Metadata-Version: 2.1
Name: fuzzy-sql
Version: 2.0.4b0
Summary: A generator of random SQL SELECT queries mainly to compare responses from a real dataset against that from a synthetic dataset.
Author: Samer Kababji
Author-email: skababji@ehealthinformation.ca
Project-URL: Home, https://github.com/skababji-ehil/fuzzy_sql
Project-URL: Documentation, https://fuzzy-sql.readthedocs.io/en/latest/index.html#
Keywords: sql,synthetic,clinical trials,generative,testing,fuzzy,fuzzing
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.8
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: Jinja2
Requires-Dist: seaborn
Requires-Dist: scikit-learn
Requires-Dist: jupyter
Requires-Dist: jsonschema
Requires-Dist: multiprocess
Requires-Dist: scipy
Requires-Dist: matplotlib
Provides-Extra: dev
Requires-Dist: wheel ; extra == 'dev'
Requires-Dist: dvc ; extra == 'dev'
Requires-Dist: Sphinx ; extra == 'dev'
Requires-Dist: sphinxcontrib-bibtex ; extra == 'dev'

# Fuzzy SQL

The package generates semantically and syntactically correct random SELECT SQL Statements. It is developed by <a href="https://www.ehealthinformation.ca/home" target="_blank">EHIL</a> mainly to check the query response from *synthetic* data against that of *real* data. The package supports both tabular and longitudinal datasets. Table shapes, variable names and relations in both real and synthetic datasets shall be identical. 

To install:
```
pip install fuzzy-sql
```

For further details, please refer to the <a href="https://fuzzy-sql.readthedocs.io/en/latest/" target="_blank">Documentation</a>.

Detailed examples comprising three sample datasets are provided under the **examples** folder in the <a href="https://github.com/skababji-ehil/fuzzy_sql" target="_blank">repository</a>. To generate the random queries, you first need to download the sample data by running  **0.0-download_data.ipynb**. You may then proceed with the remaining notebooks to construct the necessary databases and generate the random queries. For details, please refer to the usage/code-examples  subsection in the <a href="https://fuzzy-sql.readthedocs.io/en/latest/" target="_blank">Documentation</a>.
