review comments

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

@@ -69,7 +69,8 @@ public static void Example()
             };
 
             var model = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text")
-                .Append(mlContext.Transforms.Conversion.MapValue("VariableLenghtFeatures", lookupMap, "Words", "Ids", "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"))

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

@@ -38,15 +38,13 @@ public static void Example()
 
             // 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 key-value pairs based on the Education values from the dataset. 
-            var educationKeyValuePairs = new List<KeyValuePair<string, string>>()
-            {
-                new KeyValuePair<string, string>("0-5yrs", "Undergraduate"),
-                new KeyValuePair<string, string>("6-11yrs", "Postgraduate"),
-                new KeyValuePair<string, string>("12+yrs", "Postgraduate")
-            };
+            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("EducationCategory", educationKeyValuePairs, "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);

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

@@ -29,15 +29,13 @@ public static void Example()
 
             // 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 key-value pairs based on the induced value from the dataset
-            var temperatureKeyValuePairs = new List<KeyValuePair<float, string>>()
-            {
-                new KeyValuePair<float, string>(36.0f, "T1"),
-                new KeyValuePair<float, string>(35.0f, "T2"),
-                new KeyValuePair<float, string>(34.0f, "T3")
-            };
-
+            var temperatureMap = new Dictionary<float, string>();
+            temperatureMap[36.0f] = "T1";
+            temperatureMap[35.0f] = "T2";
+            temperatureMap[34.0f] = "T3";
+
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue("TemperatureCategory", temperatureKeyValuePairs, "Temperature");
+            var pipeline = mlContext.Transforms.Conversion.MapValue("TemperatureCategory", temperatureMap, "Temperature");
 
             // Fits the ValueMappingEstimator and transforms the data adding the TemperatureCategory column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

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

@@ -32,15 +32,13 @@ public static void Example()
 
             // 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 key-value pairs based on the Education values from the dataset
-            var educationKeyValuePairs = new List<KeyValuePair<string, int[]>>()
-            {
-                new KeyValuePair<string, int[]>("0-5yrs",  new int[] { 1,2,3 }),
-                new KeyValuePair<string, int[]>("6-11yrs",  new int[] { 1,2,3 }),
-                new KeyValuePair<string, int[]>("12+yrs",  new int[] { 1,2,3 })
-            };
+            var educationMap = new Dictionary<string, int[]>();
+            educationMap["0-5yrs"] = new int[] { 1, 2, 3 };
+            educationMap["6-11yrs"] = new int[] { 5, 6, 7 };
+            educationMap["12+yrs"] = new int[] { 42, 32, 64 };
 
             // Constructs the ValueMappingEstimator making the ML.net pipeline
-            var pipeline = mlContext.Transforms.Conversion.MapValue<string, int>("EducationFeature", educationKeyValuePairs, "Education");
+            var pipeline = mlContext.Transforms.Conversion.MapValue<string, int>("EducationFeature", educationMap, "Education");
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationFeature column.
             IDataView transformedData = pipeline.Fit(trainData).Transform(trainData);

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

@@ -36,17 +36,15 @@ public static void Example()
 
             // Creating a list of key-value pairs based on the Education values from the dataset
             // These lists are created by hand for the demonstration, but the ValueMappingEstimator does take an IEnumerable.
-            var educationKeyValuePairs = new List<KeyValuePair<string,string>>()
-            {
-                new KeyValuePair<string,string>("0-5yrs", "Undergraduate"),
-                new KeyValuePair<string,string>("6-11yrs", "Postgraduate"),
-                new KeyValuePair<string,string>("12+yrs", "Postgraduate")
-            };
+            var educationMap = new Dictionary<string, string>();
+            educationMap["0-5yrs"] = "Undergraduate";
+            educationMap["6-11yrs"] = "Postgraduate";
+            educationMap["12+yrs"] = "Postgraduate";
 
             // 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("EducationKeyType", educationKeyValuePairs, "Education", true)
+            var pipeline = mlContext.Transforms.Conversion.MapValue("EducationKeyType", educationMap, "Education", true)
                               .Append(mlContext.Transforms.Conversion.MapKeyToValue("EducationCategory", "EducationKeyType"));
 
             // Fits the ValueMappingEstimator and transforms the data adding the EducationKeyType column.

