public void TestCrossValidationMacro()
{
var dataPath = GetDataPath(@"housing.txt");
using (var env = new TlcEnvironment())
{
var subGraph = env.CreateExperiment();
var nop = new ML.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var learnerInput = new ML.Trainers.StochasticDualCoordinateAscentRegressor
{
TrainingData = nopOutput.OutputData,
NumThreads = 1
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <ITransformModel>(nopOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new ML.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var crossValidate = new ML.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
Kind = ML.Models.MacroUtilsTrainerKinds.SignatureRegressorTrainer,
TransformModel = null
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics[0]);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("L1(avg)", out int metricCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == metricCol))
{
var getter = cursor.GetGetter <double>(metricCol);
b = cursor.MoveNext();
Assert.True(b);
double val = 0;
getter(ref val);
Assert.Equal(3.32, val, 1);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
public void TestTrainTestMacro()
{
var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
using (var env = new TlcEnvironment())
{
var subGraph = env.CreateExperiment();
var catInput = new ML.Transforms.CategoricalOneHotVectorizer();
catInput.AddColumn("Categories");
var catOutput = subGraph.Add(catInput);
var concatInput = new ML.Transforms.ColumnConcatenator
{
Data = catOutput.OutputData
};
concatInput.AddColumn("Features", "Categories", "NumericFeatures");
var concatOutput = subGraph.Add(concatInput);
var sdcaInput = new ML.Trainers.StochasticDualCoordinateAscentBinaryClassifier
{
TrainingData = concatOutput.OutputData,
LossFunction = new HingeLossSDCAClassificationLossFunction()
{
Margin = 1.1f
},
NumThreads = 1,
Shuffle = false
};
var sdcaOutput = subGraph.Add(sdcaInput);
var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <ITransformModel>(catOutput.Model, concatOutput.Model),
PredictorModel = sdcaOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new ML.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var trainTestInput = new ML.Models.TrainTestBinaryEvaluator
{
TrainingData = importOutput.Data,
TestingData = importOutput.Data,
Nodes = subGraph
};
trainTestInput.Inputs.Data = catInput.Data;
trainTestInput.Outputs.Model = modelCombineOutput.PredictorModel;
var trainTestOutput = experiment.Add(trainTestInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(trainTestOutput.OverallMetrics);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int aucCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == aucCol))
{
var getter = cursor.GetGetter <double>(aucCol);
b = cursor.MoveNext();
Assert.True(b);
double auc = 0;
getter(ref auc);
Assert.Equal(0.93, auc, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
public void TestCrossValidationBinaryMacro()
{
var dataPath = GetDataPath(@"adult.tiny.with-schema.txt");
using (var env = new TlcEnvironment())
{
var subGraph = env.CreateExperiment();
var catInput = new ML.Transforms.CategoricalOneHotVectorizer();
catInput.AddColumn("Categories");
var catOutput = subGraph.Add(catInput);
var concatInput = new ML.Transforms.ColumnConcatenator
{
Data = catOutput.OutputData
};
concatInput.AddColumn("Features", "Categories", "NumericFeatures");
var concatOutput = subGraph.Add(concatInput);
var lrInput = new ML.Trainers.LogisticRegressionBinaryClassifier
{
TrainingData = concatOutput.OutputData,
NumThreads = 1
};
var lrOutput = subGraph.Add(lrInput);
var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <ITransformModel>(catOutput.Model, concatOutput.Model),
PredictorModel = lrOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new ML.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var crossValidateBinary = new ML.Models.BinaryCrossValidator
{
Data = importOutput.Data,
Nodes = subGraph
};
crossValidateBinary.Inputs.Data = catInput.Data;
crossValidateBinary.Outputs.Model = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidateBinary);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics[0]);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int aucCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == aucCol))
{
var getter = cursor.GetGetter <double>(aucCol);
b = cursor.MoveNext();
Assert.True(b);
double auc = 0;
getter(ref auc);
Assert.Equal(0.87, auc, 1);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
public void TestCrossValidationMacroWithStratification()
{
var dataPath = GetDataPath(@"breast-cancer.txt");
using (var env = new TlcEnvironment())
{
var subGraph = env.CreateExperiment();
var nop = new ML.Transforms.NoOperation();
var nopOutput = subGraph.Add(nop);
var learnerInput = new ML.Trainers.StochasticDualCoordinateAscentBinaryClassifier
{
TrainingData = nopOutput.OutputData,
NumThreads = 1
};
var learnerOutput = subGraph.Add(learnerInput);
var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner
{
TransformModels = new ArrayVar <ITransformModel>(nopOutput.Model),
PredictorModel = learnerOutput.PredictorModel
};
var modelCombineOutput = subGraph.Add(modelCombine);
var experiment = env.CreateExperiment();
var importInput = new ML.Data.TextLoader(dataPath);
importInput.Arguments.Column = new ML.Data.TextLoaderColumn[]
{
new ML.Data.TextLoaderColumn {
Name = "Label", Source = new[] { new ML.Data.TextLoaderRange(0) }
},
new ML.Data.TextLoaderColumn {
Name = "Strat", Source = new[] { new ML.Data.TextLoaderRange(1) }
},
new ML.Data.TextLoaderColumn {
Name = "Features", Source = new[] { new ML.Data.TextLoaderRange(2, 9) }
}
};
var importOutput = experiment.Add(importInput);
var crossValidate = new ML.Models.CrossValidator
{
Data = importOutput.Data,
Nodes = subGraph,
TransformModel = null,
StratificationColumn = "Strat"
};
crossValidate.Inputs.Data = nop.Data;
crossValidate.Outputs.Model = modelCombineOutput.PredictorModel;
var crossValidateOutput = experiment.Add(crossValidate);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(crossValidateOutput.OverallMetrics[0]);
var schema = data.Schema;
var b = schema.TryGetColumnIndex("AUC", out int metricCol);
Assert.True(b);
using (var cursor = data.GetRowCursor(col => col == metricCol))
{
var getter = cursor.GetGetter <double>(metricCol);
b = cursor.MoveNext();
Assert.True(b);
double val = 0;
getter(ref val);
Assert.Equal(0.99, val, 2);
b = cursor.MoveNext();
Assert.False(b);
}
}
}
