/// <summary>
/// Save schema associations of role/column-name in <paramref name="rep"/>.
/// </summary>
internal static void SaveRoleMappings(IHostEnvironment env, IChannel ch, RoleMappedSchema schema, RepositoryWriter rep)
{
// REVIEW: Should we also save this stuff, for instance, in some portion of the
// score command or transform?
Contracts.AssertValue(env);
env.AssertValue(ch);
ch.AssertValue(schema);
ArrayDataViewBuilder builder = new ArrayDataViewBuilder(env);
List<string> rolesList = new List<string>();
List<string> columnNamesList = new List<string>();
// OrderBy is stable, so there is no danger in it "reordering" columns
// when a role is filled by multiple columns.
foreach (var role in schema.GetColumnRoleNames().OrderBy(r => r.Key.Value))
{
rolesList.Add(role.Key.Value);
columnNamesList.Add(role.Value);
}
builder.AddColumn("Role", rolesList.ToArray());
builder.AddColumn("Column", columnNamesList.ToArray());
using (var entry = rep.CreateEntry(DirTrainingInfo, RoleMappingFile))
{
// REVIEW: It seems very important that we have the role mappings
// be easily human interpretable and even manipulable, but relying on the
// text saver/loader means that special characters like '\n' won't be reinterpretable.
// On the other hand, no one is such a big lunatic that they will actually
// ever go ahead and do something so stupid as that.
var saver = new TextSaver(env, new TextSaver.Arguments() { Dense = true, Silent = true });
var view = builder.GetDataView();
saver.SaveData(entry.Stream, view, Utils.GetIdentityPermutation(view.Schema.ColumnCount));
}
}
public static void Write(ModelSaveContext ctx, RoleMappedSchema schema)
{
var array = schema.GetColumnRoleNames().ToArray();
ctx.Writer.Write(array.Length);
foreach (var pair in array)
{
ctx.Writer.Write(pair.Key.Value);
ctx.Writer.Write(pair.Value);
}
}
private void Run(IChannel ch)
{
IDataLoader loader = null;
IPredictor rawPred = null;
IDataView view;
RoleMappedSchema trainSchema = null;
if (_model == null)
{
if (string.IsNullOrEmpty(Args.InputModelFile))
{
loader = CreateLoader();
rawPred = null;
trainSchema = null;
Host.CheckUserArg(Args.LoadPredictor != true, nameof(Args.LoadPredictor),
"Cannot be set to true unless " + nameof(Args.InputModelFile) + " is also specifified.");
}
else
{
LoadModelObjects(ch, _loadPredictor, out rawPred, true, out trainSchema, out loader);
}
view = loader;
}
else
{
view = _model.Apply(Host, new EmptyDataView(Host, _model.InputSchema));
}
// Get the transform chain.
IDataView source;
IDataView end;
LinkedList <ITransformCanSaveOnnx> transforms;
GetPipe(ch, view, out source, out end, out transforms);
Host.Assert(transforms.Count == 0 || transforms.Last.Value == end);
var assembly = System.Reflection.Assembly.GetExecutingAssembly();
var versionInfo = System.Diagnostics.FileVersionInfo.GetVersionInfo(assembly.Location);
var ctx = new OnnxContextImpl(Host, _name, ProducerName, versionInfo.FileVersion,
ModelVersion, _domain);
// If we have a predictor, try to get the scorer for it.
if (rawPred != null)
{
RoleMappedData data;
if (trainSchema != null)
{
data = RoleMappedData.Create(end, trainSchema.GetColumnRoleNames());
}
else
{
// We had a predictor, but no roles stored in the model. Just suppose
// default column names are OK, if present.
data = TrainUtils.CreateExamplesOpt(end, DefaultColumnNames.Label,
DefaultColumnNames.Features, DefaultColumnNames.GroupId, DefaultColumnNames.Weight, DefaultColumnNames.Name);
}
var scorePipe = ScoreUtils.GetScorer(rawPred, data, Host, trainSchema);
var scoreOnnx = scorePipe as ITransformCanSaveOnnx;
if (scoreOnnx?.CanSaveOnnx == true)
{
Host.Assert(scorePipe.Source == end);
end = scorePipe;
transforms.AddLast(scoreOnnx);
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but we do not know how to save it as ONNX.");
ch.Warning("We do not know how to save the predictor as ONNX. Ignoring.");
}
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but one was not present.");
}
HashSet <string> inputColumns = new HashSet <string>();
//Create graph inputs.
for (int i = 0; i < source.Schema.ColumnCount; i++)
{
string colName = source.Schema.GetColumnName(i);
if (_inputsToDrop.Contains(colName))
{
continue;
}
ctx.AddInputVariable(source.Schema.GetColumnType(i), colName);
inputColumns.Add(colName);
}
//Create graph nodes, outputs and intermediate values.
foreach (var trans in transforms)
{
Host.Assert(trans.CanSaveOnnx);
trans.SaveAsOnnx(ctx);
}
//Add graph outputs.
