include random_state to make cv equal across models and cv checks

autoemulate/compare.py

@@ -22,6 +22,7 @@
 from autoemulate.save import ModelSerialiser
 from autoemulate.sensitivity_analysis import plot_sensitivity_analysis
 from autoemulate.sensitivity_analysis import sensitivity_analysis
+from autoemulate.utils import _check_cv
 from autoemulate.utils import _ensure_2d
 from autoemulate.utils import _get_full_model_name
 from autoemulate.utils import _redirect_warnings
@@ -54,7 +55,9 @@ def setup(
         scaler=StandardScaler(),
         reduce_dim=False,
         dim_reducer=PCA(),
-        cross_validator=KFold(n_splits=5, shuffle=True),
+        cross_validator=KFold(
+            n_splits=5, shuffle=True, random_state=np.random.randint(1e5)
+        ),
         n_jobs=None,
         models=None,
         verbose=0,
@@ -121,7 +124,7 @@ def setup(
             dim_reducer=dim_reducer,
         )
         self.metrics = self._get_metrics(METRIC_REGISTRY)
-        self.cross_validator = cross_validator
+        self.cross_validator = _check_cv(cross_validator)
         self.param_search = param_search
         self.search_type = param_search_type
         self.param_search_iters = param_search_iters

autoemulate/utils.py

@@ -9,6 +9,7 @@
 import torch
 from sklearn.base import RegressorMixin
 from sklearn.exceptions import ConvergenceWarning
+from sklearn.model_selection import KFold
 from sklearn.multioutput import MultiOutputRegressor
 from sklearn.pipeline import Pipeline
 
@@ -370,3 +371,18 @@ def _ensure_2d(arr):
     if arr.ndim == 1:
         arr = arr.reshape(-1, 1)
     return arr
+
+
+# checkers for scikit-learn objects --------------------------------------------
+
+
+def _check_cv(cv):
+    """Ensure that cross-validation method is valid"""
+    if cv is None:
+        raise ValueError("cross_validator cannot be None")
+    if not isinstance(cv, KFold):
+        raise ValueError(
+            "cross_validator should be an instance of KFold cross-validation. We do not "
+            "currently support other cross-validation methods."
+        )
+    return cv

tests/test_ui.py

@@ -44,7 +44,7 @@ def test_cross_validators():
     X = np.random.rand(100, 5)
     y = np.random.rand(100, 1)
 
-    cross_validators = [KFold(n_splits=5), TimeSeriesSplit(n_splits=5)]
+    cross_validators = [KFold(n_splits=5)]
 
     for cross_validator in cross_validators:
         ae = AutoEmulate()

tests/test_utils.py

@@ -3,6 +3,8 @@
 import pytest
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import KFold
+from sklearn.model_selection import LeaveOneOut
 from sklearn.multioutput import MultiOutputRegressor
 from sklearn.pipeline import Pipeline
 from sklearn.preprocessing import StandardScaler
@@ -12,6 +14,7 @@
 from autoemulate.utils import _add_prefix_to_param_space
 from autoemulate.utils import _add_prefix_to_single_grid
 from autoemulate.utils import _adjust_param_space
+from autoemulate.utils import _check_cv
 from autoemulate.utils import _denormalise_y
 from autoemulate.utils import _ensure_2d
 from autoemulate.utils import _get_full_model_name
@@ -340,3 +343,14 @@ def test_ensure_2d_2d():
     y = np.array([[1, 2], [3, 4], [5, 6]])
     y_2d = _ensure_2d(y)
     assert y_2d.ndim == 2
+
+
+# test checkers for scikit-learn objects --------------------------------------
+def test_check_cv():
+    cv = KFold(n_splits=5, shuffle=True, random_state=np.random.randint(1e5))
+    _check_cv(cv)
+
+
+def test_check_cv_error():
+    with pytest.raises(ValueError):
+        _check_cv(LeaveOneOut())