[13865] | 1 | using System;
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| 2 | using System.Collections;
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| 3 | using System.Collections.Generic;
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| 4 | using System.Collections.Specialized;
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| 5 | using System.Drawing.Design;
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| 6 | using System.Linq;
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| 7 | using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
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| 8 | using HeuristicLab.Problems.DataAnalysis;
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| 9 |
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| 10 | namespace HeuristicLab.Problems.GeneticProgramming.GlucosePrediction {
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| 11 | public static class Interpreter {
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| 12 | private class Data {
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| 13 | public double[] realGluc;
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| 14 | public double[] realIns;
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| 15 | public double[] realCh;
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| 16 | public double[] predGluc;
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| 17 | }
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| 18 |
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| 19 | public static IEnumerable<double> Apply(ISymbolicExpressionTreeNode model, IDataset dataset, IEnumerable<int> rows) {
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| 20 | double[] targetGluc = dataset.GetDoubleValues("Glucose_target", rows).ToArray(); // only for skipping rows for which we should not produce an output
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| 21 |
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| 22 | var data = new Data {
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| 23 | realGluc = dataset.GetDoubleValues("Glucose_Interpol", rows).ToArray(),
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| 24 | realIns = dataset.GetDoubleValues("Insuline", rows).ToArray(),
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| 25 | realCh = dataset.GetDoubleValues("CH", rows).ToArray(),
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| 26 | };
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| 27 | data.predGluc = new double[data.realGluc.Length];
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| 28 | for (int k = 0; k < data.predGluc.Length; k++) {
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| 29 | if (double.IsNaN(targetGluc[k])) {
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| 30 | data.predGluc[k] = double.NaN;
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| 31 | } else {
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| 32 | var rawPred = InterpretRec(model, data, k);
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| 33 | data.predGluc[k] = Math.Max(0, Math.Min(400, rawPred)); // limit output values of the model to 0 ... 400
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| 34 | }
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| 35 | }
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| 36 | return data.predGluc;
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| 37 | }
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| 38 |
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| 39 | private static double InterpretRec(ISymbolicExpressionTreeNode node, Data data, int k) {
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| 40 | if (node.Symbol is SimpleSymbol) {
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| 41 | switch (node.Symbol.Name) {
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| 42 | case "+":
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| 43 | case "+Ins":
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| 44 | case "+Ch": {
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| 45 | return InterpretRec(node.GetSubtree(0), data, k) + InterpretRec(node.GetSubtree(1), data, k);
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| 46 | }
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| 47 | case "-":
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| 48 | case "-Ins":
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| 49 | case "-Ch": {
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| 50 | return InterpretRec(node.GetSubtree(0), data, k) - InterpretRec(node.GetSubtree(1), data, k);
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| 51 | }
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| 52 | case "*":
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| 53 | case "*Ins":
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| 54 | case "*Ch": {
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| 55 | return InterpretRec(node.GetSubtree(0), data, k) * InterpretRec(node.GetSubtree(1), data, k);
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| 56 | }
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| 57 | case "/Ch":
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| 58 | case "/Ins":
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| 59 | case "/": {
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| 60 | return InterpretRec(node.GetSubtree(0), data, k) / InterpretRec(node.GetSubtree(1), data, k);
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| 61 | }
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| 62 | case "Exp":
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| 63 | case "ExpIns":
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| 64 | case "ExpCh": {
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| 65 | return Math.Exp(InterpretRec(node.GetSubtree(0), data, k));
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| 66 | }
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| 67 | case "Sin":
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| 68 | case "SinIns":
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| 69 | case "SinCh": {
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| 70 | return Math.Sin(InterpretRec(node.GetSubtree(0), data, k));
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| 71 | }
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| 72 | case "CosCh":
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| 73 | case "CosIns":
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| 74 | case "Cos": {
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| 75 | return Math.Cos(InterpretRec(node.GetSubtree(0), data, k));
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| 76 | }
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| 77 | case "LogCh":
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| 78 | case "LogIns":
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| 79 | case "Log": {
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| 80 | return Math.Log(InterpretRec(node.GetSubtree(0), data, k));
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| 81 | }
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| 82 | case "Func": {
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| 83 | // <exprgluc> + <exprch> - <exprins>
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| 84 | return InterpretRec(node.GetSubtree(0), data, k)
