Skip to main content

Reversible Data Transformsi

Project description

DAI-Lab An open source project from Data to AI Lab at MIT.

PyPi Shield Travis CI Shield Coverage Status Downloads

RDT: Reversible Data Transforms

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

Requirements

RDT has been developed and tested on Python 3.5, 3.6 and 3.7

Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid interfering with other software installed in the system where RDT is run.

These are the minimum commands needed to create a virtualenv using python3.6 for RDT:

pip install virtualenv
virtualenv -p $(which python3.6) rdt-venv

Afterwards, you have to execute this command to have the virtualenv activated:

source rdt-venv/bin/activate

Remember about executing it every time you start a new console to work on RDT!

Install with pip

After creating the virtualenv and activating it, we recommend using pip in order to install RDT:

pip install rdt

This will pull and install the latest stable release from PyPi.

Install from source

With your virtualenv activated, you can clone the repository and install it from source by running make install on the stable branch:

git clone git@github.com:sdv-dev/RDT.git
cd RDT
git checkout stable
make install

Install for Development

If you want to contribute to the project, a few more steps are required to make the project ready for development.

Please head to the Contributing Guide for more details about this process.

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['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['3_datetime'])

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

What's next?

For more details about Reversible Data Transforms, how to contribute to the project, and its complete API reference, please visit the documentation site.

History

0.2.1 - 2020-01-17

Bugs Fixed

  • Boolean Transformer fails to revert when there are NO nulls - Issue #103 by @JDTheRipperPC

0.2.0 - 2019-10-15

This version comes with a brand new API and internal implementation, removing the old metadata JSON from the user provided arguments, and making each transformer work only with pandas.Series of their corresponding data type.

As part of this change, several transformer names have been changed and a new BooleanTransformer and a feature to automatically decide which transformers to use based on dtypes have been added.

Unit test coverage has also been increased to 100%.

Special thanks to @JDTheRipperPC and @csala for the big efforts put in making this release possible.

Issues

  • Drop the usage of meta - Issue #72 by @JDTheRipperPC
  • Make CatTransformer.probability_map deterministic - Issue #25 by @csala

0.1.3 - 2019-09-24

New Features

  • Add attributes NullTransformer and col_meta - Issue #30 by @ManuelAlvarezC

General Improvements

  • Integrate with CodeCov - Issue #89 by @csala
  • Remake Sphinx Documentation - Issue #96 by @JDTheRipperPC
  • Improve README - Issue #92 by @JDTheRipperPC
  • Document RELEASE workflow - Issue #93 by @JDTheRipperPC
  • Add support to Python 3.7 - Issue #38 by @ManuelAlvarezC
  • Create way to pass HyperTransformer table dict - Issue #45 by @ManuelAlvarezC

0.1.2

  • Add a numerical transformer for positive numbers.
  • Add option to anonymize data on categorical transformer.
  • Move the col_meta argument from method-level to class-level.
  • Move the logic for missing values from the transformers into the HyperTransformer.
  • Removed unreacheble lines in NullTransformer.
  • Numbertransfomer to set default value to 0 when the column is null.
  • Add a CLA for collaborators.
  • Refactor performance-wise the transformers.

0.1.1

  • Improve handling of NaN in NumberTransformer and CatTransformer.
  • Add unittests for HyperTransformer.
  • Remove unused methods get_types and impute_table from HyperTransformer.
  • Make NumberTransformer enforce dtype int on integer data.
  • Make DTTransformer check data format before transforming.
  • Add minimal API Reference.
  • Merge rdt.utils into HyperTransformer class.

0.1.0

  • First release on PyPI.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for rdt, version 0.2.1
Filename, size File type Python version Upload date Hashes
Filename, size rdt-0.2.1-py2.py3-none-any.whl (16.9 kB) File type Wheel Python version py2.py3 Upload date Hashes View hashes
Filename, size rdt-0.2.1.tar.gz (68.2 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page