source: branches/2994-AutoDiffForIntervals/HeuristicLab.Problems.DataAnalysis.Regression.Symbolic.Extensions/ConstrainedConstantOptimizationEvaluator.cs @ 16914

#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2019 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 <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;
using System.Linq;
using HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Optimization;
using HeuristicLab.Parameters;
using HEAL.Attic;

namespace HeuristicLab.Problems.DataAnalysis.Symbolic.Regression {
  [Item("Constant Optimization Evaluator (with constraints)", "")]
  [StorableType("A8958E06-C54A-4193-862E-8315C86EB5C1")]
  public class ConstrainedConstantOptimizationEvaluator : SymbolicRegressionSingleObjectiveEvaluator {
    private const string ConstantOptimizationIterationsParameterName = "ConstantOptimizationIterations";
    private const string ConstantOptimizationImprovementParameterName = "ConstantOptimizationImprovement";
    private const string ConstantOptimizationProbabilityParameterName = "ConstantOptimizationProbability";
    private const string ConstantOptimizationRowsPercentageParameterName = "ConstantOptimizationRowsPercentage";
    private const string UpdateConstantsInTreeParameterName = "UpdateConstantsInSymbolicExpressionTree";
    private const string UpdateVariableWeightsParameterName = "Update Variable Weights";

    private const string FunctionEvaluationsResultParameterName = "Constants Optimization Function Evaluations";
    private const string GradientEvaluationsResultParameterName = "Constants Optimization Gradient Evaluations";
    private const string CountEvaluationsParameterName = "Count Function and Gradient Evaluations";

    public IFixedValueParameter<IntValue> ConstantOptimizationIterationsParameter {
      get { return (IFixedValueParameter<IntValue>)Parameters[ConstantOptimizationIterationsParameterName]; }
    }
    public IFixedValueParameter<DoubleValue> ConstantOptimizationImprovementParameter {
      get { return (IFixedValueParameter<DoubleValue>)Parameters[ConstantOptimizationImprovementParameterName]; }
    }
    public IFixedValueParameter<PercentValue> ConstantOptimizationProbabilityParameter {
      get { return (IFixedValueParameter<PercentValue>)Parameters[ConstantOptimizationProbabilityParameterName]; }
    }
    public IFixedValueParameter<PercentValue> ConstantOptimizationRowsPercentageParameter {
      get { return (IFixedValueParameter<PercentValue>)Parameters[ConstantOptimizationRowsPercentageParameterName]; }
    }
    public IFixedValueParameter<BoolValue> UpdateConstantsInTreeParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateConstantsInTreeParameterName]; }
    }
    public IFixedValueParameter<BoolValue> UpdateVariableWeightsParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[UpdateVariableWeightsParameterName]; }
    }

    public IResultParameter<IntValue> FunctionEvaluationsResultParameter {
      get { return (IResultParameter<IntValue>)Parameters[FunctionEvaluationsResultParameterName]; }
    }
    public IResultParameter<IntValue> GradientEvaluationsResultParameter {
      get { return (IResultParameter<IntValue>)Parameters[GradientEvaluationsResultParameterName]; }
    }
    public IFixedValueParameter<BoolValue> CountEvaluationsParameter {
      get { return (IFixedValueParameter<BoolValue>)Parameters[CountEvaluationsParameterName]; }
    }


    public IntValue ConstantOptimizationIterations {
      get { return ConstantOptimizationIterationsParameter.Value; }
    }
    public DoubleValue ConstantOptimizationImprovement {
      get { return ConstantOptimizationImprovementParameter.Value; }
    }
    public PercentValue ConstantOptimizationProbability {
      get { return ConstantOptimizationProbabilityParameter.Value; }
    }
    public PercentValue ConstantOptimizationRowsPercentage {
      get { return ConstantOptimizationRowsPercentageParameter.Value; }
    }
    public bool UpdateConstantsInTree {
      get { return UpdateConstantsInTreeParameter.Value.Value; }
      set { UpdateConstantsInTreeParameter.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 false; }
    }

