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());
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())).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());
expNodes.AddRange(subgraphNodes);
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = expNodes
});
}
/// <summary>
/// Extract all values of one column of the data view in a form of an <see cref="IEnumerable{T}"/>.
/// </summary>
/// <typeparam name="T">The type of the values. This must match the actual column type.</typeparam>
/// <param name="data">The data view to get the column from.</param>
/// <param name="env">The current host environment.</param>
/// <param name="columnName">The name of the column to extract.</param>
public static IEnumerable <T> GetColumn <T>(this IDataView data, IHostEnvironment env, string columnName)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(data, nameof(data));
env.CheckNonEmpty(columnName, nameof(columnName));
if (!data.Schema.TryGetColumnIndex(columnName, out int col))
{
throw env.ExceptSchemaMismatch(nameof(columnName), "input", columnName);
}
// There are two decisions that we make here:
// - Is the T an array type?
// - If yes, we need to map VBuffer to array and densify.
// - If no, this is not needed.
// - Does T (or item type of T if it's an array) equal to the data view type?
// - If this is the same type, we can map directly.
// - Otherwise, we need a conversion delegate.
var colType = data.Schema[col].Type;
if (colType.RawType == typeof(T))
{
// Direct mapping is possible.
return(GetColumnDirect <T>(data, col));
}
else if (typeof(T) == typeof(string) && colType.IsText)
{
// Special case of ROM<char> to string conversion.
Delegate convert = (Func <ReadOnlyMemory <char>, string>)((ReadOnlyMemory <char> txt) => txt.ToString());
Func <IDataView, int, Func <int, T>, IEnumerable <T> > del = GetColumnConvert;
var meth = del.Method.GetGenericMethodDefinition().MakeGenericMethod(typeof(T), colType.RawType);
return((IEnumerable <T>)(meth.Invoke(null, new object[] { data, col, convert })));
}
else if (typeof(T).IsArray)
{
// Output is an array type.
if (!colType.IsVector)
{
throw env.ExceptSchemaMismatch(nameof(columnName), "input", columnName, "vector", "scalar");
}
var elementType = typeof(T).GetElementType();
if (elementType == colType.ItemType.RawType)
{
// Direct mapping of items.
Func <IDataView, int, IEnumerable <int[]> > del = GetColumnArrayDirect <int>;
var meth = del.Method.GetGenericMethodDefinition().MakeGenericMethod(elementType);
return((IEnumerable <T>)meth.Invoke(null, new object[] { data, col }));
}
else if (elementType == typeof(string) && colType.ItemType.IsText)
{
// Conversion of DvText items to string items.
Delegate convert = (Func <ReadOnlyMemory <char>, string>)((ReadOnlyMemory <char> txt) => txt.ToString());
Func <IDataView, int, Func <int, long>, IEnumerable <long[]> > del = GetColumnArrayConvert;
var meth = del.Method.GetGenericMethodDefinition().MakeGenericMethod(elementType, colType.ItemType.RawType);
return((IEnumerable <T>)meth.Invoke(null, new object[] { data, col, convert }));
}
// Fall through to the failure.
}
throw env.Except($"Could not map a data view column '{columnName}' of type {colType} to {typeof(T)}.");
}
private static void RunGraphCore(EnvironmentBlock *penv, IHostEnvironment env, string graphStr, int cdata, DataSourceBlock **ppdata)
{
Contracts.AssertValue(env);
var args = new RunGraphArgs();
string err = null;
if (!CmdParser.ParseArguments(env, graphStr, args, e => err = err ?? e))
{
throw env.Except(err);
}
int?maxThreadsAllowed = Math.Min(args.parallel > 0 ? args.parallel.Value : penv->maxThreadsAllowed, penv->maxThreadsAllowed);
maxThreadsAllowed = penv->maxThreadsAllowed > 0 ? maxThreadsAllowed : args.parallel;
var host = env.Register("RunGraph", args.randomSeed, null);
JObject graph;
try
{
graph = JObject.Parse(args.graph);
}
catch (JsonReaderException ex)
{
throw host.Except(ex, "Failed to parse experiment graph: {0}", ex.Message);
}
var runner = new GraphRunner(host, graph["nodes"] as JArray);
var dvNative = new IDataView[cdata];
try
{
for (int i = 0; i < cdata; i++)
{
dvNative[i] = new NativeDataView(host, ppdata[i]);
}
// Setting inputs.
