#region License Information
/* HeuristicLab
* Copyright (C) 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 .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
using HEAL.Attic;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
[Item("Parameter Optimization Evaluator", "Calculates Pearson Rē of a symbolic regression solution and optimizes the parameters used.")]
[StorableType("24B68851-036D-4446-BD6F-3823E9028FF4")]
public class SymbolicRegressionParameterOptimizationEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
private const string ParameterOptimizationIterationsParameterName = "ParameterOptimizationIterations";
private const string ParameterOptimizationImprovementParameterName = "ParameterOptimizationImprovement";
private const string ParameterOptimizationProbabilityParameterName = "ParameterOptimizationProbability";
private const string ParameterOptimizationRowsPercentageParameterName = "ParameterOptimizationRowsPercentage";
private const string UpdateParametersInTreeParameterName = "UpdateParametersInSymbolicExpressionTree";
private const string UpdateVariableWeightsParameterName = "Update Variable Weights";
private const string FunctionEvaluationsResultParameterName = "Parameters Optimization Function Evaluations";
private const string GradientEvaluationsResultParameterName = "Parameters Optimization Gradient Evaluations";
private const string CountEvaluationsParameterName = "Count Function and Gradient Evaluations";
public IFixedValueParameter ParameterOptimizationIterationsParameter {
get { return (IFixedValueParameter)Parameters[ParameterOptimizationIterationsParameterName]; }
}
public IFixedValueParameter ParameterOptimizationImprovementParameter {
get { return (IFixedValueParameter)Parameters[ParameterOptimizationImprovementParameterName]; }
}
public IFixedValueParameter ParameterOptimizationProbabilityParameter {
get { return (IFixedValueParameter)Parameters[ParameterOptimizationProbabilityParameterName]; }
}
public IFixedValueParameter ParameterOptimizationRowsPercentageParameter {
get { return (IFixedValueParameter)Parameters[ParameterOptimizationRowsPercentageParameterName]; }
}
public IFixedValueParameter UpdateParametersInTreeParameter {
get { return (IFixedValueParameter)Parameters[UpdateParametersInTreeParameterName]; }
}
public IFixedValueParameter UpdateVariableWeightsParameter {
get { return (IFixedValueParameter)Parameters[UpdateVariableWeightsParameterName]; }
}
public IResultParameter FunctionEvaluationsResultParameter {
get { return (IResultParameter)Parameters[FunctionEvaluationsResultParameterName]; }
}
public IResultParameter GradientEvaluationsResultParameter {
get { return (IResultParameter)Parameters[GradientEvaluationsResultParameterName]; }
}
public IFixedValueParameter CountEvaluationsParameter {
get { return (IFixedValueParameter)Parameters[CountEvaluationsParameterName]; }
}
public IntValue ParameterOptimizationIterations {
get { return ParameterOptimizationIterationsParameter.Value; }
}
public DoubleValue ParameterOptimizationImprovement {
get { return ParameterOptimizationImprovementParameter.Value; }
}
public PercentValue ParameterOptimizationProbability {
get { return ParameterOptimizationProbabilityParameter.Value; }
}
public PercentValue ParameterOptimizationRowsPercentage {
get { return ParameterOptimizationRowsPercentageParameter.Value; }
}
public bool UpdateParametersInTree {
get { return UpdateParametersInTreeParameter.Value.Value; }
set { UpdateParametersInTreeParameter.Value.Value = value; }
}
public bool UpdateVariableWeights {
get { return UpdateVariableWeightsParameter.Value.Value; }
set { UpdateVariableWeightsParameter.Value.Value = value; }
}
public bool CountEvaluations {
get { return CountEvaluationsParameter.Value.Value; }
set { CountEvaluationsParameter.Value.Value = value; }
}
public override bool Maximization {
get { return true; }
}
[StorableConstructor]
protected SymbolicRegressionParameterOptimizationEvaluator(StorableConstructorFlag _) : base(_) { }
protected SymbolicRegressionParameterOptimizationEvaluator(SymbolicRegressionParameterOptimizationEvaluator original, Cloner cloner)
: base(original, cloner) {
}
public SymbolicRegressionParameterOptimizationEvaluator()
: base() {
