tonic-reporting 1.0.0

Tools for evaluating fidelity and privacy of synthetic data

Overview

This library contains tools for evaluating fidelity and privacy of synthetic data.

Usage

Import the desired modules from the library:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from tonic_reporting import univariate, multivariate, privacy

Preface

Numeric columns refer to columns encoded as numeric. Numerical data types in the schema underlying a model may be encoded as other types.

Categorical columns refer to columns encoded as categorical.

source_df is a Pandas DataFrame of original data from the source database

synth_df is a Pandas DataFrame of sampled data from trained models

The source and synthetic DataFrames should be equal in row count and schema.

Numeric Column Statistics

univariate.summarize_numeric(source_df, synth_df, numeric_cols)

Categorical Column Statistics

univariate.summarize_categorical(source_df, synth_df, categorical_cols)

Numeric Column Comparative Histograms

fig, axarr = plt.subplots(1, len(numeric_cols), figsize = (9,12))
axarr = axarr.ravel()

for col, ax in zip(numeric_cols, axarr):
    univariate.plot_histogram(source_df, synth_df, col,ax)

Numeric Column Multivariate Correlations Table

multivariate.summarize_correlations(source_df, synth_df, numeric_cols)

Numeric Column Multivariate Correlations Heat Map

fig, axarr = plt.subplots(1, 2, figsize=(13, 8))
multivariate.plot_correlations(source_df, synth_df, numeric_cols, axarr=axarr, )
fig.tight_layout()

Distance to Closest Record Comparison

syn_dcr, real_dcr = privacy.compute_dcr(source_df, synth_df, numeric_cols, categorical_cols)

fig, ax = plt.subplots(1,1,figsize=(8,6))
ax.hist(real_dcr,bins=300,label = 'Real vs. real', color='mediumpurple');
ax.hist(syn_dcr,bins=300,label='Synthetic vs. real', color='mediumturquoise');
ax.tick_params(axis='both', which='major', labelsize=14)
ax.set_title('Distances to closest record',fontsize=22)
ax.legend(fontsize=16);