From 0545dbe2ca029f27faca77e1bdc35c6f6838417a Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 4 Oct 2024 10:57:16 +0200
Subject: [PATCH 01/51] Add AutoSampler

---
 package/samplers/auto_sampler/LICENSE     |  21 ++++
 package/samplers/auto_sampler/README.md   | 110 +++++++++++++++++
 package/samplers/auto_sampler/__init__.py |   4 +
 package/samplers/auto_sampler/sampler.py  | 144 ++++++++++++++++++++++
 4 files changed, 279 insertions(+)
 create mode 100644 package/samplers/auto_sampler/LICENSE
 create mode 100644 package/samplers/auto_sampler/README.md
 create mode 100644 package/samplers/auto_sampler/__init__.py
 create mode 100644 package/samplers/auto_sampler/sampler.py

diff --git a/package/samplers/auto_sampler/LICENSE b/package/samplers/auto_sampler/LICENSE
new file mode 100644
index 00000000..548d31c6
--- /dev/null
+++ b/package/samplers/auto_sampler/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 <COPYRIGHT HOLDER, e.g., John Smith>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
new file mode 100644
index 00000000..d4e40702
--- /dev/null
+++ b/package/samplers/auto_sampler/README.md
@@ -0,0 +1,110 @@
+---
+author: Please fill in the author name here. (e.g., John Smith)
+title: Please fill in the title of the feature here. (e.g., Gaussian-Process Expected Improvement Sampler)
+description: Please fill in the description of the feature here. (e.g., This sampler searches for each trial based on expected improvement using Gaussian process.)
+tags: [Please fill in the list of tags here. (e.g., sampler, visualization, pruner)]
+optuna_versions: ['Please fill in the list of versions of Optuna in which you have confirmed the feature works, e.g., 3.6.1.']
+license: MIT License
+---
+
+<!--
+This is an example of the frontmatters.
+All columns must be string.
+You can omit quotes when value types are not ambiguous.
+For tags, a package placed in
+- package/samplers/ must include the tag "sampler"
+- package/visualilzation/ must include the tag "visualization"
+- package/pruners/ must include the tag "pruner"
+respectively.
+
+---
+author: Optuna team
+title: My Sampler
+description: A description for My Sampler.
+tags: [sampler, 2nd tag for My Sampler, 3rd tag for My Sampler]
+optuna_versions: [3.6.1]
+license: "MIT License"
+---
+-->
+
+Please read the [tutorial guide](https://optuna.github.io/optunahub-registry/recipes/001_first.html) to register your feature in OptunaHub.
+You can find more detailed explanation of the following contents in the tutorial.
+Looking at [other packages' implementations](https://github.com/optuna/optunahub-registry/tree/main/package) will also help you.
+
+## Abstract
+
+You can provide an abstract for your package here.
+This section will help attract potential users to your package.
+
+**Example**
+
+This package provides a sampler based on Gaussian process-based Bayesian optimization. The sampler is highly sample-efficient, so it is suitable for computationally expensive optimization problems with a limited evaluation budget, such as hyperparameter optimization of machine learning algorithms.
+
+## Class or Function Names
+
+Please fill in the class/function names which you implement here.
+
+**Example**
+
+- GPSampler
+
+## Installation
+
+If you have additional dependencies, please fill in the installation guide here.
+If no additional dependencies is required, **this section can be removed**.
+
+**Example**
+
+```shell
+$ pip install scipy torch
+```
+
+If your package has `requirements.txt`, it will be automatically uploaded to the OptunaHub, and the package dependencies will be available to install as follows.
+
+```shell
+ pip install -r https://hub.optuna.org/{category}/{your_package_name}/requirements.txt
+```
+
+## Example
+
+Please fill in the code snippet to use the implemented feature here.
+
+**Example**
+
+```python
+import optuna
+import optunahub
+
+
+def objective(trial):
+  x = trial.suggest_float("x", -5, 5)
+  return x**2
+
+
+sampler = optunahub.load_module(package="samplers/gp").GPSampler()
+study = optuna.create_study(sampler=sampler)
+study.optimize(objective, n_trials=100)
+```
+
+## Others
+
+Please fill in any other information if you have here by adding child sections (###).
+If there is no additional information, **this section can be removed**.
+
+<!--
+For example, you can add sections to introduce a corresponding paper.
+
+### Reference
+Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019.
+Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD.
+
+### Bibtex
+```
+@inproceedings{optuna_2019,
+    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
+    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
+    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
+    year={2019}
+}
+```
+-->
diff --git a/package/samplers/auto_sampler/__init__.py b/package/samplers/auto_sampler/__init__.py
new file mode 100644
index 00000000..50191329
--- /dev/null
+++ b/package/samplers/auto_sampler/__init__.py
@@ -0,0 +1,4 @@
+from .auto_sampler.sampler import AutoSampler
+
+
+__all__ = ["AutoSampler"]
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
new file mode 100644
index 00000000..924bac3b
--- /dev/null
+++ b/package/samplers/auto_sampler/sampler.py
@@ -0,0 +1,144 @@
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Any
+from typing import TYPE_CHECKING
+
+from optuna.distributions import CategoricalDistribution
+from optuna.samplers import BaseSampler
+from optuna.samplers import CmaEsSampler
+from optuna.samplers import GPSampler
+from optuna.samplers import RandomSampler
+from optuna.samplers import TPESampler
+from optuna.search_space import IntersectionSearchSpace
+from optuna.trial import TrialState
+
+
+if TYPE_CHECKING:
+    from optuna.distributions import BaseDistribution
+    from optuna.study import Study
+    from optuna.trial import FrozenTrial
+
+
+class AutoSampler(BaseSampler):
+    """Sampler automatically choosing an appropriate sampler based on search space.
+
+    This sampler is convenient when you are unsure what sampler to use.
+
+    Example:
+
+        .. testcode::
+
+            import optuna
+            from optuna.samplers import AutoSampler
+
+
+            def objective(trial):
+                x = trial.suggest_float("x", -5, 5)
+                return x**2
+
+
+            study = optuna.create_study(sampler=AutoSampler())
+            study.optimize(objective, n_trials=10)
+
+    .. note::
+        This sampler might require ``scipy``, ``torch``, and ``cmaes``.
+        You can install these dependencies with ``pip install scipy torch cmaes``.
+
+    Args:
+        seed: Seed for random number generator.
+
+    """
+
+    def __init__(self, seed: int | None = None) -> None:
+        self._seed = seed
+        self._sampler: BaseSampler = RandomSampler(seed=seed)
+
+    def reseed_rng(self) -> None:
+        self._sampler.reseed_rng()
+
+    def _include_conditional_param(self, study: Study) -> bool:
+        trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED))
+        if len(trials) == 0:
+            return False
+
+        param_key = set(trials[0].params)
+        for t in trials:
+            if param_key != set(t.params):
+                return True
+
+        return False
+
+    def _determine_sampler(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> None:
+        if isinstance(self._sampler, TPESampler):
+            return
+
+        if any(
+            isinstance(d, CategoricalDistribution) for d in search_space.values()
+        ) or self._include_conditional_param(study):
+            # NOTE(nabenabe): The statement above is always true for Trial#1.
+            # Use ``TPESampler`` if search space includes conditional or categorical parameters.
+            # TBD: group=True?
+            self._sampler = TPESampler(
+                seed=self._seed, multivariate=True, warn_independent_sampling=False
+            )
+            return
+
+        if trial.number < 250:
+            # Use ``GPSampler`` if search space is numerical and n_trials <= 250.
+            if not isinstance(self._sampler, GPSampler):
+                self._sampler = GPSampler(seed=self._seed)
+            return
+
+        if not isinstance(self._sampler, CmaEsSampler):
+            # Use ``CmaEsSampler`` if search space is numerical and n_trials > 250.
+            # Warm start CMA-ES with trials up to trial.number of 249.
+            warm_start_trials = study.get_trials(
+                deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED)
+            )
+            self._sampler = CmaEsSampler(seed=self._seed, source_trials=warm_start_trials)
+
+    def infer_relative_search_space(
+        self, study: Study, trial: FrozenTrial
+    ) -> dict[str, BaseDistribution]:
+        search_space = IntersectionSearchSpace().calculate(study)
+        sampler_key = "auto:sampler"
+        if len(search_space) == 0:
+            study._storage.set_trial_system_attr(
+                trial._trial_id, sampler_key, self._sampler.__class__.__name__
+            )
+            return {}
+
+        self._determine_sampler(study, trial, search_space)
+        study._storage.set_trial_system_attr(
+            trial._trial_id, sampler_key, self._sampler.__class__.__name__
+        )
+        return self._sampler.infer_relative_search_space(study, trial)
+
+    def sample_relative(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> dict[str, Any]:
+        return self._sampler.sample_relative(study, trial, search_space)
+
+    def sample_independent(
+        self,
+        study: Study,
+        trial: FrozenTrial,
+        param_name: str,
+        param_distribution: BaseDistribution,
+    ) -> Any:
+        return self._sampler.sample_independent(study, trial, param_name, param_distribution)
+
+    def before_trial(self, study: Study, trial: FrozenTrial) -> None:
+        self._sampler.before_trial(study, trial)
+
+    def after_trial(
+        self,
+        study: Study,
+        trial: FrozenTrial,
+        state: TrialState,
+        values: Sequence[float] | None,
+    ) -> None:
+        self._sampler.after_trial(study, trial, state, values)

From e47861a03592659995059453e0570bb8fe5c1a4d Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 4 Oct 2024 11:18:51 +0200
Subject: [PATCH 02/51] Enhance README

---
 package/samplers/auto_sampler/README.md   | 92 +++--------------------
 package/samplers/auto_sampler/__init__.py |  2 +-
 package/samplers/auto_sampler/sampler.py  |  6 +-
 3 files changed, 17 insertions(+), 83 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index d4e40702..40986a9c 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -1,78 +1,31 @@
 ---
-author: Please fill in the author name here. (e.g., John Smith)
-title: Please fill in the title of the feature here. (e.g., Gaussian-Process Expected Improvement Sampler)
-description: Please fill in the description of the feature here. (e.g., This sampler searches for each trial based on expected improvement using Gaussian process.)
-tags: [Please fill in the list of tags here. (e.g., sampler, visualization, pruner)]
-optuna_versions: ['Please fill in the list of versions of Optuna in which you have confirmed the feature works, e.g., 3.6.1.']
+author: Optuna Team
+title: AutoSampler
+description: This sampler automatically chooses an appropriate built-in sampler for the provided objective function.
+tags: [sampler, automatic, automated]
+optuna_versions: [4.0.0]
 license: MIT License
 ---
 
-<!--
-This is an example of the frontmatters.
-All columns must be string.
-You can omit quotes when value types are not ambiguous.
-For tags, a package placed in
-- package/samplers/ must include the tag "sampler"
-- package/visualilzation/ must include the tag "visualization"
-- package/pruners/ must include the tag "pruner"
-respectively.
-
----
-author: Optuna team
-title: My Sampler
-description: A description for My Sampler.
-tags: [sampler, 2nd tag for My Sampler, 3rd tag for My Sampler]
-optuna_versions: [3.6.1]
-license: "MIT License"
----
--->
-
-Please read the [tutorial guide](https://optuna.github.io/optunahub-registry/recipes/001_first.html) to register your feature in OptunaHub.
-You can find more detailed explanation of the following contents in the tutorial.
-Looking at [other packages' implementations](https://github.com/optuna/optunahub-registry/tree/main/package) will also help you.
-
 ## Abstract
 
-You can provide an abstract for your package here.
-This section will help attract potential users to your package.
-
-**Example**
-
 This package provides a sampler based on Gaussian process-based Bayesian optimization. The sampler is highly sample-efficient, so it is suitable for computationally expensive optimization problems with a limited evaluation budget, such as hyperparameter optimization of machine learning algorithms.
 
 ## Class or Function Names
 
-Please fill in the class/function names which you implement here.
-
-**Example**
+- AutoSampler
 
-- GPSampler
+This sampler currently accepts only `seed`.
 
 ## Installation
 
-If you have additional dependencies, please fill in the installation guide here.
-If no additional dependencies is required, **this section can be removed**.
-
-**Example**
-
 ```shell
-$ pip install scipy torch
-```
-
-If your package has `requirements.txt`, it will be automatically uploaded to the OptunaHub, and the package dependencies will be available to install as follows.
-
-```shell
- pip install -r https://hub.optuna.org/{category}/{your_package_name}/requirements.txt
+$ pip install scipy torch cmaes
 ```
 
 ## Example
 
-Please fill in the code snippet to use the implemented feature here.
-
-**Example**
-
 ```python
-import optuna
 import optunahub
 
 
@@ -81,30 +34,7 @@ def objective(trial):
   return x**2
 
 
-sampler = optunahub.load_module(package="samplers/gp").GPSampler()
-study = optuna.create_study(sampler=sampler)
-study.optimize(objective, n_trials=100)
-```
-
-## Others
-
-Please fill in any other information if you have here by adding child sections (###).
-If there is no additional information, **this section can be removed**.
-
-<!--
-For example, you can add sections to introduce a corresponding paper.
-
-### Reference
-Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. 2019.
-Optuna: A Next-generation Hyperparameter Optimization Framework. In KDD.
-
-### Bibtex
-```
-@inproceedings{optuna_2019,
-    title={Optuna: A Next-generation Hyperparameter Optimization Framework},
-    author={Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
-    booktitle={Proceedings of the 25th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining},
-    year={2019}
-}
+module = optunahub.load_module(package="samplers/auto_sampler")
+study = optuna.create_study(sampler=module.AutoSampler())
+study.optimize(objective, n_trials=300)
 ```
--->
diff --git a/package/samplers/auto_sampler/__init__.py b/package/samplers/auto_sampler/__init__.py
index 50191329..b74f25a5 100644
--- a/package/samplers/auto_sampler/__init__.py
+++ b/package/samplers/auto_sampler/__init__.py
@@ -1,4 +1,4 @@
-from .auto_sampler.sampler import AutoSampler
+from .sampler import AutoSampler
 
 
 __all__ = ["AutoSampler"]
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 924bac3b..662949cf 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -98,7 +98,11 @@ def _determine_sampler(
             warm_start_trials = study.get_trials(
                 deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED)
             )
-            self._sampler = CmaEsSampler(seed=self._seed, source_trials=warm_start_trials)
+            # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
+            # 1D problems.
+            self._sampler = CmaEsSampler(
+                seed=self._seed, source_trials=warm_start_trials, warn_independent_sampling=False
+            )
 
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial

From a8edf5e0ef077014f23e0b3493febe456d8b4e7d Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 4 Oct 2024 11:20:40 +0200
Subject: [PATCH 03/51] Add LICENSE

---
 package/samplers/auto_sampler/LICENSE          | 2 +-
 package/samplers/auto_sampler/requirements.txt | 3 +++
 2 files changed, 4 insertions(+), 1 deletion(-)
 create mode 100644 package/samplers/auto_sampler/requirements.txt

diff --git a/package/samplers/auto_sampler/LICENSE b/package/samplers/auto_sampler/LICENSE
index 548d31c6..4e2b34c1 100644
--- a/package/samplers/auto_sampler/LICENSE
+++ b/package/samplers/auto_sampler/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 <COPYRIGHT HOLDER, e.g., John Smith>
+Copyright (c) 2024 Preferred Networks, Inc.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
diff --git a/package/samplers/auto_sampler/requirements.txt b/package/samplers/auto_sampler/requirements.txt
new file mode 100644
index 00000000..10fd7a0f
--- /dev/null
+++ b/package/samplers/auto_sampler/requirements.txt
@@ -0,0 +1,3 @@
+scipy
+torch
+cmaes

From f101e0ffa4f0daafaa9acb6369f275149ad8ed1a Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 8 Oct 2024 08:06:28 +0200
Subject: [PATCH 04/51] Update the random seed handling

---
 package/samplers/auto_sampler/sampler.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 662949cf..bd21cb49 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -10,6 +10,7 @@
 from optuna.samplers import GPSampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
+from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.search_space import IntersectionSearchSpace
 from optuna.trial import TrialState
 
@@ -51,10 +52,12 @@ def objective(trial):
     """
 
     def __init__(self, seed: int | None = None) -> None:
-        self._seed = seed
-        self._sampler: BaseSampler = RandomSampler(seed=seed)
+        self._rng = LazyRandomState(seed)
+        seed_for_random_sampler = self._rng.rng.randint(1 << 32)
+        self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
 
     def reseed_rng(self) -> None:
+        self._rng.rng.seed()
         self._sampler.reseed_rng()
 
     def _include_conditional_param(self, study: Study) -> bool:
@@ -75,6 +78,7 @@ def _determine_sampler(
         if isinstance(self._sampler, TPESampler):
             return
 
+        seed = self._rng.rng.randint(1 << 32)
         if any(
             isinstance(d, CategoricalDistribution) for d in search_space.values()
         ) or self._include_conditional_param(study):
@@ -82,14 +86,14 @@ def _determine_sampler(
             # Use ``TPESampler`` if search space includes conditional or categorical parameters.
             # TBD: group=True?
             self._sampler = TPESampler(
-                seed=self._seed, multivariate=True, warn_independent_sampling=False
+                seed=seed, multivariate=True, warn_independent_sampling=False
             )
             return
 
         if trial.number < 250:
             # Use ``GPSampler`` if search space is numerical and n_trials <= 250.
             if not isinstance(self._sampler, GPSampler):
-                self._sampler = GPSampler(seed=self._seed)
+                self._sampler = GPSampler(seed=seed)
             return
 
         if not isinstance(self._sampler, CmaEsSampler):
@@ -101,7 +105,7 @@ def _determine_sampler(
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
             self._sampler = CmaEsSampler(
-                seed=self._seed, source_trials=warm_start_trials, warn_independent_sampling=False
+                seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
             )
 
     def infer_relative_search_space(

From 6df7a9585511817614a150c8191fa0e4c3175160 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 15 Oct 2024 11:22:13 +0200
Subject: [PATCH 05/51] Make WS-CMA-ES distributed setup robust

---
 package/samplers/auto_sampler/sampler.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index bd21cb49..83b62677 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -90,7 +90,9 @@ def _determine_sampler(
             )
             return
 
-        if trial.number < 250:
+        complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
+        complete_trials.sort(key=lambda trial: trial.datetime_complete)
+        if len(complete_trials) < 250:
             # Use ``GPSampler`` if search space is numerical and n_trials <= 250.
             if not isinstance(self._sampler, GPSampler):
                 self._sampler = GPSampler(seed=seed)
@@ -99,9 +101,7 @@ def _determine_sampler(
         if not isinstance(self._sampler, CmaEsSampler):
             # Use ``CmaEsSampler`` if search space is numerical and n_trials > 250.
             # Warm start CMA-ES with trials up to trial.number of 249.
-            warm_start_trials = study.get_trials(
-                deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED)
-            )
+            warm_start_trials = complete_trials[:250]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
             self._sampler = CmaEsSampler(

From f71caeb22e4bc236cbeeb533914d858d149bacc7 Mon Sep 17 00:00:00 2001
From: Toshihiko Yanase <toshihiko.yanase@gmail.com>
Date: Thu, 17 Oct 2024 00:43:55 +0900
Subject: [PATCH 06/51] Support multi-objective and constrained optimization.

