private static IPredictor TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, RoleMappedData validData,
IComponentFactory <ICalibratorTrainer> calibrator, int maxCalibrationExamples, bool?cacheData, IPredictor inputPredictor = null)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
ch.CheckValue(data, nameof(data));
ch.CheckValue(trainer, nameof(trainer));
ch.CheckValueOrNull(validData);
ch.CheckValueOrNull(inputPredictor);
AddCacheIfWanted(env, ch, trainer, ref data, cacheData);
ch.Trace("Training");
if (validData != null)
{
AddCacheIfWanted(env, ch, trainer, ref validData, cacheData);
}
if (inputPredictor != null && !trainer.Info.SupportsIncrementalTraining)
{
ch.Warning("Ignoring " + nameof(TrainCommand.Arguments.InputModelFile) +
": Trainer does not support incremental training.");
inputPredictor = null;
}
ch.Assert(validData == null || trainer.Info.SupportsValidation);
var predictor = trainer.Train(new TrainContext(data, validData, inputPredictor));
var caliTrainer = calibrator?.CreateComponent(env);
return(CalibratorUtils.TrainCalibratorIfNeeded(env, ch, caliTrainer, maxCalibrationExamples, trainer, predictor, data));
}
private static IDataLoader LoadStopwords(IHostEnvironment env, IChannel ch, string dataFile,
IComponentFactory <IMultiStreamSource, IDataLoader> loader, ref string stopwordsCol)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(ch, nameof(ch));
MultiFileSource fileSource = new MultiFileSource(dataFile);
IDataLoader dataLoader;
// First column using the file.
if (loader == null)
{
// Determine the default loader from the extension.
var ext = Path.GetExtension(dataFile);
bool isBinary = string.Equals(ext, ".idv", StringComparison.OrdinalIgnoreCase);
bool isTranspose = string.Equals(ext, ".tdv", StringComparison.OrdinalIgnoreCase);
if (isBinary || isTranspose)
{
ch.Assert(isBinary != isTranspose);
ch.CheckUserArg(!string.IsNullOrWhiteSpace(stopwordsCol), nameof(Arguments.StopwordsColumn),
"stopwordsColumn should be specified");
if (isBinary)
{
dataLoader = new BinaryLoader(env, new BinaryLoader.Arguments(), fileSource);
}
else
{
ch.Assert(isTranspose);
dataLoader = new TransposeLoader(env, new TransposeLoader.Arguments(), fileSource);
}
}
else
{
if (!string.IsNullOrWhiteSpace(stopwordsCol))
{
ch.Warning("{0} should not be specified when default loader is TextLoader. Ignoring stopwordsColumn={0}",
stopwordsCol);
}
dataLoader = TextLoader.Create(
env,
new TextLoader.Arguments()
{
Separator = "tab",
Column = new[]
{
new TextLoader.Column("Stopwords", DataKind.TX, 0)
}
},
fileSource);
stopwordsCol = "Stopwords";
}
ch.AssertNonEmpty(stopwordsCol);
}
else
{
dataLoader = loader.CreateComponent(env, fileSource);
}
return(dataLoader);
}
public static IPredictor Train(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, RoleMappedData validData,
IComponentFactory <ICalibratorTrainer> calibrator, int maxCalibrationExamples, bool?cacheData, IPredictor inputPredictor = null)
{
ICalibratorTrainer caliTrainer = calibrator?.CreateComponent(env);
return(TrainCore(env, ch, data, trainer, validData, caliTrainer, maxCalibrationExamples, cacheData, inputPredictor));
}
/// <summary>
/// Runs the healthcheck.
/// </summary>
/// <param name="id">The identifier.</param>
public void RunHealthcheck(string id = null)
{
using (new LanguageSwitcher(Language.Parse("en")))
{
var maximumNumberOfThreads = Settings.GetIntSetting(maxNumberOfThreadsSettingsKey, 1);
var queue = new ConcurrentQueue <BaseComponent>();
using (new DatabaseSwitcher(Factory.GetDatabase("master")))
{
using (new SecurityDisabler())
{
var settingsItem = Sitecore.Context.Database.GetItem(new ID(Constants.SettingsItemId));
int numberOfDaysToKeepLogs = 0;
if (!int.TryParse(settingsItem["Days"], out numberOfDaysToKeepLogs))
{
numberOfDaysToKeepLogs = DefaultNumberOfDaysToKeepLogs;
}
var componentsFolder = Sitecore.Context.Database.GetItem(new ID(Constants.ComponentsRootFolderId));
foreach (Item item in componentsFolder.Axes.GetDescendants())
{
if (!string.IsNullOrEmpty(id) && !item.ID.ToString().Equals(id, System.StringComparison.OrdinalIgnoreCase))
{
continue;
}
var component = componentFactory.CreateComponent(item);
if (component != null)
{
queue.Enqueue(component);
}
}
List <Action> actions = new List <Action>();
for (int i = 0; i < maximumNumberOfThreads; i++)
{
Action action = () =>
{
BaseComponent component;
while (queue.TryDequeue(out component))
{
component.RunHealthcheck();
component.SaveHealthcheckResult(numberOfDaysToKeepLogs);
}
};
actions.Add(action);
}
Parallel.Invoke(actions.ToArray());
}
}
}
}
/// <summary>
/// Given a predictor, an optional mapper factory, and an optional scorer factory settings,
/// produces a compatible ISchemaBindableMapper.
