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Code analysis: Add editorconfig and fix code according to rules (#150)
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@mdabros
mdabros authored Feb 9, 2025
1 parent 582995b commit 0e2f4f1
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362 changes: 362 additions & 0 deletions .editorconfig
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2 changes: 1 addition & 1 deletion README.md
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Expand Up @@ -55,7 +55,7 @@ However, several optimization methods are available for hyperparameter tuning:
License
-------

SharpLearning is covered under the terms of the [MIT](LICENSE.md) license. You may therefore link to it and use it in both opensource and proprietary software projects.
SharpLearning is covered under the terms of the [MIT](LICENSE) license. You may therefore link to it and use it in both opensource and proprietary software projects.

Documentation
-------------
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13 changes: 7 additions & 6 deletions SharpLearning.sln
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Expand Up @@ -64,6 +64,7 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "ci", "ci", "{45CBAF1D-3116-
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "root", "root", "{D83436F7-2EE6-4D59-ACC3-355C973BCDAF}"
ProjectSection(SolutionItems) = preProject
.editorconfig = .editorconfig
.gitattributes = .gitattributes
.gitignore = .gitignore
CONTRIBUTING.md = CONTRIBUTING.md
Expand All @@ -74,12 +75,12 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "root", "root", "{D83436F7-2
EndProject
Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "src", "src", "{2204FA16-973A-48CF-A9FD-94FA72424BA4}"
ProjectSection(SolutionItems) = preProject
Directory.Build.props = Directory.Build.props
Directory.Build.targets = Directory.Build.targets
OutputBuildProject.props = OutputBuildProject.props
OutputBuildProps.props = OutputBuildProps.props
OutputBuildTargets.props = OutputBuildTargets.props
SourceLink.GitHub.props = SourceLink.GitHub.props
src\Directory.Build.props = src\Directory.Build.props
src\Directory.Build.targets = src\Directory.Build.targets
src\OutputBuildProject.props = src\OutputBuildProject.props
src\OutputBuildProps.props = src\OutputBuildProps.props
src\OutputBuildTargets.props = src\OutputBuildTargets.props
src\SourceLink.GitHub.props = src\SourceLink.GitHub.props
EndProjectSection
EndProject
Global
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7 changes: 3 additions & 4 deletions src/Directory.Build.props
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Expand Up @@ -29,13 +29,12 @@

<GenerateDocumentationFile>true</GenerateDocumentationFile>

<!--Disabled for now-->
<!--<AnalysisLevel>latest</AnalysisLevel>
<AnalysisLevel>latest</AnalysisLevel>
<EnforceCodeStyleInBuild>true</EnforceCodeStyleInBuild>
<RunAnalyzersDuringBuild>true</RunAnalyzersDuringBuild>
<EnableNETAnalyzers>true</EnableNETAnalyzers>
<CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>-->
<NoWarn>CS1591</NoWarn>
<CodeAnalysisTreatWarningsAsErrors>true</CodeAnalysisTreatWarningsAsErrors>
<NoWarn>CS1591;CS1668</NoWarn>

</PropertyGroup>

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25 changes: 12 additions & 13 deletions src/SharpLearning.AdaBoost.Test/AssemblyInitializeCultureTest.cs
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Expand Up @@ -2,19 +2,18 @@
using System.Threading;
using Microsoft.VisualStudio.TestTools.UnitTesting;

namespace SharpLearning.AdaBoost.Test
namespace SharpLearning.AdaBoost.Test;

[TestClass]
public static class AssemblyInitializeCultureTest
{
[TestClass]
public class AssemblyInitializeCultureTest
[AssemblyInitialize]
public static void AssemblyInitializeCultureTest_InvariantCulture(TestContext c)
{
[AssemblyInitialize]
public static void AssemblyInitializeCultureTest_InvariantCulture(TestContext c)
{
CultureInfo culture = CultureInfo.InvariantCulture;
CultureInfo.DefaultThreadCurrentCulture = culture;
CultureInfo.DefaultThreadCurrentUICulture = culture;
Thread.CurrentThread.CurrentCulture = culture;
Thread.CurrentThread.CurrentUICulture = culture;
}
CultureInfo culture = CultureInfo.InvariantCulture;
CultureInfo.DefaultThreadCurrentCulture = culture;
CultureInfo.DefaultThreadCurrentUICulture = culture;
Thread.CurrentThread.CurrentCulture = culture;
Thread.CurrentThread.CurrentUICulture = culture;
}
}
}
41 changes: 20 additions & 21 deletions src/SharpLearning.AdaBoost.Test/DataSetUtilities.cs
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Expand Up @@ -2,28 +2,28 @@
using SharpLearning.Containers.Matrices;
using SharpLearning.InputOutput.Csv;

namespace SharpLearning.AdaBoost.Test
namespace SharpLearning.AdaBoost.Test;

public static class DataSetUtilities
{
public static class DataSetUtilities
public static (F64Matrix observations, double[] targets) LoadAptitudeDataSet()
{
public static (F64Matrix observations, double[] targets) LoadAptitudeDataSet()
{
var parser = new CsvParser(() => new StringReader(AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
return (observations, targets);
}
var parser = new CsvParser(() => new StringReader(AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
return (observations, targets);
}

public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
{
var parser = new CsvParser(() => new StringReader(GlassData));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
return (observations, targets);
}
public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
{
var parser = new CsvParser(() => new StringReader(GlassData));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
return (observations, targets);
}

public const string AptitudeData =
@"AptitudeTestScore;PreviousExperience_month;Pass
public const string AptitudeData =
@"AptitudeTestScore;PreviousExperience_month;Pass
5;6;0
1;15;0
1;12;0
Expand Down Expand Up @@ -51,7 +51,7 @@ public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
1;8;0
5;12;0";

public const string GlassData =
public const string GlassData =
@"F1;F2;F3;F4;F5;F6;F7;F8;F10;Target
1.52101;13.64;4.49;1.1;71.78;0.06;8.75;0;0;1
1.51761;13.89;3.6;1.36;72.73;0.48;7.83;0;0;1
Expand Down Expand Up @@ -268,5 +268,4 @@ public static (F64Matrix observations, double[] targets) LoadGlassDataSet()
1.51651;14.38;0;1.94;73.61;0;8.48;1.57;0;7
1.51711;14.23;0;2.08;73.36;0;8.62;1.67;0;7";

}
}
}
115 changes: 57 additions & 58 deletions src/SharpLearning.AdaBoost.Test/Learners/ClassificationAdaBoostLearnerTest.cs
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Expand Up @@ -5,83 +5,82 @@
using SharpLearning.Containers.Extensions;
using SharpLearning.Metrics.Classification;

namespace SharpLearning.AdaBoost.Test.Learners
namespace SharpLearning.AdaBoost.Test.Learners;

[TestClass]
public class ClassificationAdaBoostLearnerTest
{
[TestClass]
public class ClassificationAdaBoostLearnerTest
[TestMethod]
public void ClassificationAdaBoostLearner_Learn_AptitudeData()
{
[TestMethod]
public void ClassificationAdaBoostLearner_Learn_AptitudeData()
{
var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();

var sut = new ClassificationAdaBoostLearner(10);

var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var sut = new ClassificationAdaBoostLearner(10);

var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(targets, predictions);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);

Assert.AreEqual(0.038461538461538464, actual);
}
var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(targets, predictions);

[TestMethod]
public void ClassificationAdaBoostLearner_Learn_Glass()
{
var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
Assert.AreEqual(0.038461538461538464, actual);
}

var sut = new ClassificationAdaBoostLearner(10, 1, 5);
[TestMethod]
public void ClassificationAdaBoostLearner_Learn_Glass()
{
var (observations, targets) = DataSetUtilities.LoadGlassDataSet();

var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);
var sut = new ClassificationAdaBoostLearner(10, 1, 5);

var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(targets, predictions);
var model = sut.Learn(observations, targets);
var predictions = model.Predict(observations);

Assert.AreEqual(0.0, actual);
}
var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(targets, predictions);

[TestMethod]
public void ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoItemIssue_Solved()
{
var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var indices = new int[] { 22, 6, 23, 12 };
Assert.AreEqual(0.0, actual);
}

var sut = new ClassificationAdaBoostLearner(10);

var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);
[TestMethod]
public void ClassificationAdaBoostLearner_Learn_AptitudeData_SequenceContainNoItemIssue_Solved()
{
var (observations, targets) = DataSetUtilities.LoadAptitudeDataSet();
var indices = new int[] { 22, 6, 23, 12 };

var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
var sut = new ClassificationAdaBoostLearner(10);

Assert.AreEqual(0.0, actual);
}
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);

[TestMethod]
public void ClassificationAdaBoostLearner_Learn_Glass_Indexed()
{
var (observations, targets) = DataSetUtilities.LoadGlassDataSet();
var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(indexedTargets, indexedPredictions);

Assert.AreEqual(0.0, actual);
}

[TestMethod]
public void ClassificationAdaBoostLearner_Learn_Glass_Indexed()
{
var (observations, targets) = DataSetUtilities.LoadGlassDataSet();

var sut = new ClassificationAdaBoostLearner(10, 1, 5);
var sut = new ClassificationAdaBoostLearner(10, 1, 5);

var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();
var indices = Enumerable.Range(0, targets.Length).ToArray();
indices.Shuffle(new Random(42));
indices = indices.Take((int)(targets.Length * 0.7))
.ToArray();

var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);
var model = sut.Learn(observations, targets, indices);
var predictions = model.Predict(observations);
var indexedPredictions = predictions.GetIndices(indices);
var indexedTargets = targets.GetIndices(indices);

var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(indexedTargets, indexedPredictions);
var evaluator = new TotalErrorClassificationMetric<double>();
var actual = evaluator.Error(indexedTargets, indexedPredictions);

Assert.AreEqual(0.0, actual);
}
Assert.AreEqual(0.0, actual);
}
}
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