Remove deprecations from 26.02

python/cuml/cuml/internals/__init__.py

@@ -1,9 +1,6 @@
-#
-# SPDX-FileCopyrightText: Copyright (c) 2019-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
-#
-# TODO: remove in 26.04
-import cuml.internals.memory_utils
+
 from cuml.internals.base import Base, get_handle
 from cuml.internals.internals import GraphBasedDimRedCallback
 from cuml.internals.outputs import (

python/cuml/cuml/manifold/umap/umap.pyx

@@ -560,25 +560,8 @@ cdef init_params(self, lib.UMAPParams &params, n_rows, is_sparse=False, is_fit=T
         )
 
     build_kwds = self.build_kwds or {}
-    if "nnd_n_clusters" in build_kwds:
-        warnings.warn(
-            "`nnd_n_clusters` was deprecated in 26.02 and will be changed to "
-            "`knn_n_clusters` in 26.04."
-        )
-        n_clusters = build_kwds.get("nnd_n_clusters", 1)
-    else:
-        n_clusters = build_kwds.get("knn_n_clusters", 1)
-    if "nnd_overlap_factor" in build_kwds:
-        warnings.warn(
-            "`nnd_overlap_factor` was deprecated in 26.02 and will be changed to "
-            "`knn_overlap_factor` in 26.04."
-        )
-        overlap_factor = build_kwds.get("nnd_overlap_factor", 2)
-    else:
-        overlap_factor = build_kwds.get("knn_overlap_factor", 2)
-
-    params.build_params.n_clusters = n_clusters
-    params.build_params.overlap_factor = overlap_factor
+    n_clusters = build_kwds.get("knn_n_clusters", 1)
+    overlap_factor = build_kwds.get("knn_overlap_factor", 2)
 
     if n_clusters < 1:
         raise ValueError(f"Expected `knn_n_clusters >= 1`, got {n_clusters}")
@@ -588,19 +571,24 @@ cdef init_params(self, lib.UMAPParams &params, n_rows, is_sparse=False, is_fit=T
             f"knn_overlap_factor ({overlap_factor})`"
         )
 
-    # Supported metrics: L2Expanded, L2SqrtExpanded, CosineExpanded, InnerProduct
-    all_neighbors_supported_metrics = ['l2', 'euclidean', 'sqeuclidean', 'cosine',
-                                       'inner_product']
-    if (build_algo == "brute_force_knn" and
-            n_clusters > 1 and
-            self.metric.lower() not in all_neighbors_supported_metrics):
+    all_neighbors_supported_metrics = [
+        'l2', 'euclidean', 'sqeuclidean', 'cosine', 'inner_product'
+    ]
+    if (
+        build_algo == "brute_force_knn" and
+        n_clusters > 1 and
+        self.metric.lower() not in all_neighbors_supported_metrics
+    ):
         warnings.warn(
             f"metric='{self.metric}' is not supported for batched knn build with "
             f"knn_n_clusters > 1. Supported metrics are: {all_neighbors_supported_metrics}. "
             f"The knn_n_clusters parameter will be ignored and regular brute force knn "
             f"(without batching) will be used instead."
         )
 
+    params.build_params.n_clusters = n_clusters
+    params.build_params.overlap_factor = overlap_factor
+
     if build_algo == "brute_force_knn":
         params.build_algo = lib.graph_build_algo.BRUTE_FORCE_KNN
     else:
@@ -835,10 +823,6 @@ class UMAP(Base, InteropMixin, CMajorInputTagMixin, SparseInputTagMixin):
           memory usage. This is independent from knn_overlap_factor as long as
           'knn_overlap_factor' < 'knn_n_clusters'.
 