/// First, it tries to instantiate the bindable mapper using the mapper factory.
/// Next, it tries to instantiate the bindable mapper using the <paramref name="scorerFactorySettings"/>
/// (this will only succeed if there's a registered BindableMapper creation method with load name equal to the one
/// of the scorer).
/// If the above fails, it checks whether the predictor implements <see cref="ISchemaBindableMapper"/>
/// directly.
/// If this also isn't true, it will create a 'matching' standard mapper.
/// </summary>
public static ISchemaBindableMapper GetSchemaBindableMapper(
IHostEnvironment env,
IPredictor predictor,
IComponentFactory<IPredictor, ISchemaBindableMapper> mapperFactory = null,
ICommandLineComponentFactory scorerFactorySettings = null)
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(predictor, nameof(predictor));
env.CheckValueOrNull(mapperFactory);
env.CheckValueOrNull(scorerFactorySettings);
// if the mapperFactory was supplied, use it
if (mapperFactory != null)
return mapperFactory.CreateComponent(env, predictor);
// See if we can instantiate a mapper using scorer arguments.
if (scorerFactorySettings != null && TryCreateBindableFromScorer(env, predictor, scorerFactorySettings, out var bindable))
return bindable;
// The easy case is that the predictor implements the interface.
bindable = predictor as ISchemaBindableMapper;
if (bindable != null)
return bindable;
// Use one of the standard wrappers.
if (predictor is IValueMapperDist)
return new SchemaBindableBinaryPredictorWrapper(predictor);
return new SchemaBindablePredictorWrapper(predictor);
}
/// <summary>
/// Parses a single entity into an Entitydata object.
/// </summary>
/// <param name="xEntity">The x entity.</param>
/// <returns></returns>
private EntityData ParseEntity(XElement xEntity)
{
if (xEntity == null)
{
EntityIoLogger.WriteNullArgumentIoException(new ArgumentNullException(xEntity.ToString()), IoType.Component, _entityNumber);
}
EntityIoLogger.WriteIoInformation(xEntity, IoType.Entity, _entityNumber);
EntityData entityData = new EntityData();
var xComponents = xEntity.Descendants("Components");
var xSpriteComponent = xComponents.Descendants("Sprite");
var xPlayerComponent = xComponents.Descendants("Player");
var xPositionComponent = xComponents.Descendants("Position");
var xVelocitiyComponent = xComponents.Descendants("Velocity");
var xAccelerationComponent = xComponents.Descendants("Acceleration");
if (xSpriteComponent.Any())
{
entityData.Components.Add(
_componentFactory.CreateComponent <SpriteComponent>(xSpriteComponent.FirstOrDefault()));
}
if (xPlayerComponent.Any())
{
entityData.Components.Add(
_componentFactory.CreateComponent <PlayerComponent>(xPlayerComponent.FirstOrDefault()));
}
if (xPositionComponent.Any())
{
entityData.Components.Add(
_componentFactory.CreateComponent <PositionComponent>(xPositionComponent.FirstOrDefault()));
}
if (xVelocitiyComponent.Any())
{
entityData.Components.Add(
_componentFactory.CreateComponent <VelocityComponent>(xVelocitiyComponent.FirstOrDefault()));
}
if (xAccelerationComponent.Any())
{
entityData.Components.Add(
_componentFactory.CreateComponent <AccelerationComponent>(xAccelerationComponent.FirstOrDefault()));
}
return(entityData);
}
private void RunCore(IChannel ch, string cmd)
{
Host.AssertValue(ch);
Host.AssertNonEmpty(cmd);
ch.Trace("Constructing trainer");
ITrainer trainer = _trainer.CreateComponent(Host);
IPredictor inputPredictor = null;
if (Args.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, Args.InputModelFile, out inputPredictor))
{
ch.Warning("No input model file specified or model file did not contain a predictor. The model state cannot be initialized.");
}
ch.Trace("Constructing data pipeline");
IDataView view = CreateLoader();
ISchema schema = view.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.LabelColumn), _labelColumn, DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.FeatureColumn), _featureColumn, DefaultColumnNames.Features);
var group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.GroupColumn), _groupColumn, DefaultColumnNames.GroupId);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.WeightColumn), _weightColumn, DefaultColumnNames.Weight);
var name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.NameColumn), _nameColumn, DefaultColumnNames.Name);
TrainUtils.AddNormalizerIfNeeded(Host, ch, trainer, ref view, feature, Args.NormalizeFeatures);
ch.Trace("Binding columns");
var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, Args.CustomColumn);
var data = new RoleMappedData(view, label, feature, group, weight, name, customCols);
// REVIEW: Unify the code that creates validation examples in Train, TrainTest and CV commands.
