rdt 0.6.2

Reversible Data Transforms

This repository is part of The Synthetic Data Vault Project, a project from DataCebo.

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.

Important Links
:computer: Website	Check out the SDV Website for more information about the project.
:orange_book: SDV Blog	Regular publshing of useful content about Synthetic Data Generation.
:book: Documentation	Quickstarts, User and Development Guides, and API Reference.
:octocat: Repository	The link to the Github Repository of this library.
:scroll: License	The entire ecosystem is published under the MIT License.
:keyboard: Development Status	This software is in its Pre-Alpha stage.
Community	Join our Slack Workspace for announcements and discussions.
Tutorials	Run the SDV Tutorials in a Binder environment.

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 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 DataCebo team is the proud developer of The Synthetic Data Vault Project, the largest open source ecosystem for synthetic data generation & evaluation. The ecosystem 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.

History

0.6.2 - 2021-12-28

This release adds a new BayesGMMTransformer. This transformer can be used to convert a numerical column into two columns: a discrete column indicating the selected component of the GMM for each row, and a continuous column containing the normalized value of each row based on the mean and std of the selected component. It is useful when the column being transformed came from multiple distributions.

This release also adds multiple new methods to the HyperTransformer API. These allow for users to access the specfic transformers used on each input field, as well as view the entire tree of transformers that are used when running transform. The exact methods are:

• BaseTransformer.get_input_columns() - Return list of input columns for a transformer.
• BaseTransformer.get_output_columns() - Return list of output columns for a transformer.
• HyperTransformer.get_transformer(field) - Return the transformer instance used for a field.
• HyperTransformer.get_output_transformers(field) - Return dictionary mapping output columns of a field to the transformers used on them.
• HyperTransformer.get_final_output_columns(field) - Return list of all final output columns related to a field.
• HyperTransformer.get_transformer_tree_yaml() - Return YAML representation of transformers tree.

Additionally, this release fixes a bug where the HyperTransformer was incorrectly raising a NotFittedError. It also improved the DatetimeTransformer by autonomously detecting if a column needs to be converted from dtype object to dtype datetime.

New Features

• Cast column to datetime if specified in field transformers - Issue #321 by @amontanez24
• Add a BayesianGMM Transformer - Issue #183 by @fealho
• Add transformer tree structure and traversal methods - Issue #330 by @amontanez24

Bugs fixed

• HyperTransformer raises NotFittedError after fitting - Issue #332 by @amontanez24

0.6.1 - 2021-11-10

This release adds support for Python 3.9! It also removes unused document files.

Internal Improvements

• Add support for Python 3.9 - Issue #323 by @amontanez24
• Remove docs - PR #322 by @pvk-developer

0.6.0 - 2021-10-29

This release makes major changes to the underlying code for RDT as well as the API for both the HyperTransformer and BaseTransformer. The changes enable the following functionality:

• The HyperTransformer can now apply a sequence of transformers to a column.
• Transformers can now take multiple columns as an input.
• RDT has been expanded to allow for infinite data types to be added instead of being restricted to pandas.dtypes.
• Users can define acceptable output types for running HyperTransformer.transform.
• The HyperTransformer will continuously apply transformations to the input fields until only acceptable data types are in the output.
• Transformers can return data of any data type.
• Transformers now have named outputs and output types.
• Transformers can suggest which transformer to use on any of their outputs.

To take advantage of this functionality, the following API changes were made:

• The HyperTransformer has new initialization parameters that allow users to specify data types for any field in their data as well as specify which transformer to use for a field or data type. The parameters are:
  • field_transformers - A dictionary allowing users to specify which transformer to use for a field or derived field. Derived fields are fields created by running transform on the input data.
  • field_data_types - A dictionary allowing users to specify the data type of a field.
  • default_data_type_transformers - A dictionary allowing users to specify the default transformer to use for a data type.
  • transform_output_types - A dictionary allowing users to specify which data types are acceptable for the output of transform. This is a result of the fact that transformers can now be applied in a sequence, and not every transformer will return numeric data.
• Methods were also added to the HyperTransformer to allow these parameters to be modified. These include get_field_data_types, update_field_data_types, get_default_data_type_transformers, update_default_data_type_transformers and set_first_transformers_for_fields.
• The BaseTransformer now requires the column names it will transform to be provided to fit, transform and reverse_transform.
• The BaseTransformer added the following method to allow for users to see its output fields and output types: get_output_types.
• The BaseTransformer added the following method to allow for users to see the next suggested transformer for each output field: get_next_transformers.

