category-encoders 2.0.0

Categorical Encoding Methods
============================

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[](https://coveralls.io/r/scikit-learn-contrib/categorical-encoding)
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A set of scikit-learn-style transformers for encoding categorical
variables into numeric by means of different techniques.

Important Links
---------------

Documentation: [http://contrib.scikit-learn.org/categorical-encoding/](http://contrib.scikit-learn.org/categorical-encoding/)

Encoding Methods
----------------

* Backward Difference Contrast [2][3]
* BaseN [6]
* Binary [5]
* Hashing [1]
* Helmert Contrast [2][3]
* James-Stein Estimator [9]
* LeaveOneOut [4]
* M-estimator [7]
* Ordinal [2][3]
* One-Hot [2][3]
* Polynomial Contrast [2][3]
* Sum Contrast [2][3]
* Target Encoding [7]
* Weight of Evidence [8]

Installation
-----

The package requires: `numpy`, `statsmodels`, and `scipy`.

To install the package, execute:

```shell
$ python setup.py install
```

or

```shell
pip install category_encoders
```

or

```shell
conda install -c conda-forge category_encoders
```

To install the development version, you may use:

```shell
pip install --upgrade git+https://github.com/scikit-learn-contrib/categorical-encoding
```

Usage
-----

All of the encoders are fully compatible sklearn transformers, so they can be used in pipelines or in your existing scripts. Supported input formats include numpy arrays and pandas dataframes. If the cols parameter isn't passed, all columns with object or pandas categorical data type will be encoded. Please see the docs for transformer-specific configuration options.

Examples
--------
There are two types of encoders: unsupervised and supervised. An unsupervised example:
```python
from category_encoders import *
import pandas as pd
from sklearn.datasets import load_boston

# prepare some data
bunch = load_boston()
y = bunch.target
X = pd.DataFrame(bunch.data, columns=bunch.feature_names)

# use binary encoding to encode two categorical features
enc = BinaryEncoder(cols=['CHAS', 'RAD']).fit(X)

# transform the dataset
numeric_dataset = enc.transform(X)
```

And a supervised example:
```python
from category_encoders import *
import pandas as pd
from sklearn.datasets import load_boston

# prepare some data
bunch = load_boston()
y_train = bunch.target[0:250]
y_test = bunch.target[250:506]
X_train = pd.DataFrame(bunch.data[0:250], columns=bunch.feature_names)
X_test = pd.DataFrame(bunch.data[250:506], columns=bunch.feature_names)

# use target encoding to encode two categorical features
enc = TargetEncoder(cols=['CHAS', 'RAD']).fit(X_train, y_train)

# transform the datasets
training_numeric_dataset = enc.transform(X_train, y_train)
testing_numeric_dataset = enc.transform(X_test)
```

Additional examples and benchmarks can be found in the `examples` directory.

Contributing
------------

Category encoders is under active development, if you'd like to be involved, we'd love to have you. Check out the CONTRIBUTING.md file
or open an issue on the github project to get started.

References:
-----------

1. Kilian Weinberger; Anirban Dasgupta; John Langford; Alex Smola; Josh Attenberg (2009). Feature Hashing for Large Scale Multitask Learning. Proc. ICML.
2. Contrast Coding Systems for categorical variables. UCLA: Statistical Consulting Group. From https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/.
3. Gregory Carey (2003). Coding Categorical Variables. From http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
4. Strategies to encode categorical variables with many categories. From https://www.kaggle.com/c/caterpillar-tube-pricing/discussion/15748#143154.
5. Beyond One-Hot: an exploration of categorical variables. From http://www.willmcginnis.com/2015/11/29/beyond-one-hot-an-exploration-of-categorical-variables/
6. BaseN Encoding and Grid Search in categorical variables. From http://www.willmcginnis.com/2016/12/18/basen-encoding-grid-search-category_encoders/
7. Daniele Miccii-Barreca (2001). A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems. SIGKDD Explor. Newsl. 3, 1. From http://dx.doi.org/10.1145/507533.507538
8. Weight of Evidence (WOE) and Information Value Explained. From https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html
9. Empirical Bayes for multiple sample sizes. From http://chris-said.io/2017/05/03/empirical-bayes-for-multiple-sample-sizes/