public void TestPipelineIris()
{
var iris = FileHelper.GetTestFile("iris_data_id.txt");
var df = DataFrameIO.ReadCsv(iris, sep: ',', dtypes: new[] { NumberType.R4, NumberType.R4, NumberType.R4 });
var env3 = PyEnvHelper.CreateStoreEnvironment();
var pipe = PyPipelineHelper.CreateScikitPipeline(new string[] { "Concat{col=Features:Slength,Swidth}" },
"mlr", env3.Item1);
pipe.Train(df, "Features", "Label");
var sout = env3.Item2.ToString();
Assert.IsNotNull(sout);
}
public void TestTrainIris()
{
var file = FileHelper.GetTestFile("iris_data_id.txt");
var df = PyDataFrameHelper.ReadCsv(file);
df.AddColumn("LabelR4", df["Label"].AsType(NumberType.R4));
var host = PyEnvHelper.CreateConsoleEnvironment();
var pipe = PyPipelineHelper.CreateScikitPipeline(new[] { "concat{col=Feat:Slength,Swidth,Plength,Pwidth}" },
"oova{p=ap}", host);
DataFrame dfo;
using (var res = pipe.Train(df, "Feat", "LabelR4"))
dfo = PyDataFrameHelper.ReadView(pipe.Predict(df));
Assert.AreEqual(dfo.Shape, new Tuple <int, int>(150, 14));
}
public void TestPipelineDiabete()
{
var diab = FileHelper.GetTestFile("diabete.csv");
var cols = Enumerable.Range(0, 10).Select(c => NumberType.R4).ToArray();
var colsName = string.Join(',', Enumerable.Range(0, 10).Select(c => $"F{c}"));
var df = DataFrameIO.ReadCsv(diab, sep: ',', dtypes: cols);
var env3 = PyEnvHelper.CreateStoreEnvironment();
var pipe = PyPipelineHelper.CreateScikitPipeline(new string[] { $"Concat{{col=Features:{colsName}}}" }, "ols", env3.Item1);
pipe.Train(df, "Features", "Label");
var sout = env3.Item2.ToString();
Assert.IsNotNull(sout);
DataFrame pred = PyPipelineHelper.FastPredictOrTransform(pipe, df);
Assert.IsTrue(df.Shape[0] > 0);
}
