syndat 0.11.0

A library for evaluation & visualization of synthetic data.

Syndat

Syndat is a software package that provides basic functionalities for the evaluation and visualisation of synthetic data. Quality scores can be computed on 3 base metrics (Discrimation, Correlation and Distribution) and data may be visualized to inspect correlation structures or statistical distribution plots.

Installation

Install via pip:

pip install syndat

Usage

Quality metrics

Compute data quality metrics by comparing real and synthetic data in terms of their separation complexity, distribution similarity or pairwise feature correlations:

import pandas as pd
import syndat

real = pd.read_csv("real.csv")
synthetic = pd.read_csv("synthetic.csv")

# How similar are the statistical distributions of real and synthetic features 
distribution_similarity_score = syndat.scores.distribution(real, synthetic)

# How hard is it for a classifier to discriminate real and synthetic data
discrimination_score = syndat.scores.discrimination(real, synthetic)

# How well are pairwise feature correlations preserved
correlation_score = syndat.scores.correlation(real, synthetic)

Scores are defined in a range of 0-100, with a higher score corresponding to better data fidelity.

Visualization

Visualize real vs. synthetic data distributions, summary statistics and discriminating features:

import pandas as pd
import syndat

real = pd.read_csv("real.csv")
synthetic = pd.read_csv("synthetic.csv")

# plot *all* feature distribution and store image files
syndat.visualization.plot_distributions(real, synthetic, store_destination="results/plots")
syndat.visualization.plot_correlations(real, synthetic, store_destination="results/plots")

# plot and display specific feature distribution plot
syndat.visualization.plot_numerical_feature("feature_xy", real, synthetic)
syndat.visualization.plot_numerical_feature("feature_xy", real, synthetic)

# plot a shap plot of differentiating feature for real and synthetic data
syndat.visualization.plot_shap_discrimination(real, synthetic)

Postprocessing

Postprocess synthetic data to improve data fidelity:

import pandas as pd
import syndat

real = pd.read_csv("real.csv")
synthetic = pd.read_csv("synthetic.csv")

# postprocess synthetic data
synthetic_post = syndat.postprocessing.assert_minmax(real, synthetic)
synthetic_post = syndat.postprocessing.normalize_float_precision(real, synthetic)