Preprocessor YAML

YAML configuration file format for the Preprocessor module, used for data preprocessing.

Usage Examples

Click the button below to run the example in Colab:

Note: If using Colab, please see the runtime setup guide.

Using Default Preprocessing

Preprocessor:
  demo:
    method: 'default'

Using Custom Processing Sequence

Preprocessor:
  custom:
    method: 'default'
    sequence:
      - missing
      - outlier
      - encoder
      - scaler

Customizing Processors for Specific Fields

Preprocessor:
  custom_fields:
    method: 'default'
    config:
      missing:
        age: 'missing_mean'
        income: 'missing_median'
      outlier:
        age: 'outlier_zscore'
        income: 'outlier_iqr'
      encoder:
        gender: 'encoder_onehot'
        education: 'encoder_label'
      scaler:
        age: 'scaler_minmax'
        income: 'scaler_standard'

Main Parameters

• method (string, required)

  • Preprocessing method
  • Available values: 'default' (default processing sequence)

• sequence (list, optional)

  • Custom processing sequence
  • Available values: 'missing', 'outlier', 'encoder', 'scaler', 'discretizing'
  • Default value: ['missing', 'outlier', 'encoder', 'scaler']

• config (dict, optional)

  • Custom processor configuration for each field
  • Structure: {processing_type: {field_name: processing_method}}

Processing Sequence

Preprocessor supports the following processing steps, executed in order:

1. missing: Missing value handling
2. outlier: Outlier handling
3. encoder: Categorical variable encoding
4. scaler: Numerical normalization
5. discretizing: Discretization (mutually exclusive with encoder)

Default Processing Methods

Feature Documentation

For detailed processor descriptions, please refer to each feature page:

• Missing Value Handling
• Outlier Handling
• Encoding
• Scaling
• Discretizing

Precision Preservation

Preprocessor automatically preserves the precision of numerical fields:

• Precision Retention: The type_attr.precision in the schema will not be changed during transformation
• Automatic Application: Rounding is automatically applied according to precision after transformation
• Memory Mechanism: Precision information is recorded in Status for use by subsequent modules

Execution Instructions

• Experiment names (second level) can be freely named; descriptive names are recommended
• Multiple experiments can be defined and will be executed sequentially
• Preprocessing results are passed to the Synthesizer module

Notes

• discretizing and encoder cannot be used simultaneously
• discretizing must be the last step in the sequence
• Some outlier handlers (such as outlier_isolationforest, outlier_lof) are global transformations and will be applied to all fields
• Custom config will override default settings
• Precision is automatically applied after preprocessing transformation to ensure numerical consistency
• For detailed processor parameter settings, please refer to each feature page and the Processor API documentation