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

'''
    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
'''
import random

from pm4py.objects.log.obj import EventStream, EventLog




def sample_stream(event_log, no_events=100):
    """
    Randomly sample a fixed number of events from the original event log

    Parameters
    -----------
    event_log
        Event log
    no_events
        Number of events that the sample should have

    Returns
    -----------
    newLog
        Filtered log
    """
    new_log = EventStream(
        attributes=event_log.attributes,
        extensions=event_log.extensions,
        globals=event_log.omni_present,
        classifiers=event_log.classifiers,
    )
    new_log._list = random.sample(event_log, min(no_events, len(event_log)))
    return new_log





def sample_log(log, no_traces=100):
    """
    Randomly sample a fixed number of traces from the original log

    Parameters
    -----------
    log
        Log
    no_traces
        Number of traces that the sample should have

    Returns
    -----------
    newLog
        Filtered log
    """
    new_log = EventLog(
        attributes=log.attributes,
        extensions=log.extensions,
        globals=log.omni_present,
        classifiers=log.classifiers,
        properties=log.properties,
    )
    new_log._list = random.sample(log, min(no_traces, len(log)))
    return new_log





def sample(log, n=100):
    """
    Randomly sample a fixed number of traces from the original log

    Parameters
    -----------
    log
        Trace/event log
    n
        Number of elements that the sample should have

    Returns
    -----------
    newLog
        Filtered log
    """

    if type(log) is EventLog:
        return sample_log(log, no_traces=n)

    return sample_stream(log, no_events=n)