Source code for pm4py.objects.log.util.split_train_test

'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.objects.log.obj import EventLog
from typing import Tuple
import random
import math




def split(
    log: EventLog, train_percentage: float = 0.8
) -> Tuple[EventLog, EventLog]:
    """
    Split an event log in a training log and a test log (for machine learning purposes)

    Parameters
    --------------
    log
        Event log
    train_percentage
        Fraction of traces to be included in the training log (from 0.0 to 1.0)

    Returns
    --------------
    training_log
        Training event log
    test_log
        Test event log
    """
    idxs = [i for i in range(len(log))]
    random.shuffle(idxs)
    stop_idx = math.floor(len(idxs) * train_percentage) + 1
    idxs_train = idxs[:stop_idx]
    idxs_test = idxs[stop_idx:]
    train_log = EventLog(
        list(),
        attributes=log.attributes,
        extensions=log.extensions,
        classifiers=log.classifiers,
        omni_present=log.omni_present,
        properties=log.properties,
    )
    test_log = EventLog(
        list(),
        attributes=log.attributes,
        extensions=log.extensions,
        classifiers=log.classifiers,
        omni_present=log.omni_present,
        properties=log.properties,
    )
    for idx in idxs_train:
        train_log.append(log[idx])
    for idx in idxs_test:
        test_log.append(log[idx])
    return train_log, test_log