    [StorableConstructor]
    protected ConstrainedConstantOptimizationEvaluator(StorableConstructorFlag _) : base(_) { }
    protected ConstrainedConstantOptimizationEvaluator(ConstrainedConstantOptimizationEvaluator original, Cloner cloner)
      : base(original, cloner) {
    }
    public ConstrainedConstantOptimizationEvaluator()
      : base() {
      Parameters.Add(new FixedValueParameter<IntValue>(ConstantOptimizationIterationsParameterName, "Determines how many iterations should be calculated while optimizing the constant of a symbolic expression tree (0 indicates other or default stopping criterion).", new IntValue(10)));
      Parameters.Add(new FixedValueParameter<DoubleValue>(ConstantOptimizationImprovementParameterName, "Determines the relative improvement which must be achieved in the constant optimization to continue with it (0 indicates other or default stopping criterion).", new DoubleValue(0)) { Hidden = true });
      Parameters.Add(new FixedValueParameter<PercentValue>(ConstantOptimizationProbabilityParameterName, "Determines the probability that the constants are optimized", new PercentValue(1)));
      Parameters.Add(new FixedValueParameter<PercentValue>(ConstantOptimizationRowsPercentageParameterName, "Determines the percentage of the rows which should be used for constant optimization", new PercentValue(1)));
      Parameters.Add(new FixedValueParameter<BoolValue>(UpdateConstantsInTreeParameterName, "Determines if the constants in the tree should be overwritten by the optimized constants.", new BoolValue(true)) { Hidden = true });
      Parameters.Add(new FixedValueParameter<BoolValue>(UpdateVariableWeightsParameterName, "Determines if the variable weights in the tree should be  optimized.", new BoolValue(true)) { Hidden = true });

      Parameters.Add(new FixedValueParameter<BoolValue>(CountEvaluationsParameterName, "Determines if function and gradient evaluation should be counted.", new BoolValue(false)));
      Parameters.Add(new ResultParameter<IntValue>(FunctionEvaluationsResultParameterName, "The number of function evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
      Parameters.Add(new ResultParameter<IntValue>(GradientEvaluationsResultParameterName, "The number of gradient evaluations performed by the constants optimization evaluator", "Results", new IntValue()));
    }

    public override IDeepCloneable Clone(Cloner cloner) {
      return new ConstrainedConstantOptimizationEvaluator(this, cloner);
    }

    [StorableHook(HookType.AfterDeserialization)]
    private void AfterDeserialization() { }

    private static readonly object locker = new object();

    public override IOperation InstrumentedApply() {
      var solution = SymbolicExpressionTreeParameter.ActualValue;
      double quality;
      if (RandomParameter.ActualValue.NextDouble() < ConstantOptimizationProbability.Value) {
        IEnumerable<int> constantOptimizationRows = GenerateRowsToEvaluate(ConstantOptimizationRowsPercentage.Value);
        var counter = new EvaluationsCounter();
        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, counter: counter);

        if (ConstantOptimizationRowsPercentage.Value != RelativeNumberOfEvaluatedSamplesParameter.ActualValue.Value) {
          var evaluationRows = GenerateRowsToEvaluate();
          quality = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, double.MinValue, double.MaxValue, ProblemDataParameter.ActualValue, evaluationRows, applyLinearScaling: false);
        }

        if (CountEvaluations) {
          lock (locker) {
            FunctionEvaluationsResultParameter.ActualValue.Value += counter.FunctionEvaluations;
            GradientEvaluationsResultParameter.ActualValue.Value += counter.GradientEvaluations;
          }
        }

