public static CommonOutputs.MacroOutput <Output> CrossValidate(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
env.CheckValue(input, nameof(input));
// This will be the final resulting list of nodes that is returned from the macro.
var subGraphNodes = new List <EntryPointNode>();
//the input transform model
VariableBinding transformModelVarName = null;
if (input.TransformModel != null)
{
transformModelVarName = node.GetInputVariable(nameof(input.TransformModel));
}
// Split the input data into folds.
var splitArgs = new CVSplit.Input();
splitArgs.NumFolds = input.NumFolds;
splitArgs.StratificationColumn = input.StratificationColumn;
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var inputData = node.GetInputVariable(nameof(splitArgs.Data));
ParameterBinding paramBinding = new SimpleParameterBinding(nameof(splitArgs.Data));
inputBindingMap.Add(nameof(splitArgs.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, inputData);
var outputMap = new Dictionary <string, string>();
var splitOutputTrainData = new ArrayVar <IDataView>();
var splitOutputTestData = new ArrayVar <IDataView>();
outputMap.Add(nameof(CVSplit.Output.TrainData), splitOutputTrainData.VarName);
outputMap.Add(nameof(CVSplit.Output.TestData), splitOutputTestData.VarName);
var splitNode = EntryPointNode.Create(env, "Models.CrossValidatorDatasetSplitter", splitArgs,
node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(splitNode);
var predModelVars = new Var <PredictorModel> [input.NumFolds];
var inputTransformModelVars = new Var <PredictorModel> [input.NumFolds];
var warningsVars = new Var <IDataView> [input.NumFolds];
var overallMetricsVars = new Var <IDataView> [input.NumFolds];
var instanceMetricsVars = new Var <IDataView> [input.NumFolds];
var confusionMatrixVars = new Var <IDataView> [input.NumFolds];
// Instantiate the subgraph for each fold.
for (int k = 0; k < input.NumFolds; k++)
{
// Parse the nodes in input.Nodes into a temporary run context.
var context = new RunContext(env);
var graph = EntryPointNode.ValidateNodes(env, context, input.Nodes);
// Rename all the variables such that they don't conflict with the ones in the outer run context.
var mapping = new Dictionary <string, string>();
foreach (var entryPointNode in graph)
{
entryPointNode.RenameAllVariables(mapping);
}
// Instantiate a TrainTest entry point for this fold.
var args = new TrainTestMacro.Arguments
{
Nodes = new JArray(graph.Select(n => n.ToJson()).ToArray()),
TransformModel = null,
LabelColumn = input.LabelColumn,
GroupColumn = input.GroupColumn,
WeightColumn = input.WeightColumn,
NameColumn = input.NameColumn
};
if (transformModelVarName != null)
{
args.TransformModel = new Var <TransformModel> {
VarName = transformModelVarName.VariableName
}
}
;
args.Inputs.Data = new Var <IDataView>
{
VarName = mapping[input.Inputs.Data.VarName]
};
args.Outputs.PredictorModel = new Var <PredictorModel>
{
VarName = mapping[input.Outputs.PredictorModel.VarName]
};
// Set train/test trainer kind to match.
args.Kind = input.Kind;
// Set the input bindings for the TrainTest entry point.
