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

Bases: Enum

OUT_COLUMN = 'out_column'#

IN_COLUMN = 'in_column'#

SORTING_COLUMN = 'sorting_column'#

INDEX_COLUMN = 'index_column'#

LOOK_FORWARD = 'look_forward'#

KEEP_FIRST_OCCURRENCE = 'keep_first_occurrence'#

PROPAGATE = 'propagate'#

pm4py.algo.discovery.ocel.link_analysis.variants.classic.propagate_associations(associations: Dict[str, Set[str]]) → Dict[str, Set[str]][source]#

Propagate the associations, such that the eventually-follows flow between the events of the event log is considered

Parameters#

associations: Associations between events

Returns#

propagated_associations: Propagated associations

pm4py.algo.discovery.ocel.link_analysis.variants.classic.apply(dataframe: DataFrame, parameters: Dict[Any, Any] | None = None) → DataFrame[source]#

Performs a link analysis between the entries of the current dataframe. The link analysis permits advanced filtering based on events connected in an output-input relation (e.g., the OUT column of the first is equal to the IN column of the second).

When OUT_COLUMN = IN_COLUMN = CASE ID, it can be equivalent to the directly-follows graph (when Parameters.KEEP_FIRST_OCCURRENCE = True), and to the eventually-follows graph (when Parameters.KEEP_FIRST_OCCURRENCE = False).

Parameters#

dataframe: Pandas dataframe
parameters: Parameters of the algorithm, including: - Parameters.OUT_COLUMN => the output column of the dataframe - Parameters.IN_COLUMN => the input column of the dataframe - Parameters.SORTING_COLUMN => the column on top of which the - Parameters.INDEX_COLUMN => the attribute to use for the indexing - Parameters.LOOK_FORWARD => filters the relations in which the second event has an index >= than the index of the first event. - Parameters.KEEP_FIRST_OCCURRENCE => keep, for every source event, only the first-occurring relationship with a target event (OUT=IN). - Parameters.PROPAGATE => propagate the relationships between events, in such a way that the entire document flow chain can be reconstructed.

Returns#

link_analysis_dataframe: Link analysis dataframe