Changeset 15744

Timestamp:

02/09/18 10:51:27 (7 years ago)

Author:

gkronber

Message:

#2892: implemented a new class LinearRegressionModel that produces variances for predictions (IConfidenceRegressionModel). Changed LR to produce a ConfidenceRegressionSolution (old static method is kept but marked obsolete)

Location:

branches/2892_LR-prediction-intervals/HeuristicLab.Algorithms.DataAnalysis/3.4

Files:

• HeuristicLab.Algorithms.DataAnalysis-3.4.csproj (modified) (2 diffs)
• Linear/LinearRegression.cs (modified) (5 diffs)
• Linear/LinearRegressionModel.cs (added)

branches/2892_LR-prediction-intervals/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

@@ -262 +262 @@
     <Compile Include="Linear\MultinomialLogitClassification.cs" />
     <Compile Include="Linear\MultinomialLogitClassificationSolution.cs" />
+    <Compile Include="Linear\LinearRegressionModel.cs" />
     <Compile Include="Linear\MultinomialLogitModel.cs" />
     <Compile Include="Linear\Scaling.cs" />
@@ -320 +321 @@
     <Compile Include="TSNE\Distances\IndexedItemDistance.cs" />
     <Compile Include="TSNE\Distances\ManhattanDistance.cs" />
-  <Compile Include="TSNE\Distances\WeightedEuclideanDistance.cs" />
+    <Compile Include="TSNE\Distances\WeightedEuclideanDistance.cs" />
     <Compile Include="TSNE\Distances\IDistance.cs" />
     <Compile Include="TSNE\PriorityQueue.cs" />

branches/2892_LR-prediction-intervals/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

@@ -62 +62 @@
     protected override void Run(CancellationToken cancellationToken) {
       double rmsError, cvRmsError;
-      var solution = CreateLinearRegressionSolution(Problem.ProblemData, out rmsError, out cvRmsError);
+      var solution = CreateSolution(Problem.ProblemData, out rmsError, out cvRmsError);
       Results.Add(new Result(LinearRegressionModelResultName, "The linear regression solution.", solution));
       Results.Add(new Result("Root mean square error", "The root of the mean of squared errors of the linear regression solution on the training set.", new DoubleValue(rmsError)));
@@ -68 +68 @@
     }
 
+    [Obsolete("Use CreateSolution() instead")]
     public static ISymbolicRegressionSolution CreateLinearRegressionSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {
-      var dataset = problemData.Dataset;
-      string targetVariable = problemData.TargetVariable;
-      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
-      IEnumerable<int> rows = problemData.TrainingIndices;
-      var doubleVariables = allowedInputVariables.Where(dataset.VariableHasType<double>);
-      var factorVariableNames = allowedInputVariables.Where(dataset.VariableHasType<string>);
-      var factorVariables = dataset.GetFactorVariableValues(factorVariableNames, rows);
-      double[,] binaryMatrix = dataset.ToArray(factorVariables, rows);
-      double[,] doubleVarMatrix = dataset.ToArray(doubleVariables.Concat(new string[] { targetVariable }), rows);
-      var inputMatrix = binaryMatrix.HorzCat(doubleVarMatrix);
-
-      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
-        throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
+      IEnumerable<string> doubleVariables;
+      IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables;
+      double[,] inputMatrix;
+      PrepareData(problemData, out inputMatrix, out doubleVariables, out factorVariables);
 
       alglib.linearmodel lm = new alglib.linearmodel();
@@ -87 +79 @@
       int nRows = inputMatrix.GetLength(0);
       int nFeatures = inputMatrix.GetLength(1) - 1;
-      double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
 
       int retVal = 1;
@@ -95 +86 @@
       cvRmsError = ar.cvrmserror;
 
+      double[] coefficients = new double[nFeatures + 1]; // last coefficient is for the constant
       alglib.lrunpack(lm, out coefficients, out nFeatures);
-
-      int nFactorCoeff = binaryMatrix.GetLength(1);
+      
+      int nFactorCoeff = factorVariables.Sum(kvp=>kvp.Value.Count());
       int nVarCoeff = doubleVariables.Count();
       var tree = LinearModelToTreeConverter.CreateTree(factorVariables, coefficients.Take(nFactorCoeff).ToArray(),
@@ -108 +100 @@
       return solution;
     }
+
+    public static IRegressionSolution CreateSolution(IRegressionProblemData problemData, out double rmsError, out double cvRmsError) {
+      IEnumerable<string> doubleVariables;
+      IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables;
+      double[,] inputMatrix;
+      PrepareData(problemData, out inputMatrix, out doubleVariables, out factorVariables);
+
+      alglib.linearmodel lm = new alglib.linearmodel();
+      alglib.lrreport ar = new alglib.lrreport();
+      int nRows = inputMatrix.GetLength(0);
+      int nFeatures = inputMatrix.GetLength(1) - 1;
+
+      int retVal = 1;
+      alglib.lrbuild(inputMatrix, nRows, nFeatures, out retVal, out lm, out ar);
+      if (retVal != 1) throw new ArgumentException("Error in calculation of linear regression solution");
+      rmsError = ar.rmserror;
+      cvRmsError = ar.cvrmserror;
+
+      // get parameters of the model
+      double[] w;
+      int nVars;
+      alglib.lrunpack(lm, out w, out nVars);
+
+      // ar.c is the covariation matrix,  array[0..NVars,0..NVars].
+      // C[i, j] = Cov(A[i], A[j])
+
+      var solution = new LinearRegressionModel(w, ar.c, cvRmsError, problemData.TargetVariable, doubleVariables, factorVariables)
+        .CreateRegressionSolution((IRegressionProblemData)problemData.Clone());
+      solution.Name = "Linear Regression Solution";
+      return solution;
+    }
+
+    private static void PrepareData(IRegressionProblemData problemData, 
+      out double[,] inputMatrix, 
+      out IEnumerable<string> doubleVariables, 
+      out IEnumerable<KeyValuePair<string, IEnumerable<string>>> factorVariables) {
+      var dataset = problemData.Dataset;
+      string targetVariable = problemData.TargetVariable;
+      IEnumerable<string> allowedInputVariables = problemData.AllowedInputVariables;
+      IEnumerable<int> rows = problemData.TrainingIndices;
+      doubleVariables = allowedInputVariables.Where(dataset.VariableHasType<double>);
+      var factorVariableNames = allowedInputVariables.Where(dataset.VariableHasType<string>);
+      factorVariables = dataset.GetFactorVariableValues(factorVariableNames, rows);
+      double[,] binaryMatrix = dataset.ToArray(factorVariables, rows);
+      double[,] doubleVarMatrix = dataset.ToArray(doubleVariables.Concat(new string[] { targetVariable }), rows);
+      inputMatrix = binaryMatrix.HorzCat(doubleVarMatrix);
+
+      if (inputMatrix.Cast<double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
+        throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
+    }
     #endregion
   }