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SupportedTransformers 类

  • 参考

为 AutoML 支持的转换器定义面向客户的名称。

转换器被分类用于 Categorical 数据（例如，CatImputer）、DateTime 数据（例如，DataTimeTransformer）、Text 数据（例如，TfIdf）或 Generic 数据类型（例如，Imputer）。

继承
builtins.object
SupportedTransformers

构造函数

SupportedTransformers()

注解

在使用自动化 ML 中的自动预处理或使用 FeaturizationConfig 类自定义特征化时，SupportedTransformers 中定义的属性用于特征化摘要，如示例中所示。


   featurization_config = FeaturizationConfig()
   featurization_config.add_transformer_params('Imputer', ['column1'], {"strategy": "median"})
   featurization_config.add_transformer_params('HashOneHotEncoder', [], {"number_of_bits": 3})

有关详细信息，请参阅配置自动化 ML 试验。

属性

ImputationMarker 

ImputationMarker = 'ImputationMarker'

Imputer 

Imputer = 'Imputer'

MaxAbsScaler 

MaxAbsScaler = 'MaxAbsScaler'

CatImputer 

CatImputer = 'CatImputer'

HashOneHotEncoder 

HashOneHotEncoder = 'HashOneHotEncoder'

LabelEncoder 

LabelEncoder = 'LabelEncoder'

CatTargetEncoder 

CatTargetEncoder = 'CatTargetEncoder'

WoETargetEncoder 

WoETargetEncoder = 'WoETargetEncoder'

OneHotEncoder 

OneHotEncoder = 'OneHotEncoder'

DateTimeTransformer 

DateTimeTransformer = 'DateTimeTransformer'

CountVectorizer 

CountVectorizer = 'CountVectorizer'

NaiveBayes 

NaiveBayes = 'NaiveBayes'

StringCast 

StringCast = 'StringCast'

TextTargetEncoder 

TextTargetEncoder = 'TextTargetEncoder'

TfIdf 

TfIdf = 'TfIdf'

WordEmbedding 

WordEmbedding = 'WordEmbedding'

CUSTOMIZABLE_TRANSFORMERS 

CUSTOMIZABLE_TRANSFORMERS = {'HashOneHotEncoder', 'Imputer', 'TfIdf'}

BLOCK_TRANSFORMERS 

BLOCK_TRANSFORMERS = {'CatTargetEncoder', 'CountVectorizer', 'HashOneHotEncoder', 'LabelEncoder', 'NaiveBayes', 'OneHotEncoder', 'TextTargetEncoder', 'TfIdf', 'WoETargetEncoder', 'WordEmbedding'}

FULL_SET 

FULL_SET = {'CatImputer', 'CatTargetEncoder', 'CountVectorizer', 'DateTimeTransformer', 'HashOneHotEncoder', 'ImputationMarker', 'Imputer', 'LabelEncoder', 'MaxAbsScaler', 'NaiveBayes', 'OneHotEncoder', 'StringCast', 'TextTargetEncoder', 'TfIdf', 'WoETargetEncoder', 'WordEmbedding'}