black

Author: christopher-wild

causal_testing/data_collection/data_collector.py

@@ -73,8 +73,10 @@ 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 %s/%s values due to constraint violations.\n"
-                "For example%s", size_diff, len(data), 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/generation/abstract_causal_test_case.py

@@ -103,8 +103,9 @@ def _generate_concrete_tests(
             optimizer.add_soft([self.scenario.variables[v].z3 == row[v] for v in run_columns])
             if optimizer.check() == z3.unsat:
                 logger.warning(
-                    "Satisfiability of test case was unsat.\n"
-                    "Constraints \n %s \n Unsat core %s", optimizer, optimizer.unsat_core()
+                    "Satisfiability of test case was unsat.\n" "Constraints \n %s \n Unsat core %s",
+                    optimizer,
+                    optimizer.unsat_core(),
                 )
             model = optimizer.model()
 

causal_testing/specification/causal_dag.py

@@ -349,8 +349,10 @@ def adjustment_set_is_minimal(self, treatments: list[str], outcomes: list[str],
                 proper_backdoor_graph, treatments, outcomes, smaller_adjustment_set
             ):
                 logger.info(
-                    "Z=%s is not minimal because Z'=Z\\{{'%s'}}="
-                    "%s is also a valid adjustment set.", adjustment_set, variable, smaller_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
 
@@ -394,15 +396,21 @@ def constructive_backdoor_criterion(
             if not set(covariates).issubset(set(self.graph.nodes).difference(descendents_of_proper_casual_paths)):
                 logger.info(
                     "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
+                    "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(
                 "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
+                " the proper back-door graph relative to X and Y.",
+                covariates,
+                treatments,
+                outcomes,
             )
             return False
 

causal_testing/testing/causal_test_engine.py

@@ -213,7 +213,8 @@ def _check_positivity_violation(self, variables_list):
                 "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
+                " %s.",
+                missing_variables,
             )
             return True
         else: