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Changeset 15127 for stable


Ignore:
Timestamp:
07/06/17 09:22:48 (7 years ago)
Author:
gkronber
Message:

#2690: merged r14345, r14346, r14368 from trunk to stable

Location:
stable
Files:
17 edited
10 copied

Legend:

Unmodified
Added
Removed

  • stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/HeuristicLab.Algorithms.DataAnalysis.Views-3.4.csproj

    r14166 r15127  
    125125  </ItemGroup>
    126126  <ItemGroup>
     127    <Compile Include="RandomForestClassificationSolutionView.cs">
     128      <SubType>UserControl</SubType>
     129    </Compile>
     130    <Compile Include="RandomForestClassificationSolutionView.Designer.cs">
     131      <DependentUpon>RandomForestClassificationSolutionView.cs</DependentUpon>
     132    </Compile>
     133    <Compile Include="RandomForestModelView.cs">
     134      <SubType>UserControl</SubType>
     135    </Compile>
     136    <Compile Include="RandomForestModelView.Designer.cs">
     137      <DependentUpon>RandomForestModelView.cs</DependentUpon>
     138    </Compile>
     139    <Compile Include="RandomForestRegressionSolutionView.cs">
     140      <SubType>UserControl</SubType>
     141    </Compile>
     142    <Compile Include="RandomForestRegressionSolutionView.Designer.cs">
     143      <DependentUpon>RandomForestRegressionSolutionView.cs</DependentUpon>
     144    </Compile>
     145    <Compile Include="GradientBoostedTreesModelView.cs">
     146      <SubType>UserControl</SubType>
     147    </Compile>
     148    <Compile Include="GradientBoostedTreesModelView.Designer.cs">
     149      <DependentUpon>GradientBoostedTreesModelView.cs</DependentUpon>
     150    </Compile>
    127151    <Compile Include="MeanProdView.cs">
    128152      <SubType>UserControl</SubType>
    … …  
    192216    <Compile Include="SupportVectorMachineModelView.Designer.cs">
    193217      <DependentUpon>SupportVectorMachineModelView.cs</DependentUpon>
     218    </Compile>
     219    <Compile Include="GradientBoostedTreesSolutionView.cs">
     220      <SubType>UserControl</SubType>
     221    </Compile>
     222    <Compile Include="GradientBoostedTreesSolutionView.Designer.cs">
     223      <DependentUpon>GradientBoostedTreesSolutionView.cs</DependentUpon>
    194224    </Compile>
    195225  </ItemGroup>
    … …  
    245275      <Private>False</Private>
    246276    </ProjectReference>
     277    <ProjectReference Include="..\..\HeuristicLab.Encodings.SymbolicExpressionTreeEncoding\3.4\HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4.csproj">
     278      <Project>{06D4A186-9319-48A0-BADE-A2058D462EEA}</Project>
     279      <Name>HeuristicLab.Encodings.SymbolicExpressionTreeEncoding-3.4</Name>
     280    </ProjectReference>
    247281    <ProjectReference Include="..\..\HeuristicLab.MainForm.WindowsForms\3.3\HeuristicLab.MainForm.WindowsForms-3.3.csproj">
    248282      <Project>{AB687BBE-1BFE-476B-906D-44237135431D}</Project>
    … …  
    269303      <Name>HeuristicLab.PluginInfrastructure-3.3</Name>
    270304      <Private>False</Private>
     305    </ProjectReference>
     306    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.Classification\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4.csproj">
     307      <Project>{05BAE4E1-A9FA-4644-AA77-42558720159E}</Project>
     308      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.Classification-3.4</Name>
     309    </ProjectReference>
     310    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.Regression\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4.csproj">
     311      <Project>{5AC82412-911B-4FA2-A013-EDC5E3F3FCC2}</Project>
     312      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.Regression-3.4</Name>
     313    </ProjectReference>
     314    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic.Views\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic.Views-3.4.csproj">
     315      <Project>{7a2531ce-3f7c-4f13-bcca-ed6dc27a7086}</Project>
     316      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic.Views-3.4</Name>
     317    </ProjectReference>
     318    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Symbolic\3.4\HeuristicLab.Problems.DataAnalysis.Symbolic-3.4.csproj">
     319      <Project>{3d28463f-ec96-4d82-afee-38be91a0ca00}</Project>
     320      <Name>HeuristicLab.Problems.DataAnalysis.Symbolic-3.4</Name>
    271321    </ProjectReference>
    272322    <ProjectReference Include="..\..\HeuristicLab.Problems.DataAnalysis.Views\3.4\HeuristicLab.Problems.DataAnalysis.Views-3.4.csproj">

  • stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/KMeansClusteringModelView.cs

    r14186 r15127  
    1919 */
    2020#endregion
    21 using System;
    2221using System.Linq;
    23 using System.IO;
    24 using System.Windows.Forms;
    2522using HeuristicLab.MainForm;
    2623using HeuristicLab.MainForm.WindowsForms;

  • stable/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/Plugin.cs.frame

    r14190 r15127  
    3737  [PluginDependency("HeuristicLab.Data", "3.3")]
    3838  [PluginDependency("HeuristicLab.Data.Views", "3.3")]
     39  [PluginDependency("HeuristicLab.Encodings.SymbolicExpressionTreeEncoding", "3.4")]
    3940  [PluginDependency("HeuristicLab.LibSVM", "3.12")]
    4041  [PluginDependency("HeuristicLab.MainForm", "3.3")]
    … …  
    4445  [PluginDependency("HeuristicLab.Problems.DataAnalysis", "3.4")]
    4546  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Views", "3.4")]
     47  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic", "3.4")]
     48  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Regression", "3.4")]
     49  [PluginDependency("HeuristicLab.Problems.DataAnalysis.Symbolic.Classification", "3.4")]
    4650  public class HeuristicLabAlgorithmsDataAnalysisViewsPlugin : PluginBase {
    4751  }

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesAlgorithm.cs

    r15061 r15127  
    270270        } else {
    271271          // otherwise we produce a regression solution
    272           Results.Add(new Result("Solution", new RegressionSolution(model, problemData)));
     272          Results.Add(new Result("Solution", new GradientBoostedTreesSolution(model, problemData)));
    273273        }
    274274      }

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/GradientBoostedTreesSolution.cs

    r14186 r15127  
    2020#endregion
    2121
    22 using System.Collections.Generic;
    23 using System.Linq;
    2422using HeuristicLab.Common;
    2523using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/GradientBoostedTrees/RegressionTreeModel.cs

    r14186 r15127  
    2828using HeuristicLab.Common;
    2929using HeuristicLab.Core;
     30using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    3031using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    3132using HeuristicLab.Problems.DataAnalysis;
     33using HeuristicLab.Problems.DataAnalysis.Symbolic;
     34using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
    3235
    3336namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    210213    }
    211214
     215    /// <summary>
     216    /// Transforms the tree model to a symbolic regression solution
     217    /// </summary>
     218    /// <param name="problemData"></param>
     219    /// <returns>A new symbolic regression solution which matches the tree model</returns>
     220    public ISymbolicRegressionSolution CreateSymbolicRegressionSolution(IRegressionProblemData problemData) {
     221      var rootSy = new ProgramRootSymbol();
     222      var startSy = new StartSymbol();
     223      var varCondSy = new VariableCondition() { IgnoreSlope = true };
     224      var constSy = new Constant();
     225
     226      var startNode = startSy.CreateTreeNode();
     227      startNode.AddSubtree(CreateSymbolicRegressionTreeRecursive(tree, 0, varCondSy, constSy));
     228      var rootNode = rootSy.CreateTreeNode();
     229      rootNode.AddSubtree(startNode);
     230      var model = new SymbolicRegressionModel(TargetVariable, new SymbolicExpressionTree(rootNode), new SymbolicDataAnalysisExpressionTreeLinearInterpreter());
     231      return model.CreateRegressionSolution(problemData);
     232    }
     233
     234    private ISymbolicExpressionTreeNode CreateSymbolicRegressionTreeRecursive(TreeNode[] treeNodes, int nodeIdx, VariableCondition varCondSy, Constant constSy) {
     235      var curNode = treeNodes[nodeIdx];
     236      if (curNode.VarName == TreeNode.NO_VARIABLE) {
     237        var node = (ConstantTreeNode)constSy.CreateTreeNode();
     238        node.Value = curNode.Val;
     239        return node;
     240      } else {
     241        var node = (VariableConditionTreeNode)varCondSy.CreateTreeNode();
     242        node.VariableName = curNode.VarName;
     243        node.Threshold = curNode.Val;
     244
     245        var left = CreateSymbolicRegressionTreeRecursive(treeNodes, curNode.LeftIdx, varCondSy, constSy);
     246        var right = CreateSymbolicRegressionTreeRecursive(treeNodes, curNode.RightIdx, varCondSy, constSy);
     247        node.AddSubtree(left);
     248        node.AddSubtree(right);
     249        return node;
     250      }
     251    }
     252
     253
    212254    private string TreeToString(int idx, string part) {
    213255      var n = tree[idx];

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IRandomForestClassificationSolution.cs

    r14186 r15127  
    3030  public interface IRandomForestClassificationSolution : IClassificationSolution {
    3131    new IRandomForestModel Model { get; }
     32    int NumberOfTrees { get; }
    3233  }
    3334}

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IRandomForestModel.cs

    r14822 r15127  
    2020#endregion
    2121
    22 using HeuristicLab.Optimization;
     22using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2323using HeuristicLab.Problems.DataAnalysis;
    24 using HeuristicLab.Core;
    25 using System.Collections.Generic;
     24
    2625
    2726namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    3029  /// </summary>
    3130  public interface IRandomForestModel : IConfidenceRegressionModel, IClassificationModel {
     31    int NumberOfTrees { get; }
     32    ISymbolicExpressionTree ExtractTree(int treeIdx); // returns a specific tree from the random forest as a ISymbolicRegressionModel
    3233  }
    3334}

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/Interfaces/IRandomForestRegressionSolution.cs

    r14822 r15127  
    2020#endregion
    2121
    22 using HeuristicLab.Optimization;
    2322using HeuristicLab.Problems.DataAnalysis;
    24 using HeuristicLab.Core;
     23using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
    2524
    2625namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    3029  public interface IRandomForestRegressionSolution : IConfidenceRegressionSolution {
    3130    new IRandomForestModel Model { get; }
     31    int NumberOfTrees { get; }
    3232  }
    3333}

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestClassificationSolution.cs

    r14186 r15127  
    3838    }
    3939
     40    public int NumberOfTrees {
     41      get { return Model.NumberOfTrees; }
     42    }
     43
    4044    [StorableConstructor]
    4145    private RandomForestClassificationSolution(bool deserializing) : base(deserializing) { }

