pm4py.algo.filtering.pandas.ltl 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.filtering.pandas.ltl.ltl_checker 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.filtering.pandas.ltl.ltl_checker.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#
Bases:
Enum- CASE_ID_KEY = 'pm4py:param:case_id_key'#
- ATTRIBUTE_KEY = 'pm4py:param:attribute_key'#
- TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
- RESOURCE_KEY = 'pm4py:param:resource_key'#
- POSITIVE = 'positive'#
- ENABLE_TIMESTAMP = 'enable_timestamp'#
- TIMESTAMP_DIFF_BOUNDARIES = 'timestamp_diff_boundaries'#
- pm4py.algo.filtering.pandas.ltl.ltl_checker.eventually_follows(df0: DataFrame, attribute_values: List[str], parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Applies the eventually follows rule
Parameters#
- df0
Dataframe
- attribute_values
A list of attribute_values attribute_values[n] follows attribute_values[n-1] follows … follows attribute_values[0]
- parameters
Parameters of the algorithm, including the attribute key and the positive parameter: - If True, returns all the cases containing all attribute_values and in which attribute_values[i] was eventually followed by attribute_values[i + 1] - If False, returns all the cases not containing all attribute_values, or in which an instance of attribute_values[i] was not eventually followed by an instance of attribute_values[i + 1]
Returns#
- filtered_df
Filtered dataframe
- pm4py.algo.filtering.pandas.ltl.ltl_checker.A_next_B_next_C(df0: DataFrame, A: str, B: str, C: str, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Applies the A net B next C rule
Parameters#
- df0
Dataframe
- A
A Attribute value
- B
B Attribute value
- C
C Attribute value
- parameters
Parameters of the algorithm, including the attribute key and the positive parameter: - If True, returns all the cases containing A, B and C and in which A was directly followed by B and B was directly followed by C - If False, returns all the cases not containing A or B or C, or in which none instance of A was directly followed by an instance of B and B was directly followed by C
Returns#
- filtered_df
Filtered dataframe
- pm4py.algo.filtering.pandas.ltl.ltl_checker.four_eyes_principle(df0: DataFrame, A: str, B: str, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Verifies the Four Eyes Principle given A and B
Parameters#
- df0
Dataframe
- A
A attribute value
- B
B attribute value
- parameters
Parameters of the algorithm, including the attribute key and the positive parameter: - if True, then filters all the cases containing A and B which have empty intersection between the set
of resources doing A and B
if False, then filters all the cases containing A and B which have no empty intersection between the set of resources doing A and B
Returns#
- filtered_df
Filtered dataframe
- pm4py.algo.filtering.pandas.ltl.ltl_checker.attr_value_different_persons(df0: DataFrame, A: str, parameters: Dict[str | Parameters, Any] | None = None) DataFrame[source]#
Checks whether an attribute value is assumed on events done by different resources
Parameters#
- df0
Dataframe
- A
A attribute value
- parameters
- Parameters of the algorithm, including the attribute key and the positive parameter:
if True, then filters all the cases containing occurrences of A done by different resources
if False, then filters all the cases not containing occurrences of A done by different resources
Returns#
- filtered_df
Filtered dataframe