pm4py.algo.enhancement.organizational_mining.local_diagnostics package

Submodules

pm4py.algo.enhancement.organizational_mining.local_diagnostics.algorithm module

class pm4py.algo.enhancement.organizational_mining.local_diagnostics.algorithm.Outputs(value)[source]

Bases: enum.Enum

An enumeration.

GROUP_COVERAGE = 'group_coverage'
GROUP_MEMBER_CONTRIBUTION = 'group_member_contribution'
GROUP_RELATIVE_FOCUS = 'group_relative_focus'
GROUP_RELATIVE_STAKE = 'group_relative_stake'
class pm4py.algo.enhancement.organizational_mining.local_diagnostics.algorithm.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
GROUP_KEY = 'pm4py:param:group_key'
RESOURCE_KEY = 'pm4py:param:resource_key'
pm4py.algo.enhancement.organizational_mining.local_diagnostics.algorithm.apply_from_clustering_or_roles(log_obj: Union[pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventLog], ja_clustering_or_roles: Dict[str, List[str]], parameters: Optional[Dict[Any, str]] = None)[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

Deprecated since version 2.2.5: This will be removed in 3.0.0. use pm4py.algo.organizational_mining.local_diagnostics.algorithm instead

pm4py.algo.enhancement.organizational_mining.local_diagnostics.algorithm.apply_from_group_attribute(log_obj: Union[pandas.core.frame.DataFrame, pm4py.objects.log.obj.EventLog], parameters: Optional[Dict[Any, str]] = None)[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

Deprecated since version 2.2.5: This will be removed in 3.0.0. use pm4py.algo.organizational_mining.local_diagnostics.algorithm instead

Module contents