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Python API
Processor API
update_config()

update_config()

Update the processor’s configuration settings.

Syntax 

def update_config(
    config: dict
) -> None

Parameters

  • config : dict, required
    • New processor configuration
    • Required parameter
    • Structure: {processor_type: {field_name: processing_method}}

Returns

None (method modifies instance state)

Description

The update_config() method updates the processor configuration. It can:

  1. Override default processing methods
  2. Set custom processors for specific columns
  3. Disable processing for specific columns (set to None or "none")

Basic Example 

from petsard import Loader, Processor

# Load data
loader = Loader('data.csv', schema='schema.yaml')
data, schema = loader.load()

# Create processor
processor = Processor(metadata=schema)

# Update configuration
new_config = {
    'missing': {
        'age': 'missing_median',
        'income': 'missing_mean'
    },
    'encoder': {
        'gender': 'encoder_onehot',
        'education': 'encoder_label'
    }
}
processor.update_config(new_config)

# Use updated configuration
processor.fit(data)
processed_data = processor.transform(data)

Configuration Formats

1. Using Processor Name (String) 

config = {
    'missing': {
        'age': 'missing_mean',
        'income': 'missing_median'
    },
    'outlier': {
        'age': 'outlier_zscore',
        'income': 'outlier_iqr'
    },
    'encoder': {
        'gender': 'encoder_onehot'
    },
    'scaler': {
        'age': 'scaler_minmax',
        'income': 'scaler_standard'
    }
}
processor.update_config(config)

2. Using Processor with Parameters (Dictionary) 

config = {
    'missing': {
        'age': {
            'method': 'missing_simple',
            'value': 0.0
        }
    },
    'scaler': {
        'created_at': {
            'method': 'scaler_timeanchor',
            'reference': 'event_time',
            'unit': 'D'
        }
    },
    'encoder': {
        'doc_date': {
            'method': 'encoder_date',
            'input_format': '%MinguoY-%m-%d',
            'date_type': 'date'
        }
    }
}
processor.update_config(config)

3. Disable Specific Processing 

config = {
    'outlier': {
        'age': None,  # Don't process age outliers
        'income': 'outlier_iqr'
    },
    'scaler': {
        'gender': 'none'  # String "none" also disables
    }
}
processor.update_config(config)

Partial Update 

# Update only some columns, others keep default
processor = Processor(metadata=schema)

# Update only missing value handling for age
processor.update_config({
    'missing': {
        'age': 'missing_median'
    }
})

# Other columns still use default configuration

Multiple Updates 

processor = Processor(metadata=schema)

# First update
processor.update_config({
    'missing': {'age': 'missing_median'}
})

# Second update (will override or add)
processor.update_config({
    'missing': {'income': 'missing_mean'},
    'encoder': {'gender': 'encoder_onehot'}
})

# Final configuration includes all updates

Setting Configuration at Initialization 

# Can also provide configuration when creating processor
custom_config = {
    'missing': {
        'age': 'missing_median',
        'income': 'missing_mean'
    },
    'encoder': {
        'gender': 'encoder_onehot'
    }
}

processor = Processor(metadata=schema, config=custom_config)
# No need to call update_config()

Verify Configuration Update 

processor = Processor(metadata=schema)

# Update configuration
new_config = {
    'missing': {'age': 'missing_median'},
    'encoder': {'gender': 'encoder_onehot'}
}
processor.update_config(new_config)

# Verify update
config = processor.get_config(col=['age', 'gender'])
print("age missing:", type(config['missing']['age']).__name__)
print("gender encoder:", type(config['encoder']['gender']).__name__)

Available Processor Names

Missing Value Processors

  • missing_mean: Fill with mean
  • missing_median: Fill with median
  • missing_mode: Fill with mode
  • missing_simple: Fill with custom value (requires value parameter)
  • missing_drop: Drop rows with missing values

Outlier Processors

  • outlier_zscore: Z-Score method
  • outlier_iqr: Interquartile Range method
  • outlier_isolationforest: Isolation Forest (global)
  • outlier_lof: Local Outlier Factor (global)

Encoders

  • encoder_uniform: Uniform encoding
  • encoder_label: Label encoding
  • encoder_onehot: One-Hot encoding
  • encoder_date: Date format conversion (requires parameters)

Scalers

  • scaler_standard: Standardization
  • scaler_minmax: Min-Max scaling
  • scaler_zerocenter: Zero centering
  • scaler_log: Logarithmic transformation
  • scaler_log1p: log(1+x) transformation
  • scaler_timeanchor: Time anchor scaling (requires parameters)

Discretization

  • discretizing_kbins: K-bins discretization (requires parameters)

Notes

  • Updating configuration overwrites default settings for that column
  • Must call this method before fit()
  • If updating after fit(), need to retrain
  • Invalid processor names will raise ConfigError
  • Setting to None or "none" disables that processing
  • Processors with parameters must use dictionary format
  • Updates are cumulative, won’t clear other columns’ configuration
get_config()get_changes()