Rename ProduceCharacterTokens to ProduceCharactersAsKeys

docs/code/MlNetCookBook.md

@@ -775,7 +775,7 @@ var pipeline =
                 ngramLength: 2, useAllLengths: false))
 
     // NLP pipeline 3: bag of tri-character sequences with TF-IDF weighting.
-    .Append(mlContext.Transforms.Text.ProduceCharacterTokens("MessageChars", "Message"))
+    .Append(mlContext.Transforms.Text.ProduceCharactersAsKeys("MessageChars", "Message"))
     .Append(new NgramExtractingEstimator(mlContext, "BagOfTrichar", "MessageChars", 
                 ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf))
 

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

@@ -26,7 +26,7 @@ public static void NgramTransform()
             // A pipeline to tokenize text as characters and then combine them together into ngrams
             // The pipeline uses the default settings to featurize.
 
-            var charsPipeline = ml.Transforms.Text.ProduceCharacterTokens("Chars", "SentimentText", useMarkerCharacters: false);
+            var charsPipeline = ml.Transforms.Text.ProduceCharactersAsKeys("Chars", "SentimentText", useMarkerCharacters: false);
             var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength: 1);
             var ngramTwpPipeline = ml.Transforms.Text.ProduceNgrams("CharsTwograms", "Chars");
             var oneCharsPipeline = charsPipeline.Append(ngramOnePipeline);

src/Microsoft.ML.StaticPipe/TextStaticExtensions.cs

@@ -109,7 +109,7 @@ public bool Equals(Reconciler other)
         /// </summary>
         /// <param name="input">The column to apply to.</param>
         /// <param name="useMarkerCharacters">Whether to use marker characters to separate words.</param>
-        public static VarVector<Key<ushort, string>> ProduceCharacterTokens(this Scalar<string> input, bool useMarkerCharacters = true) => new OutPipelineColumn(input, useMarkerCharacters);
+        public static VarVector<Key<ushort, string>> ProduceCharactersAsKeys(this Scalar<string> input, bool useMarkerCharacters = true) => new OutPipelineColumn(input, useMarkerCharacters);
     }
 
     /// <summary>

src/Microsoft.ML.Transforms/Text/TextCatalog.cs

@@ -57,7 +57,7 @@ public static class TextCatalog
         /// <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>
         /// <param name="useMarkerCharacters">Whether to prepend a marker character, <see langword="0x02"/>, to the beginning,
         /// and append another marker character, <see langword="0x03"/>, to the end of the output vector of characters.</param>
-        public static TokenizingByCharactersEstimator ProduceCharacterTokens(this TransformsCatalog.TextTransforms catalog,
+        public static TokenizingByCharactersEstimator ProduceCharactersAsKeys(this TransformsCatalog.TextTransforms catalog,
             string outputColumnName,
             string inputColumnName = null,
             bool useMarkerCharacters = CharTokenizingDefaults.UseMarkerCharacters)
@@ -72,7 +72,7 @@ public static class TextCatalog
         /// and append another marker character, <see langword="0x03"/>, to the end of the output vector of characters.</param>
         /// <param name="columns">Pairs of columns to run the tokenization on.</param>
 
-        public static TokenizingByCharactersEstimator ProduceCharacterTokens(this TransformsCatalog.TextTransforms catalog,
+        public static TokenizingByCharactersEstimator ProduceCharactersAsKeys(this TransformsCatalog.TextTransforms catalog,
             bool useMarkerCharacters = CharTokenizingDefaults.UseMarkerCharacters,
             params ColumnOptions[] columns)
             => new TokenizingByCharactersEstimator(Contracts.CheckRef(catalog, nameof(catalog)).GetEnvironment(), useMarkerCharacters, ColumnOptions.ConvertToValueTuples(columns));

test/Microsoft.ML.StaticPipelineTesting/StaticPipeTests.cs

@@ -520,7 +520,7 @@ public void Tokenize()
                 .Append(r => (
                     r.label,
                     tokens: r.text.ProduceWordTokens(),
-                    chars: r.text.ProduceCharacterTokens()));
+                    chars: r.text.ProduceCharactersAsKeys()));
 
             var tdata = est.Fit(data).Transform(data);
             var schema = tdata.AsDynamic.Schema;

test/Microsoft.ML.Tests/Scenarios/Api/CookbookSamples/CookbookSamples.cs

@@ -467,7 +467,7 @@ private void TextFeaturizationOn(string dataPath)
                     BagOfBigrams: r.Message.NormalizeText().ProduceHashedWordBags(ngramLength: 2, useAllLengths: false),
 
                     // NLP pipeline 3: bag of tri-character sequences with TF-IDF weighting.
-                    BagOfTrichar: r.Message.ProduceCharacterTokens().ProduceNgrams(ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf),
+                    BagOfTrichar: r.Message.ProduceCharactersAsKeys().ProduceNgrams(ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf),
 
                     // NLP pipeline 4: word embeddings.
                     // PretrainedModelKind.Sswe is used here for performance of the test. In a real

test/Microsoft.ML.Tests/Scenarios/Api/CookbookSamples/CookbookSamplesDynamicApi.cs

@@ -305,7 +305,7 @@ private void TextFeaturizationOn(string dataPath)
                             ngramLength: 2, useAllLengths: false))
 
                 // NLP pipeline 3: bag of tri-character sequences with TF-IDF weighting.
-                .Append(mlContext.Transforms.Text.ProduceCharacterTokens("MessageChars", "Message"))
+                .Append(mlContext.Transforms.Text.ProduceCharactersAsKeys("MessageChars", "Message"))
                 .Append(new NgramExtractingEstimator(mlContext, "BagOfTrichar", "MessageChars", 
                             ngramLength: 3, weighting: NgramExtractingEstimator.WeightingCriteria.TfIdf))