private void RunCore(IChannel ch) { ch.Trace("Constructing data pipeline"); IDataLoader loader; IPredictor predictor; RoleMappedSchema trainSchema; LoadModelObjects(ch, true, out predictor, true, out trainSchema, out loader); ch.AssertValue(predictor); ch.AssertValueOrNull(trainSchema); ch.AssertValue(loader); ch.Trace("Binding columns"); var schema = loader.Schema; string label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.LabelColumn), Args.LabelColumn, DefaultColumnNames.Label); string features = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.FeatureColumn), Args.FeatureColumn, DefaultColumnNames.Features); string group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.GroupColumn), Args.GroupColumn, DefaultColumnNames.GroupId); string weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.WeightColumn), Args.WeightColumn, DefaultColumnNames.Weight); string name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name); var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, Args.CustomColumn); // Score. ch.Trace("Scoring and evaluating"); ch.Assert(Args.Scorer == null || Args.Scorer is ICommandLineComponentFactory, "TestCommand should only be used from the command line."); IDataScorerTransform scorePipe = ScoreUtils.GetScorer(Args.Scorer, predictor, loader, features, group, customCols, Host, trainSchema); // Evaluate. var evaluator = Args.Evaluator?.CreateComponent(Host) ?? EvaluateUtils.GetEvaluator(Host, scorePipe.Schema); var data = new RoleMappedData(scorePipe, label, null, group, weight, name, customCols); var metrics = evaluator.Evaluate(data); MetricWriter.PrintWarnings(ch, metrics); evaluator.PrintFoldResults(ch, metrics); if (!metrics.TryGetValue(MetricKinds.OverallMetrics, out var overall)) { throw ch.Except("No overall metrics found"); } overall = evaluator.GetOverallResults(overall); MetricWriter.PrintOverallMetrics(Host, ch, Args.SummaryFilename, overall, 1); evaluator.PrintAdditionalMetrics(ch, metrics); Dictionary <string, IDataView>[] metricValues = { metrics }; SendTelemetryMetric(metricValues); if (!string.IsNullOrWhiteSpace(Args.OutputDataFile)) { var perInst = evaluator.GetPerInstanceMetrics(data); var perInstData = new RoleMappedData(perInst, label, null, group, weight, name, customCols); var idv = evaluator.GetPerInstanceDataViewToSave(perInstData); MetricWriter.SavePerInstance(Host, ch, Args.OutputDataFile, idv); } }
internal static IDataScorerTransform CreateDefaultScorer(this IHostEnvironment env, RoleMappedData data, IPredictor predictor, RoleMappedSchema trainSchema = null) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(data, nameof(data)); env.CheckValue(predictor, nameof(predictor)); env.CheckValueOrNull(trainSchema); return(ScoreUtils.GetScorer(predictor, data, env, trainSchema)); }
private void RunCore(IChannel ch, string cmd) { Host.AssertValue(ch); Host.AssertNonEmpty(cmd); ch.Trace("Constructing trainer"); ITrainer trainer = ImplOptions.Trainer.CreateComponent(Host); IPredictor inputPredictor = null; if (ImplOptions.ContinueTrain && !TrainUtils.TryLoadPredictor(ch, Host, ImplOptions.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 the training pipeline"); IDataView trainPipe = CreateLoader(); var schema = trainPipe.Schema; string label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.LabelColumn), ImplOptions.LabelColumn, DefaultColumnNames.Label); string features = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.FeatureColumn), ImplOptions.FeatureColumn, DefaultColumnNames.Features); string group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.GroupColumn), ImplOptions.GroupColumn, DefaultColumnNames.GroupId); string weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.WeightColumn), ImplOptions.WeightColumn, DefaultColumnNames.Weight); string name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.NameColumn), ImplOptions.NameColumn, DefaultColumnNames.Name); TrainUtils.AddNormalizerIfNeeded(Host, ch, trainer, ref trainPipe, features, ImplOptions.NormalizeFeatures); ch.Trace("Binding columns"); var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, ImplOptions.CustomColumns); var data = new RoleMappedData(trainPipe, label, features, group, weight, name, customCols); RoleMappedData validData = null; if (!string.IsNullOrWhiteSpace(ImplOptions.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: ImplOptions.ValidationFile); validPipe = ApplyTransformUtils.ApplyAllTransformsToData(Host, trainPipe, validPipe); validData = new RoleMappedData(validPipe, data.Schema.GetColumnRoleNames()); } } // In addition to the training set, some trainers can accept two 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(ImplOptions.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: ImplOptions.TestFile); testPipeUsedInTrainer = ApplyTransformUtils.ApplyAllTransformsToData(Host, trainPipe, testPipeUsedInTrainer); testDataUsedInTrainer = new RoleMappedData(testPipeUsedInTrainer, data.Schema.GetColumnRoleNames()); } } var predictor = TrainUtils.Train(Host, ch, data, trainer, validData, ImplOptions.Calibrator, ImplOptions.MaxCalibrationExamples, ImplOptions.CacheData, inputPredictor, testDataUsedInTrainer); ILegacyDataLoader testPipe; bool hasOutfile = !string.IsNullOrEmpty(ImplOptions.OutputModelFile); var tempFilePath = hasOutfile ? null : Path.GetTempFileName(); using (var file = new SimpleFileHandle(ch, hasOutfile ? ImplOptions.OutputModelFile : tempFilePath, true, !hasOutfile)) { TrainUtils.SaveModel(Host, ch, file, predictor, data, cmd); ch.Trace("Constructing the testing pipeline"); using (var stream = file.OpenReadStream()) using (var rep = RepositoryReader.Open(stream, ch)) testPipe = LoadLoader(rep, ImplOptions.TestFile, true); } // Score. ch.Trace("Scoring and evaluating"); ch.Assert(ImplOptions.Scorer == null || ImplOptions.Scorer is ICommandLineComponentFactory, "TrainTestCommand should only be used from the command line."); IDataScorerTransform scorePipe = ScoreUtils.GetScorer(ImplOptions.Scorer, predictor, testPipe, features, group, customCols, Host, data.Schema); // Evaluate. var evaluator = ImplOptions.Evaluator?.CreateComponent(Host) ?? EvaluateUtils.GetEvaluator(Host, scorePipe.Schema); var dataEval = new RoleMappedData(scorePipe, label, features, group, weight, name, customCols, opt: true); var metrics = evaluator.Evaluate(dataEval); MetricWriter.PrintWarnings(ch, metrics); evaluator.PrintFoldResults(ch, metrics); if (!metrics.TryGetValue(MetricKinds.OverallMetrics, out var overall)) { throw ch.Except("No overall metrics found"); } overall = evaluator.GetOverallResults(overall); MetricWriter.PrintOverallMetrics(Host, ch, ImplOptions.SummaryFilename, overall, 1); evaluator.PrintAdditionalMetrics(ch, metrics); Dictionary <string, IDataView>[] metricValues = { metrics }; SendTelemetryMetric(metricValues); if (!string.IsNullOrWhiteSpace(ImplOptions.OutputDataFile)) { var perInst = evaluator.GetPerInstanceMetrics(dataEval); var perInstData = new RoleMappedData(perInst, label, null, group, weight, name, customCols); var idv = evaluator.GetPerInstanceDataViewToSave(perInstData); MetricWriter.SavePerInstance(Host, ch, ImplOptions.OutputDataFile, idv); } }