RoleMappedData validData = null;
if (!string.IsNullOrWhiteSpace(Args.ValidationFile))
{
if (!trainer.Info.SupportsValidation)
{
ch.Warning("Ignoring validationFile: Trainer does not accept validation dataset.");
}
else
{
ch.Trace("Constructing the validation pipeline");
IDataView validPipe = CreateRawLoader(dataFile: Args.ValidationFile);
validPipe = ApplyTransformUtils.ApplyAllTransformsToData(Host, view, validPipe);
validData = new RoleMappedData(validPipe, data.Schema.GetColumnRoleNames());
}
}
var predictor = TrainUtils.Train(Host, ch, data, trainer, validData,
Args.Calibrator, Args.MaxCalibrationExamples, Args.CacheData, inputPredictor);
using (var file = Host.CreateOutputFile(Args.OutputModelFile))
TrainUtils.SaveModel(Host, ch, file, predictor, data, cmd);
}
private IComponent CreateComponent(ComponentTemplate componentTemplate)
{
var component = componentFactory.CreateComponent(componentTemplate.ComponentType);
/*
* // Iterate virtual template components
* foreach (var childComponentTemplate in componentTemplate.Templates)
* {
* var child = CreateComponent(childComponentTemplate);
* component.Children.Add(child);
* child.Parent = component;
* }
*/
styleSetter.ApplyStyle(component);
foreach (var property in componentTemplate.PropertySetters)
{
styleSetter.ApplyProperty(component, property);
}
return(component);
}
private static byte[] GetBytesOne(IHost host, string dataFile, IComponentFactory <IMultiStreamSource, IDataLoader> loaderFactory,
string termColumn, string valueColumn)
{
Contracts.AssertValue(host);
host.Assert(!string.IsNullOrWhiteSpace(dataFile));
host.AssertNonEmpty(termColumn);
host.AssertNonEmpty(valueColumn);
IMultiStreamSource fileSource = new MultiFileSource(dataFile);
IDataLoader loader;
if (loaderFactory == null)
{
// REVIEW: Should there be defaults for loading from text?
var ext = Path.GetExtension(dataFile);
bool isBinary = string.Equals(ext, ".idv", StringComparison.OrdinalIgnoreCase);
bool isTranspose = string.Equals(ext, ".tdv", StringComparison.OrdinalIgnoreCase);
if (!isBinary && !isTranspose)
{
throw host.ExceptUserArg(nameof(Arguments.Loader), "must specify the loader");
}
host.Assert(isBinary != isTranspose); // One or the other must be true.
if (isBinary)
{
loader = new BinaryLoader(host, new BinaryLoader.Arguments(), fileSource);
}
else
{
loader = new TransposeLoader(host, new TransposeLoader.Arguments(), fileSource);
}
}
else
{
loader = loaderFactory.CreateComponent(host, fileSource);
}
return(GetBytesFromDataView(host, loader, termColumn, valueColumn));
}
private FoldResult RunFold(int fold)
{
var host = GetHost();
host.Assert(0 <= fold && fold <= _numFolds);
// REVIEW: Make channels buffered in multi-threaded environments.
using (var ch = host.Start($"Fold {fold}"))
{
ch.Trace("Constructing trainer");
ITrainer trainer = _trainer.CreateComponent(host);
// Train pipe.
var trainFilter = new RangeFilter.Arguments();
trainFilter.Column = _splitColumn;
trainFilter.Min = (Double)fold / _numFolds;
trainFilter.Max = (Double)(fold + 1) / _numFolds;
trainFilter.Complement = true;
IDataView trainPipe = new RangeFilter(host, trainFilter, _inputDataView);
trainPipe = new OpaqueDataView(trainPipe);
var trainData = _createExamples(host, ch, trainPipe, trainer);
// Test pipe.
var testFilter = new RangeFilter.Arguments();
testFilter.Column = trainFilter.Column;
testFilter.Min = trainFilter.Min;
testFilter.Max = trainFilter.Max;
ch.Assert(!testFilter.Complement);
IDataView testPipe = new RangeFilter(host, testFilter, _inputDataView);
testPipe = new OpaqueDataView(testPipe);
var testData = _applyTransformsToTestData(host, ch, testPipe, trainData, trainPipe);
// Validation pipe and examples.
