public static EntryPointNode Create(
IHostEnvironment env,
string entryPointName,
object arguments,
ModuleCatalog catalog,
RunContext context,
Dictionary <string, string> inputMap,
Dictionary <string, string> outputMap,
bool checkpoint = false,
string stageId = "",
float cost = float.NaN)
{
ModuleCatalog.EntryPointInfo info;
bool success = catalog.TryFindEntryPoint(entryPointName, out info);
env.Assert(success);
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var inputParamBindingMap = new Dictionary <ParameterBinding, VariableBinding>();
foreach (var kvp in inputMap)
{
var paramBinding = new SimpleParameterBinding(kvp.Key);
inputBindingMap.Add(kvp.Key, new List <ParameterBinding>()
{
paramBinding
});
inputParamBindingMap.Add(paramBinding, new SimpleVariableBinding(kvp.Value));
}
return(Create(env, entryPointName, arguments, catalog, context, inputBindingMap, inputParamBindingMap,
outputMap, checkpoint, stageId, cost));
}
public Tuple <Var <T>, VariableBinding> AddNewVariable <T>(string uniqueName, T value)
{
// Make sure name is really unique.
if (InputBindingMap.ContainsKey(uniqueName))
{
throw _host.Except($"Key {uniqueName} already exists in binding map.");
}
// Add parameter bindings
var paramBinding = new SimpleParameterBinding(uniqueName);
InputBindingMap.Add(uniqueName, new List <ParameterBinding> {
paramBinding
});
// Create new variables
var varBinding = new SimpleVariableBinding(uniqueName);
Context.AddInputVariable(varBinding, typeof(T));
InputMap.Add(paramBinding, varBinding);
// Set value
if (value != null && Context.TryGetVariable(varBinding.VariableName, out var variable))
{
variable.SetValue(value);
}
// Return Var<> object and variable binding
return(new Tuple <Var <T>, VariableBinding>(new Var <T> {
VarName = varBinding.VariableName
}, varBinding));
}
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 exp = new Experiment(env);
var cvSplit = new Models.CrossValidatorDatasetSplitter();
cvSplit.Data.VarName = node.GetInputVariable("Data").ToJson();
cvSplit.NumFolds = input.NumFolds;
cvSplit.StratificationColumn = input.StratificationColumn;
var cvSplitOutput = exp.Add(cvSplit);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
var predModelVars = new Var <IPredictorModel> [input.NumFolds];
var transformModelVars = new Var <ITransformModel> [input.NumFolds];
var inputTransformModelVars = new Var <IPredictorModel> [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, node.Catalog);
// 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
};
if (transformModelVarName != null)
{
args.TransformModel = new Var <ITransformModel> {
VarName = transformModelVarName.VariableName
}
}
;
args.Inputs.Data = new Var <IDataView>
{
VarName = mapping[input.Inputs.Data.VarName]
};
if (input.Outputs.PredictorModel != null && mapping.ContainsKey(input.Outputs.PredictorModel.VarName))
{
args.Outputs.PredictorModel = new Var <IPredictorModel>
{
VarName = mapping[input.Outputs.PredictorModel.VarName]
};
}
else
{
args.Outputs.PredictorModel = null;
}
if (input.Outputs.TransformModel != null && mapping.ContainsKey(input.Outputs.TransformModel.VarName))
{
args.Outputs.TransformModel = new Var <ITransformModel>
{
VarName = mapping[input.Outputs.TransformModel.VarName]
};
}
else
{
args.Outputs.TransformModel = null;
}
// Set train/test trainer kind to match.
args.Kind = input.Kind;
// Set the input bindings for the TrainTest entry point.
