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Faker Synthesizer

ydata.synthesizers.FakerSynthesizer

A synthesizer for generating synthetic data based on user-defined configurations.

The FakerSynthesizer allows users to create synthetic tabular data without needing an existing dataset. Instead, it generates data based on user-provided metadata or manually defined column configurations. This approach is useful for:

  • Creating mock datasets for testing and development.
  • Generating data prototypes before real data is available.
  • Ensuring privacy-preserving synthetic data without reference to actual records.

Key Features:

  • Metadata-Driven Generation: Generates synthetic data based on predefined Metadata.
  • Customizable Column Types: Supports user-defined column structures.
  • Multi-Language Support: Uses locale settings to generate realistic names, addresses, etc.

Example Usage:

from ydata.synthesizers import FakerSynthesizer

# Initialize the synthesizer with a specific locale
faker_synth = FakerSynthesizer(locale="en")  # English data generation

faker_synth.fit(metadata)

# Generate synthetic data
synthetic_data = faker_synth.sample(n_samples=1000)

fit(metadata)

Configure the FakerSynthesizer using provided metadata.

This method sets up the synthesizer by defining the structure of the synthetic dataset based on the given Metadata. The metadata can either be:

  • Computed: Automatically extracted from an existing dataset.
  • User-Defined: Manually constructed to specify custom column types and distributions.

Once fit() is called, the synthesizer will use this metadata to generate structured synthetic data that adheres to the defined schema.

Parameters:

Name Type Description Default
metadata Metadata

A metadata object describing the structure of the synthetic dataset, including: - Column names and data types. - Faker-based data generators (e.g., names, addresses, emails). - Value constraints (e.g., numeric ranges, categorical options).

 required

sample(sample_size=1000)

Generate a synthetic dataset based on the configured metadata.

This method produces synthetic data according to the schema defined in the fit() step. The generated data adheres to the column types, constraints, and distributions specified in the provided Metadata.

Parameters:

Name Type Description Default
sample_size int

The number of synthetic records/rows to generate. Defaults to 1000.

 1000

Returns:

Name Type Description
dataset Dataset

A Dataset object with the generated synthetic records/rows.

save(path)

Saves the SYNTHESIZER and the models fitted per variable.