RoleMappedData validData = null;
if (_getValidationDataView != null)
{
ch.Assert(_applyTransformsToValidationData != null);
if (!trainer.Info.SupportsValidation)
{
ch.Warning("Trainer does not accept validation dataset.");
}
else
{
ch.Trace("Constructing the validation pipeline");
IDataView validLoader = _getValidationDataView();
var validPipe = ApplyTransformUtils.ApplyAllTransformsToData(host, _inputDataView, validLoader);
validPipe = new OpaqueDataView(validPipe);
validData = _applyTransformsToValidationData(host, ch, validPipe, trainData, trainPipe);
}
}
// Train.
var predictor = TrainUtils.Train(host, ch, trainData, trainer, validData,
_calibrator, _maxCalibrationExamples, _cacheData, _inputPredictor);
// Score.
ch.Trace("Scoring and evaluating");
ch.Assert(_scorer == null || _scorer is ICommandLineComponentFactory, "CrossValidationCommand should only be used from the command line.");
var bindable = ScoreUtils.GetSchemaBindableMapper(host, predictor, scorerFactorySettings: _scorer as ICommandLineComponentFactory);
ch.AssertValue(bindable);
var mapper = bindable.Bind(host, testData.Schema);
var scorerComp = _scorer ?? ScoreUtils.GetScorerComponent(mapper);
IDataScorerTransform scorePipe = scorerComp.CreateComponent(host, testData.Data, mapper, trainData.Schema);
// Save per-fold model.
string modelFileName = ConstructPerFoldName(_outputModelFile, fold);
if (modelFileName != null && _loader != null)
{
using (var file = host.CreateOutputFile(modelFileName))
{
var rmd = new RoleMappedData(
CompositeDataLoader.ApplyTransform(host, _loader, null, null,
(e, newSource) => ApplyTransformUtils.ApplyAllTransformsToData(e, trainData.Data, newSource)),
trainData.Schema.GetColumnRoleNames());
TrainUtils.SaveModel(host, ch, file, predictor, rmd, _cmd);
}
}
// Evaluate.
var eval = _evaluator?.CreateComponent(host) ??
EvaluateUtils.GetEvaluator(host, scorePipe.Schema);
// Note that this doesn't require the provided columns to exist (because of the "opt" parameter).
// We don't normally expect the scorer to drop columns, but if it does, we should not require
// all the columns in the test pipeline to still be present.
var dataEval = new RoleMappedData(scorePipe, testData.Schema.GetColumnRoleNames(), opt: true);
var dict = eval.Evaluate(dataEval);
RoleMappedData perInstance = null;
if (_savePerInstance)
{
var perInst = eval.GetPerInstanceMetrics(dataEval);
perInstance = new RoleMappedData(perInst, dataEval.Schema.GetColumnRoleNames(), opt: true);
}
ch.Done();
return(new FoldResult(dict, dataEval.Schema.Schema, perInstance, trainData.Schema));
}
}
private void RunCore(IChannel ch, string cmd)
{
Host.AssertValue(ch);
Host.AssertNonEmpty(cmd);
ch.Trace("Constructing trainer");
ITrainer trainer = _trainer.CreateComponent(Host);
IPredictor inputPredictor = null;
if (Args.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, Args.InputModelFile, out inputPredictor))
{
ch.Warning("No input model file specified or model file did not contain a predictor. The model state cannot be initialized.");
}
ch.Trace("Constructing data pipeline");
IDataView view = CreateLoader();
ISchema schema = view.Schema;
var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.LabelColumn), _labelColumn, DefaultColumnNames.Label);
var feature = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.FeatureColumn), _featureColumn, DefaultColumnNames.Features);
var group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.GroupColumn), _groupColumn, DefaultColumnNames.GroupId);
var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.WeightColumn), _weightColumn, DefaultColumnNames.Weight);
var name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.NameColumn), _nameColumn, DefaultColumnNames.Name);
TrainUtils.AddNormalizerIfNeeded(Host, ch, trainer, ref view, feature, Args.NormalizeFeatures);
ch.Trace("Binding columns");
var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, Args.CustomColumn);
var data = new RoleMappedData(view, label, feature, group, weight, name, customCols);
// REVIEW: Unify the code that creates validation examples in Train, TrainTest and CV commands.
