SsaSpikeDetector Class
Definition
ITransformer resulting from fitting a SsaSpikeEstimator.
public sealed class SsaSpikeDetector : Microsoft.ML.Transforms.TimeSeries.SsaAnomalyDetectionBaseWrapper, Microsoft.ML.ITransformer
type SsaSpikeDetector = class
inherit SsaAnomalyDetectionBaseWrapper
interface ITransformer
interface ICanSaveModel
Public NotInheritable Class SsaSpikeDetector
Inherits SsaAnomalyDetectionBaseWrapper
Implements ITransformer
- Inheritance
- Implements
Methods
GetOutputSchema(DataViewSchema) |
Schema propagation for transformers. Returns the output schema of the data, if the input schema is like the one provided. (Inherited from SsaAnomalyDetectionBaseWrapper) |
GetStatefulRowToRowMapper(DataViewSchema) |
Same as GetRowToRowMapper(DataViewSchema) but also supports mechanism to save the state. (Inherited from SsaAnomalyDetectionBaseWrapper) |
Transform(IDataView) |
Take the data in, make transformations, output the data. Note that IDataView's are lazy, so no actual transformations happen here, just schema validation. (Inherited from SsaAnomalyDetectionBaseWrapper) |
Explicit Interface Implementations
ICanSaveModel.Save(ModelSaveContext) |
For saving a model into a repository. (Inherited from SsaAnomalyDetectionBaseWrapper) |
ITransformer.GetRowToRowMapper(DataViewSchema) |
Constructs a row-to-row mapper based on an input schema. If IsRowToRowMapper
is |
ITransformer.IsRowToRowMapper |
Whether a call to GetRowToRowMapper(DataViewSchema) should succeed, on an appropriate schema. (Inherited from SsaAnomalyDetectionBaseWrapper) |
Extension Methods
Preview(ITransformer, IDataView, Int32) |
Preview an effect of the |
Append<TTrans>(ITransformer, TTrans) |
Create a new transformer chain, by appending another transformer to the end of this transformer chain. |
CreateTimeSeriesEngine<TSrc,TDst>(ITransformer, IHostEnvironment, Boolean, SchemaDefinition, SchemaDefinition) |
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. |