[4417] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[16140] | 3 | * Copyright (C) 2002-2018 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[4417] | 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.Drawing;
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| 25 | using System.Linq;
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| 26 | using System.Text;
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| 27 | using System.Windows.Forms;
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| 28 | using System.Windows.Forms.DataVisualization.Charting;
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| 29 | using HeuristicLab.Common;
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| 30 | using HeuristicLab.MainForm;
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| 31 | using HeuristicLab.MainForm.WindowsForms;
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[5829] | 32 | namespace HeuristicLab.Problems.DataAnalysis.Views {
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[5975] | 33 | [View("ROC Curves")]
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[5664] | 34 | [Content(typeof(IDiscriminantFunctionClassificationSolution))]
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[6642] | 35 | public partial class DiscriminantFunctionClassificationRocCurvesView : DataAnalysisSolutionEvaluationView {
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[4417] | 36 | private const string xAxisTitle = "False Positive Rate";
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| 37 | private const string yAxisTitle = "True Positive Rate";
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| 38 | private const string TrainingSamples = "Training";
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| 39 | private const string TestSamples = "Test";
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| 40 | private Dictionary<string, List<ROCPoint>> cachedRocPoints;
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| 41 |
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[5664] | 42 | public DiscriminantFunctionClassificationRocCurvesView() {
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[4417] | 43 | InitializeComponent();
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| 44 |
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| 45 | cachedRocPoints = new Dictionary<string, List<ROCPoint>>();
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| 46 |
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| 47 | cmbSamples.Items.Add(TrainingSamples);
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| 48 | cmbSamples.Items.Add(TestSamples);
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| 49 | cmbSamples.SelectedIndex = 0;
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| 50 |
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[4651] | 51 | chart.CustomizeAllChartAreas();
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[4417] | 52 | chart.ChartAreas[0].AxisX.Minimum = 0.0;
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| 53 | chart.ChartAreas[0].AxisX.Maximum = 1.0;
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| 54 | chart.ChartAreas[0].AxisX.MajorGrid.Interval = 0.2;
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| 55 | chart.ChartAreas[0].AxisY.Minimum = 0.0;
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| 56 | chart.ChartAreas[0].AxisY.Maximum = 1.0;
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| 57 | chart.ChartAreas[0].AxisY.MajorGrid.Interval = 0.2;
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| 58 |
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| 59 | chart.ChartAreas[0].AxisX.Title = xAxisTitle;
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| 60 | chart.ChartAreas[0].AxisY.Title = yAxisTitle;
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| 61 | }
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| 62 |
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[5664] | 63 | public new IDiscriminantFunctionClassificationSolution Content {
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| 64 | get { return (IDiscriminantFunctionClassificationSolution)base.Content; }
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[4417] | 65 | set { base.Content = value; }
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| 66 | }
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| 67 |
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| 68 | protected override void RegisterContentEvents() {
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| 69 | base.RegisterContentEvents();
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[5664] | 70 | Content.ModelChanged += new EventHandler(Content_ModelChanged);
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[4417] | 71 | Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged);
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| 72 | }
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| 73 | protected override void DeregisterContentEvents() {
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| 74 | base.DeregisterContentEvents();
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[5664] | 75 | Content.ModelChanged -= new EventHandler(Content_ModelChanged);
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[4417] | 76 | Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged);
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| 77 | }
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| 78 |
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[5664] | 79 | private void Content_ModelChanged(object sender, EventArgs e) {
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[4417] | 80 | UpdateChart();
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| 81 | }
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| 82 | private void Content_ProblemDataChanged(object sender, EventArgs e) {
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| 83 | UpdateChart();
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| 84 | }
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| 85 |
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| 86 | protected override void OnContentChanged() {
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| 87 | base.OnContentChanged();
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| 88 | chart.Series.Clear();
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| 89 | if (Content != null) UpdateChart();
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| 90 | }
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| 91 |
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| 92 | private void UpdateChart() {
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| 93 | if (InvokeRequired) Invoke((Action)UpdateChart);
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| 94 | else {
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| 95 | chart.Series.Clear();
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| 96 | chart.Annotations.Clear();
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| 97 | cachedRocPoints.Clear();
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| 98 |
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| 99 | int slices = 100;
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[4469] | 100 | IEnumerable<int> rows;
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[4417] | 101 |
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| 102 | if (cmbSamples.SelectedItem.ToString() == TrainingSamples) {
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[8139] | 103 | rows = Content.ProblemData.TrainingIndices;
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[4417] | 104 | } else if (cmbSamples.SelectedItem.ToString() == TestSamples) {
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[8139] | 105 | rows = Content.ProblemData.TestIndices;
