Use Schema.Column instead of string to identify which column to load

Author: wschin

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

@@ -82,8 +82,8 @@ public static void Example()
             };
 
             // Print the data that results from the transformations.
-            var countSelectColumn = transformedData.GetColumn<VBuffer<float>>("FeaturesCountSelect");
-            var MISelectColumn = transformedData.GetColumn<VBuffer<float>>("FeaturesMISelect");
+            var countSelectColumn = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema["FeaturesCountSelect"]);
+            var MISelectColumn = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema["FeaturesMISelect"]);
             printHelper("FeaturesCountSelect", countSelectColumn);
             printHelper("FeaturesMISelect", MISelectColumn);
 

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

@@ -60,7 +60,7 @@ public static void Example()
             };
 
             // Preview of the DefaultKeys column obtained after processing the input.
-            var defaultColumn = transformedData_default.GetColumn<VBuffer<uint>>(defaultColumnName);
+            var defaultColumn = transformedData_default.GetColumn<VBuffer<uint>>(transformedData_default.Schema[defaultColumnName]);
             printHelper(defaultColumnName, defaultColumn);
 
             // DefaultKeys column obtained post-transformation.
@@ -71,7 +71,7 @@ public static void Example()
             // 9 10 11 12 13 6
 
             // Previewing the CustomizedKeys column obtained after processing the input.
-            var customizedColumn = transformedData_customized.GetColumn<VBuffer<uint>>(customizedColumnName);
+            var customizedColumn = transformedData_customized.GetColumn<VBuffer<uint>>(transformedData_customized.Schema[customizedColumnName]);
             printHelper(customizedColumnName, customizedColumn);
 
             // CustomizedKeys column obtained post-transformation.
@@ -87,7 +87,7 @@ public static void Example()
             transformedData_default = pipeline.Fit(trainData).Transform(trainData);
 
             // Preview of the DefaultColumnName column obtained.
-            var originalColumnBack = transformedData_default.GetColumn<VBuffer<ReadOnlyMemory<char>>>(defaultColumnName);
+            var originalColumnBack = transformedData_default.GetColumn<VBuffer<ReadOnlyMemory<char>>>(transformedData_default.Schema[defaultColumnName]);
 
             foreach (var row in originalColumnBack)
             {

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

@@ -37,7 +37,7 @@ public static void Example()
             var transformed_data = transformer.Transform(trainData);
 
             // Column obtained after processing the input.
-            var ldaFeaturesColumn = transformed_data.GetColumn<VBuffer<float>>(ldaFeatures);
+            var ldaFeaturesColumn = transformed_data.GetColumn<VBuffer<float>>(transformed_data.Schema[ldaFeatures]);
 
             Console.WriteLine($"{ldaFeatures} column obtained post-transformation.");
             foreach (var featureRow in ldaFeaturesColumn)

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

@@ -26,8 +26,8 @@ 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.TokenizeCharacters("Chars", "SentimentText", useMarkerCharacters:false);
-            var ngramOnePipeline = ml.Transforms.Text.ProduceNgrams("CharsUnigrams", "Chars", ngramLength:1);
+            var charsPipeline = ml.Transforms.Text.TokenizeCharacters("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);
             var twoCharsPipeline = charsPipeline.Append(ngramTwpPipeline);
@@ -38,30 +38,30 @@ public static void NgramTransform()
 
             // Small helper to print the text inside the columns, in the console. 
             Action<string, IEnumerable<VBuffer<float>>, VBuffer<ReadOnlyMemory<char>>> printHelper = (columnName, column, names) =>
-           {
-               Console.WriteLine($"{columnName} column obtained post-transformation.");
-               var slots = names.GetValues();
-               foreach (var featureRow in column)
-               {
-                   foreach (var item in featureRow.Items())
-                       Console.Write($"'{slots[item.Key]}' - {item.Value} ");
-                   Console.WriteLine("");
-               }
+            {
+                Console.WriteLine($"{columnName} column obtained post-transformation.");
+                var slots = names.GetValues();
+                foreach (var featureRow in column)
+                {
+                    foreach (var item in featureRow.Items())
+                        Console.Write($"'{slots[item.Key]}' - {item.Value} ");
+                    Console.WriteLine("");
+                }
 
-               Console.WriteLine("===================================================");
-           };
+                Console.WriteLine("===================================================");
+            };
             // Preview of the CharsUnigrams column obtained after processing the input.
             VBuffer<ReadOnlyMemory<char>> slotNames = default;
             transformedData_onechars.Schema["CharsUnigrams"].GetSlotNames(ref slotNames);
-            var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>("CharsUnigrams");
+            var charsOneGramColumn = transformedData_onechars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
             printHelper("CharsUnigrams", charsOneGramColumn, slotNames);
 