var jInputs = graph["inputs"] as JObject;
if (graph["inputs"] != null && jInputs == null)
{
throw host.Except("Unexpected value for 'inputs': {0}", graph["inputs"]);
}
int iDv = 0;
if (jInputs != null)
{
foreach (var kvp in jInputs)
{
var pathValue = kvp.Value as JValue;
if (pathValue == null)
{
throw host.Except("Invalid value for input: {0}", kvp.Value);
}
var path = pathValue.Value <string>();
var varName = kvp.Key;
var type = runner.GetPortDataKind(varName);
switch (type)
{
case TlcModule.DataKind.FileHandle:
var fh = new SimpleFileHandle(host, path, false, false);
runner.SetInput(varName, fh);
break;
case TlcModule.DataKind.DataView:
IDataView dv;
if (!string.IsNullOrWhiteSpace(path))
{
var extension = Path.GetExtension(path);
if (extension == ".txt")
{
dv = TextLoader.LoadFile(host, new TextLoader.Options(), new MultiFileSource(path));
}
else
{
dv = new BinaryLoader(host, new BinaryLoader.Arguments(), path);
}
}
else
{
Contracts.Assert(iDv < dvNative.Length);
// prefetch all columns
dv = dvNative[iDv++];
var prefetch = new int[dv.Schema.Count];
for (int i = 0; i < prefetch.Length; i++)
{
prefetch[i] = i;
}
dv = new CacheDataView(host, dv, prefetch);
}
runner.SetInput(varName, dv);
break;
case TlcModule.DataKind.PredictorModel:
PredictorModel pm;
if (!string.IsNullOrWhiteSpace(path))
{
using (var fs = File.OpenRead(path))
pm = new PredictorModelImpl(host, fs);
}
else
{
throw host.Except("Model must be loaded from a file");
}
runner.SetInput(varName, pm);
break;
case TlcModule.DataKind.TransformModel:
TransformModel tm;
if (!string.IsNullOrWhiteSpace(path))
{
using (var fs = File.OpenRead(path))
tm = new TransformModelImpl(host, fs);
}
else
{
throw host.Except("Model must be loaded from a file");
}
runner.SetInput(varName, tm);
break;
default:
throw host.Except("Port type {0} not supported", type);
}
}
}
runner.RunAll();
// Reading outputs.
using (var ch = host.Start("Reading outputs"))
{
var jOutputs = graph["outputs"] as JObject;
if (jOutputs != null)
{
foreach (var kvp in jOutputs)
{
var pathValue = kvp.Value as JValue;
if (pathValue == null)
{
throw host.Except("Invalid value for input: {0}", kvp.Value);
}
var path = pathValue.Value <string>();
var varName = kvp.Key;
var type = runner.GetPortDataKind(varName);
switch (type)
{
case TlcModule.DataKind.FileHandle:
var fh = runner.GetOutput <IFileHandle>(varName);
throw host.ExceptNotSupp("File handle outputs not yet supported.");
case TlcModule.DataKind.DataView:
var idv = runner.GetOutput <IDataView>(varName);
if (!string.IsNullOrWhiteSpace(path))
{
SaveIdvToFile(idv, path, host);
}
else
{
var infos = ProcessColumns(ref idv, args.maxSlots, host);
SendViewToNative(ch, penv, idv, infos);
}
break;
case TlcModule.DataKind.PredictorModel:
var pm = runner.GetOutput <PredictorModel>(varName);
if (!string.IsNullOrWhiteSpace(path))
{
SavePredictorModelToFile(pm, path, host);
}
else
{
throw host.Except("Returning in-memory models is not supported");
}
break;
case TlcModule.DataKind.TransformModel:
var tm = runner.GetOutput <TransformModel>(varName);
if (!string.IsNullOrWhiteSpace(path))
{
using (var fs = File.OpenWrite(path))
tm.Save(host, fs);
}
else
{
throw host.Except("Returning in-memory models is not supported");
}
break;
case TlcModule.DataKind.Array:
var objArray = runner.GetOutput <object[]>(varName);
if (objArray is PredictorModel[])
{
var modelArray = (PredictorModel[])objArray;
// Save each model separately
for (var i = 0; i < modelArray.Length; i++)
{
var modelPath = string.Format(CultureInfo.InvariantCulture, path, i);
SavePredictorModelToFile(modelArray[i], modelPath, host);
}
}
else
{
throw host.Except("DataKind.Array type {0} not supported", objArray.First().GetType());
}
break;
default:
throw host.Except("Port type {0} not supported", type);
}
}
}
}
}
finally
{
// The raw data view is disposable so it lets go of unmanaged raw pointers before we return.
for (int i = 0; i < dvNative.Length; i++)
{
var view = dvNative[i];
if (view == null)
{
continue;
}
host.Assert(view is IDisposable);
var disp = (IDisposable)dvNative[i];
disp.Dispose();
}
}
}
public static CommonOutputs.MacroOutput <Output> TrainTestBinary(
IHostEnvironment env,
Arguments input,
EntryPointNode node)
{
// Parse the subgraph.
var subGraphRunContext = new RunContext(env);
var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, input.Nodes, node.Catalog);
// Change the subgraph to use the training data as input.
var varName = input.Inputs.Data.VarName;
EntryPointVariable variable;
if (!subGraphRunContext.TryGetVariable(varName, out variable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
var trainingVar = node.GetInputVariable("TrainingData");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameInputVariable(variable.Name, trainingVar);
}
subGraphRunContext.RemoveVariable(variable);
// Change the subgraph to use the model variable as output.
varName = input.Outputs.Model.VarName;
if (!subGraphRunContext.TryGetVariable(varName, out variable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
string outputVarName = node.GetOutputVariableName("PredictorModel");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameOutputVariable(variable.Name, outputVarName);
}
subGraphRunContext.RemoveVariable(variable);
// Move the variables from the subcontext to the main context.
node.Context.AddContextVariables(subGraphRunContext);
// Change all the subgraph nodes to use the main context.
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.SetContext(node.Context);
}
// Add the scoring node.
var testingVar = node.GetInputVariable("TestingData");
var exp = new Experiment(env);
var scoreNode = new Legacy.Transforms.DatasetScorer();
scoreNode.Data.VarName = testingVar.ToJson();
scoreNode.PredictorModel.VarName = outputVarName;
var scoreNodeOutput = exp.Add(scoreNode);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
// Add the evaluator node.
exp.Reset();
var evalNode = new Legacy.Models.BinaryClassificationEvaluator();
evalNode.Data.VarName = scoreNodeOutput.ScoredData.VarName;
var evalOutput = new Legacy.Models.BinaryClassificationEvaluator.Output();
string outVariableName;
if (node.OutputMap.TryGetValue("Warnings", out outVariableName))
{
evalOutput.Warnings.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("OverallMetrics", out outVariableName))
{
evalOutput.OverallMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("PerInstanceMetrics", out outVariableName))
{
evalOutput.PerInstanceMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue("ConfusionMatrix", out outVariableName))
{
evalOutput.ConfusionMatrix.VarName = outVariableName;
}
exp.Add(evalNode, evalOutput);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
var stageId = Guid.NewGuid().ToString("N");
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.StageId = stageId;
}
return(new CommonOutputs.MacroOutput <Output>()
{
Nodes = subGraphNodes
});
}
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
});
}
static IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx)
{
sourceCtx = input;
env.CheckValue(args.tag, "Tag cannot be empty.");
if (TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.tag).Any())
{
throw env.Except("Tag '{0}' is already used.", args.tag);
}
env.CheckValue(args.selectTag, "Selected tag cannot be empty.");
if (string.IsNullOrEmpty(args.filename))
{
var selected = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.selectTag);
if (!selected.Any())
{
throw env.Except("Unable to find a view to select with tag '{0}'. Did you forget to specify a filename?", args.selectTag);
