[12198] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[13727] | 3 | * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[12198] | 4 | *
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| 5 | * This file is part of HeuristicLab.
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| 6 | *
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| 7 | * HeuristicLab is free software: you can redistribute it and/or modify
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| 8 | * it under the terms of the GNU General Public License as published by
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| 9 | * the Free Software Foundation, either version 3 of the License, or
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| 10 | * (at your option) any later version.
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| 11 | *
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| 12 | * HeuristicLab is distributed in the hope that it will be useful,
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| 13 | * but WITHOUT ANY WARRANTY; without even the implied warranty of
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| 14 | * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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| 15 | * GNU General Public License for more details.
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| 16 | *
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| 17 | * You should have received a copy of the GNU General Public License
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| 18 | * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
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| 19 | */
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| 20 | #endregion
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| 21 |
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| 22 | using System;
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| 23 | using System.Collections.Generic;
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| 24 | using System.ComponentModel;
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| 25 | using System.Drawing;
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| 26 | using System.Linq;
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[13727] | 27 | using System.Text;
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[12198] | 28 | using System.Windows.Forms;
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| 29 | using HeuristicLab.Common;
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[13727] | 30 | using HeuristicLab.Core;
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| 31 | using HeuristicLab.Core.Views;
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[12198] | 32 | using HeuristicLab.Data;
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| 33 | using HeuristicLab.MainForm;
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| 34 | using HeuristicLab.Optimization;
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| 35 | using HeuristicLab.Problems.DataAnalysis;
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[13727] | 36 | using HeuristicLab.Visualization;
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[13773] | 37 | using Ellipse = HeuristicLab.Visualization.Ellipse;
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[13727] | 38 | using Rectangle = HeuristicLab.Visualization.Rectangle;
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[12198] | 39 |
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[13727] | 40 | namespace HeuristicLab.VariableInteractionNetworks.Views {
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| 41 | [View("Variable Interaction Network")]
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| 42 | [Content(typeof(RunCollection), IsDefaultView = false)]
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[12198] | 43 |
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[13727] | 44 | public sealed partial class RunCollectionVariableInteractionNetworkView : ItemView {
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| 45 | public RunCollectionVariableInteractionNetworkView() {
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| 46 | InitializeComponent();
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| 47 | ConfigureNodeShapes();
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| 48 | }
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[12229] | 49 |
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[13727] | 50 | public new RunCollection Content {
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| 51 | get { return (RunCollection)base.Content; }
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| 52 | set {
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| 53 | if (value != null && value != Content) {
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| 54 | base.Content = value;
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[12320] | 55 | }
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[13727] | 56 | }
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| 57 | }
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[12229] | 58 |
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[13773] | 59 | private VariableInteractionNetwork variableInteractionNetwork;
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[12229] | 60 |
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[13773] | 61 | private static void AssertSameProblemData(RunCollection runs) {
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| 62 | IDataset dataset = null;
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| 63 | IRegressionProblemData problemData = null;
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| 64 | foreach (var run in runs) {
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| 65 | var solution = (IRegressionSolution)run.Results.Values.Single(x => x is IRegressionSolution);
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| 66 | var ds = solution.ProblemData.Dataset;
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| 67 |
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| 68 | if (solution.ProblemData == problemData) continue;
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| 69 | if (ds == dataset) continue;
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| 70 | if (problemData == null) {
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| 71 | problemData = solution.ProblemData;