-        .. deprecated:: 26.02
-            The `nnd_n_clusters` and `nnd_overlap_factor` was deprecated in 26.02 and
-            will be changed to `knn_n_clusters` and `knn_overlap_factor` in 26.04.
-
     device_ids : list[int], "all", or None, default=None
         The device IDs to use during fitting (only used when
         `build_algo=nn_descent` and `knn_n_clusters > 1`). May be a list of

python/cuml/cuml/model_selection/_split.py

@@ -3,7 +3,6 @@
 #
 from __future__ import annotations
 
-import warnings
 from abc import ABC, abstractmethod
 
 import cudf
@@ -20,7 +19,6 @@
 
 def train_test_split(
     *arrays,
-    y="deprecated",
     test_size=None,
     train_size=None,
     random_state=None,
@@ -37,14 +35,6 @@ def train_test_split(
         arrays, numpy arrays, pandas DataFrames/Series, or any array-like
         objects with a shape attribute.
 
-    y : str, default="deprecated"
-        The name of the column that contains the target variable.
-
-        .. deprecated:: 26.02
-            The ``y`` parameter is deprecated and will be removed in 26.04.
-            Extract the column manually:
-            ``X, y = df.drop('col', axis=1), df['col']``
-
     test_size : float or int, default=None
         If float, should be between 0.0 and 1.0 and represent the proportion
         of the dataset to include in the test split. If int, represents the
@@ -87,65 +77,10 @@ def train_test_split(
         >>> X_train, X_test, y_train, y_test = train_test_split(
         ...     X, y, test_size=0.2, random_state=42
         ... )
-
-    Notes
-    -----
-    .. versionchanged:: 26.02
-        The names and the order of the optional keyword arguments was changed to
-        match the scikit-learn equivalent function. The ``y`` parameter was
-        deprecated (see above).
-
-    .. versionchanged:: 26.02
-        Output types now consistently match input types. Previously, pandas
-        inputs were converted to cudf outputs. Now pandas inputs return pandas
-        outputs, cudf inputs return cudf outputs.
     """
     if len(arrays) == 0:
         raise ValueError("At least one array required as input")
 
-    # Handle deprecated y parameter usage
-    # Case 1: y passed as keyword: train_test_split(df, y=...)
-    # Case 2: column name passed as second positional arg: train_test_split(df, "col")
-    y_is_column_name_positional = len(arrays) == 2 and isinstance(
-        arrays[1], str
-    )
-    # Use isinstance check to avoid ambiguous truth value with array-like y
-    y_was_passed = not (isinstance(y, str) and y == "deprecated")
-
-    if y_was_passed or y_is_column_name_positional:
-        warnings.warn(
-            "The explicit 'y' parameter is deprecated and will be "
-            "removed in 26.04. Extract the column manually: "
-            "X, y = df.drop('col', axis=1), df['col']",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-        if y_is_column_name_positional:
-            # User passed: train_test_split(df, "colname")
-            X = arrays[0]
-            col_name = arrays[1]
-            X, y = X.drop(col_name, axis=1), X[col_name]
-            arrays = (X, y)
-        elif isinstance(y, str):
-            # User passed: train_test_split(df, y="colname")
-            X = arrays[0]
-            if not hasattr(X, "drop"):
-                raise TypeError(
-                    "X must be a DataFrame when y is a column name string"
-                )
-            X, y = X.drop(y, axis=1), X[y]
-            arrays = (X, y)
-        else:
-            # User passed: train_test_split(X, y=array)
-            if len(arrays) > 1:
-                raise ValueError(
-                    "Cannot use deprecated 'y' parameter with multiple "
-                    "positional arrays. Pass all arrays as positional "
-                    "arguments instead: train_test_split(X, y, ...)"
-                )
-            arrays = (arrays[0], y)
-
     # Validate arrays have consistent first dimension
     n_samples = arrays[0].shape[0]
     for i, arr in enumerate(arrays[1:], 1):

python/cuml/cuml/svm/svm_base.pyx

@@ -1,8 +1,5 @@
 # SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
-#
-import warnings
-
 import cupy as cp
 import cupyx.scipy.sparse
 import numpy as np
@@ -137,19 +134,6 @@ cdef class _SVMModel:
         return support, support_vectors, dual_coef, intercept
 