---
 package/samplers/auto_sampler/README.md  |  4 +-
 package/samplers/auto_sampler/sampler.py | 50 ++++++++++++++++++++----
 2 files changed, 46 insertions(+), 8 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 40986a9c..ad90365f 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -19,8 +19,10 @@ This sampler currently accepts only `seed`.
 
 ## Installation
 
+This sampler requires optional dependencies of Optuna.
+
 ```shell
-$ pip install scipy torch cmaes
+$ pip install "optuna[optional]"
 ```
 
 ## Example
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 83b62677..e669a1fb 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -2,12 +2,14 @@
 
 from collections.abc import Sequence
 from typing import Any
+from typing import Callable
 from typing import TYPE_CHECKING
 
 from optuna.distributions import CategoricalDistribution
 from optuna.samplers import BaseSampler
 from optuna.samplers import CmaEsSampler
 from optuna.samplers import GPSampler
+from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
 from optuna.samplers._lazy_random_state import LazyRandomState
@@ -31,30 +33,50 @@ class AutoSampler(BaseSampler):
         .. testcode::
 
             import optuna
-            from optuna.samplers import AutoSampler
+            import optunahub
 
 
             def objective(trial):
                 x = trial.suggest_float("x", -5, 5)
                 return x**2
 
-
-            study = optuna.create_study(sampler=AutoSampler())
-            study.optimize(objective, n_trials=10)
+            module = optunahub.load_module("samplers/auto_sampler")
+            study = optuna.create_study(sampler=module.AutoSampler())
+            study.optimize(objective, n_trials=300)
 
     .. note::
-        This sampler might require ``scipy``, ``torch``, and ``cmaes``.
-        You can install these dependencies with ``pip install scipy torch cmaes``.
+        This sampler requires optional dependencies of Optuna.
+        You can install them with ``pip install "optuna[optional]"``.
+        Alternatively, you can install them with ``pip install -r https://hub.optuna.org/samplers/auto_sampler/requirements.txt``.
 
     Args:
         seed: Seed for random number generator.
+        constraints_func:
+            An optional function that computes the objective constraints. It must take a
+            :class:`~optuna.trial.FrozenTrial` and return the constraints. The return value must
+            be a sequence of :obj:`float` s. A value strictly larger than 0 means that a
+            constraints is violated. A value equal to or smaller than 0 is considered feasible.
+            If ``constraints_func`` returns more than one value for a trial, that trial is
+            considered feasible if and only if all values are equal to 0 or smaller.
+
+            The ``constraints_func`` will be evaluated after each successful trial.
+            The function won't be called when trials fail or they are pruned, but this behavior is
+            subject to change in the future releases.
+
+            .. note::
+                If you enable this feature, Optuna's default sampler will be selected automatically.
 
     """
 
-    def __init__(self, seed: int | None = None) -> None:
+    def __init__(
+        self,
+        seed: int | None = None,
+        constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
+    ) -> None:
         self._rng = LazyRandomState(seed)
         seed_for_random_sampler = self._rng.rng.randint(1 << 32)
         self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
+        self._constraints_func = constraints_func
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()
@@ -75,10 +97,24 @@ def _include_conditional_param(self, study: Study) -> bool:
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
+        if len(study.directions) > 1:
+            # Fallback to the default sampler if the study has multiple objectives.
+            if isinstance(self._sampler, NSGAIISampler):
+                return
+            # TODO(toshihikoyanase): add warning message about fallback.
+            self._sampler = NSGAIISampler(constraints_func=self._constraints_func)
+            return
+
         if isinstance(self._sampler, TPESampler):
             return
 
         seed = self._rng.rng.randint(1 << 32)
+        if self._constraints_func is not None:
+            # Fallback to the default sampler if the study has constraints.
+            # TODO(toshihikoyanase): add warning message about fallback.
+            self._sampler = TPESampler(seed=seed, constraines_func=self._constraints_func)
+            return
+
         if any(
             isinstance(d, CategoricalDistribution) for d in search_space.values()
         ) or self._include_conditional_param(study):

From 2a7d4bb047975a0365ee4d68c267eed9abd8d2e9 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 18 Oct 2024 05:51:57 +0200
Subject: [PATCH 07/51] Update auto sampler

---
 package/samplers/auto_sampler/sampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index e669a1fb..5010f4db 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -1,8 +1,8 @@
 from __future__ import annotations
 
+from collections.abc import Callable
 from collections.abc import Sequence
 from typing import Any
-from typing import Callable
 from typing import TYPE_CHECKING
 
 from optuna.distributions import CategoricalDistribution

From b5b204df97f0f7ba310a292d4677a8c699cf1066 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 08:18:31 +0200
Subject: [PATCH 08/51] Support constraints_func

---
 package/samplers/auto_sampler/sampler.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 5010f4db..af495c95 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -109,20 +109,20 @@ def _determine_sampler(
             return
 
         seed = self._rng.rng.randint(1 << 32)
-        if self._constraints_func is not None:
-            # Fallback to the default sampler if the study has constraints.
-            # TODO(toshihikoyanase): add warning message about fallback.
-            self._sampler = TPESampler(seed=seed, constraines_func=self._constraints_func)
-            return
-
-        if any(
-            isinstance(d, CategoricalDistribution) for d in search_space.values()
-        ) or self._include_conditional_param(study):
+        if (
+            self._constraints_func is not None
+            or any(isinstance(d, CategoricalDistribution) for d in search_space.values())
+            or self._include_conditional_param(study)
+        ):
             # NOTE(nabenabe): The statement above is always true for Trial#1.
             # Use ``TPESampler`` if search space includes conditional or categorical parameters.
             # TBD: group=True?
             self._sampler = TPESampler(
-                seed=seed, multivariate=True, warn_independent_sampling=False
+                seed=seed,
+                multivariate=True,
+                warn_independent_sampling=False,
+                constraines_func=self._constraints_func,
+                constant_liar=True,
             )
             return
 

From bf07aafd545a8222931506ab40fd1d9acc048911 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 08:29:58 +0200
Subject: [PATCH 09/51] Replace the random seed with the max int32

---
 package/samplers/auto_sampler/sampler.py | 30 +++++++++++++++---------
 1 file changed, 19 insertions(+), 11 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index af495c95..a0753247 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -23,6 +23,9 @@
     from optuna.trial import FrozenTrial
 
 
+MAXINT32 = (1 << 31) - 1
+
+
 class AutoSampler(BaseSampler):
     """Sampler automatically choosing an appropriate sampler based on search space.
 
@@ -74,7 +77,7 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_random_sampler = self._rng.rng.randint(1 << 32)
+        seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
         self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
         self._constraints_func = constraints_func
 
@@ -94,21 +97,26 @@ def _include_conditional_param(self, study: Study) -> bool:
 
         return False
 
+    def _determine_multi_objective_sampler(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> None:
+        if isinstance(self._sampler, NSGAIISampler):
+            return
+
+        seed = self._rng.rng.randint(MAXINT32)
+        self._sampler = NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
+
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
         if len(study.directions) > 1:
-            # Fallback to the default sampler if the study has multiple objectives.
-            if isinstance(self._sampler, NSGAIISampler):
-                return
-            # TODO(toshihikoyanase): add warning message about fallback.
-            self._sampler = NSGAIISampler(constraints_func=self._constraints_func)
+            self._determine_multi_objective_sampler(study, trial, search_space)
             return
 
         if isinstance(self._sampler, TPESampler):
             return
 
-        seed = self._rng.rng.randint(1 << 32)
+        seed = self._rng.rng.randint(MAXINT32)
         if (
             self._constraints_func is not None
             or any(isinstance(d, CategoricalDistribution) for d in search_space.values())
@@ -128,15 +136,15 @@ def _determine_sampler(
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) < 250:
-            # Use ``GPSampler`` if search space is numerical and n_trials <= 250.
+        if len(complete_trials) <= 250:
+            # Use ``GPSampler`` if search space is numerical and len(trials) <= 250.
             if not isinstance(self._sampler, GPSampler):
                 self._sampler = GPSampler(seed=seed)
             return
 
         if not isinstance(self._sampler, CmaEsSampler):
-            # Use ``CmaEsSampler`` if search space is numerical and n_trials > 250.
-            # Warm start CMA-ES with trials up to trial.number of 249.
+            # Use ``CmaEsSampler`` if search space is numerical and len(trials) > 250.
+            # Warm start CMA-ES with the first 250 complete trials.
             warm_start_trials = complete_trials[:250]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.

From c0372485ba6937419cb436a4964ae48d3047ba95 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 08:38:14 +0200
Subject: [PATCH 10/51] Add the switching algorithm for multi-objective
 optimization

---
 package/samplers/auto_sampler/sampler.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index a0753247..fe9c1172 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -13,6 +13,7 @@
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
 from optuna.samplers._lazy_random_state import LazyRandomState
+from optuna.samplers.nsgaii._sampler import _GENERATION_KEY
 from optuna.search_space import IntersectionSearchSpace
 from optuna.trial import TrialState
 
@@ -104,7 +105,21 @@ def _determine_multi_objective_sampler(
             return
 
         seed = self._rng.rng.randint(MAXINT32)
-        self._sampler = NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
+        if not isinstance(self._sampler, TPESampler):
+            self._sampler = TPESampler(
+                seed=seed,
+                multivariate=True,
+                warn_independent_sampling=False,
+                constraines_func=self._constraints_func,
+                constant_liar=True,
+            )
+            return
+
+        complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
+        complete_trials.sort(key=lambda trial: trial.datetime_complete)
+        if len(complete_trials) <= 1000:
+            # Use ``NSGAIISampler`` if search space is numerical and len(trials) <= 1000.
+            self._sampler = NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
@@ -172,6 +187,11 @@ def infer_relative_search_space(
     def sample_relative(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> dict[str, Any]:
+        if len(study.directions) > 1 and isinstance(self._sampler, TPESampler):
+            # NOTE(nabenabe): Set generation 0 so that NSGAIISampler can use the trial information
+            # obtained during the optimization using TPESampler.
+            study._storage.set_trial_system_attr(trial._trial_id, _GENERATION_KEY, 0)
+
         return self._sampler.sample_relative(study, trial, search_space)
 
     def sample_independent(

From ee0d97b491e1f4380125bb498373e5760a0ab344 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 08:40:05 +0200
Subject: [PATCH 11/51] Refactor the determination algo

---
 package/samplers/auto_sampler/sampler.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index fe9c1172..7c695b4c 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -121,13 +121,9 @@ def _determine_multi_objective_sampler(
             # Use ``NSGAIISampler`` if search space is numerical and len(trials) <= 1000.
             self._sampler = NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
 
-    def _determine_sampler(
+    def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
-        if len(study.directions) > 1:
-            self._determine_multi_objective_sampler(study, trial, search_space)
-            return
-
         if isinstance(self._sampler, TPESampler):
             return
 
@@ -167,6 +163,14 @@ def _determine_sampler(
                 seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
             )
 
+    def _determine_sampler(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> None:
+        if len(study.directions) == 1:
+            self._determine_single_objective_sampler(study, trial, search_space)
+        else:
+            self._determine_multi_objective_sampler(study, trial, search_space)
+
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial
     ) -> dict[str, BaseDistribution]:

From b3af183ec4a94d723fb54141c75d109acf4a0eb7 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 09:03:55 +0200
Subject: [PATCH 12/51] Add NSGAIIISampler

---
 package/samplers/auto_sampler/sampler.py | 27 ++++++++++++++++++------
 1 file changed, 21 insertions(+), 6 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 7c695b4c..d8636f6f 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -9,11 +9,13 @@
 from optuna.samplers import BaseSampler
 from optuna.samplers import CmaEsSampler
 from optuna.samplers import GPSampler
+from optuna.samplers import NSGAIIISampler
 from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
 from optuna.samplers._lazy_random_state import LazyRandomState
-from optuna.samplers.nsgaii._sampler import _GENERATION_KEY
+from optuna.samplers._nsgaiii._sampler import _GENERATION_KEY as NSGA3_GENERATION_KEY
+from optuna.samplers.nsgaii._sampler import _GENERATION_KEY as NSGA2_GENERATION_KEY
 from optuna.search_space import IntersectionSearchSpace
 from optuna.trial import TrialState
 
@@ -25,6 +27,7 @@
 
 
 MAXINT32 = (1 << 31) - 1
+THRESHOLD_OF_MANY_OBJECTIVES = 4
 
 
 class AutoSampler(BaseSampler):
@@ -101,7 +104,7 @@ def _include_conditional_param(self, study: Study) -> bool:
     def _determine_multi_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
-        if isinstance(self._sampler, NSGAIISampler):
+        if isinstance(self._sampler, (NSGAIISampler, NSGAIIISampler)):
             return
 
         seed = self._rng.rng.randint(MAXINT32)
@@ -110,16 +113,21 @@ def _determine_multi_objective_sampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
-                constraines_func=self._constraints_func,
+                constraints_func=self._constraints_func,
                 constant_liar=True,
             )
             return
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) <= 1000:
+        if len(complete_trials) >= 1000:
+            nsga_sampler_cls = (
+                NSGAIISampler
+                if len(study.directions) < THRESHOLD_OF_MANY_OBJECTIVES
+                else NSGAIIISampler
+            )
             # Use ``NSGAIISampler`` if search space is numerical and len(trials) <= 1000.
-            self._sampler = NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
+            self._sampler = nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
@@ -191,9 +199,16 @@ def infer_relative_search_space(
     def sample_relative(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> dict[str, Any]:
-        if len(study.directions) > 1 and isinstance(self._sampler, TPESampler):
+        n_objectives = len(study.directions)
+        if n_objectives > 1 and isinstance(self._sampler, TPESampler):
             # NOTE(nabenabe): Set generation 0 so that NSGAIISampler can use the trial information
             # obtained during the optimization using TPESampler.
+            # NOTE(nabenabe): Use NSGA-III for many objective problems.
+            _GENERATION_KEY = (
+                NSGA2_GENERATION_KEY
+                if n_objectives < THRESHOLD_OF_MANY_OBJECTIVES
+                else NSGA3_GENERATION_KEY
+            )
             study._storage.set_trial_system_attr(trial._trial_id, _GENERATION_KEY, 0)
 
         return self._sampler.sample_relative(study, trial, search_space)

From 317369c454af6357c5edfcaf0a6cabba047b0c20 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 09:08:22 +0200
Subject: [PATCH 13/51] Make magical numbers to class attributes

---
 package/samplers/auto_sampler/sampler.py | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index d8636f6f..87913448 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -31,6 +31,9 @@
 
 
 class AutoSampler(BaseSampler):
+    _N_COMPLETE_TRIALS_FOR_CMAES = 250
+    _N_COMPLETE_TRIALS_FOR_NSGA = 1000
+
     """Sampler automatically choosing an appropriate sampler based on search space.
 
     This sampler is convenient when you are unsure what sampler to use.
@@ -120,13 +123,14 @@ def _determine_multi_objective_sampler(
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) >= 1000:
+        if len(complete_trials) >= self._N_COMPLETE_TRIALS_FOR_NSGA:
             nsga_sampler_cls = (
                 NSGAIISampler
                 if len(study.directions) < THRESHOLD_OF_MANY_OBJECTIVES
                 else NSGAIIISampler
             )
-            # Use ``NSGAIISampler`` if search space is numerical and len(trials) <= 1000.
+            # Use ``NSGAIISampler`` if search space is numerical and
+            # len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
             self._sampler = nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
@@ -155,16 +159,18 @@ def _determine_single_objective_sampler(
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) <= 250:
-            # Use ``GPSampler`` if search space is numerical and len(trials) <= 250.
+        if len(complete_trials) <= self._N_COMPLETE_TRIALS_FOR_CMAES:
+            # Use ``GPSampler`` if search space is numerical and
+            # len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_CMAES.
             if not isinstance(self._sampler, GPSampler):
                 self._sampler = GPSampler(seed=seed)
             return
 
         if not isinstance(self._sampler, CmaEsSampler):
-            # Use ``CmaEsSampler`` if search space is numerical and len(trials) > 250.
-            # Warm start CMA-ES with the first 250 complete trials.
-            warm_start_trials = complete_trials[:250]
+            # Use ``CmaEsSampler`` if search space is numerical and
+            # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
+            # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
+            warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
             self._sampler = CmaEsSampler(

From 370d5227501f8233e9800c2fadcd7fcc8d70c8ce Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 09:16:51 +0200
Subject: [PATCH 14/51] Verify the implementation by a simple example

---
 package/samplers/auto_sampler/sampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 87913448..b9b76903 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -152,7 +152,7 @@ def _determine_single_objective_sampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
-                constraines_func=self._constraints_func,
+                constraints_func=self._constraints_func,
                 constant_liar=True,
             )
             return

From 45e4e35ae8136ef32a4d83b4d8f50c0012e15ff0 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 10:20:55 +0200
Subject: [PATCH 15/51] Add unittests for MO

---
 package/samplers/auto_sampler/sampler.py      | 11 ++-
 .../auto_sampler/tests/test_auto_sampler.py   | 96 +++++++++++++++++++
 2 files changed, 105 insertions(+), 2 deletions(-)
 create mode 100644 package/samplers/auto_sampler/tests/test_auto_sampler.py