      } else {
        var evaluationRows = GenerateRowsToEvaluate();
        quality = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, solution, double.MinValue, double.MaxValue, ProblemDataParameter.ActualValue, evaluationRows, applyLinearScaling: false);
      }
      QualityParameter.ActualValue = new DoubleValue(quality);

      return base.InstrumentedApply();
    }

    public override double Evaluate(IExecutionContext context, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable<int> rows) {
      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = context;
      EstimationLimitsParameter.ExecutionContext = context;
      ApplyLinearScalingParameter.ExecutionContext = context;
      FunctionEvaluationsResultParameter.ExecutionContext = context;
      GradientEvaluationsResultParameter.ExecutionContext = context;

      // MSE 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 mse = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(SymbolicDataAnalysisTreeInterpreterParameter.ActualValue, tree, double.MinValue, double.MaxValue, problemData, rows, applyLinearScaling: false);

      SymbolicDataAnalysisTreeInterpreterParameter.ExecutionContext = null;
      EstimationLimitsParameter.ExecutionContext = null;
      ApplyLinearScalingParameter.ExecutionContext = null;
      FunctionEvaluationsResultParameter.ExecutionContext = null;
      GradientEvaluationsResultParameter.ExecutionContext = null;

      return mse;
    }

    public class EvaluationsCounter {
      public int FunctionEvaluations = 0;
      public int GradientEvaluations = 0;
    }

    private static void GetParameterNodes(ISymbolicExpressionTree tree, out List<ISymbolicExpressionTreeNode> thetaNodes, out List<double> thetaValues) {
      thetaNodes = new List<ISymbolicExpressionTreeNode>();
      thetaValues = new List<double>();

      var nodes = tree.IterateNodesPrefix().ToArray();
      for (int i = 0; i < nodes.Length; ++i) {
        var node = nodes[i];
        if (node is VariableTreeNode variableTreeNode) {
          thetaValues.Add(variableTreeNode.Weight);
          thetaNodes.Add(node);
        } else if (node is ConstantTreeNode constantTreeNode) {
          thetaNodes.Add(node);
          thetaValues.Add(constantTreeNode.Value);
        }
      }
    }

    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) {

      if (!updateVariableWeights) throw new NotSupportedException("not updating variable weights is not supported");
      if (!updateConstantsInTree) throw new NotSupportedException("not updating tree parameters is not supported");
      if (applyLinearScaling) throw new NotSupportedException("linear scaling is not supported");

      // we always update constants, so we don't need to calculate initial quality
      // double originalQuality = SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling: false);

      if (counter == null) counter = new EvaluationsCounter();
      var rowEvaluationsCounter = new EvaluationsCounter();

      var intervalConstraints = problemData.IntervalConstraints;
      var dataIntervals = problemData.VariableRanges.VariableIntervals;

      // convert constants to variables named theta...
      var treeForDerivation = ReplaceConstWithVar(tree, out List<string> thetaNames, out List<double> thetaValues); // copies the tree

      // create trees for relevant derivatives
      Dictionary<string, ISymbolicExpressionTree> derivatives = new Dictionary<string, ISymbolicExpressionTree>();
      var allThetaNodes = thetaNames.Select(_ => new List<ConstantTreeNode>()).ToArray();
      var constraintTrees = new List<ISymbolicExpressionTree>();
      foreach (var constraint in intervalConstraints.Constraints) {
        if (constraint.IsDerivation) {
          if (!problemData.AllowedInputVariables.Contains(constraint.Variable))
            throw new ArgumentException($"Invalid constraint: the variable {constraint.Variable} does not exist in the dataset.");
          var df = DerivativeCalculator.Derive(treeForDerivation, constraint.Variable);

