MAP-alignment fidelity and dataset distance for synthetic tabular data
This package implements the one-sided MAP-alignment fidelity statistic introduced by Chattopadhyay et al. and described in the manuscript “How Good Is Your Synthetic Data?”.
The core idea
For a synthetic record to be realistic, each coordinate should agree with the conditional MAP prediction inferred from real data.
Formally, for a data record x and coordinate i:
υ(x, i) = φ_i(x_i | x_{-i}) / max_y φ_i(y | x_{-i})
Averaged over samples and coordinates:
Υ(D) in [0,1]
High Υ: synthetic preserves real conditional structure
Low Υ: structural distortion (even if marginals / covariance match)
Installation
pip install lsynthInterpretation
~1.0: synthetic matches conditional structure closely
~0.7: Gaussian-like distortions
< 0.7: strong structural mismatch
Why MAP-alignment?
Because covariance matching is insufficient.
Section VII of the manuscript gives explicit examples where:
Real and synthetic share identical means, variances, covariance matrices
Yet they differ strongly in conditional structure
MAP-alignment catches the discrepancy immediately
This method:
Detects nonlinear and higher-order structure
Avoids feature-embedding artifacts
Comes with finite-sample uncertainty control
Supported Generators
"LSM": use QuasiNet as a generative model via qsample
"BASELINE": independent-column null model
"CTGAN": uses SDV CTGAN synthesizer
Custom generators also supported
Citation
Chattopadhyay I, et al.
"How Good Is Your Synthetic Data?"1 maintainer
Metadata
1 maintainer
Release files for lsynth 0.1.27
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| lsynth-0.1.27.tar.gz | 12.9 kB | Details |
Release files / lsynth-0.1.27.tar.gz
| Download URL | lsynth-0.1.27.tar.gz |
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