Metadata-Version: 2.1
Name: rdt
Version: 1.0.0.dev0
Summary: Reversible Data Transforms
Home-page: https://github.com/sdv-dev/RDT
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Keywords: rdt
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6,<3.10
Description-Content-Type: text/markdown
Provides-Extra: copulas
Provides-Extra: test
Provides-Extra: dev
License-File: LICENSE
License-File: AUTHORS.rst

<div align="center">
<br/>
<p align="center">
    <i>This repository is part of <a href="https://sdv.dev">The Synthetic Data Vault Project</a>, a project from <a href="https://datacebo.com">DataCebo</a>.</i>
</p>

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<div align="left">
<br/>
<p align="center">
<a href="https://github.com/sdv-dev/RDT">
<img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/RDT-DataCebo.png"></img>
</a>
</p>
</div>

</div>

# 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 Alpha stage.                                 |
| [![][Slack Logo] **Community**][Community]    | Join our Slack Workspace for announcements and discussions.          |
| [![][Google Colab Logo] **Tutorials**][Tutorials] | Run the RDT Tutorials in a notebook.                             |

[Website]: https://sdv.dev
[SDV Blog]: https://sdv.dev/blog
[Documentation]: https://docs.sdv.dev/rdt
[Repository]: https://github.com/sdv-dev/RDT
[License]: https://github.com/sdv-dev/RDT/blob/master/LICENSE
[Development Status]: https://pypi.org/search/?q=&o=&c=Development+Status+%3A%3A+3+-+Alpha
[Slack Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/slack.png
[Community]: https://join.slack.com/t/sdv-space/shared_invite/zt-gdsfcb5w-0QQpFMVoyB2Yd6SRiMplcw
[Google Colab Logo]: https://github.com/sdv-dev/SDV/blob/master/docs/images/google_colab.png
[Tutorials]: https://colab.research.google.com/drive/1T_3XSPPOVILATsyRV9xjQPa0hvM1vnM-?usp=sharing

# 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](
https://sdv.dev/SDV/getting_started/install.html)

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

**Using `pip`:**

```bash
pip install rdt
```

**Using `conda`:**

```bash
conda install -c conda-forge rdt
```

For more installation options please visit the [RDT installation Guide](INSTALL.md)


# 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.

```python3
from rdt import get_demo

customers = get_demo()
```

This dataset contains some randomly generated values that describes 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.

```python3
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.

```python3
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(missing_value_replacement='mean')",
        "email_optin": "BinaryEncoder(missing_value_replacement='mode')",
        "credit_card": "FrequencyEncoder()",
        "age": "FloatFormatter(missing_value_replacement='mean')",
        "dollars_spent": "FloatFormatter(missing_value_replacement='mean')"
    }
}
```

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.

## Fitting & using the HyperTransformer 

The `HyperTransformer` references the config while learning the data during the `fit` stage.

```python3
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.

```python3
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.

```python3
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
```

## Transforming a single column

It is also possible to transform a single column of a `pandas.DataFrame`. To do this,
follow the following steps.

### Load the transformer

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

```python3
from rdt.transformers import UnixTimestampEncoder

transformer = UnixTimestampEncoder()
```

### 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.

```python3
transformer.fit(customers, column='last_login')
```

### 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.

```python3
transformed = transformer.transform(customers)
```

The output will be a `pandas.DataFrame` similar to the input data, except with the original
datetime column replaced with `last_login.value`.

```
  email_optin credit_card  age  dollars_spent  last_login.value
0       False        VISA   29          99.99      1.624666e+18
1       False        VISA   18            NaN      1.612915e+18
2       False        AMEX   21           2.50               NaN
3        True         NaN   45          25.00      1.601078e+18
4         NaN    DISCOVER   32          19.99      1.608595e+18
```

### 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:

```python3
reversed_data = transformer.reverse_transform(transformed)
```

The output will be a `pandas.DataFrame` containing the reverted values, which should be exactly
like the original ones, except for the order of the columns.