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index b9b76903..ed216bf2 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -13,6 +13,7 @@
 from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
+from optuna.samplers._base import _process_constraints_after_trial
 from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.samplers._nsgaiii._sampler import _GENERATION_KEY as NSGA3_GENERATION_KEY
 from optuna.samplers.nsgaii._sampler import _GENERATION_KEY as NSGA2_GENERATION_KEY
@@ -159,9 +160,9 @@ def _determine_single_objective_sampler(
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) <= self._N_COMPLETE_TRIALS_FOR_CMAES:
+        if len(complete_trials) < self._N_COMPLETE_TRIALS_FOR_CMAES:
             # Use ``GPSampler`` if search space is numerical and
-            # len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_CMAES.
+            # len(complete_trials) < _N_COMPLETE_TRIALS_FOR_CMAES.
             if not isinstance(self._sampler, GPSampler):
                 self._sampler = GPSampler(seed=seed)
             return
@@ -238,4 +239,10 @@ def after_trial(
         state: TrialState,
         values: Sequence[float] | None,
     ) -> None:
+        assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED]
+        if isinstance(self._sampler, RandomSampler) and self._constraints_func is not None:
+            # NOTE(nabenabe): Since RandomSampler does not handle constraints, we need to
+            # separately set the constraints here.
+            _process_constraints_after_trial(self._constraints_func, study, trial, state)
+
         self._sampler.after_trial(study, trial, state, values)
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
new file mode 100644
index 00000000..6c166c8c
--- /dev/null
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -0,0 +1,96 @@
+from __future__ import annotations
+
+import optuna
+import optunahub
+import pytest
+
+
+optuna.logging.set_verbosity(optuna.logging.CRITICAL)
+
+
+AutoSampler = optunahub.load_local_module(
+    package="samplers/auto_sampler", registry_root="package/"
+).AutoSampler
+
+parametrize_constraints = pytest.mark.parametrize("use_constraint", [True, False])
+
+
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -5, 5)
+    y = trial.suggest_int("y", -5, 5)
+    return x**2 + y**2
+
+
+def multi_objective(trial: optuna.Trial) -> tuple[float, float]:
+    x = trial.suggest_float("x", -5, 5)
+    y = trial.suggest_int("y", -5, 5)
+    return x**2 + y**2, (x - 2) ** 2 + (y - 2) ** 2
+
+
+def many_objective(trial: optuna.Trial) -> tuple[float, float, float, float]:
+    x = trial.suggest_float("x", -5, 5)
+    y = trial.suggest_int("y", -5, 5)
+    return (x**2 + y**2, x**2 + y**2, (x - 2) ** 2 + (y - 2) ** 2, (x + 2) ** 2 + (y + 2) ** 2)
+
+
+def constraints_func(trial: optuna.trial.FrozenTrial) -> tuple[float]:
+    return (float(trial.params["x"] >= 2),)
+
+
+def _get_used_sampler_names(study: optuna.Study) -> list[str]:
+    return [
+        study._storage.get_trial_system_attrs(t._trial_id).get("auto:sampler")
+        for t in study.trials
+    ]
+
+
+def _check_constraints_of_all_trials(study: optuna.Study) -> None:
+    target_key = optuna.samplers._base._CONSTRAINTS_KEY
+    assert all(
+        target_key in study._storage.get_trial_system_attrs(t._trial_id) for t in study.trials
+    )
+
+
+@parametrize_constraints
+def test_choose_nsga3(use_constraint: bool) -> None:
+    n_trials_of_nsga = 100
+    n_trials_before_nsga = 100
+    auto_sampler = AutoSampler(constraints_func=constraints_func if use_constraint else None)
+    auto_sampler._N_COMPLETE_TRIALS_FOR_NSGA = n_trials_before_nsga
+    study = optuna.create_study(sampler=auto_sampler, directions=["minimize"] * 4)
+    study.optimize(many_objective, n_trials=n_trials_before_nsga + n_trials_of_nsga)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["TPESampler"] * (n_trials_before_nsga - 1) + [
+        "NSGAIIISampler"
+    ] * n_trials_of_nsga == sampler_names
+    if use_constraint:
+        _check_constraints_of_all_trials(study)
+
+
+@parametrize_constraints
+def test_choose_nsga2(use_constraint: bool) -> None:
+    n_trials_of_nsga = 100
+    n_trials_before_nsga = 100
+    auto_sampler = AutoSampler(constraints_func=constraints_func if use_constraint else None)
+    auto_sampler._N_COMPLETE_TRIALS_FOR_NSGA = n_trials_before_nsga
+    study = optuna.create_study(sampler=auto_sampler, directions=["minimize"] * 2)
+    study.optimize(multi_objective, n_trials=n_trials_before_nsga + n_trials_of_nsga)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["TPESampler"] * (n_trials_before_nsga - 1) + [
+        "NSGAIISampler"
+    ] * n_trials_of_nsga == sampler_names
+    if use_constraint:
+        _check_constraints_of_all_trials(study)
+
+
+def test_choose_cmaes() -> None:
+    n_trials_of_cmaes = 100
+    n_trials_before_cmaes = 20
+    auto_sampler = AutoSampler()
+    auto_sampler._N_COMPLETE_TRIALS_FOR_CMAES = n_trials_before_cmaes
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective, n_trials=n_trials_of_cmaes + n_trials_before_cmaes)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["GPSampler"] * (n_trials_before_cmaes - 1) + [
+        "CmaEsSampler"
+    ] * n_trials_of_cmaes == sampler_names

From 0949605b3375a546e6a78798e86c5ee70c17659b Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 10:41:49 +0200
Subject: [PATCH 16/51] Add unittests for single objective

---
 .../auto_sampler/tests/test_auto_sampler.py   | 49 +++++++++++++++++++
 1 file changed, 49 insertions(+)

diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 6c166c8c..f7ee3e3e 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -21,6 +21,23 @@ def objective(trial: optuna.Trial) -> float:
     return x**2 + y**2
 
 
+def objective_with_categorical(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -5, 5)
+    y = trial.suggest_int("y", -5, 5)
+    c = trial.suggest_categorical("c", [True, False])
+    return x**2 + y**2 if c else (x - 2) ** 2 + (y - 2) ** 2
+
+
+def objective_with_conditional(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -5, 5)
+    if trial.number < 15:
+        y = trial.suggest_int("y", -5, 5)
+        return x**2 + y**2
+    else:
+        z = trial.suggest_float("z", -5, 5)
+        return x**2 + z**2
+
+
 def multi_objective(trial: optuna.Trial) -> tuple[float, float]:
     x = trial.suggest_float("x", -5, 5)
     y = trial.suggest_int("y", -5, 5)
@@ -84,6 +101,7 @@ def test_choose_nsga2(use_constraint: bool) -> None:
 
 
 def test_choose_cmaes() -> None:
+    # This test must be performed with a numerical objective function.
     n_trials_of_cmaes = 100
     n_trials_before_cmaes = 20
     auto_sampler = AutoSampler()
@@ -94,3 +112,34 @@ def test_choose_cmaes() -> None:
     assert ["RandomSampler"] + ["GPSampler"] * (n_trials_before_cmaes - 1) + [
         "CmaEsSampler"
     ] * n_trials_of_cmaes == sampler_names
+
+
+def test_choose_tpe_in_single_with_constraints() -> None:
+    n_trials = 30
+    auto_sampler = AutoSampler(constraints_func=constraints_func)
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective, n_trials=n_trials)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["TPESampler"] * (n_trials - 1) == sampler_names
+
+
+def test_choose_tpe_with_categorical_params() -> None:
+    n_trials = 30
+    auto_sampler = AutoSampler()
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective_with_categorical, n_trials=n_trials)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["TPESampler"] * (n_trials - 1) == sampler_names
+
+
+def test_choose_tpe_with_conditional_params() -> None:
+    n_trials = 30
+    auto_sampler = AutoSampler()
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective_with_conditional, n_trials=n_trials)
+    sampler_names = _get_used_sampler_names(study)
+    # NOTE(nabenabe): When the conditional parameter is detected for the first time, GPSampler
+    # simply falls back to sample_independent, so sample_relative of GPSampler is called 15 times.
+    assert ["RandomSampler"] + ["GPSampler"] * 15 + ["TPESampler"] * (
+        n_trials - 16
+    ) == sampler_names

From 820460865e975813cc57b75d92df71327704bf0b Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 10:53:08 +0200
Subject: [PATCH 17/51] Enhance README

---
 package/samplers/auto_sampler/README.md       | 17 ++++++++++++++-
 package/samplers/auto_sampler/sampler.py      | 21 +++++++++++--------
 .../auto_sampler/tests/test_auto_sampler.py   |  1 +
 3 files changed, 29 insertions(+), 10 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index ad90365f..642a627a 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -15,13 +15,16 @@ This package provides a sampler based on Gaussian process-based Bayesian optimiz
 
 - AutoSampler
 
-This sampler currently accepts only `seed`.
+This sampler currently accepts only `seed` and `constraints_func`.
+`constraints_func` enables users to handle constraints along with the objective function.
+This argument follows the same convention as the other samplers, so please take a look at [the reference](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html).
 
 ## Installation
 
 This sampler requires optional dependencies of Optuna.
 
 ```shell
+# TODO
 $ pip install "optuna[optional]"
 ```
 
@@ -40,3 +43,15 @@ module = optunahub.load_module(package="samplers/auto_sampler")
 study = optuna.create_study(sampler=module.AutoSampler())
 study.optimize(objective, n_trials=300)
 ```
+
+### Test
+
+To execute the tests for `AutoSampler`, please run the following commands. The test file is provided in the package.
+
+```sh
+pip install pytest
+```
+
+```python
+python -m pytest package/samplers/tests/test_auto_sampler.py
+```
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index ed216bf2..57123f6a 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -13,7 +13,6 @@
 from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
-from optuna.samplers._base import _process_constraints_after_trial
 from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.samplers._nsgaiii._sampler import _GENERATION_KEY as NSGA3_GENERATION_KEY
 from optuna.samplers.nsgaii._sampler import _GENERATION_KEY as NSGA2_GENERATION_KEY
@@ -85,9 +84,19 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
-        self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
+        seed_for_first_sampler = self._rng.rng.randint(MAXINT32)
         self._constraints_func = constraints_func
+        self._sampler: BaseSampler = (
+            RandomSampler(seed=seed_for_first_sampler)
+            if constraints_func is None
+            else TPESampler(
+                seed=seed_for_first_sampler,
+                multivariate=True,
+                warn_independent_sampling=False,
+                constraints_func=self._constraints_func,
+                constant_liar=True,
+            )
+        )
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()
@@ -239,10 +248,4 @@ def after_trial(
         state: TrialState,
         values: Sequence[float] | None,
     ) -> None:
-        assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED]
-        if isinstance(self._sampler, RandomSampler) and self._constraints_func is not None:
-            # NOTE(nabenabe): Since RandomSampler does not handle constraints, we need to
-            # separately set the constraints here.
-            _process_constraints_after_trial(self._constraints_func, study, trial, state)
-
         self._sampler.after_trial(study, trial, state, values)
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index f7ee3e3e..b2d45620 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -5,6 +5,7 @@
 import pytest
 
 
+# TODO(nabaenabe): Add the CI for this sampler.
 optuna.logging.set_verbosity(optuna.logging.CRITICAL)
 
 

From baf4f7ef4983a077ab0884e57bf17eaca48f20e3 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 11:00:45 +0200
Subject: [PATCH 18/51] Fix the test path in README

---
 package/samplers/auto_sampler/README.md  |  2 +-
 package/samplers/auto_sampler/sampler.py | 21 +++++++++------------
 2 files changed, 10 insertions(+), 13 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 642a627a..bd322ebd 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -53,5 +53,5 @@ pip install pytest
 ```
 
 ```python
-python -m pytest package/samplers/tests/test_auto_sampler.py
+python -m pytest package/samplers/auto_sampler/tests/test_auto_sampler.py
 ```
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 57123f6a..ed216bf2 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -13,6 +13,7 @@
 from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
+from optuna.samplers._base import _process_constraints_after_trial
 from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.samplers._nsgaiii._sampler import _GENERATION_KEY as NSGA3_GENERATION_KEY
 from optuna.samplers.nsgaii._sampler import _GENERATION_KEY as NSGA2_GENERATION_KEY
@@ -84,19 +85,9 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_first_sampler = self._rng.rng.randint(MAXINT32)
+        seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
+        self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
         self._constraints_func = constraints_func
-        self._sampler: BaseSampler = (
-            RandomSampler(seed=seed_for_first_sampler)
-            if constraints_func is None
-            else TPESampler(
-                seed=seed_for_first_sampler,
-                multivariate=True,
-                warn_independent_sampling=False,
-                constraints_func=self._constraints_func,
-                constant_liar=True,
-            )
-        )
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()
@@ -248,4 +239,10 @@ def after_trial(
         state: TrialState,
         values: Sequence[float] | None,
     ) -> None:
+        assert state in [TrialState.COMPLETE, TrialState.FAIL, TrialState.PRUNED]
+        if isinstance(self._sampler, RandomSampler) and self._constraints_func is not None:
+            # NOTE(nabenabe): Since RandomSampler does not handle constraints, we need to
+            # separately set the constraints here.
+            _process_constraints_after_trial(self._constraints_func, study, trial, state)
+
         self._sampler.after_trial(study, trial, state, values)

From fdd1558b9bc4398b47b9e1475e7b3133bb3c8d59 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 11:03:42 +0200
Subject: [PATCH 19/51] Add thumbnail

---
 package/samplers/auto_sampler/README.md        |   2 ++
 .../samplers/auto_sampler/images/thumbnail.png | Bin 0 -> 307697 bytes
 2 files changed, 2 insertions(+)
 create mode 100644 package/samplers/auto_sampler/images/thumbnail.png

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index bd322ebd..ee08f0eb 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -11,6 +11,8 @@ license: MIT License
 
 This package provides a sampler based on Gaussian process-based Bayesian optimization. The sampler is highly sample-efficient, so it is suitable for computationally expensive optimization problems with a limited evaluation budget, such as hyperparameter optimization of machine learning algorithms.
 
+![Conceptual Visualization](images/thumbnail.png "Conceptual Visualization")
+
 ## Class or Function Names
 
 - AutoSampler
diff --git a/package/samplers/auto_sampler/images/thumbnail.png b/package/samplers/auto_sampler/images/thumbnail.png
new file mode 100644
index 0000000000000000000000000000000000000000..83a4fe56574dd1154371b8cde0e055c16c59786b
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From 0c1ed85c8479655c1dc0aa1756e9df1738e72e11 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 11:10:51 +0200
Subject: [PATCH 20/51] Fix README

---
 package/samplers/auto_sampler/README.md | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index ee08f0eb..50260544 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -9,7 +9,7 @@ license: MIT License
 
 ## Abstract
 
-This package provides a sampler based on Gaussian process-based Bayesian optimization. The sampler is highly sample-efficient, so it is suitable for computationally expensive optimization problems with a limited evaluation budget, such as hyperparameter optimization of machine learning algorithms.
+This package automatically selects an appropriate sampler for the provided search space based on the developers' recommendation.
 
 ![Conceptual Visualization](images/thumbnail.png "Conceptual Visualization")
 
@@ -19,15 +19,14 @@ This package provides a sampler based on Gaussian process-based Bayesian optimiz
 
 This sampler currently accepts only `seed` and `constraints_func`.
 `constraints_func` enables users to handle constraints along with the objective function.
-This argument follows the same convention as the other samplers, so please take a look at [the reference](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html).
+These arguments follow the same convention as the other samplers, so please take a look at [the reference](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html).
 
 ## Installation
 
 This sampler requires optional dependencies of Optuna.
 
 ```shell
-# TODO
-$ pip install "optuna[optional]"
+$ pip install optunahub cmaes torch
 ```
 
 ## Example

From 133ad558d16d90a736eb9175aececa1a8299d78b Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 22 Oct 2024 11:19:40 +0200
Subject: [PATCH 21/51] Fix README and sampler

---
 package/samplers/auto_sampler/README.md  |  2 +-
 package/samplers/auto_sampler/sampler.py | 11 +++--------
 2 files changed, 4 insertions(+), 9 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 50260544..866fccc1 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -26,7 +26,7 @@ These arguments follow the same convention as the other samplers, so please take
 This sampler requires optional dependencies of Optuna.
 
 ```shell
-$ pip install optunahub cmaes torch
+$ pip install optunahub cmaes torch scipy
 ```
 
 ## Example
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index ed216bf2..15caaa12 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -57,7 +57,7 @@ def objective(trial):
 
     .. note::
         This sampler requires optional dependencies of Optuna.
-        You can install them with ``pip install "optuna[optional]"``.
+        You can install them with ``pip install optunahub scipy torch cmaes``.
         Alternatively, you can install them with ``pip install -r https://hub.optuna.org/samplers/auto_sampler/requirements.txt``.
 
     Args:
@@ -73,10 +73,6 @@ def objective(trial):
             The ``constraints_func`` will be evaluated after each successful trial.
             The function won't be called when trials fail or they are pruned, but this behavior is
             subject to change in the future releases.
-
-            .. note::
-                If you enable this feature, Optuna's default sampler will be selected automatically.
-
     """
 
     def __init__(
@@ -130,8 +126,7 @@ def _determine_multi_objective_sampler(
                 if len(study.directions) < THRESHOLD_OF_MANY_OBJECTIVES
                 else NSGAIIISampler
             )
-            # Use ``NSGAIISampler`` if search space is numerical and
-            # len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
+            # Use NSGA-II/III if len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
             self._sampler = nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
@@ -208,7 +203,7 @@ def sample_relative(
     ) -> dict[str, Any]:
         n_objectives = len(study.directions)
         if n_objectives > 1 and isinstance(self._sampler, TPESampler):
-            # NOTE(nabenabe): Set generation 0 so that NSGAIISampler can use the trial information
+            # NOTE(nabenabe): Set generation 0 so that NSGA-II/III can use the trial information
             # obtained during the optimization using TPESampler.
             # NOTE(nabenabe): Use NSGA-III for many objective problems.
             _GENERATION_KEY = (

From 45039c1c1423769c6c96bb05550e97646d96f3fe Mon Sep 17 00:00:00 2001
From: Shuhei Watanabe <47781922+nabenabe0928@users.noreply.github.com>
Date: Thu, 24 Oct 2024 08:44:04 +0200
Subject: [PATCH 22/51] Update package/samplers/auto_sampler/sampler.py

Co-authored-by: Naoto Mizuno <gobou522@gmail.com>
---
 package/samplers/auto_sampler/sampler.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 15caaa12..cab1d7d2 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -77,6 +77,7 @@ def objective(trial):
 
     def __init__(
         self,
+        *,
         seed: int | None = None,
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:

From d06a851360d25532547b02947bf70aab393488bf Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 08:58:02 +0200
Subject: [PATCH 23/51] Remove thumbnail

---
 package/samplers/auto_sampler/README.md        |   2 --
 .../samplers/auto_sampler/images/thumbnail.png | Bin 307697 -> 0 bytes
 2 files changed, 2 deletions(-)
 delete mode 100644 package/samplers/auto_sampler/images/thumbnail.png

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 866fccc1..2bd91607 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -11,8 +11,6 @@ license: MIT License
 
 This package automatically selects an appropriate sampler for the provided search space based on the developers' recommendation.
 
