diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs index c49a0f35dc..a9fa08a4dc 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/BootstrapSample.cs @@ -1,7 +1,8 @@ using System; using System.Collections.Generic; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class BootstrapSample { @@ -12,7 +13,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and them read it as ML.NET's data type. - IEnumerable enumerableOfData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5); + IEnumerable enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(5); var data = mlContext.Data.LoadFromEnumerable(enumerableOfData); // Look at the original dataset @@ -43,7 +44,7 @@ public static void Example() { var resample = mlContext.Data.BootstrapSample(data, seed: i); - var enumerable = mlContext.Data.CreateEnumerable(resample, reuseRowObject: false); + var enumerable = mlContext.Data.CreateEnumerable(resample, reuseRowObject: false); Console.WriteLine($"Label\tFeatures[0]"); foreach (var row in enumerable) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs index acc47e8cb0..2519ca86f7 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/Cache.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class Cache { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs index 4ae6b103a8..eff2cd6638 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/CrossValidationSplit.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using static Microsoft.ML.DataOperationsCatalog; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { /// /// Sample class showing how to use CrossValidationSplit. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs index 65864bc5f6..2d25bf0c73 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/DataViewEnumerable.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { /// /// Sample class showing how to use ShuffleRows. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs index 40a801dda5..2be2983e01 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByColumn.cs @@ -1,7 +1,8 @@ using System; using System.Collections.Generic; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { /// /// Sample class showing how to use FilterRowsByColumn. @@ -15,7 +16,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable. - IEnumerable enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); + IEnumerable enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); var data = mlContext.Data.LoadFromEnumerable(enumerableOfData); // Before we apply a filter, examine all the records in the dataset. @@ -42,7 +43,7 @@ public static void Example() var filteredData = mlContext.Data.FilterRowsByColumn(data, columnName: "Temperature", lowerBound: 34, upperBound: 37); // Look at the filtered data and observe that values outside [34,37) have been dropped. - var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); + var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); Console.WriteLine($"Date\tTemperature"); foreach (var row in enumerable) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs index 1a418cb8f6..0766e77f12 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByKeyColumnFraction.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { using MulticlassClassificationExample = DatasetUtils.MulticlassClassificationExample; diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs index f1f9c4a21d..a624928a96 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/FilterRowsByMissingValues.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public class FilterRowsByMissingValues { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs index 6e88e89cd2..14baddaa6c 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/ShuffleRows.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { /// /// Sample class showing how to use ShuffleRows. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs index 194fff12d1..2e38b77c88 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/SkipRows.cs @@ -1,5 +1,7 @@ using System; -namespace Microsoft.ML.Samples.Dynamic +using Microsoft.ML; + +namespace Samples.Dynamic { /// /// Sample class showing how to use Skip. @@ -13,7 +15,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable. - var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); + var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); var data = mlContext.Data.LoadFromEnumerable(enumerableOfData); // Before we apply a filter, examine all the records in the dataset. @@ -40,7 +42,7 @@ public static void Example() var filteredData = mlContext.Data.SkipRows(data, 5); // Look at the filtered data and observe that the first 5 rows have been dropped - var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); + var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); Console.WriteLine($"Date\tTemperature"); foreach (var row in enumerable) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs index 24b69de0a8..9653a67904 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TakeRows.cs @@ -1,5 +1,7 @@ using System; -namespace Microsoft.ML.Samples.Dynamic +using Microsoft.ML; + +namespace Samples.Dynamic { /// /// Sample class showing how to use Take. @@ -13,7 +15,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable. - var enumerableOfData = SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); + var enumerableOfData = Microsoft.ML.SamplesUtils.DatasetUtils.GetSampleTemperatureData(10); var data = mlContext.Data.LoadFromEnumerable(enumerableOfData); // Before we apply a filter, examine all the records in the dataset. @@ -40,7 +42,7 @@ public static void Example() var filteredData = mlContext.Data.TakeRows(data, 5); // Look at the filtered data and observe that only the first 5 rows are in the resulting dataset. - var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); + var enumerable = mlContext.Data.CreateEnumerable(filteredData, reuseRowObject: true); Console.WriteLine($"Date\tTemperature"); foreach (var row in enumerable) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs index eb3bdd5a5a..d02651c006 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/DataOperations/TrainTestSplit.cs @@ -1,11 +1,9 @@ using System; using System.Collections.Generic; -using System.Collections.Immutable; -using System.Linq; -using Microsoft.ML.Data; +using Microsoft.ML; using static Microsoft.ML.DataOperationsCatalog; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { /// /// Sample class showing how to use TrainTestSplit. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs index 1101de6d51..d58a0c71e6 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/FeatureContributionCalculationTransform.cs @@ -1,9 +1,10 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class FeatureContributionCalculationTransform { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs index 405e090b6a..c1124c8425 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/NgramExtraction.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static partial class TransformSamples { @@ -13,7 +14,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert to IDataView. - IEnumerable data = SamplesUtils.DatasetUtils.GetSentimentData(); + IEnumerable data = Microsoft.ML.SamplesUtils.DatasetUtils.GetSentimentData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs index cf94245aba..dc92f50e40 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class NormalizerTransform { @@ -13,7 +14,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert it to an IDataView. - IEnumerable data = SamplesUtils.DatasetUtils.GetInfertData(); + IEnumerable data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs index f8b7ba401c..62a7802ec1 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/OnnxTransform.