src/Microsoft.ML.Data/Transforms/ConversionsExtensionsCatalog.cs

@@ -327,7 +327,7 @@ internal static ValueMappingEstimator<TInputType, TOutputType> MapValue<TInputTy
         /// </summary>
         /// <param name="catalog">The conversion transform's catalog</param>
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="lookupMap">An instance of <see cref="IDataView"/> that contains the key and value columns.</param>
+        /// <param name="lookupMap">An instance of <see cref="IDataView"/> that contains the <paramref name="keyColumn"/> and <paramref name="valueColumn"/> columns.</param>
         /// <param name="keyColumn">The key column in <paramref name="lookupMap"/>.</param>
         /// <param name="valueColumn">The value column in <paramref name="lookupMap"/>.</param>
         /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
@@ -343,19 +343,19 @@ internal static ValueMappingEstimator<TInputType, TOutputType> MapValue<TInputTy
         /// </example>
         public static ValueMappingEstimator MapValue(
             this TransformsCatalog.ConversionTransforms catalog,
-            string outputColumnName, IDataView lookupMap, string keyColumn, string valueColumn, string inputColumnName = null)
+            string outputColumnName, IDataView lookupMap, DataViewSchema.Column keyColumn, DataViewSchema.Column valueColumn, string inputColumnName = null)
         {
-            return new ValueMappingEstimator(CatalogUtils.GetEnvironment(catalog), lookupMap, keyColumn, valueColumn,
+            return new ValueMappingEstimator(CatalogUtils.GetEnvironment(catalog), lookupMap, keyColumn.Name, valueColumn.Name,
               new[] { (outputColumnName, inputColumnName ?? outputColumnName) });
         }
 
         /// <summary>
         /// <see cref="ValueMappingEstimator"/>
         /// </summary>
         /// <param name="catalog">The conversion transform's catalog</param>
-        /// <param name="lookupMap">An instance of <see cref="IDataView"/> that contains the key and value columns.</param>
-        /// <param name="keyColumnName">Name of the key column in <paramref name="lookupMap"/>.</param>
-        /// <param name="valueColumnName">Name of the value column in <paramref name="lookupMap"/>.</param>
+        /// <param name="lookupMap">An instance of <see cref="IDataView"/> that contains the <paramref name="keyColumn"/> and <paramref name="valueColumn"/> columns.</param>
+        /// <param name="keyColumn">Name of the key column in <paramref name="lookupMap"/>.</param>
+        /// <param name="valueColumn">Name of the value column in <paramref name="lookupMap"/>.</param>
         /// <param name="columns">The columns to apply this transform on.</param>
         /// <returns>A instance of the ValueMappingEstimator</returns>
         /// <example>
@@ -370,8 +370,8 @@ public static ValueMappingEstimator MapValue(
         [BestFriend]
         internal static ValueMappingEstimator MapValue(
             this TransformsCatalog.ConversionTransforms catalog,
-            IDataView lookupMap, string keyColumnName, string valueColumnName, params ColumnOptions[] columns)
-            => new ValueMappingEstimator(CatalogUtils.GetEnvironment(catalog), lookupMap, keyColumnName, valueColumnName,
+            IDataView lookupMap, DataViewSchema.Column keyColumn, DataViewSchema.Column valueColumn, params ColumnOptions[] columns)
+            => new ValueMappingEstimator(CatalogUtils.GetEnvironment(catalog), lookupMap, keyColumn.Name, valueColumn.Name,
                 ColumnOptions.ConvertToValueTuples(columns));
     }
 }

test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/TensorflowTests.cs

@@ -999,7 +999,8 @@ public void TensorFlowSentimentClassificationTest()
             // Then this integer vector is retrieved from the pipeline and resized to fixed length.
             // The second pipeline 'tfEnginePipe' takes the resized integer vector and passes it to TensoFlow and gets the classification scores.
             var estimator = mlContext.Transforms.Text.TokenizeIntoWords("TokenizedWords", "Sentiment_Text")
-                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, "Words", "Ids", new ColumnOptions[] { ("Features", "TokenizedWords") }));
+                .Append(mlContext.Transforms.Conversion.MapValue(lookupMap, lookupMap.Schema["Words"], lookupMap.Schema["Ids"], 
+                    new ColumnOptions[] { ("Features", "TokenizedWords") }));
             var model = estimator.Fit(dataView);
             var dataPipe = mlContext.Model.CreatePredictionEngine<TensorFlowSentiment, TensorFlowSentiment>(model);