Remove reference to ricardo in tests, use CTS treatment again

Author: willGraham01

tests/fixtures/continuous_treatment.py

@@ -28,7 +28,7 @@
 
 
 @pytest.fixture
-def ricardo_regression_function(rng_key) -> RegressionBuilder:
+def cts_treatment_regression_function(rng_key) -> RegressionBuilder:
     def _inner(
         k_len: int,
         z_len: int,

tests/test_integration/test_continuous_treatment.py/test_build_loss_function.py

@@ -209,7 +209,7 @@ def test_build_loss_function_bad_rhat_shape(
 )
 def test_build_loss_function_uy_independent_regression_fn(
     jax_enable_x64,  # noqa: ARG001
-    ricardo_regression_function,
+    cts_treatment_regression_function,
     uy_independent_mlps,
     pytree_allclose,
     pytree_all_same_shape,
@@ -232,7 +232,7 @@ def test_build_loss_function_uy_independent_regression_fn(
     theta_y_solution = jnp.atleast_1d(theta_y_solution_value)
 
     mlps, r_analytic = uy_independent_mlps(k_len=k_len)
-    r = ricardo_regression_function(
+    r = cts_treatment_regression_function(
         k_len=k_len,
         z_len=z_len,
         theta_x=jnp.ones((1,)),
@@ -315,7 +315,7 @@ def test_build_loss_function_uy_independent_regression_fn(
 )
 def test_build_loss_function_ux_independent_regression_fn(
     jax_enable_x64,  # noqa: ARG001
-    ricardo_regression_function,
+    cts_treatment_regression_function,
     ux_independent_mlps,
     pytree_allclose,
     pytree_all_same_shape,
@@ -348,7 +348,7 @@ def test_build_loss_function_ux_independent_regression_fn(
     initial_guess.update(dict.fromkeys(independent_params, 1.0))
 
     mlps, r_analytic = ux_independent_mlps(k_len, n_points, f_y)
-    r = ricardo_regression_function(
+    r = cts_treatment_regression_function(
         k_len=k_len,
         z_len=z_len,
         theta_x=0.0,

tests/test_integration/test_continuous_treatment.py/test_regression_function.py

@@ -7,7 +7,7 @@
 
 def test_fy_independent_of_uy(
     jax_enable_x64,  # noqa: ARG001
-    ricardo_regression_function,
+    cts_treatment_regression_function,
     vectorise_over_dict_args,
     uy_independent_mlps,
     k_len: int = 5,
@@ -33,7 +33,7 @@ def test_fy_independent_of_uy(
     since enabling x64-precision calculations makes this issue disappear.
     """
     mlps, r_analytic = uy_independent_mlps(k_len=k_len)
-    r = ricardo_regression_function(
+    r = cts_treatment_regression_function(
         k_len=k_len,
         z_len=z_len,
         theta_x=jnp.ones((1,)),
@@ -79,7 +79,7 @@ def test_fy_independent_of_uy(
 
 
 def test_uy_independent_of_ux(
-    ricardo_regression_function,
+    cts_treatment_regression_function,
     ux_independent_mlps,
     vectorise_over_dict_args,
     k_len: int = 5,
@@ -97,7 +97,7 @@ def test_uy_independent_of_ux(
     within numerical precision, of course).
     """
     mlps, r_direct_integration = ux_independent_mlps(k_len, n_points, f_y)
-    r = ricardo_regression_function(
+    r = cts_treatment_regression_function(
         k_len=k_len,
         z_len=z_len,
         theta_x=0.0,
@@ -125,7 +125,7 @@ def test_uy_independent_of_ux(
 
 
 def test_regression_correctly_calculates_pi_ul(
-    ricardo_regression_function,
+    cts_treatment_regression_function,
     k_len: int = 3,
     z_len: int = 1,
     n_points: int = 10,
@@ -160,7 +160,7 @@ def f_m(_czl: dict, _theta_m):
     def f_y(_xuy: dict, _theta_y):
         return jnp.asarray(1.0)
 
-    r = ricardo_regression_function(
+    r = cts_treatment_regression_function(
         k_len=k_len,
         z_len=z_len,
         f_ux=f_ux,