[8638] | 1 | #region License Information
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| 2 | /* HeuristicLab
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[11185] | 3 | * Copyright (C) 2002-2014 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
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[8638] | 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 Microsoft.VisualStudio.TestTools.UnitTesting;
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[9785] | 23 |
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[9764] | 24 | namespace HeuristicLab.Problems.DataAnalysis.Tests {
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[8638] | 25 |
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| 26 | [TestClass()]
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| 27 | public class ThresholdCalculatorsTest {
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| 28 | [TestMethod]
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[9785] | 29 | [TestCategory("Problems.DataAnalysis")]
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| 30 | [TestProperty("Time", "short")]
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[8638] | 31 | public void NormalDistributionCutPointsThresholdCalculatorTest() {
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| 32 |
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| 33 | {
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| 34 | // simple two-class case
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| 35 | double[] estimatedValues = new double[] { 1.0, 0.99, 1.01, 2.0, 1.99, 2.01 };
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| 36 | double[] targetClassValues = new double[] { 0.0, 0.0, 0.0, 1.0, 1.0, 1.0 };
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| 37 | double[] classValues;
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| 38 | double[] thresholds;
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| 39 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 40 | out classValues, out thresholds);
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| 41 |
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| 42 | var expectedClassValues = new double[] { 0.0, 1.0 };
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| 43 | var expectedTresholds = new double[] { double.NegativeInfinity, 1.5 };
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| 44 |
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| 45 | AssertEqual(expectedClassValues, classValues);
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| 46 | AssertEqual(expectedTresholds, thresholds);
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| 47 | }
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| 48 |
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| 49 | {
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| 50 | // switched classes two-class case
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| 51 | double[] estimatedValues = new double[] { 1.0, 0.99, 1.01, 2.0, 1.99, 2.01 };
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| 52 | double[] targetClassValues = new double[] { 1.0, 1.0, 1.0, 0.0, 0.0, 0.0 };
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| 53 | double[] classValues;
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| 54 | double[] thresholds;
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| 55 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 56 | out classValues, out thresholds);
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| 57 |
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| 58 | var expectedClassValues = new double[] { 1.0, 0.0 };
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| 59 | var expectedTresholds = new double[] { double.NegativeInfinity, 1.5 };
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| 60 |
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| 61 | AssertEqual(expectedClassValues, classValues);
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| 62 | AssertEqual(expectedTresholds, thresholds);
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| 63 | }
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| 64 |
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| 65 | {
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| 66 | // three-class case with permutated estimated values
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| 67 | double[] estimatedValues = new double[] { 1.0, 0.99, 1.01, 2.0, 1.99, 2.01, -1.0, -0.99, -1.01 };
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| 68 | double[] targetClassValues = new double[] { 2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0 };
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| 69 | double[] classValues;
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| 70 | double[] thresholds;
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| 71 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 72 | out classValues, out thresholds);
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| 73 |
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| 74 | var expectedClassValues = new double[] { 1.0, 2.0, 0.0 };
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| 75 | var expectedTresholds = new double[] { double.NegativeInfinity, 0.0, 1.5 };
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| 76 |
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| 77 | AssertEqual(expectedClassValues, classValues);
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| 78 | AssertEqual(expectedTresholds, thresholds);
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| 79 | }
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| 80 |
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| 81 | {
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| 82 | // constant output values for all classes
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[8917] | 83 | // most frequent class is 0
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| 84 | double[] estimatedValues = new double[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 };
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| 85 | double[] targetClassValues = new double[] { 2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0 };
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[8638] | 86 | double[] classValues;
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| 87 | double[] thresholds;
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| 88 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 89 | out classValues, out thresholds);
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| 90 |
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| 91 | var expectedClassValues = new double[] { 0.0 };
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| 92 | var expectedTresholds = new double[] { double.NegativeInfinity };
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| 93 |
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| 94 | AssertEqual(expectedClassValues, classValues);
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| 95 | AssertEqual(expectedTresholds, thresholds);
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| 96 | }
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| 97 |
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| 98 | {
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| 99 | // constant output values for two of three classes
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| 100 | double[] estimatedValues = new double[] { 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, -1.0, -0.99, -1.01 };
