Update remaining strings to % format

Author: christopher-wild

causal_testing/data_collection/data_collector.py

@@ -73,8 +73,8 @@ def filter_valid_data(self, data: pd.DataFrame, check_pos: bool = True) -> pd.Da
         size_diff = len(data) - len(satisfying_data)
         if size_diff > 0:
             logger.warning(
-                "Discarded %d/%d values due to constraint violations.\n" % (size_diff, len(data)) +
-                "For example %s" % unsat_core
+                "Discarded %s/%s values due to constraint violations.\n" 
+                "For example%s" % (size_diff, len(data), unsat_core)
             )
         return satisfying_data
 

causal_testing/specification/causal_dag.py

@@ -349,8 +349,8 @@ def adjustment_set_is_minimal(self, treatments: list[str], outcomes: list[str],
                 proper_backdoor_graph, treatments, outcomes, smaller_adjustment_set
             ):
                 logger.info(
-                    f"Z={adjustment_set} is not minimal because Z'=Z\\{{'{variable}'}}="
-                    f"{smaller_adjustment_set} is also a valid adjustment set."
+                    "Z=%s is not minimal because Z'=Z\\{{'%s'}}=" 
+                    "%s is also a valid adjustment set." % (adjustment_set, variable, smaller_adjustment_set)
                 )
                 return False
 
@@ -393,16 +393,16 @@ def constructive_backdoor_criterion(
 
             if not set(covariates).issubset(set(self.graph.nodes).difference(descendents_of_proper_casual_paths)):
                 logger.info(
-                    f"Failed Condition 1: Z={covariates} **is** a descendent of some variable on a proper causal "
-                    f"path between X={treatments} and Y={outcomes}."
+                    "Failed Condition 1: Z=%s **is** a descendent of some variable on a proper causal "
+                    "path between X=%s and Y=%s." % (covariates, treatments, outcomes)
                 )
                 return False
 
         # Condition (2)
         if not nx.d_separated(proper_backdoor_graph.graph, set(treatments), set(outcomes), set(covariates)):
             logger.info(
-                f"Failed Condition 2: Z={covariates} **does not** d-separate X={treatments} and Y={outcomes} in"
-                f" the proper back-door graph relative to X and Y."
+                "Failed Condition 2: Z=%s **does not** d-separate X=%s and Y=%s in"
+                " the proper back-door graph relative to X and Y.", {covariates, treatments, outcomes}
             )
             return False