          // alglib requires constraint expressions of the form c(x) <= 0
          // -> we make two expressions, one for the lower bound and one for the upper bound

          if (constraint.Interval.UpperBound < double.PositiveInfinity) {
            var df_smaller_upper = Subtract((ISymbolicExpressionTree)df.Clone(), CreateConstant(constraint.Interval.UpperBound));
            // convert variables named theta back to constants
            var df_prepared = ReplaceVarWithConst(df_smaller_upper, thetaNames, thetaValues, allThetaNodes);
            constraintTrees.Add(df_prepared);
          }
          if (constraint.Interval.LowerBound > double.NegativeInfinity) {
            var df_larger_lower = Subtract(CreateConstant(constraint.Interval.LowerBound), (ISymbolicExpressionTree)df.Clone());
            // convert variables named theta back to constants
            var df_prepared = ReplaceVarWithConst(df_larger_lower, thetaNames, thetaValues, allThetaNodes);
            constraintTrees.Add(df_prepared);
          }
        } else {
          if (constraint.Interval.UpperBound < double.PositiveInfinity) {
            var f_smaller_upper = Subtract((ISymbolicExpressionTree)treeForDerivation.Clone(), CreateConstant(constraint.Interval.UpperBound));
            // convert variables named theta back to constants
            var df_prepared = ReplaceVarWithConst(f_smaller_upper, thetaNames, thetaValues, allThetaNodes);
            constraintTrees.Add(df_prepared);
          }
          if (constraint.Interval.LowerBound > double.NegativeInfinity) {
            var f_larger_lower = Subtract(CreateConstant(constraint.Interval.LowerBound), (ISymbolicExpressionTree)treeForDerivation.Clone());
            // convert variables named theta back to constants
            var df_prepared = ReplaceVarWithConst(f_larger_lower, thetaNames, thetaValues, allThetaNodes);
            constraintTrees.Add(df_prepared);
          }
        }
      }

      var preparedTree = ReplaceVarWithConst(treeForDerivation, thetaNames, thetaValues, allThetaNodes);


      // local function
      void UpdateThetaValues(double[] theta) {
        for (int i = 0; i < theta.Length; ++i) {
          foreach (var constNode in allThetaNodes[i]) constNode.Value = theta[i];
        }
      }

      // buffers for calculate_jacobian
      var target = problemData.TargetVariableTrainingValues.ToArray();
      var fi_eval = new double[target.Length];
      var jac_eval = new double[target.Length, thetaValues.Count];

      // define the callback used by the alglib optimizer
      // the x argument for this callback represents our theta
      // local function
      void calculate_jacobian(double[] x, double[] fi, double[,] jac, object obj) {
        UpdateThetaValues(x);

        var autoDiffEval = new VectorAutoDiffEvaluator();
        autoDiffEval.Evaluate(preparedTree, problemData.Dataset, problemData.TrainingIndices.ToArray(),
          GetParameterNodes(preparedTree, allThetaNodes), fi_eval, jac_eval);

        // calc sum of squared errors and gradient
        var sse = 0.0;
        var g = new double[x.Length];
        for (int i = 0; i < target.Length; i++) {
          var res = target[i] - fi_eval[i];
          sse += 0.5 * res * res;
          for (int j = 0; j < g.Length; j++) {
            g[j] -= res * jac_eval[i, j];
          }
        }

        fi[0] = sse / target.Length;
        for (int j = 0; j < x.Length; j++) { jac[0, j] = g[j] / target.Length; }

        var intervalEvaluator = new IntervalEvaluator();
        for (int i = 0; i < constraintTrees.Count; i++) {
          var interval = intervalEvaluator.Evaluate(constraintTrees[i], dataIntervals, GetParameterNodes(constraintTrees[i], allThetaNodes),
            out double[] lowerGradient, out double[] upperGradient);

          // we transformed this to a constraint c(x) <= 0, so only the upper bound is relevant for us
          fi[i + 1] = interval.UpperBound;
          for (int j = 0; j < x.Length; j++) {
            jac[i + 1, j] = upperGradient[j];
          }
        }
      }



      alglib.minnlcstate state;
      alglib.minnlcreport rep;
      try {
        alglib.minnlccreate(thetaValues.Count, thetaValues.ToArray(), out state);
        alglib.minnlcsetalgoslp(state);        // SLP is more robust but slower
        alglib.minnlcsetcond(state, 0, maxIterations);
        var s = Enumerable.Repeat(1d, thetaValues.Count).ToArray();  // scale is set to unit scale
        alglib.minnlcsetscale(state, s);