             // CharsUnigrams column obtained post-transformation.
             // 'B' - 1 'e' - 6 's' - 1 't' - 1 '<?>' - 4 'g' - 1 'a' - 2 'm' - 1 'I' - 1 ''' - 1 'v' - 2 ...
             // 'e' - 1 '<?>' - 2 'd' - 1 '=' - 4 'R' - 1 'U' - 1 'D' - 2 'E' - 1 'u' - 1 ',' - 1 '2' - 1
             // 'B' - 0 'e' - 6 's' - 3 't' - 6 '<?>' - 9 'g' - 2 'a' - 2 'm' - 2 'I' - 0 ''' - 0 'v' - 0 ...
             // Preview of the CharsTwoGrams column obtained after processing the input.
-            var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>("CharsTwograms");
+            var charsTwoGramColumn = transformedData_twochars.GetColumn<VBuffer<float>>(transformedData_onechars.Schema["CharsUnigrams"]);
             transformedData_twochars.Schema["CharsTwograms"].GetSlotNames(ref slotNames);
             printHelper("CharsTwograms", charsTwoGramColumn, slotNames);
 

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

@@ -44,7 +44,7 @@ public static void Example()
             var transformedData = transformer.Transform(trainData);
 
             // Getting the data of the newly created column, so we can preview it.
-            var normalizedColumn = transformedData.GetColumn<float>("Induced");
+            var normalizedColumn = transformedData.GetColumn<float>(transformedData.Schema["Induced"]);
 
             // A small printing utility.
             Action<string, IEnumerable<float>> printHelper = (colName, column) =>
@@ -72,8 +72,8 @@ public static void Example()
             var multiColtransformedData = multiColtransformer.Transform(trainData);
 
             // Getting the newly created columns. 
-            var normalizedInduced = multiColtransformedData.GetColumn<float>("LogInduced");
-            var normalizedSpont = multiColtransformedData.GetColumn<float>("LogSpontaneous");
+            var normalizedInduced = multiColtransformedData.GetColumn<float>(multiColtransformedData.Schema["LogInduced"]);
+            var normalizedSpont = multiColtransformedData.GetColumn<float>(multiColtransformedData.Schema["LogSpontaneous"]);
 
             printHelper("LogInduced", normalizedInduced);
 

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

@@ -41,7 +41,7 @@ public static void Example()
             // 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<VBuffer<float>>(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features));
+            var randomFourier = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), randomFourier);
 
@@ -59,7 +59,7 @@ public static void Example()
             // 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<VBuffer<float>>(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features));
+            var lpNormalize= transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), lpNormalize);
 
@@ -77,7 +77,7 @@ public static void Example()
             // 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<VBuffer<float>>(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features));
+            var gcNormalize = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), gcNormalize);
 

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

@@ -54,14 +54,14 @@ public static void Example()
             };
 
             // Preview the result of breaking string into array of words.
-            var originalText = transformedDataDefault.GetColumn<VBuffer<ReadOnlyMemory<char>>>(originalTextColumnName);
+            var originalText = transformedDataDefault.GetColumn<VBuffer<ReadOnlyMemory<char>>>(transformedDataDefault.Schema[originalTextColumnName]);
             printHelper(originalTextColumnName, originalText);
             // Best|game|I've|ever|played.|
             // == RUDE ==| Dude,| 2 |
             // Until | the | next | game,| this |is| the | best | Xbox | game!|
 
             // Preview the result of cleaning with default stop word remover.
-            var defaultRemoverData = transformedDataDefault.GetColumn<VBuffer<ReadOnlyMemory<char>>>("DefaultRemover");
+            var defaultRemoverData = transformedDataDefault.GetColumn<VBuffer<ReadOnlyMemory<char>>>(transformedDataDefault.Schema["DefaultRemover"]);
             printHelper("DefaultRemover", defaultRemoverData);
             // Best|game|I've|played.|
             // == RUDE ==| Dude,| 2 |
@@ -70,7 +70,7 @@ public static void Example()
 
 
             // Preview the result of cleaning with default customized stop word remover.
-            var customizeRemoverData = transformedDataCustomized.GetColumn<VBuffer<ReadOnlyMemory<char>>>("RemovedWords");
+            var customizeRemoverData = transformedDataCustomized.GetColumn<VBuffer<ReadOnlyMemory<char>>>(transformedDataCustomized.Schema["RemovedWords"]);
             printHelper("RemovedWords", customizeRemoverData);
 
             // Best|game|I've|ever|played.|

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

@@ -58,7 +58,7 @@ public static void Example()
             };
 
