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

Author: fit-alessandro-berti

examples/CHECK_MISSING.py

@@ -2,7 +2,13 @@
 
 
 if __name__ == "__main__":
-    files = [x.split(".")[0] for x in os.listdir(".") if x.endswith(".py") and not "execute_everything" in x and not "CHECK_MISSING" in x]
+    files = [
+        x.split(".")[0]
+        for x in os.listdir(".")
+        if x.endswith(".py")
+        and "execute_everything" not in x
+        and "CHECK_MISSING" not in x
+    ]
     contents_ex_everything = open("execute_everything.py", "r").read()
     for f in files:
         if f not in contents_ex_everything:

examples/concept_drift.py

@@ -1,15 +1,23 @@
-import pm4py, os
+import os
+
+import pm4py
 from pm4py.algo.concept_drift import algorithm as concept_drift_detection
 
 
 def execute_script():
-    log: "pandas.DataFrame" = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
+    log: "pandas.DataFrame" = pm4py.read_xes(
+        os.path.join("..", "tests", "input_data", "receipt.xes")
+    )
 
     # Bose's concept drift detection is solely based on the control-flow perspective
     returned_sublogs: "list[pandas.DataFrame]"
     change_timestamps: "list[int]"
     p_values: "list[float]"
-    returned_sublogs, change_timestamps, p_values = concept_drift_detection.apply(log, parameters={"max_no_change_points": 3})
+    returned_sublogs, change_timestamps, p_values = (
+        concept_drift_detection.apply(
+            log, parameters={"max_no_change_points": 3}
+        )
+    )
 
     # print the timestamps and the p-values of the detected change points
     print(change_timestamps)

examples/llm/01_2_protected_group_query.py

@@ -1,5 +1,4 @@
 import pm4py
-import os
 import duckdb
 
 
@@ -26,12 +25,12 @@ def execute_script():
     dataframe_pos = dataframe_pos.groupby("case:concept:name").first()
     dataframe_neg = dataframe_neg.groupby("case:concept:name").last()
 
-    tp = len(dataframe_pos[dataframe_pos[protected_attr] == True])
-    fp = len(dataframe_pos[dataframe_pos[protected_attr] == False])
+    tp = len(dataframe_pos[dataframe_pos[protected_attr].eq(True)])
+    fp = len(dataframe_pos[dataframe_pos[protected_attr].eq(False)])
     print("true positives", tp)
     print("false positives", fp)
-    fn = len(dataframe_neg[dataframe_neg[protected_attr] == True])
-    tn = len(dataframe_neg[dataframe_neg[protected_attr] == False])
+    fn = len(dataframe_neg[dataframe_neg[protected_attr].eq(True)])
+    tn = len(dataframe_neg[dataframe_neg[protected_attr].eq(False)])
     print("false negatives", fn)
     print("true negatives", tn)
 

examples/llm/02_group_fairness_factors.py

@@ -1,5 +1,4 @@
 import pm4py
-import os
 
 
 def execute_script():
@@ -11,8 +10,12 @@ def execute_script():
     """
     dataframe: "pandas.DataFrame" = pm4py.read_xes("../../tests/input_data/fairness/renting_log_high.xes.gz")
     protected_attr = [x for x in dataframe.columns if "protected" in x][0]
-    dataframe_prot: "pandas.DataFrame" = dataframe[dataframe[protected_attr] == True]
-    dataframe_unprot: "pandas.DataFrame" = dataframe[dataframe[protected_attr] == False]
+    dataframe_prot: "pandas.DataFrame" = dataframe[
+        dataframe[protected_attr].eq(True)
+    ]
+    dataframe_unprot: "pandas.DataFrame" = dataframe[
+        dataframe[protected_attr].eq(False)
+    ]
     prompt: "str" = "I want to identify the unfair differences between the treatment of the 'protected' group (first) and the 'unprotected' group (second). I report the process variants. Each process variant is also reported with its execution time."
     prompt += "\n\nProcess variants of the protected group:"
     prompt += pm4py.llm.abstract_variants(dataframe_prot, max_len=5000)

examples/llm/03_case_procedural.py

@@ -1,5 +1,4 @@
 import pm4py
-import os
 import duckdb
 
 
@@ -53,12 +52,12 @@ def execute_script():
     dataframe_pos = dataframe_pos.groupby("case:concept:name").first()
     dataframe_neg = dataframe_neg.groupby("case:concept:name").last()
 
