Overview
RDT (Reversible Data Transforms) is a Python library that transforms raw data into fully numerical data, ready for data science. The transforms are reversible, allowing you to convert from numerical data back into your original format.
Install
Install RDT using pip or conda. We recommend using a virtual environment to avoid
conflicts with other software on your device.
pip install rdt
conda install -c conda-forge rdt
For more information about using reversible data transformations, visit the RDT Documentation.
Quickstart
In this short series of tutorials we will guide you through a series of steps that will help you getting started using RDT to transform columns, tables and datasets.
Load the demo data
After you have installed RDT, you can get started using the demo dataset.
from rdt import get_demo
customers = get_demo()
This dataset contains some randomly generated values that describe the customers of an online marketplace.
last_login email_optin credit_card age dollars_spent
0 2021-06-26 False VISA 29 99.99
1 2021-02-10 False VISA 18 NaN
2 NaT False AMEX 21 2.50
3 2020-09-26 True NaN 45 25.00
4 2020-12-22 NaN DISCOVER 32 19.99
Let's transform this data so that each column is converted to full, numerical data ready for data science.
Creating the HyperTransformer & config
The HyperTransformer is capable of transforming multi-column datasets.
from rdt import HyperTransformer
ht = HyperTransformer()
The HyperTransformer needs to know about the columns in your dataset and which transformers to
apply to each. These are described by a config. We can ask the HyperTransformer to automatically
detect it based on the data we plan to use.
ht.detect_initial_config(data=customers)
This will create and set the config.
Config:
{
"sdtypes": {
"last_login": "datetime",
"email_optin": "boolean",
"credit_card": "categorical",
"age": "numerical",
"dollars_spent": "numerical"
},
"transformers": {
"last_login": "UnixTimestampEncoder()",
"email_optin": "BinaryEncoder()",
"credit_card": "UniformEncoder()",
"age": "FloatFormatter()",
"dollars_spent": "FloatFormatter()"
}
}
The sdtypes dictionary describes the semantic data types of each of your columns and the
transformers dictionary describes which transformer to use for each column. You can customize the
transformers and their settings. (See the Transformers Glossary for more information).
Fitting & using the HyperTransformer
The HyperTransformer references the config while learning the data during the fit stage.
ht.fit(customers)
Once the transformer is fit, it's ready to use. Use the transform method to transform all columns of your dataset at once.
transformed_data = ht.transform(customers)
last_login.value email_optin.value credit_card.value age.value dollars_spent.value
0 1.624666e+18 0.0 0.2 29 99.99
1 1.612915e+18 0.0 0.2 18 36.87
2 1.611814e+18 0.0 0.5 21 2.50
3 1.601078e+18 1.0 0.7 45 25.00
4 1.608595e+18 0.0 0.9 32 19.99
The HyperTransformer applied the assigned transformer to each individual column. Each column
now contains fully numerical data that you can use for your project!
When you're done with your project, you can also transform the data back to the original format
using the reverse_transform method.
original_format_data = ht.reverse_transform(transformed_data)
last_login email_optin credit_card age dollars_spent
0 NaT False VISA 29 99.99
1 2021-02-10 False VISA 18 NaN
2 NaT False AMEX 21 NaN
3 2020-09-26 True NaN 45 25.00
4 2020-12-22 False DISCOVER 32 19.99
What's Next?
To learn more about reversible data transformations, visit the RDT Documentation.
The Synthetic Data Vault Project was first created at MIT's Data to AI Lab in 2016. After 4 years of research and traction with enterprise, we created DataCebo in 2020 with the goal of growing the project. Today, DataCebo is the proud developer of SDV, the largest ecosystem for synthetic data generation & evaluation. It is home to multiple libraries that support synthetic data, including:
- 🔄 Data discovery & transformation. Reverse the transforms to reproduce realistic data.
- 🧠 Multiple machine learning models -- ranging from Copulas to Deep Learning -- to create tabular, multi table and time series data.
- 📊 Measuring quality and privacy of synthetic data, and comparing different synthetic data generation models.
Get started using the SDV package -- a fully integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries for specific needs.
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Metadata
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