Replace all f string logging formats

Author: christopher-wild

causal_testing/generation/abstract_causal_test_case.py

@@ -104,7 +104,7 @@ def _generate_concrete_tests(
             if optimizer.check() == z3.unsat:
                 logger.warning(
                     "Satisfiability of test case was unsat.\n"
-                    + f"Constraints\n{optimizer}\nUnsat core {optimizer.unsat_core()}"
+                    "Constraints \n %s \n Unsat core %s", optimizer, optimizer.unsat_core()
                 )
             model = optimizer.model()
 

causal_testing/json_front/json_class.py

@@ -124,8 +124,7 @@ def generate_tests(self, effects: dict, mutates: dict, estimators: dict, f_flag:
             logger.info([(v.name, v.distribution) for v in abstract_test.treatment_variables])
             logger.info("Number of concrete tests for test case: %s", str(len(concrete_tests)))
             failures = self._execute_tests(concrete_tests, estimators, test, f_flag)
-
-        logger.info(f"{failures}/{len(concrete_tests)} failed")
+        logger.info("%s/%s failed", failures, len(concrete_tests))
 
     def _execute_tests(self, concrete_tests, estimators, test, f_flag):
         failures = 0

causal_testing/testing/causal_test_engine.py

@@ -210,10 +210,10 @@ def _check_positivity_violation(self, variables_list):
         if not set(variables_list).issubset(self.scenario_execution_data_df.columns):
             missing_variables = set(variables_list) - set(self.scenario_execution_data_df.columns)
             logger.warning(
-                f"Positivity violation: missing data for variables {missing_variables}.\n"
-                f"Causal inference is only valid if a well-specified parametric model is used.\n"
-                f"Alternatively, consider restricting analysis to executions without the variables:"
-                f" {missing_variables}."
+                "Positivity violation: missing data for variables {missing_variables}.\n"
+                "Causal inference is only valid if a well-specified parametric model is used.\n"
+                "Alternatively, consider restricting analysis to executions without the variables:"
+                " %s.", missing_variables
             )
             return True
         else: