Update all unit tests to work with pd.Series refactor

Author: christopher-wild

tests/testing_tests/test_causal_test_case.py

@@ -118,7 +118,7 @@ def test_execute_test_observational_causal_forest_estimator(self):
             self.df,
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value, 4, delta=1)
+        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1)
 
     def test_invalid_causal_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -140,7 +140,7 @@ def test_execute_test_observational_linear_regression_estimator(self):
             self.df,
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value, 4, delta=1e-10)
+        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_direct_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -167,7 +167,7 @@ def test_execute_test_observational_linear_regression_estimator_direct_effect(se
             self.df,
         )
         causal_test_result = causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(causal_test_result.test_value.value, 4, delta=1e-10)
+        self.assertAlmostEqual(causal_test_result.test_value.value[0], 4, delta=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_coefficient(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
@@ -227,7 +227,7 @@ def test_execute_test_observational_linear_regression_estimator_squared_term(sel
             formula=f"C ~ A + {'+'.join(self.minimal_adjustment_set)} + (D ** 2)",
         )
         causal_test_result = self.causal_test_case.execute_test(estimation_model, self.data_collector)
-        self.assertAlmostEqual(round(causal_test_result.test_value.value, 1), 4, delta=1)
+        self.assertAlmostEqual(round(causal_test_result.test_value.value[0], 1), 4, delta=1)
 
     def test_execute_observational_causal_forest_estimator_cates(self):
         """Check that executing the causal test case returns the correct conditional average treatment effects for

tests/testing_tests/test_causal_test_outcome.py

@@ -1,4 +1,5 @@
 import unittest
+import pandas as pd
 from causal_testing.testing.causal_test_outcome import ExactValue, SomeEffect, Positive, Negative, NoEffect
 from causal_testing.testing.causal_test_result import CausalTestResult, TestValue
 from causal_testing.testing.estimators import LinearRegressionEstimator
@@ -69,7 +70,7 @@ def test_empty_adjustment_set(self):
         )
 
     def test_Positive_ate_pass(self):
-        test_value = TestValue(type="ate", value=5.05)
+        test_value = TestValue(type="ate", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -80,7 +81,7 @@ def test_Positive_ate_pass(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_Positive_risk_ratio_pass(self):
-        test_value = TestValue(type="risk_ratio", value=2)
+        test_value = TestValue(type="risk_ratio", value=pd.Series(2))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -91,7 +92,7 @@ def test_Positive_risk_ratio_pass(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_Positive_fail(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -102,7 +103,7 @@ def test_Positive_fail(self):
         self.assertFalse(ev.apply(ctr))
 
     def test_Positive_fail_ci(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -113,7 +114,7 @@ def test_Positive_fail_ci(self):
         self.assertFalse(ev.apply(ctr))
 
     def test_Negative_ate_pass(self):
-        test_value = TestValue(type="ate", value=-5.05)
+        test_value = TestValue(type="ate", value=pd.Series(-5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -124,7 +125,7 @@ def test_Negative_ate_pass(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_Negative_risk_ratio_pass(self):
-        test_value = TestValue(type="risk_ratio", value=0.2)
+        test_value = TestValue(type="risk_ratio", value=pd.Series(0.2))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -135,7 +136,7 @@ def test_Negative_risk_ratio_pass(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_Negative_fail(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -146,7 +147,7 @@ def test_Negative_fail(self):
         self.assertFalse(ev.apply(ctr))
 
     def test_Negative_fail_ci(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -157,7 +158,7 @@ def test_Negative_fail_ci(self):
         self.assertFalse(ev.apply(ctr))
 
     def test_exactValue_pass(self):
-        test_value = TestValue(type="ate", value=5.05)
+        test_value = TestValue(type="ate", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -168,7 +169,7 @@ def test_exactValue_pass(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_exactValue_pass_ci(self):
-        test_value = TestValue(type="ate", value=5.05)
+        test_value = TestValue(type="ate", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -179,7 +180,7 @@ def test_exactValue_pass_ci(self):
         self.assertTrue(ev.apply(ctr))
 
     def test_exactValue_fail(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -190,7 +191,7 @@ def test_exactValue_fail(self):
         self.assertFalse(ev.apply(ctr))
 
     def test_invalid(self):
-        test_value = TestValue(type="invalid", value=5.05)
+        test_value = TestValue(type="invalid", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -207,7 +208,7 @@ def test_invalid(self):
             Negative().apply(ctr)
 
     def test_someEffect_pass_coefficient(self):
-        test_value = TestValue(type="coefficient", value=5.05)
+        test_value = TestValue(type="coefficient", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -218,7 +219,7 @@ def test_someEffect_pass_coefficient(self):
         self.assertFalse(NoEffect().apply(ctr))
 
     def test_someEffect_pass_ate(self):
-        test_value = TestValue(type="ate", value=5.05)
+        test_value = TestValue(type="ate", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -229,7 +230,7 @@ def test_someEffect_pass_ate(self):
         self.assertFalse(NoEffect().apply(ctr))
 
     def test_someEffect_pass_rr(self):
-        test_value = TestValue(type="risk_ratio", value=5.05)
+        test_value = TestValue(type="risk_ratio", value=pd.Series(5.05))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,
@@ -240,7 +241,7 @@ def test_someEffect_pass_rr(self):
         self.assertFalse(NoEffect().apply(ctr))
 