Parameters.Add(new FixedValueParameter(ParameterOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the parameter of a symbolic expression tree (0 indicates other or default stopping criterion).", new IntValue(10)));
Parameters.Add(new FixedValueParameter(ParameterOptimizationImprovementParameterName, "Determines the relative improvement which must be achieved in the parameter optimization to continue with it (0 indicates other or default stopping criterion).", new DoubleValue(0)) { Hidden = true });
Parameters.Add(new FixedValueParameter(ParameterOptimizationProbabilityParameterName, "Determines the probability that the parameters are optimized", new PercentValue(1)));
Parameters.Add(new FixedValueParameter(ParameterOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for parameter optimization", new PercentValue(1)));
Parameters.Add(new FixedValueParameter(UpdateParametersInTreeParameterName, "Determines if the parameters in the tree should be overwritten by the optimized parameters.", new BoolValue(true)) { Hidden = true });
Parameters.Add(new FixedValueParameter(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be optimized.", new BoolValue(true)) { Hidden = true });
Parameters.Add(new FixedValueParameter(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
Parameters.Add(new ResultParameter(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the parameters optimization evaluator", "Results", new IntValue()));
Parameters.Add(new ResultParameter(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the parameters optimization evaluator", "Results", new IntValue()));
}
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicRegressionParameterOptimizationEvaluator(this, cloner);
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
if (!Parameters.ContainsKey(UpdateParametersInTreeParameterName)) {
if (Parameters.ContainsKey("UpdateConstantsInSymbolicExpressionTree")) {
Parameters.Add(new FixedValueParameter(UpdateParametersInTreeParameterName, "Determines if the parameters in the tree should be overwritten by the optimized parameters.", (BoolValue)Parameters["UpdateConstantsInSymbolicExpressionTree"].ActualValue));
Parameters.Remove("UpdateConstantsInSymbolicExpressionTree");
} else {
Parameters.Add(new FixedValueParameter(UpdateParametersInTreeParameterName, "Determines if the parameters in the tree should be overwritten by the optimized parameters.", new BoolValue(true)));
}
}
if (!Parameters.ContainsKey(UpdateVariableWeightsParameterName))
Parameters.Add(new FixedValueParameter(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be optimized.", new BoolValue(true)));
if (!Parameters.ContainsKey(CountEvaluationsParameterName))
Parameters.Add(new FixedValueParameter(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
if (!Parameters.ContainsKey(FunctionEvaluationsResultParameterName)) {
if (Parameters.ContainsKey("Constants Optimization Function Evaluations")) {
Parameters.Remove("Constants Optimization Function Evaluations");
}
Parameters.Add(new ResultParameter(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the parameters optimization evaluator", "Results", new IntValue()));
}
if (!Parameters.ContainsKey(GradientEvaluationsResultParameterName)) {
if (Parameters.ContainsKey("Constants Optimization Gradient Evaluations")) {
Parameters.Remove("Constants Optimization Gradient Evaluations");
}
Parameters.Add(new ResultParameter(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the parameters optimization evaluator", "Results", new IntValue()));
}
if (!Parameters.ContainsKey(ParameterOptimizationIterationsParameterName)) {
if (Parameters.ContainsKey("ConstantOptimizationIterations")) {
Parameters.Add(new FixedValueParameter(ParameterOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the parameter of a symbolic expression tree (0 indicates other or default stopping criterion).", (IntValue)Parameters["ConstantOptimizationIterations"].ActualValue));
Parameters.Remove("ConstantOptimizationIterations");
} else {
Parameters.Add(new FixedValueParameter(ParameterOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the parameter of a symbolic expression tree (0 indicates other or default stopping criterion).", new IntValue(10)));
}
}
if (!Parameters.ContainsKey(ParameterOptimizationImprovementParameterName)) {
if (Parameters.ContainsKey("CosntantOptimizationImprovement")) {