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var 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(cvSplitOutput.TrainData.VarName, k));
var testingData = new SimpleParameterBinding(nameof(args.TestingData));
inputBindingMap.Add(nameof(args.TestingData), new List <ParameterBinding> {
testingData
});
inputMap.Add(testingData, new ArrayIndexVariableBinding(cvSplitOutput.TestData.VarName, k));
var outputMap = new Dictionary <string, string>();
var transformModelVar = new Var <ITransformModel>();
var predModelVar = new Var <IPredictorModel>();
if (input.Outputs.PredictorModel == null)
{
outputMap.Add(nameof(TrainTestMacro.Output.TransformModel), transformModelVar.VarName);
transformModelVars[k] = transformModelVar;
ML.Transforms.ModelCombiner.Output modelCombineOutput = null;
if (transformModelVarName != null && transformModelVarName.VariableName != null)
{
var modelCombine = new ML.Transforms.ModelCombiner
{
Models = new ArrayVar <ITransformModel>(
new Var <ITransformModel>[] {
new Var <ITransformModel> {
VarName = transformModelVarName.VariableName
},
transformModelVar
}
)
};
exp.Reset();
modelCombineOutput = exp.Add(modelCombine);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
transformModelVars[k] = modelCombineOutput.OutputModel;
}
}
else
{
outputMap.Add(nameof(TrainTestMacro.Output.PredictorModel), predModelVar.VarName);
predModelVars[k] = predModelVar;
ML.Transforms.TwoHeterogeneousModelCombiner.Output modelCombineOutput = null;
if (transformModelVarName != null && transformModelVarName.VariableName != null)
{
var modelCombine = new ML.Transforms.TwoHeterogeneousModelCombiner
{
TransformModel = { VarName = transformModelVarName.VariableName },
PredictorModel = predModelVar
};
exp.Reset();
modelCombineOutput = exp.Add(modelCombine);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
predModelVars[k] = modelCombineOutput.PredictorModel;
}
}
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.Catalog, node.Context, inputBindingMap, inputMap, outputMap));
}
exp.Reset();
// Convert predictors from all folds into an array of predictors.
if (input.Outputs.PredictorModel == null)
{
var outModels = new ML.Data.TransformModelArrayConverter
{
TransformModel = new ArrayVar <ITransformModel>(transformModelVars)
};
var outModelsOutput = new ML.Data.TransformModelArrayConverter.Output();
outModelsOutput.OutputModel.VarName = node.GetOutputVariableName(nameof(Output.TransformModel));
exp.Add(outModels, outModelsOutput);
}
else
{
var outModels = new ML.Data.PredictorModelArrayConverter
{
Model = new ArrayVar <IPredictorModel>(predModelVars)
};
var outModelsOutput = new ML.Data.PredictorModelArrayConverter.Output();
outModelsOutput.OutputModel.VarName = node.GetOutputVariableName(nameof(Output.PredictorModel));
exp.Add(outModels, outModelsOutput);
}
// Convert warnings data views from all folds into an array of data views.
var warnings = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(warningsVars)
};
var warningsOutput = new ML.Data.IDataViewArrayConverter.Output();
exp.Add(warnings, warningsOutput);
// Convert overall metrics data views from all folds into an array of data views.
var overallMetrics = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(overallMetricsVars)
};
var overallMetricsOutput = new ML.Data.IDataViewArrayConverter.Output();
exp.Add(overallMetrics, overallMetricsOutput);
// Convert per instance data views from all folds into an array of data views.
var instanceMetrics = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(instanceMetricsVars)
};
var instanceMetricsOutput = new ML.Data.IDataViewArrayConverter.Output();
exp.Add(instanceMetrics, instanceMetricsOutput);
ML.Data.IDataViewArrayConverter.Output confusionMatricesOutput = null;
if (input.Kind == MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer ||
input.Kind == MacroUtils.TrainerKinds.SignatureMultiClassClassifierTrainer)
{
// Convert confusion matrix data views from all folds into an array of data views.
var confusionMatrices = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(confusionMatrixVars)
};
confusionMatricesOutput = new ML.Data.IDataViewArrayConverter.Output();
exp.Add(confusionMatrices, confusionMatricesOutput);
}
var combineArgs = new CombineMetricsInput();
combineArgs.Kind = input.Kind;
combineArgs.LabelColumn = input.LabelColumn;
combineArgs.WeightColumn = input.WeightColumn;
combineArgs.GroupColumn = input.GroupColumn;
// Set the input bindings for the CombineMetrics entry point.
var combineInputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var combineInputMap = new Dictionary <ParameterBinding, VariableBinding>();
var overallArray = new SimpleParameterBinding(nameof(combineArgs.OverallMetrics));
combineInputBindingMap.Add(nameof(combineArgs.OverallMetrics), new List <ParameterBinding> {
overallArray
});
combineInputMap.Add(overallArray, new SimpleVariableBinding(overallMetricsOutput.OutputData.VarName));
var combinePerInstArray = new SimpleParameterBinding(nameof(combineArgs.PerInstanceMetrics));
combineInputBindingMap.Add(nameof(combineArgs.PerInstanceMetrics), new List <ParameterBinding> {
combinePerInstArray
});
combineInputMap.Add(combinePerInstArray, new SimpleVariableBinding(instanceMetricsOutput.OutputData.VarName));
if (confusionMatricesOutput != null)
{
var combineConfArray = new SimpleParameterBinding(nameof(combineArgs.ConfusionMatrix));
combineInputBindingMap.Add(nameof(combineArgs.ConfusionMatrix), new List <ParameterBinding> {
combineConfArray
});
combineInputMap.Add(combineConfArray, new SimpleVariableBinding(confusionMatricesOutput.OutputData.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 (confusionMatricesOutput != null)
{
var combineConfusionMatrix = new Var <IDataView>();
combineConfusionMatrix.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix));
combineOutputMap.Add(nameof(TrainTestMacro.Output.ConfusionMatrix), combineConfusionMatrix.VarName);
}
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
subGraphNodes.Add(EntryPointNode.Create(env, "Models.CrossValidationResultsCombiner", combineArgs, node.Catalog, node.Context, combineInputBindingMap, combineInputMap, combineOutputMap));
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}
public static CommonOutputs.MacroOutput <Output> CrossValidateBinary(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
// This will be the final resulting list of nodes that is returned from the macro.
var subGraphNodes = new List <EntryPointNode>();
// Split the input data into folds.
var exp = new Experiment(env);
var cvSplit = new Legacy.Models.CrossValidatorDatasetSplitter();
cvSplit.Data.VarName = node.GetInputVariable("Data").ToJson();
cvSplit.NumFolds = input.NumFolds;
cvSplit.StratificationColumn = input.StratificationColumn;
var cvSplitOutput = exp.Add(cvSplit);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes()));
var predModelVars = 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 TrainTestBinaryMacro.Arguments
{
Nodes = new JArray(graph.Select(n => n.ToJson()).ToArray())
};
args.Inputs.Data = new Var <IDataView>
{
VarName = mapping[input.Inputs.Data.VarName]
};
args.Outputs.Model = new Var <PredictorModel>
{
VarName = mapping[input.Outputs.Model.VarName]
};
// Set the input bindings for the TrainTest entry point.
