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

CHANGELOG.md

@@ -1,6 +1,6 @@
 # Changelog of pm4py
 
-## pm4py 2.7.3 (2023.04.XX)
+## pm4py 2.7.3 (2023.04.12)
 
 ### Added
 * e561089945951e91b2ecfe0f223b35bd2d351630

pm4py/openai.py

@@ -19,7 +19,7 @@
 import pandas as pd
 from pm4py.objects.log.obj import EventLog, EventStream
 from typing import Union, Tuple
-from pm4py.utils import get_properties
+from pm4py.utils import get_properties, constants
 from pm4py.utils import __event_log_deprecation_warning
 from pm4py.objects.ocel.obj import OCEL
 from pm4py.objects.petri_net.obj import PetriNet, Marking
@@ -304,7 +304,7 @@ def compare_logs(log1: Union[pd.DataFrame, EventLog, EventStream], log2: Union[p
     return log_queries.compare_logs(log1, log2, parameters=parameters)
 
 
-def abstract_dfg(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = 10000, include_performance: bool = True, relative_frequency: bool = False, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
+def abstract_dfg(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = constants.OPENAI_MAX_LEN, include_performance: bool = True, relative_frequency: bool = False, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
     """
     Obtains the DFG abstraction of a traditional event log
 
@@ -339,7 +339,7 @@ def abstract_dfg(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: i
     return log_to_dfg_descr.apply(log_obj, parameters=parameters)
 
 
-def abstract_variants(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = 10000, include_performance: bool = True, relative_frequency: bool = False, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
+def abstract_variants(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = constants.OPENAI_MAX_LEN, include_performance: bool = True, relative_frequency: bool = False, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
     """
     Obtains the variants abstraction of a traditional event log
 
@@ -396,7 +396,7 @@ def abstract_ocel(ocel: OCEL, include_timestamps: bool = True) -> str:
     return ocel_description.apply(ocel, parameters=parameters)
 
 
-def abstract_event_stream(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = 10000, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
+def abstract_event_stream(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = constants.OPENAI_MAX_LEN, response_header: bool = True, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
     """
     Obtains the event stream abstraction of a traditional event log
 
@@ -555,7 +555,7 @@ def conformance_checking(log_obj: Union[pd.DataFrame, EventLog, EventStream], ru
     return log_queries.conformance_checking(log_obj, rule, parameters=parameters)
 
 
-def suggest_verify_hypotheses(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = 5000, api_key: Optional[str] = None, openai_model: Optional[str] = None, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
+def suggest_verify_hypotheses(log_obj: Union[pd.DataFrame, EventLog, EventStream], max_len: int = constants.OPENAI_MAX_LEN // 2, api_key: Optional[str] = None, openai_model: Optional[str] = None, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
     """
     Given an event log, identifies some hypotheses to verify against the data.
 
@@ -590,7 +590,7 @@ def suggest_verify_hypotheses(log_obj: Union[pd.DataFrame, EventLog, EventStream
     return log_queries.suggest_verify_hypotheses(log_obj, parameters=parameters)
 
 
-def filtering_query(log_obj: Union[pd.DataFrame, EventLog, EventStream], filtering_query: str,  max_len: int = 5000, api_key: Optional[str] = None, openai_model: Optional[str] = None, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
+def filtering_query(log_obj: Union[pd.DataFrame, EventLog, EventStream], filtering_query: str,  max_len: int = constants.OPENAI_MAX_LEN // 2, api_key: Optional[str] = None, openai_model: Optional[str] = None, activity_key: str = "concept:name", timestamp_key: str = "time:timestamp", case_id_key: str = "case:concept:name") -> str:
     """
     Given an event log, performs a query to filter the event data
 

pm4py/vis.py

@@ -467,7 +467,7 @@ def save_vis_dotted_chart(log: Union[EventLog, pd.DataFrame], file_path: str, at
     dotted_chart_visualizer.save(gviz, file_path)
 
 
-def view_sna(sna_metric: SNA, variant_str: str = "pyvis"):
+def view_sna(sna_metric: SNA, variant_str: Optional[str] = None):
     """
     Represents a SNA metric (.html)
 