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestModel.cs

    r14822 r15127  
    2525using HeuristicLab.Common;
    2626using HeuristicLab.Core;
     27using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
    2728using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2829using HeuristicLab.Problems.DataAnalysis;
     30using HeuristicLab.Problems.DataAnalysis.Symbolic;
    2931
    3032namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    4951    }
    5052
     53    public int NumberOfTrees {
     54      get { return nTrees; }
     55    }
    5156
    5257    // instead of storing the data of the model itself
    … …  
    6469    [Storable]
    6570    private double m;
    66 
    6771
    6872    [StorableConstructor]
    … …  
    197201    }
    198202
     203    public ISymbolicExpressionTree ExtractTree(int treeIdx) {
     204      var rf = RandomForest;
     205      // hoping that the internal representation of alglib is stable
     206
     207      // TREE FORMAT
     208      // W[Offs]      -   size of sub-array (for the tree)
     209      //     node info:
     210      // W[K+0]       -   variable number        (-1 for leaf mode)
     211      // W[K+1]       -   threshold              (class/value for leaf node)
     212      // W[K+2]       -   ">=" branch index      (absent for leaf node)
     213
     214      // skip irrelevant trees
     215      int offset = 0;
     216      for (int i = 0; i < treeIdx - 1; i++) {
     217        offset = offset + (int)Math.Round(rf.innerobj.trees[offset]);
     218      }
     219
     220      var constSy = new Constant();
     221      var varCondSy = new VariableCondition() { IgnoreSlope = true };
     222
     223      var node = CreateRegressionTreeRec(rf.innerobj.trees, offset, offset + 1, constSy, varCondSy);
     224
     225      var startNode = new StartSymbol().CreateTreeNode();
     226      startNode.AddSubtree(node);
     227      var root = new ProgramRootSymbol().CreateTreeNode();
     228      root.AddSubtree(startNode);
     229      return new SymbolicExpressionTree(root);
     230    }
     231
     232    private ISymbolicExpressionTreeNode CreateRegressionTreeRec(double[] trees, int offset, int k, Constant constSy, VariableCondition varCondSy) {
     233
     234      // alglib source for evaluation of one tree (dfprocessinternal)
     235      // offs = 0
     236      //
     237      // Set pointer to the root
     238      //
     239      // k = offs + 1;
     240      //
     241      // //
     242      // // Navigate through the tree
     243      // //
     244      // while (true) {
     245      //   if ((double)(df.trees[k]) == (double)(-1)) {
     246      //     if (df.nclasses == 1) {
     247      //       y[0] = y[0] + df.trees[k + 1];
     248      //     } else {
     249      //       idx = (int)Math.Round(df.trees[k + 1]);
     250      //       y[idx] = y[idx] + 1;
     251      //     }
     252      //     break;
     253      //   }
     254      //   if ((double)(x[(int)Math.Round(df.trees[k])]) < (double)(df.trees[k + 1])) {
     255      //     k = k + innernodewidth;
     256      //   } else {
     257      //     k = offs + (int)Math.Round(df.trees[k + 2]);
     258      //   }
     259      // }
     260
     261      if ((double)(trees[k]) == (double)(-1)) {
     262        var constNode = (ConstantTreeNode)constSy.CreateTreeNode();
     263        constNode.Value = trees[k + 1];
     264        return constNode;
     265      } else {
     266        var condNode = (VariableConditionTreeNode)varCondSy.CreateTreeNode();
     267        condNode.VariableName = AllowedInputVariables[(int)Math.Round(trees[k])];
     268        condNode.Threshold = trees[k + 1];
     269        condNode.Slope = double.PositiveInfinity;
     270
     271        var left = CreateRegressionTreeRec(trees, offset, k + 3, constSy, varCondSy);
     272        var right = CreateRegressionTreeRec(trees, offset, offset + (int)Math.Round(trees[k + 2]), constSy, varCondSy);
     273
     274        condNode.AddSubtree(left); // not 100% correct because interpreter uses: if(x <= thres) left() else right() and RF uses if(x < thres) left() else right() (see above)
     275        condNode.AddSubtree(right);
     276        return condNode;
     277      }
     278    }
     279
    199280
    200281    public IRegressionSolution CreateRegressionSolution(IRegressionProblemData problemData) {

  • stable/HeuristicLab.Algorithms.DataAnalysis/3.4/RandomForest/RandomForestRegressionSolution.cs

    r14822 r15127  
    2020#endregion
    2121
     22using System;
    2223using HeuristicLab.Common;
    2324using HeuristicLab.Core;
    2425using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
    2526using HeuristicLab.Problems.DataAnalysis;
     27using HeuristicLab.Problems.DataAnalysis.Symbolic.Regression;
    2628
    2729namespace HeuristicLab.Algorithms.DataAnalysis {
    … …  
    3638      get { return (IRandomForestModel)base.Model; }
    3739      set { base.Model = value; }
     40    }
     41
     42    public int NumberOfTrees {
     43      get { return Model.NumberOfTrees; }
    3844    }
    3945

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionCompiledTreeInterpreter.cs

    r14811 r15127  
    614614        case OpCodes.VariableCondition: {
    615615            var variableConditionTreeNode = (VariableConditionTreeNode)node;
     616            if (variableConditionTreeNode.Symbol.IgnoreSlope) throw new NotSupportedException("Strict variable conditionals are not supported");
    616617            var variableName = variableConditionTreeNode.VariableName;
    617618            var indexExpr = Expression.Constant(variableIndices[variableName]);

