PyPI 官网下载 | sdgym-0.4.1.dev1.tar.gz

<p align="left"> <a href="https://dai.lids.mit.edu"> <img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt="DAI-Lab" /> </a> <i>An Open Source Project from the <a href="https://dai.lids.mit.edu">Data to AI Lab, at MIT</a></i> </p> [](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha) [](https://travis-ci.org/sdv-dev/SDGym) [](https://pypi.python.org/pypi/sdgym) [](https://pepy.tech/project/sdgym) <img align="center" width=30% src="docs/resources/header.png"> Benchmarking framework for Synthetic Data Generators * Website: https://sdv.dev * Documentation: https://sdv.dev/SDV * Repository: https://github.com/sdv-dev/SDGym * License: [MIT](https://github.com/sdv-dev/SDGym/blob/master/LICENSE) * Development Status: [Pre-Alpha](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha) # Overview Synthetic Data Gym (SDGym) is a framework to benchmark the performance of synthetic data generators based on [SDV](https://github.com/sdv-dev/SDV) and [SDMetrics]( https://github.com/sdv-dev/SDMetrics). SDGym is a part of the [The Synthetic Data Vault](https://sdv.dev/) project. ## What is a Synthetic Data Generator? A **Synthetic Data Generator** is a Python function (or method) that takes as input some data, which we call the *real* data, learns a model from it, and outputs new *synthetic* data that has the same structure and similar mathematical properties as the *real* one. Please refer to the [synthesizers documentation](SYNTHESIZERS.md) for instructions about how to implement your own Synthetic Data Generator and integrate with SDGym. You can also read about how to use the ones already included in **SDGym** and see how to run them. ## Benchmark datasets **SDGym** evaluates the performance of **Synthetic Data Generators** using *single table*, *multi table* and *timeseries* datasets stored as CSV files alongside an [SDV Metadata]( https://sdv.dev/SDV/user_guides/relational/relational_metadata.html) JSON file. Further details about the list of available datasets and how to add your own datasets to the collection can be found in the [datasets documentation](DATASETS.md). # Install **SDGym** can be installed using the following commands: **Using `pip`:** ```bash pip install sdgym ``` **Using `conda`:** ```bash conda install -c sdv-dev -c conda-forge sdgym ``` For more installation options please visit the [SDGym installation Guide](INSTALL.md) # Usage ## Benchmarking your own Synthesizer SDGym evaluates **Synthetic Data Generators**, which are Python functions (or classes) that take as input some data, which we call the *real* data, learn a model from it, and output new *synthetic* data that has the same structure and similar mathematical properties as the *real* one. As an example, let use define a synthesizer function that applies the [GaussianCopula model from SDV ](https://sdv.dev/SDV/user_guides/single_table/gaussian_copula.html) with `gaussian` distribution. ```python3 import numpy as np from sdv.tabular import GaussianCopula def gaussian_copula(real_data, metadata): gc = GaussianCopula(default_distribution='gaussian') table_name = metadata.get_tables()[0] gc.fit(real_data[table_name]) return {table_name: gc.sample()} ``` |:information_source: You can learn how to create your own synthesizer function [here](SYNTHESIZERS.md).| |:-| We can now try to evaluate this function on the `asia` and `alarm` datasets: ```python3 import sdgym scores = sdgym.run(synthesizers=gaussian_copula, datasets=['asia', 'alarm']) ``` |:information_source: You can learn about different arguments for `sdgym.run` function [here](BENCHMARK.md).| |:-| The output of the `sdgym.run` function will be a `pd.DataFrame` containing the results obtained by your synthesizer on each dataset. | synthesizer | dataset | modality | metric | score | metric_time | model_time | |-----------------|---------|--------------|-----------------|------------|-------------|------------| | gaussian_copula | asia | single-table | BNLogLikelihood | -2.842690 | 2.762427 | 0.752364 | | gaussian_copula | alarm | single-table | BNLogLikelihood | -20.223178 | 7.009401 | 3.173832 | ## Benchmarking the SDGym Synthesizers If you want to run the SDGym benchmark on the SDGym Synthesizers you can directly pass the corresponding class, or a list of classes, to the `sdgym.run` function. For example, if you want to run the complete benchmark suite to evaluate all the existing synthesizers you can run (:warning: this will take a lot of time to run!): ```python from sdgym.synthesizers import ( CLBN, CopulaGAN, CTGAN, HMA1, Identity, Independent, MedGAN, PAR, PrivBN, SDV, TableGAN, TVAE, Uniform, VEEGAN) all_synthesizers = [ CLBN, CTGAN, CopulaGAN, HMA1, Identity, Independent, MedGAN, PAR, PrivBN, SDV, TVAE, TableGAN, Uniform, VEEGAN, ] scores = sdgym.run(synthesizers=all_synthesizers) ``` For further details about all the arguments and possibilities that the `benchmark` function offers please refer to the [benchmark documentation](BENCHMARK.md) # Additional References * Datasets used in SDGym are detailed [here](DATASETS.md). * How to write a synthesizer is detailed [here](SYNTHESIZERS.md). * How to use benchmark function is detailed [here](BENCHMARK.md). * Detailed leaderboard results for all the releases are available [here]( https://docs.google.com/spreadsheets/d/1iNJDVG_tIobcsGUG5Gn4iLa565vVhz2U/edit). # The Synthetic Data Vault <p> <a href="https://sdv.dev"> <img width=30% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/SDV-Logo-Color-Tagline.png?raw=true"> </a> <p><i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a></i></p> </p> * Website: https://sdv.dev * Documentation: https://sdv.dev/SDV