style: ruff formatting pass

neps/__init__.py

@@ -20,23 +20,23 @@
 
 __all__ = [
     "Architecture",
-    "Integer",
-    "Float",
-    "Categorical",
-    "Constant",
-    "Function",
     "ArchitectureParameter",
+    "Categorical",
     "CategoricalParameter",
+    "Constant",
     "ConstantParameter",
+    "Float",
     "FloatParameter",
-    "IntegerParameter",
+    "Function",
     "FunctionParameter",
-    "run",
-    "plot",
-    "get_summary_dict",
-    "status",
     "GraphGrammar",
     "GraphGrammarCell",
     "GraphGrammarRepetitive",
+    "Integer",
+    "IntegerParameter",
+    "get_summary_dict",
+    "plot",
+    "run",
+    "status",
     "tblogger",
 ]

neps/optimizers/bayesian_optimization/acquisition_functions/__init__.py

@@ -39,7 +39,7 @@
 
 __all__ = [
     "AcquisitionMapping",
+    "BaseAcquisition",
     "ComprehensiveExpectedImprovement",
     "UpperConfidenceBound",
-    "BaseAcquisition",
 ]

neps/optimizers/multi_fidelity/__init__.py

@@ -15,15 +15,15 @@
 )
 
 __all__ = [
-    "SuccessiveHalving",
-    "SuccessiveHalvingWithPriors",
+    "IFBO",
+    "MOBSTER",
+    "AsynchronousHyperband",
+    "AsynchronousHyperbandWithPriors",
     "AsynchronousSuccessiveHalving",
     "AsynchronousSuccessiveHalvingWithPriors",
     "Hyperband",
-    "HyperbandWithPriors",
     "HyperbandCustomDefault",
-    "AsynchronousHyperband",
-    "AsynchronousHyperbandWithPriors",
-    "MOBSTER",
-    "IFBO",
+    "HyperbandWithPriors",
+    "SuccessiveHalving",
+    "SuccessiveHalvingWithPriors",
 ]

neps/optimizers/multi_fidelity_prior/__init__.py

@@ -5,7 +5,7 @@
 from neps.optimizers.multi_fidelity_prior.priorband import PriorBand
 
 __all__ = [
+    "PriorBand",
     "PriorBandAsha",
     "PriorBandAshaHB",
-    "PriorBand",
 ]

neps/runtime.py

@@ -395,7 +395,9 @@ def _get_next_trial(self) -> Trial | Literal["break"]:
                         time_started=time.time(),
                         worker_id=self.worker_id,
                     )
-                    self.state._trial_repo.update_trial(earliest_pending, hints="metadata")
+                    self.state._trial_repo.update_trial(
+                        earliest_pending, hints="metadata"
+                    )
                     logger.info(
                         "Worker '%s' picked up pending trial: %s.",
                         self.worker_id,

neps/sampling/__init__.py

@@ -1,4 +1,4 @@
 from neps.sampling.priors import CenteredPrior, Prior, UniformPrior
 from neps.sampling.samplers import Sampler, Sobol
 
-__all__ = ["Sobol", "Sampler", "Prior", "UniformPrior", "CenteredPrior"]
+__all__ = ["CenteredPrior", "Prior", "Sampler", "Sobol", "UniformPrior"]

neps/search_spaces/hyperparameters/__init__.py

@@ -9,13 +9,13 @@
 
 __all__ = [
     "Categorical",
-    "Constant",
-    "Integer",
-    "Float",
-    "Numerical",
     "CategoricalParameter",
+    "Constant",
     "ConstantParameter",
-    "IntegerParameter",
+    "Float",
     "FloatParameter",
+    "Integer",
+    "IntegerParameter",
+    "Numerical",
     "NumericalParameter",
 ]

neps/state/__init__.py

@@ -3,9 +3,9 @@
 from neps.state.trial import Trial
 
 __all__ = [
-    "SeedSnapshot",
     "BudgetInfo",
     "OptimizationState",
     "OptimizerInfo",
+    "SeedSnapshot",
     "Trial",
 ]

neps/state/neps_state.py

@@ -18,7 +18,6 @@
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import (
-    TYPE_CHECKING,
     Literal,
     TypeAlias,
     TypeVar,
@@ -43,13 +42,10 @@
     ReaderWriterTrial,
     TrialWriteHint,
 )
-from neps.state.optimizer import OptimizerInfo
+from neps.state.optimizer import OptimizationState, OptimizerInfo
 from neps.state.trial import Report, Trial
 from neps.utils.files import atomic_write, deserialize, serialize
 