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var 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(cvSplitOutput.TrainData.VarName, k));
var testingData = new SimpleParameterBinding(nameof(args.TestingData));
inputBindingMap.Add(nameof(args.TestingData), new List <ParameterBinding> {
testingData
});
inputMap.Add(testingData, new ArrayIndexVariableBinding(cvSplitOutput.TestData.VarName, k));
var outputMap = new Dictionary <string, string>();
var predModelVar = new Var <PredictorModel>();
outputMap.Add(nameof(TrainTestBinaryMacro.Output.PredictorModel), predModelVar.VarName);
predModelVars[k] = predModelVar;
var warningVar = new Var <IDataView>();
outputMap.Add(nameof(TrainTestBinaryMacro.Output.Warnings), warningVar.VarName);
warningsVars[k] = warningVar;
var overallMetric = new Var <IDataView>();
outputMap.Add(nameof(TrainTestBinaryMacro.Output.OverallMetrics), overallMetric.VarName);
overallMetricsVars[k] = overallMetric;
var instanceMetric = new Var <IDataView>();
outputMap.Add(nameof(TrainTestBinaryMacro.Output.PerInstanceMetrics), instanceMetric.VarName);
instanceMetricsVars[k] = instanceMetric;
var confusionMatrix = new Var <IDataView>();
outputMap.Add(nameof(TrainTestBinaryMacro.Output.ConfusionMatrix), confusionMatrix.VarName);
confusionMatrixVars[k] = confusionMatrix;
subGraphNodes.Add(EntryPointNode.Create(env, "Models.TrainTestBinaryEvaluator", args, node.Context, inputBindingMap, inputMap, outputMap));
}
exp.Reset();
var outModels = new Legacy.Data.PredictorModelArrayConverter
{
Model = new ArrayVar <PredictorModel>(predModelVars)
};
var outModelsOutput = new Legacy.Data.PredictorModelArrayConverter.Output();
outModelsOutput.OutputModel.VarName = node.GetOutputVariableName(nameof(Output.PredictorModel));
exp.Add(outModels, outModelsOutput);
var warnings = new Legacy.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(warningsVars)
};
var warningsOutput = new Legacy.Data.IDataViewArrayConverter.Output();
warningsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.Warnings));
exp.Add(warnings, warningsOutput);
var overallMetrics = new Legacy.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(overallMetricsVars)
};
var overallMetricsOutput = new Legacy.Data.IDataViewArrayConverter.Output();
overallMetricsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.OverallMetrics));
exp.Add(overallMetrics, overallMetricsOutput);
var instanceMetrics = new Legacy.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(instanceMetricsVars)
};
var instanceMetricsOutput = new Legacy.Data.IDataViewArrayConverter.Output();
instanceMetricsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.PerInstanceMetrics));
exp.Add(instanceMetrics, instanceMetricsOutput);
var confusionMatrices = new Legacy.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(confusionMatrixVars)
};
var confusionMatricesOutput = new Legacy.Data.IDataViewArrayConverter.Output();
confusionMatricesOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix));
exp.Add(confusionMatrices, confusionMatricesOutput);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes()));
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}
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
});
}
/// <summary>
/// Checks the given JSON object key-value pair is a valid EntryPoint input and
/// extracts out any variables that need to be populated. These variables will be
/// added to the EntryPoint context. Input parameters that are not set to variables
/// will be immediately set using the input builder instance.
/// </summary>
private void CheckAndSetInputValue(KeyValuePair <string, JToken> pair)
{
var inputName = _inputBuilder.GetFieldNameOrNull(pair.Key);
if (VariableBinding.IsBindingToken(pair.Value))
{
Type valueType = _inputBuilder.GetFieldTypeOrNull(pair.Key);
if (valueType == null)
{
throw _host.Except($"Unexpected input name: '{pair.Key}'");
}
if (!EntryPointVariable.IsValidType(valueType))
{
throw _host.Except($"Unexpected input variable type: {valueType}");
}
var varBinding = VariableBinding.Create(_host, pair.Value.Value <string>());
_context.AddInputVariable(varBinding, valueType);
if (!_inputBindingMap.ContainsKey(inputName))
{
_inputBindingMap[inputName] = new List <ParameterBinding>();
}
var paramBinding = new SimpleParameterBinding(inputName);
_inputBindingMap[inputName].Add(paramBinding);
_inputMap[paramBinding] = varBinding;
}
else if (pair.Value is JArray &&
((JArray)pair.Value).Any(tok => VariableBinding.IsBindingToken(tok)))
{
// REVIEW: EntryPoint arrays and dictionaries containing
// variables must ONLY contain variables right now.