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| 72 | continue;
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[12320] | 73 | }
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[13773] | 74 | if (dataset == null) {
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| 75 | dataset = ds;
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| 76 | continue;
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[13727] | 77 | }
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[12198] | 78 |
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[13773] | 79 | if (problemData.TrainingPartition.Start != solution.ProblemData.TrainingPartition.Start || problemData.TrainingPartition.End != solution.ProblemData.TrainingPartition.End)
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| 80 | throw new InvalidOperationException("The runs must share the same data.");
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[12198] | 81 |
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[13773] | 82 | if (!ds.DoubleVariables.SequenceEqual(dataset.DoubleVariables))
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| 83 | throw new InvalidOperationException("The runs must share the same data.");
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| 84 |
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| 85 | foreach (var v in ds.DoubleVariables) {
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| 86 | var values1 = (IList<double>)ds.GetReadOnlyDoubleValues(v);
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| 87 | var values2 = (IList<double>)dataset.GetReadOnlyDoubleValues(v);
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| 88 |
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| 89 | if (values1.Count != values2.Count)
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| 90 | throw new InvalidOperationException("The runs must share the same data.");
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| 91 |
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| 92 | if (!values1.SequenceEqual(values2))
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| 93 | throw new InvalidOperationException("The runs must share the same data.");
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[12320] | 94 | }
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[13773] | 95 | }
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| 96 | }
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[12198] | 97 |
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[13773] | 98 | private static RegressionEnsembleSolution CreateEnsembleSolution(IEnumerable<IRun> runs) {
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| 99 | var solutions = runs.Select(x => x.Results.Values.Single(v => v is IRegressionSolution)).Cast<IRegressionSolution>();
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| 100 | return new RegressionEnsembleSolution(new RegressionEnsembleModel(solutions.Select(x => x.Model)), solutions.First().ProblemData);
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| 101 | }
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| 102 |
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| 103 | public static Dictionary<string, Tuple<IEnumerable<IRun>, Dictionary<string, double>>> CalculateVariableImpactsOnline(RunCollection runs, bool useBest) {
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| 104 | AssertSameProblemData(runs);
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| 105 | var solution = (IRegressionSolution)runs.First().Results.Values.Single(x => x is IRegressionSolution);
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| 106 | var dataset = (Dataset)solution.ProblemData.Dataset;
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| 107 | var originalValues = dataset.DoubleVariables.ToDictionary(x => x, x => dataset.GetReadOnlyDoubleValues(x).ToList());
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| 108 | var md = dataset.ToModifiable();
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| 109 | var medians = new Dictionary<string, List<double>>();
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| 110 | foreach (var v in dataset.DoubleVariables) {
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| 111 | var median = dataset.GetDoubleValues(v, solution.ProblemData.TrainingIndices).Median();
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| 112 | medians[v] = Enumerable.Repeat(median, originalValues[v].Count).ToList();
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| 113 | }
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| 114 |
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| 115 | var targetImpacts = new Dictionary<string, Tuple<IEnumerable<IRun>, Dictionary<string, double>>>();
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| 116 |
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| 117 | if (useBest) {
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| 118 | // build network using only the best run for each target
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| 119 | } else {
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| 120 | var groups = runs.GroupBy(run => {
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| 121 | var sol = (IRegressionSolution)run.Results.Values.Single(x => x is IRegressionSolution);
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| 122 | return Concatenate(sol.ProblemData.AllowedInputVariables) + sol.ProblemData.TargetVariable;
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| 123 | });
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| 124 |
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| 125 | foreach (var group in groups) {
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| 126 | // calculate average impacts
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| 127 | var averageImpacts = new Dictionary<string, double>();
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| 128 | solution = (IRegressionSolution)group.First().Results.Values.Single(x => x is IRegressionSolution);
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| 129 | foreach (var run in group) {
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| 130 | var sol = (IRegressionSolution)run.Results.Values.Single(v => v is IRegressionSolution);
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| 131 |
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| 132 | DoubleLimit estimationLimits = null;
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| 133 | if (run.Parameters.ContainsKey("EstimationLimits")) {
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| 134 | estimationLimits = (DoubleLimit)run.Parameters["EstimationLimits"];
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[12568] | 135 | }
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[13773] | 136 | var impacts = CalculateImpacts(sol, md, originalValues, medians, estimationLimits);
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| 137 | // var impacts = RegressionSolutionVariableImpactsCalculator.CalculateImpacts(sol).ToDictionary(x => x.Item1, x => x.Item2);
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| 138 | foreach (var pair in impacts) {
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| 139 | if (averageImpacts.ContainsKey(pair.Key))
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| 140 | averageImpacts[pair.Key] += pair.Value;
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| 141 | else {
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| 142 | averageImpacts[pair.Key] = pair.Value;
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| 143 | }
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[12568] | 144 | }
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[13727] | 145 | }
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[13773] | 146 | var count = group.Count();
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| 147 | var keys = averageImpacts.Keys.ToList();
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| 148 | foreach (var v in keys) {
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| 149 | averageImpacts[v] /= count;
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| 150 | }
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| 151 |
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| 152 | targetImpacts[solution.ProblemData.TargetVariable] = new Tuple<IEnumerable<IRun>, Dictionary<string, double>>(group, averageImpacts);
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[12320] | 153 | }
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[13727] | 154 | }
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[13773] | 155 | return targetImpacts;
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| 156 | }
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[12320] | 157 |
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[13773] | 158 | private static Dictionary<string, double> CalculateImpacts(IRegressionSolution solution, ModifiableDataset dataset,
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| 159 | Dictionary<string, List<double>> originalValues, Dictionary<string, List<double>> medianValues, DoubleLimit estimationLimits = null) {
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| 160 | var impacts = new Dictionary<string, double>();
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| 161 |
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| 162 | var model = solution.Model;
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| 163 | var pd = solution.ProblemData;
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| 164 |
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| 165 | var rows = pd.TrainingIndices.ToList();
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| 166 | var targetValues = pd.Dataset.GetDoubleValues(pd.TargetVariable, rows).ToList();
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| 167 |
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| 168 |
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| 169 | foreach (var v in pd.AllowedInputVariables) {
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| 170 | dataset.ReplaceVariable(v, medianValues[v]);
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| 171 |
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| 172 | var estimatedValues = model.GetEstimatedValues(dataset, rows);
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| 173 | if (estimationLimits != null)
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| 174 | estimatedValues = estimatedValues.LimitToRange(estimationLimits.Lower, estimationLimits.Upper);
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| 175 |
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| 176 | OnlineCalculatorError error;
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| 177 | var r = OnlinePearsonsRCalculator.Calculate(targetValues, estimatedValues, out error);
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| 178 | var newQuality = error == OnlineCalculatorError.None ? r * r : double.NaN;
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| 179 | var originalQuality = solution.TrainingRSquared;
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| 180 | impacts[v] = originalQuality - newQuality;
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| 181 |
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| 182 | dataset.ReplaceVariable(v, originalValues[v]);
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| 183 | }
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| 184 | return impacts;
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[13727] | 185 | }
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[12320] | 186 |
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[13773] | 187 | private static Dictionary<string, Tuple<IEnumerable<IRun>, Dictionary<string, double>>> CalculateVariableImpactsFromRunResults(RunCollection runs,
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| 188 | string qualityResultName, bool maximization, string impactsResultName, bool useBestRunsPerTarget = false) {
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[13727] | 189 | var targets = runs.GroupBy(x => ((IRegressionProblemData)x.Parameters["ProblemData"]).TargetVariable).ToList();
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[13773] | 190 | var targetImpacts = new Dictionary<string, Tuple<IEnumerable<IRun>, Dictionary<string, double>>>();
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[13727] | 191 | if (useBestRunsPerTarget) {
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[13773] | 192 | var bestRunsPerTarget = maximization
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| 193 | ? targets.Select(x => x.OrderBy(y => ((DoubleValue)y.Results[qualityResultName]).Value).Last())
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| 194 | : targets.Select(x => x.OrderBy(y => ((DoubleValue)y.Results[qualityResultName]).Value).First());
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[12229] | 195 |
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[13727] | 196 | foreach (var run in bestRunsPerTarget) {
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| 197 | var pd = (IRegressionProblemData)run.Parameters["ProblemData"];
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| 198 | var target = pd.TargetVariable;
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| 199 | var impacts = (DoubleMatrix)run.Results[impactsResultName];
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[13773] | 200 | targetImpacts[target] = new Tuple<IEnumerable<IRun>, Dictionary<string, double>>(new[] { run }, impacts.RowNames.Select((x, i) => new { Name = x, Index = i }).ToDictionary(x => x.Name, x => impacts[x.Index, 0]));
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[12320] | 201 | }
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[13727] | 202 | } else {
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| 203 | foreach (var target in targets) {
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| 204 | var averageImpacts = CalculateAverageImpacts(new RunCollection(target), impactsResultName);
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[13773] | 205 | targetImpacts[target.Key] = new Tuple<IEnumerable<IRun>, Dictionary<string, double>>(target, averageImpacts);
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[12198] | 206 | }
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[13727] | 207 | }
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[13773] | 208 | return targetImpacts;
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| 209 | }
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[12198] | 210 |
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[13773] | 211 | private static VariableInteractionNetwork CreateNetwork(Dictionary<string, Tuple<IEnumerable<IRun>, Dictionary<string, double>>> targetImpacts) {
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| 212 | var nodes = new Dictionary<string, IVertex>();
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| 213 | var vn = new VariableInteractionNetwork();
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[13727] | 214 | foreach (var ti in targetImpacts) {
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| 215 | var target = ti.Key;
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[13773] | 216 | var variableImpacts = ti.Value.Item2;
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| 217 | var targetRuns = ti.Value.Item1;
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[13727] | 218 | IVertex targetNode;
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[12198] | 219 |
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[13773] | 220 | var variables = variableImpacts.Keys.ToList();
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[13727] | 221 | if (variables.Count == 0) continue;
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[12198] | 222 |
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[13727] | 223 | if (!nodes.TryGetValue(target, out targetNode)) {
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| 224 | targetNode = new VariableNetworkNode { Label = target };
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| 225 | vn.AddVertex(targetNode);
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| 226 | nodes[target] = targetNode;
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[12320] | 227 | }
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[12229] | 228 |
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[13727] | 229 | IVertex variableNode;
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| 230 | if (variables.Count > 1) {
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| 231 | var variableList = new List<string>(variables) { target };
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| 232 | var junctionLabel = Concatenate(variableList);
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| 233 | IVertex junctionNode;
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| 234 | if (!nodes.TryGetValue(junctionLabel, out junctionNode)) {
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[13789] | 235 | var solutionsEnsemble = CreateEnsembleSolution(targetRuns);
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| 236 | junctionNode = new JunctionNetworkNode { Label = string.Empty, Data = solutionsEnsemble };
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[13727] | 237 | vn.AddVertex(junctionNode);
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| 238 | nodes[junctionLabel] = junctionNode;
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[13789] | 239 | junctionNode.Label = string.Format("Target quality: {0:0.000}", solutionsEnsemble.TrainingRSquared);
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[13727] | 240 | }
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| 241 | IArc arc;
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| 242 | foreach (var v in variables) {
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| 243 | var impact = variableImpacts[v];
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| 244 | if (!nodes.TryGetValue(v, out variableNode)) {
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| 245 | variableNode = new VariableNetworkNode { Label = v };
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| 246 | vn.AddVertex(variableNode);
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| 247 | nodes[v] = variableNode;
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[12320] | 248 | }
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[13789] | 249 | arc = new Arc(variableNode, junctionNode) { Weight = impact, Label = string.Format("Impact: {0:0.000}", impact) };
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[13727] | 250 | vn.AddArc(arc);
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| 251 | }
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[13789] | 252 | var trainingR2 = ((IRegressionSolution)((JunctionNetworkNode)junctionNode).Data).TrainingRSquared;
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| 253 | arc = new Arc(junctionNode, targetNode) { Weight = junctionNode.InArcs.Sum(x => x.Weight), Label = string.Format("Quality: {0:0.000}", trainingR2) };
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[13727] | 254 | vn.AddArc(arc);
