using System;
using System.Collections.Generic;
using Microsoft.ML;
 
namespace Samples.Dynamic
{
    public static class CustomMapping
    {
        // This example shows how to define and apply a custom mapping of input
        // columns to output columns without defining a contract. Since a contract
        // is not defined, the pipeline containing this mapping cannot be saved and
        // loaded back.
        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();
 
            // Get a small dataset as an IEnumerable and convert it to an IDataView.
            var samples = new List<InputData>
            {
                new InputData { Age = 26 },
                new InputData { Age = 35 },
                new InputData { Age = 34 },
                new InputData { Age = 28 },
            };
            var data = mlContext.Data.LoadFromEnumerable(samples);
 
            // We define the custom mapping between input and output rows that will
            // be applied by the transformation.
            Action<InputData, CustomMappingOutput> mapping =
                (input, output) => output.IsUnderThirty = input.Age < 30;
 
            // Custom transformations can be used to transform data directly, or as
            // part of a pipeline of estimators. Note: If contractName is null in
            // the CustomMapping estimator, any pipeline of estimators containing
            // it, cannot be saved and loaded back. 
            var pipeline = mlContext.Transforms.CustomMapping(mapping, contractName:
                null);
 
            // Now we can transform the data and look at the output to confirm the
            // behavior of the estimator. This operation doesn't actually evaluate
            // data until we read the data below.
            var transformer = pipeline.Fit(data);
            var transformedData = transformer.Transform(data);
 
            var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(
                transformedData, reuseRowObject: true);
 
            Console.WriteLine("Age\t IsUnderThirty");
            foreach (var row in dataEnumerable)
                Console.WriteLine($"{row.Age}\t {row.IsUnderThirty}");
 
            // Expected output:
            // Age      IsUnderThirty
            // 26       True
            // 35       False
            // 34       False
            // 28       True
        }
 
        // Defines only the column to be generated by the custom mapping
        // transformation in addition to the columns already present.
        private class CustomMappingOutput
        {
            public bool IsUnderThirty { get; set; }
        }
 
        // Defines the schema of the input data.
        private class InputData
        {
            public float Age { get; set; }
        }
 
        // Defines the schema of the transformed data, which includes the new column
        // IsUnderThirty.
        private class TransformedData : InputData
        {
            public bool IsUnderThirty { get; set; }
        }
    }
}