This is an efficient and Scikit-learn compatible implementation of the machine learning algorithm Cyclic Boosting -- an explainable supervised machine learning algorithm, specifically for predicting count-data, such as sales and demand.
- Optimized for categorical features
- Continuous features are discretized using pandas.qcut.
- Date auto-expansion (weekday + month).
- Feature importance plots: categorical, continuous and interactions.
- Metrics to stop training: RMSE, L1, SMAPE.
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.
with
pip install cyclicbmimportcbmfromsklearn.metricsimportmean_squared_error# load data using https://www.kaggle.com/c/demand-forecasting-kernels-onlytrain=pd.read_csv('data/train.csv', parse_dates=['date'])
test=pd.read_csv('data/test.csv', parse_dates=['date']) # feature engineeringmin_date=train['date'].min()
deffeaturize(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)
})
returnoutx_train_df=featurize(train)
x_test_df=featurize(test)
y_train=train['sales']
# model trainingmodel=cbm.CBM()
model.fit(x_train_df, y_train)
# test on train errory_pred_train=model.predict(x_train_df).flatten()
print('RMSE', mean_squared_error(y_pred_train, y_train, squared=False))
# plottingmodel.plot_importance(figsize=(20, 20), continuous_features=['seasonal'])
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