```
  email_optin credit_card  age  dollars_spent last_login
0       False        VISA   29          99.99 2021-06-26
1       False        VISA   18            NaN 2021-02-10
2       False        AMEX   21           2.50        NaT
3        True         NaN   45          25.00 2020-09-26
4         NaN    DISCOVER   32          19.99 2020-12-22
```

---


<div align="center">
<a href="https://datacebo.com"><img align="center" width=40% src="https://github.com/sdv-dev/SDV/blob/master/docs/images/DataCebo.png"></img></a>
</div>
<br/>
<br/>

[The Synthetic Data Vault Project](https://sdv.dev) was first created at MIT's [Data to AI Lab](
https://dai.lids.mit.edu/) in 2016. After 4 years of research and traction with enterprise, we
created [DataCebo](https://datacebo.com) 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](https://sdv.dev/SDV/getting_started/install.html) -- a fully
integrated solution and your one-stop shop for synthetic data. Or, use the standalone libraries
for specific needs.


# History

## 0.6.4 - 2022-3-7

This release fixes multiple bugs concerning the `HyperTransformer`. One is that the `get_transformer_tree_yaml` method no longer crashes on
every call. Another is that calling the `update_field_data_types` and `update_default_data_type_transformers` after fitting no longer breaks the `transform`
method. 

The `HyperTransformer` now sorts its outputs for both `transform` and `reverse_transform` based on the order of the input's columns. It is also now possible
to create transformers that simply drops columns during `transform` and don't return any new columns.

### New Features

* Support dropping a column trough a transformer - Issue [#393](https://github.com/sdv-dev/RDT/issues/393) by @pvk-developer
* HyperTransformer should sort columns after transform and reverse_transform - Issue [#405](https://github.com/sdv-dev/RDT/issues/405) by @fealho

### Bugs

* get_transformer_tree_yaml fails - Issue [#389](https://github.com/sdv-dev/RDT/issues/389) by @amontanez24
* HyperTransformer _unfit method not working correctly - Issue [#390](https://github.com/sdv-dev/RDT/issues/390) by @amontanez24
* Blank dataframe after updating the data types - Issue [#401](https://github.com/sdv-dev/RDT/issues/401) by @amontanez24

## 0.6.3 - 2022-2-4

This release adds a new module to the `RDT` library called `performance`. This module can be used to evaluate the speed and peak memory usage
of any transformer in RDT. This release also increases the maximum acceptable version of scikit-learn to make it more compatible with other libraries
in the `SDV` ecosystem. On top of that, it fixes a bug related to a new version of `pandas`.

### New Features

* Move profiling functions into RDT library - Issue [#353](https://github.com/sdv-dev/RDT/issues/353) by @amontanez24

### Housekeeping

* Increase scikit-learn dependency range - Issue [#351](https://github.com/sdv-dev/RDT/issues/351) by @amontanez24
* pandas 1.4.0 release causes a small error - Issue [#358](https://github.com/sdv-dev/RDT/issues/358) by @fealho

### Bugs

* Performance tests get stuck on Unix if multiprocessing is involved - Issue [#337](https://github.com/sdv-dev/RDT/issues/337) by @amontanez24

## 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](https://github.com/sdv-dev/RDT/issues/321) by @amontanez24
* Add a BayesianGMM Transformer - Issue [#183](https://github.com/sdv-dev/RDT/issues/183) by @fealho
* Add transformer tree structure and traversal methods - Issue [#330](https://github.com/sdv-dev/RDT/issues/330) by @amontanez24

### Bugs fixed

* HyperTransformer raises NotFittedError after fitting - Issue [#332](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/323) by @amontanez24
* Remove docs - PR [#322](https://github.com/sdv-dev/RDT/pull/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](https://github.com/sdv-dev/RDT/issues/298) by @amontanez24
* Create validate_pull_request function - Issue [#254](https://github.com/sdv-dev/RDT/issues/254) by @pvk-developer
* Create validate_transformer_unit_tests function - Issue [#249](https://github.com/sdv-dev/RDT/issues/249) by @pvk-developer
* Create validate_transformer_performance function - Issue [#251](https://github.com/sdv-dev/RDT/issues/251) by @katxiao
