| Name | enfobench JSON |
| Version |
0.5.1
JSON |
| download |
| home_page | None |
| Summary | Energy forecast benchmarking toolkit. |
| upload_time | 2024-03-25 12:38:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
benchmarking
energy
forecasting
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| coveralls test coverage |
No coveralls.
|
# Energy Forecast Benchmark Toolkit
[](https://badge.fury.io/py/enfobench)
[](https://github.com/pypa/hatch)
[](https://github.com/psf/black)
[](https://github.com/charliermarsh/ruff)
[](https://github.com/python/mypy)
Energy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to
benchmark forecast models.
---
**Documentation**: https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/
**Source code**: https://github.com/attila-balint-kul/energy-forecast-benchmark-toolkit
## Datasets
**Electricity demand**: https://huggingface.co/datasets/EDS-lab/electricity-demand
## Dashboards
**Electricity demand**: https://api.wandb.ai/links/attila-balint-kul/vqvezsl0
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
---
## Installation
Use the package manager pip to install foobar.
```bash
pip install enfobench
```
## Usage
Download the HuggingFace Dataset ['EDS-lab/electricity-demand'](https://huggingface.co/datasets/EDS-lab/electricity-demand),
and download the files from the data folder to your computer.
```python
import pandas as pd
from enfobench import Dataset
from enfobench.datasets import ElectricityDemandDataset
from enfobench.evaluation import cross_validate, evaluate_metrics
from enfobench.evaluation.metrics import mean_bias_error, mean_absolute_error, root_mean_squared_error
# Load the dataset from the folder that you downloaded the files to.
ds = ElectricityDemandDataset("/path/to/the/dataset/folder/that/contains/all/subsets")
# List all meter ids
ds.list_unique_ids()
# Get one of the meter ids
unique_id = ds.list_unique_ids()[0]
# Get dataset for a specific meter id
target, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)
# Create a dataset
dataset = Dataset(
target=target,
past_covariates=past_covariates,
future_covariates=None,
metadata=metadata
)
# Import your model and instantiate it
model = MyForecastModel()
# Run cross validation on your model
cv_results = cross_validate(
model,
dataset,
start_date=pd.Timestamp("2018-01-01"),
end_date=pd.Timestamp("2018-01-31"),
horizon=pd.Timedelta("24 hours"),
step=pd.Timedelta("1 day"),
)
# Simply pass in the cross validation results and the metrics you want to evaluate.
metrics = evaluate_metrics(
cv_results,
metrics={
"MBE": mean_bias_error,
"MAE": mean_absolute_error,
"RMSE": root_mean_squared_error,
},
)
```
To get started with some examples check out the `models` folder and the [examples](https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/examples) section of the documentation.
## Benchmarking
Once confident in your model, you can submit for evaluation.
The results of the benchmarks are openly accessible through various dashboards. The links you can find above.
## Contributing
Contributions and feedback are welcome! For major changes, please open an issue first to discuss
what you would like to change.
If you'd like to contribute to the project, please follow these steps:
Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and commit them.
Push your changes to your forked repository.
Submit a pull request describing your changes.
## License
BSD 2-Clause License
Raw data
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"description": "# Energy Forecast Benchmark Toolkit\n\n[](https://badge.fury.io/py/enfobench)\n[](https://github.com/pypa/hatch)\n[](https://github.com/psf/black)\n[](https://github.com/charliermarsh/ruff)\n[](https://github.com/python/mypy)\n\nEnergy Forecast Benchmark Toolkit is a Python project that aims to provide common tools to\nbenchmark forecast models.\n\n---\n\n**Documentation**: https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/\n\n**Source code**: https://github.com/attila-balint-kul/energy-forecast-benchmark-toolkit\n\n\n## Datasets\n\n**Electricity demand**: https://huggingface.co/datasets/EDS-lab/electricity-demand\n\n\n## Dashboards\n\n**Electricity demand**: https://api.wandb.ai/links/attila-balint-kul/vqvezsl0\n\n\n## Table of Contents\n\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n\n---\n\n## Installation\n\nUse the package manager pip to install foobar.\n\n```bash\npip install enfobench\n```\n\n## Usage\n\nDownload the HuggingFace Dataset ['EDS-lab/electricity-demand'](https://huggingface.co/datasets/EDS-lab/electricity-demand),\nand download the files from the data folder to your computer.\n\n```python\nimport pandas as pd\n\nfrom enfobench import Dataset\nfrom enfobench.datasets import ElectricityDemandDataset\nfrom enfobench.evaluation import cross_validate, evaluate_metrics\nfrom enfobench.evaluation.metrics import mean_bias_error, mean_absolute_error, root_mean_squared_error\n\n# Load the dataset from the folder that you downloaded the files to.\nds = ElectricityDemandDataset(\"/path/to/the/dataset/folder/that/contains/all/subsets\")\n\n# List all meter ids\nds.list_unique_ids()\n\n# Get one of the meter ids\nunique_id = ds.list_unique_ids()[0]\n\n# Get dataset for a specific meter id\ntarget, past_covariates, metadata = ds.get_data_by_unique_id(unique_id)\n\n# Create a dataset\ndataset = Dataset(\n target=target,\n past_covariates=past_covariates,\n future_covariates=None,\n metadata=metadata\n)\n\n# Import your model and instantiate it\nmodel = MyForecastModel()\n\n# Run cross validation on your model\ncv_results = cross_validate(\n model,\n dataset,\n start_date=pd.Timestamp(\"2018-01-01\"),\n end_date=pd.Timestamp(\"2018-01-31\"),\n horizon=pd.Timedelta(\"24 hours\"),\n step=pd.Timedelta(\"1 day\"),\n)\n\n# Simply pass in the cross validation results and the metrics you want to evaluate.\nmetrics = evaluate_metrics(\n cv_results,\n metrics={\n \"MBE\": mean_bias_error,\n \"MAE\": mean_absolute_error,\n \"RMSE\": root_mean_squared_error,\n },\n)\n```\n\nTo get started with some examples check out the `models` folder and the [examples](https://attila-balint-kul.github.io/energy-forecast-benchmark-toolkit/examples) section of the documentation.\n\n## Benchmarking\n\nOnce confident in your model, you can submit for evaluation.\nThe results of the benchmarks are openly accessible through various dashboards. The links you can find above.\n\n\n## Contributing\n\nContributions and feedback are welcome! For major changes, please open an issue first to discuss\nwhat you would like to change.\n\nIf you'd like to contribute to the project, please follow these steps:\n\nFork the repository.\nCreate a new branch for your feature or bug fix.\nMake your changes and commit them.\nPush your changes to your forked repository.\nSubmit a pull request describing your changes.\n\n## License\n\nBSD 2-Clause License\n",
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