dotnet · artidoro · Mar 19, 2019 · Mar 13, 2019 · Mar 13, 2019 · Mar 14, 2019
diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs b/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);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/TensorFlow/TextClassification.cs
@@ -69,10 +69,10 @@ public static void Example()
             };
 
             var engine = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text")
-                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, "Words", "Ids", new ColumnOptions[] { ("VariableLenghtFeatures", "TokenizedWords") }))
+                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, "Words", "Ids", "VariableLenghtFeatures", "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)
                 .CreatePredictionEngine<IMDBSentiment, OutputScores>(mlContext);
 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ConvertToGrayScale.cs b/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);
 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/DnnFeaturizeImage.cs b/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"));

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ExtractPixels.cs b/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"));
 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/LoadImages.cs b/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

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ImageAnalytics/ResizeImages.cs b/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));
 
 

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMapping.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMapping.cs
@@ -55,7 +55,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue(educationKeys, educationValues, ("EducationCategory", "Education"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(educationKeys, educationValues, "EducationCategory", "Education");
 
             // Fits the ValueMappingEstimator and transforms the data converting the Education to EducationCategory.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingFloatToString.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingFloatToString.cs
@@ -46,7 +46,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue(temperatureKeys, classificationValues, ("TemperatureCategory", "Temperature"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(temperatureKeys, classificationValues, "TemperatureCategory", "Temperature");
 
             // Fits the ValueMappingEstimator and transforms the data adding the TemperatureCategory column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToArray.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToArray.cs
@@ -49,7 +49,7 @@ public static void Example()
             };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue<string, int>(educationKeys, educationValues, ("EducationFeature", "Education"));
+            var pipeline = mlContext.Transforms.Conversion.MapValue<string, int>(educationKeys, educationValues, "EducationFeature", "Education");
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationFeature column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToKeyType.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToKeyType.cs
@@ -55,8 +55,8 @@ public static void Example()
             // Generate the ValueMappingEstimator that will output KeyTypes even though our values are strings.
             // The KeyToValueMappingEstimator is added to provide a reverse lookup of the KeyType, converting the KeyType value back
             // to the original value.
-            var pipeline = mlContext.Transforms.Conversion.MapValue<string, string>(educationKeys, educationValues, true, ("EducationKeyType", "Education"))
-                              .Append(mlContext.Transforms.Conversion.MapKeyToValue(("EducationCategory", "EducationKeyType")));
+            var pipeline = mlContext.Transforms.Conversion.MapValue(educationKeys, educationValues, "EducationKeyType", "Education", true)
+                              .Append(mlContext.Transforms.Conversion.MapKeyToValue("EducationCategory", "EducationKeyType"));
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationKeyType column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);