pm4py.algo.conformance.temporal_profile.variants package#

PM4Py – A Process Mining Library for Python

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.conformance.temporal_profile.variants.dataframe module#

PM4Py – A Process Mining Library for Python

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.conformance.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'#

ZETA = 'zeta'#

BUSINESS_HOURS = 'business_hours'#

BUSINESS_HOUR_SLOTS = 'business_hour_slots'#

WORKCALENDAR = 'workcalendar'#

pm4py.algo.conformance.temporal_profile.variants.dataframe.apply(df: DataFrame, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Dict[Any, Any] | None = None) → List[List[Tuple[float, float, float, float]]][source]#

Checks the conformance of the dataframe using the provided temporal profile.

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

Pandas dataframe

temporal_profile

Temporal profile

parameters

Parameters of the algorithm, 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.ZETA => multiplier for the standard deviation
Parameters.CASE_ID_KEY => column to use as case identifier

Returns#

list_dev: A list containing, for each case, all the deviations. Each deviation is a tuple with four elements: - 1) The source activity of the recorded deviation - 2) The target activity of the recorded deviation - 3) The time passed between the occurrence of the source activity and the target activity - 4) The value of (time passed - mean)/std for this occurrence (zeta).

pm4py.algo.conformance.temporal_profile.variants.log module#

PM4Py – A Process Mining Library for Python

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.conformance.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'#

ZETA = 'zeta'#

BUSINESS_HOURS = 'business_hours'#

BUSINESS_HOUR_SLOTS = 'business_hour_slots'#

WORKCALENDAR = 'workcalendar'#

pm4py.algo.conformance.temporal_profile.variants.log.apply(log: EventLog, temporal_profile: Dict[Tuple[str, str], Tuple[float, float]], parameters: Dict[Any, Any] | None = None) → List[List[Tuple[float, float, float, float]]][source]#

Checks the conformance of the log using the provided temporal profile.

Parameters#

log

Event log

temporal_profile

Temporal profile

parameters

Parameters of the algorithm, 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.ZETA => multiplier for the standard deviation
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#

list_dev: A list containing, for each trace, all the deviations. Each deviation is a tuple with four elements: - 1) The source activity of the recorded deviation - 2) The target activity of the recorded deviation - 3) The time passed between the occurrence of the source activity and the target activity - 4) The value of (time passed - mean)/std for this occurrence (zeta).