-![Conceptual Visualization](images/thumbnail.png "Conceptual Visualization")
-
 ## Class or Function Names
 
 - AutoSampler
diff --git a/package/samplers/auto_sampler/images/thumbnail.png b/package/samplers/auto_sampler/images/thumbnail.png
deleted file mode 100644
index 83a4fe56574dd1154371b8cde0e055c16c59786b..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 72d661f2bbde05836e240f0dcda93bcaebe2adda Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 09:02:08 +0200
Subject: [PATCH 24/51] Address mizuno's comments

---
 package/samplers/auto_sampler/sampler.py | 38 +++++++++++-------------
 1 file changed, 17 insertions(+), 21 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index cab1d7d2..29159b2a 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -96,28 +96,23 @@ def _include_conditional_param(self, study: Study) -> bool:
             return False
 
         param_key = set(trials[0].params)
-        for t in trials:
-            if param_key != set(t.params):
-                return True
-
-        return False
+        return any(param_key != set(t.params) for t in trials)
 
     def _determine_multi_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> None:
+    ) -> BaseSampler:
         if isinstance(self._sampler, (NSGAIISampler, NSGAIIISampler)):
-            return
+            return self._sampler
 
         seed = self._rng.rng.randint(MAXINT32)
         if not isinstance(self._sampler, TPESampler):
-            self._sampler = TPESampler(
+            return TPESampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
                 constraints_func=self._constraints_func,
                 constant_liar=True,
             )
-            return
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
@@ -128,13 +123,15 @@ def _determine_multi_objective_sampler(
                 else NSGAIIISampler
             )
             # Use NSGA-II/III if len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
-            self._sampler = nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
+            return nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
+
+        return self._sampler
 
     def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> None:
+    ) -> BaseSampler | None:
         if isinstance(self._sampler, TPESampler):
-            return
+            return self._sampler
 
         seed = self._rng.rng.randint(MAXINT32)
         if (
@@ -145,14 +142,13 @@ def _determine_single_objective_sampler(
             # NOTE(nabenabe): The statement above is always true for Trial#1.
             # Use ``TPESampler`` if search space includes conditional or categorical parameters.
             # TBD: group=True?
-            self._sampler = TPESampler(
+            return TPESampler(
                 seed=seed,
                 multivariate=True,
                 warn_independent_sampling=False,
                 constraints_func=self._constraints_func,
                 constant_liar=True,
             )
-            return
 
         complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
@@ -160,27 +156,27 @@ def _determine_single_objective_sampler(
             # Use ``GPSampler`` if search space is numerical and
             # len(complete_trials) < _N_COMPLETE_TRIALS_FOR_CMAES.
             if not isinstance(self._sampler, GPSampler):
-                self._sampler = GPSampler(seed=seed)
-            return
-
-        if not isinstance(self._sampler, CmaEsSampler):
+                return GPSampler(seed=seed)
+        elif not isinstance(self._sampler, CmaEsSampler):
             # Use ``CmaEsSampler`` if search space is numerical and
             # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
             # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
             warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
-            self._sampler = CmaEsSampler(
+            return CmaEsSampler(
                 seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
             )
 
+        return self._sampler
+
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> None:
         if len(study.directions) == 1:
-            self._determine_single_objective_sampler(study, trial, search_space)
+            self._sampler = self._determine_single_objective_sampler(study, trial, search_space)
         else:
-            self._determine_multi_objective_sampler(study, trial, search_space)
+            self._sampler = self._determine_multi_objective_sampler(study, trial, search_space)
 
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial

From f50962019da92ed7d881b9f9c372483df543ca44 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 09:09:20 +0200
Subject: [PATCH 25/51] Address mizuno's comments

---
 package/samplers/auto_sampler/sampler.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 29159b2a..30bdd04f 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -125,11 +125,11 @@ def _determine_multi_objective_sampler(
             # Use NSGA-II/III if len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
             return nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
 
-        return self._sampler
+        return self._sampler  # No update happens to self._sampler.
 
     def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> BaseSampler | None:
+    ) -> BaseSampler:
         if isinstance(self._sampler, TPESampler):
             return self._sampler
 
@@ -168,7 +168,7 @@ def _determine_single_objective_sampler(
                 seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
             )
 
-        return self._sampler
+        return self._sampler  # No update happens to self._sampler.
 
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]

From ae983bca8f81e8d6759320bbd51875a1967712c5 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 09:29:39 +0200
Subject: [PATCH 26/51] Address mizuno's comments

---
 package/samplers/auto_sampler/sampler.py | 23 ++++++++++++-----------
 1 file changed, 12 insertions(+), 11 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 30bdd04f..41f924ef 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -29,6 +29,7 @@
 
 MAXINT32 = (1 << 31) - 1
 THRESHOLD_OF_MANY_OBJECTIVES = 4
+SAMPLER_KEY = "auto:sampler"
 
 
 class AutoSampler(BaseSampler):
@@ -172,27 +173,19 @@ def _determine_single_objective_sampler(
 
     def _determine_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> None:
+    ) -> BaseSampler:
         if len(study.directions) == 1:
-            self._sampler = self._determine_single_objective_sampler(study, trial, search_space)
+            return self._determine_single_objective_sampler(study, trial, search_space)
         else:
-            self._sampler = self._determine_multi_objective_sampler(study, trial, search_space)
+            return self._determine_multi_objective_sampler(study, trial, search_space)
 
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial
     ) -> dict[str, BaseDistribution]:
         search_space = IntersectionSearchSpace().calculate(study)
-        sampler_key = "auto:sampler"
         if len(search_space) == 0:
-            study._storage.set_trial_system_attr(
-                trial._trial_id, sampler_key, self._sampler.__class__.__name__
-            )
             return {}
 
-        self._determine_sampler(study, trial, search_space)
-        study._storage.set_trial_system_attr(
-            trial._trial_id, sampler_key, self._sampler.__class__.__name__
-        )
         return self._sampler.infer_relative_search_space(study, trial)
 
     def sample_relative(
@@ -222,6 +215,14 @@ def sample_independent(
         return self._sampler.sample_independent(study, trial, param_name, param_distribution)
 
     def before_trial(self, study: Study, trial: FrozenTrial) -> None:
+        search_space = IntersectionSearchSpace().calculate(study)
+        if len(search_space) != 0:
+            # Cannot determine sampler in before we know the search space, i.e. len(search_space) == 0.
+            self._sampler = self._determine_sampler(study, trial, search_space)
+
+        study._storage.set_trial_system_attr(
+            trial._trial_id, SAMPLER_KEY, self._sampler.__class__.__name__
+        )
         self._sampler.before_trial(study, trial)
 
     def after_trial(

From e9e7b959c3d51daa663945036205876ef5fe1ccf Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 09:43:37 +0200
Subject: [PATCH 27/51] Address mizuno's comments

---
 package/samplers/auto_sampler/sampler.py | 11 ++++-------
 1 file changed, 4 insertions(+), 7 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index 41f924ef..d4c15539 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -182,10 +182,6 @@ def _determine_sampler(
     def infer_relative_search_space(
         self, study: Study, trial: FrozenTrial
     ) -> dict[str, BaseDistribution]:
-        search_space = IntersectionSearchSpace().calculate(study)
-        if len(search_space) == 0:
-            return {}
-
         return self._sampler.infer_relative_search_space(study, trial)
 
     def sample_relative(
@@ -215,9 +211,10 @@ def sample_independent(
         return self._sampler.sample_independent(study, trial, param_name, param_distribution)
 
     def before_trial(self, study: Study, trial: FrozenTrial) -> None:
-        search_space = IntersectionSearchSpace().calculate(study)
-        if len(search_space) != 0:
-            # Cannot determine sampler in before we know the search space, i.e. len(search_space) == 0.
+        # NOTE(nabenabe): Use the states used in IntersectionSearchSpace().calculate.
+        states_of_interest = [TrialState.COMPLETE, TrialState.WAITING, TrialState.RUNNING]
+        if len(study._get_trials(deepcopy=False, states=states_of_interest)) != 0:
+            search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
 
         study._storage.set_trial_system_attr(

From 2bfad4b5a797abe46fa2cdc29e3a5e4fe1c373be Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 24 Oct 2024 10:02:17 +0200
Subject: [PATCH 28/51] Fix

---
 package/samplers/auto_sampler/sampler.py                 | 3 +--
 package/samplers/auto_sampler/tests/test_auto_sampler.py | 3 +++
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index d4c15539..f2bf676e 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -211,8 +211,7 @@ def sample_independent(
         return self._sampler.sample_independent(study, trial, param_name, param_distribution)
 
     def before_trial(self, study: Study, trial: FrozenTrial) -> None:
-        # NOTE(nabenabe): Use the states used in IntersectionSearchSpace().calculate.
-        states_of_interest = [TrialState.COMPLETE, TrialState.WAITING, TrialState.RUNNING]
+        states_of_interest = [TrialState.COMPLETE, TrialState.WAITING]
         if len(study._get_trials(deepcopy=False, states=states_of_interest)) != 0:
             search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index b2d45620..901526a4 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -144,3 +144,6 @@ def test_choose_tpe_with_conditional_params() -> None:
     assert ["RandomSampler"] + ["GPSampler"] * 15 + ["TPESampler"] * (
         n_trials - 16
     ) == sampler_names
+
+
+# TODO: Add a test with enqueue_trial.

From 3cb7f73bd4f46b3a2b0e3278226dd95476cfe187 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 04:06:33 +0200
Subject: [PATCH 29/51] Add TODO and fix README

