Changeset 16067

Timestamp:

08/07/18 16:55:29 (6 years ago)

Author:

fholzing

Message:

#2904: Added additional Unit-Test for performance measurement

File:

• branches/2904_CalculateImpacts/HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis-3.4/ClassificationVariableImpactCalculationTest.cs (modified) (4 diffs)

branches/2904_CalculateImpacts/HeuristicLab.Tests/HeuristicLab.Problems.DataAnalysis-3.4/ClassificationVariableImpactCalculationTest.cs

@@ -1 +1 @@
 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
 using System.Linq;
 using HeuristicLab.Algorithms.DataAnalysis;
@@ -15 +16 @@
   [TestClass()]
   public class ClassificationVariableImpactCalculationTest {
+    private TestContext testContextInstance;
+    /// <summary>
+    ///Gets or sets the test context which provides
+    ///information about and functionality for the current test run.
+    ///</summary>
+    public TestContext TestContext {
+      get { return testContextInstance; }
+      set { testContextInstance = value; }
+    }
+
     private static readonly double epsilon = 0.00001;
 
@@ -82 +93 @@
       solution.ProblemData = LoadMammographyProblem();
       ClassificationSolutionVariableImpactsCalculator.CalculateImpacts(solution);
+    }
+
+
+    [TestMethod]
+    [TestCategory("Problems.DataAnalysis")]
+    [TestProperty("Time", "medium")]
+    public void PerformanceTest() {
+      int rows = 1500;
+      int columns = 77;
+      IClassificationProblemData problemData = CreateDefaultProblem(rows, columns);
+      IClassificationSolution solution = NearestNeighbourClassification.CreateNearestNeighbourClassificationSolution(problemData, 3);
+
+      Stopwatch watch = new Stopwatch();
+      watch.Start();
+      var results = ClassificationSolutionVariableImpactsCalculator.CalculateImpacts(solution);
+      watch.Stop();
+
+      TestContext.WriteLine("");
+      TestContext.WriteLine("Calculated cells per millisecond: {0}.", rows * columns / watch.ElapsedMilliseconds);
     }
 
@@ -121 +151 @@
       return ret;
     }
+
+    private IClassificationProblemData CreateDefaultProblem(int rows, int columns) {
+      List<string> allowedInputVariables = Enumerable.Range(0, columns - 1).Select(x => "x" + x.ToString()).ToList();
+      string targetVariable = "y";
+      var variableNames = allowedInputVariables.Union(targetVariable.ToEnumerable());
+      double[,] variableValues = new double[rows, columns];
+
+      FastRandom random = new FastRandom(12345);
+      int len0 = variableValues.GetLength(0);
+      int len1 = variableValues.GetLength(1);
+      for (int i = 0; i < len0; i++) {
+        for (int j = 0; j < len1; j++) {
+          if (j == len1 - 1) {
+            variableValues[i, j] = (j + i) % 2;
+          } else {
+            variableValues[i, j] = random.Next(1, 100);
+          }
+        }
+      }
+
+      Dataset dataset = new Dataset(variableNames, variableValues);
+      var ret = new ClassificationProblemData(dataset, allowedInputVariables, targetVariable);
+
+      ret.SetClassName(0, "NOK");
+      ret.SetClassName(1, "OK");
+      return ret;
+    }
+
     #endregion