ValueToKeyMappingEstimator Class
Definition
Important
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IEstimator<TTransformer> for the ValueToKeyMappingTransformer. Converts a set of categorical values (for example, US state abbreviations) into numerical key values (e.g. 1-50). The numerical key can be used directly by classification algorithms.
public sealed class ValueToKeyMappingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Transforms.ValueToKeyMappingTransformer>type ValueToKeyMappingEstimator = class
interface IEstimator<ValueToKeyMappingTransformer>Public NotInheritable Class ValueToKeyMappingEstimator
Implements IEstimator(Of ValueToKeyMappingTransformer)- Inheritance
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ValueToKeyMappingEstimator
- Implements
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | Yes |
| Input column data type | Scalar or vector of numeric, boolean, text, System.DateTime and key type. |
| Output column data type | Scalar or vector of key type. |
| Exportable to ONNX | Yes |
The ValueToKeyMappingEstimator maps the input values to keys using a dictionary that is built during training. The dictionary mapping values to keys is most commonly learnt from the unique values in input data, but can be pre-defined. The key value is the one-based index of the item in the dictionary. If the key is not found in the dictionary, it is assigned the missing value indicator. If multiple columns are used, each column builds exactly one dictionary. The dictionary data is stored as an annotation in the schema, to enable the reverse mapping to occur using KeyToValueMappingEstimator
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) |
Trains and returns a ValueToKeyMappingTransformer. |
| GetOutputSchema(SchemaShape) |
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline. |
Extension Methods
| AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment) |
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes. |
| WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>) |
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called. |