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| 101 | double[] targetClassValues = new double[] { 2.0, 2.0, 2.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0 };
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| 102 | double[] classValues;
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| 103 | double[] thresholds;
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| 104 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 105 | out classValues, out thresholds);
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| 106 |
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| 107 |
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| 108 | var expectedClassValues = new double[] { 1.0, 0.0, 1.0 };
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| 109 | double range = 1.0 + 1.01;
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| 110 | var expectedTresholds = new double[] { double.NegativeInfinity, 1.0 - 0.001 * range, 1.0 + 0.001 * range };
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| 111 |
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| 112 | AssertEqual(expectedClassValues, classValues);
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| 113 | AssertEqual(expectedTresholds, thresholds);
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| 114 | }
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| 115 |
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[8658] | 116 |
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| 117 | {
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| 118 | // normal operation
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| 119 | double[] estimatedValues = new double[]
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| 120 | {
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| 121 | 2.9937,
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| 122 | 2.9861,
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| 123 | 1.0202,
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| 124 | 0.9844,
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| 125 | 1.9912,
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| 126 | 1.9970,
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| 127 | 0.9776,
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| 128 | 0.9611,
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| 129 | 1.9882,
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| 130 | 1.9953,
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| 131 | 2.0147,
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| 132 | 2.0106,
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| 133 | 2.9949,
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| 134 | 0.9925,
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| 135 | 3.0050,
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| 136 | 1.9987,
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| 137 | 2.9973,
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| 138 | 1.0110,
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| 139 | 2.0160,
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| 140 | 2.9559,
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| 141 | 1.9943,
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| 142 | 2.9477,
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| 143 | 2.0158,
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| 144 | 2.0026,
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| 145 | 1.9837,
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| 146 | 3.0185,
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| 147 | };
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| 148 | double[] targetClassValues = new double[]
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| 149 | {
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| 150 | 3,
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| 151 | 3,
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| 152 | 1,
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| 153 | 1,
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| 154 | 2,
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| 155 | 2,
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| 156 | 1,
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| 157 | 1,
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| 158 | 2,
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| 159 | 2,
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| 160 | 2,
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| 161 | 2,
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| 162 | 3,
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| 163 | 1,
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| 164 | 3,
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| 165 | 2,
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| 166 | 3,
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| 167 | 1,
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| 168 | 2,
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| 169 | 3,
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| 170 | 2,
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| 171 | 3,
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| 172 | 2,
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| 173 | 2,
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| 174 | 2,
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| 175 | 3,
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| 176 | };
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| 177 |
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| 178 | double[] classValues;
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| 179 | double[] thresholds;
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| 180 | NormalDistributionCutPointsThresholdCalculator.CalculateThresholds(null, estimatedValues, targetClassValues,
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| 181 | out classValues, out thresholds);
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| 182 |
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| 183 |
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[8917] | 184 | var expectedClassValues = new double[] { 3.0, 1.0, 2.0, 3.0 };
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| 185 | var expectedTresholds = new double[] { double.NegativeInfinity, -18.36483129043598, 1.6574168546810319, 2.3148463106026012 };
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[8658] | 186 |
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| 187 | AssertEqual(expectedClassValues, classValues);
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| 188 | AssertEqual(expectedTresholds, thresholds);
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| 189 | }
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[8638] | 190 | }
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| 191 |
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| 192 |
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| 193 | private static void AssertEqual(double[] expected, double[] actual) {
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| 194 | Assert.AreEqual(expected.Length, actual.Length);
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| 195 | for (int i = 0; i < expected.Length; i++)
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| 196 | Assert.AreEqual(expected[i], actual[i]);
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| 197 | }
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| 198 | }
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| 199 | }
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