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Changeset 16389

Timestamp:

12/15/18 12:46:02 (6 years ago)

Message:

#2892: merged branch back to trunk

Location:

trunk

: 88 edited
: 1 copied

: Unmodified
: Added
: Removed

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/HeuristicLab.Algorithms.DataAnalysis-3.4.csproj

r15783	r16389
263	263	<Compile Include="Linear\MultinomialLogitClassification.cs" />
264	264	<Compile Include="Linear\MultinomialLogitClassificationSolution.cs" />
	265	<Compile Include="Linear\LinearRegressionModel.cs" />
265	266	<Compile Include="Linear\MultinomialLogitModel.cs" />
266	267	<Compile Include="Linear\Scaling.cs" />

trunk/HeuristicLab.Algorithms.DataAnalysis/3.4/Linear/LinearRegression.cs

-                      r15786
+                      r16389
     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)));
 …
+    }
+    [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.ContainsNanOrInfinity())
+        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();
 …
       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;
 …
       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(),
 …
       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.ContainsNanOrInfinity())
+        throw new NotSupportedException("Linear regression does not support NaN or infinity values in the input dataset.");
+    }
     #endregion
+  }

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Changeset 16389

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