---
 package/samplers/auto_sampler/README.md | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 2bd91607..11399bb4 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -30,12 +30,14 @@ $ pip install optunahub cmaes torch scipy
 ## Example
 
 ```python
+import optuna
 import optunahub
 
 
 def objective(trial):
   x = trial.suggest_float("x", -5, 5)
-  return x**2
+  y = trial.suggest_float("y", -5, 5)
+  return x**2 + y**2
 
 
 module = optunahub.load_module(package="samplers/auto_sampler")
@@ -54,3 +56,9 @@ pip install pytest
 ```python
 python -m pytest package/samplers/auto_sampler/tests/test_auto_sampler.py
 ```
+
+## Others
+
+### Algorithm Switch Heurtistic in AutoSampler
+
+TODO

From 8ac08bed4d69b099267318e438bae11fb9cb415c Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 04:07:35 +0200
Subject: [PATCH 30/51] Add TODO to tests

---
 package/samplers/auto_sampler/tests/test_auto_sampler.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 901526a4..155a2b43 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -146,4 +146,5 @@ def test_choose_tpe_with_conditional_params() -> None:
     ) == sampler_names
 
 
+# TODO(c-bata): Check the test_samplers.py to cover a wider range of edge cases.
 # TODO: Add a test with enqueue_trial.

From 5b9c9a7bccded6b6037528a569615b30a2667287 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 04:23:03 +0200
Subject: [PATCH 31/51] Remove TPESampler for MO

---
 package/samplers/auto_sampler/sampler.py      | 47 +++----------------
 .../auto_sampler/tests/test_auto_sampler.py   | 24 ++++------
 2 files changed, 14 insertions(+), 57 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index f2bf676e..ba68d9ba 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -9,14 +9,11 @@
 from optuna.samplers import BaseSampler
 from optuna.samplers import CmaEsSampler
 from optuna.samplers import GPSampler
-from optuna.samplers import NSGAIIISampler
 from optuna.samplers import NSGAIISampler
 from optuna.samplers import RandomSampler
 from optuna.samplers import TPESampler
 from optuna.samplers._base import _process_constraints_after_trial
 from optuna.samplers._lazy_random_state import LazyRandomState
-from optuna.samplers._nsgaiii._sampler import _GENERATION_KEY as NSGA3_GENERATION_KEY
-from optuna.samplers.nsgaii._sampler import _GENERATION_KEY as NSGA2_GENERATION_KEY
 from optuna.search_space import IntersectionSearchSpace
 from optuna.trial import TrialState
 
@@ -28,13 +25,11 @@
 
 
 MAXINT32 = (1 << 31) - 1
-THRESHOLD_OF_MANY_OBJECTIVES = 4
 SAMPLER_KEY = "auto:sampler"
 
 
 class AutoSampler(BaseSampler):
     _N_COMPLETE_TRIALS_FOR_CMAES = 250
-    _N_COMPLETE_TRIALS_FOR_NSGA = 1000
 
     """Sampler automatically choosing an appropriate sampler based on search space.
 
@@ -102,31 +97,11 @@ def _include_conditional_param(self, study: Study) -> bool:
     def _determine_multi_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> BaseSampler:
-        if isinstance(self._sampler, (NSGAIISampler, NSGAIIISampler)):
+        if isinstance(self._sampler, NSGAIISampler):
             return self._sampler
 
         seed = self._rng.rng.randint(MAXINT32)
-        if not isinstance(self._sampler, TPESampler):
-            return TPESampler(
-                seed=seed,
-                multivariate=True,
-                warn_independent_sampling=False,
-                constraints_func=self._constraints_func,
-                constant_liar=True,
-            )
-
-        complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
-        complete_trials.sort(key=lambda trial: trial.datetime_complete)
-        if len(complete_trials) >= self._N_COMPLETE_TRIALS_FOR_NSGA:
-            nsga_sampler_cls = (
-                NSGAIISampler
-                if len(study.directions) < THRESHOLD_OF_MANY_OBJECTIVES
-                else NSGAIIISampler
-            )
-            # Use NSGA-II/III if len(complete_trials) <= _N_COMPLETE_TRIALS_FOR_NSGA.
-            return nsga_sampler_cls(constraints_func=self._constraints_func, seed=seed)
-
-        return self._sampler  # No update happens to self._sampler.
+        return NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
@@ -187,18 +162,6 @@ def infer_relative_search_space(
     def sample_relative(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> dict[str, Any]:
-        n_objectives = len(study.directions)
-        if n_objectives > 1 and isinstance(self._sampler, TPESampler):
-            # NOTE(nabenabe): Set generation 0 so that NSGA-II/III can use the trial information
-            # obtained during the optimization using TPESampler.
-            # NOTE(nabenabe): Use NSGA-III for many objective problems.
-            _GENERATION_KEY = (
-                NSGA2_GENERATION_KEY
-                if n_objectives < THRESHOLD_OF_MANY_OBJECTIVES
-                else NSGA3_GENERATION_KEY
-            )
-            study._storage.set_trial_system_attr(trial._trial_id, _GENERATION_KEY, 0)
-
         return self._sampler.sample_relative(study, trial, search_space)
 
     def sample_independent(
@@ -211,8 +174,10 @@ def sample_independent(
         return self._sampler.sample_independent(study, trial, param_name, param_distribution)
 
     def before_trial(self, study: Study, trial: FrozenTrial) -> None:
-        states_of_interest = [TrialState.COMPLETE, TrialState.WAITING]
-        if len(study._get_trials(deepcopy=False, states=states_of_interest)) != 0:
+        # NOTE(nabenabe): Sampler must be updated in this method. If, for example, it is updated in
+        # infer_relative_search_space, the sampler for before_trial and that for sample_relative,
+        # after_trial might be different, meaning that the sampling routine could be incompatible.
+        if len(study._get_trials(deepcopy=False, states=(TrialState.COMPLETE,))) != 0:
             search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
 
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 155a2b43..4685f2d3 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -70,33 +70,25 @@ def _check_constraints_of_all_trials(study: optuna.Study) -> None:
 
 
 @parametrize_constraints
-def test_choose_nsga3(use_constraint: bool) -> None:
-    n_trials_of_nsga = 100
-    n_trials_before_nsga = 100
+def test_choose_for_many_objective(use_constraint: bool) -> None:
+    n_trials = 200
     auto_sampler = AutoSampler(constraints_func=constraints_func if use_constraint else None)
-    auto_sampler._N_COMPLETE_TRIALS_FOR_NSGA = n_trials_before_nsga
     study = optuna.create_study(sampler=auto_sampler, directions=["minimize"] * 4)
-    study.optimize(many_objective, n_trials=n_trials_before_nsga + n_trials_of_nsga)
+    study.optimize(many_objective, n_trials=n_trials)
     sampler_names = _get_used_sampler_names(study)
-    assert ["RandomSampler"] + ["TPESampler"] * (n_trials_before_nsga - 1) + [
-        "NSGAIIISampler"
-    ] * n_trials_of_nsga == sampler_names
+    assert ["RandomSampler"] + ["NSGAIISampler"] * (n_trials - 1) == sampler_names
     if use_constraint:
         _check_constraints_of_all_trials(study)
 
 
 @parametrize_constraints
-def test_choose_nsga2(use_constraint: bool) -> None:
-    n_trials_of_nsga = 100
-    n_trials_before_nsga = 100
+def test_choose_for_multi_objective(use_constraint: bool) -> None:
+    n_trials = 200
     auto_sampler = AutoSampler(constraints_func=constraints_func if use_constraint else None)
-    auto_sampler._N_COMPLETE_TRIALS_FOR_NSGA = n_trials_before_nsga
     study = optuna.create_study(sampler=auto_sampler, directions=["minimize"] * 2)
-    study.optimize(multi_objective, n_trials=n_trials_before_nsga + n_trials_of_nsga)
+    study.optimize(multi_objective, n_trials=n_trials)
     sampler_names = _get_used_sampler_names(study)
-    assert ["RandomSampler"] + ["TPESampler"] * (n_trials_before_nsga - 1) + [
-        "NSGAIISampler"
-    ] * n_trials_of_nsga == sampler_names
+    assert ["RandomSampler"] + ["NSGAIISampler"] * (n_trials - 1) == sampler_names
     if use_constraint:
         _check_constraints_of_all_trials(study)
 

From 7e75683119f64dab0130c1f130470b4092190492 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 04:42:41 +0200
Subject: [PATCH 32/51] Make sampler thread local

---
 package/samplers/auto_sampler/sampler.py | 18 +++++++++++++++++-
 1 file changed, 17 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index ba68d9ba..b317a8f8 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -2,6 +2,7 @@
 
 from collections.abc import Callable
 from collections.abc import Sequence
+import threading
 from typing import Any
 from typing import TYPE_CHECKING
 
@@ -28,6 +29,12 @@
 SAMPLER_KEY = "auto:sampler"
 
 
+class _ThreadLocalSampler(threading.local):
+    def __init__(self, sampler: BaseSampler) -> None:
+        self._sampler = sampler
+        super().__init__()
+
+
 class AutoSampler(BaseSampler):
     _N_COMPLETE_TRIALS_FOR_CMAES = 250
 
@@ -79,9 +86,18 @@ def __init__(
     ) -> None:
         self._rng = LazyRandomState(seed)
         seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
-        self._sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
+        sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
+        self._thread_local_sampler = _ThreadLocalSampler(sampler)
         self._constraints_func = constraints_func
 
+    @property
+    def _sampler(self) -> BaseSampler:
+        return self._thread_local_sampler._sampler
+
+    @_sampler.setter
+    def _sampler(self, sampler: BaseSampler) -> None:
+        self._thread_local_sampler._sampler = sampler
+
     def reseed_rng(self) -> None:
         self._rng.rng.seed()
         self._sampler.reseed_rng()

From e7362a091c33731b51ac8c81841fd1a72c638d97 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 06:06:25 +0200
Subject: [PATCH 33/51] Add logging

---
 package/samplers/auto_sampler/sampler.py                 | 8 +++++---
 package/samplers/auto_sampler/tests/test_auto_sampler.py | 3 +--
 2 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index b317a8f8..b52a4eb8 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -7,6 +7,7 @@
 from typing import TYPE_CHECKING
 
 from optuna.distributions import CategoricalDistribution
+from optuna.logging import get_logger
 from optuna.samplers import BaseSampler
 from optuna.samplers import CmaEsSampler
 from optuna.samplers import GPSampler
@@ -27,6 +28,7 @@
 
 MAXINT32 = (1 << 31) - 1
 SAMPLER_KEY = "auto:sampler"
+_logger = get_logger(f"optuna.{__name__}")
 
 
 class _ThreadLocalSampler(threading.local):
@@ -197,9 +199,9 @@ def before_trial(self, study: Study, trial: FrozenTrial) -> None:
             search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
 
-        study._storage.set_trial_system_attr(
-            trial._trial_id, SAMPLER_KEY, self._sampler.__class__.__name__
-        )
+        sampler_name = self._sampler.__class__.__name__
+        _logger.debug(f"Sample trial#{trial.number} with {sampler_name}.")
+        study._storage.set_trial_system_attr(trial._trial_id, SAMPLER_KEY, sampler_name)
         self._sampler.before_trial(study, trial)
 
     def after_trial(
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 4685f2d3..94d5e390 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -6,8 +6,7 @@
 
 
 # TODO(nabaenabe): Add the CI for this sampler.
-optuna.logging.set_verbosity(optuna.logging.CRITICAL)
-
+# optuna.logging.set_verbosity(optuna.logging.CRITICAL)
 
 AutoSampler = optunahub.load_local_module(
     package="samplers/auto_sampler", registry_root="package/"

From bc205ca1b3c136b3639d1a766cc30258e931e565 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 06:16:18 +0200
Subject: [PATCH 34/51] Add test_samplers

---
 .../auto_sampler/tests/test_auto_sampler.py   |    4 -
 .../auto_sampler/tests/test_sampler.py        | 1063 +++++++++++++++++
 2 files changed, 1063 insertions(+), 4 deletions(-)
 create mode 100644 package/samplers/auto_sampler/tests/test_sampler.py

diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 94d5e390..0eab42b8 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -135,7 +135,3 @@ def test_choose_tpe_with_conditional_params() -> None:
     assert ["RandomSampler"] + ["GPSampler"] * 15 + ["TPESampler"] * (
         n_trials - 16
     ) == sampler_names
-
-
-# TODO(c-bata): Check the test_samplers.py to cover a wider range of edge cases.
-# TODO: Add a test with enqueue_trial.
diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
new file mode 100644
index 00000000..b0f8dbeb
--- /dev/null
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -0,0 +1,1063 @@
+from __future__ import annotations
+
+from collections.abc import Callable
+from collections.abc import Sequence
+import multiprocessing
+from multiprocessing.managers import DictProxy
+import os
+import pickle
+from typing import Any
+from unittest.mock import patch
+import warnings
+
+from _pytest.fixtures import SubRequest
+from _pytest.mark.structures import MarkDecorator
+import numpy as np
+import optuna
+from optuna.distributions import BaseDistribution
+from optuna.distributions import CategoricalChoiceType
+from optuna.distributions import CategoricalDistribution
+from optuna.distributions import FloatDistribution
+from optuna.distributions import IntDistribution
+from optuna.samplers import BaseSampler
+from optuna.samplers._lazy_random_state import LazyRandomState
+from optuna.study import Study
+from optuna.testing.objectives import fail_objective
+from optuna.testing.objectives import pruned_objective
+from optuna.trial import FrozenTrial
+from optuna.trial import Trial
+from optuna.trial import TrialState
+import pytest
+
+
+# NOTE(nabenabe): This file content is mostly copied from the Optuna repository.
+
+
+def get_gp_sampler(
+    *, n_startup_trials: int = 0, deterministic_objective: bool = False, seed: int | None = None
+) -> optuna.samplers.GPSampler:
+    return optuna.samplers.GPSampler(
+        n_startup_trials=n_startup_trials,
+        seed=seed,
+        deterministic_objective=deterministic_objective,
+    )
+
+
+parametrize_sampler = pytest.mark.parametrize(
+    "sampler_class",
+    [
+        optuna.samplers.RandomSampler,
+        lambda: optuna.samplers.TPESampler(n_startup_trials=0),
+        lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True),
+        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
+        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0, use_separable_cma=True),
+        optuna.samplers.NSGAIISampler,
+        optuna.samplers.NSGAIIISampler,
+        optuna.samplers.QMCSampler,
+        lambda: get_gp_sampler(n_startup_trials=0),
+        lambda: get_gp_sampler(n_startup_trials=0, deterministic_objective=True),
+    ],
+)
+parametrize_relative_sampler = pytest.mark.parametrize(
+    "relative_sampler_class",
+    [
+        lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True),
+        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
+        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0, use_separable_cma=True),
+        lambda: get_gp_sampler(n_startup_trials=0),
+        lambda: get_gp_sampler(n_startup_trials=0, deterministic_objective=True),
+    ],
+)
+parametrize_multi_objective_sampler = pytest.mark.parametrize(
+    "multi_objective_sampler_class",
+    [
+        optuna.samplers.NSGAIISampler,
+        optuna.samplers.NSGAIIISampler,
+        lambda: optuna.samplers.TPESampler(n_startup_trials=0),
+    ],
+)
+
+
+sampler_class_with_seed: dict[str, Callable[[int], BaseSampler]] = {
+    "RandomSampler": lambda seed: optuna.samplers.RandomSampler(seed=seed),
+    "TPESampler": lambda seed: optuna.samplers.TPESampler(seed=seed),
+    "multivariate TPESampler": lambda seed: optuna.samplers.TPESampler(
+        multivariate=True, seed=seed
+    ),
+    "CmaEsSampler": lambda seed: optuna.samplers.CmaEsSampler(seed=seed),
+    "separable CmaEsSampler": lambda seed: optuna.samplers.CmaEsSampler(
+        seed=seed, use_separable_cma=True
+    ),
+    "NSGAIISampler": lambda seed: optuna.samplers.NSGAIISampler(seed=seed),
+    "NSGAIIISampler": lambda seed: optuna.samplers.NSGAIIISampler(seed=seed),
+    "QMCSampler": lambda seed: optuna.samplers.QMCSampler(seed=seed),
+    "GPSampler": lambda seed: get_gp_sampler(seed=seed, n_startup_trials=0),
+}
+param_sampler_with_seed = []
+param_sampler_name_with_seed = []
+for sampler_name, sampler_class in sampler_class_with_seed.items():
+    param_sampler_with_seed.append(pytest.param(sampler_class, id=sampler_name))
+    param_sampler_name_with_seed.append(pytest.param(sampler_name))
+parametrize_sampler_with_seed = pytest.mark.parametrize("sampler_class", param_sampler_with_seed)
+parametrize_sampler_name_with_seed = pytest.mark.parametrize(
+    "sampler_name", param_sampler_name_with_seed
+)
+
+
+@pytest.mark.parametrize(
+    "sampler_class,expected_has_rng,expected_has_another_sampler",
+    [
+        (optuna.samplers.RandomSampler, True, False),
+        (lambda: optuna.samplers.TPESampler(n_startup_trials=0), True, True),
+        (lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True), True, True),
+        (lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0), True, True),
+        (optuna.samplers.NSGAIISampler, True, True),
+        (optuna.samplers.NSGAIIISampler, True, True),
+        (
+            lambda: optuna.samplers.PartialFixedSampler(
+                fixed_params={"x": 0}, base_sampler=optuna.samplers.RandomSampler()
+            ),
+            False,
+            True,
+        ),
+        (lambda: optuna.samplers.GridSampler(search_space={"x": [0]}), True, False),
+        (lambda: optuna.samplers.QMCSampler(), False, True),
+        (lambda: get_gp_sampler(n_startup_trials=0), True, True),
+    ],
+)
+def test_sampler_reseed_rng(
+    sampler_class: Callable[[], BaseSampler],
+    expected_has_rng: bool,
+    expected_has_another_sampler: bool,
+) -> None:
+    def _extract_attr_name_from_sampler_by_cls(sampler: BaseSampler, cls: Any) -> str | None:
+        for name, attr in sampler.__dict__.items():
+            if isinstance(attr, cls):
+                return name
+        return None
+
+    sampler = sampler_class()
+
+    rng_name = _extract_attr_name_from_sampler_by_cls(sampler, LazyRandomState)
+    has_rng = rng_name is not None
+    assert expected_has_rng == has_rng
+    if has_rng:
+        rng_name = str(rng_name)
+        original_random_state = sampler.__dict__[rng_name].rng.get_state()
+        sampler.reseed_rng()
+        random_state = sampler.__dict__[rng_name].rng.get_state()
+        if not isinstance(sampler, optuna.samplers.CmaEsSampler):
+            assert str(original_random_state) != str(random_state)
+        else:
+            # CmaEsSampler has a RandomState that is not reseed by its reseed_rng method.
+            assert str(original_random_state) == str(random_state)
+
+    had_sampler_name = _extract_attr_name_from_sampler_by_cls(sampler, BaseSampler)
+    has_another_sampler = had_sampler_name is not None
+    assert expected_has_another_sampler == has_another_sampler
+
+    if has_another_sampler:
+        had_sampler_name = str(had_sampler_name)
+        had_sampler = sampler.__dict__[had_sampler_name]
+        had_sampler_rng_name = _extract_attr_name_from_sampler_by_cls(had_sampler, LazyRandomState)
+        original_had_sampler_random_state = had_sampler.__dict__[
+            had_sampler_rng_name
+        ].rng.get_state()
+        with patch.object(
+            had_sampler,
+            "reseed_rng",
+            wraps=had_sampler.reseed_rng,
+        ) as mock_object:
+            sampler.reseed_rng()
+            assert mock_object.call_count == 1
+
+        had_sampler = sampler.__dict__[had_sampler_name]
+        had_sampler_random_state = had_sampler.__dict__[had_sampler_rng_name].rng.get_state()
+        assert str(original_had_sampler_random_state) != str(had_sampler_random_state)
+
+
+def parametrize_suggest_method(name: str) -> MarkDecorator:
+    return pytest.mark.parametrize(
+        f"suggest_method_{name}",
+        [
+            lambda t: t.suggest_float(name, 0, 10),
+            lambda t: t.suggest_int(name, 0, 10),
+            lambda t: t.suggest_categorical(name, [0, 1, 2]),
+            lambda t: t.suggest_float(name, 0, 10, step=0.5),
+            lambda t: t.suggest_float(name, 1e-7, 10, log=True),
+            lambda t: t.suggest_int(name, 1, 10, log=True),
+        ],
+    )
+
+
+@pytest.mark.parametrize(
+    "sampler_class",
+    [
+        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
+    ],
+)
+def test_raise_error_for_samplers_during_multi_objectives(
+    sampler_class: Callable[[], BaseSampler],
+) -> None:
+    study = optuna.study.create_study(directions=["maximize", "maximize"], sampler=sampler_class())
+
+    distribution = FloatDistribution(0.0, 1.0)
+    with pytest.raises(ValueError):
+        study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution)
+
+    with pytest.raises(ValueError):
+        trial = _create_new_trial(study)
+        study.sampler.sample_relative(
+            study, trial, study.sampler.infer_relative_search_space(study, trial)
+        )
+
+
+@pytest.mark.parametrize("seed", [0, 169208])
+def test_pickle_random_sampler(seed: int) -> None:
+    sampler = optuna.samplers.RandomSampler(seed)
+    restored_sampler = pickle.loads(pickle.dumps(sampler))
+    assert sampler._rng.rng.bytes(10) == restored_sampler._rng.rng.bytes(10)
+
+
+@parametrize_sampler
+@pytest.mark.parametrize(
+    "distribution",
+    [
+        FloatDistribution(-1.0, 1.0),
+        FloatDistribution(0.0, 1.0),
+        FloatDistribution(-1.0, 0.0),
+        FloatDistribution(1e-7, 1.0, log=True),
+        FloatDistribution(-10, 10, step=0.1),
+        FloatDistribution(-10.2, 10.2, step=0.1),
+    ],
+)
+def test_float(
+    sampler_class: Callable[[], BaseSampler],
+    distribution: FloatDistribution,
+) -> None:
+    study = optuna.study.create_study(sampler=sampler_class())
+    points = np.array(
+        [
+            study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution)
+            for _ in range(100)
+        ]
+    )
+    assert np.all(points >= distribution.low)
+    assert np.all(points <= distribution.high)
+    assert not isinstance(
+        study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution),
+        np.floating,
+    )
+
+    if distribution.step is not None:
+        # Check all points are multiples of distribution.step.
+        points -= distribution.low
+        points /= distribution.step
+        round_points = np.round(points)
+        np.testing.assert_almost_equal(round_points, points)
+
+
+@parametrize_sampler
+@pytest.mark.parametrize(
+    "distribution",
+    [
+        IntDistribution(-10, 10),
+        IntDistribution(0, 10),
+        IntDistribution(-10, 0),
+        IntDistribution(-10, 10, step=2),
+        IntDistribution(0, 10, step=2),
+        IntDistribution(-10, 0, step=2),
+        IntDistribution(1, 100, log=True),
+    ],
+)
+def test_int(sampler_class: Callable[[], BaseSampler], distribution: IntDistribution) -> None:
+    study = optuna.study.create_study(sampler=sampler_class())
+    points = np.array(
+        [
+            study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution)
+            for _ in range(100)
+        ]
+    )
+    assert np.all(points >= distribution.low)
+    assert np.all(points <= distribution.high)
+    assert not isinstance(
+        study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution),
+        np.integer,
+    )
+
+
+@parametrize_sampler
+@pytest.mark.parametrize("choices", [(1, 2, 3), ("a", "b", "c"), (1, "a")])
+def test_categorical(
+    sampler_class: Callable[[], BaseSampler], choices: Sequence[CategoricalChoiceType]
+) -> None:
+    distribution = CategoricalDistribution(choices)
+
+    study = optuna.study.create_study(sampler=sampler_class())
+
+    def sample() -> float:
+        trial = _create_new_trial(study)
+        param_value = study.sampler.sample_independent(study, trial, "x", distribution)
+        return float(distribution.to_internal_repr(param_value))
+
+    points = np.asarray([sample() for i in range(100)])
+
+    # 'x' value is corresponding to an index of distribution.choices.
+    assert np.all(points >= 0)
+    assert np.all(points <= len(distribution.choices) - 1)
+    round_points = np.round(points)
+    np.testing.assert_almost_equal(round_points, points)
+
+
+@parametrize_relative_sampler
+@pytest.mark.parametrize(
+    "x_distribution",
+    [
+        FloatDistribution(-1.0, 1.0),
+        FloatDistribution(1e-7, 1.0, log=True),
+        FloatDistribution(-10, 10, step=0.5),
+        IntDistribution(3, 10),
+        IntDistribution(1, 100, log=True),
+        IntDistribution(3, 9, step=2),
+    ],
+)
+@pytest.mark.parametrize(
+    "y_distribution",
+    [
+        FloatDistribution(-1.0, 1.0),
+        FloatDistribution(1e-7, 1.0, log=True),
+        FloatDistribution(-10, 10, step=0.5),
+        IntDistribution(3, 10),
+        IntDistribution(1, 100, log=True),
+        IntDistribution(3, 9, step=2),
+    ],
+)
+def test_sample_relative_numerical(
+    relative_sampler_class: Callable[[], BaseSampler],
+    x_distribution: BaseDistribution,
+    y_distribution: BaseDistribution,
+) -> None:
+    search_space: dict[str, BaseDistribution] = dict(x=x_distribution, y=y_distribution)
+    study = optuna.study.create_study(sampler=relative_sampler_class())
+    trial = study.ask(search_space)
+    study.tell(trial, sum(trial.params.values()))
+
+    def sample() -> list[int | float]:
+        params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
+        return [params[name] for name in search_space]
+
+    points = np.array([sample() for _ in range(10)])
+    for i, distribution in enumerate(search_space.values()):
+        assert isinstance(
+            distribution,
+            (
+                FloatDistribution,
+                IntDistribution,
+            ),
+        )
+        assert np.all(points[:, i] >= distribution.low)
+        assert np.all(points[:, i] <= distribution.high)
+    for param_value, distribution in zip(sample(), search_space.values()):
+        assert not isinstance(param_value, np.floating)
+        assert not isinstance(param_value, np.integer)
+        if isinstance(distribution, IntDistribution):
+            assert isinstance(param_value, int)
+        else:
+            assert isinstance(param_value, float)
+
+
+@parametrize_relative_sampler
+def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSampler]) -> None:
+    search_space: dict[str, BaseDistribution] = dict(
+        x=CategoricalDistribution([1, 10, 100]), y=CategoricalDistribution([-1, -10, -100])
+    )
+    study = optuna.study.create_study(sampler=relative_sampler_class())
+    trial = study.ask(search_space)
+    study.tell(trial, sum(trial.params.values()))
+
+    def sample() -> list[float]:
+        params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
+        return [params[name] for name in search_space]
+
+    points = np.array([sample() for _ in range(10)])
+    for i, distribution in enumerate(search_space.values()):
+        assert isinstance(distribution, CategoricalDistribution)
+        assert np.all([v in distribution.choices for v in points[:, i]])
+    for param_value in sample():
+        assert not isinstance(param_value, np.floating)
+        assert not isinstance(param_value, np.integer)
+        assert isinstance(param_value, int)
+
+
+@parametrize_relative_sampler
+@pytest.mark.parametrize(
+    "x_distribution",
+    [
+        FloatDistribution(-1.0, 1.0),
+        FloatDistribution(1e-7, 1.0, log=True),
+        FloatDistribution(-10, 10, step=0.5),
+        IntDistribution(1, 10),
+        IntDistribution(1, 100, log=True),
+    ],
+)
+def test_sample_relative_mixed(
+    relative_sampler_class: Callable[[], BaseSampler], x_distribution: BaseDistribution
+) -> None:
+    search_space: dict[str, BaseDistribution] = dict(
+        x=x_distribution, y=CategoricalDistribution([-1, -10, -100])
+    )
+    study = optuna.study.create_study(sampler=relative_sampler_class())
+    trial = study.ask(search_space)
+    study.tell(trial, sum(trial.params.values()))
+
