public void DiscreteClassification_DiscreteFeatures_MultiValuesSplits_CongressVoting()
{
// Given
var randomForestBuilder = new RandomForestModelBuilder <object>(
multiValueTreeBuilderWithBetterNumercValsHandler,
new DecisionTreePredictor <object>(),
new ConfusionMatrixBuilder <object>(),
i => (int)Math.Round(Math.Sqrt(i), MidpointRounding.AwayFromZero),
() => new DecisionTreeModelBuilderParams(false));
var randomForestPredictor = new RandomForestPredictor <object>(new DecisionTreePredictor <object>(), true);
var testData = TestDataBuilder.ReadCongressData();
var crossValidator = new CrossValidator <object>();
// When
var accuracy = crossValidator.CrossValidate(
randomForestBuilder,
new RandomForestParams(100, 10),
randomForestPredictor,
new ConfusionMatrixBuilder <object>(),
testData,
"party",
0.7,
1).First();
// Then
Assert.IsTrue(accuracy.Accuracy >= 0.9);
}
public void DiscreteClassification_DiscreteFeatures_MultiValuesSplits_CongressVoting()
{
// Given
var randomForestBuilder = new RandomForestModelBuilder<object>(
multiValueTreeBuilderWithBetterNumercValsHandler,
new DecisionTreePredictor<object>(),
new ConfusionMatrixBuilder<object>(),
i => (int)Math.Round(Math.Sqrt(i), MidpointRounding.AwayFromZero),
() => new DecisionTreeModelBuilderParams(false));
var randomForestPredictor = new RandomForestPredictor<object>(new DecisionTreePredictor<object>(), true);
var testData = TestDataBuilder.ReadCongressData();
var crossValidator = new CrossValidator<object>();
// When
var accuracy = crossValidator.CrossValidate(
randomForestBuilder,
new RandomForestParams(100, 10),
randomForestPredictor,
new ConfusionMatrixBuilder<object>(),
testData,
"party",
0.7,
1).First();
// Then
Assert.IsTrue(accuracy.Accuracy >= 0.9);
}
public void DiscreteClassification_MixedFeatures_MultiValueSplits_CleanedTitanicData()
{
// Given
var randomForestBuilder = new RandomForestModelBuilder<object>(
multiValueTreeBuilderWithBetterNumercValsHandler,
new DecisionTreePredictor<object>(),
new ConfusionMatrixBuilder<object>(),
i => (int)Math.Round(Math.Sqrt(i), MidpointRounding.AwayFromZero),
() => new DecisionTreeModelBuilderParams(false, true));
var randomForestPredictor = new RandomForestPredictor<object>(new DecisionTreePredictor<object>());
var baseData = TestDataBuilder.ReadTitanicData();
baseData = baseData.GetSubsetByColumns(baseData.ColumnNames.Except(new[] { "FarePerPerson", "PassengerId", "FamilySize" }).ToList());
var crossValidator = new CrossValidator<object>();
// When
var accuracy = crossValidator.CrossValidate(
randomForestBuilder,
new RandomForestParams(200, 10),
randomForestPredictor,
new ConfusionMatrixBuilder<object>(),
baseData,
"Survived",
0.75,
1);
// Then
Assert.IsTrue(accuracy.Select(acc => acc.Accuracy).Average() >= 0.75);
/*
var qualityMeasure = new ConfusionMatrixBuilder<object>();
IPredictionModel bestModel = null;
double accuracy = Double.NegativeInfinity;
var percetnagOfTrainData = 0.8;
var trainingDataCount = (int)Math.Round(percetnagOfTrainData * baseData.RowCount);
var testDataCount = baseData.RowCount - trainingDataCount;
for (var i = 0; i < 10; i++)
{
var shuffledAllIndices = baseData.RowIndices.Shuffle(new Random());
var trainingIndices = shuffledAllIndices.Take(trainingDataCount).ToList();
var trainingData = baseData.GetSubsetByRows(trainingIndices);
var testIndices = shuffledAllIndices.Except(trainingIndices).ToList();
var testData = baseData.GetSubsetByRows(testIndices);
IPredictionModel model = randomForestBuilder.BuildModel(trainingData, "Survived", new RandomForestParams(250, 10));