-    tp = len(dataframe_pos[dataframe_pos[protected_attr] == True])
-    fp = len(dataframe_pos[dataframe_pos[protected_attr] == False])
+    tp = len(dataframe_pos[dataframe_pos[protected_attr].eq(True)])
+    fp = len(dataframe_pos[dataframe_pos[protected_attr].eq(False)])
     print("true positives", tp)
     print("false positives", fp)
-    fn = len(dataframe_neg[dataframe_neg[protected_attr] == True])
-    tn = len(dataframe_neg[dataframe_neg[protected_attr] == False])
+    fn = len(dataframe_neg[dataframe_neg[protected_attr].eq(True)])
+    tn = len(dataframe_neg[dataframe_neg[protected_attr].eq(False)])
     print("false negatives", fn)
     print("true negatives", tn)
 

examples/streaming_conformance_footprints.py

@@ -1,13 +1,19 @@
+import os
+
 import pm4py
+
 from pm4py.algo.discovery.footprints import algorithm as fp_discovery
+from pm4py.streaming.algo.conformance.footprints import (
+    algorithm as streaming_fp_conf,
+)
 from pm4py.streaming.stream.live_event_stream import LiveEventStream
-from pm4py.streaming.algo.conformance.footprints import algorithm as streaming_fp_conf
-import os, time
 
 
 def execute_script():
     # imports a XES event log
-    log: "pandas.DataFrame" = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
+    log: "pandas.DataFrame" = pm4py.read_xes(
+        os.path.join("..", "tests", "input_data", "receipt.xes")
+    )
     # converts the log into a list of events (not anymore grouped in cases)
     event_stream: "EventStream" = pm4py.convert_to_event_stream(log)
     # calculates a process tree using the IMf algorithm (50% noise)
@@ -24,7 +30,7 @@ def execute_script():
     live_stream.start()
     # append each event of the original log to the live event stream
     # (so it is sent to the conformance checking algorithm)
-    for index, event in enumerate(event_stream):
+    for event in event_stream:
         live_stream.append(event)
     # stops the live event stream
     live_stream.stop()
@@ -35,7 +41,7 @@ def execute_script():
     diagn_df = conf_obj.get()
     conf_obj.terminate_all()
     print(diagn_df)
-    print(diagn_df[diagn_df["is_fit"] == False])
+    print(diagn_df[diagn_df["is_fit"].eq(False)])
 
 
 if __name__ == "__main__":

examples/streaming_conformance_tbr.py

@@ -1,12 +1,16 @@
+import os
+
 import pm4py
-from pm4py.streaming.stream.live_event_stream import LiveEventStream
+
 from pm4py.streaming.algo.conformance.tbr import algorithm as streaming_tbr
-import os, time
+from pm4py.streaming.stream.live_event_stream import LiveEventStream
 
 
 def execute_script():
     # imports a XES event log
-    log: "pandas.DataFrame" = pm4py.read_xes(os.path.join("..", "tests", "input_data", "receipt.xes"))
+    log: "pandas.DataFrame" = pm4py.read_xes(
+        os.path.join("..", "tests", "input_data", "receipt.xes")
+    )
     # converts the log into a list of events (not anymore grouped in cases)
     event_stream: "EventStream" = pm4py.convert_to_event_stream(log)
     # calculates a process tree using the IMf algorithm (30% noise)
@@ -24,9 +28,9 @@ def execute_script():
     live_stream.start()
     # append each event of the original log to the live event stream
     # (so it is sent to the conformance checking algorithm)
-    for index, event in enumerate(event_stream):
+    for event in event_stream:
         live_stream.append(event)
-    #time.sleep(5)
+    # time.sleep(5)
     # stops the live event stream
     live_stream.stop()
     # sends a termination signal to the conformance checking algorithm;
@@ -35,7 +39,7 @@ def execute_script():
     diagn_df = conf_obj.get()
     conf_obj.terminate_all()
     print(diagn_df)
-    print(diagn_df[diagn_df["is_fit"] == False])
+    print(diagn_df[diagn_df["is_fit"].eq(False)])
 