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.OnnxRuntime; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class OnnxTransformExample { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs index 95c64e629c..6cf402bb6f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs @@ -2,8 +2,9 @@ using System.Linq; using Microsoft.ML.Trainers; using Microsoft.ML.SamplesUtils; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance +namespace Samples.Dynamic.PermutationFeatureImportance { public static class PfiHelper { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs index 46b5bc65a6..3b7f3d112b 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs @@ -1,7 +1,8 @@ using System; using System.Linq; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance +namespace Samples.Dynamic.PermutationFeatureImportance { public static class PfiRegression { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs index 8e109890e1..b3b646c35f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs @@ -1,8 +1,9 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance +namespace Samples.Dynamic.PermutationFeatureImportance { public static class PfiBinaryClassification { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs index 67d4680796..8fb934bf5c 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class ProjectionTransforms { @@ -14,7 +15,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert it to an IDataView. - IEnumerable data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); + IEnumerable data = Microsoft.ML.SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. @@ -37,13 +38,13 @@ public static void Example() }; // A pipeline to project Features column into Random fourier space. - var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4); + var rffPipeline = ml.Transforms.ApproximatedKernelMap(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), rank: 4); // The transformed (projected) data. var transformedData = rffPipeline.Fit(trainData).Transform(trainData); // Getting the data of the newly created column, so we can preview it. - var randomFourier = transformedData.GetColumn>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); + var randomFourier = transformedData.GetColumn>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); - printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier); + printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier); // Features column obtained post-transformation. // @@ -55,13 +56,15 @@ public static void Example() //0.165 0.117 -0.547 0.014 // A pipeline to project Features column into L-p normalized vector. - var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), norm: Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1); + var lpNormalizePipeline = ml.Transforms.NormalizeLpNorm(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), + norm: Microsoft.ML.Transforms.LpNormNormalizingEstimatorBase.NormFunction.L1); + // The transformed (projected) data. transformedData = lpNormalizePipeline.Fit(trainData).Transform(trainData); // Getting the data of the newly created column, so we can preview it. - var lpNormalize= transformedData.GetColumn>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); + var lpNormalize= transformedData.GetColumn>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); - printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize); + printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize); // Features column obtained post-transformation. // @@ -73,13 +76,13 @@ public static void Example() // 0.133 0.156 0.178 0.200 0.000 0.022 0.044 0.067 0.089 0.111 // A pipeline to project Features column into L-p normalized vector. - var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false); + var gcNormalizePipeline = ml.Transforms.NormalizeGlobalContrast(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false); // The transformed (projected) data. transformedData = gcNormalizePipeline.Fit(trainData).Transform(trainData); // Getting the data of the newly created column, so we can preview it. - var gcNormalize = transformedData.GetColumn>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); + var gcNormalize = transformedData.GetColumn>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); - printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize); + printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize); // Features column obtained post-transformation. // diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs index d97d7391a6..3fa852fb44 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/ImageClassification.cs @@ -4,9 +4,10 @@ using System.Net; using ICSharpCode.SharpZipLib.GZip; using ICSharpCode.SharpZipLib.Tar; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class ImageClassification { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs index efc428a731..1fa1f9541a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs @@ -1,8 +1,9 @@ using System; using System.IO; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class TextClassification { @@ -12,7 +13,7 @@ public static class TextClassification /// public static void Example() { - string modelLocation = SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel(); + string modelLocation = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadTensorFlowSentimentModel(); var mlContext = new MLContext(); var data = new[] { new IMDBSentiment() { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs index 06f7a34193..6b8c507b07 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/TextTransform.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Transforms.Text; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class TextTransform { @@ -14,7 +15,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert to IDataView. - var data = SamplesUtils.DatasetUtils.GetSentimentData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetSentimentData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs index a42c760da0..62287c4ba1 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSample.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.AnomalyDetection +namespace Samples.Dynamic.Trainers.AnomalyDetection { public static class RandomizedPcaSample { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs index f9160570c9..02b725f7ce 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/AnomalyDetection/RandomizedPcaSampleWithOptions.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.AnomalyDetection +namespace Samples.Dynamic.Trainers.AnomalyDetection { public static class RandomizedPcaSampleWithOptions { @@ -28,7 +29,7 @@ public static void Example() // Convert the List to IDataView, a consumble format to ML.NET functions. var data = mlContext.Data.LoadFromEnumerable(samples); - var options = new ML.Trainers.RandomizedPcaTrainer.Options() + var options = new Microsoft.ML.Trainers.RandomizedPcaTrainer.Options() { FeatureColumnName = nameof(DataPoint.Features), Rank = 1, diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs index b0dd1613fb..1a9635a3ad 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class AveragedPerceptron { @@ -13,7 +15,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -27,7 +29,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.86 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs index b34926f658..4165b7943a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Trainers; +using Microsoft.ML; +using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class AveragedPerceptronWithOptions { @@ -15,7 +16,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -39,7 +40,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.86 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/BinaryClassification.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/BinaryClassification.ttinclude index 50555baf04..3a9e7633c5 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/BinaryClassification.ttinclude +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/BinaryClassification.ttinclude @@ -1,12 +1,13 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; <# if (TrainerOptions != null) { #> <#=OptionsInclude#> <# } #> -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class <#=ClassName#> {<#=Comments#> diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/FixedPlatt.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/FixedPlatt.cs index 52fe41cc4b..75b25dcb86 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/FixedPlatt.