pm4py.objects.dfg.utils package#

PM4Py – A Process Mining Library for Python

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Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

pm4py.objects.dfg.utils.dfg_utils 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.objects.dfg.utils.dfg_utils.get_outgoing_edges(dfg)[source]#: Gets outgoing edges of the provided DFG graph

pm4py.objects.dfg.utils.dfg_utils.get_ingoing_edges(dfg)[source]#: Get ingoing edges of the provided DFG graph

pm4py.objects.dfg.utils.dfg_utils.infer_start_activities(dfg)[source]#

Infer start activities from a Directly-Follows Graph

Parameters#

dfg: Directly-Follows Graph

Returns#

start_activities: Start activities in the log

pm4py.objects.dfg.utils.dfg_utils.infer_end_activities(dfg)[source]#

Infer end activities from a Directly-Follows Graph

Parameters#

dfg: Directly-Follows Graph

Returns#

end_activities: End activities in the log

pm4py.objects.dfg.utils.dfg_utils.infer_start_activities_from_prev_connections_and_current_dfg(initial_dfg, dfg, activities, include_self=True)[source]#

Infer the start activities from the previous connections

Parameters#

initial_dfg: Initial DFG
dfg: Directly-follows graph
activities: List of the activities contained in DFG

pm4py.objects.dfg.utils.dfg_utils.infer_end_activities_from_succ_connections_and_current_dfg(initial_dfg, dfg, activities, include_self=True)[source]#

Infer the end activities from the previous connections

Parameters#

initial_dfg: Initial DFG
dfg: Directly-follows graph
activities: List of the activities contained in DFG

pm4py.objects.dfg.utils.dfg_utils.get_outputs_of_outside_activities_going_to_start_activities(initial_dfg, dfg, activities)[source]#

Get outputs of outside activities going to start activities

Parameters#

initial_dfg: Initial DFG
dfg: Directly-follows graph
activities: Activities contained in the DFG

pm4py.objects.dfg.utils.dfg_utils.get_inputs_of_outside_activities_reached_by_end_activities(initial_dfg, dfg, activities)[source]#

Get inputs of outside activities going to start activities

Parameters#

initial_dfg: Initial DFG
dfg: Directly-follows graph
activities: Activities contained in the DFG

pm4py.objects.dfg.utils.dfg_utils.get_activities_from_dfg(dfg)[source]#

Get the list of attributes directly from DFG graph

Parameters#

dfg: Directly-Follows graph

Returns#

list_activities: List of activities that are present in the DFG graph

pm4py.objects.dfg.utils.dfg_utils.get_max_activity_count(dfg, act)[source]#

Get maximum count of an ingoing/outgoing edge related to an activity

Parameters#

dfg: Directly-Follows graph
act: Activity

Returns#

max_value: Maximum count of ingoing/outgoing edges to attributes

pm4py.objects.dfg.utils.dfg_utils.sum_ingoutg_val_activ(dictio, activity)[source]#

Gets the sum of ingoing/outgoing values of an activity

Parameters#

dictio: Dictionary
activity: Current examined activity

Returns#

summ

pm4py.objects.dfg.utils.dfg_utils.max_occ_all_activ(dfg)[source]#: Get maximum ingoing/outgoing sum of values related to attributes in DFG graph

pm4py.objects.dfg.utils.dfg_utils.max_occ_among_specif_activ(dfg, activities)[source]#: Get maximum ingoing/outgoing sum of values related to attributes in DFG graph (here attributes to consider are specified)

pm4py.objects.dfg.utils.dfg_utils.sum_start_activities_count(dfg)[source]#

Gets the sum of start attributes count inside a DFG

Parameters#

dfg: Directly-Follows graph

Returns#

Sum of start attributes count

pm4py.objects.dfg.utils.dfg_utils.sum_end_activities_count(dfg)[source]#

Gets the sum of end attributes count inside a DFG

Parameters#

dfg: Directly-Follows graph

Returns#

Sum of start attributes count

pm4py.objects.dfg.utils.dfg_utils.sum_activities_count(dfg, activities, enable_halving=True)[source]#

Gets the sum of specified attributes count inside a DFG

Parameters#

dfg: Directly-Follows graph
activities: Activities to sum
enable_halving: Halves the sum in specific occurrences

Returns#

Sum of start attributes count

pm4py.objects.dfg.utils.dfg_utils.filter_dfg_on_act(dfg, listact)[source]#

Filter a DFG graph on a list of attributes (to produce a projected DFG graph)

Parameters#

dfg: Current DFG graph
listact: List of attributes to filter on

pm4py.objects.dfg.utils.dfg_utils.negate(dfg)[source]#

Negate relationship in the DFG graph

Parameters#

dfg: Directly-Follows graph

Returns#

negated_dfg: Negated Directly-Follows graph (for parallel cut detection)

pm4py.objects.dfg.utils.dfg_utils.get_activities_direction(dfg, activities)[source]#

