pm4py.algo.discovery.temporal_profile.variants package#
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
Submodules#
pm4py.algo.discovery.temporal_profile.variants.dataframe module#
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
- class pm4py.algo.discovery.temporal_profile.variants.dataframe.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.algo.discovery.temporal_profile.variants.dataframe.apply(df: DataFrame, parameters: Dict[Any, Any] | None = None) Dict[Tuple[str, str], Tuple[float, float]][source]#
Gets the temporal profile from a dataframe.
Implements the approach described in: Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. “Temporal Conformance Checking at Runtime based on Time-infused Process Models.” arXiv preprint arXiv:2008.07262 (2020).
Parameters#
- df
Dataframe
- parameters
Parameters, including: - Parameters.ACTIVITY_KEY => the column to use as activity - Parameters.START_TIMESTAMP_KEY => the column to use as start timestamp - Parameters.TIMESTAMP_KEY => the column to use as timestamp - Parameters.CASE_ID_KEY => the column to use as case ID
Returns#
- temporal_profile
Temporal profile of the dataframe
pm4py.algo.discovery.temporal_profile.variants.log module#
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
- class pm4py.algo.discovery.temporal_profile.variants.log.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- ACTIVITY_KEY = 'pm4py:param:activity_key'#
- START_TIMESTAMP_KEY = 'pm4py:param:start_timestamp_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- BUSINESS_HOURS = 'business_hours'#
- BUSINESS_HOUR_SLOTS = 'business_hour_slots'#
- WORKCALENDAR = 'workcalendar'#
- pm4py.algo.discovery.temporal_profile.variants.log.apply(log: EventLog, parameters: Dict[Any, Any] | None = None) Dict[Tuple[str, str], Tuple[float, float]][source]#
Gets the temporal profile from the log.
Implements the approach described in: Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. “Temporal Conformance Checking at Runtime based on Time-infused Process Models.” arXiv preprint arXiv:2008.07262 (2020).
Parameters#
- log
Event log
- parameters
Parameters, including: - Parameters.ACTIVITY_KEY => the attribute to use as activity - Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp - Parameters.TIMESTAMP_KEY => the attribute to use as timestamp - Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time.
Default: False
Parameters.BUSINESS_HOURS_SLOTS =>
work schedule of the company, provided as a list of tuples where each tuple represents one time slot of business hours. One slot i.e. one tuple consists of one start and one end time given in seconds since week start, e.g. [
(7 * 60 * 60, 17 * 60 * 60), ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60), ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
] meaning that business hours are Mondays 07:00 - 17:00 and Tuesdays 07:00 - 12:00 and 13:00 - 17:00
Returns#
- temporal_profile
Temporal profile of the log