public void Regression_NumericAttrsAndOutcomesOnly_RegularizedRegression()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator <double>(randomizer);
var testData = TestDataBuilder.ReadHousingDataNormalizedAttrs();
var predictor = new DecisionTreePredictor <double>();
var numericTreeBuilder = new BinaryDecisionTreeModelBuilder(
new VarianceBasedSplitQualityChecker(),
new BestSplitSelectorForNumericValues(new BinaryNumericDataSplitter()),
new RegressionAndModelDecisionTreeLeafBuilder(new RegularizedLinearRegressionModelBuilder(0.005)));
// When
var accuracies = splitter.CrossValidate(
modelBuilder: numericTreeBuilder,
modelBuilderParams: modelBuilderParams,
predictor: predictor,
qualityMeasure: new GoodnessOfFitQualityMeasure(),
dataFrame: testData,
dependentFeatureName: "MEDV",
percetnagOfTrainData: 0.7,
folds: 15);
// Then
var averegeRsquared = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averegeRsquared >= 0.6);
}
public void Mushroom_BinarySplit()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator <string>(randomizer);
var testData = TestDataBuilder.ReadMushroomDataWithCategoricalAttributes();
var predictor = new DecisionTreePredictor <string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: binaryTreeBuilder,
modelBuilderParams: modelBuilderParams,
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder <string>(),
dataFrame: testData,
dependentFeatureName: "type",
percetnagOfTrainData: 0.7,
folds: 2);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.99);
}
public void Mushroom_MultiSplit_StatisticalSignificanceHeuristic()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator <string>(randomizer);
var testData = TestDataBuilder.ReadMushroomDataWithCategoricalAttributes();
var predictor = new DecisionTreePredictor <string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: this.BuildCustomModelBuilder(statisticalSignificanceChecker: new ChiSquareStatisticalSignificanceChecker()),
modelBuilderParams: new DecisionTreeModelBuilderParams(false, true),
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder <string>(),
dataFrame: testData,
dependentFeatureName: "type",
percetnagOfTrainData: 0.7,
folds: 2);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.99);
}
public void DiscreteClassification_CategoricalFeatures_BinarySplits_ConvressVotingData_StatisticalSignificanceTest_CrossValidation()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator <string>(randomizer);
var testData = TestDataBuilder.ReadCongressData() as DataFrame;
var predictor = new DecisionTreePredictor <string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: this.BuildCustomModelBuilder(true, statisticalSignificanceChecker: new ChiSquareStatisticalSignificanceChecker()),
modelBuilderParams: new DecisionTreeModelBuilderParams(false, true),
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder <string>(),
dataFrame: testData,
dependentFeatureName: "party",
percetnagOfTrainData: 0.7,
folds: 10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
public void DiscreteClassification_CategoricalFeatures_MultiValuesSplits_CongressVotingData_CrossValidation()
{
// Given
var randomizer = new Random();
var splitter = new CrossValidator <string>(randomizer);
var testData = TestDataBuilder.ReadCongressData() as DataFrame;
var predictor = new DecisionTreePredictor <string>();
// When
var accuracies = splitter.CrossValidate(modelBuilder: this.multiValueTreeBuilder, modelBuilderParams: modelBuilderParams, predictor: predictor, qualityMeasure: new ConfusionMatrixBuilder <string>(), dataFrame: testData, dependentFeatureName: "party", percetnagOfTrainData: 0.7, folds: 10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
public void DiscreteClassification_CategoricalFeatures_BinarySplits_ConvressVotingData_CrossValidation()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator<string>(randomizer);
var testData = TestDataBuilder.ReadCongressData() as DataFrame;
var predictor = new DecisionTreePredictor<string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: binaryTreeBuilder,
modelBuilderParams: modelBuilderParams,
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder<string>(),
dataFrame: testData,
dependentFeatureName: "party",
percetnagOfTrainData: 0.7,
folds: 10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
public void DiscreteClassification_NumericFeatures_MultiValuesSplits_AdultCensusData_CrossValidation()
{
// Given
var splitter = new CrossValidator <object>();
var testData = TestDataBuilder.ReadAdultCensusDataFrame();
var predictor = new DecisionTreePredictor <object>();
// When
var accuracies = splitter.CrossValidate(
multiValueTreeBuilderWithBetterNumercValsHandler,
modelBuilderParams,
predictor,
new ConfusionMatrixBuilder <object>(),
testData,
