42 references to Append
Microsoft.ML.AutoML (26)
API\AutoCatalog.cs (9)
403return new SweepablePipeline().Append(res.ToArray()); 526return new SweepablePipeline().Append(res.ToArray()); 618return new SweepablePipeline().Append(res.ToArray()); 694return new SweepablePipeline().Append(new SweepableEstimator[] { SweepableEstimatorFactory.CreateOneHotEncoding(option), SweepableEstimatorFactory.CreateOneHotHashEncoding(option) }); 837pipeline = pipeline.Append(this.NumericFeaturizer(numericFeatureColumnNames, numericFeatureColumnNames)); 842pipeline = pipeline.Append(this.BooleanFeaturizer(booleanFeatureColumnNames, booleanFeatureColumnNames)); 847pipeline = pipeline.Append(this.CatalogFeaturizer(catalogFeatureColumnNames, catalogFeatureColumnNames)); 852pipeline = pipeline.Append(this.ImagePathFeaturizer(imagePathColumn, imagePathColumn)); 857pipeline = pipeline.Append(this.TextFeaturizer(textColumn, textColumn));
API\BinaryClassificationExperiment.cs (2)
335.Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features)); 340.Append(Context.Auto().BinaryClassification(labelColumnName: columnInformation.LabelColumnName, useSdcaLogisticRegression: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsLogisticRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
API\MulticlassClassificationExperiment.cs (2)
331pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features)); 333pipeline = pipeline.Append(Context.Auto().MultiClassification(label, useSdcaMaximumEntrophy: useSdcaMaximumEntrophy, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsMaximumEntrophy: uselbfgsME, useLbfgsLogisticRegression: uselbfgsLR, useFastForest: useFastForest, featureColumnName: Features));
API\RegressionExperiment.cs (2)
312pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features)); 313pipeline = pipeline.Append(Context.Auto().Regression(label, useSdca: useSdca, useFastTree: useFastTree, useLgbm: useLgbm, useLbfgsPoissonRegression: uselbfgs, useFastForest: useFastForest, featureColumnName: Features));
API\SweepableExtension.cs (11)
11return new SweepablePipeline().Append(estimator).Append(estimator1); 16return pipeline.Append(new SweepableEstimator((context, parameter) => estimator, new SearchSpace.SearchSpace())); 21return new SweepablePipeline().Append(estimator).Append(estimator1); 26return new SweepablePipeline().Append(estimator).Append(estimator1); 32var res = new SweepablePipeline().Append(sweepableEstimator).Append(pipeline); 40pipeline = pipeline.Append(estimator); 42return pipeline.Append(estimators); 48var pipeline = new SweepablePipeline().Append(sweepableEstimator).Append(estimators);
Microsoft.ML.AutoML.Samples (1)
Cifar10.cs (1)
39.Append(context.Auto().MultiClassification());
Microsoft.ML.AutoML.Tests (15)
AutoMLExperimentTests.cs (8)
223.Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdcaLogisticRegression: false, useLbfgsLogisticRegression: false)); 248.Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", exampleWeightColumnName: "signedWeight", useLgbm: false, useSdcaLogisticRegression: false, useLbfgsLogisticRegression: false)); 273.Append(context.Auto().BinaryClassification(DatasetUtil.UciAdultLabel, "_Features_", useLgbm: false, useSdcaLogisticRegression: false, useLbfgsLogisticRegression: false)); 300.Append(context.Auto().MultiClassification(label, useLgbm: false, useSdcaMaximumEntrophy: false, useLbfgsMaximumEntrophy: false)); 327.Append(context.Auto().MultiClassification(label, useLgbm: false, useSdcaMaximumEntrophy: false, useLbfgsMaximumEntrophy: false)); 354.Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgsPoissonRegression: false)); 384.Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgsPoissonRegression: false)); 403.Append(context.Auto().Regression(label, useLgbm: false, useSdca: false, useLbfgsPoissonRegression: false));
SweepableEstimatorPipelineTest.cs (1)
51pipeline = pipeline.Append(e1).Append(e2);
SweepableExtensionTest.cs (2)
135.Append(context.Auto().MultiClassification()); 149.Append(context.Auto().MultiClassification().Estimators.Select(kv => kv.Value).ToArray());
SweepablePipelineTests.cs (4)
59pipeline = pipeline.Append(SweepableEstimatorFactory.CreateConcatenate(concatOption)); 62pipeline = pipeline.Append(SweepableEstimatorFactory.CreateLightGbmBinary(lgbmOption), SweepableEstimatorFactory.CreateConcatenate(concatOption)); 65pipeline = pipeline.Append(SweepableEstimatorFactory.CreateConcatenate(concatOption), pipeline); 68pipeline = pipeline.Append(pipeline, pipeline);