}
var first = selected.First();
if (selected.Skip(1).Any())
{
throw env.Except("Tag '{0}' is ambiguous, {1} views were found.", args.selectTag, selected.Count());
}
var tagged = input as ITaggedDataView;
if (tagged == null)
{
var ag = new TagViewTransform.Arguments {
tag = args.tag
};
tagged = new TagViewTransform(env, ag, input);
}
first.Item2.AddRange(new[] { new Tuple <string, ITaggedDataView>(args.tag, tagged) });
tagged.AddRange(new[] { new Tuple <string, ITaggedDataView>(args.selectTag, first.Item2) });
#if (DEBUG_TIP)
long count = DataViewUtils.ComputeRowCount(tagged);
if (count == 0)
{
throw env.Except("Replaced view is empty.");
}
count = DataViewUtils.ComputeRowCount(first.Item2);
if (count == 0)
{
throw env.Except("Selected view is empty.");
}
#endif
var tr = first.Item2 as IDataTransform;
env.AssertValue(tr);
return(tr);
}
else
{
if (!File.Exists(args.filename))
{
throw env.Except("Unable to find file '{0}'.", args.filename);
}
var selected = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.selectTag);
if (selected.Any())
{
throw env.Except("Tag '{0}' was already given. It cannot be assigned to the new file.", args.selectTag);
}
var loaderArgs = new BinaryLoader.Arguments();
var file = new MultiFileSource(args.filename);
var loadSettings = ScikitSubComponent <IDataLoader, SignatureDataLoader> .AsSubComponent(args.loaderSettings);
IDataView loader = loadSettings.CreateInstance(env, file);
var ag = new TagViewTransform.Arguments {
tag = args.selectTag
};
var newInput = new TagViewTransform(env, ag, loader);
var tagged = input as ITaggedDataView;
if (tagged == null)
{
ag = new TagViewTransform.Arguments {
tag = args.tag
};
tagged = new TagViewTransform(env, ag, input);
}
newInput.AddRange(new[] { new Tuple <string, ITaggedDataView>(args.tag, tagged) });
tagged.AddRange(new[] { new Tuple <string, ITaggedDataView>(args.selectTag, newInput) });
var schema = loader.Schema;
if (schema.Count == 0)
{
throw env.Except("The loaded view '{0}' is empty (empty schema).", args.filename);
}
return(newInput);
}
}
IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx)
{
sourceCtx = input;
Contracts.CheckValue(env, "env");
env.CheckValue(args, "args");
env.CheckValue(input, "input");
env.CheckValue(args.tag, "tag is empty");
env.CheckValue(args.trainer, "trainer",
"Trainer cannot be null. If your model is already trained, please use ScoreTransform instead.");
var views = TagHelper.EnumerateTaggedView(true, input).Where(c => c.Item1 == args.tag);
if (views.Any())
{
throw env.Except("Tag '{0}' is already used.", args.tag);
}
var host = env.Register("TagTrainOrScoreTransform");
using (var ch = host.Start("Train"))
{
ch.Trace("Constructing trainer");
var trainerSett = ScikitSubComponent <ITrainer, SignatureTrainer> .AsSubComponent(args.trainer);
ITrainer trainer = trainerSett.CreateInstance(host);
var customCols = TrainUtils.CheckAndGenerateCustomColumns(env, args.CustomColumn);
string feat;
string group;
var data = CreateDataFromArgs(_host, ch, new OpaqueDataView(input), args, out feat, out group);
ICalibratorTrainer calibrator = args.calibrator == null
? null
: ScikitSubComponent <ICalibratorTrainer, SignatureCalibrator> .AsSubComponent(args.calibrator).CreateInstance(host);
var nameTrainer = args.trainer.ToString().Replace("{", "").Replace("}", "").Replace(" ", "").Replace("=", "").Replace("+", "Y").Replace("-", "N");
var extTrainer = new ExtendedTrainer(trainer, nameTrainer);
_predictor = extTrainer.Train(host, ch, data, null, calibrator, args.maxCalibrationExamples);
