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TableVerticalFeaturizationSettings.cs
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TableVerticalFeaturizationSettings.cs
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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
// <auto-generated/>
#nullable disable
using System.Collections.Generic;
using Azure.Core;
namespace Azure.ResourceManager.MachineLearning.Models
{
/// <summary> Featurization Configuration. </summary>
public partial class TableVerticalFeaturizationSettings : MachineLearningFeaturizationSettings
{
/// <summary> Initializes a new instance of <see cref="TableVerticalFeaturizationSettings"/>. </summary>
public TableVerticalFeaturizationSettings()
{
BlockedTransformers = new ChangeTrackingList<BlockedTransformer>();
ColumnNameAndTypes = new ChangeTrackingDictionary<string, string>();
TransformerParams = new ChangeTrackingDictionary<string, IList<ColumnTransformer>>();
}
/// <summary> Initializes a new instance of <see cref="TableVerticalFeaturizationSettings"/>. </summary>
/// <param name="datasetLanguage"> Dataset language, useful for the text data. </param>
/// <param name="blockedTransformers"> These transformers shall not be used in featurization. </param>
/// <param name="columnNameAndTypes"> Dictionary of column name and its type (int, float, string, datetime etc). </param>
/// <param name="enableDnnFeaturization"> Determines whether to use Dnn based featurizers for data featurization. </param>
/// <param name="mode">
/// Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase.
/// If 'Off' is selected then no featurization is done.
/// If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
/// </param>
/// <param name="transformerParams"> User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. </param>
internal TableVerticalFeaturizationSettings(string datasetLanguage, IList<BlockedTransformer> blockedTransformers, IDictionary<string, string> columnNameAndTypes, bool? enableDnnFeaturization, MachineLearningFeaturizationMode? mode, IDictionary<string, IList<ColumnTransformer>> transformerParams) : base(datasetLanguage)
{
BlockedTransformers = blockedTransformers;
ColumnNameAndTypes = columnNameAndTypes;
EnableDnnFeaturization = enableDnnFeaturization;
Mode = mode;
TransformerParams = transformerParams;
}
/// <summary> These transformers shall not be used in featurization. </summary>
public IList<BlockedTransformer> BlockedTransformers { get; set; }
/// <summary> Dictionary of column name and its type (int, float, string, datetime etc). </summary>
public IDictionary<string, string> ColumnNameAndTypes { get; set; }
/// <summary> Determines whether to use Dnn based featurizers for data featurization. </summary>
public bool? EnableDnnFeaturization { get; set; }
/// <summary>
/// Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase.
/// If 'Off' is selected then no featurization is done.
/// If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.
/// </summary>
public MachineLearningFeaturizationMode? Mode { get; set; }
/// <summary> User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. </summary>
public IDictionary<string, IList<ColumnTransformer>> TransformerParams { get; set; }
}
}