"income",
0.7,
5);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.8);
}
public void DiscreteClassification_NumericFeatures_BinarySplits_IrisData_CrossValidation()
{
// Given
var randomizer = new Random();
var splitter = new CrossValidator <object>();
var testData = TestDataBuilder.ReadIrisData();
var predictor = new DecisionTreePredictor <object>();
// When
var accuracies = splitter.CrossValidate(
binaryTreeBuilder,
modelBuilderParams,
predictor,
new ConfusionMatrixBuilder <object>(),
testData,
"iris_class",
0.7,
10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
public void Regression_NumericAttrsAndOutcomesOnly_RegularizedRegression()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator<double>(randomizer);
var testData = TestDataBuilder.ReadHousingDataNormalizedAttrs();
var predictor = new DecisionTreePredictor<double>();
var numericTreeBuilder = new BinaryDecisionTreeModelBuilder(
new VarianceBasedSplitQualityChecker(),
new BestSplitSelectorForNumericValues(new BinaryNumericDataSplitter()),
new RegressionAndModelDecisionTreeLeafBuilder(new RegularizedLinearRegressionModelBuilder(0.005)));
// When
var accuracies = splitter.CrossValidate(
modelBuilder: numericTreeBuilder,
modelBuilderParams: modelBuilderParams,
predictor: predictor,
qualityMeasure: new GoodnessOfFitQualityMeasure(),
dataFrame: testData,
dependentFeatureName: "MEDV",
percetnagOfTrainData: 0.7,
folds: 15);
// Then
var averegeRsquared = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averegeRsquared >= 0.6);
}
public void Mushroom_MultiSplit_StatisticalSignificanceHeuristic()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator<string>(randomizer);
var testData = TestDataBuilder.ReadMushroomDataWithCategoricalAttributes();
var predictor = new DecisionTreePredictor<string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: this.BuildCustomModelBuilder(statisticalSignificanceChecker: new ChiSquareStatisticalSignificanceChecker()),
modelBuilderParams: new DecisionTreeModelBuilderParams(false, true),
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder<string>(),
dataFrame: testData,
dependentFeatureName: "type",
percetnagOfTrainData: 0.7,
folds: 2);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.99);
}
public void Mushroom_MultiSplit()
{
// Given
var randomizer = new Random(3);
var splitter = new CrossValidator<string>(randomizer);
var testData = TestDataBuilder.ReadMushroomDataWithCategoricalAttributes();
var predictor = new DecisionTreePredictor<string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: multiValueTreeBuilder,
modelBuilderParams: modelBuilderParams,
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder<string>(),
dataFrame: testData,
dependentFeatureName: "type",
percetnagOfTrainData: 0.7,
folds: 2);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.99);
}
public void DiscreteClassification_NumericFeatures_MultiValuesSplits_AdultCensusData_CrossValidation()
{
// Given
var splitter = new CrossValidator<object>();
var testData = TestDataBuilder.ReadAdultCensusDataFrame();
var predictor = new DecisionTreePredictor<object>();
// When
var accuracies = splitter.CrossValidate(
multiValueTreeBuilderWithBetterNumercValsHandler,
modelBuilderParams,
predictor,
new ConfusionMatrixBuilder<object>(),
testData,
"income",
0.7,
5);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.8);
}
public void DiscreteClassification_NumericFeatures_BinarySplits_IrisData_CrossValidation()
{
// Given
var randomizer = new Random();
var splitter = new CrossValidator<object>();
var testData = TestDataBuilder.ReadIrisData();
var predictor = new DecisionTreePredictor<object>();
// When
var accuracies = splitter.CrossValidate(
binaryTreeBuilder,
modelBuilderParams,
predictor,
new ConfusionMatrixBuilder<object>(),
testData,
"iris_class",
0.7,
10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
public void DiscreteClassification_CategoricalFeatures_MultiValuesSplits_CongressVotingData_StatisticalSignificanceHeuristic_CrossValidation()
{
// Given
var randomizer = new Random();
var splitter = new CrossValidator<string>(randomizer);
var testData = TestDataBuilder.ReadCongressData() as DataFrame;
var predictor = new DecisionTreePredictor<string>();
// When
var accuracies = splitter.CrossValidate(
modelBuilder: this.BuildCustomModelBuilder(true, statisticalSignificanceChecker: new ChiSquareStatisticalSignificanceChecker(0.05)),
modelBuilderParams: new DecisionTreeModelBuilderParams(false, true),
predictor: predictor,
qualityMeasure: new ConfusionMatrixBuilder<string>(),
dataFrame: testData,
dependentFeatureName: "party",
percetnagOfTrainData: 0.7,
folds: 10);
// Then
var averageAccuracy = accuracies.Select(report => report.Accuracy).Average();
Assert.IsTrue(averageAccuracy >= 0.9);
}
