Applied black.

Author: AndrewC19

causal_testing/testing/estimators.py

@@ -300,7 +300,7 @@ def __init__(
         self.intercept = intercept
 
         if product_terms:
-            for (term_a, term_b) in product_terms:
+            for term_a, term_b in product_terms:
                 self.add_product_term_to_df(term_a, term_b)
         for term in self.effect_modifiers:
             self.adjustment_set.add(term)

tests/testing_tests/test_estimators.py

@@ -339,17 +339,17 @@ class TestLinearRegressionInteraction(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         # Y = 2X1 - 3X2 + 2*X1*X2 + 10
-        df = pd.DataFrame({'X1': np.random.uniform(-1000, 1000, 1000),
-                           'X2': np.random.uniform(-1000, 1000, 1000)})
-        df['Y'] = 2*df['X1'] - 3*df['X2'] + 2*df['X1']*df['X2'] + 10
+        df = pd.DataFrame({"X1": np.random.uniform(-1000, 1000, 1000), "X2": np.random.uniform(-1000, 1000, 1000)})
+        df["Y"] = 2 * df["X1"] - 3 * df["X2"] + 2 * df["X1"] * df["X2"] + 10
         cls.df = df
         print(df)
 
     def test_X1_effect(self):
         """When we fix the value of X2 to 0, the effect of X1 on Y should become ~2 (because X2 terms are cancelled)."""
-        x2 = Input('X2', float)
-        lr_model = LinearRegressionEstimator(('X1',), 1, 0, {'X2'}, ('Y',), effect_modifiers={x2: 0},
-                                             product_terms=[('X1', 'X2')], df=self.df)
+        x2 = Input("X2", float)
+        lr_model = LinearRegressionEstimator(
+            ("X1",), 1, 0, {"X2"}, ("Y",), effect_modifiers={x2: 0}, product_terms=[("X1", "X2")], df=self.df
+        )
         test_results = lr_model.estimate_ate()
         ate = test_results[0]
-        self.assertAlmostEqual(ate, 2.0)
+        self.assertAlmostEqual(ate, 2.0)