ExperimentBase<TMetrics,TExperimentSettings>.Execute Method
Definition
Important
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Overloads
| Execute(IDataView, ColumnInformation, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>) |
Executes an AutoML experiment. |
| Execute(IDataView, IDataView, ColumnInformation, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>) |
Executes an AutoML experiment. |
| Execute(IDataView, IDataView, String, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>) |
Executes an AutoML experiment. |
| Execute(IDataView, String, String, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>) |
Executes an AutoML experiment. |
| Execute(IDataView, UInt32, ColumnInformation, IEstimator<ITransformer>, IProgress<CrossValidationRunDetail<TMetrics>>) |
Executes an AutoML experiment. |
| Execute(IDataView, UInt32, String, String, IEstimator<ITransformer>, IProgress<CrossValidationRunDetail<TMetrics>>) |
Executes an AutoML experiment. |
Execute(IDataView, ColumnInformation, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.ExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, Microsoft.ML.AutoML.ColumnInformation columnInformation, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.RunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, columnInformation As ColumnInformation, Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of RunDetail(Of TMetrics)) = Nothing) As ExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data to be used by the AutoML experiment.
- columnInformation
- ColumnInformation
Column information for the dataset.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.
Applies to
Execute(IDataView, IDataView, ColumnInformation, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.ExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, Microsoft.ML.IDataView validationData, Microsoft.ML.AutoML.ColumnInformation columnInformation, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.RunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * Microsoft.ML.IDataView * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * Microsoft.ML.IDataView * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, validationData As IDataView, columnInformation As ColumnInformation, Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of RunDetail(Of TMetrics)) = Nothing) As ExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data to be used by the AutoML experiment.
- validationData
- IDataView
The validation data to be used by the AutoML experiment.
- columnInformation
- ColumnInformation
Column information for the dataset.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.
Applies to
Execute(IDataView, IDataView, String, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.ExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, Microsoft.ML.IDataView validationData, string labelColumnName = "Label", Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.RunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * Microsoft.ML.IDataView * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * Microsoft.ML.IDataView * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, validationData As IDataView, Optional labelColumnName As String = "Label", Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of RunDetail(Of TMetrics)) = Nothing) As ExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data to be used by the AutoML experiment.
- validationData
- IDataView
The validation data to be used by the AutoML experiment.
- labelColumnName
- String
The name of the label column.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.
Applies to
Execute(IDataView, String, String, IEstimator<ITransformer>, IProgress<RunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.ExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, string labelColumnName = "Label", string samplingKeyColumn = default, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.RunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * string * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * string * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.RunDetail<'Metrics>> -> Microsoft.ML.AutoML.ExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, Optional labelColumnName As String = "Label", Optional samplingKeyColumn As String = Nothing, Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of RunDetail(Of TMetrics)) = Nothing) As ExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data used by the AutoML experiment.
- labelColumnName
- String
The dataset column used as the label.
- samplingKeyColumn
- String
The dataset column used as the sampling key column. See SamplingKeyColumnName for more information.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.
Applies to
Execute(IDataView, UInt32, ColumnInformation, IEstimator<ITransformer>, IProgress<CrossValidationRunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.CrossValidationExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, uint numberOfCVFolds, Microsoft.ML.AutoML.ColumnInformation columnInformation = default, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * uint32 * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<'Metrics>> -> Microsoft.ML.AutoML.CrossValidationExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * uint32 * Microsoft.ML.AutoML.ColumnInformation * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<'Metrics>> -> Microsoft.ML.AutoML.CrossValidationExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, numberOfCVFolds As UInteger, Optional columnInformation As ColumnInformation = Nothing, Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of CrossValidationRunDetail(Of TMetrics)) = Nothing) As CrossValidationExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data to be used by the AutoML experiment.
- numberOfCVFolds
- UInt32
The number of cross validation folds into which the training data should be divided when fitting a model.
- columnInformation
- ColumnInformation
Column information for the dataset.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
- progressHandler
- IProgress<CrossValidationRunDetail<TMetrics>>
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The cross validation experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.
Applies to
Execute(IDataView, UInt32, String, String, IEstimator<ITransformer>, IProgress<CrossValidationRunDetail<TMetrics>>)
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
- Source:
- ExperimentBase.cs
Executes an AutoML experiment.
public virtual Microsoft.ML.AutoML.CrossValidationExperimentResult<TMetrics> Execute(Microsoft.ML.IDataView trainData, uint numberOfCVFolds, string labelColumnName = "Label", string samplingKeyColumn = default, Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> preFeaturizer = default, IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<TMetrics>> progressHandler = default);abstract member Execute : Microsoft.ML.IDataView * uint32 * string * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<'Metrics>> -> Microsoft.ML.AutoML.CrossValidationExperimentResult<'Metrics (requires 'Metrics : null)>
override this.Execute : Microsoft.ML.IDataView * uint32 * string * string * Microsoft.ML.IEstimator<Microsoft.ML.ITransformer> * IProgress<Microsoft.ML.AutoML.CrossValidationRunDetail<'Metrics>> -> Microsoft.ML.AutoML.CrossValidationExperimentResult<'Metrics (requires 'Metrics : null)>Public Overridable Function Execute (trainData As IDataView, numberOfCVFolds As UInteger, Optional labelColumnName As String = "Label", Optional samplingKeyColumn As String = Nothing, Optional preFeaturizer As IEstimator(Of ITransformer) = Nothing, Optional progressHandler As IProgress(Of CrossValidationRunDetail(Of TMetrics)) = Nothing) As CrossValidationExperimentResult(Of TMetrics)Parameters
- trainData
- IDataView
The training data to be used by the AutoML experiment.
- numberOfCVFolds
- UInt32
The number of cross validation folds into which the training data should be divided when fitting a model.
- labelColumnName
- String
The name of the label column.
- samplingKeyColumn
- String
The name of the sampling key column.
- preFeaturizer
- IEstimator<ITransformer>
Pre-featurizer that AutoML will apply to the data during an experiment. (The pre-featurizer will be fit only on the training data split to produce a trained transform. Then, the trained transform will be applied to both the training data split and corresponding validation data split.)
- progressHandler
- IProgress<CrossValidationRunDetail<TMetrics>>
A user-defined object that implements the IProgress<T> interface. AutoML will invoke the method Report(T) after each model it produces during the course of the experiment.
Returns
The cross validation experiment result.
Remarks
Depending on the size of your data, the AutoML experiment could take a long time to execute.