Calculate activities direction (in a similar way to Heuristics Miner)

Parameters#

dfg: Directly-follows graph
activities: (if provided) activities of the subtree

Returns#

direction: Dictionary that contains for each direction a number that goes from -1 (all ingoing edges) to 1 (all outgoing edges)

pm4py.objects.dfg.utils.dfg_utils.get_activities_dirlist(activities_direction)[source]#

Form an ordered list out of a dictionary that contains for each activity the direction (going from -1 if all ingoing edges, to 1 if all outgoing edges)

Parameters#

activities_direction: Dictionary that contains for each direction a number that goes from -1 (all ingoing edges) to 1 (all outgoing edges)

Returns#

dirlist: Sorted list of couples of activity plus the direction

pm4py.objects.dfg.utils.dfg_utils.get_activities_self_loop(dfg)[source]#

Get attributes that are in self-loop in this subtree

Parameters#

dfg: Directly-follows graph

Returns#

self_loop_act: Activities of the graph that are in subloop

pm4py.objects.dfg.utils.dfg_utils.get_connected_components(ingoing, outgoing, activities, force_insert_missing_acti=True)[source]#

Get connected components in the DFG graph

Parameters#

ingoing: Ingoing attributes
outgoing: Outgoing attributes
activities: Activities to consider
force_insert_missing_acti: Force the insertion of a missing activity

pm4py.objects.dfg.utils.dfg_utils.add_to_most_probable_component(comps, act2, ingoing, outgoing)[source]#

Adds a lost component in parallel cut detection to the most probable component

Parameters#

comps: Connected components
act2: Activity that has been missed
ingoing: Map of ingoing attributes
outgoing: Map of outgoing attributes

Returns#

comps: Fixed connected components

pm4py.objects.dfg.utils.dfg_utils.get_all_activities_connected_as_output_to_activity(dfg, activity)[source]#

Gets all the activities that are connected as output to a given activity

Parameters#

dfg: Directly-follows graph
activity: Activity

Returns#

all_activities: All activities connected as output to a given activity

pm4py.objects.dfg.utils.dfg_utils.get_all_activities_connected_as_input_to_activity(dfg, activity)[source]#

Gets all the activities that are connected as input to a given activity

Parameters#

dfg: Directly-follows graph
activity: Activity

Returns#

all_activities: All activities connected as input to a given activities

pm4py.objects.dfg.utils.dfg_utils.get_dfg_np_matrix(dfg)[source]#

Gets a Numpy matrix describing the DFG graph, along with a dictionary making correspondence between indexes and activities names

Parameters#

dfg: Directly-Follows graph

Returns#

matrix: Matrix describing the DFG
index_corresp: Dictionary making correspondence between indexes and activities names

pm4py.objects.dfg.utils.dfg_utils.get_dfg_sa_ea_act_from_variants(variants, parameters=None)[source]#

Gets the DFG, the start and end activities, and the activities from the dictionary/set/list of variants in the log

Parameters#

variants: Dictionary/set/list of variants
parameters: Parameters of the algorithm, including: - variants_sep: the delimiter splitting activities in a variant

Returns#

dfg: DFG
list_act: List of different activities
start_activities: Start activities
end_activities: End activities

pm4py.objects.dfg.utils.dfg_utils.transform_dfg_to_directed_nx_graph(dfg, activities=None)[source]#

Transform DFG to directed NetworkX graph

Returns#

G: NetworkX digraph
nodes_map: Correspondence between digraph nodes and activities

pm4py.objects.dfg.utils.dfg_utils.get_successors(dfg, activities_model=None)[source]#

Gets the successors of any node of the DFG graph

Parameters#

dfg: DFG
activities_model: Activities of the process model (if None, it is inferred from the process model)

Returns#

successors: Dictionary associating to each node all the descendants

pm4py.objects.dfg.utils.dfg_utils.get_predecessors(dfg, activities_model=None)[source]#

Gets the predecessors of any node of the DFG graph

Parameters#

dfg: DFG
activities_model: Activities of the process model (if None, it is inferred from the process model)

Returns#

predecessors: Dictionary associating to each node all the ascendants

pm4py.objects.dfg.utils.dfg_utils.get_transitive_relations(dfg, alphabet)[source]#

Parameters#

dfg: directly follows relation (counter describing activity pairs)

Returns#

tuple with two dicts. first argument maps an activit on all other activities that are able to reach the activity (‘transitive pre set’) second argument maps an activity on all other activities that it can reach (transitively) (‘transitive post set’)

pm4py.objects.dfg.utils.dfg_utils.get_alphabet(dfg)[source]#