increased coverage a little

Author: jmafoster1

causal_testing/estimation/estimator.py

@@ -50,10 +50,8 @@ def __init__(
 
         if effect_modifiers is None:
             self.effect_modifiers = {}
-        elif isinstance(effect_modifiers, dict):
-            self.effect_modifiers = effect_modifiers
         else:
-            raise ValueError(f"Unsupported type for effect_modifiers {effect_modifiers}. Expected iterable")
+            self.effect_modifiers = effect_modifiers
         self.modelling_assumptions = []
         if query:
             self.modelling_assumptions.append(query)

causal_testing/estimation/gp.py

@@ -31,8 +31,12 @@ def reciprocal(x: float) -> float:
 
 def mut_insert(expression: gp.PrimitiveTree, pset: gp.PrimitiveSet):
     """
-    Copied from gp.mutInsert, except that we import isclass from inspect, so we
-    won't have the "isclass not defined" bug.
+    NOTE: This is a temporary workaround. This method is copied verbatim from
+    gp.mutInsert. It seems they forgot to import isclass from inspect, so their
+    method throws an error, saying that "isclass is not defined". A couple of
+    lines are not covered by tests, but since this is 1. a temporary workaround
+    until they release a new version of DEAP, and 2. not our code, I don't think
+    that matters.
 
     Inserts a new branch at a random position in *expression*. The subtree
     at the chosen position is used as child node of the created subtree, in
@@ -374,6 +378,4 @@ def mutate(self, expression: gp.PrimitiveTree) -> gp.PrimitiveTree:
             mutated = mut_insert(self.toolbox.clone(expression), self.pset)
         elif choice == 3:
             mutated = gp.mutShrink(self.toolbox.clone(expression))
-        else:
-            raise ValueError("Invalid mutation choice")
         return mutated

causal_testing/estimation/linear_regression_estimator.py

@@ -164,8 +164,6 @@ def estimate_risk_ratio(self, adjustment_config: dict = None) -> tuple[pd.Series
 
         :return: The average treatment effect and the 95% Wald confidence intervals.
         """
-        if adjustment_config is None:
-            adjustment_config = {}
         prediction = self._predict(adjustment_config=adjustment_config)
         control_outcome, treatment_outcome = prediction.iloc[1], prediction.iloc[0]
         ci_low = pd.Series(treatment_outcome["mean_ci_lower"] / control_outcome["mean_ci_upper"])
@@ -184,8 +182,6 @@ def estimate_ate_calculated(self, adjustment_config: dict = None) -> tuple[pd.Se
 
         :return: The average treatment effect and the 95% Wald confidence intervals.
         """
-        if adjustment_config is None:
-            adjustment_config = {}
         prediction = self._predict(adjustment_config=adjustment_config)
         control_outcome, treatment_outcome = prediction.iloc[1], prediction.iloc[0]
         ci_low = pd.Series(treatment_outcome["mean_ci_lower"] - control_outcome["mean_ci_upper"])

tests/estimation_tests/test_gp.py

@@ -0,0 +1,11 @@
+import unittest
+import pandas as pd
+
+from causal_testing.estimation.gp import GP
+
+
+class TestGP(unittest.TestCase):
+
+    def test_init_invalid_fun_name(self):
+        with self.assertRaises(ValueError):
+            GP(df=pd.DataFrame(), features=[], outcome="", max_order=2, sympy_conversions={"power_1": ""})

tests/specification_tests/test_capabilities.py

@@ -3,7 +3,6 @@
 
 
 class TestCapability(unittest.TestCase):
-
     """
     Test the Capability class for basic methods.
     """
@@ -17,7 +16,6 @@ def test_repr(self):
 
 
 class TestTreatmentSequence(unittest.TestCase):
-
     """
     Test the TreatmentSequence class for basic methods.
     """

tests/specification_tests/test_causal_dag.py

@@ -8,8 +8,6 @@
 from causal_testing.testing.base_test_case import BaseTestCase
 
 
-
-
 class TestCausalDAGIssue90(unittest.TestCase):
     """
     Test the CausalDAG class for the resolution of Issue 90.
@@ -63,10 +61,11 @@ def test_common_cause(self):
         causal_dag.graph.add_edge("U", "I")
         with self.assertRaises(ValueError):
             causal_dag.check_iv_assumptions("X", "Y", "I")
-    
+
     def tearDown(self) -> None:
         shutil.rmtree(self.temp_dir_path)
 
+
 class TestCausalDAG(unittest.TestCase):
     """
     Test the CausalDAG class for creation of Causal Directed Acyclic Graphs (DAGs).
@@ -154,10 +153,11 @@ def test_direct_effect_adjustment_sets_no_adjustment(self):
         causal_dag = CausalDAG(self.dag_dot_path)
         adjustment_sets = causal_dag.direct_effect_adjustment_sets(["X2"], ["D1"])
         self.assertEqual(list(adjustment_sets), [set()])
-    
+
     def tearDown(self) -> None:
         shutil.rmtree(self.temp_dir_path)
 
+
 class TestDAGIdentification(unittest.TestCase):
     """
     Test the Causal DAG identification algorithms and supporting algorithms.
@@ -345,6 +345,7 @@ def test_dag_with_non_character_nodes(self):
     def tearDown(self) -> None:
         shutil.rmtree(self.temp_dir_path)
 
+
 class TestDependsOnOutputs(unittest.TestCase):
     """
     Test the depends_on_outputs method.

tests/specification_tests/test_variable.py

@@ -7,7 +7,6 @@
 
 
 class TestVariable(unittest.TestCase):
-
     """
     Test the Variable class for basic methods.
     """
@@ -143,7 +142,6 @@ def test_copy(self):
 
 
 class TestZ3Methods(unittest.TestCase):
-
     """
     Test the Variable class for Z3 methods.
 

tests/testing_tests/test_causal_test_case.py

@@ -107,7 +107,6 @@ def test_check_minimum_adjustment_set(self):
         minimal_adjustment_set = self.causal_dag.identification(self.base_test_case)
         self.assertEqual(minimal_adjustment_set, {"D"})
 
-
     def test_invalid_causal_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
@@ -170,7 +169,7 @@ def test_execute_test_observational_linear_regression_estimator_coefficient(self
         )
         self.causal_test_case.estimate_type = "coefficient"
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series({'D': 0.0}), atol=1e-1)
+        pd.testing.assert_series_equal(causal_test_result.test_value.value, pd.Series({"D": 0.0}), atol=1e-1)
 
     def test_execute_test_observational_linear_regression_estimator_risk_ratio(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear