public static CommonOutputs.MacroOutput <Output> PipelineSweep(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
env.Check(input.StateArguments != null || input.State is AutoInference.AutoMlMlState,
"Must have a valid AutoML State, or pass arguments to create one.");
env.Check(input.BatchSize > 0, "Batch size must be > 0.");
// Get the user-defined column roles (if any)
var dataRoles = GetDataRoles(env, input);
// If no current state, create object and set data.
if (input.State == null)
{
input.State = input.StateArguments?.CreateComponent(env);
if (input.State is AutoInference.AutoMlMlState inState)
{
inState.SetTrainTestData(input.TrainingData, input.TestingData);
}
else
{
throw env.Except($"Incompatible type. Expecting type {typeof(AutoInference.AutoMlMlState)}, received type {input.State?.GetType()}.");
}
var result = node.AddNewVariable("State", input.State);
node.Context.AddInputVariable(result.Item2, typeof(IMlState));
}
var autoMlState = (AutoInference.AutoMlMlState)input.State;
// The indicators are just so the macro knows those pipelines need to
// be run before performing next expansion. If we add them as inputs
// to the next iteration, the next iteration cannot run until they have
// their values set. Thus, indicators are needed.
var pipelineIndicators = new List <Var <IDataView> >();
var expNodes = new List <EntryPointNode>();
// Keep versions of the training and testing var names
var training = new Var <IDataView> {
VarName = node.GetInputVariable("TrainingData").VariableName
};
var testing = new Var <IDataView> {
VarName = node.GetInputVariable("TestingData").VariableName
};
var amlsVarObj =
new Var <IMlState>()
{
VarName = node.GetInputVariable(nameof(input.State)).VariableName
};
// Make sure search space is defined. If not, infer,
// with default number of transform levels.
if (!autoMlState.IsSearchSpaceDefined())
{
autoMlState.InferSearchSpace(numTransformLevels: 1, dataRoles);
}
// Extract performance summaries and assign to previous candidate pipelines.
foreach (var pipeline in autoMlState.BatchCandidates)
{
if (node.Context.TryGetVariable(ExperimentUtils.GenerateOverallMetricVarName(pipeline.UniqueId), out var v) &&
node.Context.TryGetVariable(AutoMlUtils.GenerateOverallTrainingMetricVarName(pipeline.UniqueId), out var v2))
{
pipeline.PerformanceSummary = AutoMlUtils.ExtractRunSummary(env, (IDataView)v.Value, autoMlState.Metric.Name, (IDataView)v2.Value);
autoMlState.AddEvaluated(pipeline);
}
}
node.OutputMap.TryGetValue("Results", out string outDvName);
var outDvVar = new Var <IDataView>()
{
VarName = outDvName
};
node.OutputMap.TryGetValue("State", out string outStateName);
var outStateVar = new Var <IMlState>()
{
VarName = outStateName
};
// Get next set of candidates.
var candidatePipelines = autoMlState.GetNextCandidates(input.BatchSize);
// Check if termination condition was met, i.e. no more candidates were returned.
// If so, end expansion and add a node to extract the sweep result.
if (candidatePipelines == null || candidatePipelines.Length == 0)
{
// Add a node to extract the sweep result.
var resultSubgraph = new Experiment(env);
var resultNode = new Microsoft.ML.Legacy.Models.SweepResultExtractor()
{
State = amlsVarObj
};
var resultOutput = new Legacy.Models.SweepResultExtractor.Output()
{
State = outStateVar, Results = outDvVar
};
resultSubgraph.Add(resultNode, resultOutput);
var resultSubgraphNodes = EntryPointNode.ValidateNodes(env, node.Context, resultSubgraph.GetNodes(), node.Catalog);
expNodes.AddRange(resultSubgraphNodes);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = expNodes
});
}
// Prep all returned candidates
foreach (var p in candidatePipelines)
{
// Add train test experiments to current graph for candidate pipeline
var subgraph = new Experiment(env);
var trainTestOutput = p.AddAsTrainTest(training, testing, autoMlState.TrainerKind, subgraph, true);
// Change variable name to reference pipeline ID in output map, context and entrypoint output.
var uniqueName = ExperimentUtils.GenerateOverallMetricVarName(p.UniqueId);
var uniqueNameTraining = AutoMlUtils.GenerateOverallTrainingMetricVarName(p.UniqueId);
var sgNode = EntryPointNode.ValidateNodes(env, node.Context,
new JArray(subgraph.GetNodes().Last()), node.Catalog).Last();
sgNode.RenameOutputVariable(trainTestOutput.OverallMetrics.VarName, uniqueName, cascadeChanges: true);
sgNode.RenameOutputVariable(trainTestOutput.TrainingOverallMetrics.VarName, uniqueNameTraining, cascadeChanges: true);
trainTestOutput.OverallMetrics.VarName = uniqueName;
trainTestOutput.TrainingOverallMetrics.VarName = uniqueNameTraining;
expNodes.Add(sgNode);
// Store indicators, to pass to next iteration of macro.
