rdt-identity 0.6.1.dev0

Reversible Data Transforms

An Open Source Project from the Data to AI Lab, at MIT

• Website: https://sdv.dev
• Documentation: https://sdv.dev/SDV
• Repository: https://github.com/sdv-dev/RDT
• License: MIT
• Development Status: Pre-Alpha

Overview

RDT is a Python library used to transform data for data science libraries and preserve the transformations in order to revert them as needed.

Install

RDT is part of the SDV project and is automatically installed alongside it. For details about this process please visit the SDV Installation Guide

Optionally, RDT can also be installed as a standalone library using the following commands:

Using pip:

pip install rdt

Using conda:

conda install -c sdv-dev -c conda-forge rdt

For more installation options please visit the RDT installation Guide

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.

Transforming a column

In this first guide, you will learn how to use RDT in its simplest form, transforming a single column loaded as a pandas.DataFrame object.

1. Load the demo data

You can load some demo data using the rdt.get_demo function, which will return some random data for you to play with.

from rdt import get_demo

data = get_demo()

This will return a pandas.DataFrame with 10 rows and 4 columns, one of each data type supported:

   0_int    1_float 2_str          3_datetime
0   38.0  46.872441     b 2021-02-10 21:50:00
1   77.0  13.150228   NaN 2021-07-19 21:14:00
2   21.0        NaN     b                 NaT
3   10.0  37.128869     c 2019-10-15 21:39:00
4   91.0  41.341214     a 2020-10-31 11:57:00
5   67.0  92.237335     a                 NaT
6    NaN  51.598682   NaN 2020-04-01 01:56:00
7    NaN  42.204396     c 2020-03-12 22:12:00
8   68.0        NaN     c 2021-02-25 16:04:00
9    7.0  31.542918     a 2020-07-12 03:12:00

Notice how the data is random, so your output might look a bit different. Also notice how RDT introduced some null values randomly.

2. Load the transformer

In this example we will use the datetime column, so let's load a DatetimeTransformer.

from rdt.transformers import DatetimeTransformer

transformer = DatetimeTransformer()

3. Fit the Transformer

Before being able to transform the data, we need the transformer to learn from it.

We will do this by calling its fit method passing the column that we want to transform.

transformer.fit(data, columns=['3_datetime'])

4. Transform the data

Once the transformer is fitted, we can pass the data again to its transform method in order to get the transformed version of the data.

transformed = transformer.transform(data)

The output will be a numpy.ndarray with two columns, one with the datetimes transformed to integer timestamps, and another one indicating with 1s which values were null in the original data.

array([[1.61299380e+18, 0.00000000e+00],
       [1.62672924e+18, 0.00000000e+00],
       [1.59919923e+18, 1.00000000e+00],
       [1.57117554e+18, 0.00000000e+00],
       [1.60414542e+18, 0.00000000e+00],
       [1.59919923e+18, 1.00000000e+00],
       [1.58570616e+18, 0.00000000e+00],
       [1.58405112e+18, 0.00000000e+00],
       [1.61426904e+18, 0.00000000e+00],
       [1.59452352e+18, 0.00000000e+00]])

5. Revert the column transformation

In order to revert the previous transformation, the transformed data can be passed to the reverse_transform method of the transformer:

reversed_data = transformer.reverse_transform(transformed)

The output will be a pandas.Series containing the reverted values, which should be exactly like the original ones.

0   2021-02-10 21:50:00
1   2021-07-19 21:14:00
2                   NaT
3   2019-10-15 21:39:00
4   2020-10-31 11:57:00
5                   NaT
6   2020-04-01 01:56:00
7   2020-03-12 22:12:00
8   2021-02-25 16:04:00
9   2020-07-12 03:12:00
dtype: datetime64[ns]

Transforming a table

Once we know how to transform a single column, we can try to go the next level and transform a table with multiple columns.

1. Load the HyperTransformer

In order to manuipulate a complete table we will need to load a rdt.HyperTransformer.

from rdt import HyperTransformer

ht = HyperTransformer()

2. Fit the HyperTransformer

Just like the transfomer, the HyperTransformer needs to be fitted before being able to transform data.

This is done by calling its fit method passing the data DataFrame.

ht.fit(data)

3. Transform the table data

Once the HyperTransformer is fitted, we can pass the data again to its transform method in order to get the transformed version of the data.

transformed = ht.transform(data)

The output, will now be another pandas.DataFrame with the numerical representation of our data.

    0_int  0_int#1    1_float  1_float#1  2_str    3_datetime  3_datetime#1
0  38.000      0.0  46.872441        0.0   0.70  1.612994e+18           0.0
1  77.000      0.0  13.150228        0.0   0.90  1.626729e+18           0.0
2  21.000      0.0  44.509511        1.0   0.70  1.599199e+18           1.0
3  10.000      0.0  37.128869        0.0   0.15  1.571176e+18           0.0
4  91.000      0.0  41.341214        0.0   0.45  1.604145e+18           0.0
5  67.000      0.0  92.237335        0.0   0.45  1.599199e+18           1.0
6  47.375      1.0  51.598682        0.0   0.90  1.585706e+18           0.0
7  47.375      1.0  42.204396        0.0   0.15  1.584051e+18           0.0
8  68.000      0.0  44.509511        1.0   0.15  1.614269e+18           0.0
9   7.000      0.0  31.542918        0.0   0.45  1.594524e+18           0.0

4. Revert the table transformation

In order to revert the transformation and recover the original data from the transformed one, we need to call reverse_transform method of the HyperTransformer instance passing it the transformed data.

reversed_data = ht.reverse_transform(transformed)

Which should output, again, a table that looks exactly like the original one.

   0_int    1_float 2_str          3_datetime
0   38.0  46.872441     b 2021-02-10 21:50:00
1   77.0  13.150228   NaN 2021-07-19 21:14:00
2   21.0        NaN     b                 NaT
3   10.0  37.128869     c 2019-10-15 21:39:00
4   91.0  41.341214     a 2020-10-31 11:57:00
5   67.0  92.237335     a                 NaT
6    NaN  51.598682   NaN 2020-04-01 01:56:00
7    NaN  42.204396     c 2020-03-12 22:12:00
8   68.0        NaN     c 2021-02-25 16:04:00
9    7.0  31.542918     a 2020-07-12 03:12:00

The Synthetic Data Vault

This repository is part of The Synthetic Data Vault Project

• Website: https://sdv.dev
• Documentation: https://sdv.dev/SDV