pm4py.algo.filtering.dfg 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.filtering.dfg.dfg_filtering 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.filtering.dfg.dfg_filtering.generate_nx_graph_from_dfg(dfg, start_activities, end_activities, activities_count)[source]#

Generate a NetworkX graph for reachability-checking purposes out of the DFG

Parameters#

dfg: DFG
start_activities: Start activities
end_activities: End activities
activities_count: Activities of the DFG along with their count

Returns#

G: NetworkX digraph
start_node: Identifier of the start node (connected to all the start activities)
end_node: Identifier of the end node (connected to all the end activities)

pm4py.algo.filtering.dfg.dfg_filtering.build_adjacency_structures(dfg, start_activities, end_activities)[source]#

Build forward (adj) and reverse (rev_adj) adjacency lists for the DFG, plus two synthetic nodes for the “start” and “end”. - start_node points to each node in start_activities. - each node in end_activities points to end_node.

Returns:: adj, rev_adj, start_node, end_node

pm4py.algo.filtering.dfg.dfg_filtering.bfs_reachable(start, adj)[source]#: Returns the set of nodes reachable from ‘start’ in the directed graph defined by adjacency list ‘adj’.

pm4py.algo.filtering.dfg.dfg_filtering.remove_unreachable_nodes(dfg, start_activities, end_activities, activities_count, adj, rev_adj, start_node, end_node)[source]#: Removes from the DFG (and related dictionaries) any activity/node that is not reachable from start_node or cannot reach end_node, based on the current adjacency structure ‘adj’ and ‘rev_adj’.

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_on_activities_percentage(dfg0, start_activities0, end_activities0, activities_count0, percentage)[source]#

Filters a DFG (complete, and so connected) on the specified percentage of activities (but ensuring that every node is still reachable from the start and can reach the end).

Parameters#

dfg0: (Complete, and so connected) DFG
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities of the DFG along with their count
percentage: Percentage of activities

Returns#

dfg: (Filtered) DFG
start_activities: (Filtered) start activities
end_activities: (Filtered) end activities
activities_count: (Filtered) activities of the DFG along with their count

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_on_paths_percentage(dfg0, start_activities0, end_activities0, activities_count0, percentage, keep_all_activities=False)[source]#

Filters a DFG (complete, and so connected) on the specified percentage of paths (but ensuring that every node is still reachable from the start and can reach the end).

Parameters#

dfg0: (Complete, and so connected) DFG
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities of the DFG along with their count
percentage: Percentage of paths
keep_all_activities: If True, keep all activities (only remove edges) and preserve connectivity; otherwise, only guarantee that the activities in the high-percentage edges remain connected.

Returns#

dfg: (Filtered) DFG
start_activities: (Filtered) start activities
end_activities: (Filtered) end activities
activities_count: (Filtered) activities of the DFG along with their count

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_keep_connected(dfg0, start_activities0, end_activities0, activities_count0, threshold, keep_all_activities=False)[source]#

Filters a DFG (complete, and so connected) on the specified dependency threshold (similar to Heuristics Miner dependency), but ensuring every node is still reachable from the start and can reach the end.

Parameters#

dfg0: (Complete, and so connected) DFG
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities of the DFG along with their count
threshold: Dependency threshold as in the Heuristics Miner
keep_all_activities: If True, keep all activities (only remove edges that fall below threshold); otherwise, remove activities not connected by high-dependency edges.

Returns#

dfg: (Filtered) DFG
start_activities: (Filtered) start activities
end_activities: (Filtered) end activities
activities_count: (Filtered) activities of the DFG along with their count

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_to_activity(dfg0, start_activities0, end_activities0, activities_count0, target_activity, parameters=None)[source]#

Filters the DFG, making “target_activity” the only possible end activity of the graph

Parameters#

dfg0: Directly-follows graph
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities count
target_activity: Target activity (only possible end activity after the filtering)
parameters: Parameters

Returns#

dfg: Filtered DFG
start_activities: Filtered start activities
end_activities: Filtered end activities
activities_count: Filtered activities count

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_from_activity(dfg0, start_activities0, end_activities0, activities_count0, source_activity, parameters=None)[source]#

Filters the DFG, making “source_activity” the only possible source activity of the graph

Parameters#

dfg0: Directly-follows graph
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities count
source_activity: Source activity (only possible start activity after the filtering)
parameters: Parameters

Returns#

dfg: Filtered DFG
start_activities: Filtered start activities
end_activities: Filtered end activities
activities_count: Filtered activities count

pm4py.algo.filtering.dfg.dfg_filtering.filter_dfg_contain_activity(dfg0, start_activities0, end_activities0, activities_count0, activity, parameters=None)[source]#

Filters the DFG keeping only nodes that can reach / are reachable from activity

Parameters#

dfg0: Directly-follows graph
start_activities0: Start activities
end_activities0: End activities
activities_count0: Activities count
activity: Activity that should be reachable / should reach all the nodes of the filtered graph
parameters: Parameters

Returns#

dfg: Filtered DFG
start_activities: Filtered start activities
end_activities: Filtered end activities
activities_count: Filtered activities count

pm4py.algo.filtering.dfg.dfg_filtering.clean_dfg_based_on_noise_thresh(dfg, activities, noise_threshold, parameters=None)[source]#

Clean Directly-Follows graph based on noise threshold

Parameters#

dfg: Directly-Follows graph
activities: Activities in the DFG graph
noise_threshold: Noise threshold

Returns#

newDfg: Cleaned dfg based on noise threshold