pm4py.algo.organizational_mining.local_diagnostics.algorithm module

class pm4py.algo.organizational_mining.local_diagnostics.algorithm.Parameters(*values)

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)

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]

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]

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