using System;
using System.Collections.Generic;
using Microsoft.ML;
using Microsoft.ML.Transforms.Text;
 
namespace Samples.Dynamic
{
    public static class FeaturizeTextWithOptions
    {
        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 FeaturizeText API uses a " +
                "composition of several basic transforms to convert text into " +
                "numeric features." },
 
                new TextData(){ Text = "This API can be used as a featurizer to " +
                "perform text classification." },
 
                new TextData(){ Text = "There are a number of approaches to text " +
                "classification." },
 
                new TextData(){ Text = "One of the simplest and most common " +
                "approaches is called “Bag of Words”." },
 
                new TextData(){ Text = "Text classification can be used for a " +
                "wide variety of tasks" },
 
                new TextData(){ Text = "such as sentiment analysis, topic " +
                "detection, intent identification etc." },
            };
 
            // Convert training data to IDataView.
            var dataview = mlContext.Data.LoadFromEnumerable(samples);
 
            // A pipeline for converting text into numeric features.
            // The following call to 'FeaturizeText' instantiates
            // 'TextFeaturizingEstimator' with given parameters. The length of the
            // output feature vector depends on these settings.
            var options = new TextFeaturizingEstimator.Options()
            {
                // Also output tokenized words
                OutputTokensColumnName = "OutputTokens",
                CaseMode = TextNormalizingEstimator.CaseMode.Lower,
                // Use ML.NET's built-in stop word remover
                StopWordsRemoverOptions = new StopWordsRemovingEstimator.Options()
                {
                    Language = TextFeaturizingEstimator.Language.English
                },
 
                WordFeatureExtractor = new WordBagEstimator.Options()
                {
                    NgramLength
                    = 2,
                    UseAllLengths = true
                },
 
                CharFeatureExtractor = new WordBagEstimator.Options()
                {
                    NgramLength
                    = 3,
                    UseAllLengths = false
                },
            };
            var textPipeline = mlContext.Transforms.Text.FeaturizeText("Features",
                options, "Text");
 
            // Fit to data.
            var textTransformer = textPipeline.Fit(dataview);
 
            // Create the prediction engine to get the 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.Features.Length}");
 
            // Print feature values and tokens.
            Console.Write("Features: ");
            for (int i = 0; i < 10; i++)
                Console.Write($"{prediction.Features[i]:F4}  ");
 
            Console.WriteLine("\nTokens: " + string.Join(",", prediction
                .OutputTokens));
 
            //  Expected output:
            //   Number of Features: 282
            //   Features: 0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.0941  0.1881 ...
            //   Tokens: ml.net's,featurizetext,api,uses,composition,basic,transforms,convert,text,numeric,features.
        }
 
        private class TextData
        {
            public string Text { get; set; }
        }
 
        private class TransformedTextData : TextData
        {
            public float[] Features { get; set; }
            public string[] OutputTokens { get; set; }
        }
    }
}