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();
}
/// <summary>
/// <see cref="TimeSeriesPredictionEngine{TSrc, TDst}"/> creates a prediction engine for a time series pipeline.
/// It updates the state of time series model with observations seen at prediction phase and allows checkpointing the model.
/// </summary>
/// <typeparam name="TSrc">Class describing input schema to the model.</typeparam>
/// <typeparam name="TDst">Class describing the output schema of the prediction.</typeparam>
/// <param name="transformer">The time series pipeline in the form of a <see cref="ITransformer"/>.</param>
/// <param name="env">Usually <see cref="MLContext"/></param>
/// <param name="options">Advanced configuration options.</param>
/// <p>Example code can be found by searching for <i>TimeSeriesPredictionEngine</i> in <a href='https://github.com/dotnet/machinelearning'>ML.NET.</a></p>
/// <example>
/// <format type="text/markdown">
/// <]
/// ]]>
/// </format>
/// </example>
public static TimeSeriesPredictionEngine <TSrc, TDst> CreateTimeSeriesEngine <TSrc, TDst>(this ITransformer transformer, IHostEnvironment env,
PredictionEngineOptions options)
where TSrc : class
where TDst : class, new()
{
Contracts.CheckValue(env, nameof(env));
env.CheckValue(options, nameof(options));
return(new TimeSeriesPredictionEngine <TSrc, TDst>(env, transformer, options));
}
/// <summary>
/// Contructor for creating time series specific prediction engine. It allows the time series model to be updated with the observations
/// seen at prediction time via <see cref="CheckPoint(IHostEnvironment, string)"/>
/// </summary>
internal TimeSeriesPredictionEngine(IHostEnvironment env, ITransformer transformer, PredictionEngineOptions options) :
base(env, CloneTransformers(transformer), options.IgnoreMissingColumns, options.InputSchemaDefinition, options.OutputSchemaDefinition, options.OwnsTransformer)
{
}
