sefef 2.3.3

SeFEF: Seizure Forecast Evaluation Framework

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:

$ 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.

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)