将自定义列添加到 ML.NET 中的 IDataView

Question

I'd like to add a custom column after loading my IDataView from file. In each row, the column value should be the sum of previous 2 values. A sort of Fibonacci series.

I was wondering to create a custom transformer but I wasn't able to find something that could help me to understand how to proceed. I also tried to clone ML.Net Git repository in order to see how other transformers were implemented but I saw many classes are marked as internal so I cannot re-use them in my project.

Answer 1

There is a way to create a custom transform with CustomMapping

Here's an example I used for this answer .

The input and output classes:

class InputData
{
    public int Age { get; set; }
}

class CustomMappingOutput
{
    public string AgeName { get; set; }
}

class TransformedData
{
    public int Age { get; set; }

    public string AgeName { get; set; }
}

Then, in the ML.NET program:

MLContext mlContext = new MLContext();

var samples = new List<InputData>
{
    new InputData { Age = 16 },
    new InputData { Age = 35 },
    new InputData { Age = 60 },
    new InputData { Age = 28 },
};

var data = mlContext.Data.LoadFromEnumerable(samples);

Action<InputData, CustomMappingOutput> mapping =
    (input, output) =>
    {
        if (input.Age < 18)
        {
            output.AgeName = "Child";
        }
        else if (input.Age < 55)
        {
            output.AgeName = "Man";
        }
        else
        {
            output.AgeName = "Grandpa";
        }
    };

var pipeline = mlContext.Transforms.CustomMapping(mapping, contractName: null);

var transformer = pipeline.Fit(data);
var transformedData = transformer.Transform(data);

var dataEnumerable = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, reuseRowObject: true);

foreach (var row in dataEnumerable)
{
    Console.WriteLine($"{row.Age}\t {row.AgeName}");
}

Answer 2

Easy thing. I am assuming, you know how to use pipelines.

This is a part of my project, where I merge two columns together:

IEstimator<ITransformer> pipeline = mlContext.Transforms.CustomMapping(mapping, contractName: null)
                            .Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName: "question1", outputColumnName: "question1Featurized"))
                            .Append(mlContext.Transforms.Text.FeaturizeText(inputColumnName: "question2", outputColumnName: "question2Featurized"))
                            .Append(mlContext.Transforms.Concatenate("Features", "question1Featurized", "question2Featurized"))
                            //.Append(mlContext.Transforms.NormalizeMinMax("Features"))
                            //.AppendCacheCheckpoint(mlContext)
                            .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: nameof(customTransform.Label), featureColumnName: "Features"));

As you can see the two columns question1Featurized and question2Featurized are combined into Features which will be created and can be used as any other column of IDataView . The Features column does not need to be declared in a separate class.

So in your case you should transform the columns firs in their data type, if strings you can do what I did and in case of numeric values use a custom Transformer/customMapping .

The documentation of the Concatenate function might help as well!