| Name | sefef JSON |
| Version |
1.2.3
JSON |
| download |
| home_page | None |
| Summary | SeFEF: Seizure Forecast Evaluation Framework |
| upload_time | 2024-12-16 14:28:15 |
| maintainer | None |
| docs_url | None |
| author | Ana Sofia Carmo |
| requires_python | None |
| license | BSD 3-clause |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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Welcome to ``SeFEF``
======================
.. image:: https://raw.githubusercontent.com/anascacais/sefef/main/docs/logo/sefef-logo.png
:align: center
:alt: SeFEF logo
|
``SeFEF`` is a Seizure Forecast Evaluation Framework written in Python.
The framework standardizes the development, evaluation, and reporting of individualized algorithms for seizure likelihood forecast.
``SeFEF`` aims to decrease development time and minimize implementation errors by automating key procedures within data preparation, training/testing, and computation of evaluation metrics.
Highlights:
-----------
- ``evaluation`` module: implements time series cross-validation.
- ``labeling`` module: automatically labels samples according to the desired pre-ictal duration and prediction latency.
- ``postprocessing`` module: processes individual predicted probabilities into a unified forecast according to the desired forecast horizon.
- ``scoring`` module: computes both deterministic and probabilistic metrics according to the horizon of the forecast.
Installation
------------
Installation can be easily done with ``pip``:
.. code:: bash
$ pip install sefef
Simple Example
--------------
The code below loads the metadata from an existing dataset from the ``examples`` folder, create a ``Dataset`` instance, and creates an adequate split for a time series cross-validation.
.. code:: python
import json
import pandas as pd
from sefef import evaluation
# read example files
files_metadata = pd.read_csv('examples/files_metadata.csv')
with open('examples/sz_onsets.txt', 'r') as f:
sz_onsets = json.load(f)
# create Dataset instance and perform TSCV
dataset = evaluation.Dataset(files_metadata, sz_onsets, sampling_frequency=128)
tscv = evaluation.TimeSeriesCV()
tscv.split(dataset, iteratively=False, plot=True)
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"description": "Welcome to ``SeFEF``\n======================\n\n.. image:: https://raw.githubusercontent.com/anascacais/sefef/main/docs/logo/sefef-logo.png\n :align: center\n :alt: SeFEF logo\n\n|\n\n``SeFEF`` is a Seizure Forecast Evaluation Framework written in Python.\nThe framework standardizes the development, evaluation, and reporting of individualized algorithms for seizure likelihood forecast. \n``SeFEF`` aims to decrease development time and minimize implementation errors by automating key procedures within data preparation, training/testing, and computation of evaluation metrics. \n\nHighlights:\n-----------\n\n- ``evaluation`` module: implements time series cross-validation.\n- ``labeling`` module: automatically labels samples according to the desired pre-ictal duration and prediction latency.\n- ``postprocessing`` module: processes individual predicted probabilities into a unified forecast according to the desired forecast horizon.\n- ``scoring`` module: computes both deterministic and probabilistic metrics according to the horizon of the forecast. \n\n\n\nInstallation\n------------\n\nInstallation can be easily done with ``pip``:\n\n.. code:: bash\n\n $ pip install sefef\n\nSimple Example\n--------------\n\nThe code below loads the metadata from an existing dataset from the ``examples`` folder, create a ``Dataset`` instance, and creates an adequate split for a time series cross-validation.\n\n.. code:: python\n\n import json\n import pandas as pd\n from sefef import evaluation\n\n # read example files\n files_metadata = pd.read_csv('examples/files_metadata.csv')\n with open('examples/sz_onsets.txt', 'r') as f:\n sz_onsets = json.load(f)\n \n # create Dataset instance and perform TSCV\n dataset = evaluation.Dataset(files_metadata, sz_onsets, sampling_frequency=128)\n tscv = evaluation.TimeSeriesCV()\n tscv.split(dataset, iteratively=False, plot=True)\n",
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