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

pm4py/algo/conformance/declare/variants/classic.py

@@ -301,7 +301,7 @@ def apply_list(projected_log: List[List[str]], model: Dict[str, Dict[Any, Dict[s
         __check_non_coexistence(trace, model, ret, parameters)
 
         ret["no_dev_total"] = len(ret["deviations"])
-        ret["dev_fitness"] = 1 - ret["no_dev_total"] / ret["no_constr_total"]
+        ret["dev_fitness"] = 1.0 - ret["no_dev_total"] / ret["no_constr_total"] if ret["no_constr_total"] > 0 else 1.0
         ret["is_fit"] = ret["no_dev_total"] == 0
 
         conf_cases.append(ret)

pm4py/algo/discovery/declare/variants/classic.py

@@ -577,19 +577,20 @@ def get_rules_from_rules_df(rules_df, parameters: Optional[Dict[Any, Any]] = Non
         min_support_ratio = float(supp) / float(len(rules_df)) * auto_selection_multiplier
         min_confidence_ratio = float(len(col[col == 1])) / float(supp) * auto_selection_multiplier
 
-    for col_name in rules_df:
-        col = rules_df[col_name]
-        supp = len(col[col != 0])
+    if rules_df is not None and len(rules_df) > 0:
+        for col_name in rules_df:
+            col = rules_df[col_name]
+            supp = len(col[col != 0])
 
-        if supp > len(rules_df) * min_support_ratio:
-            conf = len(col[col == 1])
+            if supp > len(rules_df) * min_support_ratio:
+                conf = len(col[col == 1])
 
-            if conf > supp * min_confidence_ratio:
-                rule, key = __col_to_dict_rule(col_name)
-                if rule not in rules:
-                    rules[rule] = {}
+                if conf > supp * min_confidence_ratio:
+                    rule, key = __col_to_dict_rule(col_name)
+                    if rule not in rules:
+                        rules[rule] = {}
 
-                rules[rule][key] = {"support": supp, "confidence": conf}
+                    rules[rule][key] = {"support": supp, "confidence": conf}
 
     return rules
 

pm4py/algo/discovery/log_skeleton/variants/classic.py

@@ -108,7 +108,10 @@ def always_after(logs_traces, all_activs, noise_threshold=0):
         for k in rs:
             rs[k] = rs[k] * logs_traces[trace]
         ret0 += rs
-    ret = set(x for x, y in ret0.items() if y >= all_activs[x[0]] * (1.0 - noise_threshold))
+    first_count = Counter()
+    for x, y in ret0.items():
+        first_count[x[0]] += y
+    ret = set(x for x, y in ret0.items() if y >= first_count[x[0]] * (1.0 - noise_threshold))
     return ret
 
 
@@ -136,7 +139,10 @@ def always_before(logs_traces, all_activs, noise_threshold=0):
         for k in rs:
             rs[k] = rs[k] * logs_traces[trace]
         ret0 += rs
-    ret = set(x for x, y in ret0.items() if y >= all_activs[x[0]] * (1.0 - noise_threshold))
+    first_count = Counter()
+    for x, y in ret0.items():
+        first_count[x[0]] += y
+    ret = set(x for x, y in ret0.items() if y >= first_count[x[0]] * (1.0 - noise_threshold))
     return ret
 
 

pm4py/algo/transformation/log_to_trie/algorithm.py

@@ -51,11 +51,10 @@ def apply(log: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Union[st
                     trie = c
                     match = True
                     break
-            if match:
-                continue
-            node = Trie(label=activity, parent=trie, depth=trie.depth + 1)
-            trie.children.append(node)
-            trie = node
+            if not match:
+                node = Trie(label=activity, parent=trie, depth=trie.depth + 1)
+                trie.children.append(node)
+                trie = node
             if i == len(variant) - 1:
                 trie.final = True
     return root