public void TestI_Q_KMeansInnerAPIWithDataFrame()
{
var methodName = System.Reflection.MethodBase.GetCurrentMethod().Name;
var outModelFilePath = FileHelper.GetOutputFile("outModelFilePath.zip", methodName);
var iris = FileHelper.GetTestFile("iris.txt");
using (var env = new ConsoleEnvironment(conc: 1))
{
ComponentHelper.AddStandardComponents(env);
var df = Scikit.ML.DataManipulation.DataFrameIO.ReadCsv(iris, sep: '\t', dtypes: new ColumnType[] { NumberType.R4 });
var conc = env.CreateTransform("Concat{col=Feature:Sepal_length,Sepal_width}", df);
var roleMap = env.CreateExamples(conc, "Feature", label: "Label");
var trainer = CreateTrainer(env, "km");
IPredictor model;
using (var ch = env.Start("test"))
model = TrainUtils.Train(env, ch, roleMap, trainer, null, 0);
using (var ch = env.Start("Save"))
using (var fs = File.Create(outModelFilePath))
TrainUtils.SaveModel(env, ch, fs, model, roleMap);
Predictor pred;
using (var fs = File.OpenRead(outModelFilePath))
pred = env.LoadPredictorOrNull(fs);
#pragma warning disable CS0618
var scorer = ScoreUtils.GetScorer(pred.GetPredictorObject() as IPredictor, roleMap, env, null);
#pragma warning restore CS0618
var dfout = Scikit.ML.DataManipulation.DataFrameIO.ReadView(scorer);
Assert.AreEqual(dfout.Shape, new Tuple <int, int>(150, 13));
}
}
