public void TestSimpleTrainExperiment()
{
var dataPath = GetDataPath("adult.tiny.with-schema.txt");
var env = new MLContext();
var experiment = env.CreateExperiment();
var importInput = new Legacy.Data.TextLoader(dataPath);
var importOutput = experiment.Add(importInput);
var catInput = new Legacy.Transforms.CategoricalOneHotVectorizer
{
Data = importOutput.Data
};
catInput.AddColumn("Categories");
var catOutput = experiment.Add(catInput);
var concatInput = new Legacy.Transforms.ColumnConcatenator
{
Data = catOutput.OutputData
};
concatInput.AddColumn("Features", "Categories", "NumericFeatures");
var concatOutput = experiment.Add(concatInput);
var sdcaInput = new Legacy.Trainers.StochasticDualCoordinateAscentBinaryClassifier
{
TrainingData = concatOutput.OutputData,
LossFunction = new HingeLossSDCAClassificationLossFunction()
{
Margin = 1.1f
},
NumThreads = 1,
Shuffle = false
};
var sdcaOutput = experiment.Add(sdcaInput);
var scoreInput = new Legacy.Transforms.DatasetScorer
{
Data = concatOutput.OutputData,
PredictorModel = sdcaOutput.PredictorModel
};
var scoreOutput = experiment.Add(scoreInput);
var evalInput = new Legacy.Models.BinaryClassificationEvaluator
{
Data = scoreOutput.ScoredData
};
var evalOutput = experiment.Add(evalInput);
experiment.Compile();
experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false));
experiment.Run();
var data = experiment.GetOutput(evalOutput.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 static CommonOutputs.MacroOutput <Output> TrainTestBinary(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
// Parse the subgraph.
var subGraphRunContext = new RunContext(env);
var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, input.Nodes);
// Change the subgraph to use the training data as input.
var varName = input.Inputs.Data.VarName;
EntryPointVariable variable;
if (!subGraphRunContext.TryGetVariable(varName, out variable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
var trainingVar = node.GetInputVariable("TrainingData");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameInputVariable(variable.Name, trainingVar);
}
subGraphRunContext.RemoveVariable(variable);
// Change the subgraph to use the model variable as output.
varName = input.Outputs.Model.VarName;
if (!subGraphRunContext.TryGetVariable(varName, out variable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
string outputVarName = node.GetOutputVariableName("PredictorModel");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameOutputVariable(variable.Name, outputVarName);
}
subGraphRunContext.RemoveVariable(variable);
// Move the variables from the subcontext to the main context.
node.Context.AddContextVariables(subGraphRunContext);
// Change all the subgraph nodes to use the main context.
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.SetContext(node.Context);
}
// Add the scoring node.
var testingVar = node.GetInputVariable("TestingData");
var exp = new Experiment(env);
var scoreNode = new Legacy.Transforms.DatasetScorer();
scoreNode.Data.VarName = testingVar.ToJson();
scoreNode.PredictorModel.VarName = outputVarName;
var scoreNodeOutput = exp.Add(scoreNode);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes()));
// Add the evaluator node.
exp.Reset();
var evalNode = new Legacy.Models.BinaryClassificationEvaluator();
evalNode.Data.VarName = scoreNodeOutput.ScoredData.VarName;
var evalOutput = new Legacy.Models.BinaryClassificationEvaluator.Output();
string outVariableName;
if (node.OutputMap.TryGetValue("Warnings", out outVariableName))
{
evalOutput.Warnings.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("OverallMetrics", out outVariableName))
{
evalOutput.OverallMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("PerInstanceMetrics", out outVariableName))
{
evalOutput.PerInstanceMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("ConfusionMatrix", out outVariableName))
{
evalOutput.ConfusionMatrix.VarName = outVariableName;
}
exp.Add(evalNode, evalOutput);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes()));
var stageId = Guid.NewGuid().ToString("N");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.StageId = stageId;
}
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}