Fixed pytest

Author: jmafoster1

causal_testing/generation/enum_gen.py

@@ -0,0 +1,13 @@
+from scipy.stats import rv_discrete
+
+
+class EnumGen(rv_discrete):
+    def __init__(self, dt: Enum):
+        self.dt = dict(enumerate(dt, 1))
+        self.inverse_dt = {v: k for k, v in self.dt.items()}
+
+    def ppf(self, q, *args, **kwds):
+        return np.vectorize(self.dt.get)(np.ceil(len(self.dt) * q))
+
+    def cdf(self, q, *args, **kwds):
+        return np.vectorize(self.inverse_dt.get)(q) / len(Car)

causal_testing/testing/causal_test_outcome.py

@@ -64,26 +64,26 @@ def __str__(self):
         return f"ExactValue: {self.value}±{self.tolerance}"
 
 
-class Positive(CausalTestOutcome):
+class Positive(SomeEffect):
     """An extension of TestOutcome representing that the expected causal effect should be positive."""
 
     def apply(self, res: CausalTestResult) -> bool:
         if res.ci_valid() and not super().apply(res):
             return False
-        if res.test_value.type == "ate":
+        if res.test_value.type in {"ate", "coefficient"}:
             return res.test_value.value > 0
-        if res.test_value.type == "risk_ratio":
+        elif res.test_value.type == "risk_ratio":
             return res.test_value.value > 1
         raise ValueError(f"Test Value type {res.test_value.type} is not valid for this TestOutcome")
 
 
-class Negative(CausalTestOutcome):
+class Negative(SomeEffect):
     """An extension of TestOutcome representing that the expected causal effect should be negative."""
 
     def apply(self, res: CausalTestResult) -> bool:
         if res.ci_valid() and not super().apply(res):
             return False
-        if res.test_value.type == "ate":
+        if res.test_value.type in {"ate", "coefficient"}:
             return res.test_value.value < 0
         if res.test_value.type == "risk_ratio":
             return res.test_value.value < 1

examples/lr91/example_max_conductances.py

@@ -153,7 +153,7 @@ def effects_on_APD90(observational_data_path, treatment_var, control_val, treatm
     return causal_test_result.test_value.value, causal_test_result.confidence_intervals
 
 
-def plot_ates_with_cis(results_dict: dict, xs: list, save: bool = True, show: bool = False):
+def plot_ates_with_cis(results_dict: dict, xs: list, save: bool = False, show: bool = False):
     """Plot the average treatment effects for a given treatment against a list of x-values with confidence intervals.
 
     :param results_dict: A dictionary containing results for sensitivity analysis of each input parameter.

tests/testing_tests/test_estimators.py

@@ -166,15 +166,15 @@ def test_estimate_coefficient(self):
         Test we get the correct coefficient.
         """
         iv_estimator = InstrumentalVariableEstimator(
+            df=self.df,
             treatment="X",
             treatment_value=None,
             control_value=None,
             adjustment_set=set(),
             outcome="Y",
             instrument="Z",
-            df=self.df,
         )
-        self.assertEqual(iv_estimator.estimate_coefficient(), 2)
+        self.assertEqual(iv_estimator.estimate_coefficient(self.df), 2)
 
 
 class TestLinearRegressionEstimator(unittest.TestCase):
@@ -192,21 +192,21 @@ def setUpClass(cls) -> None:
     def test_program_11_2(self):
         """Test whether our linear regression implementation produces the same results as program 11.2 (p. 141)."""
         df = self.chapter_11_df
-        linear_regression_estimator = LinearRegressionEstimator("treatments", 100, 90, set(), "outcomes", df)
+        linear_regression_estimator = LinearRegressionEstimator("treatments", None, None, set(), "outcomes", df)
         model = linear_regression_estimator._run_linear_regression()
         ate, _ = linear_regression_estimator.estimate_unit_ate()
 
         print(model.summary())
         self.assertEqual(round(model.params["Intercept"] + 90 * model.params["treatments"], 1), 216.9)
 
         # Increasing treatments from 90 to 100 should be the same as 10 times the unit ATE
-        self.assertEqual(round(10 * model.params["treatments"], 1), round(ate, 1))
+        self.assertEqual(round(model.params["treatments"], 1), round(ate, 1))
 
     def test_program_11_3(self):
         """Test whether our linear regression implementation produces the same results as program 11.3 (p. 144)."""
         df = self.chapter_11_df.copy()
         linear_regression_estimator = LinearRegressionEstimator(
-            "treatments", 100, 90, set(), "outcomes", df, formula="outcomes ~ treatments + np.power(treatments, 2)"
+            "treatments", None, None, set(), "outcomes", df, formula="outcomes ~ treatments + np.power(treatments, 2)"
         )
         model = linear_regression_estimator._run_linear_regression()
         ate, _ = linear_regression_estimator.estimate_unit_ate()
@@ -220,7 +220,7 @@ def test_program_11_3(self):
             197.1,
         )
         # Increasing treatments from 90 to 100 should be the same as 10 times the unit ATE
-        self.assertEqual(round(10 * model.params["treatments"], 3), round(ate, 3))
+        self.assertEqual(round(model.params["treatments"], 3), round(ate, 3))
 
     def test_program_15_1A(self):
         """Test whether our linear regression implementation produces the same results as program 15.1 (p. 163, 184)."""