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeInterpreter.cs

    r14811 r15127  
    471471            if (row < 0 || row >= dataset.Rows) return double.NaN;
    472472            var variableConditionTreeNode = (VariableConditionTreeNode)currentInstr.dynamicNode;
    473             double variableValue = ((IList<double>)currentInstr.data)[row];
    474             double x = variableValue - variableConditionTreeNode.Threshold;
    475             double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
    476 
    477             double trueBranch = Evaluate(dataset, ref row, state);
    478             double falseBranch = Evaluate(dataset, ref row, state);
    479 
    480             return trueBranch * p + falseBranch * (1 - p);
     473            if (!variableConditionTreeNode.Symbol.IgnoreSlope) {
     474              double variableValue = ((IList<double>)currentInstr.data)[row];
     475              double x = variableValue - variableConditionTreeNode.Threshold;
     476              double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
     477
     478              double trueBranch = Evaluate(dataset, ref row, state);
     479              double falseBranch = Evaluate(dataset, ref row, state);
     480
     481              return trueBranch * p + falseBranch * (1 - p);
     482            } else {
     483              // strict threshold
     484              double variableValue = ((IList<double>)currentInstr.data)[row];
     485              if (variableValue <= variableConditionTreeNode.Threshold) {
     486                var left = Evaluate(dataset, ref row, state);
     487                state.SkipInstructions();
     488                return left;
     489              } else {
     490                state.SkipInstructions();
     491                return Evaluate(dataset, ref row, state);
     492              }
     493            }
    481494          }
    482495        default:

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Interpreter/SymbolicDataAnalysisExpressionTreeLinearInterpreter.cs

    r14974 r15127  
    126126    private readonly object syncRoot = new object();
    127127    public IEnumerable<double> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows) {
     128      if (!rows.Any()) return Enumerable.Empty<double>();
    128129      if (CheckExpressionsWithIntervalArithmetic)
    129130        throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
    … …  
    159160          if (row < 0 || row >= dataset.Rows) instr.value = double.NaN;
    160161          var variableConditionTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
    161           double variableValue = ((IList<double>)instr.data)[row];
    162           double x = variableValue - variableConditionTreeNode.Threshold;
    163           double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
    164 
    165           double trueBranch = code[instr.childIndex].value;
    166           double falseBranch = code[instr.childIndex + 1].value;
    167 
    168           instr.value = trueBranch * p + falseBranch * (1 - p);
     162          if (!variableConditionTreeNode.Symbol.IgnoreSlope) {
     163            double variableValue = ((IList<double>)instr.data)[row];
     164            double x = variableValue - variableConditionTreeNode.Threshold;
     165            double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
     166
     167            double trueBranch = code[instr.childIndex].value;
     168            double falseBranch = code[instr.childIndex + 1].value;
     169
     170            instr.value = trueBranch * p + falseBranch * (1 - p);
     171          } else {
     172            double variableValue = ((IList<double>)instr.data)[row];
     173            if (variableValue <= variableConditionTreeNode.Threshold) {
     174              instr.value = code[instr.childIndex].value;
     175            } else {
     176              instr.value = code[instr.childIndex + 1].value;
     177            }
     178          }
    169179        } else if (instr.opCode == OpCodes.Add) {
    170180          double s = code[instr.childIndex].value;
    … …  
    411421              for (int j = 1; j != seq.Length; ++j)
    412422                seq[j].skip = true;
    413             }
    414             break;
     423              break;
     424            }
    415425        }
    416426        #endregion

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableCondition.cs

    r14811 r15127  
    149149        }
    150150      }
     151    }
     152
     153    /// <summary>
     154    /// Flag to indicate if the interpreter should ignore the slope parameter (introduced for representation of expression trees)
     155    /// </summary>
     156    [Storable]
     157    private bool ignoreSlope;
     158    public bool IgnoreSlope {
     159      get { return ignoreSlope; }
     160      set { ignoreSlope = value; }
    151161    }
    152162

  • stable/HeuristicLab.Problems.DataAnalysis.Symbolic/3.4/Symbols/VariableConditionTreeNode.cs

    r14186 r15127  
    9696
    9797    public override string ToString() {
    98       if (slope.IsAlmost(0.0))
     98      if (slope.IsAlmost(0.0) || Symbol.IgnoreSlope) {
     99        return variableName + " < " + threshold.ToString("E4");
     100      } else {
    99101        return variableName + " > " + threshold.ToString("E4") + Environment.NewLine +
    100           "slope: " + slope.ToString("E4");
    101       else
    102         return variableName + " > " + threshold.ToString("E4");
     102               "slope: " + slope.ToString("E4");
     103      }
    103104    }
    104105  }
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