if (!string.IsNullOrEmpty(args.outputModel))
{
ch.Info("Saving model into '{0}'", args.outputModel);
using (var fs = File.Create(args.outputModel))
TrainUtils.SaveModel(env, ch, fs, _predictor, data);
ch.Info("Done.");
}
if (_cali != null)
{
throw ch.ExceptNotImpl("Calibrator is not implemented yet.");
}
ch.Trace("Scoring");
if (_args.scorer != null)
{
var mapper = new SchemaBindablePredictorWrapper(_predictor);
var roles = new RoleMappedSchema(input.Schema, null, feat, group: group);
var bound = (mapper as ISchemaBindableMapper).Bind(_host, roles);
var scorPars = ScikitSubComponent <IDataScorerTransform, SignatureDataScorer> .AsSubComponent(_args.scorer);
_scorer = scorPars.CreateInstance(_host, input, bound, roles);
}
else
{
_scorer = PredictorHelper.CreateDefaultScorer(_host, input, feat, group, _predictor);
}
ch.Info("Tagging with tag '{0}'.", args.tag);
var ar = new TagViewTransform.Arguments {
tag = args.tag
};
var res = new TagViewTransform(env, ar, _scorer, _predictor);
return(res);
}
}
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, node.Catalog);
// 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($"Invalid variable name '{varName}'.");
}
var trainingVar = node.GetInputVariable(nameof(input.TrainingData));
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameInputVariable(dataVariable.Name, trainingVar);
}
subGraphRunContext.RemoveVariable(dataVariable);
// Change the subgraph to use the model variable as output.
varName = input.Outputs.PredictorModel == null ? input.Outputs.TransformModel.VarName : input.Outputs.PredictorModel.VarName;
if (!subGraphRunContext.TryGetVariable(varName, out dataVariable))
{
throw env.Except($"Invalid variable name '{varName}'.");
}
string outputVarName = input.Outputs.PredictorModel == null?node.GetOutputVariableName(nameof(Output.TransformModel)) :
node.GetOutputVariableName(nameof(Output.PredictorModel));
foreach (var subGraphNode in subGraphNodes)
{
subGraphNode.RenameOutputVariable(dataVariable.Name, outputVarName);
}
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);
DatasetScorer.Output scoreNodeOutput = null;
ML.Models.DatasetTransformer.Output datasetTransformNodeOutput = null;
if (input.Outputs.PredictorModel == null)
{
//combine the predictor model with any potential transfrom model passed from the outer graph
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
},
new Var <ITransformModel> {
VarName = outputVarName
}
}
)
};
var modelCombineOutput = exp.Add(modelCombine);
outputVarName = modelCombineOutput.OutputModel.VarName;
}
var datasetTransformerNode = new Models.DatasetTransformer
{
Data = { VarName = testingVar.ToJson() },
TransformModel = { VarName = outputVarName }
};
datasetTransformNodeOutput = exp.Add(datasetTransformerNode);
}
else
{
//combine the predictor model with any potential transfrom model passed from the outer graph
if (transformModelVarName != null && transformModelVarName.VariableName != null)
{
var modelCombine = new TwoHeterogeneousModelCombiner
{
TransformModel = { VarName = transformModelVarName.VariableName },
PredictorModel = { VarName = outputVarName }
};
var modelCombineOutput = exp.Add(modelCombine);
outputVarName = modelCombineOutput.PredictorModel.VarName;
}
// Add the scoring node for testing.
var scoreNode = new DatasetScorer
{
Data = { VarName = testingVar.ToJson() },
PredictorModel = { VarName = outputVarName }
};
scoreNodeOutput = exp.Add(scoreNode);
}
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
// Do not double-add previous nodes.
exp.Reset();
// REVIEW: we need to extract the proper label column name here to pass to the evaluators.
