pm4py.visualization.ocel.interleavings.variants 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.visualization.ocel.interleavings.variants.graphviz 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.visualization.ocel.interleavings.variants.graphviz.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[source]#

Bases: Enum

FORMAT = 'format'#

BGCOLOR = 'bgcolor'#

RANKDIR = 'rankdir'#

ANNOTATION = 'annotation'#

AGGREGATION_MEASURE = 'aggregation_measure'#

ACTIVITY_KEY = 'pm4py:param:activity_key'#

ACTIVITY_PERCENTAGE = 'activity_percentage'#

PATHS_PERCENTAGE = 'paths_percentage'#

DEPENDENCY_THRESHOLD = 'dependency_threshold'#

MIN_FACT_EDGES_INTERLEAVINGS = 'min_fact_edges_interleavings'#

pm4py.visualization.ocel.interleavings.variants.graphviz.apply(dataframe1: DataFrame, dataframe2: DataFrame, interleavings: DataFrame, parameters: Dict[Any, Any] | None = None) → Digraph[source]#

Visualizes the interleavings discovered between two different processes. We suppose to provide both event logs, and the discovered interleavings. The visualization includes the DFG of both processes, along with the arcs discovered between them. Both frequency and performance visualization are available.

Parameters#

dataframe1: Dataframe of the first process
dataframe2: Dataframe of the second process
interleavings: Interleavings between the two considered processes
parameters: Parameters of the algorithm, including: - Parameters.FORMAT => the format of the visualization - Parameters.BGCOLOR => the background color - Parameters.RANKDIR => the rank direction (LR or TB; default: TB) - Parameters.ANNOTATION => the annotation to represent (possible values: frequency or performance) - Parameters.AGGREGATION_MEASURE => which aggregation should be used when considering performance - Parameters.ACTIVITY_KEY => the activity key - Parameters.ACTIVITY_PERCENTAGE => the percentage of activities to include for the DFG of the single processes - Parameters.PATHS_PERCENTAGE => the percentage of paths to include for the DFG of the single processes - Parameters.DEPENDENCY_THRESHOLD => the dependency threshold to consider for the DFG of the single processes - Parameters.MIN_FACT_EDGES_INTERLEAVINGS => factor that is multiplied to the minimum number of occurrences of

edges in the single processes, to decide if the interleavings edge should be included. E.g., if 0.3 is provided, only interleavings edges having a frequency of at least 0.3 * MIN_EDGE_COUNT_IN_PROCESSES are included.

Returns#

digraph: Graphviz Digraph