using System;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
 
namespace Samples.Dynamic.Transforms.Categorical
{
    public static class OneHotEncoding
    {
        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[]
            {
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "0-5yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "6-11yrs"},
                new DataPoint {Education = "11-15yrs"}
            };
 
            // Convert training data to IDataView.
            IDataView data = mlContext.Data.LoadFromEnumerable(samples);
 
            // A pipeline for one hot encoding the Education column.
            var pipeline = mlContext.Transforms.Categorical.OneHotEncoding(
                "EducationOneHotEncoded", "Education");
 
            // Fit and transform the data.
            IDataView oneHotEncodedData = pipeline.Fit(data).Transform(data);
 
            PrintDataColumn(oneHotEncodedData, "EducationOneHotEncoded");
 
            // We have 3 slots because there are three categories in the
            // 'Education' column.
 
            // 1 0 0
            // 1 0 0
            // 0 1 0
            // 0 1 0
            // 0 0 1
 
            // A pipeline for one hot encoding the Education column (using keying).
            var keyPipeline = mlContext.Transforms.Categorical.OneHotEncoding(
                "EducationOneHotEncoded", "Education",
                OneHotEncodingEstimator.OutputKind.Key);
 
            // Fit and Transform data.
            oneHotEncodedData = keyPipeline.Fit(data).Transform(data);
 
            var keyEncodedColumn =
                oneHotEncodedData.GetColumn<uint>("EducationOneHotEncoded");
 
            Console.WriteLine(
                "One Hot Encoding of single column 'Education', with key type " +
                "output.");
 
            // One Hot Encoding of single column 'Education', with key type output.
 
            foreach (uint element in keyEncodedColumn)
                Console.WriteLine(element);
 
            // 1
            // 1
            // 2
            // 2
            // 3
        }
 
        private static void PrintDataColumn(IDataView transformedData,
            string columnName)
        {
            var countSelectColumn = transformedData.GetColumn<float[]>(
                transformedData.Schema[columnName]);
 
            foreach (var row in countSelectColumn)
            {
                for (var i = 0; i < row.Length; i++)
                    Console.Write($"{row[i]}\t");
 
                Console.WriteLine();
            }
        }
 
        private class DataPoint
        {
            public string Education { get; set; }
        }
    }
}