release 2.7.6

CHANGELOG.md

@@ -1,6 +1,29 @@
 # Changelog of pm4py
 
 
+## pm4py 2.7.6 (2023.08.28)
+
+### Added
+* 69e6692ff08868586f9d4d29c6b8e7dd6609c732
+  * rankdir option for simplified interface's visualizations (and fixing here and there support in main methods).
+
+### Changed
+* 95bbaee94e177644ac12d526abbba0eafdf6eb00
+  * refactoring of the textual abstractions of the DFG and variants (split in two methods + provision of primary and
+    secondary performance metrics).
+* 710b09619ebff74a0166e9518e2390289b0f686d
+  * refactoring OC-Petri nets discovery and visualization.
+
+### Deprecated
+
+### Fixed
+
+### Removed
+
+### Other
+
+---
+
 ## pm4py 2.7.5.2 (2023.08.30)
 
 ### Added

docs/source/conf.py

@@ -26,7 +26,7 @@
 # The short X.Y version
 version = '2.7'
 # The full version, including alpha/beta/rc tags
-release = '2.7.5.1'
+release = '2.7.6'
 
 # -- General configuration ---------------------------------------------------
 

pm4py/algo/discovery/ocel/ocpn/algorithm.py

@@ -15,17 +15,18 @@
     along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
 '''
 from enum import Enum
-from pm4py.algo.discovery.ocel.ocpn.variants import wo_annotation
+from pm4py.algo.discovery.ocel.ocpn.variants import classic
 from pm4py.objects.ocel.obj import OCEL
 from pm4py.util import exec_utils
 from typing import Optional, Dict, Any
 
 
 class Variants(Enum):
-    WO_ANNOTATION = wo_annotation
+    WO_ANNOTATION = classic
+    CLASSIC = classic
 
 
-def apply(ocel: OCEL, variant=Variants.WO_ANNOTATION, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
+def apply(ocel: OCEL, variant=Variants.CLASSIC, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
     """
     Discovers an object-centric Petri net from the provided object-centric event log.
 

pm4py/algo/discovery/ocel/ocpn/variants/__init__.py

@@ -1,17 +1 @@
-'''
-    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
-
-    PM4Py is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    PM4Py 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 General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
-'''
-from pm4py.algo.discovery.ocel.ocpn.variants import wo_annotation
+from pm4py.algo.discovery.ocel.ocpn.variants import classic