pipelineIndicators.Add(trainTestOutput.OverallMetrics);
}
// Add recursive macro node
var macroSubgraph = new Experiment(env);
var macroNode = new Legacy.Models.PipelineSweeper()
{
BatchSize = input.BatchSize,
CandidateOutputs = new ArrayVar <IDataView>(pipelineIndicators.ToArray()),
TrainingData = training,
TestingData = testing,
State = amlsVarObj
};
var output = new Legacy.Models.PipelineSweeper.Output()
{
Results = outDvVar, State = outStateVar
};
macroSubgraph.Add(macroNode, output);
var subgraphNodes = EntryPointNode.ValidateNodes(env, node.Context, macroSubgraph.GetNodes(), node.Catalog);
expNodes.AddRange(subgraphNodes);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = expNodes
});
}
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,
NameColumn = input.NameColumn
};
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;
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(warningsOutput.OutputData.VarName));
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
});
}
private static Tuple <List <EntryPointNode>, Var <IPredictorModel> > ProcessClass(IHostEnvironment env, int k, string label, Arguments input, EntryPointNode node)
{
var macroNodes = new List <EntryPointNode>();
// Convert label into T,F based on k.
var remapper = new ML.Transforms.LabelIndicator
{
ClassIndex = k,
Column = new[]
{
new ML.Transforms.LabelIndicatorTransformColumn
{
ClassIndex = k,
Name = label,
Source = label
}
},
Data = { VarName = node.GetInputVariable(nameof(input.TrainingData)).ToJson() }
};
var exp = new Experiment(env);
var remapperOutNode = exp.Add(remapper);
var subNodes = EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog);
macroNodes.AddRange(subNodes);
// Parse the nodes in input.Nodes into a temporary run context.
var subGraphRunContext = new RunContext(env);
var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, 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>();
bool foundOutput = false;
Var <IPredictorModel> 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 <IPredictorModel> {
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(remapperOutNode.OutputData.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(new Tuple <List <EntryPointNode>, Var <IPredictorModel> >(macroNodes, predModelVar));
}
public static CommonOutputs.MacroOutput <Output> OVA(
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 <IPredictorModel> [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++)
{
var result = ProcessClass(env, k, label, input, node);
predModelVars[k] = result.Item2;
macroNodes.AddRange(result.Item1);
}
// 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 macroExperiment = new Experiment(env);
var combinerNode = new Models.OvaModelCombiner
{
ModelArray = new ArrayVar <IPredictorModel>(predModelVars),
TrainingData = new Var <IDataView> {
VarName = node.GetInputVariable(nameof(input.TrainingData)).VariableName
},
Caching = (Models.CachingOptions)input.Caching,
FeatureColumn = input.FeatureColumn,
NormalizeFeatures = (Models.NormalizeOption)input.NormalizeFeatures,
LabelColumn = input.LabelColumn,
UseProbabilities = input.UseProbabilities
};
// Get output model variable.
if (!node.OutputMap.TryGetValue(nameof(Output.PredictorModel), out var outVariableName))
{
throw new Exception("Cannot find OVA model output.");
}
// Map macro's output back to OVA combiner (so OVA combiner will set the value on our output variable).
var combinerOutput = new Models.OvaModelCombiner.Output {
PredictorModel = new Var <IPredictorModel> {
VarName = outVariableName
}
};
// Add to experiment (must be done AFTER we assign variable name to output).
macroExperiment.Add(combinerNode, combinerOutput);
// Add nodes to main experiment.
var nodes = macroExperiment.GetNodes();
var expNodes = EntryPointNode.ValidateNodes(env, node.Context, nodes, node.Catalog);
macroNodes.AddRange(expNodes);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = macroNodes
});
}
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 ML.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 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 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 <IPredictorModel>
{
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 <IPredictorModel>();
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.Catalog, node.Context, inputBindingMap, inputMap, outputMap));
}
exp.Reset();
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);
var warnings = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(warningsVars)
};
var warningsOutput = new ML.Data.IDataViewArrayConverter.Output();
warningsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.Warnings));
exp.Add(warnings, warningsOutput);
var overallMetrics = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(overallMetricsVars)
};
var overallMetricsOutput = new ML.Data.IDataViewArrayConverter.Output();
overallMetricsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.OverallMetrics));
exp.Add(overallMetrics, overallMetricsOutput);
var instanceMetrics = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(instanceMetricsVars)
};
var instanceMetricsOutput = new ML.Data.IDataViewArrayConverter.Output();
instanceMetricsOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.PerInstanceMetrics));
exp.Add(instanceMetrics, instanceMetricsOutput);
var confusionMatrices = new ML.Data.IDataViewArrayConverter
{
Data = new ArrayVar <IDataView>(confusionMatrixVars)
};
var confusionMatricesOutput = new ML.Data.IDataViewArrayConverter.Output();
confusionMatricesOutput.OutputData.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix));
exp.Add(confusionMatrices, confusionMatricesOutput);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
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
});
}