// This is where you would add code to do it.
var settings = new MacroUtils.EvaluatorSettings
{
LabelColumn = DefaultColumnNames.Label
};
string outVariableName;
if (input.IncludeTrainingMetrics)
{
DatasetScorer.Output scoreNodeTrainingOutput = null;
ML.Models.DatasetTransformer.Output datasetTransformNodeTrainingOutput = null;
if (input.Outputs.PredictorModel == null)
{
var datasetTransformerNode = new Models.DatasetTransformer
{
Data = { VarName = testingVar.ToJson() },
TransformModel = { VarName = outputVarName }
};
datasetTransformNodeTrainingOutput = exp.Add(datasetTransformerNode);
}
else
{
// Add the scoring node for training.
var scoreNodeTraining = new DatasetScorer
{
Data = { VarName = trainingVar.ToJson() },
PredictorModel = { VarName = outputVarName }
};
scoreNodeTrainingOutput = exp.Add(scoreNodeTraining);
}
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
// Do not double-add previous nodes.
exp.Reset();
// Add the evaluator node for training.
var evalInputOutputTraining = MacroUtils.GetEvaluatorInputOutput(input.Kind, settings);
var evalNodeTraining = evalInputOutputTraining.Item1;
var evalOutputTraining = evalInputOutputTraining.Item2;
evalNodeTraining.Data.VarName = input.Outputs.PredictorModel == null ? datasetTransformNodeTrainingOutput.OutputData.VarName :
scoreNodeTrainingOutput.ScoredData.VarName;
if (node.OutputMap.TryGetValue(nameof(Output.TrainingWarnings), out outVariableName))
{
evalOutputTraining.Warnings.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingOverallMetrics), out outVariableName))
{
evalOutputTraining.OverallMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingPerInstanceMetrics), out outVariableName))
{
evalOutputTraining.PerInstanceMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.TrainingConfusionMatrix), out outVariableName) &&
evalOutputTraining is CommonOutputs.IClassificationEvaluatorOutput eoTraining)
{
eoTraining.ConfusionMatrix.VarName = outVariableName;
}
exp.Add(evalNodeTraining, evalOutputTraining);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
}
// Do not double-add previous nodes.
exp.Reset();
// Add the evaluator node for testing.
var evalInputOutput = MacroUtils.GetEvaluatorInputOutput(input.Kind, settings);
var evalNode = evalInputOutput.Item1;
var evalOutput = evalInputOutput.Item2;
evalNode.Data.VarName = input.Outputs.PredictorModel == null ? datasetTransformNodeOutput.OutputData.VarName : scoreNodeOutput.ScoredData.VarName;
if (node.OutputMap.TryGetValue(nameof(Output.Warnings), out outVariableName))
{
evalOutput.Warnings.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.OverallMetrics), out outVariableName))
{
evalOutput.OverallMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.PerInstanceMetrics), out outVariableName))
{
evalOutput.PerInstanceMetrics.VarName = outVariableName;
}
if (node.OutputMap.TryGetValue(nameof(Output.ConfusionMatrix), out outVariableName) &&
evalOutput is CommonOutputs.IClassificationEvaluatorOutput eo)
{
eo.ConfusionMatrix.VarName = outVariableName;
}
exp.Add(evalNode, evalOutput);
subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog));
// 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
});
}
public static ScikitOnnxContext ToOnnx(IDataTransform trans, ref string[] inputs, ref string[] outputs,
string name = null, string producer = "Scikit.ML",
long version = 0, string domain = "onnx.ai.ml",
OnnxVersion onnxVersion = OnnxVersion.Stable,
IDataView[] begin = null, IHostEnvironment host = null)
{
if (host == null)
{
using (var env = new DelegateEnvironment())
return(ToOnnx(trans, ref inputs, ref outputs, name, producer, version, domain, onnxVersion, begin, env));
}
if (name == null)
{
name = trans.GetType().Name;
}
GuessOutputs(trans, ref outputs);
GuessInputs(trans, ref inputs, begin);
if (inputs == null || inputs.Length == 0)
{
throw host.Except("Inputs cannot be empty.");
}
if (outputs == null || outputs.Length == 0)