        // set non-linear constraints: 0 equality constraints, constraintTrees inequality constraints
        alglib.minnlcsetnlc(state, 0, constraintTrees.Count);

        alglib.minnlcoptimize(state, calculate_jacobian, null, null);
        alglib.minnlcresults(state, out double[] xOpt, out rep);


        // counter.FunctionEvaluations += rep.nfev; TODO
        counter.GradientEvaluations += rep.nfev;

        if (rep.terminationtype != -8) {
          // update parameters in tree
          var pIdx = 0;
          foreach (var node in tree.IterateNodesPostfix().OfType<ConstantTreeNode>()) {
            node.Value = xOpt[pIdx++];
          }

          // note: we keep the optimized constants even when the tree is worse.
        }

      } catch (ArithmeticException) {
        // eval MSE of original tree
        return SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling: false);

      } catch (alglib.alglibexception) {
        // eval MSE of original tree
        return SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling: false);
      }


      // evaluate tree with updated constants
      return SymbolicRegressionSingleObjectiveMeanSquaredErrorEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling: false);
    }

    #region helper
    private static ISymbolicExpressionTreeNode[] GetParameterNodes(ISymbolicExpressionTree tree, List<ConstantTreeNode>[] allNodes) {
      // TODO better solution necessary
      var treeConstNodes = tree.IterateNodesPostfix().OfType<ConstantTreeNode>().ToArray();
      var paramNodes = new ISymbolicExpressionTreeNode[allNodes.Length];
      for (int i = 0; i < paramNodes.Length; i++) {
        paramNodes[i] = allNodes[i].SingleOrDefault(n => treeConstNodes.Contains(n));
      }
      return paramNodes;
    }

    private static ISymbolicExpressionTree ReplaceVarWithConst(ISymbolicExpressionTree tree, List<string> thetaNames, List<double> thetaValues, List<ConstantTreeNode>[] thetaNodes) {
      var copy = (ISymbolicExpressionTree)tree.Clone();
      var nodes = copy.IterateNodesPostfix().ToList();
      for (int i = 0; i < nodes.Count; i++) {
        var n = nodes[i] as VariableTreeNode;
        if (n != null) {
          var thetaIdx = thetaNames.IndexOf(n.VariableName);
          if (thetaIdx >= 0) {
            var parent = n.Parent;
            if (thetaNodes[thetaIdx].Any()) {
              // HACK: REUSE CONSTANT TREE NODE IN SEVERAL TREES
              // we use this trick to allow autodiff over thetas when thetas occurr multiple times in the tree (e.g. in derived trees)
              var constNode = thetaNodes[thetaIdx].First();
              var childIdx = parent.IndexOfSubtree(n);
              parent.RemoveSubtree(childIdx);
              parent.InsertSubtree(childIdx, constNode);
            } else {
              var constNode = (ConstantTreeNode)CreateConstant(thetaValues[thetaIdx]);
              var childIdx = parent.IndexOfSubtree(n);
              parent.RemoveSubtree(childIdx);
              parent.InsertSubtree(childIdx, constNode);
              thetaNodes[thetaIdx].Add(constNode);
            }
          }
        }
      }
      return copy;
    }

    private static ISymbolicExpressionTree ReplaceConstWithVar(ISymbolicExpressionTree tree, out List<string> thetaNames, out List<double> thetaValues) {
      thetaNames = new List<string>();
      thetaValues = new List<double>();
      var copy = (ISymbolicExpressionTree)tree.Clone();
      var nodes = copy.IterateNodesPostfix().ToList();

