ColumnSelectingEstimator Class
Definition
Important
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Keeps or drops selected columns from an IDataView.
public sealed class ColumnSelectingEstimator : Microsoft.ML.Data.TrivialEstimator<Microsoft.ML.Transforms.ColumnSelectingTransformer>type ColumnSelectingEstimator = class
inherit TrivialEstimator<ColumnSelectingTransformer>Public NotInheritable Class ColumnSelectingEstimator
Inherits TrivialEstimator(Of ColumnSelectingTransformer)- Inheritance
Remarks
Estimator Characteristics
| Does this estimator need to look at the data to train its parameters? | No |
| Input columns data type | Any |
| Exportable to ONNX | Yes |
The resulting ColumnSelectingTransformer operates on the schema of a given IDataView by dropping or keeping selected columns from the schema.
It is commonly used to remove unwanted columns before serializing a dataset or writing it to a file. It is not necessary to drop unused columns before training or performing transforms, as the IDataView is lazily evaluated and will not actually materialize the columns until needed. In the case of serialization, every column in the schema will be written out. If there are columns that should not be saved, this estimator can be used to remove them.
Check the See Also section for links to usage examples.
Methods
| Fit(IDataView) | (Inherited from TrivialEstimator<TTransformer>) |
| 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. |