-if TYPE_CHECKING:
-    from neps.state.optimizer import OptimizationState
-
 logger = logging.getLogger(__name__)
 
 

pyproject.toml

@@ -28,7 +28,7 @@ keywords = [
 classifiers = [
   "Development Status :: 4 - Beta",
   "Intended Audience :: Developers",
-  "Intended Audience :: Science/Research", 
+  "Intended Audience :: Science/Research",
   "Natural Language :: English",
   "License :: OSI Approved :: Apache Software License",
   "Operating System :: POSIX :: Linux",
@@ -214,7 +214,7 @@ ignore = [
   "S101",    # Use of assert detected.
   "W292",    # No newline at end of file
   "PLC1901", # "" can be simplified to be falsey
-  "TCH003",  # Move stdlib import into TYPE_CHECKING
+  "TC003",   # Move stdlib import into TYPE_CHECKING
   "B010",    # Do not use `setattr`
   "PD901",   # Use a better name than 'df'
   "PD011",   # Use .to_numpy() instead of .values  (triggers on report.values)

tests/joint_config_space.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import ConfigSpace as CS
 from jahs_bench.lib.core.constants import Activations
 
@@ -16,37 +18,37 @@
             "Op1",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the first edge of the cell."),
+            meta={"help": "The operation on the first edge of the cell."},
         ),
         CS.CategoricalHyperparameter(
             "Op2",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the second edge of the cell."),
+            meta={"help": "The operation on the second edge of the cell."},
         ),
         CS.CategoricalHyperparameter(
             "Op3",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the third edge of the cell."),
+            meta={"help": "The operation on the third edge of the cell."},
         ),
         CS.CategoricalHyperparameter(
             "Op4",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the fourth edge of the cell."),
+            meta={"help": "The operation on the fourth edge of the cell."},
         ),
         CS.CategoricalHyperparameter(
             "Op5",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the fifth edge of the cell."),
+            meta={"help": "The operation on the fifth edge of the cell."},
         ),
         CS.CategoricalHyperparameter(
             "Op6",
             choices=list(range(5)),
             default_value=0,
-            meta=dict(help="The operation on the sixth edge of the cell."),
+            meta={"help": "The operation on the sixth edge of the cell."},
         ),
         # CS.OrdinalHyperparameter("Resolution", sequence=[0.25, 0.5, 1.], default_value=1.,
         #     meta=dict(help="The sample resolution of the input images w.r.t. one side of the "
@@ -57,21 +59,21 @@
             "TrivialAugment",
             choices=[True, False],
             default_value=False,
-            meta=dict(
-                help="Controls whether or not TrivialAugment is used for pre-processing "
+            meta={
+                "help": "Controls whether or not TrivialAugment is used for pre-processing "
                 "data. If False (default), a set of manually chosen transforms is "
                 "applied during pre-processing. If True, these are skipped in favor of "
                 "applying random transforms selected by TrivialAugment."
-            ),
+            },
         ),
         CS.CategoricalHyperparameter(
             "Activation",
             choices=list(Activations.__members__.keys()),
             default_value="ReLU",
-            meta=dict(
-                help="Which activation function is to be used for the network. "
+            meta={
+                "help": "Which activation function is to be used for the network. "
                 "Default is ReLU."
-            ),
+            },
         ),
     ]
 )
@@ -81,31 +83,31 @@
     "Optimizer",
     choices=["SGD"],
     default_value="SGD",
-    meta=dict(
-        help="Which optimizer to use for training this model. "
+    meta={
+        "help": "Which optimizer to use for training this model. "
         "This is just a placeholder for now, to be used "
         "properly in future versions."
-    ),
+    },
 )
 lr = CS.UniformFloatHyperparameter(
     "LearningRate",
     lower=1e-3,
     upper=1e0,
     default_value=1e-1,
     log=True,
-    meta=dict(
-        help="The learning rate for the optimizer used during model training. In the "
+    meta={
+        "help": "The learning rate for the optimizer used during model training. In the "
         "case of adaptive learning rate optimizers such as Adam, this is the "
         "initial learning rate."
-    ),
+    },
 )
 weight_decay = CS.UniformFloatHyperparameter(
     "WeightDecay",
     lower=1e-5,
     upper=1e-2,
     default_value=5e-4,
     log=True,
-    meta=dict(help="Weight decay to be used by the " "optimizer during model training."),
+    meta={"help": "Weight decay to be used by the " "optimizer during model training."},
 )
 
 joint_config_space.add_hyperparameters([optimizers, lr, weight_decay])