      int n = 1;
      for (int i = 0; i < nodes.Count; ++i) {
        var node = nodes[i];
        if (node is ConstantTreeNode constantTreeNode) {
          var thetaVar = (VariableTreeNode)new Problems.DataAnalysis.Symbolic.Variable().CreateTreeNode();
          thetaVar.Weight = 1;
          thetaVar.VariableName = $"θ{n++}";

          thetaNames.Add(thetaVar.VariableName);
          thetaValues.Add(constantTreeNode.Value);

          var parent = constantTreeNode.Parent;
          if (parent != null) {
            var index = constantTreeNode.Parent.IndexOfSubtree(constantTreeNode);
            parent.RemoveSubtree(index);
            parent.InsertSubtree(index, thetaVar);
          }
        }
      }
      return copy;
    }

    private static ISymbolicExpressionTreeNode CreateConstant(double value) {
      var constantNode = (ConstantTreeNode)new Constant().CreateTreeNode();
      constantNode.Value = value;
      return constantNode;
    }

    private static ISymbolicExpressionTree Subtract(ISymbolicExpressionTree t, ISymbolicExpressionTreeNode b) {
      var sub = MakeNode<Subtraction>(t.Root.GetSubtree(0).GetSubtree(0), b);
      t.Root.GetSubtree(0).RemoveSubtree(0);
      t.Root.GetSubtree(0).InsertSubtree(0, sub);
      return t;
    }
    private static ISymbolicExpressionTree Subtract(ISymbolicExpressionTreeNode b, ISymbolicExpressionTree t) {
      var sub = MakeNode<Subtraction>(b, t.Root.GetSubtree(0).GetSubtree(0));
      t.Root.GetSubtree(0).RemoveSubtree(0);
      t.Root.GetSubtree(0).InsertSubtree(0, sub);
      return t;
    }

    private static ISymbolicExpressionTreeNode MakeNode<T>(params ISymbolicExpressionTreeNode[] fs) where T : ISymbol, new() {
      var node = new T().CreateTreeNode();
      foreach (var f in fs) node.AddSubtree(f);
      return node;
    }
    #endregion

    private static void UpdateConstants(ISymbolicExpressionTreeNode[] nodes, double[] constants) {
      if (nodes.Length != constants.Length) throw new InvalidOperationException();
      for (int i = 0; i < nodes.Length; i++) {
        if (nodes[i] is VariableTreeNode varNode) varNode.Weight = constants[i];
        else if (nodes[i] is ConstantTreeNode constNode) constNode.Value = constants[i];
      }
    }

    private static alglib.ndimensional_fvec CreateFunc(ISymbolicExpressionTree tree, VectorEvaluator eval, ISymbolicExpressionTreeNode[] parameterNodes, IDataset ds, string targetVar, int[] rows) {
      var y = ds.GetDoubleValues(targetVar, rows).ToArray();
      return (double[] c, double[] fi, object o) => {
        UpdateConstants(parameterNodes, c);
        var pred = eval.Evaluate(tree, ds, rows);
        for (int i = 0; i < fi.Length; i++)
          fi[i] = pred[i] - y[i];

        var counter = (EvaluationsCounter)o;
        counter.FunctionEvaluations++;
      };
    }

    private static alglib.ndimensional_jac CreateJac(ISymbolicExpressionTree tree, VectorAutoDiffEvaluator eval, ISymbolicExpressionTreeNode[] parameterNodes, IDataset ds, string targetVar, int[] rows) {
      var y = ds.GetDoubleValues(targetVar, rows).ToArray();
      return (double[] c, double[] fi, double[,] jac, object o) => {
        UpdateConstants(parameterNodes, c);
        eval.Evaluate(tree, ds, rows, parameterNodes, fi, jac);

        for (int i = 0; i < fi.Length; i++)
          fi[i] -= y[i];

        var counter = (EvaluationsCounter)o;
        counter.GradientEvaluations++;
      };
    }
    public static bool CanOptimizeConstants(ISymbolicExpressionTree tree) {
      return TreeToAutoDiffTermConverter.IsCompatible(tree);
    }
  }
}