* Create validate_transformer_quality function - Issue [#253](https://github.com/sdv-dev/RDT/issues/253) by @amontanez24
* Create validate_transformer_code_style function - Issue [#248](https://github.com/sdv-dev/RDT/issues/248) by @pvk-developer
* Create validate_transformer_integration function - Issue [#250](https://github.com/sdv-dev/RDT/issues/250) by @katxiao
* Enable users to specify transformers to use in HyperTransformer - Issue [#233](https://github.com/sdv-dev/RDT/issues/233) by @amontanez24 and @csala
* Addons implementation - Issue [#225](https://github.com/sdv-dev/RDT/issues/225) by @pvk-developer
* Create ways for HyperTransformer to know which transformers to apply to each data type - Issue [#232](https://github.com/sdv-dev/RDT/issues/232) by @amontanez24 and @csala
* Update categorical transformers - PR [#231](https://github.com/sdv-dev/RDT/pull/231) by @fealho
* Update numerical transformer - PR [#227](https://github.com/sdv-dev/RDT/pull/227) by @fealho
* Update datetime transformer - PR [#230](https://github.com/sdv-dev/RDT/pull/230) by @fealho
* Update boolean transformer - PR [#228](https://github.com/sdv-dev/RDT/pull/228) by @fealho
* Update null transformer - PR [#229](https://github.com/sdv-dev/RDT/pull/229) by @fealho
* Update the baseclass - PR [#224](https://github.com/sdv-dev/RDT/pull/224) by @fealho

### Bugs fixed

* If the input data has a different index, the reverse transformed data may be out of order - Issue [#277](https://github.com/sdv-dev/RDT/issues/277) by @amontanez24

### Documentation changes

* RDT contributing guide - Issue [#301](https://github.com/sdv-dev/RDT/issues/301) by @katxiao and @amontanez24

### Internal improvements

* Add PR template for new transformers - Issue [#307](https://github.com/sdv-dev/RDT/issues/307) by @katxiao
* Implement Quality Tests for Transformers - Issue [#252](https://github.com/sdv-dev/RDT/issues/252) by @amontanez24
* Update performance test structure - Issue [#257](https://github.com/sdv-dev/RDT/issues/257) by @katxiao
* Automated integration test for transformers - Issue [#223](https://github.com/sdv-dev/RDT/issues/223) by @katxiao
* Move datasets to its own module - Issue [#235](https://github.com/sdv-dev/RDT/issues/235) by @katxiao
* Fix missing coverage in rdt unit tests - Issue [#219](https://github.com/sdv-dev/RDT/issues/219) by @fealho
* Add repo-wide automation - Issue [#309](https://github.com/sdv-dev/RDT/issues/309) by @katxiao

### Other issues closed

* DeprecationWarning: np.float is a deprecated alias for the builtin float - Issue [#304](https://github.com/sdv-dev/RDT/issues/304) by @csala
* Add pip check to CI workflows - Issue [#290](https://github.com/sdv-dev/RDT/issues/290) by @csala
* Should Transformers subclasses exist for specific configurations? - Issue [#243](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/244) by @katxiao
* Adapt to latest PyLint housekeeping - Issue [#216](https://github.com/sdv-dev/RDT/issues/216) by @fealho

## 0.5.2 - 2021-08-16

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

### Issues closed

* BooleanTransformer.reverse_transform sometimes crashes with TypeError - Issue [#210](https://github.com/sdv-dev/RDT/issues/210) by @katxiao
* OneHotEncodingTransformer causing shape misalignment in CopulaGAN, CTGAN, and TVAE - Issue [#208](https://github.com/sdv-dev/RDT/issues/208) by @sarahmish
* Boolean.transformer.reverse_transform modifies the input data - Issue [#211](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/194) by @amontanez24
* Audit the NullTransformer - Issue [#192](https://github.com/sdv-dev/RDT/issues/192) by @amontanez24
* Audit DatetimeTransformer - Issue [#189](https://github.com/sdv-dev/RDT/issues/189) by @sarahmish
* Audit the LabelEncodingTransformer - Issue [#184](https://github.com/sdv-dev/RDT/issues/184) by @amontanez24
* Audit the NumericalTransformer - Issue [#181](https://github.com/sdv-dev/RDT/issues/181) by @fealho
* Audit CategoricalTransformer - Issue [#180](https://github.com/sdv-dev/RDT/issues/180) by @katxiao
* Audit BooleanTransformer - Issue [#179](https://github.com/sdv-dev/RDT/issues/179) by @katxiao
