BUG: Fix scalar hidden layer sizes

src/gfdl/model.py

@@ -49,7 +49,7 @@ def fit(self, X, Y):
         # Y shape: (n_samples, n_classes-1)
         if self.reg_alpha is not None and self.reg_alpha < 0.0:
             raise ValueError("Negative reg_alpha. Expected range : None or [0.0, inf).")
-        hidden_layer_sizes = np.asarray(self.hidden_layer_sizes)
+        hidden_layer_sizes = np.atleast_1d(np.asarray(self.hidden_layer_sizes))
         if hidden_layer_sizes.min() < 1:
             raise ValueError("hidden_layer_sizes must be > 0, "
                              f"got {hidden_layer_sizes}")
@@ -144,7 +144,7 @@ def partial_fit(self, X, y):
             fn = resolve_activation(self.activation)[1]
             self._activation_fn = fn
             self._N = X.shape[1]
-            hidden_layer_sizes = np.asarray(self.hidden_layer_sizes)
+            hidden_layer_sizes = np.atleast_1d(np.asarray(self.hidden_layer_sizes))
             self._weight_mode = resolve_weight(self.weight_scheme)
 
             # weights shape: (n_layers,)
@@ -565,7 +565,7 @@ def fit(self, X, Y):
         if self.reg_alpha is not None and self.reg_alpha < 0.0:
             raise ValueError("Negative reg_alpha. Expected range : None or [0.0, inf).")
 
-        hidden_layer_sizes = np.asarray(self.hidden_layer_sizes)
+        hidden_layer_sizes = np.atleast_1d(np.asarray(self.hidden_layer_sizes))
         if hidden_layer_sizes.min() < 1:
             raise ValueError("hidden_layer_sizes must be > 0, "
                              f"got {hidden_layer_sizes}")
@@ -632,7 +632,7 @@ def partial_fit(self, X, Y):
             fn = resolve_activation(self.activation)[1]
             self._activation_fn = fn
             self._N = X.shape[1]
-            hidden_layer_sizes = np.asarray(self.hidden_layer_sizes)
+            hidden_layer_sizes = np.atleast_1d(np.asarray(self.hidden_layer_sizes))
             self._weight_mode = resolve_weight(self.weight_scheme)
 
             self.W_ = []

src/gfdl/tests/test_model.py

@@ -822,3 +822,23 @@ def test_gh_85_classifiers(hidden_layer_sizes, classifier):
     clf = classifier(hidden_layer_sizes=hidden_layer_sizes, seed=0)
     with pytest.raises(ValueError, match="must be > 0"):
         clf.fit(X, y)
+
+@pytest.mark.parametrize(
+    "Classifier",
+    [GFDLClassifier, EnsembleGFDLClassifier],
+)
+def test_scalar_hidden_layer_sizes(Classifier):
+    X, y = make_classification(
+        n_samples=60,
+        n_features=10,
+        n_informative=5,
+        random_state=42,
+    )
+
+    model = Classifier(hidden_layer_sizes=3, seed=0)
+    model.fit(X, y)
+
+    predictions = model.predict(X[:5])
+    assert predictions.shape == (5,)
+
+