public void PredictTrueTest()
{
string getPath = Path.GetFullPath(Path.Combine(Environment.CurrentDirectory, @"..\..\..\"));
string _trainDataPath = getPath + "\\GoDiet\\DietData\\DataModel\\trainData\\weight_lr_train1.csv";
MLContext mlContext = new MLContext(seed: 0);
TextLoader _textLoader = mlContext.Data.CreateTextReader(new TextLoader.Arguments()
{
Separator = ",",
HasHeader = true,
Column = new[]
{
new TextLoader.Column("Label", DataKind.Bool, 1),
new TextLoader.Column("BMI", DataKind.Text, 0)
}
});
IDataView dataView = _textLoader.Read(_trainDataPath);
var pipeline = mlContext.Transforms.Text.FeaturizeText("BMI", "Features")
.Append(mlContext.BinaryClassification.Trainers.FastTree(numLeaves: 10, numTrees: 10, minDatapointsInLeaves: 5));
var exp = pipeline.Fit(dataView);
var model = BinaryClassML.Train(mlContext, _trainDataPath);
bool res = BinaryClassML.Predict(mlContext, model, "58");
Assert.AreEqual(true, res);
}
public void SaveModelAsFile()
{
string getPath = Path.GetFullPath(Path.Combine(Environment.CurrentDirectory, @"..\..\..\"));
string _trainDataPath = getPath + "\\GoDiet\\DietData\\DataModel\\trainData\\weight_lr_train1.csv";
MLContext mlContext = new MLContext(seed: 0);
ITransformer model = BinaryClassML.Train(mlContext, _trainDataPath);
BinaryClassML.SaveModelAsFile(mlContext, model);
Assert.AreEqual("Saved", BinaryClassML.check);
}
public void EvaluateFailTest()
{
string getPath = Path.GetFullPath(Path.Combine(Environment.CurrentDirectory, @"..\..\..\"));
string trainDataPath = getPath + "\\GoDiet\\DietData\\DataModel\\trainData\\weight_lr_train1.csv";
MLContext mlContext = new MLContext(seed: 0);
var model = BinaryClassML.Train(mlContext, trainDataPath);
BinaryClassML.Evaluate(mlContext, model);
Assert.AreNotEqual("", BinaryClassML.check);
}
public void SaveModelAsFileExceptionThrownTest()
{
try
{
string getPath = Path.GetFullPath(Path.Combine(Environment.CurrentDirectory, @"..\..\..\"));
string _trainDataPath = getPath + "\\DietData\\DataModel\\trainData\\weight_lr_train1.csv";
MLContext mlContext = new MLContext(seed: 0);
ITransformer model = BinaryClassML.Train(mlContext, _trainDataPath);
BinaryClassML.SaveModelAsFile(mlContext, model);
Assert.Fail();
}
catch (Exception) { }
}