Parameters.Add(new FixedValueParameter(ParameterOptimizationImprovementParameterName, "Determines the relative improvement which must be achieved in the parameter optimization to continue with it (0 indicates other or default stopping criterion).",
(DoubleValue)Parameters["CosntantOptimizationImprovement"].ActualValue) { Hidden = true });
Parameters.Remove("CosntantOptimizationImprovement");
} else {
Parameters.Add(new FixedValueParameter(ParameterOptimizationImprovementParameterName, "Determines the relative improvement which must be achieved in the parameter optimization to continue with it (0 indicates other or default stopping criterion).", new DoubleValue(0)) { Hidden = true });
}
}
if (!Parameters.ContainsKey(ParameterOptimizationProbabilityParameterName)) {
if (Parameters.ContainsKey("ConstantOptimizationProbability")) {
Parameters.Add(new FixedValueParameter(ParameterOptimizationProbabilityParameterName, "Determines the probability that the parameters are optimized",
(PercentValue)Parameters["ConstantOptimizationProbability"].ActualValue));
Parameters.Remove("ConstantOptimizationProbability");
} else {
Parameters.Add(new FixedValueParameter(ParameterOptimizationProbabilityParameterName, "Determines the probability that the parameters are optimized", new PercentValue(1)));
}
}
if (!Parameters.ContainsKey(ParameterOptimizationRowsPercentageParameterName)) {
if (Parameters.ContainsKey("ConstantOptimizationRowsPercentage")) {
Parameters.Add(new FixedValueParameter(ParameterOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for parameter optimization", (PercentValue)Parameters["ConstantOptimizationRowsPercentage"].ActualValue));
Parameters.Remove("ConstantOptimizationRowsPercentage");
} else {
Parameters.Add(new FixedValueParameter(ParameterOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for parameter optimization", new PercentValue(1)));
}
}
}
private static readonly object locker = new object();
public override IOperation InstrumentedApply() {
var solution = SymbolicExpressionTreeParameter.ActualValue;
double quality;
if (RandomParameter.ActualValue.NextDouble() < ParameterOptimizationProbability.Value) {
IEnumerable parameterOptimizationRows = GenerateRowsToEvaluate(ParameterOptimizationRowsPercentage.Value);
var counter = new EvaluationsCounter();
quality = OptimizeParameters(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, ProblemDataParameter.ActualValue,
parameterOptimizationRows, ApplyLinearScalingParameter.ActualValue.Value, ParameterOptimizationIterations.Value, updateVariableWeights: UpdateVariableWeights, lowerEstimationLimit: EstimationLimitsParameter.ActualValue.Lower, upperEstimationLimit: EstimationLimitsParameter.ActualValue.Upper, updateParametersInTree: UpdateParametersInTree, counter: counter);
if (ParameterOptimizationRowsPercentage.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);
}
if (CountEvaluations) {
lock (locker) {
FunctionEvaluationsResultParameter.ActualValue.Value += counter.FunctionEvaluations;
GradientEvaluationsResultParameter.ActualValue.Value += counter.GradientEvaluations;
}
}
} 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();
}
public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows) {
SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
EstimationLimitsParameter.ExecutionContext = context;
ApplyLinearScalingParameter.ExecutionContext = context;
FunctionEvaluationsResultParameter.ExecutionContext = context;
GradientEvaluationsResultParameter.ExecutionContext = context;
// Pearson Rē 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;
FunctionEvaluationsResultParameter.ExecutionContext = null;
GradientEvaluationsResultParameter.ExecutionContext = null;
return r2;
}
public class EvaluationsCounter {
public int FunctionEvaluations = 0;
public int GradientEvaluations = 0;
}
public static double OptimizeParameters(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter,
ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable rows, bool applyLinearScaling,
int maxIterations, bool updateVariableWeights = true,
double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue,
bool updateParametersInTree = true, Action iterationCallback = null, EvaluationsCounter counter = null) {
// Numeric parameters in the tree become variables for parameter optimization.