On top of the changes to the API and the capabilities of RDT, many automated checks and tests were also added to ensure that contributions to the library abide by the current code style, stay performant and result in data of a high quality. These tests run on every push to the repository. They can also be run locally via the following functions:

• validate_transformer_code_style - Checks that new code follows the code style.
• validate_transformer_quality - Tests that new transformers yield data that maintains relationships between columns.
• validate_transformer_performance - Tests that new transformers don't take too much time or memory.
• validate_transformer_unit_tests - Checks that the unit tests cover all new code, follow naming conventions and pass.
• validate_transformer_integration - Checks that the integration tests follow naming conventions and pass.

New Features

• Update HyperTransformer API - Issue #298 by @amontanez24
• Create validate_pull_request function - Issue #254 by @pvk-developer
• Create validate_transformer_unit_tests function - Issue #249 by @pvk-developer
• Create validate_transformer_performance function - Issue #251 by @katxiao
• Create validate_transformer_quality function - Issue #253 by @amontanez24
• Create validate_transformer_code_style function - Issue #248 by @pvk-developer
• Create validate_transformer_integration function - Issue #250 by @katxiao
• Enable users to specify transformers to use in HyperTransformer - Issue #233 by @amontanez24 and @csala
• Addons implementation - Issue #225 by @pvk-developer
• Create ways for HyperTransformer to know which transformers to apply to each data type - Issue #232 by @amontanez24 and @csala
• Update categorical transformers - PR #231 by @fealho
• Update numerical transformer - PR #227 by @fealho
• Update datetime transformer - PR #230 by @fealho
• Update boolean transformer - PR #228 by @fealho
• Update null transformer - PR #229 by @fealho
• Update the baseclass - PR #224 by @fealho

Bugs fixed

• If the input data has a different index, the reverse transformed data may be out of order - Issue #277 by @amontanez24

Documentation changes

• RDT contributing guide - Issue #301 by @katxiao and @amontanez24

Internal improvements

• Add PR template for new transformers - Issue #307 by @katxiao
• Implement Quality Tests for Transformers - Issue #252 by @amontanez24
• Update performance test structure - Issue #257 by @katxiao
• Automated integration test for transformers - Issue #223 by @katxiao
• Move datasets to its own module - Issue #235 by @katxiao
• Fix missing coverage in rdt unit tests - Issue #219 by @fealho
• Add repo-wide automation - Issue #309 by @katxiao

Other issues closed

• DeprecationWarning: np.float is a deprecated alias for the builtin float - Issue #304 by @csala
• Add pip check to CI workflows - Issue #290 by @csala
• Should Transformers subclasses exist for specific configurations? - Issue #243 by @fealho

0.5.3 - 2021-10-07

This release fixes a bug with learning rounding digits in the NumericalTransformer, and includes a few housekeeping improvements.

Issues closed

• Update learn rounding digits to handle all nan data - Issue #244 by @katxiao
• Adapt to latest PyLint housekeeping - Issue #216 by @fealho

0.5.2 - 2021-08-16

This release fixes a couple of bugs introduced by the previous release regarding the OneHotEncoder and the BooleanTransformer.

Issues closed

• BooleanTransformer.reverse_transform sometimes crashes with TypeError - Issue #210 by @katxiao
• OneHotEncoder causing shape misalignment in CopulaGAN, CTGAN, and TVAE - Issue #208 by @sarahmish
• Boolean.transformer.reverse_transform modifies the input data - Issue #211 by @katxiao

0.5.1 - 2021-08-11

This release improves the overall performance of the library, both in terms of memory and time consumption. More specifically, it makes the following modules more efficient: NullTransformer, DatetimeTransformer, LabelEncodingTransformer, NumericalTransformer, CategoricalTransformer, BooleanTransformer and OneHotEncodingTransformer.

It also adds performance-based testing and a script for profiling the performance.

Issues closed

• Add performance-based testing - Issue #194 by @amontanez24
• Audit the NullTransformer - Issue #192 by @amontanez24
• Audit DatetimeTransformer - Issue #189 by @sarahmish
• Audit the LabelEncodingTransformer - Issue #184 by @amontanez24
• Audit the NumericalTransformer - Issue #181 by @fealho
• Audit CategoricalTransformer - Issue #180 by @katxiao
• Audit BooleanTransformer - Issue #179 by @katxiao
• Auditing OneHotEncodingTransformer - Issue #178 by @sarahmish
• Create script for profiling - Issue #176 by @amontanez24
• Create folder structure for performance testing - Issue #174 by @amontanez24

0.5.0 - 2021-07-12

This release updates the NumericalTransformer by adding a new rounding argument. Users can now obtain numerical values with precision, either pre-specified or automatically computed from the given data.