* Auditing OneHotEncodingTransformer - Issue [#178](https://github.com/sdv-dev/RDT/issues/178) by @sarahmish
* Create script for profiling - Issue [#176](https://github.com/sdv-dev/RDT/issues/176) by @amontanez24
* Create folder structure for performance testing - Issue [#174](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/166) by @amontanez24 and @csala
* `NumericalTransformer` rounding error with infinity - Issue [#169](https://github.com/sdv-dev/RDT/issues/169) by @amontanez24
* Add min and max arguments to NumericalTransformer - Issue [#106](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/164) by @pvk-developer and @csala

### Documentation changes

* docs: fix typo - PR [#163](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/160) by @pvk-developer
* `HyperTransformer`: Memory usage increase when `reverse_transform` is called - Issue [#156](https://github.com/sdv-dev/RDT/issues/152) 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](https://github.com/sdv-dev/RDT/issues/152) by @csala
* Remove faker - Issue [#150](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/148) by @csala
* Makes Copulas an optional dependency - Issue [#144](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/132) by @csala
* Example does not work on Windows - Issue [#114](https://github.com/sdv-dev/RDT/issues/114) by @csala
* OneHotEncodingTransformer producing all zeros - Issue [#135](https://github.com/sdv-dev/RDT/issues/135) by @fealho
* OneHotEncodingTransformer support for lists and lists of lists - Issue [#137](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/129) by @csala
* Add option to drop constant parts of the datetimes - Issue [#130](https://github.com/sdv-dev/RDT/issues/130) by @csala

## 0.2.6 - 2020-10-05

* Add GaussianCopulaTransformer - Issue [#125](https://github.com/sdv-dev/RDT/issues/125) by @csala
* dtype category error - Issue [#124](https://github.com/sdv-dev/RDT/issues/124) by @csala

## 0.2.5 - 2020-09-18

Miunor bugfixing release.

# Bugs Fixed

* Handle NaNs in OneHotEncodingTransformer - Issue [#118](https://github.com/sdv-dev/RDT/issues/118) by @csala
* OneHotEncodingTransformer fails if there is only one category - Issue [#119](https://github.com/sdv-dev/RDT/issues/119) by @csala
* All NaN column produces NaN values enhancement - Issue [#121](https://github.com/sdv-dev/RDT/issues/121) by @csala
* Make the CategoricalTransformer learn the column dtype and restore it back - Issue [#122](https://github.com/sdv-dev/RDT/issues/122) by @csala

## 0.2.4 - 2020-08-08

### General Improvements

* Support Python 3.8 - Issue [#117](https://github.com/sdv-dev/RDT/issues/117) by @csala
* Support pandas >1 - Issue [#116](https://github.com/sdv-dev/RDT/issues/116) by @csala

## 0.2.3 - 2020-07-09

* Implement OneHot and Label encoding as transformers - Issue [#112](https://github.com/sdv-dev/RDT/issues/112) by @csala

## 0.2.2 - 2020-06-26

### Bugs Fixed

* Escape `column_name` in hypertransformer - Issue [#110](https://github.com/sdv-dev/RDT/issues/110) by @csala

## 0.2.1 - 2020-01-17

### Bugs Fixed

* Boolean Transformer fails to revert when there are NO nulls - Issue [#103](https://github.com/sdv-dev/RDT/issues/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](https://github.com/sdv-dev/RDT/issues/72) by @JDTheRipperPC
* Make CatTransformer.probability_map deterministic - Issue [#25](https://github.com/sdv-dev/RDT/issues/25) by @csala

## 0.1.3 - 2019-09-24

### New Features

* Add attributes NullTransformer and col_meta - Issue [#30](https://github.com/sdv-dev/RDT/issues/30) by @ManuelAlvarezC

### General Improvements

* Integrate with CodeCov - Issue [#89](https://github.com/sdv-dev/RDT/issues/89) by @csala
* Remake Sphinx Documentation - Issue [#96](https://github.com/sdv-dev/RDT/issues/96) by @JDTheRipperPC
* Improve README - Issue [#92](https://github.com/sdv-dev/RDT/issues/92) by @JDTheRipperPC
* Document RELEASE workflow - Issue [#93](https://github.com/sdv-dev/RDT/issues/93) by @JDTheRipperPC
* Add support to Python 3.7 - Issue [#38](https://github.com/sdv-dev/RDT/issues/38) by @ManuelAlvarezC
* Create way to pass HyperTransformer table dict - Issue [#45](https://github.com/sdv-dev/RDT/issues/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.