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var trainingData = new SimpleParameterBinding(nameof(args.TrainingData));
inputBindingMap.Add(nameof(args.TrainingData), new List <ParameterBinding> {
trainingData
});
inputMap.Add(trainingData, new ArrayIndexVariableBinding(splitOutputTrainData.VarName, k));
var testingData = new SimpleParameterBinding(nameof(args.TestingData));
inputBindingMap.Add(nameof(args.TestingData), new List <ParameterBinding> {
testingData
});
inputMap.Add(testingData, new ArrayIndexVariableBinding(splitOutputTestData.VarName, k));
outputMap = new Dictionary <string, string>();
var transformModelVar = new Var <TransformModel>();
var predModelVar = new Var <PredictorModel>();
outputMap.Add(nameof(TrainTestMacro.Output.PredictorModel), predModelVar.VarName);
predModelVars[k] = predModelVar;
if (transformModelVarName != null && transformModelVarName.VariableName != null)
{
var combineModelsArgs = new ModelOperations.SimplePredictorModelInput();
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var inputTransformModel = new SimpleVariableBinding(transformModelVarName.VariableName);
var inputPredictorModel = new SimpleVariableBinding(predModelVar.VarName);
paramBinding = new SimpleParameterBinding(nameof(combineModelsArgs.TransformModel));
inputBindingMap.Add(nameof(combineModelsArgs.TransformModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, inputTransformModel);
paramBinding = new SimpleParameterBinding(nameof(combineModelsArgs.PredictorModel));
inputBindingMap.Add(nameof(combineModelsArgs.PredictorModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, inputPredictorModel);
outputMap = new Dictionary <string, string>();
var combineNodeOutputPredictorModel = new Var <PredictorModel>();
predModelVars[k] = combineNodeOutputPredictorModel;
outputMap.Add(nameof(ModelOperations.PredictorModelOutput.PredictorModel), combineNodeOutputPredictorModel.VarName);
EntryPointNode combineNode = EntryPointNode.Create(env, "Transforms.TwoHeterogeneousModelCombiner", combineModelsArgs,
node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(combineNode);
}
var warningVar = new Var <IDataView>();
outputMap.Add(nameof(TrainTestMacro.Output.Warnings), warningVar.VarName);
warningsVars[k] = warningVar;
var overallMetric = new Var <IDataView>();
outputMap.Add(nameof(TrainTestMacro.Output.OverallMetrics), overallMetric.VarName);
overallMetricsVars[k] = overallMetric;
var instanceMetric = new Var <IDataView>();
outputMap.Add(nameof(TrainTestMacro.Output.PerInstanceMetrics), instanceMetric.VarName);
instanceMetricsVars[k] = instanceMetric;
var confusionMatrix = new Var <IDataView>();
outputMap.Add(nameof(TrainTestMacro.Output.ConfusionMatrix), confusionMatrix.VarName);
confusionMatrixVars[k] = confusionMatrix;
const string trainTestEvaluatorMacroEntryPoint = "Models.TrainTestEvaluator";
subGraphNodes.Add(EntryPointNode.Create(env, trainTestEvaluatorMacroEntryPoint, args, node.Context, inputBindingMap, inputMap, outputMap));
}
// Convert the predictor models to an array of predictor models.
MacroUtils.ConvertIPredictorModelsToArray(env, node.Context, subGraphNodes, predModelVars, node.GetOutputVariableName(nameof(Output.PredictorModel)));
// Convert the warnings, overall, per instance and confusion matrix data views into an array.
var warningsArrayVar = new ArrayVar <IDataView>();
var overallArrayVar = new ArrayVar <IDataView>();
var instanceArrayVar = new ArrayVar <IDataView>();
ArrayVar <IDataView> confusionMatrixArrayVar = null;
MacroUtils.ConvertIdataViewsToArray(env, node.Context, subGraphNodes, warningsVars, warningsArrayVar.VarName);
MacroUtils.ConvertIdataViewsToArray(env, node.Context, subGraphNodes, overallMetricsVars, overallArrayVar.VarName);
MacroUtils.ConvertIdataViewsToArray(env, node.Context, subGraphNodes, instanceMetricsVars, instanceArrayVar.VarName);
if (input.Kind == MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer ||
input.Kind == MacroUtils.TrainerKinds.SignatureMultiClassClassifierTrainer)
{
confusionMatrixArrayVar = new ArrayVar <IDataView>();
MacroUtils.ConvertIdataViewsToArray(env, node.Context, subGraphNodes, confusionMatrixVars, confusionMatrixArrayVar.VarName);
}
var combineArgs = new CombineMetricsInput();
combineArgs.Kind = input.Kind;
combineArgs.LabelColumn = input.LabelColumn;
combineArgs.WeightColumn = input.WeightColumn;
combineArgs.GroupColumn = input.GroupColumn;
combineArgs.NameColumn = input.NameColumn;
// Set the input bindings for the CombineMetrics entry point.
var combineInputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var combineInputMap = new Dictionary <ParameterBinding, VariableBinding>();
var warningsArray = new SimpleParameterBinding(nameof(combineArgs.Warnings));
combineInputBindingMap.Add(nameof(combineArgs.Warnings), new List <ParameterBinding> {
warningsArray
});
combineInputMap.Add(warningsArray, new SimpleVariableBinding(warningsArrayVar.VarName));
var overallArray = new SimpleParameterBinding(nameof(combineArgs.OverallMetrics));
combineInputBindingMap.Add(nameof(combineArgs.OverallMetrics), new List <ParameterBinding> {
overallArray
});
combineInputMap.Add(overallArray, new SimpleVariableBinding(overallArrayVar.VarName));
var combinePerInstArray = new SimpleParameterBinding(nameof(combineArgs.PerInstanceMetrics));
combineInputBindingMap.Add(nameof(combineArgs.PerInstanceMetrics), new List <ParameterBinding> {
combinePerInstArray
});
combineInputMap.Add(combinePerInstArray, new SimpleVariableBinding(instanceArrayVar.VarName));
if (confusionMatrixArrayVar != null)
{
var combineConfArray = new SimpleParameterBinding(nameof(combineArgs.ConfusionMatrix));
combineInputBindingMap.Add(nameof(combineArgs.ConfusionMatrix), new List <ParameterBinding> {
combineConfArray
});
combineInputMap.Add(combineConfArray, new SimpleVariableBinding(confusionMatrixArrayVar.VarName));
}
var combineOutputMap = new Dictionary <string, string>();
var combineWarningVar = new Var <IDataView>();
combineWarningVar.VarName = node.GetOutputVariableName(nameof(Output.Warnings));
combineOutputMap.Add(nameof(Output.Warnings), combineWarningVar.VarName);
var combineOverallMetric = new Var <IDataView>();
combineOverallMetric.VarName = node.GetOutputVariableName(nameof(Output.OverallMetrics));
combineOutputMap.Add(nameof(Output.OverallMetrics), combineOverallMetric.VarName);
var combineInstanceMetric = new Var <IDataView>();
combineInstanceMetric.VarName = node.GetOutputVariableName(nameof(Output.PerInstanceMetrics));
combineOutputMap.Add(nameof(Output.PerInstanceMetrics), combineInstanceMetric.VarName);
if (confusionMatrixArrayVar != null)
{
var combineConfusionMatrix = new Var <IDataView>();
combineConfusionMatrix.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix));
combineOutputMap.Add(nameof(TrainTestMacro.Output.ConfusionMatrix), combineConfusionMatrix.VarName);
}
var combineMetricsNode = EntryPointNode.Create(env, "Models.CrossValidationResultsCombiner",
combineArgs, node.Context, combineInputBindingMap, combineInputMap, combineOutputMap);
subGraphNodes.Add(combineMetricsNode);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}
public static CommonOutputs.MacroOutput <Output> OneVersusAll(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(input, nameof(input));
env.Assert(input.Nodes.Count > 0);
var numClasses = GetNumberOfClasses(env, input, out var label);
var predModelVars = new Var <PredictorModel> [numClasses];
// This will be the final resulting list of nodes that is returned from the macro.
var macroNodes = new List <EntryPointNode>();
// Instantiate the subgraph for each label value.
for (int k = 0; k < numClasses; k++)
{
predModelVars[k] = ProcessClass(env, macroNodes, k, label, input, node);
}
// Convert the predictor models to an array of predictor models.
var modelsArray = new Var <PredictorModel[]>();
MacroUtils.ConvertIPredictorModelsToArray(env, node.Context, macroNodes, predModelVars, modelsArray.VarName);
// Use OVA model combiner to combine these models into one.
// Takes in array of models that are binary predictor models and
// produces single multiclass predictor model.
var combineArgs = new ModelOperations.CombineOvaPredictorModelsInput();
combineArgs.Caching = input.Caching;
combineArgs.FeatureColumn = input.FeatureColumn;
combineArgs.LabelColumn = input.LabelColumn;
combineArgs.NormalizeFeatures = input.NormalizeFeatures;
combineArgs.UseProbabilities = input.UseProbabilities;
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var combineNodeModelArrayInput = new SimpleVariableBinding(modelsArray.VarName);
var paramBinding = new SimpleParameterBinding(nameof(combineArgs.ModelArray));
inputBindingMap.Add(nameof(combineArgs.ModelArray), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, combineNodeModelArrayInput);
paramBinding = new SimpleParameterBinding(nameof(combineArgs.TrainingData));
inputBindingMap.Add(nameof(combineArgs.TrainingData), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, node.GetInputVariable(nameof(input.TrainingData)));
var outputMap = new Dictionary <string, string>();
outputMap.Add(nameof(Output.PredictorModel), node.GetOutputVariableName(nameof(Output.PredictorModel)));
var combineModelsNode = EntryPointNode.Create(env, "Models.OvaModelCombiner",
combineArgs, node.Context, inputBindingMap, inputMap, outputMap);
macroNodes.Add(combineModelsNode);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = macroNodes
});
}