for (int i = 0; i < end.Schema.ColumnCount; ++i)
{
if (end.Schema.IsHidden(i))
{
continue;
}
var idataviewColumnName = end.Schema.GetColumnName(i);;
if (_outputsToDrop.Contains(idataviewColumnName) || _inputsToDrop.Contains(idataviewColumnName))
{
continue;
}
var variableName = ctx.TryGetVariableName(idataviewColumnName);
if (variableName != null)
{
ctx.AddOutputVariable(end.Schema.GetColumnType(i), variableName);
}
}
var model = ctx.MakeModel();
if (_outputModelPath != null)
{
using (var file = Host.CreateOutputFile(_outputModelPath))
using (var stream = file.CreateWriteStream())
model.WriteTo(stream);
}
if (_outputJsonModelPath != null)
{
using (var file = Host.CreateOutputFile(_outputJsonModelPath))
using (var stream = file.CreateWriteStream())
using (var writer = new StreamWriter(stream))
{
var parsedJson = JsonConvert.DeserializeObject(model.ToString());
writer.Write(JsonConvert.SerializeObject(parsedJson, Formatting.Indented));
}
}
if (!string.IsNullOrWhiteSpace(Args.OutputModelFile))
{
Contracts.Assert(loader != null);
ch.Trace("Saving the data pipe");
// Should probably include "end"?
SaveLoader(loader, Args.OutputModelFile);
}
}
private void Run(IChannel ch)
{
ILegacyDataLoader loader = null;
IPredictor rawPred = null;
IDataView view;
RoleMappedSchema trainSchema = null;
if (_model == null)
{
if (string.IsNullOrEmpty(ImplOptions.InputModelFile))
{
loader = CreateLoader();
rawPred = null;
trainSchema = null;
Host.CheckUserArg(ImplOptions.LoadPredictor != true, nameof(ImplOptions.LoadPredictor),
"Cannot be set to true unless " + nameof(ImplOptions.InputModelFile) + " is also specifified.");
}
else
{
LoadModelObjects(ch, _loadPredictor, out rawPred, true, out trainSchema, out loader);
}
view = loader;
}
else
{
view = _model.Apply(Host, new EmptyDataView(Host, _model.InputSchema));
}
// Create the ONNX context for storing global information
var assembly = System.Reflection.Assembly.GetExecutingAssembly();
var versionInfo = System.Diagnostics.FileVersionInfo.GetVersionInfo(assembly.Location);
var ctx = new OnnxContextImpl(Host, _name, ProducerName, versionInfo.FileVersion,
ModelVersion, _domain, ImplOptions.OnnxVersion);
// Get the transform chain.
IDataView source;
IDataView end;
LinkedList <ITransformCanSaveOnnx> transforms;
GetPipe(ctx, ch, view, out source, out end, out transforms);
Host.Assert(transforms.Count == 0 || transforms.Last.Value == end);
// If we have a predictor, try to get the scorer for it.
if (rawPred != null)
{
RoleMappedData data;
if (trainSchema != null)
{
data = new RoleMappedData(end, trainSchema.GetColumnRoleNames());
}
else
{
// We had a predictor, but no roles stored in the model. Just suppose
// default column names are OK, if present.
data = new RoleMappedData(end, DefaultColumnNames.Label,
DefaultColumnNames.Features, DefaultColumnNames.GroupId, DefaultColumnNames.Weight, DefaultColumnNames.Name, opt: true);
}
var scorePipe = ScoreUtils.GetScorer(rawPred, data, Host, trainSchema);
var scoreOnnx = scorePipe as ITransformCanSaveOnnx;
if (scoreOnnx?.CanSaveOnnx(ctx) == true)
{
Host.Assert(scorePipe.Source == end);
end = scorePipe;
transforms.AddLast(scoreOnnx);
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but we do not know how to save it as ONNX.");
ch.Warning("We do not know how to save the predictor as ONNX. Ignoring.");
}
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but one was not present.");
}
var model = ConvertTransformListToOnnxModel(ctx, ch, source, end, transforms, _inputsToDrop, _outputsToDrop);
using (var file = Host.CreateOutputFile(_outputModelPath))
using (var stream = file.CreateWriteStream())
model.WriteTo(stream);
if (_outputJsonModelPath != null)
{
using (var file = Host.CreateOutputFile(_outputJsonModelPath))
using (var stream = file.CreateWriteStream())
using (var writer = new StreamWriter(stream))
{
var parsedJson = JsonConvert.DeserializeObject(model.ToString());
writer.Write(JsonConvert.SerializeObject(parsedJson, Formatting.Indented));
}
}
if (!string.IsNullOrWhiteSpace(ImplOptions.OutputModelFile))
{
Contracts.Assert(loader != null);
ch.Trace("Saving the data pipe");
// Should probably include "end"?