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/FixedPlatt.cs @@ -1,7 +1,8 @@ using System; using System.Linq; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification.Calibrators +namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators { public static class FixedPlatt { @@ -13,7 +14,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.3); @@ -53,11 +54,11 @@ public static void Example() // Score 5.36571 Probability 0.9955735 } - private static void PrintRowViewValues(Data.DataDebuggerPreview data) + private static void PrintRowViewValues(Microsoft.ML.Data.DataDebuggerPreview data) { var firstRows = data.RowView.Take(5); - foreach (Data.DataDebuggerPreview.RowInfo row in firstRows) + foreach (Microsoft.ML.Data.DataDebuggerPreview.RowInfo row in firstRows) { foreach (var kvPair in row.Values) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Isotonic.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Isotonic.cs index 9c856d1455..1bddd25d94 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Isotonic.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Isotonic.cs @@ -1,7 +1,8 @@ using System; using System.Linq; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification.Calibrators +namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators { public static class Isotonic { @@ -13,7 +14,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.3); @@ -53,11 +54,11 @@ public static void Example() // Score 5.36571 Probability 0.8958333 } - private static void PrintRowViewValues(Data.DataDebuggerPreview data) + private static void PrintRowViewValues(Microsoft.ML.Data.DataDebuggerPreview data) { var firstRows = data.RowView.Take(5); - foreach (Data.DataDebuggerPreview.RowInfo row in firstRows) + foreach (Microsoft.ML.Data.DataDebuggerPreview.RowInfo row in firstRows) { foreach (var kvPair in row.Values) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Naive.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Naive.cs index edb38b5cc5..81f23a0974 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Naive.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Naive.cs @@ -1,7 +1,8 @@ using System; using System.Linq; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification.Calibrators +namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators { public static class Naive { @@ -13,7 +14,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.3); @@ -53,11 +54,11 @@ public static void Example() // Score 5.36571 Probability 0.9117647 } - private static void PrintRowViewValues(Data.DataDebuggerPreview data) + private static void PrintRowViewValues(Microsoft.ML.Data.DataDebuggerPreview data) { var firstRows = data.RowView.Take(5); - foreach (Data.DataDebuggerPreview.RowInfo row in firstRows) + foreach (Microsoft.ML.Data.DataDebuggerPreview.RowInfo row in firstRows) { foreach (var kvPair in row.Values) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Platt.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Platt.cs index 12ff762d14..f78f61de22 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Platt.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/Calibrators/Platt.cs @@ -1,7 +1,8 @@ using System; using System.Linq; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification.Calibrators +namespace Samples.Dynamic.Trainers.BinaryClassification.Calibrators { public static class Platt { @@ -13,7 +14,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.3); @@ -53,11 +54,11 @@ public static void Example() // Score 5.36571 Probability 0.9065308 } - private static void PrintRowViewValues(Data.DataDebuggerPreview data) + private static void PrintRowViewValues(Microsoft.ML.Data.DataDebuggerPreview data) { var firstRows = data.RowView.Take(5); - foreach (Data.DataDebuggerPreview.RowInfo row in firstRows) + foreach (Microsoft.ML.Data.DataDebuggerPreview.RowInfo row in firstRows) { foreach (var kvPair in row.Values) { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForest.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForest.cs index 587499997d..f280209107 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForest.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForest.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class FastForest { @@ -50,7 +51,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.74 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForestWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForestWithOptions.cs index d243c54c69..dc7152ebd4 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForestWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastForestWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class FastForestWithOptions { @@ -62,7 +63,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.73 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTree.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTree.cs index eae52f11ab..d5308822dd 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTree.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTree.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class FastTree { @@ -50,7 +51,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.81 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTreeWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTreeWithOptions.cs index 26493f0b12..0b33ac7277 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTreeWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FastTreeWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class FastTreeWithOptions { @@ -62,7 +63,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.78 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs index 8c87c899a2..839feed672 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs @@ -1,8 +1,9 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class FFMBinaryClassification { @@ -13,7 +14,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataviews = SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); + var dataviews = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); var trainData = dataviews[0]; var testData = dataviews[1]; @@ -61,7 +62,7 @@ public static void Example() var dataWithPredictions = model.Transform(testData); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, "Sentiment"); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Accuracy: 0.72 // AUC: 0.75 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithoutArguments.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithoutArguments.cs index 1f4d6bd5be..45bfcc1550 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithoutArguments.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithoutArguments.cs @@ -1,8 +1,8 @@ using System; using System.Linq; -using Microsoft.ML.Data; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class FFMBinaryClassificationWithoutArguments { @@ -13,7 +13,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataviews = SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); + var dataviews = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); var trainData = dataviews[0]; var testData = dataviews[1]; @@ -62,7 +62,7 @@ public static void Example() var dataWithPredictions = model.Transform(testData); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, "Sentiment"); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.61 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs index 5e7c2ae3ef..ab63c51441 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class FFMBinaryClassificationWithOptions { @@ -14,7 +15,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataviews = SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); + var dataviews = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedSentimentDataset(mlContext); var trainData = dataviews[0]; var testData = dataviews[1]; @@ -69,7 +70,7 @@ public static void Example() var dataWithPredictions = model.Transform(testData); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions, "Sentiment"); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Accuracy: 0.78 // AUC: 0.81 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegression.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegression.cs index 7ce8b9fb27..2d5dd25b78 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegression.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegression.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class LbfgsLogisticRegression { @@ -48,7 +49,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.88 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegressionWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegressionWithOptions.cs index a0308d61f7..0d8dc44282 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegressionWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LbfgsLogisticRegressionWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class LbfgsLogisticRegressionWithOptions { @@ -57,7 +58,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.BinaryClassification.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.87 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbm.cs index 64498940d9..574b226df2 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbm.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbm.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.BinaryClassification { public class LightGbm { @@ -9,7 +11,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataview = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var dataview = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1); @@ -24,7 +26,7 @@ public static void Example() var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.88 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbmWithOptions.cs index 41f85c327c..e38011dcba 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbmWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbmWithOptions.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Trainers.LightGbm; +using Microsoft.ML; +using Microsoft.ML.Trainers.LightGbm; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { class LightGbmWithOptions { @@ -11,7 +12,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataview = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var dataview = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var split = mlContext.Data.TrainTestSplit(dataview, testFraction: 0.1); @@ -34,7 +35,7 @@ public static void Example() var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.88 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/PriorTrainerSample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/PriorTrainerSample.cs index 08819c8a01..3b81b87cd6 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/PriorTrainerSample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/PriorTrainerSample.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Data; +using Microsoft.ML; +using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public class PriorTrainer { @@ -11,7 +12,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataFiles = SamplesUtils.DatasetUtils.DownloadSentimentDataset(); + var dataFiles = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadSentimentDataset(); var trainFile = dataFiles[0]; var testFile = dataFiles[1]; @@ -48,7 +49,7 @@ public static void Example() // Step 4: Evaluate on the test set var transformedData = trainedPipeline.Transform(loader.Load(testFile)); var evalMetrics = mlContext.BinaryClassification.Evaluate(transformedData, labelColumnName: "Sentiment"); - SamplesUtils.ConsoleUtils.PrintMetrics(evalMetrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(evalMetrics); // The Prior trainer outputs the proportion of a label in the dataset as the probability of that label. // In this case 'Accuracy: 0.50' means that there is a split of around 50%-50% of positive and negative labels in the test dataset. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs index 5210996cd2..2d82383924 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscent.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class StochasticDualCoordinateAscent { @@ -12,7 +13,7 @@ public static void Example() // Downloading the dataset from github.com/dotnet/machinelearning. // This will create a sentiment.tsv file in the filesystem. // You can open this file, if you want to see the data. - string dataFile = SamplesUtils.DatasetUtils.DownloadSentimentDataset()[0]; + string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadSentimentDataset()[0]; // A preview of the data. // Sentiment SentimentText diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentNonCalibrated.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentNonCalibrated.cs index b73f9e6867..db5b697623 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentNonCalibrated.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentNonCalibrated.cs @@ -1,15 +1,16 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class StochasticDualCoordinateAscentNonCalibrated { public static void Example() { // Generate IEnumerable as training examples. - var rawData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(100); + var rawData = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorFloatWeightSamples(100); // Information in first example. // Label: true @@ -49,7 +50,7 @@ public static void Example() // Step 4: Make prediction and evaluate its quality (on training set). var prediction = model.Transform(data); - var rawPrediction = mlContext.Data.CreateEnumerable(prediction, false); + var rawPrediction = mlContext.Data.CreateEnumerable(prediction, false); // Step 5: Inspect the prediction of the first example. // Note that positive/negative label may be associated with positive/negative score diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs index c08e9c56fa..2f1bbbb8a1 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs @@ -1,7 +1,7 @@ using Microsoft.ML; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class StochasticDualCoordinateAscentWithOptions { @@ -16,7 +16,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -41,7 +41,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescent.cs index b0e6f1b6c7..e3423c61d2 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescent.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class StochasticGradientDescent { @@ -13,7 +15,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -27,7 +29,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibrated.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibrated.cs index 2d3dd293df..e28921b75b 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibrated.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibrated.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic +using Microsoft.ML; + +namespace Samples.Dynamic { public static class StochasticGradientDescentNonCalibrated { @@ -13,7 +15,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -27,7 +29,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibratedWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibratedWithOptions.cs index 826f0a6bc7..9ae4269b51 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibratedWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentNonCalibratedWithOptions.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Trainers; +using Microsoft.ML; +using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class StochasticGradientDescentNonCalibratedWithOptions { @@ -15,7 +16,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -37,7 +38,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentWithOptions.cs index f03f620bde..891c17576f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentWithOptions.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Trainers; +using Microsoft.ML; +using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class StochasticGradientDescentWithOptions { @@ -15,7 +16,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -40,7 +41,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(trainTestData.TestSet); var metrics = mlContext.BinaryClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs index 8d35c621a1..3a9a842206 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescent.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class SymbolicStochasticGradientDescent { @@ -14,7 +16,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); @@ -25,7 +27,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.85 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs index bb6c74bebb..234d075551 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.BinaryClassification { public static class SymbolicStochasticGradientDescentWithOptions { @@ -14,13 +16,13 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Download and featurize the dataset. - var data = SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext); // Leave out 10% of data for testing. var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.1); // Create data training pipeline var pipeline = mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression( - new ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options() + new Microsoft.ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options() { LearningRate = 0.2f, NumberOfIterations = 10, @@ -33,7 +35,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Accuracy: 0.84 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs index fb9c867e95..438a7bb5e7 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public class KMeans { @@ -12,7 +13,7 @@ public static void Example() var ml = new MLContext(seed: 1); // Get a small dataset as an IEnumerable and convert it to an IDataView. - var data = SamplesUtils.DatasetUtils.GetInfertData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs index 4aa171b7c3..88c2652c94 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs @@ -1,8 +1,9 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public class KMeansWithOptions { @@ -13,7 +14,7 @@ public static void Example() var ml = new MLContext(seed: 1); // Get a small dataset as an IEnumerable and convert it to an IDataView. - var data = SamplesUtils.DatasetUtils.GetInfertData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs index 367e50ff47..0528be95e9 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbm.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification +namespace Samples.Dynamic.Trainers.MulticlassClassification { public static class LightGbm { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs index 2b24423e5e..84e206ac99 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LightGbmWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; using Microsoft.ML.Trainers.LightGbm; -namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification +namespace Samples.Dynamic.Trainers.MulticlassClassification { public static class LightGbmWithOptions { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscent.cs index 616d495738..40f7c2223a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscent.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.SamplesUtils; +using Microsoft.ML; +using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification +namespace Samples.Dynamic.Trainers.MulticlassClassification { public static class StochasticDualCoordinateAscent { @@ -43,7 +44,7 @@ public static void Example() // Evaluate the trained model using the test set. var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Micro Accuracy: 0.82 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs index 10bc9c7918..f1ca1b4f56 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs @@ -1,7 +1,8 @@ -using Microsoft.ML.SamplesUtils; +using Microsoft.ML; +using Microsoft.ML.SamplesUtils; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.MulticlassClassification +namespace Samples.Dynamic.Trainers.MulticlassClassification { public static class StochasticDualCoordinateAscentWithOptions { @@ -54,7 +55,7 @@ public static void Example() // Evaluate the trained model using the test set. var metrics = mlContext.MulticlassClassification.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Micro Accuracy: 0.82 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs index 76cabc36ee..9afc6872fa 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbm.cs @@ -1,4 +1,6 @@ -namespace Microsoft.ML.Samples.Dynamic.Trainers.Ranking +using Microsoft.ML; + +namespace Samples.Dynamic.Trainers.Ranking { public class LightGbm { @@ -9,7 +11,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the dataset. - var dataview = SamplesUtils.DatasetUtils.LoadFeaturizedMslrWeb10kDataset(mlContext); + var dataview = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedMslrWeb10kDataset(mlContext); // Leave out 10% of the dataset for testing. Since this is a ranking problem, we must ensure that the split // respects the GroupId column, i.e. rows with the same GroupId are either all in the train split or all in @@ -30,7 +32,7 @@ public static void Example() var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.Ranking.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // DCG: @1:1.71, @2:3.88, @3:7.93 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs index e31eeeac0c..312d9e7e56 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Ranking/LightGbmWithOptions.cs @@ -1,6 +1,7 @@ -using Microsoft.ML.Trainers.LightGbm; +using Microsoft.ML; +using Microsoft.ML.Trainers.LightGbm; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Ranking +namespace Samples.Dynamic.Trainers.Ranking { public class LightGbmWithOptions { @@ -11,7 +12,7 @@ public static void Example() var mlContext = new MLContext(); // Download and featurize the train and validation datasets. - var dataview = SamplesUtils.DatasetUtils.LoadFeaturizedMslrWeb10kDataset(mlContext); + var dataview = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedMslrWeb10kDataset(mlContext); // Leave out 10% of the dataset for testing. Since this is a ranking problem, we must ensure that the split // respects the GroupId column, i.e. rows with the same GroupId are either all in the train split or all in @@ -40,7 +41,7 @@ public static void Example() var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.Ranking.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // DCG: @1:1.71, @2:3.88, @3:7.93 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs index 05b1b1553d..1ec86a25c1 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using static Microsoft.ML.SamplesUtils.DatasetUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Recommendation +namespace Samples.Dynamic.Trainers.Recommendation { public static class MatrixFactorization { @@ -38,7 +39,7 @@ public static void Example() var metrics = mlContext.Recommendation().Evaluate(prediction, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElementForScore.Score)); // Print out some metrics for checking the model's quality. - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // L1: 0.17 // L2: 0.05 // LossFunction: 0.05 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs index d48f231015..d91032df4f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationWithOptions.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Trainers; using static Microsoft.ML.SamplesUtils.DatasetUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Recommendation +namespace Samples.Dynamic.Trainers.Recommendation { public static class MatrixFactorizationWithOptions { @@ -48,7 +49,7 @@ public static void Example() var metrics = mlContext.Recommendation().Evaluate(prediction, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElementForScore.Score)); // Print out some metrics for checking the model's quality. - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // L1: 0.16 // L2: 0.04 // LossFunction: 0.04 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs index 7263aef771..172a24e874 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastForest { @@ -50,7 +51,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.06 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs index 4629d882e1..e09bbdf9fd 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastForestWithOptions { @@ -62,7 +63,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.06 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs index 082bc340f3..51e9ec2e3a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastTree { @@ -50,7 +51,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.05 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.cs index ead29a678f..7fcfb974c6 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedie.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastTreeTweedie { @@ -50,7 +51,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.05 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedieWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedieWithOptions.cs index dd75a9c4f4..7f582e88c4 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedieWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeTweedieWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastTreeTweedieWithOptions { @@ -62,7 +63,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.05 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeWithOptions.cs index 594c80868c..4efd915ed5 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTreeWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class FastTreeWithOptions { @@ -62,7 +63,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.05 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/Gam.