Esempio n. 1
0
        private void RunCore(IChannel ch, string cmd)
        {
            Host.AssertValue(ch);
            Host.AssertNonEmpty(cmd);

            ch.Trace("Constructing trainer");
            ITrainer trainer = Args.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 the training pipeline");
            IDataView trainPipe = CreateLoader();

            ISchema schema = trainPipe.Schema;
            string  label  = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.LabelColumn),
                                                                 Args.LabelColumn, DefaultColumnNames.Label);
            string features = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.FeatureColumn),
                                                                  Args.FeatureColumn, DefaultColumnNames.Features);
            string group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.GroupColumn),
                                                               Args.GroupColumn, DefaultColumnNames.GroupId);
            string weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.WeightColumn),
                                                                Args.WeightColumn, DefaultColumnNames.Weight);
            string name = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Arguments.NameColumn),
                                                              Args.NameColumn, DefaultColumnNames.Name);

            TrainUtils.AddNormalizerIfNeeded(Host, ch, trainer, ref trainPipe, features, Args.NormalizeFeatures);

            ch.Trace("Binding columns");
            var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, Args.CustomColumn);
            var data       = new RoleMappedData(trainPipe, label, features, group, weight, name, customCols);

            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, 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(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, trainPipe, 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);

            IDataLoader testPipe;

            using (var file = !string.IsNullOrEmpty(Args.OutputModelFile) ?
                              Host.CreateOutputFile(Args.OutputModelFile) : Host.CreateTempFile(".zip"))
            {
                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, Args.TestFile, true);
            }

            // Score.
            ch.Trace("Scoring and evaluating");
            ch.Assert(Args.Scorer == null || Args.Scorer is ICommandLineComponentFactory, "TrainTestCommand should only be used from the command line.");
            IDataScorerTransform scorePipe = ScoreUtils.GetScorer(Args.Scorer, predictor, testPipe, features, group, customCols, Host, data.Schema);

            // Evaluate.
            var evaluator = Args.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, Args.SummaryFilename, overall, 1);
            evaluator.PrintAdditionalMetrics(ch, metrics);
            Dictionary <string, IDataView>[] metricValues = { metrics };
            SendTelemetryMetric(metricValues);
            if (!string.IsNullOrWhiteSpace(Args.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, Args.OutputDataFile, idv);
            }
        }
        private void RunCore(IChannel ch)
        {
            Host.AssertValue(ch);

            ch.Trace("Creating loader");

            IPredictor       predictor;
            IDataLoader      loader;
            RoleMappedSchema trainSchema;

            LoadModelObjects(ch, true, out predictor, true, out trainSchema, out loader);
            ch.AssertValue(predictor);
            ch.AssertValueOrNull(trainSchema);
            ch.AssertValue(loader);

            ch.Trace("Creating pipeline");
            var scorer   = Args.Scorer;
            var bindable = ScoreUtils.GetSchemaBindableMapper(Host, predictor, scorer);

            ch.AssertValue(bindable);

            // REVIEW: We probably ought to prefer role mappings from the training schema.
            string feat = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema,
                                                              nameof(Args.FeatureColumn), Args.FeatureColumn, DefaultColumnNames.Features);
            string group = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema,
                                                               nameof(Args.GroupColumn), Args.GroupColumn, DefaultColumnNames.GroupId);
            var customCols = TrainUtils.CheckAndGenerateCustomColumns(ch, Args.CustomColumn);
            var schema     = TrainUtils.CreateRoleMappedSchemaOpt(loader.Schema, feat, group, customCols);
            var mapper     = bindable.Bind(Host, schema);

            if (!scorer.IsGood())
            {
                scorer = ScoreUtils.GetScorerComponent(mapper);
            }

            loader = CompositeDataLoader.ApplyTransform(Host, loader, "Scorer", scorer.ToString(),
                                                        (env, view) => scorer.CreateInstance(env, view, mapper, trainSchema));

            loader = CompositeDataLoader.Create(Host, loader, Args.PostTransform);

            if (!string.IsNullOrWhiteSpace(Args.OutputModelFile))
            {
                ch.Trace("Saving the data pipe");
                SaveLoader(loader, Args.OutputModelFile);
            }

            ch.Trace("Creating saver");
            var saver = Args.Saver;

            if (!saver.IsGood())
            {
                var ext    = Path.GetExtension(Args.OutputDataFile);
                var isText = ext == ".txt" || ext == ".tlc";
                saver = new SubComponent <IDataSaver, SignatureDataSaver>(isText ? "TextSaver" : "BinarySaver");
            }
            var writer = saver.CreateInstance(Host);

            ch.Assert(writer != null);
            var outputIsBinary = writer is BinaryWriter;

            bool outputAllColumns =
                Args.OutputAllColumns == true ||
                (Args.OutputAllColumns == null && Utils.Size(Args.OutputColumn) == 0 && outputIsBinary);