+    def sample() -> list[float]:
+        params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
+        return [params[name] for name in search_space]
+
+    points = np.array([sample() for _ in range(10)])
+    assert isinstance(
+        search_space["x"],
+        (
+            FloatDistribution,
+            IntDistribution,
+        ),
+    )
+    assert np.all(points[:, 0] >= search_space["x"].low)
+    assert np.all(points[:, 0] <= search_space["x"].high)
+    assert isinstance(search_space["y"], CategoricalDistribution)
+    assert np.all([v in search_space["y"].choices for v in points[:, 1]])
+    for param_value, distribution in zip(sample(), search_space.values()):
+        assert not isinstance(param_value, np.floating)
+        assert not isinstance(param_value, np.integer)
+        if isinstance(
+            distribution,
+            (
+                IntDistribution,
+                CategoricalDistribution,
+            ),
+        ):
+            assert isinstance(param_value, int)
+        else:
+            assert isinstance(param_value, float)
+
+
+@parametrize_sampler
+def test_conditional_sample_independent(sampler_class: Callable[[], BaseSampler]) -> None:
+    # This test case reproduces the error reported in #2734.
+    # See https://github.com/optuna/optuna/pull/2734#issuecomment-857649769.
+
+    study = optuna.study.create_study(sampler=sampler_class())
+    categorical_distribution = CategoricalDistribution(choices=["x", "y"])
+    dependent_distribution = CategoricalDistribution(choices=["a", "b"])
+
+    study.add_trial(
+        optuna.create_trial(
+            params={"category": "x", "x": "a"},
+            distributions={"category": categorical_distribution, "x": dependent_distribution},
+            value=0.1,
+        )
+    )
+
+    study.add_trial(
+        optuna.create_trial(
+            params={"category": "y", "y": "b"},
+            distributions={"category": categorical_distribution, "y": dependent_distribution},
+            value=0.1,
+        )
+    )
+
+    _trial = _create_new_trial(study)
+    category = study.sampler.sample_independent(
+        study, _trial, "category", categorical_distribution
+    )
+    assert category in ["x", "y"]
+    value = study.sampler.sample_independent(study, _trial, category, dependent_distribution)
+    assert value in ["a", "b"]
+
+
+def _create_new_trial(study: Study) -> FrozenTrial:
+    trial_id = study._storage.create_new_trial(study._study_id)
+    return study._storage.get_trial(trial_id)
+
+
+class FixedSampler(BaseSampler):
+    def __init__(
+        self,
+        relative_search_space: dict[str, BaseDistribution],
+        relative_params: dict[str, Any],
+        unknown_param_value: Any,
+    ) -> None:
+        self.relative_search_space = relative_search_space
+        self.relative_params = relative_params
+        self.unknown_param_value = unknown_param_value
+
+    def infer_relative_search_space(
+        self, study: Study, trial: FrozenTrial
+    ) -> dict[str, BaseDistribution]:
+        return self.relative_search_space
+
+    def sample_relative(
+        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
+    ) -> dict[str, Any]:
+        return self.relative_params
+
+    def sample_independent(
+        self,
+        study: Study,
+        trial: FrozenTrial,
+        param_name: str,
+        param_distribution: BaseDistribution,
+    ) -> Any:
+        return self.unknown_param_value
+
+
+def test_sample_relative() -> None:
+    relative_search_space: dict[str, BaseDistribution] = {
+        "a": FloatDistribution(low=0, high=5),
+        "b": CategoricalDistribution(choices=("foo", "bar", "baz")),
+        "c": IntDistribution(low=20, high=50),  # Not exist in `relative_params`.
+    }
+    relative_params = {
+        "a": 3.2,
+        "b": "baz",
+    }
+    unknown_param_value = 30
+
+    sampler = FixedSampler(relative_search_space, relative_params, unknown_param_value)
+    study = optuna.study.create_study(sampler=sampler)
+
+    def objective(trial: Trial) -> float:
+        # Predefined parameters are sampled by `sample_relative()` method.
+        assert trial.suggest_float("a", 0, 5) == 3.2
+        assert trial.suggest_categorical("b", ["foo", "bar", "baz"]) == "baz"
+
+        # Other parameters are sampled by `sample_independent()` method.
+        assert trial.suggest_int("c", 20, 50) == unknown_param_value
+        assert trial.suggest_float("d", 1, 100, log=True) == unknown_param_value
+        assert trial.suggest_float("e", 20, 40) == unknown_param_value
+
+        return 0.0
+
+    study.optimize(objective, n_trials=10, catch=())
+    for trial in study.trials:
+        assert trial.params == {"a": 3.2, "b": "baz", "c": 30, "d": 30, "e": 30}
+
+
+@parametrize_sampler
+def test_nan_objective_value(sampler_class: Callable[[], BaseSampler]) -> None:
+    study = optuna.create_study(sampler=sampler_class())
+
+    def objective(trial: Trial, base_value: float) -> float:
+        return trial.suggest_float("x", 0.1, 0.2) + base_value
+
+    # Non NaN objective values.
+    for i in range(10, 1, -1):
+        study.optimize(lambda t: objective(t, i), n_trials=1, catch=())
+    assert int(study.best_value) == 2
+
+    # NaN objective values.
+    study.optimize(lambda t: objective(t, float("nan")), n_trials=1, catch=())
+    assert int(study.best_value) == 2
+
+    # Non NaN objective value.
+    study.optimize(lambda t: objective(t, 1), n_trials=1, catch=())
+    assert int(study.best_value) == 1
+
+
+@parametrize_sampler
+def test_partial_fixed_sampling(sampler_class: Callable[[], BaseSampler]) -> None:
+    study = optuna.create_study(sampler=sampler_class())
+
+    def objective(trial: Trial) -> float:
+        x = trial.suggest_float("x", -1, 1)
+        y = trial.suggest_int("y", -1, 1)
+        z = trial.suggest_float("z", -1, 1)
+        return x + y + z
+
+    # First trial.
+    study.optimize(objective, n_trials=1)
+
+    # Second trial. Here, the parameter ``y`` is fixed as 0.
+    fixed_params = {"y": 0}
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        study.sampler = optuna.samplers.PartialFixedSampler(fixed_params, study.sampler)
+    study.optimize(objective, n_trials=1)
+    trial_params = study.trials[-1].params
+    assert trial_params["y"] == fixed_params["y"]
+
+
+@parametrize_multi_objective_sampler
+@pytest.mark.parametrize(
+    "distribution",
+    [
+        FloatDistribution(-1.0, 1.0),
+        FloatDistribution(0.0, 1.0),
+        FloatDistribution(-1.0, 0.0),
+        FloatDistribution(1e-7, 1.0, log=True),
+        FloatDistribution(-10, 10, step=0.1),
+        FloatDistribution(-10.2, 10.2, step=0.1),
+        IntDistribution(-10, 10),
+        IntDistribution(0, 10),
+        IntDistribution(-10, 0),
+        IntDistribution(-10, 10, step=2),
+        IntDistribution(0, 10, step=2),
+        IntDistribution(-10, 0, step=2),
+        IntDistribution(1, 100, log=True),
+        CategoricalDistribution((1, 2, 3)),
+        CategoricalDistribution(("a", "b", "c")),
+        CategoricalDistribution((1, "a")),
+    ],
+)
+def test_multi_objective_sample_independent(
+    multi_objective_sampler_class: Callable[[], BaseSampler], distribution: BaseDistribution
+) -> None:
+    study = optuna.study.create_study(
+        directions=["minimize", "maximize"], sampler=multi_objective_sampler_class()
+    )
+    for i in range(100):
+        value = study.sampler.sample_independent(
+            study, _create_new_trial(study), "x", distribution
+        )
+        assert distribution._contains(distribution.to_internal_repr(value))
+
+        if not isinstance(distribution, CategoricalDistribution):
+            # Please see https://github.com/optuna/optuna/pull/393 why this assertion is needed.
+            assert not isinstance(value, np.floating)
+
+        if isinstance(distribution, FloatDistribution):
+            if distribution.step is not None:
+                # Check the value is a multiple of `distribution.step` which is
+                # the quantization interval of the distribution.
+                value -= distribution.low
+                value /= distribution.step
+                round_value = np.round(value)
+                np.testing.assert_almost_equal(round_value, value)
+
+
+def test_before_trial() -> None:
+    n_calls = 0
+    n_trials = 3
+
+    class SamplerBeforeTrial(optuna.samplers.RandomSampler):
+        def before_trial(self, study: Study, trial: FrozenTrial) -> None:
+            assert len(study.trials) - 1 == trial.number
+            assert trial.state == TrialState.RUNNING
+            assert trial.values is None
+            nonlocal n_calls
+            n_calls += 1
+
+    sampler = SamplerBeforeTrial()
+    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
+
+    study.optimize(
+        lambda t: [t.suggest_float("y", -3, 3), t.suggest_int("x", 0, 10)], n_trials=n_trials
+    )
+    assert n_calls == n_trials
+
+
+def test_after_trial() -> None:
+    n_calls = 0
+    n_trials = 3
+
+    class SamplerAfterTrial(optuna.samplers.RandomSampler):
+        def after_trial(
+            self,
+            study: Study,
+            trial: FrozenTrial,
+            state: TrialState,
+            values: Sequence[float] | None,
+        ) -> None:
+            assert len(study.trials) - 1 == trial.number
+            assert trial.state == TrialState.RUNNING
+            assert trial.values is None
+            assert state == TrialState.COMPLETE
+            assert values is not None
+            assert len(values) == 2
+            nonlocal n_calls
+            n_calls += 1
+
+    sampler = SamplerAfterTrial()
+    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
+
+    study.optimize(lambda t: [t.suggest_float("y", -3, 3), t.suggest_int("x", 0, 10)], n_trials=3)
+
+    assert n_calls == n_trials
+
+
+def test_after_trial_pruning() -> None:
+    n_calls = 0
+    n_trials = 3
+
+    class SamplerAfterTrial(optuna.samplers.RandomSampler):
+        def after_trial(
+            self,
+            study: Study,
+            trial: FrozenTrial,
+            state: TrialState,
+            values: Sequence[float] | None,
+        ) -> None:
+            assert len(study.trials) - 1 == trial.number
+            assert trial.state == TrialState.RUNNING
+            assert trial.values is None
+            assert state == TrialState.PRUNED
+            assert values is None
+            nonlocal n_calls
+            n_calls += 1
+
+    sampler = SamplerAfterTrial()
+    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
+
+    study.optimize(pruned_objective, n_trials=n_trials)
+
+    assert n_calls == n_trials
+
+
+def test_after_trial_failing() -> None:
+    n_calls = 0
+    n_trials = 3
+
+    class SamplerAfterTrial(optuna.samplers.RandomSampler):
+        def after_trial(
+            self,
+            study: Study,
+            trial: FrozenTrial,
+            state: TrialState,
+            values: Sequence[float] | None,
+        ) -> None:
+            assert len(study.trials) - 1 == trial.number
+            assert trial.state == TrialState.RUNNING
+            assert trial.values is None
+            assert state == TrialState.FAIL
+            assert values is None
+            nonlocal n_calls
+            n_calls += 1
+
+    sampler = SamplerAfterTrial()
+    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
+
+    with pytest.raises(ValueError):
+        study.optimize(fail_objective, n_trials=n_trials)
+
+    # Called once after the first failing trial before returning from optimize.
+    assert n_calls == 1
+
+
+def test_after_trial_failing_in_after_trial() -> None:
+    n_calls = 0
+    n_trials = 3
+
+    class SamplerAfterTrialAlwaysFail(optuna.samplers.RandomSampler):
+        def after_trial(
+            self,
+            study: Study,
+            trial: FrozenTrial,
+            state: TrialState,
+            values: Sequence[float] | None,
+        ) -> None:
+            nonlocal n_calls
+            n_calls += 1
+            raise NotImplementedError  # Arbitrary error for testing purpose.
+
+    sampler = SamplerAfterTrialAlwaysFail()
+    study = optuna.create_study(sampler=sampler)
+
+    with pytest.raises(NotImplementedError):
+        study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=n_trials)
+
+    assert len(study.trials) == 1
+    assert n_calls == 1
+
+    sampler = SamplerAfterTrialAlwaysFail()
+    study = optuna.create_study(sampler=sampler)
+
+    # Not affected by `catch`.
+    with pytest.raises(NotImplementedError):
+        study.optimize(
+            lambda t: t.suggest_int("x", 0, 10), n_trials=n_trials, catch=(NotImplementedError,)
+        )
+
+    assert len(study.trials) == 1
+    assert n_calls == 2
+
+
+def test_after_trial_with_study_tell() -> None:
+    n_calls = 0
+
+    class SamplerAfterTrial(optuna.samplers.RandomSampler):
+        def after_trial(
+            self,
+            study: Study,
+            trial: FrozenTrial,
+            state: TrialState,
+            values: Sequence[float] | None,
+        ) -> None:
+            nonlocal n_calls
+            n_calls += 1
+
+    sampler = SamplerAfterTrial()
+    study = optuna.create_study(sampler=sampler)
+
+    assert n_calls == 0
+
+    study.tell(study.ask(), 1.0)
+
+    assert n_calls == 1
+
+
+@parametrize_sampler
+def test_sample_single_distribution(sampler_class: Callable[[], BaseSampler]) -> None:
+    relative_search_space = {
+        "a": CategoricalDistribution([1]),
+        "b": IntDistribution(low=1, high=1),
+        "c": IntDistribution(low=1, high=1, log=True),
+        "d": FloatDistribution(low=1.0, high=1.0),
+        "e": FloatDistribution(low=1.0, high=1.0, log=True),
+        "f": FloatDistribution(low=1.0, high=1.0, step=1.0),
+    }
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        sampler = sampler_class()
+    study = optuna.study.create_study(sampler=sampler)
+
+    # We need to test the construction of the model, so we should set `n_trials >= 2`.
+    for _ in range(2):
+        trial = study.ask(fixed_distributions=relative_search_space)
+        study.tell(trial, 1.0)
+        for param_name in relative_search_space.keys():
+            assert trial.params[param_name] == 1
+
+
+@parametrize_sampler
+@parametrize_suggest_method("x")
+def test_single_parameter_objective(
+    sampler_class: Callable[[], BaseSampler], suggest_method_x: Callable[[Trial], float]
+) -> None:
+    def objective(trial: Trial) -> float:
+        return suggest_method_x(trial)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        sampler = sampler_class()
+
+    study = optuna.study.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=10)
+
+    assert len(study.trials) == 10
+    assert all(t.state == TrialState.COMPLETE for t in study.trials)
+
+
+@parametrize_sampler
+def test_conditional_parameter_objective(sampler_class: Callable[[], BaseSampler]) -> None:
+    def objective(trial: Trial) -> float:
+        x = trial.suggest_categorical("x", [True, False])
+        if x:
+            return trial.suggest_float("y", 0, 1)
+        return trial.suggest_float("z", 0, 1)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        sampler = sampler_class()
+
+    study = optuna.study.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=10)
+
+    assert len(study.trials) == 10
+    assert all(t.state == TrialState.COMPLETE for t in study.trials)
+
+
+@parametrize_sampler
+@parametrize_suggest_method("x")
+@parametrize_suggest_method("y")
+def test_combination_of_different_distributions_objective(
+    sampler_class: Callable[[], BaseSampler],
+    suggest_method_x: Callable[[Trial], float],
+    suggest_method_y: Callable[[Trial], float],
+) -> None:
+    def objective(trial: Trial) -> float:
+        return suggest_method_x(trial) + suggest_method_y(trial)
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        sampler = sampler_class()
+
+    study = optuna.study.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=3)
+
+    assert len(study.trials) == 3
+    assert all(t.state == TrialState.COMPLETE for t in study.trials)
+
+
+@parametrize_sampler
+@pytest.mark.parametrize(
+    "second_low,second_high",
+    [
+        (0, 5),  # Narrow range.
+        (0, 20),  # Expand range.
+        (20, 30),  # Set non-overlapping range.
+    ],
+)
+def test_dynamic_range_objective(
+    sampler_class: Callable[[], BaseSampler], second_low: int, second_high: int
+) -> None:
+    def objective(trial: Trial, low: int, high: int) -> float:
+        v = trial.suggest_float("x", low, high)
+        v += trial.suggest_int("y", low, high)
+        return v
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
+        sampler = sampler_class()
+
+    study = optuna.study.create_study(sampler=sampler)
+    study.optimize(lambda t: objective(t, 0, 10), n_trials=10)
+    study.optimize(lambda t: objective(t, second_low, second_high), n_trials=10)
+
+    assert len(study.trials) == 20
+    assert all(t.state == TrialState.COMPLETE for t in study.trials)
+
+
+# We add tests for constant objective functions to ensure the reproducibility of sorting.
+@parametrize_sampler_with_seed
+@pytest.mark.slow
+@pytest.mark.parametrize("objective_func", [lambda *args: sum(args), lambda *args: 0.0])
+def test_reproducible(sampler_class: Callable[[int], BaseSampler], objective_func: Any) -> None:
+    def objective(trial: Trial) -> float:
+        a = trial.suggest_float("a", 1, 9)
+        b = trial.suggest_float("b", 1, 9, log=True)
+        c = trial.suggest_float("c", 1, 9, step=1)
+        d = trial.suggest_int("d", 1, 9)
+        e = trial.suggest_int("e", 1, 9, log=True)
+        f = trial.suggest_int("f", 1, 9, step=2)
+        g = trial.suggest_categorical("g", range(1, 10))
+        return objective_func(a, b, c, d, e, f, g)
+
+    study = optuna.create_study(sampler=sampler_class(1))
+    study.optimize(objective, n_trials=15)
+
+    study_same_seed = optuna.create_study(sampler=sampler_class(1))
+    study_same_seed.optimize(objective, n_trials=15)
+    for i in range(15):
+        assert study.trials[i].params == study_same_seed.trials[i].params
+
+    study_different_seed = optuna.create_study(sampler=sampler_class(2))
+    study_different_seed.optimize(objective, n_trials=15)
+    assert any(
+        [study.trials[i].params != study_different_seed.trials[i].params for i in range(15)]
+    )
+
+
+@pytest.mark.slow
+@parametrize_sampler_with_seed
+def test_reseed_rng_change_sampling(sampler_class: Callable[[int], BaseSampler]) -> None:
+    def objective(trial: Trial) -> float:
+        a = trial.suggest_float("a", 1, 9)
+        b = trial.suggest_float("b", 1, 9, log=True)
+        c = trial.suggest_float("c", 1, 9, step=1)
+        d = trial.suggest_int("d", 1, 9)
+        e = trial.suggest_int("e", 1, 9, log=True)
+        f = trial.suggest_int("f", 1, 9, step=2)
+        g = trial.suggest_categorical("g", range(1, 10))
+        return a + b + c + d + e + f + g
+
+    sampler = sampler_class(1)
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=15)
+
+    sampler_different_seed = sampler_class(1)
+    sampler_different_seed.reseed_rng()
+    study_different_seed = optuna.create_study(sampler=sampler_different_seed)
+    study_different_seed.optimize(objective, n_trials=15)
+    assert any(
+        [study.trials[i].params != study_different_seed.trials[i].params for i in range(15)]
+    )
+
+
+# This function is used only in test_reproducible_in_other_process, but declared at top-level
+# because local function cannot be pickled, which occurs within multiprocessing.
+def run_optimize(
+    k: int,
+    sampler_name: str,
+    sequence_dict: DictProxy,
+    hash_dict: DictProxy,
+) -> None:
+    def objective(trial: Trial) -> float:
+        a = trial.suggest_float("a", 1, 9)
+        b = trial.suggest_float("b", 1, 9, log=True)
+        c = trial.suggest_float("c", 1, 9, step=1)
+        d = trial.suggest_int("d", 1, 9)
+        e = trial.suggest_int("e", 1, 9, log=True)
+        f = trial.suggest_int("f", 1, 9, step=2)
+        g = trial.suggest_categorical("g", range(1, 10))
+        return a + b + c + d + e + f + g
+
+    hash_dict[k] = hash("nondeterministic hash")
+    sampler = sampler_class_with_seed[sampler_name](1)
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=15)
+    sequence_dict[k] = list(study.trials[-1].params.values())
+
+
+@pytest.fixture
+def unset_seed_in_test(request: SubRequest) -> None:
+    # Unset the hashseed at beginning and restore it at end regardless of an exception in the test.
+    # See https://docs.pytest.org/en/stable/how-to/fixtures.html#adding-finalizers-directly
+    # for details.
+
+    hash_seed = os.getenv("PYTHONHASHSEED")
+    if hash_seed is not None:
+        del os.environ["PYTHONHASHSEED"]
+
+    def restore_seed() -> None:
+        if hash_seed is not None:
+            os.environ["PYTHONHASHSEED"] = hash_seed
+
+    request.addfinalizer(restore_seed)
+
+
+@pytest.mark.slow
+@parametrize_sampler_name_with_seed
+def test_reproducible_in_other_process(sampler_name: str, unset_seed_in_test: None) -> None:
+    # This test should be tested without `PYTHONHASHSEED`. However, some tool such as tox
+    # set the environmental variable "PYTHONHASHSEED" by default.
+    # To do so, this test calls a finalizer: `unset_seed_in_test`.
+
+    # Multiprocessing supports three way to start a process.
+    # We use `spawn` option to create a child process as a fresh python process.
+    # For more detail, see https://github.com/optuna/optuna/pull/3187#issuecomment-997673037.
+    multiprocessing.set_start_method("spawn", force=True)
+    manager = multiprocessing.Manager()
+    sequence_dict: DictProxy = manager.dict()
+    hash_dict: DictProxy = manager.dict()
+    for i in range(3):
+        p = multiprocessing.Process(
+            target=run_optimize, args=(i, sampler_name, sequence_dict, hash_dict)
+        )
+        p.start()
+        p.join()
+
+    # Hashes are expected to be different because string hashing is nondeterministic per process.
+    assert not (hash_dict[0] == hash_dict[1] == hash_dict[2])
+    # But the sequences are expected to be the same.
+    assert sequence_dict[0] == sequence_dict[1] == sequence_dict[2]
+
+
+@pytest.mark.parametrize("n_jobs", [1, 2])
+@parametrize_relative_sampler
+def test_cache_is_invalidated(
+    n_jobs: int, relative_sampler_class: Callable[[], BaseSampler]
+) -> None:
+    sampler = relative_sampler_class()
+    study = optuna.study.create_study(sampler=sampler)
+
+    def objective(trial: Trial) -> float:
+        assert trial._relative_params is None
+        assert study._thread_local.cached_all_trials is None
+
+        trial.suggest_float("x", -10, 10)
+        trial.suggest_float("y", -10, 10)
+        assert trial._relative_params is not None
+        return -1
+
+    study.optimize(objective, n_trials=10, n_jobs=n_jobs)

From 8c4346a68e479ac8ee6ff4612a653c3673219d50 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Fri, 25 Oct 2024 07:54:15 +0200
Subject: [PATCH 35/51] Fix unittests so that everything passes

---
 .../auto_sampler/tests/test_sampler.py        | 385 +-----------------
 1 file changed, 20 insertions(+), 365 deletions(-)

diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
index b0f8dbeb..039c8fc8 100644
--- a/package/samplers/auto_sampler/tests/test_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -5,9 +5,7 @@
 import multiprocessing
 from multiprocessing.managers import DictProxy
 import os
-import pickle
 from typing import Any
-from unittest.mock import patch
 import warnings
 
 from _pytest.fixtures import SubRequest
@@ -20,78 +18,29 @@
 from optuna.distributions import FloatDistribution
 from optuna.distributions import IntDistribution
 from optuna.samplers import BaseSampler
-from optuna.samplers._lazy_random_state import LazyRandomState
 from optuna.study import Study
-from optuna.testing.objectives import fail_objective
-from optuna.testing.objectives import pruned_objective
 from optuna.trial import FrozenTrial
 from optuna.trial import Trial
 from optuna.trial import TrialState
+import optunahub
 import pytest
 
 
 # NOTE(nabenabe): This file content is mostly copied from the Optuna repository.
+AutoSampler = optunahub.load_local_module(
+    package="samplers/auto_sampler", registry_root="package/"
+).AutoSampler
 
 
-def get_gp_sampler(
-    *, n_startup_trials: int = 0, deterministic_objective: bool = False, seed: int | None = None
-) -> optuna.samplers.GPSampler:
-    return optuna.samplers.GPSampler(
-        n_startup_trials=n_startup_trials,
-        seed=seed,
-        deterministic_objective=deterministic_objective,
-    )
-
-
-parametrize_sampler = pytest.mark.parametrize(
-    "sampler_class",
-    [
-        optuna.samplers.RandomSampler,
-        lambda: optuna.samplers.TPESampler(n_startup_trials=0),
-        lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True),
-        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
-        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0, use_separable_cma=True),
-        optuna.samplers.NSGAIISampler,
-        optuna.samplers.NSGAIIISampler,
-        optuna.samplers.QMCSampler,
-        lambda: get_gp_sampler(n_startup_trials=0),
-        lambda: get_gp_sampler(n_startup_trials=0, deterministic_objective=True),
-    ],
-)
-parametrize_relative_sampler = pytest.mark.parametrize(
-    "relative_sampler_class",
-    [
-        lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True),
-        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
-        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0, use_separable_cma=True),
-        lambda: get_gp_sampler(n_startup_trials=0),
-        lambda: get_gp_sampler(n_startup_trials=0, deterministic_objective=True),
-    ],
-)
+parametrize_sampler = pytest.mark.parametrize("sampler_class", [AutoSampler])
+parametrize_relative_sampler = pytest.mark.parametrize("relative_sampler_class", [AutoSampler])
 parametrize_multi_objective_sampler = pytest.mark.parametrize(
-    "multi_objective_sampler_class",
-    [
-        optuna.samplers.NSGAIISampler,
-        optuna.samplers.NSGAIIISampler,
-        lambda: optuna.samplers.TPESampler(n_startup_trials=0),
-    ],
+    "multi_objective_sampler_class", [AutoSampler]
 )
 
 
 sampler_class_with_seed: dict[str, Callable[[int], BaseSampler]] = {
-    "RandomSampler": lambda seed: optuna.samplers.RandomSampler(seed=seed),
-    "TPESampler": lambda seed: optuna.samplers.TPESampler(seed=seed),
-    "multivariate TPESampler": lambda seed: optuna.samplers.TPESampler(
-        multivariate=True, seed=seed
-    ),
-    "CmaEsSampler": lambda seed: optuna.samplers.CmaEsSampler(seed=seed),
-    "separable CmaEsSampler": lambda seed: optuna.samplers.CmaEsSampler(
-        seed=seed, use_separable_cma=True
-    ),
-    "NSGAIISampler": lambda seed: optuna.samplers.NSGAIISampler(seed=seed),
-    "NSGAIIISampler": lambda seed: optuna.samplers.NSGAIIISampler(seed=seed),
-    "QMCSampler": lambda seed: optuna.samplers.QMCSampler(seed=seed),
-    "GPSampler": lambda seed: get_gp_sampler(seed=seed, n_startup_trials=0),
+    "AutoSampler": lambda seed: AutoSampler(seed=seed)
 }
 param_sampler_with_seed = []
 param_sampler_name_with_seed = []
@@ -104,26 +53,10 @@ def get_gp_sampler(
 )
 
 
+"""
 @pytest.mark.parametrize(
-    "sampler_class,expected_has_rng,expected_has_another_sampler",
-    [
-        (optuna.samplers.RandomSampler, True, False),
-        (lambda: optuna.samplers.TPESampler(n_startup_trials=0), True, True),
-        (lambda: optuna.samplers.TPESampler(n_startup_trials=0, multivariate=True), True, True),
-        (lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0), True, True),
-        (optuna.samplers.NSGAIISampler, True, True),
-        (optuna.samplers.NSGAIIISampler, True, True),
-        (
-            lambda: optuna.samplers.PartialFixedSampler(
-                fixed_params={"x": 0}, base_sampler=optuna.samplers.RandomSampler()
-            ),
-            False,
-            True,
-        ),
-        (lambda: optuna.samplers.GridSampler(search_space={"x": [0]}), True, False),