// Variables in the tree become parameters (fixed values) for parameter optimization.
// 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[] initialParameters;
var parameters = new List();
TreeToAutoDiffTermConverter.ParametricFunction func;
TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad;
if (!TreeToAutoDiffTermConverter.TryConvertToAutoDiff(tree, updateVariableWeights, applyLinearScaling, out parameters, out initialParameters, out func, out func_grad))
throw new NotSupportedException("Could not optimize parameters of symbolic expression tree due to not supported symbols used in the tree.");
if (parameters.Count == 0) return 0.0; // constant expressions always have a Rē of 0.0
var parameterEntries = parameters.ToArray(); // order of entries must be the same for x
// extract inital parameters
double[] c;
if (applyLinearScaling) {
c = new double[initialParameters.Length + 2];
c[0] = 0.0;
c[1] = 1.0;
Array.Copy(initialParameters, 0, c, 2, initialParameters.Length);
} else {
c = (double[])initialParameters.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(info.variableName)) {
x[row, col] = ds.GetDoubleValue(info.variableName, r + info.lag);
} else if (ds.VariableHasType(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, maxIterations);
alglib.lsfitsetxrep(state, iterationCallback != null);
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 => parameter optimization failed due to wrong gradient
// -8 => optimizer detected NAN / INF in the target
// function and/ or gradient
if (retVal != -7 && retVal != -8) {
if (applyLinearScaling) {
var tmp = new double[c.Length - 2];
Array.Copy(c, 2, tmp, 0, tmp.Length);
UpdateParameters(tree, tmp, updateVariableWeights);
} else UpdateParameters(tree, c, updateVariableWeights);
}
var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
if (!updateParametersInTree) UpdateParameters(tree, initialParameters, updateVariableWeights);
if (originalQuality - quality > 0.001 || double.IsNaN(quality)) {
UpdateParameters(tree, initialParameters, updateVariableWeights);
return originalQuality;
}
return quality;
}
private static void UpdateParameters(ISymbolicExpressionTree tree, double[] parameters, bool updateVariableWeights) {
int i = 0;
foreach (var node in tree.Root.IterateNodesPrefix().OfType()) {
NumberTreeNode numberTreeNode = node as NumberTreeNode;
VariableTreeNodeBase variableTreeNodeBase = node as VariableTreeNodeBase;
FactorVariableTreeNode factorVarTreeNode = node as FactorVariableTreeNode;
if (numberTreeNode != null) {
if (numberTreeNode.Parent.Symbol is Power
&& numberTreeNode.Parent.GetSubtree(1) == numberTreeNode) continue; // exponents in powers are not optimizated (see TreeToAutoDiffTermConverter)
numberTreeNode.Value = parameters[i++];
} else if (updateVariableWeights && variableTreeNodeBase != null)
variableTreeNodeBase.Weight = parameters[i++];
else if (factorVarTreeNode != null) {
for (int j = 0; j < factorVarTreeNode.Weights.Length; j++)
factorVarTreeNode.Weights[j] = parameters[i++];
}
}
}
private static alglib.ndimensional_pfunc CreatePFunc(TreeToAutoDiffTermConverter.ParametricFunction func) {
return (double[] c, double[] x, ref double fx, object o) => {
fx = func(c, x);
var counter = (EvaluationsCounter)o;
counter.FunctionEvaluations++;
};
}
private static alglib.ndimensional_pgrad CreatePGrad(TreeToAutoDiffTermConverter.ParametricFunctionGradient func_grad) {
return (double[] c, double[] x, ref double fx, double[] grad, object o) => {
var tuple = func_grad(c, x);
fx = tuple.Item2;
Array.Copy(tuple.Item1, grad, grad.Length);
var counter = (EvaluationsCounter)o;
counter.GradientEvaluations++;
};
}
public static bool CanOptimizeParameters(ISymbolicExpressionTree tree) {
return TreeToAutoDiffTermConverter.IsCompatible(tree);
}
}
}