FeaturizationConfig 类

构造函数

FeaturizationConfig(blocked_transformers: List[str] | None = None, column_purposes: Dict[str, str] | None = None, transformer_params: Dict[str, List[Tuple[List[str], Dict[str, Any]]]] | None = None, drop_columns: List[str] | None = None, dataset_language: str | None = None, prediction_transform_type: str | None = None)

参数

注解

特征化自定义具有允许你：

• 添加或删除列用途。 add_column_purpose使用和remove_column_purpose方法可以替代指定列的功能类型，例如，当列的功能类型无法正确反映其用途时。 add 方法支持添加类的 FULL_SET 属性 FeatureType 中提供的所有功能类型。

• 添加或删除转换器参数。 add_transformer_params使用和remove_transformer_params方法可以更改可自定义转换器的参数，如 Imputer、HashOneHotEncoder 和 TfIdf。 可自定义转换器列在类CUSTOMIZABLE_TRANSFORMERS属性中 SupportedTransformers 。 get_transformer_params使用查找自定义参数。

• 块转换器。 用于采用该方法的特征化过程的 add_blocked_transformers 块转换器。 转换器必须是类BLOCKED_TRANSFORMERS属性中列出的 SupportedTransformers 转换器之一。

• 使用该方法添加删除列以忽略特征化和训练 add_drop_columns 。 例如，可以删除不包含有用信息的列。

• 添加或删除预测转换类型。 使用 add_prediction_transform_type 和

remove_prediction_transform_type 方法可以重写现有的目标列类型。 预测转换类型列在 PredictionTransformTypes 属性中。

下面的代码示例演示如何在自动化 ML 中自定义特征化以用于预测。 在示例代码中，将显示删除列并添加转换参数。

   featurization_config = FeaturizationConfig()
   # Force the CPWVOL5 feature to be numeric type.
   featurization_config.add_column_purpose("CPWVOL5", "Numeric")
   # Fill missing values in the target column, Quantity, with zeros.
   featurization_config.add_transformer_params(
       "Imputer", ["Quantity"], {"strategy": "constant", "fill_value": 0}
   )
   # Fill missing values in the INCOME column with median value.
   featurization_config.add_transformer_params(
       "Imputer", ["INCOME"], {"strategy": "median"}
   )
   # Fill missing values in the Price column with forward fill (last value carried forward).
   featurization_config.add_transformer_params("Imputer", ["Price"], {"strategy": "ffill"})

完整示例可从 https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb

下一个示例演示如何使用硬件性能数据集在回归问题中自定义特征化。 在示例代码中，定义了阻止的转换器、添加列用途以及添加转换器参数。

   featurization_config = FeaturizationConfig()
   featurization_config.blocked_transformers = ["LabelEncoder"]
   # featurization_config.drop_columns = ['MMIN']
   featurization_config.add_column_purpose("MYCT", "Numeric")
   featurization_config.add_column_purpose("VendorName", "CategoricalHash")
   # default strategy mean, add transformer param for for 3 columns
   featurization_config.add_transformer_params("Imputer", ["CACH"], {"strategy": "median"})
   featurization_config.add_transformer_params(
       "Imputer", ["CHMIN"], {"strategy": "median"}
   )
   featurization_config.add_transformer_params(
       "Imputer", ["PRP"], {"strategy": "most_frequent"}
   )
   # featurization_config.add_transformer_params('HashOneHotEncoder', [], {"number_of_bits": 3})

完整示例可从 https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

然后，上述代码示例中定义的 FeaturizationConfig 可以在自动化 ML 试验的配置中使用，如下一个代码示例所示。

   automl_settings = {
       "enable_early_stopping": True,
       "experiment_timeout_hours": 0.25,
       "max_concurrent_iterations": 4,
       "max_cores_per_iteration": -1,
       "n_cross_validations": 5,
       "primary_metric": "normalized_root_mean_squared_error",
       "verbosity": logging.INFO,
   }

   automl_config = AutoMLConfig(
       task="regression",
       debug_log="automl_errors.log",
       compute_target=compute_target,
       featurization=featurization_config,
       training_data=train_data,
       label_column_name=label,
       **automl_settings,
   )

完整示例可从 https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb

add_blocked_transformers

add_blocked_transformers(transformers: str | List[str]) -> None

add_column_purpose

add_column_purpose(column_name: str, feature_type: str) -> None

add_drop_columns

add_drop_columns(drop_columns: str | List[str]) -> None

add_prediction_transform_type

add_prediction_transform_type(prediction_transform_type: str) -> None

add_transformer_params

add_transformer_params(transformer: str, cols: List[str], params: Dict[str, Any]) -> None

   featurization_config = FeaturizationConfig()
   # Force the CPWVOL5 feature to be numeric type.
   featurization_config.add_column_purpose("CPWVOL5", "Numeric")
   # Fill missing values in the target column, Quantity, with zeros.
   featurization_config.add_transformer_params(
       "Imputer", ["Quantity"], {"strategy": "constant", "fill_value": 0}
   )
   # Fill missing values in the INCOME column with median value.
   featurization_config.add_transformer_params(
       "Imputer", ["INCOME"], {"strategy": "median"}
   )
   # Fill missing values in the Price column with forward fill (last value carried forward).
   featurization_config.add_transformer_params("Imputer", ["Price"], {"strategy": "ffill"})

get_transformer_params

get_transformer_params(transformer: str, cols: List[str]) -> Dict[str, Any]

remove_column_purpose

remove_column_purpose(column_name: str) -> None

remove_prediction_transform_type

remove_prediction_transform_type() -> None

remove_transformer_params

remove_transformer_params(transformer: str, cols: List[str] | None = None) -> None

blocked_transformers

column_purposes

dataset_language

drop_columns

prediction_transform_type

transformer_params