if (!((JArray)pair.Value).All(tok => VariableBinding.IsBindingToken(tok)))
{
throw _host.Except($"Input {pair.Key} may ONLY contain variables.");
}
Type valueType = _inputBuilder.GetFieldTypeOrNull(pair.Key);
if (valueType == null || !valueType.HasElementType)
{
throw _host.Except($"Unexpected input name: '{pair.Key}'");
}
valueType = valueType.GetElementType();
int i = 0;
foreach (var varName in (JArray)pair.Value)
{
var varBinding = VariableBinding.Create(_host, varName.Value <string>());
_context.AddInputVariable(varBinding, valueType);
if (!_inputBindingMap.ContainsKey(inputName))
{
_inputBindingMap[inputName] = new List <ParameterBinding>();
}
var paramBinding = new ArrayIndexParameterBinding(inputName, i++);
_inputBindingMap[inputName].Add(paramBinding);
_inputMap[paramBinding] = varBinding;
}
}
// REVIEW: Implement support for Dictionary of variable values. We need to differentiate
// between a Dictionary and a Component here, and likely need to support nested components
// all of which might have variables. Our current machinery only works at the 'Node' level.
else
{
// This is not a variable.
if (!_inputBuilder.TrySetValueJson(pair.Key, pair.Value))
{
throw _host.Except($"Unexpected input: '{pair.Key}'");
}
}
}
public static CommonOutputs.MacroOutput <Output> TrainTest(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
// Create default pipeline ID if one not given.
input.PipelineId = input.PipelineId ?? Guid.NewGuid().ToString("N");
// Parse the subgraph.
var subGraphRunContext = new RunContext(env);
var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, input.Nodes, label: input.LabelColumn,
input.GroupColumn.IsExplicit ? input.GroupColumn.Value : null,
input.WeightColumn.IsExplicit ? input.WeightColumn.Value : null,
input.NameColumn.IsExplicit ? input.NameColumn.Value : null);
// Change the subgraph to use the training data as input.
var varName = input.Inputs.Data.VarName;
VariableBinding transformModelVarName = null;
if (input.TransformModel != null)
{
transformModelVarName = node.GetInputVariable(nameof(input.TransformModel));
}
if (!subGraphRunContext.TryGetVariable(varName, out var dataVariable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
var trainingVar = node.GetInputVariable(nameof(input.TrainingData));
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameInputVariable(dataVariable.Name, trainingVar);
}
subGraphRunContext.RemoveVariable(dataVariable);
// Change the subgraph to use the model variable as output.
varName = input.Outputs.PredictorModel.VarName;
if (!subGraphRunContext.TryGetVariable(varName, out dataVariable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
string predictorModelVarName = node.GetOutputVariableName(nameof(Output.PredictorModel));
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameOutputVariable(dataVariable.Name, predictorModelVarName);
}
subGraphRunContext.RemoveVariable(dataVariable);
// 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);
}
// Testing using test data set
var testingVar = node.GetInputVariable(nameof(input.TestingData));
//var exp = new Experiment(env);
Dictionary <string, List <ParameterBinding> > inputBindingMap;
Dictionary <ParameterBinding, VariableBinding> inputMap;
ParameterBinding paramBinding;
Dictionary <string, string> outputMap;
//combine the predictor model with any potential transfrom model passed from the outer graph
if (transformModelVarName != null && transformModelVarName.VariableName != null)
{
var combineArgs = new ModelOperations.SimplePredictorModelInput();
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var inputTransformModel = new SimpleVariableBinding(transformModelVarName.VariableName);
var inputPredictorModel = new SimpleVariableBinding(predictorModelVarName);
paramBinding = new SimpleParameterBinding(nameof(combineArgs.TransformModel));
inputBindingMap.Add(nameof(combineArgs.TransformModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, inputTransformModel);
paramBinding = new SimpleParameterBinding(nameof(combineArgs.PredictorModel));
inputBindingMap.Add(nameof(combineArgs.PredictorModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, inputPredictorModel);
outputMap = new Dictionary <string, string>();
var combineNodeOutputPredictorModel = new Var <PredictorModel>();
predictorModelVarName = combineNodeOutputPredictorModel.VarName;
outputMap.Add(nameof(ModelOperations.PredictorModelOutput.PredictorModel), combineNodeOutputPredictorModel.VarName);
EntryPointNode combineNode = EntryPointNode.Create(env, "Transforms.TwoHeterogeneousModelCombiner", combineArgs,
node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(combineNode);
}
// Add the scoring node for testing.