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| 85 | + InterpretRec(node.GetSubtree(1), data, k)
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| 86 | - InterpretRec(node.GetSubtree(2), data, k);
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| 87 | }
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| 88 | case "ExprGluc": {
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| 89 | return InterpretRec(node.GetSubtree(0), data, k);
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| 90 | }
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| 91 | case "ExprCh": {
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| 92 | return InterpretRec(node.GetSubtree(0), data, k);
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| 93 | }
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| 94 | case "ExprIns": {
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| 95 | return InterpretRec(node.GetSubtree(0), data, k);
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| 96 | }
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| 97 | default: {
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| 98 | throw new InvalidProgramException("Found an unknown symbol " + node.Symbol);
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| 99 | }
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| 100 | }
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| 101 | } else if (node.Symbol is PredictedGlucoseVariableSymbol) {
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| 102 | var n = (PredictedGlucoseVariableTreeNode)node;
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| 103 | return data.predGluc[k + n.RowOffset];
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| 104 | } else if (node.Symbol is RealGlucoseVariableSymbol) {
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| 105 | var n = (RealGlucoseVariableTreeNode)node;
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| 106 | return data.realGluc[k + n.RowOffset];
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| 107 | } else if (node.Symbol is CurvedChVariableSymbol) {
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| 108 | var n = (CurvedChVariableTreeNode)node;
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| 109 | double prevVal;
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| 110 | int prevValDistance;
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| 111 | GetPrevDataAndDistance(data.realCh, k, out prevVal, out prevValDistance, maxDistance: 48);
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| 112 | return prevVal * Beta(prevValDistance / 48.0, n.Symbol.Alpha, n.Symbol.Beta);
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| 113 | } else if (node.Symbol is RealInsulineVariableSymbol) {
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| 114 | var n = (RealInsulineVariableTreeNode)node;
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| 115 | return data.realIns[k + n.RowOffset];
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| 116 | } else if (node.Symbol is CurvedInsVariableSymbol) {
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| 117 | var n = (CurvedInsVariableTreeNode)node;
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| 118 | double maxVal;
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| 119 | int maxValDistance;
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| 120 | var sum = GetSumOfValues(48, k, data.realIns);
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| 121 |
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| 122 | GetMaxValueAndDistance(data.realIns, k, out maxVal, out maxValDistance, maxDistance: 48);
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| 123 | return (sum - maxVal) * maxVal * Beta(maxValDistance / 48.0, n.Symbol.Alpha, n.Symbol.Beta);
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| 124 | } else {
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| 125 | throw new InvalidProgramException("found unknown symbol " + node.Symbol);
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| 126 | }
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| 127 | }
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| 128 |
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| 129 | private static double Beta(double x, double alpha, double beta) {
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| 130 | return alglib.invincompletebeta(alpha, beta, x);
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| 131 | }
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| 132 |
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| 133 | private static void GetPrevDataAndDistance(double[] vals, int k, out double val, out int dist, int maxDistance = 48, double threshold = 0.0) {
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| 134 | // look backward from the current idx k and find the first value above the threshold
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| 135 | for (int i = k; i >= 0 && i >= (k - maxDistance); i--) {
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| 136 | if (vals[i] > threshold) {
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| 137 | val = vals[i];
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| 138 | dist = k - i;
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| 139 | return;
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| 140 | }
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| 141 | }
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| 142 | val = 0;
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| 143 | dist = maxDistance;
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| 144 | }
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| 145 |
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| 146 | private static double GetSumOfValues(int windowSize, int k, double[] vals) {
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| 147 | var sum = 0.0;
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| 148 | for (int i = k; i >= 0 && i >= k - windowSize; i--)
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| 149 | sum += vals[i];
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| 150 | return sum;
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| 151 | }
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| 152 |
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| 153 | private static void GetMaxValueAndDistance(double[] vals, int k, out double maxVal, out int dist, int maxDistance = 48) {
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| 154 | // look backward from the current idx k and find the max value and it's distance
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| 155 | maxVal = vals[k];
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| 156 | dist = 0;
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| 157 | for (int i = k; i >= 0 && i >= (k - maxDistance); i--) {
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| 158 | if (vals[i] > maxVal) {
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| 159 | maxVal = vals[i];
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| 160 | dist = k - i;
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| 161 | }
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| 162 | }
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| 163 | }
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| 164 | }
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| 165 | }
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