            bool outputNamesAndLabels =
                Args.OutputAllColumns == true || Utils.Size(Args.OutputColumn) == 0;

            if (Args.OutputAllColumns == true && Utils.Size(Args.OutputColumn) != 0)
            {
                ch.Warning("outputAllColumns=+ always writes all columns irrespective of outputColumn specified.");
            }

            if (!outputAllColumns && Utils.Size(Args.OutputColumn) != 0)
            {
                foreach (var outCol in Args.OutputColumn)
                {
                    int dummyColIndex;
                    if (!loader.Schema.TryGetColumnIndex(outCol, out dummyColIndex))
                    {
                        throw ch.ExceptUserArg(nameof(Arguments.OutputColumn), "Column '{0}' not found.", outCol);
                    }
                }
            }

            int  colMax;
            uint maxScoreId = 0;

            if (!outputAllColumns)
            {
                maxScoreId = loader.Schema.GetMaxMetadataKind(out colMax, MetadataUtils.Kinds.ScoreColumnSetId);
            }
            ch.Assert(outputAllColumns || maxScoreId > 0); // score set IDs are one-based
            var cols = new List <int>();

            for (int i = 0; i < loader.Schema.ColumnCount; i++)
            {
                if (!Args.KeepHidden && loader.Schema.IsHidden(i))
                {
                    continue;
                }
                if (!(outputAllColumns || ShouldAddColumn(loader.Schema, i, maxScoreId, outputNamesAndLabels)))
                {
                    continue;
                }
                var type = loader.Schema.GetColumnType(i);
                if (writer.IsColumnSavable(type))
                {
                    cols.Add(i);
                }
                else
                {
                    ch.Warning("The column '{0}' will not be written as it has unsavable column type.",
                               loader.Schema.GetColumnName(i));
                }
            }

            ch.Check(cols.Count > 0, "No valid columns to save");

            ch.Trace("Scoring and saving data");
            using (var file = Host.CreateOutputFile(Args.OutputDataFile))
                using (var stream = file.CreateWriteStream())
                    writer.SaveData(stream, loader, cols.ToArray());
        }
Esempio n. 3
0
            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(host, 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);
                    }
                    return(new FoldResult(dict, dataEval.Schema.Schema, perInstance, trainData.Schema));
                }
            }
Esempio n. 4
0
        private string GetSplitColumn(IChannel ch, IDataView input, ref IDataView output)
        {
            // The stratification column and/or group column, if they exist at all, must be present at this point.
            var schema = input.Schema;

            output = input;
            // If no stratification column was specified, but we have a group column of type Single, Double or
            // Key (contiguous) use it.
            string stratificationColumn = null;

            if (!string.IsNullOrWhiteSpace(Args.StratificationColumn))
            {
                stratificationColumn = Args.StratificationColumn;
            }
            else
            {
                string group = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(Args.GroupColumn), Args.GroupColumn, DefaultColumnNames.GroupId);
                int    index;
                if (group != null && schema.TryGetColumnIndex(group, out index))
                {
                    // Check if group column key type with known cardinality.
                    var type = schema.GetColumnType(index);
                    if (type.KeyCount > 0)
                    {
                        stratificationColumn = group;
                    }
                }
            }

            if (string.IsNullOrEmpty(stratificationColumn))
            {
                stratificationColumn = "StratificationColumn";
                int tmp;
                int inc = 0;
                while (input.Schema.TryGetColumnIndex(stratificationColumn, out tmp))
                {
                    stratificationColumn = string.Format("StratificationColumn_{0:000}", ++inc);
                }
                var keyGenArgs = new GenerateNumberTransform.Arguments();
                var col        = new GenerateNumberTransform.Column();
                col.Name          = stratificationColumn;
                keyGenArgs.Column = new[] { col };
                output            = new GenerateNumberTransform(Host, keyGenArgs, input);
            }
            else
            {
                int col;
                if (!input.Schema.TryGetColumnIndex(stratificationColumn, out col))
                {
                    throw ch.ExceptUserArg(nameof(Arguments.StratificationColumn), "Column '{0}' does not exist", stratificationColumn);
                }
                var type = input.Schema.GetColumnType(col);
                if (!RangeFilter.IsValidRangeFilterColumnType(ch, type))
                {
                    ch.Info("Hashing the stratification column");
                    var origStratCol = stratificationColumn;
                    int tmp;
                    int inc = 0;
                    while (input.Schema.TryGetColumnIndex(stratificationColumn, out tmp))
                    {
                        stratificationColumn = string.Format("{0}_{1:000}", origStratCol, ++inc);
                    }
                    output = new HashingEstimator(Host, origStratCol, stratificationColumn, 30).Fit(input).Transform(input);
                }
            }

            return(stratificationColumn);
        }
Esempio n. 5
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        private void RunCore(IChannel ch, string cmd)
        {
            Host.AssertValue(ch);

