Synthetic Data Vault
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  • Welcome to the SDV!
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      • ❖ AI Connectors
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  • Single Table Data
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      • Synthesizers
        • GaussianCopulaSynthesizer
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        • ❖ XGCSynthesizer
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        • * DayZSynthesizer
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      • Customizations
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  • Multi Table Data
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        • ❖ AlloyDB
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      • Cleaning Your Data
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    • Modeling
      • Synthesizers
        • * DayZSynthesizer
        • * IndependentSynthesizer
        • HMASynthesizer
        • * HSASynthesizer
      • Customizations
        • Constraints
        • Preprocessing
      • * Performance Estimates
    • Sampling
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  • Sequential Data
    • Data Preparation
      • Loading Data
      • Cleaning Your Data
      • Creating Metadata
    • Modeling
      • PARSynthesizer
      • Customizations
    • Sampling
      • Sample Realistic Data
      • Conditional Sampling
    • Evaluation
  • Concepts
    • Metadata
      • Sdtypes
      • Metadata API
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    • Constraints
      • Predefined Constraints
        • Positive
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        • ScalarInequality
        • ScalarRange
        • FixedIncrements
        • FixedCombinations
        • ❖ FixedNullCombinations
        • ❖ MixedScales
        • OneHotEncoding
        • Inequality
        • Range
        • * ChainedInequality
      • Custom Logic
        • Example: IfTrueThenZero
      • ❖ Constraint Augmented Generation (CAG)
        • ❖ CarryOverColumns
        • ❖ CompositeKey
        • ❖ ForeignToForeignKey
        • ❖ ForeignToPrimaryKeySubset
        • ❖ PrimaryToPrimaryKey
        • ❖ PrimaryToPrimaryKeySubset
        • ❖ SelfReferentialHierarchy
        • ❖ ReferenceTable
        • ❖ UniqueBridgeTable
  • Support
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    • Versioning & Backwards Compatibility Policy
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On this page
  1. Sequential Data
  2. Modeling

Customizations

PreviousPARSynthesizerNextSampling

Last updated 11 months ago

Customize your synthesizer for better quality synthetic data. The guides in this section walk you through the programmable options available in the SDV.

**Limited functionality available. Constraints currently capture rules between columns, not inter-row dependencies. Constraints can only be applied on columns that all contextual or all non-contextual.

**

Add logical rules to the synthesizer. Capture business logic that may be hard for machine learning to capture on its own.

Control the pre- and post-processing steps for your data. This also includes anonymizing sensitive data.

Constraints
Transform & Anonymize