#region License Information /* HeuristicLab * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL) * * This file is part of HeuristicLab. * * HeuristicLab is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, either version 3 of the License, or * (at your option) any later version. * * HeuristicLab is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with HeuristicLab. If not, see . */ #endregion using System; using System.Collections.Generic; using System.Linq; using System.Windows.Forms; using HeuristicLab.MainForm; using HeuristicLab.MainForm.WindowsForms; namespace HeuristicLab.Problems.DataAnalysis.Views { [View("Confusion Matrix")] [Content(typeof(IClassificationSolution))] public partial class ClassificationSolutionConfusionMatrixView : DataAnalysisSolutionEvaluationView { private const string TrainingSamples = "Training"; private const string TestSamples = "Test"; public ClassificationSolutionConfusionMatrixView() { InitializeComponent(); cmbSamples.Items.Add(TrainingSamples); cmbSamples.Items.Add(TestSamples); cmbSamples.SelectedIndex = 0; } public new IClassificationSolution Content { get { return (IClassificationSolution)base.Content; } set { base.Content = value; } } protected override void RegisterContentEvents() { base.RegisterContentEvents(); Content.ModelChanged += new EventHandler(Content_ModelChanged); Content.ProblemDataChanged += new EventHandler(Content_ProblemDataChanged); } protected override void DeregisterContentEvents() { base.DeregisterContentEvents(); Content.ModelChanged -= new EventHandler(Content_ModelChanged); Content.ProblemDataChanged -= new EventHandler(Content_ProblemDataChanged); } private void Content_ModelChanged(object sender, EventArgs e) { FillDataGridView(); } private void Content_ProblemDataChanged(object sender, EventArgs e) { UpdateDataGridView(); } protected override void OnContentChanged() { base.OnContentChanged(); UpdateDataGridView(); } private void UpdateDataGridView() { if (InvokeRequired) Invoke((Action)UpdateDataGridView); else { if (Content == null) { dataGridView.RowCount = 1; dataGridView.ColumnCount = 1; dataGridView.TopLeftHeaderCell.Value = string.Empty; } else { dataGridView.ColumnCount = Content.ProblemData.Classes + 1; dataGridView.RowCount = Content.ProblemData.Classes + 1; int i = 0; foreach (string headerText in Content.ProblemData.ClassNames) { dataGridView.Columns[i].HeaderText = "Actual " + headerText; dataGridView.Rows[i].HeaderCell.Value = "Predicted " + headerText; i++; } dataGridView.Columns[i].HeaderText = "Actual not classified"; dataGridView.Rows[i].HeaderCell.Value = "Predicted not classified"; dataGridView.AutoResizeColumns(DataGridViewAutoSizeColumnsMode.ColumnHeader); dataGridView.AutoResizeRowHeadersWidth(DataGridViewRowHeadersWidthSizeMode.AutoSizeToAllHeaders); dataGridView.TopLeftHeaderCell.Style.Alignment = DataGridViewContentAlignment.MiddleCenter; dataGridView.TopLeftHeaderCell.Value = Content.Model.TargetVariable; FillDataGridView(); } } } private void FillDataGridView() { if (InvokeRequired) Invoke((Action)FillDataGridView); else { if (Content == null) return; double[,] confusionMatrix = new double[Content.ProblemData.Classes + 1, Content.ProblemData.Classes + 1]; IEnumerable rows; double[] predictedValues; if (cmbSamples.SelectedItem.ToString() == TrainingSamples) { rows = Content.ProblemData.TrainingIndices; predictedValues = Content.EstimatedTrainingClassValues.ToArray(); } else if (cmbSamples.SelectedItem.ToString() == TestSamples) { rows = Content.ProblemData.TestIndices; predictedValues = Content.EstimatedTestClassValues.ToArray(); } else throw new InvalidOperationException(); double[] targetValues = Content.ProblemData.Dataset.GetDoubleValues(Content.ProblemData.TargetVariable, rows).ToArray(); Dictionary classValueIndexMapping = new Dictionary(); int index = 0; foreach (double classValue in Content.ProblemData.ClassValues.OrderBy(x => x)) { classValueIndexMapping.Add(classValue, index); index++; } for (int i = 0; i < targetValues.Length; i++) { double targetValue = targetValues[i]; double predictedValue = predictedValues[i]; int targetIndex; int predictedIndex; if (!classValueIndexMapping.TryGetValue(targetValue, out targetIndex)) { targetIndex = Content.ProblemData.Classes; } if (!classValueIndexMapping.TryGetValue(predictedValue, out predictedIndex)) { predictedIndex = Content.ProblemData.Classes; } confusionMatrix[predictedIndex, targetIndex] += 1; } for (int row = 0; row < confusionMatrix.GetLength(0); row++) { for (int col = 0; col < confusionMatrix.GetLength(1); col++) { //TODO add scaling to relative values; dataGridView[col, row].Value = confusionMatrix[row, col]; } } } } private void cmbSamples_SelectedIndexChanged(object sender, System.EventArgs e) { FillDataGridView(); } } }