Created samples for 'ProduceWordBags' and 'ProduceHashedWordBags' API.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceHashedWordBags.cs

@@ -0,0 +1,89 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.Text;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class ProduceHashedWordBags
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Create a small dataset as an IEnumerable.
+            var samples = new List<TextData>()
+            {
+                new TextData(){ Text = "This is an example to compute bag-of-word features using hashing." },
+                new TextData(){ Text = "ML.NET's ProduceHashedWordBags API produces count of n-grams and hashes it as an index into a vector of given bit length." },
+                new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
+                new TextData(){ Text = "computing n-grams and hash them to the index given by hash value." },
+                new TextData(){ Text = "The hashing reduces the size of the output feature vector" },
+                new TextData(){ Text = "which is useful in case when number of n-grams is very large." },
+            };
+
+            // Convert training data to IDataView.
+            var dataview = mlContext.Data.LoadFromEnumerable(samples);
+
+            // A pipeline for converting text into numeric bag-of-word features using hashing.
+            // The following call to 'ProduceHashedWordBags' implicitly tokenizes the text/string into words/tokens.
+            // Please note that the length of the output feature vector depends on the 'numberOfBits' settings.
+            var textPipeline = mlContext.Transforms.Text.ProduceHashedWordBags("BagOfWordFeatures", "Text",
+                numberOfBits: 5,
+                ngramLength: 3,
+                useAllLengths: false,
+                maximumNumberOfInverts: 1);
+
+            // Fit to data.
+            var textTransformer = textPipeline.Fit(dataview);
+            var transformedDataView = textTransformer.Transform(dataview);
+
+            // Create the prediction engine to get the bag-of-word features extracted from the text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
+
+            // Convert the text into numeric features.
+            var prediction = predictionEngine.Predict(samples[0]);
+
+            // Print the length of the feature vector.
+            Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");
+
+            // Preview of the produced n-grams.
+            // Get the slot names from the column's metadata.
+            // The slot names for a vector column corresponds to the names associated with each position in the vector.
+            VBuffer<ReadOnlyMemory<char>> slotNames = default;
+            transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
+            var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
+            var slots = slotNames.GetValues();
+            Console.Write("N-grams: ");
+            foreach (var featureRow in BagOfWordFeaturesColumn)
+            {
+                foreach (var item in featureRow.Items())
+                    Console.Write($"{slots[item.Key]}  ");
+                Console.WriteLine();
+            }
+
+            // Print the first 10 feature values.
+            Console.Write("Features: ");
+            for (int i = 0; i < 10; i++)
+                Console.Write($"{prediction.BagOfWordFeatures[i]:F4}  ");
+
+            //  Expected output:
+            //   Number of Features: 32
+            //   N-grams:  an|example|to  is|an|example  example|to|compute  This|is|an  compute|bag-of-word|features  bag-of-word|features|using  to|compute|bag-of-word  ML.NET's|ProduceHashedWordBags|API  as|an|index  API|produces|count  ...
+            //   Features:     0.0000        0.0000            0.0000           0.0000              0.0000                          0.0000               1.0000                         2.0000                   0.0000          0.0000         ...
+        }
+
+        private class TextData
+        {
+            public string Text { get; set; }
+        }
+
+        private class TransformedTextData : TextData
+        {
+            public float[] BagOfWordFeatures { get; set; }
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ProduceWordBags.cs

@@ -0,0 +1,89 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Microsoft.ML.Data;
+using Microsoft.ML.Transforms.Text;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class ProduceWordBags
+    {
+        public static void Example()
+        {
+            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
+            // as well as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Create a small dataset as an IEnumerable.
+            var samples = new List<TextData>()
+            {
+                new TextData(){ Text = "This is an example to compute bag-of-word features." },
+                new TextData(){ Text = "ML.NET's ProduceWordBags API produces bag-of-word features from input text." },
+                new TextData(){ Text = "It does so by first tokenizing text/string into words/tokens then " },
+                new TextData(){ Text = "computing n-grams and their neumeric values." },
+                new TextData(){ Text = "Each position in the output vector corresponds to a particular n-gram." },
+                new TextData(){ Text = "The value at each position corresponds to," },
+                new TextData(){ Text = "the number of times n-gram occured in the data (Tf), or" },
+                new TextData(){ Text = "the inverse of the number of documents contain the n-gram (Idf)," },
+                new TextData(){ Text = "or compute both and multipy together (Tf-Idf)." },
+            };
+
+            // Convert training data to IDataView.
+            var dataview = mlContext.Data.LoadFromEnumerable(samples);
+
+            // A pipeline for converting text into numeric bag-of-word features.
+            // The following call to 'ProduceWordBags' implicitly tokenizes the text/string into words/tokens.
+            // Please note that the length of the output feature vector depends on the n-gram settings.
+            var textPipeline = mlContext.Transforms.Text.ProduceWordBags("BagOfWordFeatures", "Text",
+                ngramLength: 3, useAllLengths: false, weighting: NgramExtractingEstimator.WeightingCriteria.Tf);
+
+            // Fit to data.
+            var textTransformer = textPipeline.Fit(dataview);
+            var transformedDataView = textTransformer.Transform(dataview);
+
+            // Create the prediction engine to get the bag-of-word features extracted from the text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
+
+            // Convert the text into numeric features.
+            var prediction = predictionEngine.Predict(samples[0]);
+
+            // Print the length of the feature vector.
+            Console.WriteLine($"Number of Features: {prediction.BagOfWordFeatures.Length}");
+
+            // Preview of the produced n-grams.
+            // Get the slot names from the column's metadata.
+            // The slot names for a vector column corresponds to the names associated with each position in the vector.
+            VBuffer<ReadOnlyMemory<char>> slotNames = default;
+            transformedDataView.Schema["BagOfWordFeatures"].GetSlotNames(ref slotNames);
+            var BagOfWordFeaturesColumn = transformedDataView.GetColumn<VBuffer<float>>(transformedDataView.Schema["BagOfWordFeatures"]);
+            var slots = slotNames.GetValues();
+            Console.Write("N-grams: ");
+            foreach (var featureRow in BagOfWordFeaturesColumn)
+            {
+                foreach (var item in featureRow.Items())
+                    Console.Write($"{slots[item.Key]}  ");
+                Console.WriteLine();
+            }
+
+            // Print the first 10 feature values.
+            Console.Write("Features: ");
+            for (int i = 0; i < 10; i++)
+                Console.Write($"{prediction.BagOfWordFeatures[i]:F4}  ");
+
+            //  Expected output:
+            //   Number of Features: 62
+            //   N-grams:   This|is|an  is|an|example  an|example|to  example|to|compute  to|compute|bag-of-word  compute|bag-of-word|features.  ML.NET's|ProduceWordBags|API  ProduceWordBags|API|produces  API|produces|bag-of-word  produces|bag-of-word|features  ...
+            //   Features:    1.0000       1.0000        1.0000           1.0000                1.0000                      1.0000                           0.0000                         0.0000                  0.0000                       0.0000              ...
+        }
+
+        private class TextData
+        {
+            public string Text { get; set; }
+        }
+
+        private class TransformedTextData : TextData
+        {
+            public float[] BagOfWordFeatures { get; set; }
+        }
+    }
+}