Documentation | Sample Reports | Technical White Paper
Assess the fidelity and novelty of synthetic samples with respect to original samples:
- calculate a rich set of accuracy, similarity and distance metrics
- visualize statistics for easy comparison to training and holdout samples
- generate a standalone, easy-to-share, easy-to-read HTML summary report
...all with a few lines of Python code 💥.
mostlyai-qa.mp4
The latest release of mostlyai-qa can be installed via pip:
pip install -U mostlyai-qaOn Linux, one can explicitly install the CPU-only variant of torch together with mostlyai-qa:
pip install -U torch==2.6.0+cpu torchvision==0.21.0+cpu mostlyai-qa --extra-index-url https://download.pytorch.org/whl/cpuimport pandas as pd
import webbrowser
from mostlyai import qa
# initialize logging to stdout
qa.init_logging()
# fetch original + synthetic data
base_url = "https://github.com/mostly-ai/mostlyai-qa/raw/refs/heads/main/examples/quick-start"
syn = pd.read_csv(f"{base_url}/census2k-syn_mostly.csv.gz")
# syn = pd.read_csv(f'{base_url}/census2k-syn_flip30.csv.gz') # a 30% perturbation of trn
trn = pd.read_csv(f"{base_url}/census2k-trn.csv.gz")
hol = pd.read_csv(f"{base_url}/census2k-hol.csv.gz")
# calculate metrics
report_path, metrics = qa.report(
syn_tgt_data=syn,
trn_tgt_data=trn,
hol_tgt_data=hol,
)
# pretty print metrics
print(metrics.model_dump_json(indent=4))
# open up HTML report in new browser window
webbrowser.open(f"file://{report_path.absolute()}")from mostlyai import qa
# initialize logging to stdout
qa.init_logging()
# analyze single-table data
report_path, metrics = qa.report(
syn_tgt_data = synthetic_df,
trn_tgt_data = training_df,
hol_tgt_data = holdout_df, # optional
)
# analyze sequential data
report_path, metrics = qa.report(
syn_tgt_data = synthetic_df,
trn_tgt_data = training_df,
hol_tgt_data = holdout_df, # optional
tgt_context_key = "user_id",
)
# analyze sequential data with context
report_path, metrics = qa.report(
syn_tgt_data = synthetic_df,
trn_tgt_data = training_df,
hol_tgt_data = holdout_df, # optional
syn_ctx_data = synthetic_context_df,
trn_ctx_data = training_context_df,
hol_ctx_data = holdout_context_df, # optional
ctx_primary_key = "id",
tgt_context_key = "user_id",
)- Baseball Players (Flat Data)
- Baseball Seasons (Sequential Data)
Please consider citing our project if you find it useful:
@misc{mostlyai-qa,
title={Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework},
author={Andrey Sidorenko and Michael Platzer and Mario Scriminaci and Paul Tiwald},
year={2025},
eprint={2504.01908},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2504.01908},
}
