Remove Standardize.load_state_dict

botorch/models/transforms/input.py

@@ -781,7 +781,6 @@ def __init__(
         transform_on_fantasize: bool = True,
         approximate: bool = False,
         tau: float = 1e-3,
-        **kwargs,
     ) -> None:
         r"""Initialize transform.
 
@@ -800,13 +799,6 @@ def __init__(
                 rounding should be used. Default: False.
             tau: The temperature parameter for approximate rounding.
         """
-        indices = kwargs.get("indices")
-        if indices is not None:
-            warn(
-                "`indices` is marked for deprecation in favor of `integer_indices`.",
-                DeprecationWarning,
-            )
-            integer_indices = indices
         if approximate and categorical_features is not None:
             raise NotImplementedError
         super().__init__()

botorch/models/transforms/outcome.py

@@ -22,11 +22,9 @@
 
 from __future__ import annotations
 
-import warnings
-
 from abc import ABC, abstractmethod
 from collections import OrderedDict
-from typing import Any, List, Mapping, Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import torch
 from botorch.models.transforms.utils import (
@@ -256,19 +254,6 @@ def __init__(
         self._batch_shape = batch_shape
         self._min_stdv = min_stdv
 
-    def load_state_dict(
-        self, state_dict: Mapping[str, Any], strict: bool = True
-    ) -> None:
-        r"""Custom logic for loading the state dict."""
-        if "_is_trained" not in state_dict:
-            warnings.warn(
-                "Key '_is_trained' not found in state_dict. Setting to True. "
-                "In a future release, this will result in an error.",
-                DeprecationWarning,
-            )
-            state_dict = {**state_dict, "_is_trained": torch.tensor(True)}
-        super().load_state_dict(state_dict, strict=strict)
-
     def forward(
         self, Y: Tensor, Yvar: Optional[Tensor] = None
     ) -> Tuple[Tensor, Optional[Tensor]]:

test/models/transforms/test_input.py

@@ -5,12 +5,10 @@
 # LICENSE file in the root directory of this source tree.
 
 import itertools
-import warnings
 from copy import deepcopy
 from random import randint
 
 import torch
-from botorch import settings
 from botorch.exceptions.errors import BotorchTensorDimensionError
 from botorch.exceptions.warnings import UserInputWarning
 from botorch.models.transforms.input import (
@@ -620,10 +618,6 @@ def test_round_transform_init(self) -> None:
         # basic init
         int_idcs = [0, 4]
         categorical_feats = {2: 2, 5: 3}
-        # test deprecation warning
-        with warnings.catch_warnings(record=True) as ws, settings.debug(True):
-            Round(indices=int_idcs)
-            self.assertTrue(any(issubclass(w.category, DeprecationWarning) for w in ws))
         round_tf = Round(
             integer_indices=int_idcs, categorical_features=categorical_feats
         )

test/models/transforms/test_outcome.py

@@ -355,13 +355,6 @@ def test_standardize_state_dict(self):
                 self.assertFalse(new_transform._is_trained)
                 new_transform.load_state_dict(state_dict)
                 self.assertTrue(new_transform._is_trained)
-                # test deprecation error when loading state dict without _is_trained
-                state_dict.pop("_is_trained")
-                with self.assertWarnsRegex(
-                    DeprecationWarning,
-                    "Key '_is_trained' not found in state_dict. Setting to True.",
-                ):
-                    new_transform.load_state_dict(state_dict)
 
     def test_log(self):
         ms = (1, 2)