IList<object> evalPredictions = randomForestPredictor.Predict(testData, model, "Survived");
IList<object> expected = testData.GetColumnVector<object>("Survived");
IDataQualityReport<object> qualityReport = qualityMeasure.GetReport(expected, evalPredictions);
if (qualityReport.Accuracy > accuracy)
{
accuracy = qualityReport.Accuracy;
bestModel = model;
}
}
var queryData = TestDataBuilder.ReadTitanicQuery();
var predictions = randomForestPredictor.Predict(queryData, bestModel, "Survived").Select(elem => (double)Convert.ChangeType(elem, typeof(double))).ToList();
var passengerIds = queryData.GetNumericColumnVector("PassengerId");
var matrix = Matrix.Build.DenseOfColumns(new List<IEnumerable<double>>() { passengerIds, predictions });
DelimitedWriter.Write(@"c:\Users\Filip\Downloads\prediction.csv", matrix, ",");
Assert.IsTrue(true);
*/
}
public void DiscreteClassification_MixedFeatures_MultiValueSplits_CleanedTitanicData()
{
// Given
var randomForestBuilder = new RandomForestModelBuilder <object>(
multiValueTreeBuilderWithBetterNumercValsHandler,
new DecisionTreePredictor <object>(),
new ConfusionMatrixBuilder <object>(),
i => (int)Math.Round(Math.Sqrt(i), MidpointRounding.AwayFromZero),
() => new DecisionTreeModelBuilderParams(false, true));
var randomForestPredictor = new RandomForestPredictor <object>(new DecisionTreePredictor <object>());
var baseData = TestDataBuilder.ReadTitanicData();
baseData = baseData.GetSubsetByColumns(baseData.ColumnNames.Except(new[] { "FarePerPerson", "PassengerId", "FamilySize" }).ToList());
var crossValidator = new CrossValidator <object>();
// When
var accuracy = crossValidator.CrossValidate(
randomForestBuilder,
new RandomForestParams(200, 10),
randomForestPredictor,
new ConfusionMatrixBuilder <object>(),
baseData,
"Survived",
0.75,
1);
// Then
Assert.IsTrue(accuracy.Select(acc => acc.Accuracy).Average() >= 0.75);
/*
* var qualityMeasure = new ConfusionMatrixBuilder<object>();
* IPredictionModel bestModel = null;
* double accuracy = Double.NegativeInfinity;
* var percetnagOfTrainData = 0.8;
*
* var trainingDataCount = (int)Math.Round(percetnagOfTrainData * baseData.RowCount);
* var testDataCount = baseData.RowCount - trainingDataCount;
* for (var i = 0; i < 10; i++)
* {
* var shuffledAllIndices = baseData.RowIndices.Shuffle(new Random());
* var trainingIndices = shuffledAllIndices.Take(trainingDataCount).ToList();
* var trainingData = baseData.GetSubsetByRows(trainingIndices);
*
* var testIndices = shuffledAllIndices.Except(trainingIndices).ToList();
* var testData = baseData.GetSubsetByRows(testIndices);
* IPredictionModel model = randomForestBuilder.BuildModel(trainingData, "Survived", new RandomForestParams(250, 10));
* IList<object> evalPredictions = randomForestPredictor.Predict(testData, model, "Survived");
* IList<object> expected = testData.GetColumnVector<object>("Survived");
* IDataQualityReport<object> qualityReport = qualityMeasure.GetReport(expected, evalPredictions);
* if (qualityReport.Accuracy > accuracy)
* {
* accuracy = qualityReport.Accuracy;
* bestModel = model;
* }
* }
*
* var queryData = TestDataBuilder.ReadTitanicQuery();
* var predictions = randomForestPredictor.Predict(queryData, bestModel, "Survived").Select(elem => (double)Convert.ChangeType(elem, typeof(double))).ToList();
* var passengerIds = queryData.GetNumericColumnVector("PassengerId");
*
* var matrix = Matrix.Build.DenseOfColumns(new List<IEnumerable<double>>() { passengerIds, predictions });
* DelimitedWriter.Write(@"c:\Users\Filip\Downloads\prediction.csv", matrix, ",");
* Assert.IsTrue(true);
*/
}