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.

## Features

* Optimized for categorical features
* Continuous features are discretized using [pandas.qcut](https://pandas.pydata.org/docs/reference/api/pandas.qcut.html).
* Date auto-expansion (weekday + month).
* Feature importance plots: categorical, continuous and interactions.
* Metrics to stop training: RMSE, L1, SMAPE.

## Usage

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 \}">

```bash
pip install cyclicbm
```

```python
import cbm
from sklearn.metrics import mean_squared_error

# load data using https://www.kaggle.com/c/demand-forecasting-kernels-only
train = pd.read_csv('data/train.csv', parse_dates=['date'])
test  = pd.read_csv('data/test.csv',  parse_dates=['date']) 

# feature engineering
min_date = train['date'].min()

def featurize(df):
    out = pd.DataFrame({
        # TODO: for prediction such features need separate modelling
        'seasonal' : (df['date'] - min_date).dt.days // 60,
        'store'    : df['store'], 
        'item'     : df['item'], 
        'date'     : df['date'],
        # <name-1> _X_ <name-2> to mark interaction features
        'item_X_month': df['item'].astype(str) + '_' + df['date'].dt.month.astype(str)
    })
    
    return out

x_train_df = featurize(train)
x_test_df  = featurize(test)
y_train = train['sales']

# model training
model = cbm.CBM()
model.fit(x_train_df, y_train)

# test on train error
y_pred_train = model.predict(x_train_df).flatten()
print('RMSE', mean_squared_error(y_pred_train, y_train, squared=False))

# plotting
model.plot_importance(figsize=(20, 20), continuous_features=['seasonal'])
```

![Feature Importance Plot](images/cbm_kaggle.png)

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
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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.