{
throw host.Except("Outputs cannot be empty.");
}
var assembly = System.Reflection.Assembly.GetExecutingAssembly();
var versionInfo = System.Diagnostics.FileVersionInfo.GetVersionInfo(assembly.Location);
var ctx = new ScikitOnnxContext(host, name, producer, versionInfo.FileVersion,
version, domain, onnxVersion);
var hasin = new HashSet <string>(inputs);
var uniqueVars = EnumerateVariables(trans, begin).ToArray();
var mapInputType = new Dictionary <string, ColumnType>();
foreach (var it in uniqueVars.Where(c => c.position == TagHelper.GraphPositionEnum.first))
{
mapInputType[it.variableName] = it.variableType;
}
foreach (var col in inputs)
{
ctx.AddInputVariable(mapInputType[col], col);
}
var views = TagHelper.EnumerateAllViews(trans, begin);
var transforms = views.Where(c => (c.Item1 as IDataTransform) != null)
.Select(c => c.Item1)
.ToArray();
foreach (var tr in transforms.Reverse())
{
var tron = tr as ICanSaveOnnx;
if (tron == null)
{
throw host.ExceptNotSupp($"Transform {tr.GetType()} cannot be saved in Onnx format.");
}
if (!tron.CanSaveOnnx(ctx))
{
throw host.ExceptNotSupp($"Transform {tr.GetType()} cannot be saved in ONNX format.");
}
var tron2 = tron as ISaveAsOnnx;
if (!tron2.CanSaveOnnx(ctx))
{
throw host.ExceptNotSupp($"Transform {tr.GetType()} does not implement SaveAsOnnx.");
}
tron2.SaveAsOnnx(ctx);
}
var mapOuputType = new Dictionary <string, ColumnType>();
foreach (var it in uniqueVars.Where(c => c.position == TagHelper.GraphPositionEnum.last))
{
mapOuputType[it.variableName] = it.variableType;
}
foreach (var col in outputs)
{
var variableName = ctx.TryGetVariableName(col);
var trueVariableName = ctx.AddIntermediateVariable(null, col, true);
ctx.CreateNode("Identity", variableName, trueVariableName, ctx.GetNodeName("Identity"), "");
ctx.AddOutputVariable(mapOuputType[col], trueVariableName);
}
return(ctx);
}
public JArray GetNodes()
{
JObject json;
try
{
json = JObject.Parse($"{{'nodes': [{string.Join(",", _jsonNodes)}]}}");
}
catch (JsonReaderException ex)
{
throw _env.Except(ex, "Failed to parse experiment graph: {0}", ex.Message);
}
return(json["nodes"] as JArray);
}
protected virtual IDataTransform Create(IHostEnvironment env, Arguments args, IDataView input, out IDataView sourceCtx, IPredictor overwritePredictor)
{
sourceCtx = input;
Contracts.CheckValue(env, "env");
env.CheckValue(args, "args");
env.CheckValue(input, "input");
IPredictor predictor;
if (overwritePredictor == null)
{
throw env.Except("No defined predictor.");
}
else
{
predictor = overwritePredictor;
}
// The function is returning something and modifying a member of the class. Not very fancy.
_predictor = predictor;
string feat = TrainUtils.MatchNameOrDefaultOrNull(env, input.Schema,
"featureColumn", args.featureColumn, DefaultColumnNames.Features);
int index = SchemaHelper.GetColumnIndex(input.Schema, feat);
var type = input.Schema[index].Type;
if (!type.IsVector() || type.AsVector().ItemType().RawKind() != DataKind.R4)
{
throw env.Except("Features must a vector of floats");
}
if (args.useProb)
{
var valueMapper = predictor as IValueMapperDist;
if (valueMapper == null)
{
throw env.Except("Predictor must be a IValueMapper.");
}
var output = valueMapper.DistType;
if (output.IsVector())
{
return(CreateTransformValueMapperDist <VBuffer <float>, VBuffer <float>, VBuffer <float> >(valueMapper, feat, args.outputColumn));
}
else
{
return(CreateTransformValueMapperDist <VBuffer <float>, VBuffer <float>, float>(valueMapper, feat, args.outputColumn));
}
}
else
{
var valueMapper = predictor as IValueMapper;
if (valueMapper == null)
{
throw env.Except("Predictor must be a IValueMapper.");
}
var output = valueMapper.OutputType;
if (output.IsVector())
{
return(CreateTransformValueMapper <VBuffer <float>, VBuffer <float> >(valueMapper, feat, args.outputColumn));
}
else
{
return(CreateTransformValueMapper <VBuffer <float>, float>(valueMapper, feat, args.outputColumn));
}
}
}