 
-class TotalIters(int):
-    """Indicates the maximum number of total iterations the solver may run.
-
-    .. deprecated:: 26.02
-
-        TotalIters was deprecated in 26.02 and will be removed in 26.04.
-        The `max_iter` parameter now always places a limit on total iterations,
-        wrapping with `TotalIters` is no longer necessary.
-    """
-    def __repr__(self):
-        return f"TotalIters({int(self)})"
-
-
 class SVMBase(Base,
               InteropMixin,
               FMajorInputTagMixin,
@@ -200,11 +184,6 @@ class SVMBase(Base,
         }
 
     def _params_to_cpu(self):
-        if isinstance(self.max_iter, TotalIters):
-            max_iter = int(self.max_iter)
-        else:
-            max_iter = self.max_iter
-
         return {
             "kernel": self.kernel,
             "degree": self.degree,
@@ -213,7 +192,7 @@ class SVMBase(Base,
             "tol": self.tol,
             "C": self.C,
             "cache_size": self.cache_size,
-            "max_iter": max_iter,
+            "max_iter": self.max_iter,
             "epsilon": self.epsilon,
         }
 
@@ -418,21 +397,8 @@ class SVMBase(Base,
         param.verbosity = self._verbose_level
         param.epsilon = self.epsilon
         param.svmType = lib.SvmType.C_SVC if is_classifier else lib.SvmType.EPSILON_SVR
-
         param.max_outer_iter = -1
-        if isinstance(self.max_iter, TotalIters):
-            warnings.warn(
-                (
-                    "Passing `TotalIters` to `max_iter` was deprecated in 26.02 "
-                    "and will be removed in 26.04. `max_iter` now always places a "
-                    "limit on total iterations, please pass an integer directly "
-                    "instead of wrapping with `TotalIters`."
-                ),
-                FutureWarning,
-            )
-            param.max_iter = int(self.max_iter)
-        else:
-            param.max_iter = self.max_iter
+        param.max_iter = self.max_iter
 
         handle = get_handle(model=self)
         cdef handle_t* handle_ = <handle_t*><size_t>handle.getHandle()
@@ -679,7 +645,3 @@ class SVMBase(Base,
         handle.sync()
 
         return out
-
-
-# Add TotalIters to the SVC/SVR class for easier access
-SVMBase.TotalIters = TotalIters

python/cuml/tests/test_reflection.py

@@ -102,17 +102,6 @@ def returns_array_one_arg(n):
     return cp.ones(n)
 
 
-def test_deprecated_memory_utils():
-    for name in ["set_global_output_type", "using_output_type"]:
-        with pytest.warns(FutureWarning, match=name):
-            func = getattr(cuml.internals.memory_utils, name)
-        assert func is getattr(cuml, name)
-
-    # Unknown attributes error
-    with pytest.raises(AttributeError, match="not_a_real_attr"):
-        cuml.internals.memory_utils.not_a_real_attr
-
-
 def test_set_global_output_type():
     gs = GlobalSettings()
     assert gs.output_type is None

python/cuml/tests/test_svm.py

@@ -613,12 +613,6 @@ def test_max_iter_n_iter(classifier):
     model = cls(max_iter=5).fit(X, y)
     assert (model.n_iter_.item() if classifier else model.n_iter_) == 5
 
-    # Using TotalIters results in the same behavior, but warns
-    model = cls(max_iter=cls.TotalIters(5))
-    with pytest.warns(FutureWarning, match="TotalIters"):
-        model.fit(X, y)
-    assert (model.n_iter_.item() if classifier else model.n_iter_) == 5
-
 
 def test_svc_multiclass_n_iter():
     X, y = make_classification(random_state=42, n_classes=3, n_informative=4)