MAINT unpack 0-dim NumPy array instead of implicit conversion (scikit…

…-learn#26345)

Co-authored-by: Jérémie du Boisberranger <[email protected]>

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 <[email protected]>
 # Giuseppe Vettigli <[email protected]>
 # 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