var args = new ScoreModel.Input();
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
paramBinding = new SimpleParameterBinding(nameof(args.Data));
inputBindingMap.Add(nameof(args.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, testingVar);
var scoreNodeInputPredictorModel = new SimpleVariableBinding(predictorModelVarName);
paramBinding = new SimpleParameterBinding(nameof(args.PredictorModel));
inputBindingMap.Add(nameof(args.PredictorModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, scoreNodeInputPredictorModel);
var scoreNodeOutputScoredData = new Var <IDataView>();
var scoreNodeOutputScoringTransform = new Var <TransformModel>();
outputMap = new Dictionary <string, string>();
outputMap.Add(nameof(ScoreModel.Output.ScoredData), scoreNodeOutputScoredData.VarName);
outputMap.Add(nameof(ScoreModel.Output.ScoringTransform), scoreNodeOutputScoringTransform.VarName);
EntryPointNode scoreNode = EntryPointNode.Create(env, "Transforms.DatasetScorer", args,
node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(scoreNode);
var evalDataVarName = scoreNodeOutputScoredData.VarName;
// REVIEW: add similar support for FeatureColumn.
var settings = new MacroUtils.EvaluatorSettings
{
LabelColumn = input.LabelColumn,
WeightColumn = input.WeightColumn.IsExplicit ? input.WeightColumn.Value : null,
GroupColumn = input.GroupColumn.IsExplicit ? input.GroupColumn.Value : null,
NameColumn = input.NameColumn.IsExplicit ? input.NameColumn.Value : null
};
if (input.IncludeTrainingMetrics)
{
string evalTrainingDataVarName;
args = new ScoreModel.Input();
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
paramBinding = new SimpleParameterBinding(nameof(args.Data));
inputBindingMap.Add(nameof(args.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, trainingVar);
scoreNodeInputPredictorModel = new SimpleVariableBinding(predictorModelVarName);
paramBinding = new SimpleParameterBinding(nameof(args.PredictorModel));
inputBindingMap.Add(nameof(args.PredictorModel), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, scoreNodeInputPredictorModel);
scoreNodeOutputScoredData = new Var <IDataView>();
scoreNodeOutputScoringTransform = new Var <TransformModel>();
outputMap = new Dictionary <string, string>();
outputMap.Add(nameof(ScoreModel.Output.ScoredData), scoreNodeOutputScoredData.VarName);
outputMap.Add(nameof(ScoreModel.Output.ScoringTransform), scoreNodeOutputScoringTransform.VarName);
scoreNode = EntryPointNode.Create(env, "Transforms.DatasetScorer", args,
node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(scoreNode);
evalTrainingDataVarName = scoreNodeOutputScoredData.VarName;
// Add the evaluator node for training.
var evalTrainingArgs = MacroUtils.GetEvaluatorArgs(input.Kind, out var evalTrainingEntryPointName, settings);
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var evalTrainingNodeInputData = new SimpleVariableBinding(evalTrainingDataVarName);
paramBinding = new SimpleParameterBinding(nameof(evalTrainingArgs.Data));
inputBindingMap.Add(nameof(evalTrainingArgs.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, evalTrainingNodeInputData);
outputMap = new Dictionary <string, string>();
if (node.OutputMap.TryGetValue(nameof(Output.TrainingWarnings), out var outTrainingVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.Warnings), outTrainingVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingOverallMetrics), out outTrainingVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.OverallMetrics), outTrainingVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingPerInstanceMetrics), out outTrainingVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.PerInstanceMetrics), outTrainingVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingConfusionMatrix), out outTrainingVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.ConfusionMatrix), outTrainingVariableName);
}
EntryPointNode evalTrainingNode = EntryPointNode.Create(env, evalTrainingEntryPointName, evalTrainingArgs, node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(evalTrainingNode);
}
// Add the evaluator node for testing.
