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Description:

analyticsdf 0.0.8.3

Overview
The AutoGen (analyticsdf) is a Python library that allows you to generate synthetic data with any statistical characteristics desired.
Features
This library provides a set of functionality to enable the specification and generation of a wide range of datasets with specified statistical characteristics. Specification includes the predictor matrix and the response vector.
Some common congifuration:

High correlation and multi-collinearity among predictor variables
Interaction effects between variables
Skewed distributions of predictor and response variables
Nonlinear relationships between predictor and response variables

Check the Analyticsdf documentation for more details.
Inspirations

Sklearn Make Datasets functionality
MIT Synthetic Data Vault project

MIT Data to AI Lab
datacebo
2016 IEEE conference paper, The Synthetic Data Vault.

Install
The beta package of this library is publicly available on both PyPI and Anaconda.
Install analyticsdf using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
pip install analyticsdf

conda install -c faye-yufan analyticsdf

Getting Started
Import the dataset generation class from the package, and play with the class functions.
from analyticsdf.analyticsdataframe import AnalyticsDataframe
ad = AnalyticsDataframe(1000, 6)
ad.predictor_matrix.head()

The predictor matrix is initialized with all null values.
Now let's update the predictors with some distributions:
for var in ['X1', 'X2', 'X3', 'X4', 'X5']:
ad.update_predictor_uniform(var, 0, 100)
ad.update_predictor_categorical('X6', ["Red", "Yellow", "Blue"], [0.3, 0.4, 0.3])

Once we have a dataframe desired and would like to visualize it, we can do:
df_visualization_bi(ad)

Next Steps
We plan to integrate an user interface to the library, aiming to let users configure, manipulate, and view datasets more easily.
Code Contributors

License
AutoGen is released under the MIT License.