public void ReconfigurablePredictionNoPipeline()
{
var mlContext = new MLContext(seed: 1);
var data = mlContext.Data.LoadFromEnumerable(TypeTestData.GenerateDataset());
var pipeline = mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
new Trainers.LbfgsLogisticRegressionBinaryTrainer.Options {
NumberOfThreads = 1
});
var model = pipeline.Fit(data);
var newModel = mlContext.BinaryClassification.ChangeModelThreshold(model, -2.0f);
var rnd = new Random(1);
var randomDataPoint = TypeTestData.GetRandomInstance(rnd);
var engine = mlContext.Model.CreatePredictionEngine <TypeTestData, Prediction>(model);
var pr = engine.Predict(randomDataPoint);
// Score is -1.38 so predicted label is false.
Assert.False(pr.PredictedLabel);
Assert.True(pr.Score <= 0);
var newEngine = mlContext.Model.CreatePredictionEngine <TypeTestData, Prediction>(newModel);
pr = newEngine.Predict(randomDataPoint);
// Score is still -1.38 but since threshold is no longer 0 but -2 predicted label now is true.
Assert.True(pr.PredictedLabel);
Assert.True(pr.Score <= 0);
}
public void ReadFromIEnumerable()
{
var mlContext = new MLContext(seed: 1, conc: 1);
// Read the dataset from an enumerable.
var data = mlContext.Data.ReadFromEnumerable(TypeTestData.GenerateDataset());
Common.AssertTypeTestDataset(data);
}
public void WriteAndReadAFromABinaryFile()
{
var mlContext = new MLContext(seed: 1, conc: 1);
var dataBefore = mlContext.Data.ReadFromEnumerable(TypeTestData.GenerateDataset());
// Serialize a dataset with a known schema to a file.
var filePath = SerializeDatasetToBinaryFile(mlContext, dataBefore);
var dataAfter = mlContext.Data.ReadFromBinary(filePath);
Common.AssertTestTypeDatasetsAreEqual(mlContext, dataBefore, dataAfter);
}
public void ExportToIEnumerable()
{
var mlContext = new MLContext(seed: 1, conc: 1);
// Read the dataset from an enumerable.
var enumerableBefore = TypeTestData.GenerateDataset();
var data = mlContext.Data.ReadFromEnumerable(enumerableBefore);
// Export back to an enumerable.
var enumerableAfter = mlContext.CreateEnumerable <TypeTestData>(data, true);
Common.AssertEqual(enumerableBefore, enumerableAfter);
}
public void WriteToAndReadASchemaFromADelimitedFile()
{
var mlContext = new MLContext(seed: 1, conc: 1);
var dataBefore = mlContext.Data.ReadFromEnumerable(TypeTestData.GenerateDataset());
foreach (var separator in _separators)
{
// Serialize a dataset with a known schema to a file.
var filePath = SerializeDatasetToFile(mlContext, dataBefore, separator);
var dataAfter = mlContext.Data.ReadFromTextFile <TypeTestData>(filePath, hasHeader: true, separatorChar: separator);
Common.AssertTestTypeDatasetsAreEqual(mlContext, dataBefore, dataAfter);
}
}
public void WriteToAndReadFromADelimetedFile()
{
var mlContext = new MLContext(seed: 1);
var dataBefore = mlContext.Data.LoadFromEnumerable(TypeTestData.GenerateDataset());
foreach (var separator in _separators)
{
// Serialize a dataset with a known schema to a file.
var filePath = SerializeDatasetToFile(mlContext, dataBefore, separator);
var dataAfter = TypeTestData.GetTextLoader(mlContext, separator).Load(filePath);
Common.AssertTestTypeDatasetsAreEqual(mlContext, dataBefore, dataAfter);
}
}
public void PredictionEngineModelDisposal()
{
var mlContext = new MLContext(seed: 1);
var data = mlContext.Data.LoadFromEnumerable(TypeTestData.GenerateDataset());
var pipeline = mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
new Trainers.LbfgsLogisticRegressionBinaryTrainer.Options {
NumberOfThreads = 1
});
var model = pipeline.Fit(data);
var engine = mlContext.Model.CreatePredictionEngine <TypeTestData, Prediction>(model, new PredictionEngineOptions());
// Dispose of prediction engine, should dispose of model
engine.Dispose();
// Get disposed flag using reflection
var bfIsDisposed = BindingFlags.Instance | BindingFlags.NonPublic;
var field = model.GetType().BaseType.BaseType.GetField("_disposed", bfIsDisposed);
// Make sure the model is actually disposed
Assert.True((bool)field.GetValue(model));
// Make a new model/prediction engine. Set the options so prediction engine doesn't dispose
model = pipeline.Fit(data);
var options = new PredictionEngineOptions()
{
OwnsTransformer = false
};
engine = mlContext.Model.CreatePredictionEngine <TypeTestData, Prediction>(model, options);
// Dispose of prediction engine, shouldn't dispose of model
engine.Dispose();
// Make sure model is not disposed of.
Assert.False((bool)field.GetValue(model));
// Dispose of the model for test cleanliness
model.Dispose();
}
