public void LpNormWorkout()
{
string dataSource = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
var data = TextLoader.CreateReader(ML,
c => (label: c.LoadFloat(11), features: c.LoadFloat(0, 10)),
separator: ';', hasHeader: true)
.Read(dataSource);
var invalidData = TextLoader.CreateReader(ML,
c => (label: c.LoadFloat(11), features: c.LoadText(0, 10)),
separator: ';', hasHeader: true)
.Read(dataSource);
var est = new LpNormalizingEstimator(ML, "features", "lpNorm1")
.Append(new LpNormalizingEstimator(ML, "features", "lpNorm2", normKind: LpNormalizingEstimatorBase.NormalizerKind.L1Norm, substractMean: true));
TestEstimatorCore(est, data.AsDynamic, invalidInput: invalidData.AsDynamic);
var outputPath = GetOutputPath("NormalizerEstimator", "lpNorm.tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(ML, new TextSaver.Arguments {
Silent = true, OutputHeader = false
});
IDataView savedData = TakeFilter.Create(ML, est.Fit(data.AsDynamic).Transform(data.AsDynamic), 4);
savedData = ColumnSelectingTransformer.CreateKeep(Env, savedData, new[] { "lpNorm1", "lpNorm2" });
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("NormalizerEstimator", "lpNorm.tsv");
Done();
}
public void TestLpNormOldSavingAndLoading()
{
string dataSource = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
var dataView = TextLoader.CreateReader(ML,
c => (label: c.LoadFloat(11), features: c.LoadFloat(0, 10)),
separator: ';', hasHeader: true)
.Read(dataSource).AsDynamic;
var pipe = new LpNormalizingEstimator(ML, "features", "whitened");
var result = pipe.Fit(dataView).Transform(dataView);
var resultRoles = new RoleMappedData(result);
using (var ms = new MemoryStream())
{
TrainUtils.SaveModel(ML, Env.Start("saving"), ms, null, resultRoles);
ms.Position = 0;
var loadedView = ModelFileUtils.LoadTransforms(ML, dataView, ms);
}
}
public void LpGcNormAndWhiteningWorkout()
{
string dataSource = GetDataPath(TestDatasets.generatedRegressionDataset.trainFilename);
var data = TextLoader.CreateReader(ML,
c => (label: c.LoadFloat(11), features: c.LoadFloat(0, 10)),
separator: ';', hasHeader: true)
.Read(dataSource);
var invalidData = TextLoader.CreateReader(ML,
c => (label: c.LoadFloat(11), features: c.LoadText(0, 10)),
separator: ';', hasHeader: true)
.Read(dataSource);
var est = new LpNormalizingEstimator(ML, "features", "lpnorm")
.Append(new GlobalContrastNormalizingEstimator(ML, "features", "gcnorm"))
.Append(new VectorWhiteningEstimator(ML, "features", "whitened"));
TestEstimatorCore(est, data.AsDynamic, invalidInput: invalidData.AsDynamic);
var outputPath = GetOutputPath("NormalizerEstimator", "lpnorm_gcnorm_whitened.tsv");
using (var ch = Env.Start("save"))
{
var saver = new TextSaver(ML, new TextSaver.Arguments {
Silent = true, OutputHeader = false
});
IDataView savedData = TakeFilter.Create(ML, est.Fit(data.AsDynamic).Transform(data.AsDynamic), 4);
savedData = ColumnSelectingTransformer.CreateKeep(ML, savedData, new[] { "lpnorm", "gcnorm", "whitened" });
using (var fs = File.Create(outputPath))
DataSaverUtils.SaveDataView(ch, saver, savedData, fs, keepHidden: true);
}
CheckEquality("NormalizerEstimator", "lpnorm_gcnorm_whitened.tsv", digitsOfPrecision: 4);
Done();
}