 
 if __name__ == "__main__":

examples/streaming_csv_reader_event_stream.py

@@ -1,15 +1,18 @@
-from pm4py.streaming.stream.live_event_stream import LiveEventStream
+import os
+
 from pm4py.streaming.importer.csv import importer as streaming_csv_importer
+from pm4py.streaming.stream.live_event_stream import LiveEventStream
 from pm4py.streaming.util.event_stream_printer import EventStreamPrinter
-import os, time
 
 
 def execute_script():
     live_event_stream: "LiveEventStream" = LiveEventStream()
     event_stream_printer: "EventStreamPrinter" = EventStreamPrinter()
     live_event_stream.register(event_stream_printer)
     live_event_stream.start()
-    importer = streaming_csv_importer.apply(os.path.join("..", "tests", "input_data", "running-example.csv"))
+    importer = streaming_csv_importer.apply(
+        os.path.join("..", "tests", "input_data", "running-example.csv")
+    )
     importer.to_event_stream(live_event_stream)
     live_event_stream.stop()
 

examples/streaming_xes_reader_event_stream.py

@@ -1,16 +1,19 @@
-from pm4py.streaming.stream.live_event_stream import LiveEventStream
+import os
+
 from pm4py.streaming.importer.xes import importer as streaming_xes_importer
+from pm4py.streaming.stream.live_event_stream import LiveEventStream
 from pm4py.streaming.util.event_stream_printer import EventStreamPrinter
-import os, time
 
 
 def execute_script():
     live_event_stream: "LiveEventStream" = LiveEventStream()
     event_stream_printer: "EventStreamPrinter" = EventStreamPrinter()
     live_event_stream.register(event_stream_printer)
     live_event_stream.start()
-    importer = streaming_xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes"),
-                                            variant=streaming_xes_importer.Variants.XES_EVENT_STREAM)
+    importer = streaming_xes_importer.apply(
+        os.path.join("..", "tests", "input_data", "running-example.xes"),
+        variant=streaming_xes_importer.Variants.XES_EVENT_STREAM,
+    )
     importer.to_event_stream(live_event_stream)
     live_event_stream.stop()
 

examples/streaming_xes_reader_trace_stream.py

@@ -1,16 +1,19 @@
-from pm4py.streaming.stream.live_trace_stream import LiveTraceStream
+import os
+
 from pm4py.streaming.importer.xes import importer as streaming_xes_importer
+from pm4py.streaming.stream.live_trace_stream import LiveTraceStream
 from pm4py.streaming.util.trace_stream_printer import TraceStreamPrinter
-import os, time
 
 
 def execute_script():
     live_trace_stream: "LiveTraceStream" = LiveTraceStream()
     trace_stream_printer: "TraceStreamPrinter" = TraceStreamPrinter()
     live_trace_stream.register(trace_stream_printer)
     live_trace_stream.start()
-    importer = streaming_xes_importer.apply(os.path.join("..", "tests", "input_data", "running-example.xes"),
-                                            variant=streaming_xes_importer.Variants.XES_TRACE_STREAM)
+    importer = streaming_xes_importer.apply(
+        os.path.join("..", "tests", "input_data", "running-example.xes"),
+        variant=streaming_xes_importer.Variants.XES_TRACE_STREAM,
+    )
     importer.to_trace_stream(live_trace_stream)
     live_trace_stream.stop()