pm4py.algo.organizational_mining.local_diagnostics.algorithm module#

PM4Py – A Process Mining Library for Python

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class pm4py.algo.organizational_mining.local_diagnostics.algorithm.Parameters(*values)[source]#

Bases: Enum

RESOURCE_KEY = 'pm4py:param:resource_key'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
GROUP_KEY = 'pm4py:param:group_key'#
class pm4py.algo.organizational_mining.local_diagnostics.algorithm.Outputs(*values)[source]#

Bases: Enum

GROUP_RELATIVE_FOCUS = 'group_relative_focus'#
GROUP_RELATIVE_STAKE = 'group_relative_stake'#
GROUP_COVERAGE = 'group_coverage'#
GROUP_MEMBER_CONTRIBUTION = 'group_member_contribution'#
pm4py.algo.organizational_mining.local_diagnostics.algorithm.apply_from_clustering_or_roles(log_obj: DataFrame | EventLog, ja_clustering_or_roles: Dict[str, List[str]], parameters: Dict[Any, str] | None = None) → Dict[str, Any][source]#

Provides the local diagnostics for the organizational model starting from a log object and the results of the similar activities clustering / the roles detection algorithm.

The approach implemented is the one described in: Yang, Jing, et al. “OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs.” arXiv preprint arXiv:2011.12445 (2020).

Parameters:

  • log_obj – Log object

  • ja_clustering_or_roles – Result of the similar activities clustering / the roles detection algorithm

  • parameters – Parameters of the algorithm, including: - pm4py:param:resource_key => the resource attribute - pm4py:param:activity_key => the activity attribute - pm4py:param:group_key => the group

Returns:

  • group_relative_focus => relative focus metric

  • group_relative_stake => relative stake metric

  • group_coverage => group coverage metric

  • group_member_contribution => group member contribution metric

Return type:

Dictionary containing four keys

pm4py.algo.organizational_mining.local_diagnostics.algorithm.apply_from_group_attribute(log_obj: DataFrame | EventLog, parameters: Dict[Any, str] | None = None) → Dict[str, Any][source]#

Provides the local diagnostics for the organizational model starting from a log object and considering the group specified by the attribute

The approach implemented is the one described in: Yang, Jing, et al. “OrgMining 2.0: A Novel Framework for Organizational Model Mining from Event Logs.” arXiv preprint arXiv:2011.12445 (2020).

Parameters:

  • log_obj – Log object

  • parameters – Parameters of the algorithm, including: - pm4py:param:resource_key => the resource attribute - pm4py:param:activity_key => the activity attribute - pm4py:param:group_key => the group

Returns:

  • group_relative_focus => relative focus metric

  • group_relative_stake => relative stake metric

  • group_coverage => group coverage metric

  • group_member_contribution => group member contribution metric

Return type:

Dictionary containing four keys