public void RegisterTypeWithAttribute()
{
// Build in-memory data.
var tribe = new List <AlienHero>()
{
new AlienHero("ML.NET", 2, 1000, 2000, 3000, 4000, 5000, 6000, 7000)
};
// Build a ML.NET pipeline and make prediction.
var tribeDataView = ML.Data.LoadFromEnumerable(tribe);
var heroEstimator = new CustomMappingEstimator <AlienHero, SuperAlienHero>(ML, AlienFusionProcess.MergeBody, "LambdaAlienHero");
var model = heroEstimator.Fit(tribeDataView);
var tribeTransformed = model.Transform(tribeDataView);
var tribeEnumerable = ML.Data.CreateEnumerable <SuperAlienHero>(tribeTransformed, false).ToList();
// Make sure the pipeline output is correct.
Assert.Equal(tribeEnumerable[0].Name, "Super " + tribe[0].Name);
Assert.Equal(tribeEnumerable[0].Merged.Age, tribe[0].One.Age + tribe[0].Two.Age);
Assert.Equal(tribeEnumerable[0].Merged.Height, tribe[0].One.Height + tribe[0].Two.Height);
Assert.Equal(tribeEnumerable[0].Merged.Weight, tribe[0].One.Weight + tribe[0].Two.Weight);
Assert.Equal(tribeEnumerable[0].Merged.HandCount, tribe[0].One.HandCount + tribe[0].Two.HandCount);
// Build prediction engine from the trained pipeline.
var engine = ML.Model.CreatePredictionEngine <AlienHero, SuperAlienHero>(model);
var alien = new AlienHero("TEN.LM", 1, 2, 3, 4, 5, 6, 7, 8);
var superAlien = engine.Predict(alien);
// Make sure the prediction engine produces expected result.
Assert.Equal(superAlien.Name, "Super " + alien.Name);
Assert.Equal(superAlien.Merged.Age, alien.One.Age + alien.Two.Age);
Assert.Equal(superAlien.Merged.Height, alien.One.Height + alien.Two.Height);
Assert.Equal(superAlien.Merged.Weight, alien.One.Weight + alien.Two.Weight);
Assert.Equal(superAlien.Merged.HandCount, alien.One.HandCount + alien.Two.HandCount);
}
public void TestCustomTransformer()
{
string dataPath = GetDataPath("adult.tiny.with-schema.txt");
var source = new MultiFileSource(dataPath);
var loader = ML.Data.CreateTextLoader(new[] {
new TextLoader.Column("Float1", DataKind.Single, 9),
new TextLoader.Column("Float4", DataKind.Single, new[] { new TextLoader.Range(9), new TextLoader.Range(10), new TextLoader.Range(11), new TextLoader.Range(12) })
}, hasHeader: true);
var data = loader.Load(source);
IDataView transformedData;
// We create a temporary environment to instantiate the custom transformer. This is to ensure that we don't need the same
// environment for saving and loading.
var tempoEnv = new MLContext();
var customEst = new CustomMappingEstimator <MyInput, MyOutput>(tempoEnv, MyLambda.MyAction, "MyLambda");
try
{
TestEstimatorCore(customEst, data);
Assert.True(false, "Cannot work without RegisterAssembly");
}
catch (InvalidOperationException ex)
{
if (!ex.IsMarked())
{
throw;
}
}
ML.ComponentCatalog.RegisterAssembly(typeof(MyLambda).Assembly);
TestEstimatorCore(customEst, data);
transformedData = customEst.Fit(data).Transform(data);
var inputs = ML.Data.CreateEnumerable <MyInput>(transformedData, true);
var outputs = ML.Data.CreateEnumerable <MyOutput>(transformedData, true);
Assert.True(inputs.Zip(outputs, (x, y) => y.Together == $"{x.Float1} + {string.Join(", ", x.Float4)}").All(x => x));
Done();
}
public void TestCustomTransformer()
{
string dataPath = GetDataPath("adult.test");
var source = new MultiFileSource(dataPath);
var loader = ML.Data.TextReader(new[] {
new TextLoader.Column("Float1", DataKind.R4, 0),
new TextLoader.Column("Float4", DataKind.R4, new[] { new TextLoader.Range(0), new TextLoader.Range(2), new TextLoader.Range(4), new TextLoader.Range(10) })
}, s => { s.Separator = ","; s.HasHeader = true; });
var data = loader.Read(source);
IDataView transformedData;
// We create a temporary environment to instantiate the custom transformer. This is to ensure that we don't need the same
// environment for saving and loading.
using (var tempoEnv = new ConsoleEnvironment())
{
var customEst = new CustomMappingEstimator <MyInput, MyOutput>(tempoEnv, MyLambda.MyAction, "MyLambda");
try
{
TestEstimatorCore(customEst, data);
Assert.True(false, "Cannot work without MEF injection");
}
catch (Exception)
{
// REVIEW: we should have a common mechanism that will make sure this is 'our' exception thrown.
}
ML.CompositionContainer = new CompositionContainer(new TypeCatalog(typeof(MyLambda)));
TestEstimatorCore(customEst, data);
transformedData = customEst.Fit(data).Transform(data);
}
var inputs = transformedData.AsEnumerable <MyInput>(ML, true);
var outputs = transformedData.AsEnumerable <MyOutput>(ML, true);
Assert.True(inputs.Zip(outputs, (x, y) => y.Together == $"{x.Float1} + {string.Join(", ", x.Float4)}").All(x => x));
Done();
}
public void TestCustomTransformer(bool registerAssembly)
{
string dataPath = GetDataPath("adult.tiny.with-schema.txt");
var source = new MultiFileSource(dataPath);
var loader = ML.Data.CreateTextLoader(new[] {
new TextLoader.Column("Float1", DataKind.Single, 9),
new TextLoader.Column("Float4", DataKind.Single, new[] { new TextLoader.Range(9), new TextLoader.Range(10), new TextLoader.Range(11), new TextLoader.Range(12) })
}, hasHeader: true);
var data = loader.Load(source);
IDataView transformedData;
// We create a temporary environment to instantiate the custom transformer. This is to ensure that we don't need the same
// environment for saving and loading.
var tempoEnv = new MLContext(1);
var customEst = new CustomMappingEstimator <MyInput, MyOutput>(tempoEnv, MyLambda.MyAction, "MyLambda");
// Before 1.5-preview3 it was required to register the assembly.
// Now, the assembly information is automatically saved in the model and the assembly is registered
// when loading.
// This tests the case that the CustomTransformer still works even if you explicitly register the assembly
if (registerAssembly)
{
ML.ComponentCatalog.RegisterAssembly(typeof(MyLambda).Assembly);
}
TestEstimatorCore(customEst, data);
transformedData = customEst.Fit(data).Transform(data);
var inputs = ML.Data.CreateEnumerable <MyInput>(transformedData, true);
var outputs = ML.Data.CreateEnumerable <MyOutput>(transformedData, true);
Assert.True(inputs.Zip(outputs, (x, y) => y.Together == $"{x.Float1} + {string.Join(", ", x.Float4)}").All(x => x));
Done();
}
