source: stable/HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis/3.4/SymbolicTimeSeriesPrognosisExpressionTreeInterpreter.cs @ 14811

Last change on this file since 14811 was 14811, checked in by mkommend, 2 years ago

#2442: Merged r12807, r13039, r13139, r13140, r13141, r13222, r13247, r13248, r13251, r13254, r13255, r13256, r1326, r13288, r13313, r13314, r13315, r13318, r14282, r14809, r14810 into stable.

File size: 7.0 KB
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1#region License Information
2/* HeuristicLab
3 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
4 *
5 * This file is part of HeuristicLab.
6 *
7 * HeuristicLab is free software: you can redistribute it and/or modify
8 * it under the terms of the GNU General Public License as published by
9 * the Free Software Foundation, either version 3 of the License, or
10 * (at your option) any later version.
11 *
12 * HeuristicLab is distributed in the hope that it will be useful,
13 * but WITHOUT ANY WARRANTY; without even the implied warranty of
14 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15 * GNU General Public License for more details.
16 *
17 * You should have received a copy of the GNU General Public License
18 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
19 */
20#endregion
21
22using System;
23using System.Collections.Generic;
24using System.Linq;
25using HeuristicLab.Common;
26using HeuristicLab.Core;
27using HeuristicLab.Data;
28using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
29using HeuristicLab.Parameters;
30using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
31
32namespace HeuristicLab.Problems.DataAnalysis.Symbolic.TimeSeriesPrognosis {
33  [StorableClass]
34  [Item("SymbolicTimeSeriesPrognosisInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.")]
35  public sealed class SymbolicTimeSeriesPrognosisExpressionTreeInterpreter : SymbolicDataAnalysisExpressionTreeInterpreter, ISymbolicTimeSeriesPrognosisExpressionTreeInterpreter {
36    private const string TargetVariableParameterName = "TargetVariable";
37
38    public IFixedValueParameter<StringValue> TargetVariableParameter {
39      get { return (IFixedValueParameter<StringValue>)Parameters[TargetVariableParameterName]; }
40    }
41
42    public string TargetVariable {
43      get { return TargetVariableParameter.Value.Value; }
44      set { TargetVariableParameter.Value.Value = value; }
45    }
46
47    [ThreadStatic]
48    private static double[] targetVariableCache;
49    [ThreadStatic]
50    private static List<int> invalidateCacheIndexes;
51
52    [StorableConstructor]
53    private SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(bool deserializing) : base(deserializing) { }
54    private SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(SymbolicTimeSeriesPrognosisExpressionTreeInterpreter original, Cloner cloner) : base(original, cloner) { }
55    public override IDeepCloneable Clone(Cloner cloner) {
56      return new SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(this, cloner);
57    }
58
59    internal SymbolicTimeSeriesPrognosisExpressionTreeInterpreter()
60      : base("SymbolicTimeSeriesPrognosisInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.") {
61      Parameters.Add(new FixedValueParameter<StringValue>(TargetVariableParameterName));
62      TargetVariableParameter.Hidden = true;
63    }
64
65    public SymbolicTimeSeriesPrognosisExpressionTreeInterpreter(string targetVariable)
66      : this() {
67      TargetVariable = targetVariable;
68    }
69
70    // for each row several (=#horizon) future predictions
71    public IEnumerable<IEnumerable<double>> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows, int horizon) {
72      return GetSymbolicExpressionTreeValues(tree, dataset, rows, rows.Select(row => horizon));
73    }
74
75    private readonly object syncRoot = new object();
76    public IEnumerable<IEnumerable<double>> GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable<int> rows, IEnumerable<int> horizons) {
77      if (CheckExpressionsWithIntervalArithmetic)
78        throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
79      if (targetVariableCache == null || targetVariableCache.GetLength(0) < dataset.Rows)
80        targetVariableCache = dataset.GetDoubleValues(TargetVariable).ToArray();
81      if (invalidateCacheIndexes == null)
82        invalidateCacheIndexes = new List<int>(10);
83
84      string targetVariable = TargetVariable;
85      lock (syncRoot) {
86        EvaluatedSolutions++; // increment the evaluated solutions counter
87      }
88      var state = PrepareInterpreterState(tree, dataset, targetVariableCache, TargetVariable);
89      var rowsEnumerator = rows.GetEnumerator();
90      var horizonsEnumerator = horizons.GetEnumerator();
91
92      // produce a n-step forecast for all rows
93      while (rowsEnumerator.MoveNext() & horizonsEnumerator.MoveNext()) {
94        int row = rowsEnumerator.Current;
95        int horizon = horizonsEnumerator.Current;
96        double[] vProgs = new double[horizon];
97
98        for (int i = 0; i < horizon; i++) {
99          int localRow = i + row; // create a local variable for the ref parameter
100          vProgs[i] = Evaluate(dataset, ref localRow, state);
101          targetVariableCache[localRow] = vProgs[i];
102          invalidateCacheIndexes.Add(localRow);
103          state.Reset();
104        }
105        yield return vProgs;
106
107        int j = 0;
108        foreach (var targetValue in dataset.GetDoubleValues(targetVariable, invalidateCacheIndexes)) {
109          targetVariableCache[invalidateCacheIndexes[j]] = targetValue;
110          j++;
111        }
112        invalidateCacheIndexes.Clear();
113      }
114
115      if (rowsEnumerator.MoveNext() || horizonsEnumerator.MoveNext())
116        throw new ArgumentException("Number of elements in rows and horizon enumerations doesn't match.");
117    }
118
119    private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, IDataset dataset, double[] targetVariableCache, string targetVariable) {
120      Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
121      int necessaryArgStackSize = 0;
122      foreach (Instruction instr in code) {
123        if (instr.opCode == OpCodes.Variable) {
124          var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
125          if (variableTreeNode.VariableName == targetVariable)
126            instr.data = targetVariableCache;
127          else
128            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
129        } else if (instr.opCode == OpCodes.LagVariable) {
130          var variableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
131          if (variableTreeNode.VariableName == targetVariable)
132            instr.data = targetVariableCache;
133          else
134            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
135        } else if (instr.opCode == OpCodes.VariableCondition) {
136          var variableTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
137          if (variableTreeNode.VariableName == targetVariable)
138            instr.data = targetVariableCache;
139          else
140            instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
141        } else if (instr.opCode == OpCodes.Call) {
142          necessaryArgStackSize += instr.nArguments + 1;
143        }
144      }
145
146      return new InterpreterState(code, necessaryArgStackSize);
147    }
148  }
149}
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