-        (lambda: optuna.samplers.QMCSampler(), False, True),
-        (lambda: get_gp_sampler(n_startup_trials=0), True, True),
-    ],
+    # TODO: 知らん
+    "sampler_class,expected_has_rng,expected_has_another_sampler", [(AutoSampler, ..., ...)]
 )
 def test_sampler_reseed_rng(
     sampler_class: Callable[[], BaseSampler],
@@ -174,6 +107,7 @@ def _extract_attr_name_from_sampler_by_cls(sampler: BaseSampler, cls: Any) -> st
         had_sampler = sampler.__dict__[had_sampler_name]
         had_sampler_random_state = had_sampler.__dict__[had_sampler_rng_name].rng.get_state()
         assert str(original_had_sampler_random_state) != str(had_sampler_random_state)
+"""
 
 
 def parametrize_suggest_method(name: str) -> MarkDecorator:
@@ -190,33 +124,10 @@ def parametrize_suggest_method(name: str) -> MarkDecorator:
     )
 
 
-@pytest.mark.parametrize(
-    "sampler_class",
-    [
-        lambda: optuna.samplers.CmaEsSampler(n_startup_trials=0),
-    ],
-)
-def test_raise_error_for_samplers_during_multi_objectives(
-    sampler_class: Callable[[], BaseSampler],
-) -> None:
-    study = optuna.study.create_study(directions=["maximize", "maximize"], sampler=sampler_class())
-
-    distribution = FloatDistribution(0.0, 1.0)
-    with pytest.raises(ValueError):
-        study.sampler.sample_independent(study, _create_new_trial(study), "x", distribution)
-
-    with pytest.raises(ValueError):
-        trial = _create_new_trial(study)
-        study.sampler.sample_relative(
-            study, trial, study.sampler.infer_relative_search_space(study, trial)
-        )
-
-
-@pytest.mark.parametrize("seed", [0, 169208])
-def test_pickle_random_sampler(seed: int) -> None:
-    sampler = optuna.samplers.RandomSampler(seed)
-    restored_sampler = pickle.loads(pickle.dumps(sampler))
-    assert sampler._rng.rng.bytes(10) == restored_sampler._rng.rng.bytes(10)
+def _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study: optuna.Study) -> None:
+    # NOTE(nabenabe): Choose a sampler inside AutoSampler.
+    study.sampler.before_trial(study, trial=_create_new_trial(study))
+    study.sampler._sampler._n_startup_trials = 0
 
 
 @parametrize_sampler
@@ -231,10 +142,7 @@ def test_pickle_random_sampler(seed: int) -> None:
         FloatDistribution(-10.2, 10.2, step=0.1),
     ],
 )
-def test_float(
-    sampler_class: Callable[[], BaseSampler],
-    distribution: FloatDistribution,
-) -> None:
+def test_float(sampler_class: Callable[[], BaseSampler], distribution: FloatDistribution) -> None:
     study = optuna.study.create_study(sampler=sampler_class())
     points = np.array(
         [
@@ -341,6 +249,7 @@ def test_sample_relative_numerical(
     study = optuna.study.create_study(sampler=relative_sampler_class())
     trial = study.ask(search_space)
     study.tell(trial, sum(trial.params.values()))
+    _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study)
 
     def sample() -> list[int | float]:
         params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
@@ -374,6 +283,7 @@ def test_sample_relative_categorical(relative_sampler_class: Callable[[], BaseSa
     study = optuna.study.create_study(sampler=relative_sampler_class())
     trial = study.ask(search_space)
     study.tell(trial, sum(trial.params.values()))
+    _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study)
 
     def sample() -> list[float]:
         params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
@@ -409,6 +319,7 @@ def test_sample_relative_mixed(
     study = optuna.study.create_study(sampler=relative_sampler_class())
     trial = study.ask(search_space)
     study.tell(trial, sum(trial.params.values()))
+    _choose_sampler_in_auto_sampler_and_set_n_startup_trials_to_zero(study)
 
     def sample() -> list[float]:
         params = study.sampler.sample_relative(study, _create_new_trial(study), search_space)
@@ -480,69 +391,6 @@ def _create_new_trial(study: Study) -> FrozenTrial:
     return study._storage.get_trial(trial_id)
 
 
-class FixedSampler(BaseSampler):
-    def __init__(
-        self,
-        relative_search_space: dict[str, BaseDistribution],
-        relative_params: dict[str, Any],
-        unknown_param_value: Any,
-    ) -> None:
-        self.relative_search_space = relative_search_space
-        self.relative_params = relative_params
-        self.unknown_param_value = unknown_param_value
-
-    def infer_relative_search_space(
-        self, study: Study, trial: FrozenTrial
-    ) -> dict[str, BaseDistribution]:
-        return self.relative_search_space
-
-    def sample_relative(
-        self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
-    ) -> dict[str, Any]:
-        return self.relative_params
-
-    def sample_independent(
-        self,
-        study: Study,
-        trial: FrozenTrial,
-        param_name: str,
-        param_distribution: BaseDistribution,
-    ) -> Any:
-        return self.unknown_param_value
-
-
-def test_sample_relative() -> None:
-    relative_search_space: dict[str, BaseDistribution] = {
-        "a": FloatDistribution(low=0, high=5),
-        "b": CategoricalDistribution(choices=("foo", "bar", "baz")),
-        "c": IntDistribution(low=20, high=50),  # Not exist in `relative_params`.
-    }
-    relative_params = {
-        "a": 3.2,
-        "b": "baz",
-    }
-    unknown_param_value = 30
-
-    sampler = FixedSampler(relative_search_space, relative_params, unknown_param_value)
-    study = optuna.study.create_study(sampler=sampler)
-
-    def objective(trial: Trial) -> float:
-        # Predefined parameters are sampled by `sample_relative()` method.
-        assert trial.suggest_float("a", 0, 5) == 3.2
-        assert trial.suggest_categorical("b", ["foo", "bar", "baz"]) == "baz"
-
-        # Other parameters are sampled by `sample_independent()` method.
-        assert trial.suggest_int("c", 20, 50) == unknown_param_value
-        assert trial.suggest_float("d", 1, 100, log=True) == unknown_param_value
-        assert trial.suggest_float("e", 20, 40) == unknown_param_value
-
-        return 0.0
-
-    study.optimize(objective, n_trials=10, catch=())
-    for trial in study.trials:
-        assert trial.params == {"a": 3.2, "b": "baz", "c": 30, "d": 30, "e": 30}
-
-
 @parametrize_sampler
 def test_nan_objective_value(sampler_class: Callable[[], BaseSampler]) -> None:
     study = optuna.create_study(sampler=sampler_class())
@@ -564,29 +412,6 @@ def objective(trial: Trial, base_value: float) -> float:
     assert int(study.best_value) == 1
 
 
-@parametrize_sampler
-def test_partial_fixed_sampling(sampler_class: Callable[[], BaseSampler]) -> None:
-    study = optuna.create_study(sampler=sampler_class())
-
-    def objective(trial: Trial) -> float:
-        x = trial.suggest_float("x", -1, 1)
-        y = trial.suggest_int("y", -1, 1)
-        z = trial.suggest_float("z", -1, 1)
-        return x + y + z
-
-    # First trial.
-    study.optimize(objective, n_trials=1)
-
-    # Second trial. Here, the parameter ``y`` is fixed as 0.
-    fixed_params = {"y": 0}
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore", optuna.exceptions.ExperimentalWarning)
-        study.sampler = optuna.samplers.PartialFixedSampler(fixed_params, study.sampler)
-    study.optimize(objective, n_trials=1)
-    trial_params = study.trials[-1].params
-    assert trial_params["y"] == fixed_params["y"]
-
-
 @parametrize_multi_objective_sampler
 @pytest.mark.parametrize(
     "distribution",
@@ -635,176 +460,6 @@ def test_multi_objective_sample_independent(
                 np.testing.assert_almost_equal(round_value, value)
 
 
-def test_before_trial() -> None:
-    n_calls = 0
-    n_trials = 3
-
-    class SamplerBeforeTrial(optuna.samplers.RandomSampler):
-        def before_trial(self, study: Study, trial: FrozenTrial) -> None:
-            assert len(study.trials) - 1 == trial.number
-            assert trial.state == TrialState.RUNNING
-            assert trial.values is None
-            nonlocal n_calls
-            n_calls += 1
-
-    sampler = SamplerBeforeTrial()
-    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
-
-    study.optimize(
-        lambda t: [t.suggest_float("y", -3, 3), t.suggest_int("x", 0, 10)], n_trials=n_trials
-    )
-    assert n_calls == n_trials
-
-
-def test_after_trial() -> None:
-    n_calls = 0
-    n_trials = 3
-
-    class SamplerAfterTrial(optuna.samplers.RandomSampler):
-        def after_trial(
-            self,
-            study: Study,
-            trial: FrozenTrial,
-            state: TrialState,
-            values: Sequence[float] | None,
-        ) -> None:
-            assert len(study.trials) - 1 == trial.number
-            assert trial.state == TrialState.RUNNING
-            assert trial.values is None
-            assert state == TrialState.COMPLETE
-            assert values is not None
-            assert len(values) == 2
-            nonlocal n_calls
-            n_calls += 1
-
-    sampler = SamplerAfterTrial()
-    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
-
-    study.optimize(lambda t: [t.suggest_float("y", -3, 3), t.suggest_int("x", 0, 10)], n_trials=3)
-
-    assert n_calls == n_trials
-
-
-def test_after_trial_pruning() -> None:
-    n_calls = 0
-    n_trials = 3
-
-    class SamplerAfterTrial(optuna.samplers.RandomSampler):
-        def after_trial(
-            self,
-            study: Study,
-            trial: FrozenTrial,
-            state: TrialState,
-            values: Sequence[float] | None,
-        ) -> None:
-            assert len(study.trials) - 1 == trial.number
-            assert trial.state == TrialState.RUNNING
-            assert trial.values is None
-            assert state == TrialState.PRUNED
-            assert values is None
-            nonlocal n_calls
-            n_calls += 1
-
-    sampler = SamplerAfterTrial()
-    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
-
-    study.optimize(pruned_objective, n_trials=n_trials)
-
-    assert n_calls == n_trials
-
-
-def test_after_trial_failing() -> None:
-    n_calls = 0
-    n_trials = 3
-
-    class SamplerAfterTrial(optuna.samplers.RandomSampler):
-        def after_trial(
-            self,
-            study: Study,
-            trial: FrozenTrial,
-            state: TrialState,
-            values: Sequence[float] | None,
-        ) -> None:
-            assert len(study.trials) - 1 == trial.number
-            assert trial.state == TrialState.RUNNING
-            assert trial.values is None
-            assert state == TrialState.FAIL
-            assert values is None
-            nonlocal n_calls
-            n_calls += 1
-
-    sampler = SamplerAfterTrial()
-    study = optuna.create_study(directions=["minimize", "minimize"], sampler=sampler)
-
-    with pytest.raises(ValueError):
-        study.optimize(fail_objective, n_trials=n_trials)
-
-    # Called once after the first failing trial before returning from optimize.
-    assert n_calls == 1
-
-
-def test_after_trial_failing_in_after_trial() -> None:
-    n_calls = 0
-    n_trials = 3
-
-    class SamplerAfterTrialAlwaysFail(optuna.samplers.RandomSampler):
-        def after_trial(
-            self,
-            study: Study,
-            trial: FrozenTrial,
-            state: TrialState,
-            values: Sequence[float] | None,
-        ) -> None:
-            nonlocal n_calls
-            n_calls += 1
-            raise NotImplementedError  # Arbitrary error for testing purpose.
-
-    sampler = SamplerAfterTrialAlwaysFail()
-    study = optuna.create_study(sampler=sampler)
-
-    with pytest.raises(NotImplementedError):
-        study.optimize(lambda t: t.suggest_int("x", 0, 10), n_trials=n_trials)
-
-    assert len(study.trials) == 1
-    assert n_calls == 1
-
-    sampler = SamplerAfterTrialAlwaysFail()
-    study = optuna.create_study(sampler=sampler)
-
-    # Not affected by `catch`.
-    with pytest.raises(NotImplementedError):
-        study.optimize(
-            lambda t: t.suggest_int("x", 0, 10), n_trials=n_trials, catch=(NotImplementedError,)
-        )
-
-    assert len(study.trials) == 1
-    assert n_calls == 2
-
-
-def test_after_trial_with_study_tell() -> None:
-    n_calls = 0
-
-    class SamplerAfterTrial(optuna.samplers.RandomSampler):
-        def after_trial(
-            self,
-            study: Study,
-            trial: FrozenTrial,
-            state: TrialState,
-            values: Sequence[float] | None,
-        ) -> None:
-            nonlocal n_calls
-            n_calls += 1
-
-    sampler = SamplerAfterTrial()
-    study = optuna.create_study(sampler=sampler)
-
-    assert n_calls == 0
-
-    study.tell(study.ask(), 1.0)
-
-    assert n_calls == 1
-
-
 @parametrize_sampler
 def test_sample_single_distribution(sampler_class: Callable[[], BaseSampler]) -> None:
     relative_search_space = {

From 5304f9dcafde28bf54be77051e4e3b75e119d09b Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 03:49:15 +0100
Subject: [PATCH 36/51] Apply c-bata's comments

---
 package/samplers/auto_sampler/sampler.py | 25 +++++++++++++++---------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/sampler.py
index b52a4eb8..c0ab815e 100644
--- a/package/samplers/auto_sampler/sampler.py
+++ b/package/samplers/auto_sampler/sampler.py
@@ -26,8 +26,8 @@
     from optuna.trial import FrozenTrial
 
 
-MAXINT32 = (1 << 31) - 1
-SAMPLER_KEY = "auto:sampler"
+_MAXINT32 = (1 << 31) - 1
+_SAMPLER_KEY = "auto:sampler"
 _logger = get_logger(f"optuna.{__name__}")
 
 
@@ -87,7 +87,7 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_random_sampler = self._rng.rng.randint(MAXINT32)
+        seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
         sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
         self._thread_local_sampler = _ThreadLocalSampler(sampler)
         self._constraints_func = constraints_func
@@ -105,7 +105,9 @@ def reseed_rng(self) -> None:
         self._sampler.reseed_rng()
 
     def _include_conditional_param(self, study: Study) -> bool:
-        trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED))
+        trials = study._get_trials(
+            deepcopy=False, states=(TrialState.COMPLETE, TrialState.PRUNED), use_cache=True
+        )
         if len(trials) == 0:
             return False
 
@@ -118,7 +120,7 @@ def _determine_multi_objective_sampler(
         if isinstance(self._sampler, NSGAIISampler):
             return self._sampler
 
-        seed = self._rng.rng.randint(MAXINT32)
+        seed = self._rng.rng.randint(_MAXINT32)
         return NSGAIISampler(constraints_func=self._constraints_func, seed=seed)
 
     def _determine_single_objective_sampler(
@@ -127,7 +129,7 @@ def _determine_single_objective_sampler(
         if isinstance(self._sampler, TPESampler):
             return self._sampler
 
-        seed = self._rng.rng.randint(MAXINT32)
+        seed = self._rng.rng.randint(_MAXINT32)
         if (
             self._constraints_func is not None
             or any(isinstance(d, CategoricalDistribution) for d in search_space.values())
@@ -144,7 +146,9 @@ def _determine_single_objective_sampler(
                 constant_liar=True,
             )
 
-        complete_trials = study.get_trials(deepcopy=False, states=(TrialState.COMPLETE,))
+        complete_trials = study._get_trials(
+            deepcopy=False, states=(TrialState.COMPLETE,), use_cache=True
+        )
         complete_trials.sort(key=lambda trial: trial.datetime_complete)
         if len(complete_trials) < self._N_COMPLETE_TRIALS_FOR_CMAES:
             # Use ``GPSampler`` if search space is numerical and
@@ -195,13 +199,16 @@ def before_trial(self, study: Study, trial: FrozenTrial) -> None:
         # NOTE(nabenabe): Sampler must be updated in this method. If, for example, it is updated in
         # infer_relative_search_space, the sampler for before_trial and that for sample_relative,
         # after_trial might be different, meaning that the sampling routine could be incompatible.
-        if len(study._get_trials(deepcopy=False, states=(TrialState.COMPLETE,))) != 0:
+        if (
+            len(study._get_trials(deepcopy=False, states=(TrialState.COMPLETE,), use_cache=True))
+            != 0
+        ):
             search_space = IntersectionSearchSpace().calculate(study)
             self._sampler = self._determine_sampler(study, trial, search_space)
 
         sampler_name = self._sampler.__class__.__name__
         _logger.debug(f"Sample trial#{trial.number} with {sampler_name}.")
-        study._storage.set_trial_system_attr(trial._trial_id, SAMPLER_KEY, sampler_name)
+        study._storage.set_trial_system_attr(trial._trial_id, _SAMPLER_KEY, sampler_name)
         self._sampler.before_trial(study, trial)
 
     def after_trial(

From 3d4dd5ee287b33907899bf3ccfbdbae9abc4d075 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 05:04:16 +0100
Subject: [PATCH 37/51] Enable cache

---
 package/samplers/auto_sampler/tests/test_sampler.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
index 039c8fc8..470c47e4 100644
--- a/package/samplers/auto_sampler/tests/test_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -6,6 +6,7 @@
 from multiprocessing.managers import DictProxy
 import os
 from typing import Any
+from unittest.mock import Mock
 import warnings
 
 from _pytest.fixtures import SubRequest
@@ -704,11 +705,17 @@ def test_cache_is_invalidated(
     n_jobs: int, relative_sampler_class: Callable[[], BaseSampler]
 ) -> None:
     sampler = relative_sampler_class()
+    original_before_trial = sampler.before_trial
+
+    def mock_before_trial(study: Study, trial: FrozenTrial) -> None:
+        assert study._thread_local.cached_all_trials is None
+        original_before_trial(study, trial)
+
+    sampler.before_trial = Mock(side_effect=mock_before_trial)
     study = optuna.study.create_study(sampler=sampler)
 
     def objective(trial: Trial) -> float:
         assert trial._relative_params is None
-        assert study._thread_local.cached_all_trials is None
 
         trial.suggest_float("x", -10, 10)
         trial.suggest_float("y", -10, 10)

From 7bd35d1ce89dcc5f60e0a738ab38dccfc6af1ce0 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 05:06:49 +0100
Subject: [PATCH 38/51] Apply c-bata's comment

---
 package/samplers/auto_sampler/__init__.py                 | 2 +-
 package/samplers/auto_sampler/{sampler.py => _sampler.py} | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename package/samplers/auto_sampler/{sampler.py => _sampler.py} (100%)

diff --git a/package/samplers/auto_sampler/__init__.py b/package/samplers/auto_sampler/__init__.py
index b74f25a5..fbe3b9f9 100644
--- a/package/samplers/auto_sampler/__init__.py
+++ b/package/samplers/auto_sampler/__init__.py
@@ -1,4 +1,4 @@
-from .sampler import AutoSampler
+from ._sampler import AutoSampler
 
 
 __all__ = ["AutoSampler"]
diff --git a/package/samplers/auto_sampler/sampler.py b/package/samplers/auto_sampler/_sampler.py
similarity index 100%
rename from package/samplers/auto_sampler/sampler.py
rename to package/samplers/auto_sampler/_sampler.py

From c2be1351ef54b038bfccb7c0a41f307c264ca435 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 05:09:40 +0100
Subject: [PATCH 39/51] Apply c-bata's comment

---
 package/samplers/auto_sampler/_sampler.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index c0ab815e..dc997956 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -32,9 +32,7 @@
 
 
 class _ThreadLocalSampler(threading.local):
-    def __init__(self, sampler: BaseSampler) -> None:
-        self._sampler = sampler
-        super().__init__()
+    _sampler: BaseSampler | None = None
 
 
 class AutoSampler(BaseSampler):
@@ -89,11 +87,13 @@ def __init__(
         self._rng = LazyRandomState(seed)
         seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
         sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
-        self._thread_local_sampler = _ThreadLocalSampler(sampler)
+        self._thread_local_sampler = _ThreadLocalSampler()
+        self._thread_local_sampler._sampler = sampler
         self._constraints_func = constraints_func
 
     @property
     def _sampler(self) -> BaseSampler:
+        assert self._sampler is not None
         return self._thread_local_sampler._sampler
 
     @_sampler.setter

From 9a4d0d9114ea204dc6760db98d6f8e6b02693a06 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 07:08:02 +0100
Subject: [PATCH 40/51] Fix local thread and its tests

---
 package/samplers/auto_sampler/_sampler.py            | 11 +++++++----
 .../samplers/auto_sampler/tests/test_auto_sampler.py | 12 ++++++++++++
 2 files changed, 19 insertions(+), 4 deletions(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index dc997956..9fa4115a 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -85,15 +85,18 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
-        sampler: BaseSampler = RandomSampler(seed=seed_for_random_sampler)
         self._thread_local_sampler = _ThreadLocalSampler()
-        self._thread_local_sampler._sampler = sampler
         self._constraints_func = constraints_func
 
     @property
     def _sampler(self) -> BaseSampler:
-        assert self._sampler is not None
+        if self._thread_local_sampler._sampler is None:
+            # NOTE(nabenabe): Do not do this process in the __init__ method because the
+            # substitution at the init does not update attributes in self._thread_local_sampler
+            # in each thread.
+            seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
+            self._sampler = RandomSampler(seed=seed_for_random_sampler)
+
         return self._thread_local_sampler._sampler
 
     @_sampler.setter
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index 0eab42b8..b765ae2a 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -135,3 +135,15 @@ def test_choose_tpe_with_conditional_params() -> None:
     assert ["RandomSampler"] + ["GPSampler"] * 15 + ["TPESampler"] * (
         n_trials - 16
     ) == sampler_names
+
+
+def test_multi_thread() -> None:
+    n_trials = 30
+    auto_sampler = AutoSampler()
+    auto_sampler._N_COMPLETE_TRIALS_FOR_CMAES = 10
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective, n_trials=n_trials)
+    sampler_names = _get_used_sampler_names(study)
+    assert "RandomSampler" in sampler_names
+    assert "GPSampler" in sampler_names
+    assert "CmaEsSampler" in sampler_names

From b8c45886185694ace8ecb5d46f9afd6701e6cc46 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 07:10:38 +0100
Subject: [PATCH 41/51] Remove algo description

---
 package/samplers/auto_sampler/README.md | 6 ------
 1 file changed, 6 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 11399bb4..1d726e74 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -56,9 +56,3 @@ pip install pytest
 ```python
 python -m pytest package/samplers/auto_sampler/tests/test_auto_sampler.py
 ```
-
-## Others
-
-### Algorithm Switch Heurtistic in AutoSampler
-
-TODO

From a2a1896eda091c78fd3628dd29ebe5dfa7dc3832 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 08:22:20 +0100
Subject: [PATCH 42/51] Add ref of test

---
 .../auto_sampler/tests/test_sampler.py        | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
index 470c47e4..de9e6f46 100644
--- a/package/samplers/auto_sampler/tests/test_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -1,3 +1,26 @@
+"""MIT License
+
+Copyright (c) 2018 Preferred Networks, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
+associated documentation files (the "Software"), to deal in the Software without restriction,
+including without limitation the rights to use, copy, modify, merge, publish, distribute,
+sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT
+NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT
+OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+This file is taken from Optuna (https://github.com/optuna/optuna/blob/master/tests/samplers_tests/test_samplers.py)
+and modified to test AutoSampler.
+"""
+
 from __future__ import annotations
 
 from collections.abc import Callable

From d56cbe3637feb01bb34bc6ea54b9ff8b2e77251e Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 08:55:56 +0100
Subject: [PATCH 43/51] Apply not's comment

---
 package/samplers/auto_sampler/_sampler.py                | 7 +++++--
 package/samplers/auto_sampler/tests/test_auto_sampler.py | 1 -
 2 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index 9fa4115a..c555ec19 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -28,6 +28,7 @@
 
 _MAXINT32 = (1 << 31) - 1
 _SAMPLER_KEY = "auto:sampler"
+# NOTE(nabenabe): The prefix `optuna.` enables us to use the optuna logger externally.
 _logger = get_logger(f"optuna.{__name__}")
 
 
@@ -52,7 +53,8 @@ class AutoSampler(BaseSampler):
 
             def objective(trial):
                 x = trial.suggest_float("x", -5, 5)
-                return x**2
+                y = trial.suggest_float("y", -5, 5)
+                return x**2 + y**2
 
             module = optunahub.load_module("samplers/auto_sampler")
             study = optuna.create_study(sampler=module.AutoSampler())
@@ -120,6 +122,7 @@ def _include_conditional_param(self, study: Study) -> bool:
     def _determine_multi_objective_sampler(
         self, study: Study, trial: FrozenTrial, search_space: dict[str, BaseDistribution]
     ) -> BaseSampler:
+        # TODO(nabenabe): Add more efficient heuristic for MO.
         if isinstance(self._sampler, NSGAIISampler):
             return self._sampler
 
@@ -152,7 +155,6 @@ def _determine_single_objective_sampler(
         complete_trials = study._get_trials(
             deepcopy=False, states=(TrialState.COMPLETE,), use_cache=True
         )
-        complete_trials.sort(key=lambda trial: trial.datetime_complete)
         if len(complete_trials) < self._N_COMPLETE_TRIALS_FOR_CMAES:
             # Use ``GPSampler`` if search space is numerical and
             # len(complete_trials) < _N_COMPLETE_TRIALS_FOR_CMAES.
@@ -162,6 +164,7 @@ def _determine_single_objective_sampler(
             # Use ``CmaEsSampler`` if search space is numerical and
             # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
             # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
+            complete_trials.sort(key=lambda trial: trial.datetime_complete)
             warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index b765ae2a..b5432636 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -6,7 +6,6 @@
 
 
 # TODO(nabaenabe): Add the CI for this sampler.
-# optuna.logging.set_verbosity(optuna.logging.CRITICAL)
 
 AutoSampler = optunahub.load_local_module(
     package="samplers/auto_sampler", registry_root="package/"

From 90193ffcd980b214666cabad53c1d05d4ac6327d Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Tue, 29 Oct 2024 09:01:14 +0100
Subject: [PATCH 44/51] Apply not's comment

---
 package/samplers/auto_sampler/_sampler.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index c555ec19..daa18160 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -141,7 +141,6 @@ def _determine_single_objective_sampler(
             or any(isinstance(d, CategoricalDistribution) for d in search_space.values())
             or self._include_conditional_param(study)
         ):
-            # NOTE(nabenabe): The statement above is always true for Trial#1.
             # Use ``TPESampler`` if search space includes conditional or categorical parameters.
             # TBD: group=True?
             return TPESampler(

From d37f6c9e01583f45c81150f30feb57c8f7c1bdc1 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Wed, 30 Oct 2024 05:44:05 +0100
Subject: [PATCH 45/51] Apply c-bata's comments

---
 package/samplers/auto_sampler/README.md   |  2 +-
 package/samplers/auto_sampler/_sampler.py | 12 ++++++------
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 1d726e74..96bddde9 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -54,5 +54,5 @@ pip install pytest
 ```
 
 ```python
-python -m pytest package/samplers/auto_sampler/tests/test_auto_sampler.py
+pytest package/samplers/auto_sampler/tests/
 ```
diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index daa18160..85aa971d 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -32,8 +32,8 @@
 _logger = get_logger(f"optuna.{__name__}")
 
