| Name | ts-hyperparam-opt JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | |
| Summary | Convenience package for parallelized hyperparameter optimization (e.g. in Jupyter Notebooks) using grid search and CV |
| upload_time | 2023-03-26 21:00:35 |
| maintainer | |
| docs_url | None |
| author | nick2202 |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2023 nick2202 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
python
time series
hyperparameter optimization
cross validation
parallel
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# Time Series Hyperparameter Optimization (CV + Parallel)
Convenience package for optimizing hyperparameters for Time Series forecasting
using methods like _ExponentialSmoothing_ or _SARIMAX_. Especially useful
for Jupyter Notebooks where parallelization (with e.g. `ProcessPoolExecutor`)
only works when importing the function used in parallel.
## Install it from PyPI
```bash
pip install ts-hyperparam-opt
```
## Usage
```py
from ts_hyperparam_opt import parallel_hyperparameter_optimization as pho
params_sarima = [
[(1,1,1), (1,1,1,7)],
[(1,1,0), (1,1,1,7)]
]
if __name__ == '__main__':
freeze_support()
results = process_map(functools.partial(pho.optimize_hyperparams,
data=df_data, func="sarima",
n_steps=15), params_sarima)
results_sorted = pho.sort_results(results)
```
## Development
Alpha Version
Currently supported methods:
- (Triple) Exponential Smoothing (Holt-Winters)
- SARIMA(X)
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