RoleMappedData validData = null;
if (!string.IsNullOrWhiteSpace(Args.ValidationFile))
{
if (!trainer.Info.SupportsValidation)
{
ch.Warning("Ignoring validationFile: Trainer does not accept validation dataset.");
}
else
{
ch.Trace("Constructing the validation pipeline");
IDataView validPipe = CreateRawLoader(dataFile: Args.ValidationFile);
validPipe = ApplyTransformUtils.ApplyAllTransformsToData(Host, view, validPipe);
validData = new RoleMappedData(validPipe, data.Schema.GetColumnRoleNames());
}
}
// In addition to the training set, some trainers can accept two extra data sets, validation set and test set,
// in training phase. The major difference between validation set and test set is that training process may
// indirectly use validation set to improve the model but the learned model should totally independent of test set.
// Similar to validation set, the trainer can report the scores computed using test set.
RoleMappedData testDataUsedInTrainer = null;
if (!string.IsNullOrWhiteSpace(Args.TestFile))
{
// In contrast to the if-else block for validation above, we do not throw a warning if test file is provided
// because this is TrainTest command.
if (trainer.Info.SupportsTest)
{
ch.Trace("Constructing the test pipeline");
IDataView testPipeUsedInTrainer = CreateRawLoader(dataFile: Args.TestFile);
testPipeUsedInTrainer = ApplyTransformUtils.ApplyAllTransformsToData(Host, view, testPipeUsedInTrainer);
testDataUsedInTrainer = new RoleMappedData(testPipeUsedInTrainer, data.Schema.GetColumnRoleNames());
}
}
var predictor = TrainUtils.Train(Host, ch, data, trainer, validData,
Args.Calibrator, Args.MaxCalibrationExamples, Args.CacheData, inputPredictor, testDataUsedInTrainer);
using (var file = Host.CreateOutputFile(Args.OutputModelFile))
TrainUtils.SaveModel(Host, ch, file, predictor, data, cmd);
}
public void Train(List <FeatureSubsetModel <IPredictorProducing <TOutput> > > models, RoleMappedData data, IHostEnvironment env)
{
Contracts.CheckValue(env, nameof(env));
var host = env.Register(Stacking.LoadName);
host.CheckValue(models, nameof(models));
host.CheckValue(data, nameof(data));
using (var ch = host.Start("Training stacked model"))
{
ch.Check(Meta == null, "Train called multiple times");
ch.Check(BasePredictorType != null);
var maps = new ValueMapper <VBuffer <Single>, TOutput> [models.Count];
for (int i = 0; i < maps.Length; i++)
{
Contracts.Assert(models[i].Predictor is IValueMapper);
var m = (IValueMapper)models[i].Predictor;
maps[i] = m.GetMapper <VBuffer <Single>, TOutput>();
}
// REVIEW: Should implement this better....
var labels = new Single[100];
var features = new VBuffer <Single> [100];
int count = 0;
// REVIEW: Should this include bad values or filter them?
using (var cursor = new FloatLabelCursor(data, CursOpt.AllFeatures | CursOpt.AllLabels))
{
TOutput[] predictions = new TOutput[maps.Length];
var vBuffers = new VBuffer <Single> [maps.Length];
while (cursor.MoveNext())
{
Parallel.For(0, maps.Length, i =>
{
var model = models[i];
if (model.SelectedFeatures != null)
{
EnsembleUtils.SelectFeatures(ref cursor.Features, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]);
maps[i](ref vBuffers[i], ref predictions[i]);
}
else
{
maps[i](ref cursor.Features, ref predictions[i]);
}
});
Utils.EnsureSize(ref labels, count + 1);
Utils.EnsureSize(ref features, count + 1);
labels[count] = cursor.Label;
FillFeatureBuffer(predictions, ref features[count]);
count++;
}
}
ch.Info("The number of instances used for stacking trainer is {0}", count);
var bldr = new ArrayDataViewBuilder(host);
Array.Resize(ref labels, count);
Array.Resize(ref features, count);
bldr.AddColumn(DefaultColumnNames.Label, NumberType.Float, labels);
bldr.AddColumn(DefaultColumnNames.Features, NumberType.Float, features);
var view = bldr.GetDataView();
var rmd = new RoleMappedData(view, DefaultColumnNames.Label, DefaultColumnNames.Features);
var trainer = BasePredictorType.CreateComponent(host);
if (trainer.Info.NeedNormalization)
{
ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this.");
}
Meta = trainer.Train(rmd);
CheckMeta();
}
}
