Hiding of ColumnOptions (#2959)

Author: artidoro

docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs

@@ -58,7 +58,8 @@ public static void Example()
 
             // Composing a different pipeline if we wanted to normalize more than one column at a time. 
             // Using log scale as the normalization mode. 
-            var multiColPipeline = ml.Transforms.Normalize(NormalizingEstimator.NormalizationMode.LogMeanVariance, new ColumnOptions[] { ("LogInduced", "Induced"), ("LogSpontaneous", "Spontaneous") });
+            var multiColPipeline = ml.Transforms.Normalize("LogInduced", "Induced", NormalizingEstimator.NormalizationMode.LogMeanVariance)
+                .Append(ml.Transforms.Normalize("LogSpontaneous", "Spontaneous", NormalizingEstimator.NormalizationMode.LogMeanVariance));
             // The transformed data.
             var multiColtransformer = multiColPipeline.Fit(trainData);
             var multiColtransformedData = multiColtransformer.Transform(trainData);

docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs

@@ -69,10 +69,11 @@ public static void Example()
             };
 
             var model = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text")
-                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, "Words", "Ids", new ColumnOptions[] { ("VariableLenghtFeatures", "TokenizedWords") }))
+                .Append(mlContext.Transforms.Conversion.MapValue("VariableLenghtFeatures", lookupMap,
+                    lookupMap.Schema["Words"], lookupMap.Schema["Ids"], "TokenizedWords"))
                 .Append(mlContext.Transforms.CustomMapping(ResizeFeaturesAction, "Resize"))
                 .Append(tensorFlowModel.ScoreTensorFlowModel(new[] { "Prediction/Softmax" }, new[] { "Features" }))
-                .Append(mlContext.Transforms.CopyColumns(("Prediction", "Prediction/Softmax")))
+                .Append(mlContext.Transforms.CopyColumns("Prediction", "Prediction/Softmax"))
                 .Fit(dataView);
             var engine = mlContext.Model.CreatePredictionEngine<IMDBSentiment, OutputScores>(model);
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs

@@ -36,8 +36,8 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, ("ImageObject", "ImagePath"))
-                           .Append(mlContext.Transforms.ConvertToGrayscale(("Grayscale", "ImageObject")));
+            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, "ImageObject", "ImagePath")
+                           .Append(mlContext.Transforms.ConvertToGrayscale("Grayscale", "ImageObject"));
 
             var transformedData = pipeline.Fit(data).Transform(data);
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs

@@ -40,7 +40,7 @@ public static void Example()
             // Installing the Microsoft.ML.DNNImageFeaturizer packages copies the models in the
             // `DnnImageModels` folder. 
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, ("ImageObject", "ImagePath"))
+            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, "ImageObject", "ImagePath")
                           .Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth: 224, imageHeight: 224))
                           .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"))
                           .Append(mlContext.Transforms.DnnFeaturizeImage("FeaturizedImage", m => m.ModelSelector.ResNet18(mlContext, m.OutputColumn, m.InputColumn), "Pixels"));

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs

@@ -37,7 +37,7 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, ("ImageObject", "ImagePath"))
+            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, "ImageObject", "ImagePath")
                           .Append(mlContext.Transforms.ResizeImages("ImageObject", imageWidth: 100, imageHeight: 100 ))
                           .Append(mlContext.Transforms.ExtractPixels("Pixels", "ImageObject"));
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs

@@ -36,7 +36,7 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, ("ImageReal", "ImagePath"));
+            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, "ImageReal", "ImagePath");
             var transformedData = pipeline.Fit(data).Transform(data);
 
             // The transformedData IDataView contains the loaded images now

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs

@@ -36,7 +36,7 @@ public static void Example()
 
             var imagesFolder = Path.GetDirectoryName(imagesDataFile);
             // Image loading pipeline. 
-            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, ("ImageReal", "ImagePath"))
+            var pipeline = mlContext.Transforms.LoadImages(imagesFolder, "ImageReal", "ImagePath")
                         .Append(mlContext.Transforms.ResizeImages("ImageReal", imageWidth: 100, imageHeight: 100));
 
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithColumnOptions.cs renamed to docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

@@ -5,7 +5,7 @@
 
 namespace Microsoft.ML.Samples.Dynamic
 {
-    public sealed class VectorWhitenWithColumnOptions
+    public sealed class VectorWhitenWithOptions
     {
         /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
         public static void Example()
@@ -39,8 +39,7 @@ public static void Example()
 
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.VectorWhiten(new Transforms.VectorWhiteningEstimator.ColumnOptions(
-                nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4));
+            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: 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.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ReplaceMissingValues.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Linq;
 using Microsoft.ML.Data;
-using static Microsoft.ML.Transforms.MissingValueReplacingEstimator.ColumnOptions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Samples.Dynamic
 {
@@ -25,7 +25,7 @@ public static void Example()
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
             // ReplaceMissingValues is used to create a column where missing values are replaced according to the ReplacementMode.
-            var meanPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features", ReplacementMode.Mean);
+            var meanPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features", MissingValueReplacingEstimator.ReplacementMode.Mean);
 
             // Now we can transform the data and look at the output to confirm the behavior of the estimator.
             // This operation doesn't actually evaluate data until we read the data below.
@@ -36,7 +36,7 @@ public static void Example()
             var meanRowEnumerable = mlContext.Data.CreateEnumerable<SampleDataTransformed>(meanTransformedData, reuseRowObject: false);
 
             // ReplaceMissingValues is used to create a column where missing values are replaced according to the ReplacementMode.
-            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features", ReplacementMode.DefaultValue);
+            var defaultPipeline = mlContext.Transforms.ReplaceMissingValues("MissingReplaced", "Features", MissingValueReplacingEstimator.ReplacementMode.DefaultValue);
 
             // Now we can transform the data and look at the output to confirm the behavior of the estimator.
             // This operation doesn't actually evaluate data until we read the data below.

docs/samples/Microsoft.ML.Samples/Dynamic/ValueMapping.cs

@@ -37,24 +37,14 @@ public static void Example()
             // 35.0   1.0   6-11yrs     1.0         3.0       32.0     5.0  ...
 
             // If the list of keys and values are known, they can be passed to the API. The ValueMappingEstimator can also get the mapping through an IDataView
-            // Creating a list of keys based on the Education values from the dataset. 
-            var educationKeys = new List<string>()
-            {
-                "0-5yrs",
-                "6-11yrs",
-                "12+yrs"
-            };
-
-            // Creating a list of associated values that will map respectively to each educationKey
-            var educationValues = new List<string>()
-            {
-                "Undergraduate",
-                "Postgraduate",
-                "Postgraduate"
-            };
-
+            // Creating a list of key-value pairs based on the Education values from the dataset. 
+            var educationMap = new Dictionary<string, string> ();
+            educationMap["0-5yrs"] = "Undergraduate";
+            educationMap["6-11yrs"] = "Postgraduate";
+            educationMap["12+yrs"] = "Postgraduate";
+            
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue(educationKeys, educationValues, ("EducationCategory", "Education"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue("EducationCategory", educationMap, "Education");
 
             // Fits the ValueMappingEstimator and transforms the data converting the Education to EducationCategory.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);