зеркало из https://github.com/microsoft/CBM.git
93 строки
4.3 KiB
Markdown
93 строки
4.3 KiB
Markdown
# Cyclic Boosting Machines
|
|
|
|
|
|
|
|
[](https://codecov.io/gh/microsoft/CBM)
|
|
[](https://badge.fury.io/py/cyclicbm)
|
|
[](https://opensource.org/licenses/MIT)
|
|
[](https://arxiv.org/abs/2002.03425)
|
|
|
|
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'])
|
|
```
|
|
|
|
|
|
|
|
## 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
|
|
a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions
|
|
provided by the bot. You will only need to do this once across all repos using our CLA.
|
|
|
|
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.
|