pm4py.algo.transformation.ocel.graphs 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.transformation.ocel.graphs.object_cobirth_graph 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

pm4py.algo.transformation.ocel.graphs.object_cobirth_graph.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Set[Tuple[str, str]][source]#

Calculates the object cobirth graph. This is calculated as follows:

Given the set of objects related to an event, they belong to two different categories:

The “seen” objects (they have appeared in some earlier event)

The “unseen” objects (they appear for the first time in the current event).

Every “unseen” object is connected to every “unseen” object

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm

Returns#

object_cobirth_graph: Object cobirth graph (undirected)

pm4py.algo.transformation.ocel.graphs.object_codeath_graph 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

pm4py.algo.transformation.ocel.graphs.object_codeath_graph.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Set[Tuple[str, str]][source]#

Calculates the object codeath graph.

This is calculated like the object cobirth graph, but visiting the list of events in the reverse order.

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm

Returns#

object_codeath_graph: Object codeath graph (undirected)

pm4py.algo.transformation.ocel.graphs.object_descendants_graph 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

pm4py.algo.transformation.ocel.graphs.object_descendants_graph.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Set[Tuple[str, str]][source]#

Calculates the object descendant graph. This is calculated as follows: - Given the set of objects related to an event, they belong to two different categories:

The “seen” objects (they have appeared in some earlier event)

The “unseen” objects (they appear for the first time in the current event).

Every “seen” object is connected to every “unseen” object.

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm

Returns#

object_descendant_graph: Object descendant graph (directed)

pm4py.algo.transformation.ocel.graphs.object_inheritance_graph 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

pm4py.algo.transformation.ocel.graphs.object_inheritance_graph.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Set[Tuple[str, str]][source]#

Calculates the object descendants graph. Two objects o1 and o2, both related to an event e, are connected if: - e is the last event of the lifecycle of o1 - e is the first event of the lifecycle of o2

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm

Returns#

object_inheritance_graph: Object inheritance graph (directed)

pm4py.algo.transformation.ocel.graphs.object_interaction_graph 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

pm4py.algo.transformation.ocel.graphs.object_interaction_graph.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → Set[Tuple[str, str]][source]#

Calculates the object interaction graph. Two objects are connected iff they are both related to an event of the OCEL.

Parameters#

ocel: Object-centric event log
parameters: Parameters of the algorithm

Returns#

object_interaction_graph: Object interaction graph (as set of tuples; undirected)

pm4py.algo.transformation.ocel.graphs.ocel20_computation 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.transformation.ocel.graphs.ocel20_computation.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

INCLUDED_GRAPHS = 'included_graphs'#

pm4py.algo.transformation.ocel.graphs.ocel20_computation.apply(ocel: OCEL, parameters: Dict[Any, Any] | None = None) → OCEL[source]#

Inserts the information inferred from the graph computations in the list of O2O relations of the OCEL

Parameters#

ocel: Object-centric event log
parameters: Possible parameters of the algorithm: - Parameters.INCLUDED_GRAPHS => graphs to include in the list of O2O relations (object_interaction_graph, object_descendants_graph, object_inheritance_graph, object_cobirth_graph, object_codeath_graph)

Returns#

enriched_ocel: Enriched object-centric event log