pm4py/algo/discovery/ocel/ocpn/variants/classic.py

@@ -0,0 +1,171 @@
+from pm4py.objects.ocel.obj import OCEL
+from pm4py.algo.discovery.ocel.ocdfg.variants import classic as ocdfg_discovery
+from pm4py.algo.discovery.inductive import algorithm as inductive_miner
+from enum import Enum
+from pm4py.util import exec_utils
+from pm4py.objects.ocel import constants as ocel_constants
+from collections import Counter
+from typing import Optional, Dict, Any
+from pm4py.objects.dfg.obj import DFG
+from pm4py.objects.conversion.process_tree import converter as tree_converter
+from pm4py.algo.conformance.tokenreplay import algorithm as token_based_replay
+from pm4py.objects.ocel.util import flattening
+from copy import copy
+
+
+class Parameters(Enum):
+    EVENT_ACTIVITY = ocel_constants.PARAM_EVENT_ACTIVITY
+    OBJECT_TYPE = ocel_constants.PARAM_OBJECT_TYPE
+    INDUCTIVE_MINER_VARIANT = "inductive_miner_variant"
+    DOUBLE_ARC_THRESHOLD = "double_arc_threshold"
+    DIAGNOSTICS_WITH_TBR = "diagnostics_with_token_based_replay"
+
+
+def apply(ocel: OCEL, parameters: Optional[Dict[Any, Any]] = None) -> Dict[str, Any]:
+    """
+    Discovers an object-centric Petri net (without annotation) from the given object-centric event log,
+    using the Inductive Miner as process discovery algorithm.
+
+    Reference paper: van der Aalst, Wil MP, and Alessandro Berti. "Discovering object-centric Petri nets." Fundamenta informaticae 175.1-4 (2020): 1-40.
+
+    Parameters
+    -----------------
+    ocel
+        Object-centric event log
+    parameters
+        Parameters of the algorithm, including:
+        - Parameters.EVENT_ACTIVITY => the activity attribute to be used
+        - Parameters.OBJECT_TYPE => the object type attribute to be used
+        - Parameters.DOUBLE_ARC_THRESHOLD => the threshold for the attribution of the "double arc", as
+        described in the paper.
+        - Parameters.DIAGNOSTICS_WITH_TBR => performs token-based replay and stores the result in the return dict
+
+    Returns
+    -----------------
+    ocpn
+        Object-centric Petri net model, presented as a dictionary of properties:
+        - activities: complete set of activities derived from the object-centric event log
+        - object_types: complete set of object types derived from the object-centric event log
+        - edges: dictionary connecting each object type to a set of directly-followed arcs between activities (expressed as tuples,
+                  e.g., (act1, act2)). Every pair of activities is linked to some sets:
+                - event_pairs: the tuples of event identifiers where the directly-follows arc occurs
+                - total_objects: set of tuples containing two event and one object identifier, uniquely identifying an
+                                  occurrence of the arc.
+        - activities_indep: dictionary linking each activity, regardless of the object type, to some sets:
+            - events: the event identifiers where the activity occurs
+            - unique_objects: the object identifiers where the activity occurs
+            - total_objects: the tuples of event and object identifiers where the activity occurs.
+        - activities_ot: dictionary linking each object type to another dictionary, where the activities are linked to some sets:
+            - events: the event identifiers where the activity occurs (with at least one object of the given object type)
+            - unique_objects: the object identifiers of the given object type where the activity occurs
+            - total_objects: the tuples of event and object identifiers where the activity occurs.
+        - start_activities: dictionary linking each object type to another dictionary, where the start activities
+                            of the given object type are linked to some sets:
+            - events: the event identifiers where the start activity occurs (with at least one object of the given object type)
+            - unique_objects: the object identifiers of the given object type where the start activity occurs
+            - total_objects: the tuples of event and object identifiers where the start activity occurs.
+        - end_activities: dictionary linking each object type to another dictionary, where the end activities
+                          of the given object type are linked to some sets:
+            - events: the event identifiers where the end activity occurs (with at least one object of the given object type)
+            - unique_objects: the object identifiers of the given object type where the end activity occurs
+            - total_objects: the tuples of event and object identifiers where the end activity occurs.
+        - petri_nets: the accepted Petri nets (Petri net + initial marking + final marking) discovered by the process discovery algorithm
+        - double_arcs_on_activity: dictionary linking each object type to another dictionary, where each arc of the Petri net
+                                    is linked to a boolean (True if it is a double arc)
+        - tbr_results: the results of the token-based replay operation (if required)
+    """
+    if parameters is None:
+        parameters = {}
+
+    double_arc_threshold = exec_utils.get_param_value(Parameters.DOUBLE_ARC_THRESHOLD, parameters, 0.8)
+    inductive_miner_variant = exec_utils.get_param_value(Parameters.INDUCTIVE_MINER_VARIANT, parameters, "im")
+    diagnostics_with_tbr = exec_utils.get_param_value(Parameters.DIAGNOSTICS_WITH_TBR, parameters, False)
+
+    ocdfg_parameters = copy(parameters)
+    ocdfg_parameters["compute_edges_performance"] = False
+    ocpn = ocdfg_discovery.apply(ocel, parameters=ocdfg_parameters)
+
+    petri_nets = {}
+    double_arcs_on_activity = {}
+    tbr_results = {}
+
+    for ot in ocpn["object_types"]:
+        activities_eo = ocpn["activities_ot"]["total_objects"][ot]
+
+        start_activities = {x: len(y) for x, y in ocpn["start_activities"]["events"][ot].items()}
+        end_activities = {x: len(y) for x, y in ocpn["end_activities"]["events"][ot].items()}
+        dfg = {}
+        if ot in ocpn["edges"]["event_couples"]:
+            dfg = {x: len(y) for x, y in ocpn["edges"]["event_couples"][ot].items()}
+
+        is_activity_double = {}
+        for act in activities_eo:
+            ev_obj_count = Counter([x[0] for x in activities_eo[act]])
+            this_single_amount = 0
+            for y in ev_obj_count.values():
+                if y == 1:
+                    this_single_amount += 1
+            this_single_amount = this_single_amount / len(ev_obj_count)
+
+            if this_single_amount <= double_arc_threshold:
+                is_activity_double[act] = True
+            else:
+                is_activity_double[act] = False
+
+        double_arcs_on_activity[ot] = is_activity_double
+
+        im_parameters = copy(parameters)
+        # disables the fallthroughs, as computing the model on a myriad of different object types
+        # could be really expensive
+        im_parameters["disable_fallthroughs"] = True
+        # for performance reasons, also disable the strict sequence cut (use the normal sequence cut)
+        im_parameters["disable_strict_sequence_cut"] = True
+
+        process_tree = None
+        flat_log = None
+
+        if inductive_miner_variant == "im" or diagnostics_with_tbr:
+            # do the flattening only if it is required
+            flat_log = flattening.flatten(ocel, ot, parameters=parameters)
+
+        if inductive_miner_variant == "imd":
+            obj = DFG()
+            obj._graph = Counter(dfg)
+            obj._start_activities = Counter(start_activities)
+            obj._end_activities = Counter(end_activities)
+            process_tree = inductive_miner.apply(obj, variant=inductive_miner.Variants.IMd, parameters=im_parameters)
+        elif inductive_miner_variant == "im":
+            process_tree = inductive_miner.apply(flat_log, parameters=im_parameters)
+
+        petri_net = tree_converter.apply(process_tree, parameters=parameters)
+
+        if diagnostics_with_tbr:
+            tbr_parameters = copy(parameters)
+            tbr_parameters["enable_pltr_fitness"] = True
+            tbr_parameters["show_progress_bar"] = False
+
+            replayed_traces, place_fitness_per_trace, transition_fitness_per_trace, notexisting_activities_in_model = token_based_replay.apply(
+                flat_log, petri_net[0], petri_net[1], petri_net[2], parameters=tbr_parameters)
+            place_diagnostics = {place: {"m": 0, "r": 0, "c": 0, "p": 0} for place in place_fitness_per_trace}
+            trans_count = {trans: 0 for trans in petri_net[0].transitions}
+            # computes the missing, remaining, consumed, and produced tokens per place.
+            for place, res in place_fitness_per_trace.items():
+                place_diagnostics[place]['m'] += res['m']
+                place_diagnostics[place]['r'] += res['r']
+                place_diagnostics[place]['c'] += res['c']
+                place_diagnostics[place]['p'] += res['p']
+
+            # counts the number of times a transition has been fired during the replay.
+            for trace in replayed_traces:
+                for trans in trace['activated_transitions']:
+                    trans_count[trans] += 1
+
+            tbr_results[ot] = (place_diagnostics, trans_count)
+
+        petri_nets[ot] = petri_net
+
+    ocpn["petri_nets"] = petri_nets
+    ocpn["double_arcs_on_activity"] = double_arcs_on_activity
+    ocpn["tbr_results"] = tbr_results
+
+    return ocpn