public BoundBase(SchemaBindablePipelineEnsembleBase parent, RoleMappedSchema schema)
{
Parent = parent;
InputRoleMappedSchema = schema;
OutputSchema = ScoreSchemaFactory.Create(Parent.ScoreType, Parent._scoreColumnKind);
_inputColIndices = new HashSet <int>();
for (int i = 0; i < Parent._inputCols.Length; i++)
{
var name = Parent._inputCols[i];
var col = InputRoleMappedSchema.Schema.GetColumnOrNull(name);
if (!col.HasValue)
{
throw Parent.Host.ExceptSchemaMismatch(nameof(InputRoleMappedSchema), "input", name);
}
_inputColIndices.Add(col.Value.Index);
}
Mappers = new ISchemaBoundRowMapper[Parent.PredictorModels.Length];
BoundPipelines = new IRowToRowMapper[Parent.PredictorModels.Length];
ScoreCols = new int[Parent.PredictorModels.Length];
for (int i = 0; i < Mappers.Length; i++)
{
// Get the RoleMappedSchema to pass to the predictor.
var emptyDv = new EmptyDataView(Parent.Host, schema.Schema);
Parent.PredictorModels[i].PrepareData(Parent.Host, emptyDv, out RoleMappedData rmd, out IPredictor predictor);
// Get the predictor as a bindable mapper, and bind it to the RoleMappedSchema found above.
var bindable = ScoreUtils.GetSchemaBindableMapper(Parent.Host, Parent.PredictorModels[i].Predictor);
Mappers[i] = bindable.Bind(Parent.Host, rmd.Schema) as ISchemaBoundRowMapper;
if (Mappers[i] == null)
{
throw Parent.Host.Except("Predictor {0} is not a row to row mapper", i);
}
// Make sure there is a score column, and remember its index.
var scoreCol = Mappers[i].OutputSchema.GetColumnOrNull(MetadataUtils.Const.ScoreValueKind.Score);
if (!scoreCol.HasValue)
{
throw Parent.Host.Except("Predictor {0} does not contain a score column", i);
}
ScoreCols[i] = scoreCol.Value.Index;
// Get the pipeline.
var dv = new EmptyDataView(Parent.Host, schema.Schema);
var tm = new TransformModelImpl(Parent.Host, dv, dv);
var pipeline = Parent.PredictorModels[i].TransformModel.Apply(Parent.Host, tm);
BoundPipelines[i] = pipeline.AsRowToRowMapper(Parent.Host);
if (BoundPipelines[i] == null)
{
throw Parent.Host.Except("Transform pipeline {0} contains transforms that do not implement IRowToRowMapper", i);
}
}
}
public void SetInputFromPath(GraphRunner runner, string varName, string path, TlcModule.DataKind kind)
{
_host.CheckUserArg(runner != null, nameof(runner), "Provide a GraphRunner instance.");
_host.CheckUserArg(!string.IsNullOrWhiteSpace(varName), nameof(varName), "Specify a graph variable name.");
_host.CheckUserArg(!string.IsNullOrWhiteSpace(path), nameof(path), "Specify a valid file path.");
switch (kind)
{
case TlcModule.DataKind.FileHandle:
var fh = new SimpleFileHandle(_host, path, false, false);
runner.SetInput(varName, fh);
break;
case TlcModule.DataKind.DataView:
IDataView loader = new BinaryLoader(_host, new BinaryLoader.Arguments(), path);
runner.SetInput(varName, loader);
break;
case TlcModule.DataKind.PredictorModel:
PredictorModelImpl pm;
using (var fs = File.OpenRead(path))
pm = new PredictorModelImpl(_host, fs);
runner.SetInput(varName, pm);
break;
case TlcModule.DataKind.TransformModel:
TransformModelImpl tm;
using (var fs = File.OpenRead(path))
tm = new TransformModelImpl(_host, fs);
runner.SetInput(varName, tm);
break;
default:
throw _host.Except("Port type {0} not supported", kind);
}
}
private static void RunGraphCore(EnvironmentBlock *penv, IHostEnvironment env, string graphStr, int cdata, DataSourceBlock **ppdata)
{
Contracts.AssertValue(env);
var host = env.Register("RunGraph", penv->seed, null);
JObject graph;
try
{
graph = JObject.Parse(graphStr);
}
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 if (extension == ".dprep")
{
dv = LoadDprepFile(BytesToString(penv->pythonPath), 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 (path == CSR_MATRIX)
{
SendViewToNativeAsCsr(ch, penv, idv);
}
else if (!string.IsNullOrWhiteSpace(path))
{
SaveIdvToFile(idv, path, host);
}
else
{
var infos = ProcessColumns(ref idv, penv->maxSlots, host);
SendViewToNativeAsDataFrame(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();
}
}
}
internal PredictionModel(Stream stream)
{
_env = new MLContext();
AssemblyRegistration.RegisterAssemblies(_env);
PredictorModel = new TransformModelImpl(_env, stream);
}