Issues closed

• Add rounding argument to NumericalTransformer - Issue #166 by @amontanez24 and @csala
• NumericalTransformer rounding error with infinity - Issue #169 by @amontanez24
• Add min and max arguments to NumericalTransformer - Issue #106 by @amontanez24

0.4.2 - 2021-06-08

This release adds a new method to the CategoricalTransformer to solve a bug where the transformer becomes unusable after being pickled and unpickled if it had NaN values in the data which it was fit on.

It also fixes some grammar mistakes in the documentation.

Issues closed

• CategoricalTransformer with NaN values cannot be pickled bug - Issue #164 by @pvk-developer and @csala

Documentation changes

• docs: fix typo - PR #163 by @sbrugman

0.4.1 - 2021-03-29

This release improves the HyperTransformer memory usage when working with a high number of columns or a high number of categorical values when using one hot encoding.

Issues closed

• Boolean, Datetime and LabelEncoding transformers fail with 2D ndarray - Issue #160 by @pvk-developer
• HyperTransformer: Memory usage increase when reverse_transform is called - Issue #156 by @pvk-developer and @AnupamaGangadhar

0.4.0 - 2021-02-24

In this release a change in the HyperTransformer allows using it to transform and reverse transform a subset of the columns seen during training.

The anonymization functionality which was deprecated and not being used has also been removed along with the Faker dependency.

Issues closed

• Allow the HyperTransformer to be used on a subset of the columns - Issue #152 by @csala
• Remove faker - Issue #150 by @csala

0.3.0 - 2021-01-27

This release changes the behavior of the HyperTransformer to prevent it from modifying any column in the given DataFrame if the transformers dictionary is passed empty.

Issues closed

• If transformers is an empty dict, do nothing - Issue #149 by @csala

0.2.10 - 2020-12-18

This release adds a new argument to the HyperTransformer which gives control over which transformers to use by default for each dtype if no specific transformer has been specified for the field.

This is also the first version to be officially released on conda.

Issues closed

• Add dtype_transformers argument to HyperTransformer - Issue #148 by @csala
• Makes Copulas an optional dependency - Issue #144 by @fealho

0.2.9 - 2020-11-27

This release fixes a bug that prevented the CategoricalTransformer from working properly when being passed data that contained numerical data only, without any strings, but also contained None or NaN values.

Issues closed

• KeyError: nan - CategoricalTransformer fails on numerical + nan data only - Issue #142 by @csala

0.2.8 - 2020-11-20

This release fixes a few minor bugs, including some which prevented RDT from fully working on Windows systems.

Thanks to this fixes, as well as a new testing infrastructure that has been set up, from now on RDT is officially supported on Windows systems, as well as on the Linux and macOS systems which were previously supported.

Issues closed

• TypeError: unsupported operand type(s) for: 'NoneType' and 'int' - Issue #132 by @csala
• Example does not work on Windows - Issue #114 by @csala
• OneHotEncodingTransformer producing all zeros - Issue #135 by @fealho
• OneHotEncodingTransformer support for lists and lists of lists - Issue #137 by @fealho

0.2.7 - 2020-10-16

In this release we drop the support for the now officially dead Python 3.5 and introduce a new feature in the DatetimeTransformer which reduces the dimensionality of the generated numerical values while also ensuring that the reverted datetimes maintain the same level as time unit precision as the original ones.

• Drop Py35 support - Issue #129 by @csala
• Add option to drop constant parts of the datetimes - Issue #130 by @csala

0.2.6 - 2020-10-05

• Add GaussianCopulaTransformer - Issue #125 by @csala
• dtype category error - Issue #124 by @csala

0.2.5 - 2020-09-18

Miunor bugfixing release.

Bugs Fixed

• Handle NaNs in OneHotEncodingTransformer - Issue #118 by @csala
• OneHotEncodingTransformer fails if there is only one category - Issue #119 by @csala
• All NaN column produces NaN values enhancement - Issue #121 by @csala
• Make the CategoricalTransformer learn the column dtype and restore it back - Issue #122 by @csala

0.2.4 - 2020-08-08

General Improvements

• Support Python 3.8 - Issue #117 by @csala
• Support pandas >1 - Issue #116 by @csala

0.2.3 - 2020-07-09

• Implement OneHot and Label encoding as transformers - Issue #112 by @csala

0.2.2 - 2020-06-26

Bugs Fixed

• Escape column_name in hypertransformer - Issue #110 by @csala

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.