معرفی شرکت ها
cyclicbm-0.0.9
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
Cyclic Boosting Machines
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cyclicbm-0.0.9 |
| نام | cyclicbm |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Markus Cozowicz |
| ایمیل نویسنده | marcozo@microsoft.com |
| آدرس صفحه اصلی | https://github.com/Microsoft/CBM |
| آدرس اینترنتی | https://pypi.org/project/cyclicbm/ |
| مجوز | MIT |
# 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.
نیازمندی
| مقدار | نام |
|---|---|
| >=2.2 | pybind11 |
| - | numpy |
| - | scikit-learn |
| - | pandas |
| >=2.2.0 | matplotlib |
نحوه نصب
نصب پکیج whl cyclicbm-0.0.9:
pip install cyclicbm-0.0.9.whl
نصب پکیج tar.gz cyclicbm-0.0.9:
pip install cyclicbm-0.0.9.tar.gz