[BUG] bugfix on #387 - changed paramset 3 to use `ConditionUncensored…

…` instead of `CoxPH` (#388)

#### Reference Issues/PRs

Fixes #387 . Changed paramset3 to use `ConditionUncensored` instead of
`CoxPH` since it doesn't seem stable on smaller datasets.

Discussion thread on #291

skpro/model_selection/_tuning.py

@@ -488,8 +488,7 @@ def get_test_params(cls, parameter_set="default"):
 
         from skpro.metrics import CRPS, PinballLoss
         from skpro.regression.residual import ResidualDouble
-        from skpro.survival.coxph import CoxPH
-        from skpro.utils.validation._dependencies import _check_estimator_deps
+        from skpro.survival.compose._reduce_cond_unc import ConditionUncensored
 
         linreg1 = LinearRegression()
         linreg2 = LinearRegression(fit_intercept=False)
@@ -510,18 +509,14 @@ def get_test_params(cls, parameter_set="default"):
             "error_score": "raise",
         }
 
-        params = [param1, param2]
-
-        # testing with survival predictor
-        if _check_estimator_deps(CoxPH, severity="none"):
-            param3 = {
-                "estimator": CoxPH(alpha=0.05),
-                "cv": KFold(n_splits=4),
-                "param_grid": {"method": ["lpl", "elastic_net"]},
-                "scoring": PinballLoss(),
-                "error_score": "raise",
-            }
-            params.append(param3)
+        params3 = {
+            "estimator": ConditionUncensored(ResidualDouble(LinearRegression())),
+            "cv": KFold(n_splits=4),
+            "param_grid": {"estimator__fit_intercept": [True, False]},
+            "scoring": PinballLoss(),
+            "error_score": "raise",
+        }
+        params = [param1, param2, params3]
 
         return params
 
@@ -747,8 +742,7 @@ def get_test_params(cls, parameter_set="default"):
 
         from skpro.metrics import CRPS, PinballLoss
         from skpro.regression.residual import ResidualDouble
-        from skpro.survival.coxph import CoxPH
-        from skpro.utils.validation._dependencies import _check_estimator_deps
+        from skpro.survival.compose._reduce_cond_unc import ConditionUncensored
 
         linreg1 = LinearRegression()
         linreg2 = LinearRegression(fit_intercept=False)
@@ -769,17 +763,13 @@ def get_test_params(cls, parameter_set="default"):
             "error_score": "raise",
         }
 
-        params = [param1, param2]
-
-        # testing with survival predictor
-        if _check_estimator_deps(CoxPH, severity="none"):
-            param3 = {
-                "estimator": CoxPH(alpha=0.05),
-                "cv": KFold(n_splits=4),
-                "param_distributions": {"method": ["lpl", "elastic_net"]},
-                "scoring": PinballLoss(),
-                "error_score": "raise",
-            }
-            params += [param3]
+        params3 = {
+            "estimator": ConditionUncensored(ResidualDouble(LinearRegression())),
+            "cv": KFold(n_splits=4),
+            "param_distributions": {"estimator__fit_intercept": [True, False]},
+            "scoring": PinballLoss(),
+            "error_score": "raise",
+        }
+        params = [param1, param2, params3]
 
         return params