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| 255 | } else {
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| 256 | foreach (var v in variables) {
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| 257 | var impact = variableImpacts[v];
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| 258 | if (!nodes.TryGetValue(v, out variableNode)) {
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| 259 | variableNode = new VariableNetworkNode { Label = v };
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| 260 | vn.AddVertex(variableNode);
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| 261 | nodes[v] = variableNode;
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[12320] | 262 | }
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[13789] | 263 | var arc = new Arc(variableNode, targetNode) { Weight = impact, Label = string.Format("Impact: {0:0.000}", impact) };
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[13727] | 264 | vn.AddArc(arc);
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| 265 | }
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[12320] | 266 | }
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[13727] | 267 | }
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| 268 | return vn;
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| 269 | }
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[12320] | 270 |
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[13806] | 271 | private static VariableInteractionNetwork ApplyThreshold(VariableInteractionNetwork originalNetwork, double threshold) {
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| 272 | var arcs = originalNetwork.Arcs.Where(x => x.Weight >= threshold).ToList();
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| 273 | if (!arcs.Any()) return originalNetwork;
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| 274 | var filteredNetwork = new VariableInteractionNetwork();
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| 275 | var cloner = new Cloner();
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| 276 | var vertices = arcs.SelectMany(x => new[] { x.Source, x.Target }).Select(cloner.Clone).Distinct(); // arcs are not cloned
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| 277 | filteredNetwork.AddVertices(vertices);
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| 278 | filteredNetwork.AddArcs(arcs.Select(x => (IArc)x.Clone(cloner)));
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| 279 |
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| 280 | var unusedJunctions = filteredNetwork.Vertices.Where(x => x.InDegree == 0 && x is JunctionNetworkNode).ToList();
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| 281 | filteredNetwork.RemoveVertices(unusedJunctions);
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| 282 | var orphanedNodes = filteredNetwork.Vertices.Where(x => x.Degree == 0).ToList();
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| 283 | filteredNetwork.RemoveVertices(orphanedNodes);
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| 284 | return filteredNetwork.Vertices.Any() ? filteredNetwork : originalNetwork;
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| 285 | }
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| 286 |
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[13727] | 287 | private static double CalculateAverageQuality(RunCollection runs) {
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| 288 | var pd = (IRegressionProblemData)runs.First().Parameters["ProblemData"];
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| 289 | var target = pd.TargetVariable;
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| 290 | var inputs = pd.AllowedInputVariables;
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[12198] | 291 |
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[13727] | 292 | if (!runs.All(x => {
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| 293 | var problemData = (IRegressionProblemData)x.Parameters["ProblemData"];
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| 294 | return target == problemData.TargetVariable && inputs.SequenceEqual(problemData.AllowedInputVariables);
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| 295 | })) {
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| 296 | throw new ArgumentException("All runs must have the same target and inputs.");
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| 297 | }
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| 298 | return runs.Average(x => ((DoubleValue)x.Results["Best training solution quality"]).Value);
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| 299 | }
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[12320] | 300 |
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[13727] | 301 | private static Dictionary<string, double> CalculateAverageImpacts(RunCollection runs, string resultName) {
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| 302 | var pd = (IRegressionProblemData)runs.First().Parameters["ProblemData"];
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| 303 | var target = pd.TargetVariable;
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| 304 | var inputs = pd.AllowedInputVariables.ToList();
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[12320] | 305 |
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[13727] | 306 | var impacts = inputs.ToDictionary(x => x, x => 0d);
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[12320] | 307 |
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[13727] | 308 | // check if all the runs have the same target and same inputs
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| 309 | if (!runs.All(x => {
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| 310 | var problemData = (IRegressionProblemData)x.Parameters["ProblemData"];
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| 311 | return target == problemData.TargetVariable && inputs.SequenceEqual(problemData.AllowedInputVariables);
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| 312 | })) {
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| 313 | throw new ArgumentException("All runs must have the same target and inputs.");
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| 314 | }
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[12320] | 315 |
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[13727] | 316 | foreach (var run in runs) {