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[4417] | 106 | } else throw new InvalidOperationException();
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| 107 |
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[4469] | 108 | double[] estimatedValues = Content.GetEstimatedValues(rows).ToArray();
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[6740] | 109 | double[] targetClassValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable, rows).ToArray();
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[4417] | 110 | double minThreshold = estimatedValues.Min();
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| 111 | double maxThreshold = estimatedValues.Max();
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| 112 | double thresholdIncrement = (maxThreshold - minThreshold) / slices;
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| 113 | minThreshold -= thresholdIncrement;
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| 114 | maxThreshold += thresholdIncrement;
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| 115 |
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[8831] | 116 | List<double> classValues = Content.ProblemData.ClassValues.ToList();
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[4417] | 117 |
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| 118 | foreach (double classValue in classValues) {
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| 119 | List<ROCPoint> rocPoints = new List<ROCPoint>();
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| 120 | int positives = targetClassValues.Where(c => c.IsAlmost(classValue)).Count();
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[4469] | 121 | int negatives = targetClassValues.Length - positives;
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[4417] | 122 |
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| 123 | for (double lowerThreshold = minThreshold; lowerThreshold < maxThreshold; lowerThreshold += thresholdIncrement) {
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| 124 | for (double upperThreshold = lowerThreshold + thresholdIncrement; upperThreshold < maxThreshold; upperThreshold += thresholdIncrement) {
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[5417] | 125 | //only adapt lower threshold for binary classification problems and upper class prediction
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| 126 | if (classValues.Count == 2 && classValue == classValues[1]) upperThreshold = double.PositiveInfinity;
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| 127 |
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[4417] | 128 | int truePositives = 0;
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| 129 | int falsePositives = 0;
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| 130 |
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| 131 | for (int row = 0; row < estimatedValues.Length; row++) {
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| 132 | if (lowerThreshold < estimatedValues[row] && estimatedValues[row] < upperThreshold) {
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| 133 | if (targetClassValues[row].IsAlmost(classValue)) truePositives++;
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| 134 | else falsePositives++;
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| 135 | }
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| 136 | }
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| 137 |
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| 138 | double truePositiveRate = ((double)truePositives) / positives;
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| 139 | double falsePositiveRate = ((double)falsePositives) / negatives;
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| 140 |
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| 141 | ROCPoint rocPoint = new ROCPoint(truePositiveRate, falsePositiveRate, lowerThreshold, upperThreshold);
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[6912] | 142 | if (!rocPoints.Any(x => x.TruePositiveRate >= rocPoint.TruePositiveRate && x.FalsePositiveRate <= rocPoint.FalsePositiveRate)) {
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| 143 | rocPoints.RemoveAll(x => x.FalsePositiveRate >= rocPoint.FalsePositiveRate && x.TruePositiveRate <= rocPoint.TruePositiveRate);
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[4417] | 144 | rocPoints.Add(rocPoint);
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| 145 | }
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| 146 | }
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[5417] | 147 | //only adapt upper threshold for binary classification problems and upper class prediction
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| 148 | if (classValues.Count == 2 && classValue == classValues[0]) lowerThreshold = double.PositiveInfinity;
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[4417] | 149 | }
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| 150 |
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| 151 | string className = Content.ProblemData.ClassNames.ElementAt(classValues.IndexOf(classValue));
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[6912] | 152 | cachedRocPoints[className] = rocPoints.OrderBy(x => x.FalsePositiveRate).ToList(); ;
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[4417] | 153 |
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| 154 | Series series = new Series(className);
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| 155 | series.ChartType = SeriesChartType.Line;
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| 156 | series.MarkerStyle = MarkerStyle.Diamond;
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| 157 | series.MarkerSize = 5;
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| 158 | chart.Series.Add(series);
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| 159 | FillSeriesWithDataPoints(series, cachedRocPoints[className]);
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| 160 |
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| 161 | double auc = CalculateAreaUnderCurve(series);
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| 162 | series.LegendToolTip = "AUC: " + auc;
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| 163 | }
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| 164 | }
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| 165 | }
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| 166 |
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| 167 | private void FillSeriesWithDataPoints(Series series, IEnumerable<ROCPoint> rocPoints) {
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| 168 | series.Points.Add(new DataPoint(0, 0));
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| 169 | foreach (ROCPoint rocPoint in rocPoints) {
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| 170 | DataPoint point = new DataPoint();
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[6912] | 171 | point.XValue = rocPoint.FalsePositiveRate;
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| 172 | point.YValues[0] = rocPoint.TruePositiveRate;
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[4417] | 173 | point.Tag = rocPoint;
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| 174 |
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| 175 | StringBuilder sb = new StringBuilder();
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[6912] | 176 | sb.AppendLine("True Positive Rate: " + rocPoint.TruePositiveRate);
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| 177 | sb.AppendLine("False Positive Rate: " + rocPoint.FalsePositiveRate);
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| 178 | sb.AppendLine("Upper Threshold: " + rocPoint.UpperThreshold);