var evalArgs = MacroUtils.GetEvaluatorArgs(input.Kind, out var evalEntryPointName, settings);
inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var evalNodeInputData = new SimpleVariableBinding(evalDataVarName);
paramBinding = new SimpleParameterBinding(nameof(evalArgs.Data));
inputBindingMap.Add(nameof(evalArgs.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, evalNodeInputData);
outputMap = new Dictionary <string, string>();
if (node.OutputMap.TryGetValue(nameof(Output.Warnings), out var outVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.Warnings), outVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.OverallMetrics), out outVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.OverallMetrics), outVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.PerInstanceMetrics), out outVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.PerInstanceMetrics), outVariableName);
}
if (node.OutputMap.TryGetValue(nameof(Output.ConfusionMatrix), out outVariableName))
{
outputMap.Add(nameof(CommonOutputs.ClassificationEvaluateOutput.ConfusionMatrix), outVariableName);
}
EntryPointNode evalNode = EntryPointNode.Create(env, evalEntryPointName, evalArgs, node.Context, inputBindingMap, inputMap, outputMap);
subGraphNodes.Add(evalNode);
// Marks as an atomic unit that can be run in
// a distributed fashion.
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.StageId = input.PipelineId;
}
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}
private static Var <PredictorModel> ProcessClass(IHostEnvironment env, List <EntryPointNode> macroNodes, int k, string label, Arguments input, EntryPointNode node)
{
Contracts.AssertValue(macroNodes);
// Convert label into T,F based on k.
var labelIndicatorArgs = new LabelIndicatorTransform.Arguments();
labelIndicatorArgs.ClassIndex = k;
labelIndicatorArgs.Column = new[] { new LabelIndicatorTransform.Column()
{
Name = label, Source = label
} };
var inputBindingMap = new Dictionary <string, List <ParameterBinding> >();
var inputMap = new Dictionary <ParameterBinding, VariableBinding>();
var paramBinding = new SimpleParameterBinding(nameof(labelIndicatorArgs.Data));
inputBindingMap.Add(nameof(labelIndicatorArgs.Data), new List <ParameterBinding>()
{
paramBinding
});
inputMap.Add(paramBinding, node.GetInputVariable(nameof(input.TrainingData)));
var outputMap = new Dictionary <string, string>();
var remappedLabelVar = new Var <IDataView>();
outputMap.Add(nameof(CommonOutputs.TransformOutput.OutputData), remappedLabelVar.VarName);
var labelIndicatorNode = EntryPointNode.Create(env, "Transforms.LabelIndicator", labelIndicatorArgs, node.Context,
inputBindingMap, inputMap, outputMap);
macroNodes.Add(labelIndicatorNode);
// Parse the nodes in input.Nodes into a temporary run context.
var subGraphRunContext = new RunContext(env);
var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, 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>();
bool foundOutput = false;
Var <PredictorModel> predModelVar = null;
foreach (var entryPointNode in subGraphNodes)
{
// Rename variables in input/output maps, and in subgraph context.
entryPointNode.RenameAllVariables(mapping);
foreach (var kvp in mapping)
{
subGraphRunContext.RenameContextVariable(kvp.Key, kvp.Value);
}
// Grab a hold of output model from this subgraph.
if (entryPointNode.GetOutputVariableName("PredictorModel") is string mvn)
{
predModelVar = new Var <PredictorModel> {
VarName = mvn
};
foundOutput = true;
}
// Connect label remapper output to wherever training data was expected within the input graph.
if (entryPointNode.GetInputVariable(nameof(input.TrainingData)) is VariableBinding vb)
{
vb.Rename(remappedLabelVar.VarName);
}
// Change node to use the main context.
entryPointNode.SetContext(node.Context);
}
// Move the variables from the subcontext to the main context.
node.Context.AddContextVariables(subGraphRunContext);
// Make sure we found the output variable for this model.
if (!foundOutput)
{
throw new Exception("Invalid input graph. Does not output predictor model.");
}
// Add training subgraph to our context.
macroNodes.AddRange(subGraphNodes);
return(predModelVar);
}
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
});
}
