microsoft · debonte · May 5, 2025 · May 3, 2025 · May 5, 2025 · May 5, 2025
diff --git a/pyproject.toml b/pyproject.toml
@@ -33,6 +33,7 @@ extend-select = [
     "PYI", # flake8-pyi
     "UP", # pyupgrade
     "W", # pycodestyle Warning
+    "PIE790", # unnecessary-placeholder
 ]
 ignore = [
     ###
@@ -56,7 +57,6 @@ ignore = [
 
     # TODO: Investigate and fix or configure
     "PYI024",
-    "PYI048",
     "PYI051", # Request for autofix: https://github.com/astral-sh/ruff/issues/14185
 ]
 [tool.ruff.lint.per-file-ignores]

diff --git a/stubs/sklearn/cluster/_k_means_elkan.pyi b/stubs/sklearn/cluster/_k_means_elkan.pyi
@@ -41,7 +41,6 @@ def init_bounds_dense(
     n_threads : int
         The number of threads to be used by openmp.
     """
-    ...
 
 def init_bounds_sparse(
     X: spmatrix,
@@ -83,7 +82,6 @@ def init_bounds_sparse(
     n_threads : int
         The number of threads to be used by openmp.
     """
-    ...
 
 def elkan_iter_chunked_dense(
     X: np.ndarray,
@@ -141,7 +139,6 @@ def elkan_iter_chunked_dense(
           the algorithm. This is useful especially when calling predict on a
           fitted model.
     """
-    ...
 
 def elkan_iter_chunked_sparse(
     X: spmatrix,
@@ -199,4 +196,3 @@ def elkan_iter_chunked_sparse(
           the algorithm. This is useful especially when calling predict on a
           fitted model.
     """
-    ...
diff --git a/stubs/sklearn/cluster/_k_means_lloyd.pyi b/stubs/sklearn/cluster/_k_means_lloyd.pyi
@@ -44,7 +44,6 @@ def lloyd_iter_chunked_dense(
           the algorithm. This is useful especially when calling predict on a
           fitted model.
     """
-    ...
 
 def lloyd_iter_chunked_sparse(
     X: np.ndarray,
@@ -90,4 +89,3 @@ def lloyd_iter_chunked_sparse(
           the algorithm. This is useful especially when calling predict on a
           fitted model.
     """
-    ...
diff --git a/stubs/sklearn/decomposition/_online_lda_fast.pyi b/stubs/sklearn/decomposition/_online_lda_fast.pyi
@@ -5,6 +5,5 @@ def mean_change(arr_1: np.ndarray, arr_2: np.ndarray) -> float:
 
     Equivalent to np.abs(arr_1 - arr2).mean().
     """
-    ...
 
 def psi(x: float) -> float: ...
diff --git a/stubs/sklearn/ensemble/_gradient_boosting.pyi b/stubs/sklearn/ensemble/_gradient_boosting.pyi
@@ -5,11 +5,9 @@ def predict_stages(estimators: np.ndarray, X, scale: float, out: np.ndarray) ->
     Each estimator is scaled by ``scale`` before its prediction
     is added to ``out``.
     """
-    ...
 
 def predict_stage(estimators: np.ndarray, stage: int, X, scale: float, out: np.ndarray) -> None:
     """Add predictions of ``estimators[stage]`` to ``out``.
     Each estimator in the stage is scaled by ``scale`` before
     its prediction is added to ``out``.
     """
-    ...
diff --git a/stubs/sklearn/ensemble/_hist_gradient_boosting/histogram.pyi b/stubs/sklearn/ensemble/_hist_gradient_boosting/histogram.pyi
@@ -58,7 +58,6 @@ class HistogramBuilder:
         histograms : ndarray of HISTOGRAM_DTYPE, shape (n_features, n_bins)
             The computed histograms of the current node.
         """
-        ...
 
     def compute_histograms_subtraction(
         self,
@@ -85,4 +84,3 @@ class HistogramBuilder:
         histograms : ndarray of HISTOGRAM_DTYPE, shape(n_features, n_bins)
             The computed histograms of the current node.
         """
-        ...
diff --git a/stubs/sklearn/ensemble/_hist_gradient_boosting/splitting.pyi b/stubs/sklearn/ensemble/_hist_gradient_boosting/splitting.pyi
@@ -159,7 +159,6 @@ class Splitter:
         right_child_position : int
             The position of the right child in ``sample_indices``.
         """
-        ...
 