             // Preview of the DefaultTextFeatures column obtained after processing the input.
-            var defaultColumn = transformedData_default.GetColumn<VBuffer<float>>(defaultColumnName);
+            var defaultColumn = transformedData_default.GetColumn<VBuffer<float>>(transformedData_default.Schema[defaultColumnName]);
             printHelper(defaultColumnName, defaultColumn);
 
             // DefaultTextFeatures column obtained post-transformation.
@@ -68,7 +68,7 @@ public static void Example()
             // 0 0.1230915 0.1230915 0.1230915 0.1230915 0.246183 0.246183 0.246183 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.1230915 0 0 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.3692745 0.246183 0.246183 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.246183 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.1230915 0.2886751 0 0 0 0 0 0 0 0.2886751 0.5773503 0.2886751 0.2886751 0.2886751 0.2886751 0.2886751 0.2886751
 
             // Preview of the CustomizedTextFeatures column obtained after processing the input.
-            var customizedColumn = transformedData_customized.GetColumn<VBuffer<float>>(customizedColumnName);
+            var customizedColumn = transformedData_customized.GetColumn<VBuffer<float>>(transformedData_customized.Schema[customizedColumnName]);
             printHelper(customizedColumnName, customizedColumn);
 
             // CustomizedTextFeatures column obtained post-transformation.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

@@ -45,7 +45,7 @@ public static void Example()
             // 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<VBuffer<float>>(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features));
+            var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening);
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithColumnInfo.cs

@@ -44,7 +44,7 @@ public static void Example()
             // 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<VBuffer<float>>(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features));
+            var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), whitening);
 

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

@@ -31,7 +31,7 @@ public static void Example()
 
             var wordsDataview = wordsPipeline.Fit(trainData).Transform(trainData);
             // Preview of the CleanWords column obtained after processing SentimentText.
-            var cleanWords = wordsDataview.GetColumn<ReadOnlyMemory<char>[]>("CleanWords");
+            var cleanWords = wordsDataview.GetColumn<ReadOnlyMemory<char>[]>(wordsDataview.Schema["CleanWords"]);
             Console.WriteLine($" CleanWords column obtained post-transformation.");
             foreach (var featureRow in cleanWords)
             {
@@ -86,7 +86,7 @@ public static void Example()
             // And do all required transformations.
             var embeddingDataview = pipeline.Fit(wordsDataview).Transform(wordsDataview);
 
-            var customEmbeddings = embeddingDataview.GetColumn<float[]>("CustomEmbeddings");
+            var customEmbeddings = embeddingDataview.GetColumn<float[]>(embeddingDataview.Schema["CustomEmbeddings"]);
             printEmbeddings("GloveEmbeddings", customEmbeddings);
 
             // -1  -2   -3  -0.5   -1  8.5  0   0   20
@@ -98,7 +98,7 @@ public static void Example()
             // Second set of 3 floats in output represent average (for each dimension) for extracted values.
             // Third set of 3 floats in output represent maximum values (for each dimension) for extracted values.
             // Preview of GloveEmbeddings.
-            var gloveEmbeddings = embeddingDataview.GetColumn<float[]>("GloveEmbeddings");
+            var gloveEmbeddings = embeddingDataview.GetColumn<float[]>(embeddingDataview.Schema["GloveEmbeddings"]);
             printEmbeddings("GloveEmbeddings", gloveEmbeddings);
             // 0.23166 0.048825 0.26878 -1.3945 -0.86072 -0.026778 0.84075 -0.81987 -1.6681 -1.0658 -0.30596 0.50974 ...
             //-0.094905 0.61109 0.52546 - 0.2516 0.054786 0.022661 1.1801 0.33329 - 0.85388 0.15471 - 0.5984 0.4364  ...

docs/samples/Microsoft.ML.Samples/Static/FeatureSelectionTransform.cs

@@ -83,8 +83,8 @@ public static void FeatureSelectionTransform()
             };
 
             // Print the data that results from the transformations.
-            var countSelectColumn = transformedData.AsDynamic.GetColumn<VBuffer<float>>("FeaturesCountSelect");
-            var MISelectColumn = transformedData.AsDynamic.GetColumn<VBuffer<float>>("FeaturesMISelect");
+            var countSelectColumn = transformedData.AsDynamic.GetColumn<VBuffer<float>>(transformedData.AsDynamic.Schema["FeaturesCountSelect"]);
+            var MISelectColumn = transformedData.AsDynamic.GetColumn<VBuffer<float>>(transformedData.AsDynamic.Schema["FeaturesMISelect"]);
             printHelper("FeaturesCountSelect", countSelectColumn);
             printHelper("FeaturesMISelect", MISelectColumn);