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/Gam.cs index 67e57697f2..dd8107452a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/Gam.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/Gam.cs @@ -1,8 +1,9 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class Gam { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/GamWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/GamWithOptions.cs index 6545e27022..33617b2d94 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/GamWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/GamWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class GamWithOptions { @@ -60,7 +61,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.06 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegression.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegression.cs index c9f3d83faa..dafb2a3d7c 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegression.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegression.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class LbfgsPoissonRegression { @@ -48,7 +49,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.07 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegressionWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegressionWithOptions.cs index d9fa086dd0..0cb3c2554f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegressionWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LbfgsPoissonRegressionWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class LbfgsPoissonRegressionWithOptions { @@ -60,7 +61,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // Mean Absolute Error: 0.07 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbm.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbm.cs index c30d980650..f721aed215 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbm.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbm.cs @@ -1,8 +1,9 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { class LightGbm { @@ -14,7 +15,7 @@ public static void Example() var mlContext = new MLContext(); // Download and load the housing dataset into an IDataView. - var dataView = SamplesUtils.DatasetUtils.LoadHousingRegressionDataset(mlContext); + var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadHousingRegressionDataset(mlContext); //////////////////// Data Preview //////////////////// /// Only 6 columns are displayed here. @@ -52,7 +53,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.Regression.Evaluate(dataWithPredictions, labelColumnName: labelName); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output // L1: 4.97 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbmWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbmWithOptions.cs index f6d3eeb1f9..05cf8386b1 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbmWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/LightGbmWithOptions.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers.LightGbm; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { class LightGbmWithOptions { @@ -15,7 +16,7 @@ public static void Example() var mlContext = new MLContext(); // Download and load the housing dataset into an IDataView. - var dataView = SamplesUtils.DatasetUtils.LoadHousingRegressionDataset(mlContext); + var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadHousingRegressionDataset(mlContext); //////////////////// Data Preview //////////////////// /// Only 6 columns are displayed here. @@ -61,7 +62,7 @@ public static void Example() // Evaluate how the model is doing on the test data. var dataWithPredictions = model.Transform(split.TestSet); var metrics = mlContext.Regression.Evaluate(dataWithPredictions, labelColumnName: labelName); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output // L1: 4.97 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescent.cs index 1b7110f13e..42a2e2ef93 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescent.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class OnlineGradientDescent { @@ -43,7 +44,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // TODO #2425: OGD is missing baseline tests and seems numerically unstable } diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescentWithOptions.cs index 1e3d9b1b32..4789b457bb 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OnlineGradientDescentWithOptions.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class OnlineGradientDescentWithOptions { @@ -57,7 +58,7 @@ public static void Example() // Evaluate the overall metrics var metrics = mlContext.Regression.Evaluate(transformedTestData); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // TODO #2425: OGD is missing baseline tests and seems numerically unstable } diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs index 2d43970d17..b1485b7d75 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquares.cs @@ -1,8 +1,9 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class OrdinaryLeastSquares { @@ -12,7 +13,7 @@ public static class OrdinaryLeastSquares public static void Example() { // Downloading a regression dataset from github.com/dotnet/machinelearning - string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); + string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, // as well as the source of randomness. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs index 3d1af5555d..97daa181bc 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs @@ -1,9 +1,10 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.SamplesUtils; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class OrdinaryLeastSquaresWithOptions { @@ -13,7 +14,7 @@ public static class OrdinaryLeastSquaresWithOptions public static void Example() { // Downloading a regression dataset from github.com/dotnet/machinelearning - string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); + string dataFile = DatasetUtils.DownloadHousingRegressionDataset(); // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, // as well as the source of randomness. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/RegressionSamplesTemplate.ttinclude b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/RegressionSamplesTemplate.ttinclude index 10b58238c8..46a2bca015 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/RegressionSamplesTemplate.ttinclude +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/RegressionSamplesTemplate.ttinclude @@ -1,12 +1,13 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; <# if (TrainerOptions != null) { #> using Microsoft.ML.Trainers; <# } #> -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class <#=ClassName#> { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscent.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscent.cs index e674bec00c..54c440cc96 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscent.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscent.cs @@ -1,8 +1,6 @@ -using System; -using System.Linq; -using Microsoft.ML.Data; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class StochasticDualCoordinateAscent { @@ -14,7 +12,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Create in-memory examples as C# native class and convert to IDataView - var data = SamplesUtils.DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(1000); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(1000); var dataView = mlContext.Data.LoadFromEnumerable(data); // Split the data into training and test sets. Only training set is used in fitting @@ -30,7 +28,7 @@ public static void Example() // Evaluate the trained model using the test set. var metrics = mlContext.Regression.