Created sample for 'LatentDirichletAllocation' API.

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

@@ -0,0 +1,74 @@
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class LatentDirichletAllocation
+    {
+        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 = "ML.NET's LatentDirichletAllocation API computes topic models." },
+                new TextData(){ Text = "ML.NET's LatentDirichletAllocation API is the best for topic models." },
+                new TextData(){ Text = "I like to eat broccoli and bananas." },
+                new TextData(){ Text = "I eat bananas for breakfast." },
+                new TextData(){ Text = "This car is expensive compared to last week's price." },
+                new TextData(){ Text = "This car was $X last week." },
+            };
+
+            // Convert training data to IDataView.
+            var dataview = mlContext.Data.LoadFromEnumerable(samples);
+
+            // A pipeline for featurizing the text/string using LatentDirichletAllocation API.
+            // To be more accurate in computing the LDA features, the pipeline first normalizes text and removes stop words
+            // before passing tokens (the individual words, lower cased, with common words removed) to LatentDirichletAllocation.
+            var pipeline = mlContext.Transforms.Text.NormalizeText("NormalizedText", "Text")
+                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "NormalizedText"))
+                .Append(mlContext.Transforms.Text.RemoveDefaultStopWords("Tokens"))
+                .Append(mlContext.Transforms.Conversion.MapValueToKey("Tokens"))
+                .Append(mlContext.Transforms.Text.ProduceNgrams("Tokens"))
+                .Append(mlContext.Transforms.Text.LatentDirichletAllocation("Features", "Tokens", numberOfTopics: 3));
+
+            // Fit to data.
+            var transformer = pipeline.Fit(dataview);
+
+            // Create the prediction engine to get the LDA features extracted from the text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(transformer);
+
+            // Convert the sample text into LDA features and print it.
+            PrintLdaFeatures(predictionEngine.Predict(samples[0]));
+            PrintLdaFeatures(predictionEngine.Predict(samples[1]));
+
+            // Features obtained post-transformation.
+            // For LatentDirichletAllocation, we had specified numTopic:3. Hence each prediction has been featurized as a vector of floats with length 3.
+
+            //  Topic1  Topic2  Topic3
+            //  0.6364  0.2727  0.0909
+            //  0.5455  0.1818  0.2727
+        }
+
+        private static void PrintLdaFeatures(TransformedTextData prediction)
+        {
+            for (int i = 0; i < prediction.Features.Length; i++)
+                Console.Write($"{prediction.Features[i]:F4}  ");
+            Console.WriteLine();
+        }
+
+        private class TextData
+        {
+            public string Text { get; set; }
+        }
+
+        private class TransformedTextData : TextData
+        {
+            public float[] Features { get; set; }
+        }
+    }
+}

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

@@ -509,7 +509,7 @@ internal static NgramHashingEstimator ProduceHashedNgrams(this TransformsCatalog
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
-        /// [!code-csharp[LatentDirichletAllocation](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/LdaTransform.cs)]
+        /// [!code-csharp[LatentDirichletAllocation](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/LatentDirichletAllocation.cs)]
         /// ]]>
         /// </format>
         /// </example>