public void TestEstimatorMulticlassLogisticRegression()
{
(IEstimator <ITransformer> pipe, IDataView dataView) = GetMultiClassPipeline();
var trainer = new MulticlassLogisticRegression(Env, "Label", "Features");
var pipeWithTrainer = pipe.Append(trainer);
TestEstimatorCore(pipeWithTrainer, dataView);
var transformedDataView = pipe.Fit(dataView).Transform(dataView);
var model = trainer.Fit(transformedDataView);
trainer.Train(transformedDataView, model.Model);
Done();
}
public Arguments()
{
BasePredictors = new[]
{
ComponentFactoryUtils.CreateFromFunction(
env => {
// Note that this illustrates a fundamnetal problem with the mixture of `ITrainer` and `ITrainerEstimator`
// present in this class. The options to the estimator have no way of being communicated to the `ITrainer`
// implementation, so there is a fundamnetal disconnect if someone chooses to ever use the *estimator* with
// non-default column names. Unfortuantely no method of resolving this temporary strikes me as being any
// less laborious than the proper fix, which is that this "meta" component should itself be a trainer
// estimator, as opposed to a regular trainer.
var trainerEstimator = new MulticlassLogisticRegression(env, LabelColumn, FeatureColumn);
return(TrainerUtils.MapTrainerEstimatorToTrainer <MulticlassLogisticRegression,
MulticlassLogisticRegressionModelParameters, MulticlassLogisticRegressionModelParameters>(env, trainerEstimator));
})
};
}
public void Train(string dest)
{
using (var env = new ConsoleEnvironment(verbose: false))
{
var args = new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new TextLoader.Column[] {
new TextLoader.Column("Label", DataKind.R4, 0),
new TextLoader.Column("Sepal_length", DataKind.R4, 1),
new TextLoader.Column("Sepal_width", DataKind.R4, 2),
new TextLoader.Column("Petal_length", DataKind.R4, 3),
new TextLoader.Column("Petal_width", DataKind.R4, 4),
}
};
var reader = new TextLoader(env, args);
var concat = new ColumnConcatenatingEstimator(env,
"Features", "Sepal_length",
"Sepal_width", "Petal_length", "Petal_width");
var km = new MulticlassLogisticRegression(env, "Label", "Features");
var pipeline = concat.Append(km);
IDataView trainingDataView = reader.Read(new MultiFileSource(_dataset));
var model = pipeline.Fit(trainingDataView);
var obs = new IrisObservation()
{
Sepal_length = 3.3f,
Sepal_width = 1.6f,
Petal_length = 0.2f,
Petal_width = 5.1f,
};
_fct = model.MakePredictionFunction <IrisObservation, IrisPrediction>(env);
using (var stdest = File.OpenWrite(dest))
model.SaveTo(env, stdest);
}
}
