public override IOperation InstrumentedApply() { var solution = SymbolicExpressionTreeParameter.ActualValue; double quality; if (RandomParameter.ActualValue.NextDouble() < ConstantOptimizationProbability.Value) { IEnumerable <int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value); quality = OptimizeConstants(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue, constantOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ConstantOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateConstantsInTree: UpdateConstantsInTree); if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) { var evaluationRows = GenerateRowsToEvaluate(); quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value); } } else { var evaluationRows = GenerateRowsToEvaluate(); quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, ProblemDataParameter.ActualValue, evaluationRows, ApplyLinearScalingParameter.ActualValue.Value); } QualityParameter.ActualValue = new DoubleValue(quality); return(base.InstrumentedApply()); }
} // maximize R² and minimize average similarity public override IOperation InstrumentedApply() { IEnumerable <int> rows = GenerateRowsToEvaluate(); var solution = SymbolicExpressionTreeParameter.ActualValue; var problemData = ProblemDataParameter.ActualValue; var interpreter = SymbolicDataAnalysisTreeInterpreterParameter.ActualValue; var estimationLimits = EstimationLimitsParameter.ActualValue; var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value; if (UseConstantOptimization) { SymbolicRegressionConstantOptimizationEvaluator.OptimizeConstants(interpreter, solution, problemData, rows, applyLinearScaling, ConstantOptimizationIterations, updateVariableWeights: ConstantOptimizationUpdateVariableWeights, lowerEstimationLimit: estimationLimits.Lower, upperEstimationLimit: estimationLimits.Upper); } double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling); if (DecimalPlaces >= 0) { r2 = Math.Round(r2, DecimalPlaces); } lock (locker) { if (AverageSimilarityParameter.ActualValue == null) { var context = new ExecutionContext(null, SimilarityCalculator, ExecutionContext.Scope.Parent); SimilarityCalculator.StrictSimilarity = StrictSimilarity; SimilarityCalculator.Execute(context, CancellationToken); } } var avgSimilarity = AverageSimilarityParameter.ActualValue.Value; QualitiesParameter.ActualValue = new DoubleArray(new[] { r2, avgSimilarity }); return(base.InstrumentedApply()); }
public override double[] Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; AverageSimilarityParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; var estimationLimits = EstimationLimitsParameter.ActualValue; var applyLinearScaling = ApplyLinearScalingParameter.ActualValue.Value; double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, estimationLimits.Lower, estimationLimits.Upper, problemData, rows, applyLinearScaling); lock (locker) { if (AverageSimilarityParameter.ActualValue == null) { var ctx = new ExecutionContext(null, SimilarityCalculator, context.Scope.Parent); SimilarityCalculator.StrictSimilarity = StrictSimilarity; SimilarityCalculator.Execute(context, CancellationToken); } } var avgSimilarity = AverageSimilarityParameter.ActualValue.Value; SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return(new[] { r2, avgSimilarity }); }
public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int decimalPlaces) { double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (decimalPlaces >= 0) { r2 = Math.Round(r2, decimalPlaces); } return(new double[2] { r2, SymbolicDataAnalysisModelComplexityCalculator.CalculateComplexity(solution) }); }
public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int decimalPlaces) { double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (decimalPlaces >= 0) { r2 = Math.Round(r2, decimalPlaces); } return(new double[2] { r2, solution.IterateNodesPostfix().OfType <IVariableTreeNode>().Count() }); // count the number of variables }
public static double[] Calculate(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree solution, double lowerEstimationLimit, double upperEstimationLimit, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int decimalPlaces) { double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, solution, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (decimalPlaces >= 0) { r2 = Math.Round(r2, decimalPlaces); } return(new double[2] { r2, solution.IterateNodesPostfix().Sum(n => n.GetLength()) }); // sum of the length of the whole sub-tree for each node }
public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows) { SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context; EstimationLimitsParameter.ExecutionContext = context; ApplyLinearScalingParameter.ExecutionContext = context; // Pearson R² evaluator is used on purpose instead of the const-opt evaluator, // because Evaluate() is used to get the quality of evolved models on // different partitions of the dataset (e.g., best validation model) double r2 = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, EstimationLimitsParameter.ActualValue.Lower, EstimationLimitsParameter.ActualValue.Upper, problemData, rows, ApplyLinearScalingParameter.ActualValue.Value); SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null; EstimationLimitsParameter.ExecutionContext = null; ApplyLinearScalingParameter.ExecutionContext = null; return(r2); }
protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner) : base(original, cloner) { }
