CBM/README.md

# Cyclic Boosting Machines

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This is an efficient and Scikit-learn compatible implementation of the machine learning algorithm [Cyclic Boosting -- an explainable supervised machine learning algorithm](https://arxiv.org/abs/2002.03425), specifically for predicting count-data, such as sales and demand.

## Usage

```bash
pip install cyclicbm
```

```python
import cbm
import numpy as np

x_train: np.ndarray = ... # will be cast to uint8, so make sure you featurize before hand
y_train: np.ndarray = ... # will be cast to uint32

model = cbm.CBM()
model.fit(x_train, y_train)

x_test: np.numpy = ...
y_pred = model.predict(x_test)
```

## Explainability

The CBM model predicts by multiplying the global mean with each weight estimate for each bin and feature. Thus the weights can be interpreted as % increase or decrease from the global mean. e.g. a weight of 1.2 for the bin _Monday_ of the feature _Day-of-Week_ can be interpreted as a 20% increase of the target.

<img src="https://render.githubusercontent.com/render/math?math=\hat{y}_i = \mu \cdot \product^{p}_{j=1} f^k_j"> with <img src="https://render.githubusercontent.com/render/math?math=k = \{x_{j,_i} \in b^k_j \}">

```python
model = cbm.CBM()
model.fit(x_train, y_train)

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 
                         int(np.ceil(x_train.shape[1] / 2)),
                         figsize=(25, 20),
                         sharex=True)

for feature in np.arange(x_train.shape[1]):
    w = model.weights[feature]
    
    ax = axes[feature % 2, feature // 2]
    (ax.barh(x_train.iloc[:,feature].cat.categories.astype(str),
             np.array(w) - 1, # make sure it looks nice w/ bars go up and down from zero
             )
    )
    
    ax.set_title(x_train.columns[feature])
    ax.xaxis.set_tick_params(which='both', labelbottom=True)
    
fig.tight_layout()
``` 

## Featurization

Categorical features can be passed as 0-based indices, with a maximum of 255 categories supported at the moment.

Continuous features need to be discretized. [pandas.qcut](https://pandas.pydata.org/docs/reference/api/pandas.qcut.html) for equal-sized bins or [numpy.interp](https://numpy.org/doc/stable/reference/generated/numpy.interp.html) for equal-distant bins yield good results for us.

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide
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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft 
trademarks or logos is subject to and must follow 
[Microsoft's Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks/usage/general).
Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
Any use of third-party trademarks or logos are subject to those third-party's policies.