Discretizing

Converts continuous numerical data into discrete categories or intervals, mutually exclusive with encoding (encoder).

Usage Examples

Custom K-bins Parameters

Preprocessor:
  custom:
    method: 'default'
    sequence:
      - missing
      - outlier
      - discretizing
    config:
      discretizing:
        age:
          method: 'discretizing_kbins'
          n_bins: 10                    # Divide into 10 bins
        income:
          method: 'discretizing_kbins'
          n_bins: 5                     # Divide into 5 bins

Available Processors

Note: In the discretizing sequence, categorical data will automatically use encoder_label for encoding.

Processor Details

discretizing_kbins

K-bins Discretization: Divides continuous values into k equal-width intervals.

Parameters:

• n_bins (int, optional)
  • Number of bins (k value)
  • Default value: 5
  • Example: n_bins: 10

Features:

• Equal-width binning
• Outputs integer labels (0, 1, 2, …)
• Reduces data dimensionality and complexity

Discretization Example:

Original values: [18, 25, 35, 45, 55, 65]
n_bins = 5

Bin division:
[18-27.4) → 0
[27.4-36.8) → 1
[36.8-46.2) → 2
[46.2-55.6) → 3
[55.6-65] → 4

Discretized result: [0, 0, 1, 2, 3, 4]

Processing Logic

Numerical Data (discretizing_kbins)

• Training phase (fit): Calculate and store bin boundaries
• Transform phase (transform): Map values to integer labels based on bin boundaries
• Inverse transform phase (inverse_transform): Restore integer labels to bin midpoint values

Categorical Data (encoder_label)

• Training phase (fit): Create mapping from categories to integers
• Transform phase (transform): Convert categories to integers based on mapping
• Inverse transform phase (inverse_transform): Restore integers to categories based on mapping

Default Behavior

Default processing when using discretizing sequence:

Differences from encoder

Usage Restrictions

1. Mutually Exclusive with encoder

# ❌ Wrong: Cannot use both
Preprocessor:
  wrong:
    method: 'default'
    sequence:
      - missing
      - encoder       # Error!
      - discretizing  # Mutually exclusive with encoder

# ✅ Correct: Use only one
Preprocessor:
  correct:
    method: 'default'
    sequence:
      - missing
      - outlier
      - discretizing  # Correct

2. Must Be Last Step

# ❌ Wrong: Steps after discretizing
Preprocessor:
  wrong:
    method: 'default'
    sequence:
      - missing
      - discretizing
      - scaler        # Error! discretizing must be last

# ✅ Correct: discretizing is the last step
Preprocessor:
  correct:
    method: 'default'
    sequence:
      - missing
      - outlier
      - discretizing  # Correct: last step

Notes

• Mutual Exclusivity: Cannot be used with encoder simultaneously
• Position Restriction: Must be the last step in the sequence
• Restoration Precision: Restoration uses bin midpoints, won’t fully restore original values
• Number of Bins: n_bins should be adjusted based on data range and requirements
• Synthesizer Impact: Some synthesizers (like PAC-Synth, DPCTGAN) may produce floating-point numbers, which the system will automatically round
• Use Case: Suitable for situations requiring discretized output, such as certain privacy protection algorithms
• NA Handling: During post-processing, NA values will be removed before restoration