public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int maxIterations, bool updateVariableWeights = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue, bool updateConstantsInTree = true) { List <AutoDiff.Variable> variables = new List <AutoDiff.Variable>(); List <AutoDiff.Variable> parameters = new List <AutoDiff.Variable>(); List <string> variableNames = new List <string>(); AutoDiff.Term func; if (!TryTransformToAutoDiff(tree.Root.GetSubtree(0), variables, parameters, variableNames, updateVariableWeights, out func)) { throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree."); } if (variableNames.Count == 0) { return(0.0); } AutoDiff.IParametricCompiledTerm compiledFunc = func.Compile(variables.ToArray(), parameters.ToArray()); List <SymbolicExpressionTreeTerminalNode> terminalNodes = null; if (updateVariableWeights) { terminalNodes = tree.Root.IterateNodesPrefix().OfType <SymbolicExpressionTreeTerminalNode>().ToList(); } else { terminalNodes = new List <SymbolicExpressionTreeTerminalNode>(tree.Root.IterateNodesPrefix().OfType <ConstantTreeNode>()); } //extract inital constants double[] c = new double[variables.Count]; { c[0] = 0.0; c[1] = 1.0; int i = 2; foreach (var node in terminalNodes) { ConstantTreeNode constantTreeNode = node as ConstantTreeNode; VariableTreeNode variableTreeNode = node as VariableTreeNode; if (constantTreeNode != null) { c[i++] = constantTreeNode.Value; } else if (updateVariableWeights && variableTreeNode != null) { c[i++] = variableTreeNode.Weight; } } } double[] originalConstants = (double[])c.Clone(); double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); alglib.lsfitstate state; alglib.lsfitreport rep; int info; IDataset ds = problemData.Dataset; double[,] x = new double[rows.Count(), variableNames.Count]; int row = 0; foreach (var r in rows) { for (int col = 0; col < variableNames.Count; col++) { x[row, col] = ds.GetDoubleValue(variableNames[col], r); } row++; } double[] y = ds.GetDoubleValues(problemData.TargetVariable, rows).ToArray(); int n = x.GetLength(0); int m = x.GetLength(1); int k = c.Length; alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(compiledFunc); alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(compiledFunc); try { alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state); alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations); //alglib.lsfitsetgradientcheck(state, 0.001); alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, null, null); alglib.lsfitresults(state, out info, out c, out rep); } catch (ArithmeticException) { return(originalQuality); } catch (alglib.alglibexception) { return(originalQuality); } //info == -7 => constant optimization failed due to wrong gradient if (info != -7) { UpdateConstants(tree, c.Skip(2).ToArray(), updateVariableWeights); } var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (!updateConstantsInTree) { UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights); } if (originalQuality - quality > 0.001 || double.IsNaN(quality)) { UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights); return(originalQuality); } return(quality); }
public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int maxIterations, bool updateVariableWeights = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue, bool updateConstantsInTree = true, Action <double[], double, object> iterationCallback = null, EvaluationsCounter counter = null) { // numeric constants in the tree become variables for constant opt // variables in the tree become parameters (fixed values) for constant opt // for each parameter (variable in the original tree) we store the // variable name, variable value (for factor vars) and lag as a DataForVariable object. // A dictionary is used to find parameters double[] initialConstants; var parameters = new List <TreeToAutoDiffTermConverter.DataForVariable>(); TreeToAutoDiffTermConverter.ParametricFunction func; TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad; if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, applyLinearScaling, out parameters, out initialConstants, out func, out func_grad)) { throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree."); } if (parameters.Count == 0) { return(0.0); // gkronber: constant expressions always have a R² of 0.0 } var parameterEntries = parameters.ToArray(); // order of entries must be the same for x //extract inital constants double[] c; if (applyLinearScaling) { c = new double[initialConstants.Length + 2]; c[0] = 0.0; c[1] = 1.0; Array.Copy(initialConstants, 0, c, 2, initialConstants.Length); } else { c = (double[])initialConstants.Clone(); } double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (counter == null) { counter = new EvaluationsCounter(); } var rowEvaluationsCounter = new EvaluationsCounter(); alglib.lsfitstate state; alglib.lsfitreport rep; int retVal; IDataset ds = problemData.Dataset; double[,] x = new double[rows.Count(), parameters.Count]; int row = 0; foreach (var r in rows) { int col = 0; foreach (var info in parameterEntries) { if (ds.VariableHasType <double>(info.variableName)) { x[row, col] = ds.GetDoubleValue(info.variableName, r + info.lag); } else if (ds.VariableHasType <string>(info.variableName)) { x[row, col] = ds.GetStringValue(info.variableName, r) == info.variableValue ? 1 : 0; } else { throw new InvalidProgramException("found a variable of unknown type"); } col++; } row++; } double[] y = ds.GetDoubleValues(problemData.TargetVariable, rows).ToArray(); int n = x.GetLength(0); int m = x.GetLength(1); int k = c.Length; alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(func); alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(func_grad); alglib.ndimensional_rep xrep = (p, f, obj) => iterationCallback(p, f, obj); try { alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state); alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations); alglib.lsfitsetxrep(state, iterationCallback != null); //alglib.lsfitsetgradientcheck(state, 0.001); alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, xrep, rowEvaluationsCounter); alglib.lsfitresults(state, out retVal, out c, out rep); } catch (ArithmeticException) { return(originalQuality); } catch (alglib.alglibexception) { return(originalQuality); } counter.FunctionEvaluations += rowEvaluationsCounter.FunctionEvaluations / n; counter.GradientEvaluations += rowEvaluationsCounter.GradientEvaluations / n; //retVal == -7 => constant optimization failed due to wrong gradient if (retVal != -7) { if (applyLinearScaling) { var tmp = new double[c.Length - 2]; Array.Copy(c, 2, tmp, 0, tmp.Length); UpdateConstants(tree, tmp, updateVariableWeights); } else { UpdateConstants(tree, c, updateVariableWeights); } } var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling); if (!updateConstantsInTree) { UpdateConstants(tree, initialConstants, updateVariableWeights); } if (originalQuality - quality > 0.001 || double.IsNaN(quality)) { UpdateConstants(tree, initialConstants, updateVariableWeights); return(originalQuality); } return(quality); }
protected SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator(SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator original, Cloner cloner) : base(original, cloner) { }