@@ -481,6 +481,12 @@ def view_sna(sna_metric: SNA, variant_str: str = "pyvis"):
         metric = pm4py.discover_subcontracting_network(dataframe, resource_key='org:resource', timestamp_key='time:timestamp', case_id_key='case:concept:name')
         pm4py.view_sna(metric)
     """
+    if variant_str is None:
+        if constants.DEFAULT_GVIZ_VIEW == "matplotlib_view":
+            variant_str = "networkx"
+        else:
+            variant_str = "pyvis"
+
     from pm4py.visualization.sna import visualizer as sna_visualizer
     variant = sna_visualizer.Variants.PYVIS
     if variant_str == "networkx":
@@ -489,12 +495,13 @@ def view_sna(sna_metric: SNA, variant_str: str = "pyvis"):
     sna_visualizer.view(gviz, variant=variant)
 
 
-def save_vis_sna(sna_metric: SNA, file_path: str):
+def save_vis_sna(sna_metric: SNA, file_path: str, variant_str: Optional[str] = None):
     """
     Saves the visualization of a SNA metric in a .html file
 
     :param sna_metric: Values of the metric
     :param file_path: Destination path
+    :param variant_str: variant to be used (default: pyvis)
 
     .. code-block:: python3
 
@@ -504,9 +511,20 @@ def save_vis_sna(sna_metric: SNA, file_path: str):
         pm4py.save_vis_sna(metric, 'sna.png')
     """
     file_path = str(file_path)
+
+    if variant_str is None:
+        if constants.DEFAULT_GVIZ_VIEW == "matplotlib_view":
+            variant_str = "networkx"
+        else:
+            variant_str = "pyvis"
+
     from pm4py.visualization.sna import visualizer as sna_visualizer
-    gviz = sna_visualizer.apply(sna_metric, variant=sna_visualizer.Variants.PYVIS)
-    sna_visualizer.save(gviz, file_path, variant=sna_visualizer.Variants.PYVIS)
+    variant = sna_visualizer.Variants.PYVIS
+    if variant_str == "networkx":
+        variant = sna_visualizer.Variants.NETWORKX
+
+    gviz = sna_visualizer.apply(sna_metric, variant=variant)
+    sna_visualizer.save(gviz, file_path, variant=variant)
 
 
 def view_case_duration_graph(log: Union[EventLog, pd.DataFrame], format: str = "png", activity_key="concept:name", timestamp_key="time:timestamp", case_id_key="case:concept:name"):

safety_checks/20230412

@@ -0,0 +1,47 @@
++==============================================================================+
+
+                               /$$$$$$            /$$
+                              /$$__  $$          | $$
+           /$$$$$$$  /$$$$$$ | $$  \__//$$$$$$  /$$$$$$   /$$   /$$
+          /$$_____/ |____  $$| $$$$   /$$__  $$|_  $$_/  | $$  | $$
+         |  $$$$$$   /$$$$$$$| $$_/  | $$$$$$$$  | $$    | $$  | $$
+          \____  $$ /$$__  $$| $$    | $$_____/  | $$ /$$| $$  | $$
+          /$$$$$$$/|  $$$$$$$| $$    |  $$$$$$$  |  $$$$/|  $$$$$$$
+         |_______/  \_______/|__/     \_______/   \___/   \____  $$
+                                                          /$$  | $$
+                                                         |  $$$$$$/
+  by pyup.io                                              \______/
+
++==============================================================================+
+
+ REPORT 
+
+  Safety is using PyUp's free open-source vulnerability database. This
+data is 30 days old and limited. 
+  For real-time enhanced vulnerability data, fix recommendations, severity
+reporting, cybersecurity support, team and project policy management and more
+sign up at https://pyup.io or email [email protected]
+
+  Safety v2.3.5 is scanning for Vulnerabilities...
+  Scanning dependencies in your files:
+
+  -> requirements_stable.txt
+
+  Using non-commercial database
+  Found and scanned 24 packages
+  Timestamp 2023-04-12 12:22:23
+  0 vulnerabilities found
+  0 vulnerabilities ignored
++==============================================================================+
+
+ No known security vulnerabilities found. 
+
++==============================================================================+
+
+  Safety is using PyUp's free open-source vulnerability database. This
+data is 30 days old and limited. 
+  For real-time enhanced vulnerability data, fix recommendations, severity
+reporting, cybersecurity support, team and project policy management and more
+sign up at https://pyup.io or email [email protected]
+
++==============================================================================+