stepshifter3 0.0.1

A general purpose stepshifting algorithm for tabular data, based on BaseEstimator.

StepShifter3 🛠️:

A general purpose Python package for time series analysis of tabular data

StepShifter3 is a Python package designed to facilitate time series analysis of tabular data. It is developed and maintained by the Peace Research Institute Oslo (PRIO) as part of the VIEWS project.

📚 Table of Contents

1. 🛠 Installation
2. 📝 Usage
3. 🤝 Contributing
4. 🔖 License
5. ❓ FAQs
6. 🙏 Credits
7. 📚 References

🛠 Installation

To install StepShifter3, you have two options:

1. Using pip: 📦

   pip install StepShifter3
   

2. From GitHub: 🐱‍💻

   git clone https://github.com/YourUsername/StepShifter3.git
   cd StepShifter3
   python setup.py install
   

📖 More Details

📝 Usage

The Stepshifter class is the main class of the package. It handles all models which is herited from the sklearn BaseEstimator class.

Basic Usage with XGBRegressor and dummy data from synthetic data generator

from StepShifter3 import StepShifter, SyntheticDataGenerator
from xgboost import XGBRegressor 

# generates a pandas multiindex dataframe with dummy data Indexes: month_id, country_id
df = SyntheticDataGenerator("cm", 516, 60000, 242).generate_dataframe()

#initialize the StepShifter3 object with the relevant parameters:
# define some XGBRegressor parameters:
params_xgb_reg = {
    'objective': 'reg:squarederror',
    'n_estimators': 500,
    'max_depth': 3,
    'learning_rate': 0.1,
    'n_jobs': -1,
    'eval_metric': 'rmse',
    'early_stopping_rounds': 30
}

stepshifter_config_xgb_reg = { "target_column" : "ln_ged_sb_dep",
                       "ID_columns" : ["month_id","country_id"],
                       "time_column" : "month_id",
                       "run_name" : 'xgb_reg_test_run_0',
                       "experiment_name" : 'xgb_reg_experiment_0',
                       "mlflow_tracking_uri" : 'http://127.0.0.1:5000',
                       "tau^e_0" : 121, # training period start
                       "tau^e_t" : 141, # training period end
                       "tau^e_c" : 146, # calibration period end
                       "tau^e_f" : 468, # forecast period end
                       "tau_f" : 516,   # Test Forfecast end 
                       "k": 13,
                       "S": 5,
                       "metrics_report": True,
                       "combine_method": 'mean_of_all',
                       }

# initialize class
stepshifter = StepShifter(XGBRegressor(**params_xgb_reg),df, stepshifter_config_xgb_reg)

# fit and predict
models = stepshifter.fit(stepshifter_config_xgb_reg['tau^e_0'],stepshifter_config_xgb_reg['tau^e_t'])
predictions = stepshifter.predict(stepshifter_config_xgb_reg['tau^e_t'],stepshifter_config_xgb_reg['tau^e_c'])

# Make model ensamble from training models and predict with a blended mean algorithm:

M_ssa = stepshifter.stepcombine()

X_test = stepshifter.X.loc[slice(136,136),slice(None),:]

# Make predictions for 1 and 3 months ahead of time:
blended_predictions = stepshifter.predict_ansamble(X_test,steps=[1,3])

Contributing

Contributions are welcome! To contribute:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please read our Contributing Guidelines for more information.

References

1. Hegre et.al: Partitioning and time-shifting in VIEWS, fatalities002∗†

License

Distributed under the MIT License. See LICENSE for more information.