     def find_node_split(
         self,
@@ -208,4 +207,3 @@ class Splitter:
         best_split_info : SplitInfo
             The info about the best possible split among all features.
         """
-        ...
diff --git a/stubs/sklearn/feature_extraction/_hashing_fast.pyi b/stubs/sklearn/feature_extraction/_hashing_fast.pyi
@@ -8,4 +8,3 @@ def transform(
     indices, indptr, values : lists
         For constructing a scipy.sparse.csr_matrix.
     """
-    ...
diff --git a/stubs/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.pyi b/stubs/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.pyi
@@ -20,7 +20,6 @@ def sqeuclidean_row_norms(X: np.ndarray | csr_matrix, num_threads: int) -> np.nd
     sqeuclidean_row_norms : ndarray of shape (n_samples,)
         Arrays containing the squared euclidean norm of each row of X.
     """
-    ...
 
 class BaseDistancesReductionDispatcher:
     """Abstract base dispatcher for pairwise distance computation & reduction.
@@ -53,7 +52,6 @@ class BaseDistancesReductionDispatcher:
         -------
         True if the dispatcher can be used, else False.
         """
-        ...
 
     @classmethod
     @abstractmethod
@@ -170,7 +168,6 @@ class ArgKmin(BaseDistancesReductionDispatcher):
         for the concrete implementation are therefore freed when this classmethod
         returns.
         """
-        ...
 
 class RadiusNeighbors(BaseDistancesReductionDispatcher):
     """Compute radius-based neighbors for two sets of vectors.
@@ -286,4 +283,3 @@ class RadiusNeighbors(BaseDistancesReductionDispatcher):
         for the concrete implementation are therefore freed when this classmethod
         returns.
         """
-        ...
diff --git a/stubs/sklearn/utils/arrayfuncs.pyi b/stubs/sklearn/utils/arrayfuncs.pyi
@@ -5,6 +5,5 @@ def min_pos(X: np.ndarray) -> float:
     Returns the maximum representable value of the input dtype if none of the
     values are positive.
     """
-    ...
 
 def cholesky_delete(L: np.ndarray, go_out: int) -> None: ...
diff --git a/stubs/sklearn/utils/sparsefuncs_fast.pyi b/stubs/sklearn/utils/sparsefuncs_fast.pyi
@@ -3,7 +3,6 @@ from scipy.sparse import csc_matrix, csr_matrix
 
 def csr_row_norms(X: np.ndarray) -> np.ndarray:
     """Squared L2 norm of each row in CSR matrix X."""
-    ...
 
 def csr_mean_variance_axis0(
     X: csr_matrix, weights: np.ndarray | None = None, return_sum_weights: bool = False
@@ -29,7 +28,6 @@ def csr_mean_variance_axis0(
     sum_weights : ndarray of shape (n_features,), dtype=floating
         Returned if return_sum_weights is True.
     """
-    ...
 
 def csc_mean_variance_axis0(
     X: csc_matrix, weights: np.ndarray | None = None, return_sum_weights: bool = False
@@ -55,7 +53,6 @@ def csc_mean_variance_axis0(
     sum_weights : ndarray of shape (n_features,), dtype=floating
         Returned if return_sum_weights is True.
     """
-    ...
 
 def incr_mean_variance_axis0(
     X: csr_matrix | csc_matrix, last_mean: np.ndarray, last_var: np.ndarray, last_n: np.ndarray, weights: np.ndarray | None = None
@@ -86,15 +83,12 @@ def incr_mean_variance_axis0(
     updated_n : int array with shape (n_features,)
       Updated number of samples seen
     """
-    ...
 
 def inplace_csr_row_normalize_l1(X: np.ndarray) -> None:
     """Inplace row normalize using the l1 norm"""
-    ...
 
 def inplace_csr_row_normalize_l2(X: np.ndarray) -> None:
     """Inplace row normalize using the l2 norm"""
-    ...
 
 def assign_rows_csr(
     X: csr_matrix,
@@ -112,4 +106,3 @@ def assign_rows_csr(
     out_rows : array, dtype=np.intp, shape=n_rows
     out : array, shape=(arbitrary, n_features)
     """
-    ...
diff --git a/stubs/sympy-stubs/core/cache.pyi b/stubs/sympy-stubs/core/cache.pyi
@@ -13,6 +13,4 @@ SYMPY_CACHE_SIZE = ...
 cacheit = ...
 
 def cached_property(func) -> property: ...
-def lazy_function(module: str, name: str) -> Callable:
-    class LazyFunctionMeta(type): ...
-    class LazyFunction(metaclass=LazyFunctionMeta): ...
+def lazy_function(module: str, name: str) -> Callable: ...
-Original file line number
+Diff line change
@@ Expand Up / @@ -5,6 +5,5 @@ def mean_change(arr_1: np.ndarray, arr_2: np.ndarray) -> float: @@
         Equivalent to np.abs(arr_1 - arr2).mean().
         """
-        ...
     def psi(x: float) -> float: ...