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| 179 | sb.AppendLine("Lower Threshold: " + rocPoint.LowerThreshold);
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[4417] | 180 | point.ToolTip = sb.ToString();
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| 181 |
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| 182 | series.Points.Add(point);
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| 183 | }
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| 184 | series.Points.Add(new DataPoint(1, 1));
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| 185 | }
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| 186 |
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| 187 | private double CalculateAreaUnderCurve(Series series) {
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| 188 | if (series.Points.Count < 1) throw new ArgumentException("Could not calculate area under curve if less than 1 data points were given.");
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| 189 |
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| 190 | double auc = 0.0;
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| 191 | for (int i = 1; i < series.Points.Count; i++) {
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| 192 | double width = series.Points[i].XValue - series.Points[i - 1].XValue;
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| 193 | double y1 = series.Points[i - 1].YValues[0];
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| 194 | double y2 = series.Points[i].YValues[0];
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| 195 |
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| 196 | auc += (y1 + y2) * width / 2;
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| 197 | }
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| 198 |
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| 199 | return auc;
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| 200 | }
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| 201 |
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| 202 | private void cmbSamples_SelectedIndexChanged(object sender, System.EventArgs e) {
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| 203 | if (Content != null)
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| 204 | UpdateChart();
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| 205 | }
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| 206 |
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| 207 |
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| 208 | #region show / hide series
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| 209 | private void ToggleSeries(Series series) {
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| 210 | if (series.Points.Count == 0)
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| 211 | FillSeriesWithDataPoints(series, cachedRocPoints[series.Name]);
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| 212 | else
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| 213 | series.Points.Clear();
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| 214 | }
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| 215 | private void chart_MouseDown(object sender, MouseEventArgs e) {
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| 216 | HitTestResult result = chart.HitTest(e.X, e.Y);
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| 217 | if (result.ChartElementType == ChartElementType.LegendItem) {
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| 218 | if (result.Series != null) ToggleSeries(result.Series);
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| 219 | }
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| 220 | }
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| 221 | private void chart_CustomizeLegend(object sender, CustomizeLegendEventArgs e) {
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| 222 | foreach (LegendItem legendItem in e.LegendItems) {
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| 223 | var series = chart.Series[legendItem.SeriesName];
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| 224 | if (series != null) {
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| 225 | bool seriesIsInvisible = series.Points.Count == 0;
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| 226 | foreach (LegendCell cell in legendItem.Cells)
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| 227 | cell.ForeColor = seriesIsInvisible ? Color.Gray : Color.Black;
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| 228 | }
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| 229 | }
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| 230 | }
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| 231 | private void chart_MouseMove(object sender, MouseEventArgs e) {
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| 232 | HitTestResult result = chart.HitTest(e.X, e.Y);
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| 233 | if (result.ChartElementType == ChartElementType.LegendItem)
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| 234 | this.Cursor = Cursors.Hand;
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| 235 | else
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| 236 | this.Cursor = Cursors.Default;
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| 237 |
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| 238 | string newTooltipText = string.Empty;
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| 239 | if (result.ChartElementType == ChartElementType.DataPoint)
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| 240 | newTooltipText = ((DataPoint)result.Object).ToolTip;
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| 241 |
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| 242 | string oldTooltipText = this.toolTip.GetToolTip(chart);
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| 243 | if (newTooltipText != oldTooltipText)
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| 244 | this.toolTip.SetToolTip(chart, newTooltipText);
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| 245 | }
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| 246 | #endregion
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| 247 |
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| 248 |
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| 249 | private class ROCPoint {
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| 250 | public ROCPoint(double truePositiveRate, double falsePositiveRate, double lowerThreshold, double upperThreshold) {
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[6912] | 251 | this.TruePositiveRate = truePositiveRate;
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| 252 | this.FalsePositiveRate = falsePositiveRate;
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| 253 | this.LowerThreshold = lowerThreshold;
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| 254 | this.UpperThreshold = upperThreshold;
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[4417] | 255 |
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| 256 | }
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[6912] | 257 | public double TruePositiveRate { get; private set; }
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| 258 | public double FalsePositiveRate { get; private set; }
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| 259 | public double LowerThreshold { get; private set; }
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| 260 | public double UpperThreshold { get; private set; }
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[4417] | 261 | }
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| 262 |
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| 263 | }
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| 264 | }
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