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| 317 | var impactsMatrix = (DoubleMatrix)run.Results[resultName];
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[12320] | 318 |
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[13727] | 319 | int i = 0;
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| 320 | foreach (var v in impactsMatrix.RowNames) {
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| 321 | impacts[v] += impactsMatrix[i, 0];
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| 322 | ++i;
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[12320] | 323 | }
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[13727] | 324 | }
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[12320] | 325 |
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[13727] | 326 | foreach (var v in inputs) {
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| 327 | impacts[v] /= runs.Count;
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| 328 | }
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[12320] | 329 |
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[13727] | 330 | return impacts;
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| 331 | }
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[12263] | 332 |
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[13727] | 333 | private static string Concatenate(IEnumerable<string> strings) {
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| 334 | var sb = new StringBuilder();
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| 335 | foreach (var s in strings) {
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| 336 | sb.Append(s);
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| 337 | }
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| 338 | return sb.ToString();
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| 339 | }
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[12320] | 340 |
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[13727] | 341 | private void ConfigureNodeShapes() {
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| 342 | graphChart.ClearShapes();
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| 343 | var font = new Font(FontFamily.GenericSansSerif, 12);
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| 344 | graphChart.AddShape(typeof(VariableNetworkNode), new LabeledPrimitive(new Ellipse(graphChart.Chart, new PointD(0, 0), new PointD(30, 30), Pens.Black, Brushes.White), "", font));
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| 345 | graphChart.AddShape(typeof(JunctionNetworkNode), new LabeledPrimitive(new Rectangle(graphChart.Chart, new PointD(0, 0), new PointD(15, 15), Pens.Black, Brushes.DarkGray), "", font));
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| 346 | }
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[12320] | 347 |
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[13727] | 348 | #region events
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| 349 | protected override void OnContentChanged() {
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| 350 | base.OnContentChanged();
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| 351 | var run = Content.First();
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| 352 | var pd = (IRegressionProblemData)run.Parameters["ProblemData"];
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| 353 | var variables = new HashSet<string>(new List<string>(pd.Dataset.DoubleVariables));
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| 354 | impactResultNameComboBox.Items.Clear();
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| 355 | foreach (var result in run.Results.Where(x => x.Value is DoubleMatrix)) {
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| 356 | var m = (DoubleMatrix)result.Value;
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| 357 | if (m.RowNames.All(x => variables.Contains(x)))
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| 358 | impactResultNameComboBox.Items.Add(result.Key);
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| 359 | }
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| 360 | qualityResultNameComboBox.Items.Clear();
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| 361 | foreach (var result in run.Results.Where(x => x.Value is DoubleValue)) {
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| 362 | qualityResultNameComboBox.Items.Add(result.Key);
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| 363 | }
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| 364 | if (impactResultNameComboBox.Items.Count > 0) {
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| 365 | impactResultNameComboBox.Text = (string)impactResultNameComboBox.Items[0];
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| 366 | }
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| 367 | if (qualityResultNameComboBox.Items.Count > 0) {
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| 368 | qualityResultNameComboBox.Text = (string)qualityResultNameComboBox.Items[0];
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| 369 | }
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| 370 | if (impactResultNameComboBox.Items.Count > 0 && qualityResultNameComboBox.Items.Count > 0)
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| 371 | NetworkConfigurationChanged(this, EventArgs.Empty);
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| 372 | }
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[12320] | 373 |
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[13727] | 374 | private void TextBoxValidating(object sender, CancelEventArgs e) {
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| 375 | double v;
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| 376 | string errorMsg = "Could not parse the entered value. Please input a real number.";
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| 377 | var tb = (TextBox)sender;
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| 378 | if (!double.TryParse(tb.Text, out v)) {
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| 379 | e.Cancel = true;
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| 380 | tb.Select(0, tb.Text.Length);
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[12320] | 381 |
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[13727] | 382 | // Set the ErrorProvider error with the text to display.
