public void TestEP_PassThroughTransform()
{
var methodName = System.Reflection.MethodBase.GetCurrentMethod().Name;
var iris = FileHelper.GetTestFile("iris.txt");
var outPass = FileHelper.GetOutputFile("data.idv", methodName);
var df = DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Scikit.ML.EntryPoints.Scaler("Features"));
learningPipeline.Add(new Scikit.ML.EntryPoints.PassThrough()
{
Filename = outPass, SaveOnDisk = true
});
learningPipeline.Add(new Legacy.Trainers.StochasticDualCoordinateAscentRegressor());
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 8));
Assert.IsTrue(File.Exists(outPass));
}
public void TestEP_NearestNeighborsLPMc()
{
using (var env = EnvHelper.NewTestEnvironment(conc: 1))
{
var iris = FileHelper.GetTestFile("iris.txt");
var df = DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Scikit.ML.EntryPoints.NearestNeighborsMultiClass());
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 11));
}
}
public void TestEP_Q_KMeansEntryPointAPIWithDataFrame()
{
var iris = FileHelper.GetTestFile("iris.txt");
var df = Scikit.ML.DataManipulation.DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Legacy.Trainers.KMeansPlusPlusClusterer());
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = Scikit.ML.DataManipulation.DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 13));
}
public void TestEP_ScalerTransform()
{
var iris = FileHelper.GetTestFile("iris.txt");
var df = DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Scikit.ML.EntryPoints.Scaler("Features"));
learningPipeline.Add(new Legacy.Trainers.StochasticDualCoordinateAscentRegressor());
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 8));
}
public void TestEntryPointXGBoostBinary()
{
var env = EnvHelper.NewTestEnvironment(conc: 1);
var iris = FileHelper.GetTestFile("iris_binary.txt");
var df = DataFrame.ReadCsv(iris, sep: '\t', dtypes: new DataKind?[] { DataKind.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline(conc: 1);
learningPipeline.Add(importData);
learningPipeline.Add(new ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Scikit.ML.XGBoostWrapper.XGBoostBinary());
// Fails here due to missing variable.
return;
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrame.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 9));
}
public void TestTreePathNewAPI()
{
using (var env = EnvHelper.NewTestEnvironment(conc: 1))
{
var methodName = System.Reflection.MethodBase.GetCurrentMethod().Name;
var iris = FileHelper.GetTestFile("iris.txt");
var df = DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var importData = df.EPTextLoader(iris, sep: '\t', header: true);
var learningPipeline = new GenericLearningPipeline();
learningPipeline.Add(importData);
learningPipeline.Add(new Legacy.Transforms.ColumnConcatenator("Features", "Sepal_length", "Sepal_width"));
learningPipeline.Add(new Legacy.Trainers.StochasticDualCoordinateAscentRegressor()
{
MaxIterations = 2
});
var predictor = learningPipeline.Train();
var predictions = predictor.Predict(df);
var dfout = DataFrameIO.ReadView(predictions);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 8));
var outfile = FileHelper.GetOutputFile("iris_path.txt", methodName);
dfout.ToCsv(outfile);
Assert.IsTrue(File.Exists(outfile));
}
}