ml.net train model input from string instead of a file

Question

I\'ve been using the following line of code to load text data:

pipeline.Add(new TextLoader(dataPath).CreateFrom(separator: \',\'));
         


        

Answer 1

You'll want to use a CollectionDataSource which was introduced in v0.2 of ML.NET. You can either grab fresh github bits or the nuget, and then you can use CollectionDataSource on top of an enumerable.

You can find a full example in its tests: https://github.com/dotnet/machinelearning/blob/6d5a41d39face9e98c242d3db3ff10ea8e233cc1/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs

One example on Iris data:

var data = new List() {
    new IrisData { SepalLength = 1f, SepalWidth = 1f ,PetalLength=0.3f, PetalWidth=5.1f, Label=1},
    new IrisData { SepalLength = 1f, SepalWidth = 1f ,PetalLength=0.3f, PetalWidth=5.1f, Label=1},
    new IrisData { SepalLength = 1.2f, SepalWidth = 0.5f ,PetalLength=0.3f, PetalWidth=5.1f, Label=0}
};
var collection = CollectionDataSource.Create(data);

pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
    "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
PredictionModel model = pipeline.Train();

IrisPrediction prediction = model.Predict(new IrisData()
{
    SepalLength = 3.3f,
    SepalWidth = 1.6f,
    PetalLength = 0.2f,
    PetalWidth = 5.1f,
});

pipeline = new LearningPipeline();
collection = CollectionDataSource.Create(data.AsEnumerable());
pipeline.Add(collection);
pipeline.Add(new ColumnConcatenator(outputColumn: "Features",
    "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));
pipeline.Add(new StochasticDualCoordinateAscentClassifier());
model = pipeline.Train();