Addressed Chris's comments

Author: jmafoster1

causal_testing/data_collection/data_collector.py

@@ -50,10 +50,6 @@ def filter_valid_data(self, data: pd.DataFrame, check_pos: bool = True) -> pd.Da
         for _, row in data.iterrows():
             solver.push()
             # Need to explicitly cast variables to their specified type. Z3 will not take e.g. np.int64 to be an int.
-            for var in self.scenario.variables:
-                self.scenario.variables[var].z3 == self.scenario.variables[var].z3_val(
-                    self.scenario.variables[var].z3, row[var]
-                )
             model = [
                 self.scenario.variables[var].z3
                 == self.scenario.variables[var].z3_val(self.scenario.variables[var].z3, row[var])
@@ -145,7 +141,7 @@ def collect_data(self, **kwargs) -> pd.DataFrame:
         for meta in self.scenario.metas():
             meta.populate(execution_data_df)
         scenario_execution_data_df = self.filter_valid_data(execution_data_df)
-        for vname, var in self.scenario.variables.items():
+        for var_name, var in self.scenario.variables.items():
             if issubclass(var.datatype, Enum):
-                scenario_execution_data_df[vname] = [var.datatype(x) for x in scenario_execution_data_df[vname]]
+                scenario_execution_data_df[var_name] = [var.datatype(x) for x in scenario_execution_data_df[var_name]]
         return scenario_execution_data_df

causal_testing/generation/abstract_causal_test_case.py

@@ -111,7 +111,6 @@ def _generate_concrete_tests(
                     == self.scenario.variables[v].z3_val(self.scenario.variables[v].z3, row[v])
                 )
 
-            # optimizer.add_soft([optimizer.add_soft(self.scenario.variables[v].z3 == self.scenario.variables[v].z3_val(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",

causal_testing/json_front/json_class.py

@@ -93,7 +93,6 @@ def _create_abstract_test_case(self, test, mutates, effects):
         abstract_test = AbstractCausalTestCase(
             scenario=self.modelling_scenario,
             intervention_constraints=[mutates[v](k) for k, v in test["mutations"].items()],
-            # TODO: Could change JSON to be treatment_var and mutation to it rather than a dict of mutations
             treatment_variable=next(self.modelling_scenario.variables[v] for v in test["mutations"]),
             expected_causal_effect={
                 self.modelling_scenario.variables[variable]: effects[effect]

causal_testing/specification/variable.py

@@ -173,7 +173,7 @@ def z3_val(self, z3_var, val: Any) -> T:
         native_val = self.cast(val)
         if isinstance(native_val, Enum):
             values = [z3_var.sort().constructor(c)() for c in range(z3_var.sort().num_constructors())]
-            values = [v for v in values if type(val)(str(v)) == val]
+            values = [v for v in values if val.__class__(str(v)) == val]
             assert len(values) == 1, f"Expected {values} to be length 1"
             return values[0]
         return native_val