Merge remote-tracking branch 'upstream/hotfixes' into release

Author: fit-alessandro-berti

docs/source/api.rst

@@ -65,6 +65,7 @@ The following conversions are currently available:
   * :meth:`pm4py.convert.convert_to_bpmn` converts a process model to BPMN
   * :meth:`pm4py.convert.convert_to_petri_net` converts a process model to a Petri net
   * :meth:`pm4py.convert.convert_to_process_tree` converts a process model to a process tree
+  * :meth:`pm4py.convert.convert_to_powl` converts a process model to a POWL model
   * :meth:`pm4py.convert.convert_to_reachability_graph` converts a process model to a reachability graph
   * :meth:`pm4py.convert.convert_log_to_ocel` converts an event log to an object-centric event log
   * :meth:`pm4py.convert.convert_log_to_networkx` converts a traditional event log (dataframe) to a directed graph (NetworkX)
@@ -402,8 +403,14 @@ Other algorithms, which do not belong to the aforementioned categories, are coll
   * :meth:`pm4py.analysis.reduce_petri_net_invisibles`; reduces the invisible transitions of a Petri net when possible.
   * :meth:`pm4py.analysis.reduce_petri_net_implicit_places`; reduces the implicit places in the Petri net (using MURATA).
   * :meth:`pm4py.analysis.get_enabled_transitions`; gets the transitions enabled in a given marking.
-
-
+  * :meth:`pm4py.analysis.simplicity_petri_net`; computes the simplicity metric on the given Petri net.
+  * :meth:`pm4py.analysis.behavioral_similarity`; computes the behavioral similarity between two process models.
+  * :meth:`pm4py.analysis.structural_similarity`; computes the structural similarity between two process models.
+  * :meth:`pm4py.analysis.embeddings_similarity`; computes the embeddings similarity between two process models.
+  * :meth:`pm4py.analysis.label_sets_similarity`; computes the label-sets-similarity between two process models.
+  * :meth:`pm4py.analysis.get_activity_labels`; gets the activity labels from the given event log or process model.
+  * :meth:`pm4py.analysis.replace_activity_labels`; uses a substitution dictionary to replace the names of the activities in the process model.
+  * :meth:`pm4py.analysis.map_labels_from_second_model`; maps the activity labels of the second process model on top of the first.
 
 
 List of Methods
@@ -451,6 +458,7 @@ List of Methods
    pm4py.convert.convert_ocel_to_networkx
    pm4py.convert.convert_petri_net_to_networkx
    pm4py.convert.convert_petri_net_type
+   pm4py.convert.convert_to_powl
    pm4py.discovery
    pm4py.discovery.discover_dfg
    pm4py.discovery.discover_performance_dfg
@@ -670,6 +678,14 @@ List of Methods
    pm4py.analysis.reduce_petri_net_invisibles
    pm4py.analysis.reduce_petri_net_implicit_places
    pm4py.analysis.get_enabled_transitions
+   pm4py.analysis.simplicity_petri_net
+   pm4py.analysis.behavioral_similarity
+   pm4py.analysis.structural_similarity
+   pm4py.analysis.embeddings_similarity
+   pm4py.analysis.get_activity_labels
+   pm4py.analysis.replace_activity_labels
+   pm4py.analysis.label_sets_similarity
+   pm4py.analysis.map_labels_from_second_model
    pm4py.utils
    pm4py.utils.rebase
    pm4py.utils.parse_process_tree

examples/bpmn_merging.py

@@ -202,8 +202,7 @@ def print_bpmn_summary(bpmn, name):
             print(f"  - {flow.get_source().get_name()} -> {flow.get_target().get_name()}")
 
 
-# Main execution
-if __name__ == "__main__":
+def execute_script():
     # Create the main BPMN with swimlanes
     main_bpmn = create_manual_bpmn_with_swimlanes()
 
@@ -231,3 +230,8 @@ def print_bpmn_summary(bpmn, name):
     print_bpmn_summary(merged_bpmn, "Merged BPMN with Swimlanes")
 
     pm4py.view_bpmn(merged_bpmn, format="svg")
+
+
+# Main execution
+if __name__ == "__main__":
+    execute_script()

examples/execute_everything.py

@@ -179,6 +179,30 @@ def cost_based_dfg():
     cost_based_dfg.execute_script()
 
 
+def bpmn_merging():
+    from examples import bpmn_merging
+    print("\n\nbpmn_merging")
+    bpmn_merging.execute_script()
+
+
+def label_replacement():
+    from examples import label_replacement
+    print("\n\nlabel_replacement")
+    label_replacement.execute_script()
+
+
+def model_to_model_sim():
+    from examples import model_to_model_sim
+    print("\n\nmodel_to_model_sim")
+    model_to_model_sim.execute_script()
+
+
+def monte_carlo_example():
+    from examples import monte_carlo_example
+    print("\n\nmonte_carlo_example")
+    monte_carlo_example.execute_script()
+
+
 def df_to_log_postpro():
     from examples import df_to_log_postpro
     print("\n\ndf_to_log_postpro")
@@ -1032,7 +1056,7 @@ def main():
         execute_script(link_analysis_vbfa)
         execute_script(ocel_streaming)
         execute_script(petri_manual_generation)
-        #execute_script(timestamp_interleavings)
+        execute_script(timestamp_interleavings)
         execute_script(object_centric_petri_net_discovery)
         execute_script(trans_system_stochastic_view)
         execute_script(network_analysis)
@@ -1091,8 +1115,8 @@ def main():
         execute_script(streaming_discovery_dfg)
         execute_script(streaming_xes_reader_event_stream)
         execute_script(streaming_xes_reader_trace_stream)
-        #execute_script(monte_carlo_dfg)
-        #execute_script(monte_carlo_petri_net)
+        execute_script(monte_carlo_dfg)
+        execute_script(monte_carlo_petri_net)
 
     print_versions()
 

examples/label_replacement.py

@@ -0,0 +1,27 @@
+import pm4py
+import os
+
+
+def execute_script():
+    log = pm4py.read_xes(os.path.join("..", "tests", "input_data", "running-example.xes"))
+
+    # discovers a process moedl from the log
+    process_tree = pm4py.discover_process_tree_inductive(log)
+    # gets the label from the process models
+    labels = pm4py.get_activity_labels(process_tree)
+    # modify the labels
+    labels = {x: x+"ADD" for x in labels}
+
+    # replace the labels on the process model
+    process_tree_mod = pm4py.replace_activity_labels(labels, process_tree)
+    # view the modified process tree
+    pm4py.view_process_tree(process_tree_mod, format="svg")
+
+    # now, let's replace the labels back, with labels projection from another process model
+    process_tree_mod2 = pm4py.map_labels_from_second_model(process_tree_mod, process_tree)
+    # show the process tree modified
+    pm4py.view_process_tree(process_tree_mod2, format="svg")
+
+
+if __name__ == "__main__":
+    execute_script()

examples/model_to_model_sim.py

@@ -0,0 +1,42 @@
+import pm4py
+import os
+import traceback
+
+
+def execute_script():
+    log = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
+
+    # can be anything here :))
+    process_tree = pm4py.discover_process_tree_inductive(log)
+
+    # can be anything here too :)))
+    powl = pm4py.discover_powl(pm4py.filter_variants_top_k(log, 20))
+
+    # computes the footprints-based behavioral similarity between a process tree
+    # and a POWL model
+    behavioral_similarity = pm4py.behavioral_similarity(powl, process_tree)
+    print("behavioral similarity", behavioral_similarity)
+
+    # computes the structural similarity between a POWL and a process tree
+    structural_similarity = pm4py.structural_similarity(process_tree, powl)
+    print("structural similarity", structural_similarity)
+
+    # computes the label-sets-similarity between a process tree and a POWL
+    label_sets_similarity = pm4py.label_sets_similarity(powl, process_tree)
+    print("label sets similarity", label_sets_similarity)
+
+    try:
+        # computes the embeddings-based similarity between a process tree and a POWL
+        embeddings_similarity = pm4py.embeddings_similarity(powl, process_tree)
+        print("embeddings similarity", embeddings_similarity)
+
+        # embeddings are non-deterministic, so even comparing a model with itself,
+        # it would probably give a value below 1
+        embeddings_similarity2 = pm4py.embeddings_similarity(process_tree, process_tree)
+        print("embeddings similarity", embeddings_similarity2)
+    except:
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    execute_script()