SaveLoader(loader, ImplOptions.OutputModelFile);
}
}
private void Run(IChannel ch)
{
ILegacyDataLoader loader = null;
IPredictor rawPred = null;
IDataView view;
RoleMappedSchema trainSchema = null;
if (_model == null && _predictiveModel == null)
{
if (string.IsNullOrEmpty(ImplOptions.InputModelFile))
{
loader = CreateLoader();
rawPred = null;
trainSchema = null;
Host.CheckUserArg(ImplOptions.LoadPredictor != true, nameof(ImplOptions.LoadPredictor),
"Cannot be set to true unless " + nameof(ImplOptions.InputModelFile) + " is also specified.");
}
else
{
LoadModelObjects(ch, _loadPredictor, out rawPred, true, out trainSchema, out loader);
}
view = loader;
}
else if (_model != null)
{
view = _model.Apply(Host, new EmptyDataView(Host, _model.InputSchema));
}
else
{
view = _predictiveModel.TransformModel.Apply(Host, new EmptyDataView(Host, _predictiveModel.TransformModel.InputSchema));
rawPred = _predictiveModel.Predictor;
trainSchema = _predictiveModel.GetTrainingSchema(Host);
}
// Create the ONNX context for storing global information
var assembly = System.Reflection.Assembly.GetExecutingAssembly();
var versionInfo = System.Diagnostics.FileVersionInfo.GetVersionInfo(assembly.Location);
var ctx = new OnnxContextImpl(Host, _name, ProducerName, versionInfo.FileVersion,
ModelVersion, _domain, ImplOptions.OnnxVersion);
// Get the transform chain.
IDataView source;
IDataView end;
LinkedList <ITransformCanSaveOnnx> transforms;
GetPipe(ctx, ch, view, out source, out end, out transforms);
Host.Assert(transforms.Count == 0 || transforms.Last.Value == end);
// If we have a predictor, try to get the scorer for it.
if (rawPred != null)
{
RoleMappedData data;
if (trainSchema != null)
{
data = new RoleMappedData(end, trainSchema.GetColumnRoleNames());
}
else
{
// We had a predictor, but no roles stored in the model. Just suppose
// default column names are OK, if present.
data = new RoleMappedData(end, DefaultColumnNames.Label,
DefaultColumnNames.Features, DefaultColumnNames.GroupId, DefaultColumnNames.Weight, DefaultColumnNames.Name, opt: true);
}
var scorePipe = ScoreUtils.GetScorer(rawPred, data, Host, trainSchema);
var scoreOnnx = scorePipe as ITransformCanSaveOnnx;
if (scoreOnnx?.CanSaveOnnx(ctx) == true)
{
Host.Assert(scorePipe.Source == end);
end = scorePipe;
transforms.AddLast(scoreOnnx);
if (rawPred.PredictionKind == PredictionKind.BinaryClassification || rawPred.PredictionKind == PredictionKind.MulticlassClassification)
{
// Check if the PredictedLabel Column is a KeyDataViewType and has KeyValue Annotations.
// If it does, add a KeyToValueMappingTransformer, to enable NimbusML to get the values back
// when using an ONNX model, as described in https://github.com/dotnet/machinelearning/pull/4841
var predictedLabelColumn = scorePipe.Schema.GetColumnOrNull(DefaultColumnNames.PredictedLabel);
if (predictedLabelColumn.HasValue && HasKeyValues(predictedLabelColumn.Value))
{
var outputData = new KeyToValueMappingTransformer(Host, DefaultColumnNames.PredictedLabel).Transform(scorePipe);
end = outputData;
transforms.AddLast(outputData as ITransformCanSaveOnnx);
}
}
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but we do not know how to save it as ONNX.");
ch.Warning("We do not know how to save the predictor as ONNX. Ignoring.");
}
}
else
{
Contracts.CheckUserArg(_loadPredictor != true,
nameof(Arguments.LoadPredictor), "We were explicitly told to load the predictor but one was not present.");
}
// Convert back to values the KeyDataViewType "pass-through" columns
// (i.e those that remained untouched by the model). This is done to enable NimbusML to get these values
// as described in https://github.com/dotnet/machinelearning/pull/4841
var passThroughColumnNames = GetPassThroughKeyDataViewTypeColumnsNames(source, end);
foreach (var name in passThroughColumnNames)
{
var outputData = new KeyToValueMappingTransformer(Host, name).Transform(end);
end = outputData;
transforms.AddLast(end as ITransformCanSaveOnnx);
}
var model = ConvertTransformListToOnnxModel(ctx, ch, source, end, transforms, _inputsToDrop, _outputsToDrop);
using (var file = Host.CreateOutputFile(_outputModelPath))
using (var stream = file.CreateWriteStream())
model.WriteTo(stream);
if (_outputJsonModelPath != null)
{
using (var file = Host.CreateOutputFile(_outputJsonModelPath))
using (var stream = file.CreateWriteStream())
using (var writer = new StreamWriter(stream))
{
var parsedJson = JsonConvert.DeserializeObject(model.ToString());
writer.Write(JsonConvert.SerializeObject(parsedJson, Formatting.Indented));
}
}
if (!string.IsNullOrWhiteSpace(ImplOptions.OutputModelFile))
{
Contracts.Assert(loader != null);
ch.Trace("Saving the data pipe");
// Should probably include "end"?
SaveLoader(loader, ImplOptions.OutputModelFile);
}
}