     def test_someEffect_fail(self):
-        test_value = TestValue(type="ate", value=0)
+        test_value = TestValue(type="ate", value=pd.Series(0))
         ctr = CausalTestResult(
             estimator=self.estimator,
             test_value=test_value,

tests/testing_tests/test_causal_test_suite.py

@@ -98,7 +98,7 @@ def test_execute_test_suite_single_base_test_case(self):
 
         causal_test_results = self.test_suite.execute_test_suite(self.data_collector, self.causal_specification)
         causal_test_case_result = causal_test_results[self.base_test_case]
-        self.assertAlmostEqual(causal_test_case_result["LinearRegressionEstimator"][0].test_value.value, 4, delta=1e-10)
+        self.assertAlmostEqual(causal_test_case_result["LinearRegressionEstimator"][0].test_value.value[0], 4, delta=1e-10)
 
     def test_execute_test_suite_multiple_estimators(self):
         """Check that executing a test suite with multiple estimators returns correct results for the dummy data
@@ -114,5 +114,5 @@ def test_execute_test_suite_multiple_estimators(self):
         causal_test_case_result = causal_test_results[self.base_test_case]
         linear_regression_result = causal_test_case_result["LinearRegressionEstimator"][0]
         causal_forrest_result = causal_test_case_result["CausalForestEstimator"][0]
-        self.assertAlmostEqual(linear_regression_result.test_value.value, 4, delta=1e-1)
-        self.assertAlmostEqual(causal_forrest_result.test_value.value, 4, delta=1e-1)
+        self.assertAlmostEqual(linear_regression_result.test_value.value[0], 4, delta=1e-1)
+        self.assertAlmostEqual(causal_forrest_result.test_value.value[0], 4, delta=1e-1)

tests/testing_tests/test_estimators.py

@@ -199,7 +199,7 @@ def test_estimate_coefficient(self):
             instrument="Z",
         )
         coefficient, [low, high] = iv_estimator.estimate_coefficient()
-        self.assertEqual(coefficient, 2)
+        self.assertEqual(coefficient[0], 2)
 
 
 class TestLinearRegressionEstimator(unittest.TestCase):
@@ -364,8 +364,8 @@ def test_program_15_no_interaction_ate(self):
         # terms_to_square = ["age", "wt71", "smokeintensity", "smokeyrs"]
         # for term_to_square in terms_to_square:
         ate, [ci_low, ci_high] = linear_regression_estimator.estimate_ate()
-        self.assertEqual(round(ate, 1), 3.5)
-        self.assertEqual([round(ci_low, 1), round(ci_high, 1)], [2.6, 4.3])
+        self.assertEqual(round(ate[0], 1), 3.5)
+        self.assertEqual([round(ci_low[0], 1), round(ci_high[0], 1)], [2.6, 4.3])
 
     def test_program_15_no_interaction_ate_calculated(self):
         """Test whether our linear regression implementation produces the same results as program 15.1 (p. 163, 184)
@@ -402,8 +402,8 @@ def test_program_15_no_interaction_ate_calculated(self):
         ate, [ci_low, ci_high] = linear_regression_estimator.estimate_ate_calculated(
             adjustment_config={k: self.nhefs_df.mean()[k] for k in covariates}
         )
-        self.assertEqual(round(ate, 1), 3.5)
-        self.assertEqual([round(ci_low, 1), round(ci_high, 1)], [1.9, 5])
+        self.assertEqual(round(ate[0], 1), 3.5)
+        self.assertEqual([round(ci_low[0], 1), round(ci_high[0], 1)], [1.9, 5])
 
     def test_program_11_2_with_robustness_validation(self):
         """Test whether our linear regression estimator, as used in test_program_11_2 can correctly estimate robustness."""
@@ -449,8 +449,8 @@ def test_program_11_3_cublic_spline(self):
         ate_2 = cublic_spline_estimator.estimate_ate_calculated()
 
         # Doubling the treatemebnt value should roughly but not exactly double the ATE
-        self.assertNotEqual(ate_1 * 2, ate_2)
-        self.assertAlmostEqual(ate_1 * 2, ate_2)
+        self.assertNotEqual(ate_1[0] * 2, ate_2[0])
+        self.assertAlmostEqual(ate_1[0] * 2, ate_2[0])
 
 
 
@@ -488,8 +488,8 @@ def test_program_15_ate(self):
         }
         causal_forest = CausalForestEstimator("qsmk", 1, 0, covariates, "wt82_71", df, {"smokeintensity": 40})
         ate, _ = causal_forest.estimate_ate()
-        self.assertGreater(round(ate, 1), 2.5)
-        self.assertLess(round(ate, 1), 4.5)
+        self.assertGreater(round(ate[0], 1), 2.5)
+        self.assertLess(round(ate[0], 1), 4.5)
 
     def test_program_15_cate(self):
         """Test whether our causal forest implementation produces the similar CATE to program 15.1 (p. 163, 184)."""
@@ -535,7 +535,7 @@ def test_X1_effect(self):
             "X1", 1, 0, {"X2"}, "Y", effect_modifiers={x2.name: 0}, formula="Y ~ X1 + X2 + (X1 * X2)", df=self.df
         )
         test_results = lr_model.estimate_ate()
-        ate = test_results[0]
+        ate = test_results[0][0]
         self.assertAlmostEqual(ate, 2.0)