MAINT unpack 0-dim NumPy array instead of implicit conversion (scikit-learn#26345)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

Author: glemaitre

sklearn/linear_model/_logistic.py

@@ -506,6 +506,9 @@ def _logistic_regression_path(
                 w0 = np.concatenate([coef_.ravel(), intercept_])
             else:
                 w0 = coef_.ravel()
+            # n_iter_i is an array for each class. However, `target` is always encoded
+            # in {-1, 1}, so we only take the first element of n_iter_i.
+            n_iter_i = n_iter_i.item()
 
         elif solver in ["sag", "saga"]:
             if multi_class == "multinomial":

sklearn/linear_model/tests/test_sag.py

@@ -95,7 +95,7 @@ def sag(
 
     for epoch in range(n_iter):
         for k in range(n_samples):
-            idx = int(rng.rand(1) * n_samples)
+            idx = int(rng.rand() * n_samples)
             # idx = k
             entry = X[idx]
             seen.add(idx)
@@ -167,7 +167,7 @@ def sag_sparse(
     for epoch in range(n_iter):
         for k in range(n_samples):
             # idx = k
-            idx = int(rng.rand(1) * n_samples)
+            idx = int(rng.rand() * n_samples)
             entry = X[idx]
             seen.add(idx)
 

sklearn/metrics/tests/test_pairwise.py

@@ -298,7 +298,8 @@ def test_pairwise_precomputed_non_negative():
 def callable_rbf_kernel(x, y, **kwds):
     # Callable version of pairwise.rbf_kernel.
     K = rbf_kernel(np.atleast_2d(x), np.atleast_2d(y), **kwds)
-    return K
+    # unpack the output since this is a scalar packed in a 0-dim array
+    return K.item()
 
 
 @pytest.mark.parametrize(

sklearn/mixture/tests/test_bayesian_mixture.py

@@ -58,7 +58,7 @@ def test_log_wishart_norm():
                 ),
                 0,
             )
-        )
+        ).item()
     predected_norm = _log_wishart_norm(
         degrees_of_freedom, log_det_precisions_chol, n_features
     )

sklearn/tests/test_discriminant_analysis.py

@@ -187,7 +187,7 @@ def generate_dataset(n_samples, centers, covariances, random_state=None):
     sample = np.array([[-22, 22]])
 
     def discriminant_func(sample, coef, intercept, clazz):
-        return np.exp(intercept[clazz] + np.dot(sample, coef[clazz]))
+        return np.exp(intercept[clazz] + np.dot(sample, coef[clazz])).item()
 
     prob = np.array(
         [

sklearn/tree/_export.py

@@ -11,6 +11,7 @@
 #          Li Li <aiki.nogard@gmail.com>
 #          Giuseppe Vettigli <vettigli@gmail.com>
 # License: BSD 3 clause
+from collections.abc import Iterable
 from io import StringIO
 from numbers import Integral
 
@@ -247,7 +248,7 @@ def get_color(self, value):
             color = list(self.colors["rgb"][np.argmax(value)])
             sorted_values = sorted(value, reverse=True)
             if len(sorted_values) == 1:
-                alpha = 0
+                alpha = 0.0
             else:
                 alpha = (sorted_values[0] - sorted_values[1]) / (1 - sorted_values[1])
         else:
@@ -256,8 +257,6 @@ def get_color(self, value):
             alpha = (value - self.colors["bounds"][0]) / (
                 self.colors["bounds"][1] - self.colors["bounds"][0]
             )
-        # unpack numpy scalars
-        alpha = float(alpha)
         # compute the color as alpha against white
         color = [int(round(alpha * c + (1 - alpha) * 255, 0)) for c in color]
         # Return html color code in #RRGGBB format
@@ -277,8 +276,12 @@ def get_fill_color(self, tree, node_id):
         if tree.n_outputs == 1:
             node_val = tree.value[node_id][0, :] / tree.weighted_n_node_samples[node_id]
             if tree.n_classes[0] == 1:
-                # Regression
+                # Regression or degraded classification with single class
                 node_val = tree.value[node_id][0, :]
+                if isinstance(node_val, Iterable) and self.colors["bounds"] is not None:
+                    # Only unpack the float only for the regression tree case.
+                    # Classification tree requires an Iterable in `get_color`.
+                    node_val = node_val.item()
         else:
             # If multi-output color node by impurity
             node_val = -tree.impurity[node_id]

sklearn/utils/random.py

@@ -75,7 +75,8 @@ def _random_choice_csc(n_samples, classes, class_probability=None, random_state=
         # If there are nonzero classes choose randomly using class_probability
         rng = check_random_state(random_state)
         if classes[j].shape[0] > 1:
-            p_nonzero = 1 - class_prob_j[classes[j] == 0]
+            index_class_0 = np.flatnonzero(classes[j] == 0).item()
+            p_nonzero = 1 - class_prob_j[index_class_0]
             nnz = int(n_samples * p_nonzero)
             ind_sample = sample_without_replacement(
                 n_population=n_samples, n_samples=nnz, random_state=random_state