            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");
            IDataLoader loader = CreateRawLoader();

            // If the per-instance results are requested and there is no name column, add a GenerateNumberTransform.
            var preXf = Args.PreTransform;

            if (!string.IsNullOrEmpty(Args.OutputDataFile))
            {
                string name = TrainUtils.MatchNameOrDefaultOrNull(ch, loader.Schema, nameof(Args.NameColumn), Args.NameColumn, DefaultColumnNames.Name);
                if (name == null)
                {
                    preXf = preXf.Concat(
                        new[]
                    {
                        new KeyValuePair <string, IComponentFactory <IDataView, IDataTransform> >(
                            "", ComponentFactoryUtils.CreateFromFunction <IDataView, IDataTransform>(
                                (env, input) =>
                        {
                            var args    = new GenerateNumberTransform.Arguments();
                            args.Column = new[] { new GenerateNumberTransform.Column()
                                                  {
                                                      Name = DefaultColumnNames.Name
                                                  }, };
                            args.UseCounter = true;
                            return(new GenerateNumberTransform(env, args, input));
                        }))
                    }).ToArray();
                }
            }
            loader = CompositeDataLoader.Create(Host, loader, preXf);

            ch.Trace("Binding label and features columns");

            IDataView pipe = loader;
            var       stratificationColumn = GetSplitColumn(ch, loader, ref pipe);
            var       scorer    = Args.Scorer;
            var       evaluator = Args.Evaluator;

            Func <IDataView> validDataCreator = null;

            if (Args.ValidationFile != null)
            {
                validDataCreator =
                    () =>
                {
                    // Fork the command.
                    var impl = new CrossValidationCommand(this);
                    return(impl.CreateRawLoader(dataFile: Args.ValidationFile));
                };
            }

            FoldHelper fold = new FoldHelper(Host, RegistrationName, pipe, stratificationColumn,
                                             Args, CreateRoleMappedData, ApplyAllTransformsToData, scorer, evaluator,
                                             validDataCreator, ApplyAllTransformsToData, inputPredictor, cmd, loader, !string.IsNullOrEmpty(Args.OutputDataFile));
            var tasks = fold.GetCrossValidationTasks();

            var eval = evaluator?.CreateComponent(Host) ??
                       EvaluateUtils.GetEvaluator(Host, tasks[0].Result.ScoreSchema);

            // Print confusion matrix and fold results for each fold.
            for (int i = 0; i < tasks.Length; i++)
            {
                var dict = tasks[i].Result.Metrics;
                MetricWriter.PrintWarnings(ch, dict);
                eval.PrintFoldResults(ch, dict);
            }

            // Print the overall results.
            if (!TryGetOverallMetrics(tasks.Select(t => t.Result.Metrics).ToArray(), out var overallList))
            {
                throw ch.Except("No overall metrics found");
            }

            var overall = eval.GetOverallResults(overallList.ToArray());

            MetricWriter.PrintOverallMetrics(Host, ch, Args.SummaryFilename, overall, Args.NumFolds);
            eval.PrintAdditionalMetrics(ch, tasks.Select(t => t.Result.Metrics).ToArray());
            Dictionary <string, IDataView>[] metricValues = tasks.Select(t => t.Result.Metrics).ToArray();
            SendTelemetryMetric(metricValues);

            // Save the per-instance results.
            if (!string.IsNullOrWhiteSpace(Args.OutputDataFile))
            {
                var perInstance = EvaluateUtils.ConcatenatePerInstanceDataViews(Host, eval, Args.CollateMetrics,
                                                                                Args.OutputExampleFoldIndex, tasks.Select(t => t.Result.PerInstanceResults).ToArray(), out var variableSizeVectorColumnNames);
                if (variableSizeVectorColumnNames.Length > 0)
                {
                    ch.Warning("Detected columns of variable length: {0}. Consider setting collateMetrics- for meaningful per-Folds results.",
                               string.Join(", ", variableSizeVectorColumnNames));
                }
                if (Args.CollateMetrics)
                {
                    ch.Assert(perInstance.Length == 1);
                    MetricWriter.SavePerInstance(Host, ch, Args.OutputDataFile, perInstance[0]);
                }
                else
                {
                    int i = 0;
                    foreach (var idv in perInstance)
                    {
                        MetricWriter.SavePerInstance(Host, ch, ConstructPerFoldName(Args.OutputDataFile, i), idv);
                        i++;
                    }
                }
            }
        }
Esempio n. 6
0
        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");
            ISchema 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 evalComp = Args.Evaluator;

            if (!evalComp.IsGood())
            {
                evalComp = EvaluateUtils.GetEvaluatorType(ch, scorePipe.Schema);
            }
            var evaluator = evalComp.CreateInstance(Host);
            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);
            }
        }