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // L1: 0.27 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs index c0110454a4..cfe96f1a4f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs @@ -1,7 +1,7 @@ -using Microsoft.ML.Data; +using Microsoft.ML; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression +namespace Samples.Dynamic.Trainers.Regression { public static class StochasticDualCoordinateAscentWithOptions { @@ -13,7 +13,7 @@ public static void Example() var mlContext = new MLContext(seed: 0); // Create in-memory examples as C# native class and convert to IDataView - var data = SamplesUtils.DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(1000); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GenerateFloatLabelFloatFeatureVectorSamples(1000); var dataView = mlContext.Data.LoadFromEnumerable(data); // Split the data into training and test sets. Only training set is used in fitting @@ -40,7 +40,7 @@ public static void Example() // Evaluate the trained model using the test set. var metrics = mlContext.Regression.Evaluate(dataWithPredictions); - SamplesUtils.ConsoleUtils.PrintMetrics(metrics); + Microsoft.ML.SamplesUtils.ConsoleUtils.PrintMetrics(metrics); // Expected output: // L1: 0.26 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs index 22f6b7e321..e25d1e0d94 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Concatenate.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class ConcatTransform { @@ -12,7 +13,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and them read it as ML.NET's data type. - var data = SamplesUtils.DatasetUtils.GetInfertData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = mlContext.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs index 5296c5bd78..08f9f48fb8 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CopyColumns.cs @@ -1,7 +1,8 @@ using System; using System.Collections.Generic; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class CopyColumns { @@ -12,7 +13,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and them read it as ML.NET's data type. - IEnumerable data = SamplesUtils.DatasetUtils.GetInfertData(); + IEnumerable data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = mlContext.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs index d4b5b5904e..4214451924 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CustomMappingSample.cs @@ -1,5 +1,7 @@ using System; -namespace Microsoft.ML.Samples.Dynamic +using Microsoft.ML; + +namespace Samples.Dynamic { public static class CustomMapping { @@ -10,7 +12,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and convert it to an IDataView. - var data = SamplesUtils.DatasetUtils.GetInfertData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = mlContext.Data.LoadFromEnumerable(data); // Preview of the data. @@ -22,7 +24,7 @@ public static void Example() // 35 4 6-11yrs ... // We define the custom mapping between input and output rows that will be applied by the transformation. - Action mapping = + Action mapping = (input, output) => output.IsUnderThirty = input.Age < 30; // Custom transformations can be used to transform data directly, or as part of a pipeline. Below we transform data directly. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs index bcc812bee3..ff3e2cd5dc 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/DropColumns.cs @@ -1,7 +1,8 @@ using System; using System.Collections.Generic; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class DropColumns { @@ -12,7 +13,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and them read it as ML.NET's data type. - IEnumerable data = SamplesUtils.DatasetUtils.GetInfertData(); + IEnumerable data = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var trainData = mlContext.Data.LoadFromEnumerable(data); // Preview of the data. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs index 15d448deee..9bda79b6d8 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/IndicateMissingValues.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class IndicateMissingValues { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs index 6076d40a39..0f0f1d5370 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public sealed class VectorWhiten { @@ -16,7 +17,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert it to an IDataView. - var data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. @@ -39,14 +40,14 @@ public static void Example() }; // A pipeline to project Features column into white noise vector. - var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), - kind: Transforms.WhiteningKind.ZeroPhaseComponentAnalysis); + var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), + kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis); // The transformed (projected) data. var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData); // Getting the data of the newly created column, so we can preview it. - var whitening = transformedData.GetColumn>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); + var whitening = transformedData.GetColumn>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); - printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening); + printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening); // Features column obtained post-transformation. // diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs index bf314064e1..4349140c62 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public sealed class VectorWhitenWithOptions { @@ -15,7 +16,7 @@ public static void Example() var ml = new MLContext(); // Get a small dataset as an IEnumerable and convert it to an IDataView. - var data = SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); + var data = Microsoft.ML.SamplesUtils.DatasetUtils.GetVectorOfNumbersData(); var trainData = ml.Data.LoadFromEnumerable(data); // Preview of the data. @@ -39,13 +40,13 @@ public static void Example() // A pipeline to project Features column into white noise vector. - var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4); + var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4); // The transformed (projected) data. var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData); // Getting the data of the newly created column, so we can preview it. - var whitening = transformedData.GetColumn>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); + var whitening = transformedData.GetColumn>(transformedData.Schema[nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]); - printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening); + printHelper(nameof(Microsoft.ML.SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening); // Features column obtained post-transformation. // -0.979 0.867 1.449 1.236 diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs index 01fce1ad06..757242acfe 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.Linq; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Transforms; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { class ReplaceMissingValues { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs index 9f365694cc..8747bc45ae 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/SelectColumns.cs @@ -1,5 +1,7 @@ using System; -namespace Microsoft.ML.Samples.Dynamic +using Microsoft.ML; + +namespace Samples.Dynamic { public static class SelectColumns { @@ -10,7 +12,7 @@ public static void Example() var mlContext = new MLContext(); // Get a small dataset as an IEnumerable and them read it as ML.NET's data type. - var enumerableData = SamplesUtils.DatasetUtils.GetInfertData(); + var enumerableData = Microsoft.ML.SamplesUtils.DatasetUtils.GetInfertData(); var data = mlContext.Data.LoadFromEnumerable(enumerableData); // Before transformation, take a look at the dataset diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs index 268c6865e7..0e14dd6f6c 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectChangePointBySsa.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class DetectChangePointBySsa { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs index d19b1be2e1..268c28238a 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidChangePoint.cs @@ -4,9 +4,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class DetectIidChangePoint { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs index d43b581842..862c390ac7 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectIidSpike.cs @@ -1,10 +1,11 @@ using System; using System.Collections.Generic; using System.IO; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Transforms.TimeSeries; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class DetectIidSpike { diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs index f0352e6331..c7293052f2 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/TimeSeries/DetectSpikeBySsa.cs @@ -1,8 +1,9 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Dynamic { public static class DetectSpikeBySsa { diff --git a/docs/samples/Microsoft.ML.Samples/Program.cs b/docs/samples/Microsoft.ML.Samples/Program.cs index 2013183f98..03b6ce19b6 100644 --- a/docs/samples/Microsoft.ML.Samples/Program.cs +++ b/docs/samples/Microsoft.ML.Samples/Program.cs @@ -1,5 +1,4 @@ -using Microsoft.ML.Samples.Dynamic; -using Samples.Dynamic; +using Samples.Dynamic; namespace Microsoft.ML.Samples { diff --git a/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs b/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs index 9ffb2c3c32..c5eb417fdd 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/AveragedPerceptronBinaryClassification.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.StaticPipe; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class AveragedPerceptronBinaryClassificationExample { @@ -9,7 +10,7 @@ public static void Example() { // Downloading a classification dataset from github.com/dotnet/machinelearning. // It will be stored in the same path as the executable - string dataFilePath = SamplesUtils.DatasetUtils.DownloadAdultDataset(); + string dataFilePath = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadAdultDataset(); // Data Preview // 1. Column [Label]: IsOver50K (boolean) diff --git a/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs b/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs index 1dfa53d9bf..cda4a54ef8 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/FastTreeBinaryClassification.cs @@ -1,7 +1,8 @@ using System; +using Microsoft.ML; using Microsoft.ML.StaticPipe; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class FastTreeBinaryClassificationExample { @@ -10,7 +11,7 @@ public static void Example() { // Downloading a classification dataset from github.com/dotnet/machinelearning. // It will be stored in the same path as the executable - string dataFilePath = SamplesUtils.DatasetUtils.DownloadAdultDataset(); + string dataFilePath = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadAdultDataset(); // Data Preview // 1. Column [Label]: IsOver50K (boolean) diff --git a/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs b/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs index d0f5d6336b..66ca7176d4 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/FastTreeRegression.cs @@ -1,9 +1,10 @@ using System; using System.Linq; +using Microsoft.ML; using Microsoft.ML.StaticPipe; using Microsoft.ML.Trainers.FastTree; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class FastTreeRegressionExample { @@ -13,7 +14,7 @@ public static void Example() // Downloading a regression dataset from github.com/dotnet/machinelearning // this will create a housing.txt file in the filsystem this code will run // you can open the file to see the data. - string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); + string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, // as well as the source of randomness. diff --git a/docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs b/docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs index 6d5da0d326..aa52b3a468 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.StaticPipe; -namespace Microsoft.ML.Samples.Dynamic +namespace Samples.Static { public class FeatureSelectionTransformStaticExample { @@ -11,7 +12,7 @@ public static void Example() { // Downloading a classification dataset from github.com/dotnet/machinelearning. // It will be stored in the same path as the executable - string dataFilePath = SamplesUtils.DatasetUtils.DownloadBreastCancerDataset(); + string dataFilePath = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadBreastCancerDataset(); // Data Preview // 1. Label 0=benign, 1=malignant diff --git a/docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs b/docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs index 04e3a2488d..4723d3e1f8 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/LightGBMBinaryClassification.cs @@ -1,8 +1,9 @@ using System; using Microsoft.ML.Trainers.LightGbm.StaticPipe; using Microsoft.ML.StaticPipe; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class LightGbmBinaryClassificationExample { @@ -10,7 +11,7 @@ public static void Example() { // Downloading a classification dataset from github.com/dotnet/machinelearning. // It will be stored in the same path as the executable - string dataFilePath = SamplesUtils.DatasetUtils.DownloadAdultDataset(); + string dataFilePath = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadAdultDataset(); // Data Preview // 1. Column [Label]: IsOver50K (boolean) diff --git a/docs/samples/Microsoft.ML.Samples/Static/LightGBMMulticlassWithInMemoryData.cs b/docs/samples/Microsoft.ML.Samples/Static/LightGBMMulticlassWithInMemoryData.cs index 53a0ccca70..8f08f86889 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/LightGBMMulticlassWithInMemoryData.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/LightGBMMulticlassWithInMemoryData.cs @@ -4,8 +4,9 @@ using Microsoft.ML.Trainers.LightGbm.StaticPipe; using Microsoft.ML.SamplesUtils; using Microsoft.ML.StaticPipe; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { class LightGBMMulticlassWithInMemoryData { diff --git a/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs b/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs index 7dd2d46e98..a7c1d7bdae 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/LightGBMRegression.cs @@ -3,8 +3,9 @@ using Microsoft.ML.Trainers.LightGbm; using Microsoft.ML.Trainers.LightGbm.StaticPipe; using Microsoft.ML.StaticPipe; +using Microsoft.ML; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class LightGbmRegressionExample { @@ -13,7 +14,7 @@ public static void Example() // Downloading a regression dataset from github.com/dotnet/machinelearning // this will create a housing.txt file in the filsystem. // You can open the file to see the data. - string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); + string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, // as well as the source of randomness. diff --git a/docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs b/docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs index cdf2c4c0b8..e43d1cfcb9 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs @@ -1,9 +1,10 @@ using System; using System.Collections.Generic; +using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.StaticPipe; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class SdcaBinaryClassificationExample { @@ -11,7 +12,7 @@ public static void Example() { // Downloading a classification dataset from github.com/dotnet/machinelearning. // It will be stored in the same path as the executable - string dataFilePath = SamplesUtils.DatasetUtils.DownloadAdultDataset(); + string dataFilePath = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadAdultDataset(); // Data Preview // 1. Column [Label]: IsOver50K (boolean) diff --git a/docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs b/docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs index 2107b59d37..df8cf17267 100644 --- a/docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs +++ b/docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs @@ -1,8 +1,9 @@ using System; +using Microsoft.ML; using Microsoft.ML.StaticPipe; using Microsoft.ML.Trainers; -namespace Microsoft.ML.Samples.Static +namespace Samples.Static { public class SdcaRegressionExample { @@ -11,7 +12,7 @@ public static void Example() // Downloading a regression dataset from github.com/dotnet/machinelearning // this will create a housing.txt file in the filsystem this code will run // you can open the file to see the data. - string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); + string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset(); // Creating the ML.Net IHostEnvironment object, needed for the pipeline var mlContext = new MLContext();