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| 383 | this.errorProvider.SetError(tb, errorMsg);
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| 384 | errorProvider.BlinkStyle = ErrorBlinkStyle.NeverBlink;
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| 385 | errorProvider.SetIconPadding(tb, -20);
|
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| 386 | }
|
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| 387 | }
|
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[12320] | 388 |
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[13773] | 389 | private void ImpactThresholdTextBoxValidated(object sender, EventArgs e) {
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[13727] | 390 | var tb = (TextBox)sender;
|
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| 391 | errorProvider.SetError(tb, string.Empty);
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[13806] | 392 | double impact;
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| 393 | if (!double.TryParse(tb.Text, out impact))
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| 394 | impact = 0.1;
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| 395 | var network = ApplyThreshold(variableInteractionNetwork, impact);
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[13773] | 396 | graphChart.Graph = network;
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[13727] | 397 | }
|
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[12320] | 398 |
|
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[13727] | 399 | private void LayoutConfigurationBoxValidated(object sender, EventArgs e) {
|
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| 400 | var tb = (TextBox)sender;
|
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| 401 | errorProvider.SetError(tb, string.Empty);
|
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| 402 | LayoutConfigurationChanged(sender, e);
|
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| 403 | }
|
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[12320] | 404 |
|
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[13727] | 405 | private void NetworkConfigurationChanged(object sender, EventArgs e) {
|
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| 406 | var useBest = impactAggregationComboBox.SelectedIndex <= 0;
|
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| 407 | var threshold = double.Parse(impactThresholdTextBox.Text);
|
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| 408 | var qualityResultName = qualityResultNameComboBox.Text;
|
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| 409 | var impactsResultName = impactResultNameComboBox.Text;
|
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| 410 | if (string.IsNullOrEmpty(qualityResultName) || string.IsNullOrEmpty(impactsResultName))
|
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| 411 | return;
|
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| 412 | var maximization = maximizationCheckBox.Checked;
|
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[13773] | 413 | var impacts = CalculateVariableImpactsFromRunResults(Content, qualityResultName, maximization, impactsResultName, useBest);
|
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| 414 | variableInteractionNetwork = CreateNetwork(impacts);
|
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| 415 | var network = ApplyThreshold(variableInteractionNetwork, threshold);
|
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| 416 | graphChart.Graph = network;
|
---|
[12263] | 417 | }
|
---|
[13727] | 418 |
|
---|
| 419 | private void LayoutConfigurationChanged(object sender, EventArgs e) {
|
---|
| 420 | ConstrainedForceDirectedLayout.EdgeRouting routingMode;
|
---|
| 421 | switch (edgeRoutingComboBox.SelectedIndex) {
|
---|
| 422 | case 0:
|
---|
| 423 | routingMode = ConstrainedForceDirectedLayout.EdgeRouting.None;
|
---|
| 424 | break;
|
---|
| 425 | case 1:
|
---|
| 426 | routingMode = ConstrainedForceDirectedLayout.EdgeRouting.Polyline;
|
---|
| 427 | break;
|
---|
| 428 | case 2:
|
---|
| 429 | routingMode = ConstrainedForceDirectedLayout.EdgeRouting.Orthogonal;
|
---|
| 430 | break;
|
---|
| 431 | default:
|
---|
| 432 | throw new ArgumentException("Invalid edge routing mode.");
|
---|
| 433 | }
|
---|
| 434 | var idealEdgeLength = double.Parse(idealEdgeLengthTextBox.Text);
|
---|
| 435 | if (routingMode == graphChart.RoutingMode && idealEdgeLength.IsAlmost(graphChart.IdealEdgeLength)) return;
|
---|
| 436 | graphChart.RoutingMode = routingMode;
|
---|
| 437 | graphChart.PerformEdgeRouting = routingMode != ConstrainedForceDirectedLayout.EdgeRouting.None;
|
---|
| 438 | graphChart.IdealEdgeLength = idealEdgeLength;
|
---|
| 439 | graphChart.Draw();
|
---|
| 440 | }
|
---|
[13773] | 441 |
|
---|
| 442 | private void onlineImpactCalculationButton_Click(object sender, EventArgs args) {
|
---|
| 443 | var button = (Button)sender;
|
---|
| 444 | var worker = new BackgroundWorker();
|
---|
| 445 | worker.DoWork += (o, e) => {
|
---|
| 446 | button.Enabled = false;
|
---|
| 447 | var impacts = CalculateVariableImpactsOnline(Content, false);
|
---|
[13789] | 448 | variableInteractionNetwork = CreateNetwork(impacts);
|
---|
[13773] | 449 | var threshold = double.Parse(impactThresholdTextBox.Text);
|
---|
[13789] | 450 | graphChart.Graph = ApplyThreshold(variableInteractionNetwork, threshold);
|
---|
[13773] | 451 | };
|
---|
| 452 | worker.RunWorkerCompleted += (o, e) => button.Enabled = true;
|
---|
| 453 | worker.RunWorkerAsync();
|
---|
| 454 | }
|
---|
[13727] | 455 | #endregion
|
---|
| 456 | }
|
---|
| 457 | }
|
---|