TST: Simplify test_bad_ensemble_params and fix guard code in Ensemble…

…Optimizer
dynamicslab · Jun 30, 2023 · b36ccf6 · b36ccf6
1 parent 59f8e6c
commit b36ccf6
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Showing 2 changed files with 12 additions and 17 deletions.
diff --git a/pysindy/optimizers/base.py b/pysindy/optimizers/base.py
@@ -281,19 +281,19 @@ def __init__(
             raise ValueError(
                 "If not ensembling data or library terms, use another optimizer"
             )
-        if n_subset is not None and n_subset <= 0:
+        if bagging and (n_subset is None or n_subset < 1):
             raise ValueError("n_subset must be a positive integer if bagging")
-        if n_candidates_to_drop is not None and n_candidates_to_drop <= 0:
+        if library_ensemble and (
+            n_candidates_to_drop is None or n_candidates_to_drop < 1
+        ):
             raise ValueError(
                 "n_candidates_to_drop must be a positive integer if ensembling library"
             )
-        self.opt = opt
-        if n_models is None or n_models == 0:
-            warnings.warn(
-                "n_models must be a positive integer.  Explicitly initialized to zero"
-                " or None, defaulting to 20."
+        if n_models < 1:
+            raise ValueError(
+                "n_candidates_to_drop must be a positive integer if ensembling library"
             )
-            n_models = 20
+        self.opt = opt
         self.n_models = n_models
         self.n_subset = n_subset
         self.bagging = bagging

diff --git a/test/test_optimizers.py b/test/test_optimizers.py
@@ -1046,15 +1046,10 @@ def test_ensemble_optimizer(data_lorenz, optimizer_params):
 @pytest.mark.parametrize(
     "params",
     [
-        dict(ensemble=False, n_models=-1, n_subset=1),
-        dict(ensemble=False, n_models=0, n_subset=1),
-        dict(ensemble=False, n_models=1, n_subset=0),
-        dict(ensemble=False, n_models=1, n_subset=-1),
-        dict(ensemble=True, n_models=-1, n_subset=1),
-        dict(ensemble=True, n_models=0, n_subset=1),
-        dict(ensemble=True, n_models=1, n_subset=0),
-        dict(ensemble=True, n_models=1, n_subset=-1),
-        dict(ensemble=True, n_models=1, n_subset=0),
+        dict(),
+        dict(bagging=True, n_models=0),
+        dict(bagging=True, n_subset=0),
+        dict(library_ensemble=True, n_candidates_to_drop=0),
     ],
 )
 def test_bad_ensemble_params(data_lorenz, params):