MinimalNodeSizeParameter.Value.Value = value; }
     }
     public ILeafModel LeafModel {
@@ -91 +116 @@
     public int Seed {
       get { return SeedParameter.Value.Value; }
+      set { SeedParameter.Value.Value = value; }
     }
     public bool SetSeedRandomly {
       get { return SetSeedRandomlyParameter.Value.Value; }
+      set { SetSeedRandomlyParameter.Value.Value = value; }
     }
     public bool UseHoldout {
       get { return UseHoldoutParameter.Value.Value; }
+      set { UseHoldoutParameter.Value.Value = value; }
     }
     #endregion
@@ -107 +135 @@
     #region Constructors and Cloning
     [StorableConstructor]
-    private M5Regression(bool deserializing) : base(deserializing) { }
+    private M5Regression(StorableConstructorFlag _) : base(_) { }
     private M5Regression(M5Regression original, Cloner cloner) : base(original, cloner) {
       stateScope = cloner.Clone(stateScope);
@@ -114 +142 @@
       var modelSet = new ItemSet<ILeafModel>(ApplicationManager.Manager.GetInstances<ILeafModel>());
       var pruningSet = new ItemSet<IPruning>(ApplicationManager.Manager.GetInstances<IPruning>());
-      var impuritySet = new ItemSet<ISplitter>(ApplicationManager.Manager.GetInstances<ISplitter>());
-      Parameters.Add(new FixedValueParameter<BoolValue>(GenerateRulesParameterName, "Whether a set of rules or a decision tree shall be created", new BoolValue(false)));
-      Parameters.Add(new FixedValueParameter<PercentValue>(HoldoutSizeParameterName, "How much of the training set shall be reserved for pruning", new PercentValue(0.2)));
-      Parameters.Add(new ConstrainedValueParameter<ISplitter>(SpliterParameterName, "The type of split function used to create node splits", impuritySet, impuritySet.OfType<M5Splitter>().First()));
-      Parameters.Add(new FixedValueParameter<IntValue>(MinimalNodeSizeParameterName, "The minimal number of samples in a leaf node", new IntValue(1)));
-      Parameters.Add(new ConstrainedValueParameter<ILeafModel>(LeafModelParameterName, "The type of model used for the nodes", modelSet, modelSet.OfType<LinearLeaf>().First()));
-      Parameters.Add(new ConstrainedValueParameter<IPruning>(PruningTypeParameterName, "The type of pruning used", pruningSet, pruningSet.OfType<ComplexityPruning>().First()));
+      var splitterSet = new ItemSet<ISplitter>(ApplicationManager.Manager.GetInstances<ISplitter>());
+      Parameters.Add(new FixedValueParameter<BoolValue>(GenerateRulesParameterName, "Whether a set of rules or a decision tree shall be created (default=false)", new BoolValue(false)));
+      Parameters.Add(new FixedValueParameter<PercentValue>(HoldoutSizeParameterName, "How much of the training set shall be reserved for pruning (default=20%).", new PercentValue(0.2)));
+      Parameters.Add(new ConstrainedValueParameter<ISplitter>(SplitterParameterName, "The type of split function used to create node splits (default='M5Splitter').", splitterSet, splitterSet.OfType<M5Splitter>().First()));
+      Parameters.Add(new FixedValueParameter<IntValue>(MinimalNodeSizeParameterName, "The minimal number of samples in a leaf node (default=1).", new IntValue(1)));
+      Parameters.Add(new ConstrainedValueParameter<ILeafModel>(LeafModelParameterName, "The type of model used for the nodes (default='LinearLeaf').", modelSet, modelSet.OfType<LinearLeaf>().First()));
+      Parameters.Add(new ConstrainedValueParameter<IPruning>(PruningTypeParameterName, "The type of pruning used (default='ComplexityPruning').", pruningSet, pruningSet.OfType<ComplexityPruning>().First()));
       Parameters.Add(new FixedValueParameter<IntValue>(SeedParameterName, "The random seed used to initialize the new pseudo random number generator.", new IntValue(0)));
       Parameters.Add(new FixedValueParameter<BoolValue>(SetSeedRandomlyParameterName, "True if the random seed should be set to a random value, otherwise false.", new BoolValue(true)));
-      Parameters.Add(new FixedValueParameter<BoolValue>(UseHoldoutParameterName, "True if a holdout set should be generated, false if splitting and pruning shall be performed on the same data ", new BoolValue(false)));
+      Parameters.Add(new FixedValueParameter<BoolValue>(UseHoldoutParameterName, "True if a holdout set should be generated, false if splitting and pruning shall be performed on the same data (default=false).", new BoolValue(false)));
       Problem = new RegressionProblem();
     }
@@ -134 +162 @@
       base.Initialize(cancellationToken);
       var random = new MersenneTwister();
-      if (SetSeedRandomly) SeedParameter.Value.Value = new System.Random().Next();
+      if (SetSeedRandomly) Seed = RandomSeedGenerator.GetSeed();
       random.Reset(Seed);
       stateScope = InitializeScope(random, Problem.ProblemData, Pruning, MinimalNodeSize, LeafModel, Splitter, GenerateRules, UseHoldout, HoldoutSize);