using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Data;
 
namespace 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; }
        }
    }
}