 
-class _ThreadLocalSampler(threading.local):
-    _sampler: BaseSampler | None = None
+class ThreadLocalSampler(threading.local):
+    sampler: BaseSampler | None = None
 
 
 class AutoSampler(BaseSampler):
@@ -87,23 +87,23 @@ def __init__(
         constraints_func: Callable[[FrozenTrial], Sequence[float]] | None = None,
     ) -> None:
         self._rng = LazyRandomState(seed)
-        self._thread_local_sampler = _ThreadLocalSampler()
+        self._thread_local_sampler = ThreadLocalSampler()
         self._constraints_func = constraints_func
 
     @property
     def _sampler(self) -> BaseSampler:
-        if self._thread_local_sampler._sampler is None:
+        if self._thread_local_sampler.sampler is None:
             # NOTE(nabenabe): Do not do this process in the __init__ method because the
             # substitution at the init does not update attributes in self._thread_local_sampler
             # in each thread.
             seed_for_random_sampler = self._rng.rng.randint(_MAXINT32)
             self._sampler = RandomSampler(seed=seed_for_random_sampler)
 
-        return self._thread_local_sampler._sampler
+        return self._thread_local_sampler.sampler
 
     @_sampler.setter
     def _sampler(self, sampler: BaseSampler) -> None:
-        self._thread_local_sampler._sampler = sampler
+        self._thread_local_sampler.sampler = sampler
 
     def reseed_rng(self) -> None:
         self._rng.rng.seed()

From 13e63a6977b228bbb9c053269ec1a1782a2f39b6 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Wed, 30 Oct 2024 06:24:18 +0100
Subject: [PATCH 46/51] Apply c-bata's comments

---
 .../auto_sampler/tests/test_sampler.py        | 57 -------------------
 1 file changed, 57 deletions(-)

diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
index de9e6f46..6677d1a8 100644
--- a/package/samplers/auto_sampler/tests/test_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -77,63 +77,6 @@
 )
 
 
-"""
-@pytest.mark.parametrize(
-    # TODO: 知らん
-    "sampler_class,expected_has_rng,expected_has_another_sampler", [(AutoSampler, ..., ...)]
-)
-def test_sampler_reseed_rng(
-    sampler_class: Callable[[], BaseSampler],
-    expected_has_rng: bool,
-    expected_has_another_sampler: bool,
-) -> None:
-    def _extract_attr_name_from_sampler_by_cls(sampler: BaseSampler, cls: Any) -> str | None:
-        for name, attr in sampler.__dict__.items():
-            if isinstance(attr, cls):
-                return name
-        return None
-
-    sampler = sampler_class()
-
-    rng_name = _extract_attr_name_from_sampler_by_cls(sampler, LazyRandomState)
-    has_rng = rng_name is not None
-    assert expected_has_rng == has_rng
-    if has_rng:
-        rng_name = str(rng_name)
-        original_random_state = sampler.__dict__[rng_name].rng.get_state()
-        sampler.reseed_rng()
-        random_state = sampler.__dict__[rng_name].rng.get_state()
-        if not isinstance(sampler, optuna.samplers.CmaEsSampler):
-            assert str(original_random_state) != str(random_state)
-        else:
-            # CmaEsSampler has a RandomState that is not reseed by its reseed_rng method.
-            assert str(original_random_state) == str(random_state)
-
-    had_sampler_name = _extract_attr_name_from_sampler_by_cls(sampler, BaseSampler)
-    has_another_sampler = had_sampler_name is not None
-    assert expected_has_another_sampler == has_another_sampler
-
-    if has_another_sampler:
-        had_sampler_name = str(had_sampler_name)
-        had_sampler = sampler.__dict__[had_sampler_name]
-        had_sampler_rng_name = _extract_attr_name_from_sampler_by_cls(had_sampler, LazyRandomState)
-        original_had_sampler_random_state = had_sampler.__dict__[
-            had_sampler_rng_name
-        ].rng.get_state()
-        with patch.object(
-            had_sampler,
-            "reseed_rng",
-            wraps=had_sampler.reseed_rng,
-        ) as mock_object:
-            sampler.reseed_rng()
-            assert mock_object.call_count == 1
-
-        had_sampler = sampler.__dict__[had_sampler_name]
-        had_sampler_random_state = had_sampler.__dict__[had_sampler_rng_name].rng.get_state()
-        assert str(original_had_sampler_random_state) != str(had_sampler_random_state)
-"""
-
-
 def parametrize_suggest_method(name: str) -> MarkDecorator:
     return pytest.mark.parametrize(
         f"suggest_method_{name}",

From 396b70344e5541b96194621924e3b5cfbfe373f9 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 31 Oct 2024 05:29:49 +0100
Subject: [PATCH 47/51] Apply ozaki's comment

---
 .../auto_sampler/tests/test_sampler.py        | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/package/samplers/auto_sampler/tests/test_sampler.py b/package/samplers/auto_sampler/tests/test_sampler.py
index 6677d1a8..13b3933a 100644
--- a/package/samplers/auto_sampler/tests/test_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_sampler.py
@@ -29,7 +29,7 @@
 from multiprocessing.managers import DictProxy
 import os
 from typing import Any
-from unittest.mock import Mock
+from unittest.mock import patch
 import warnings
 
 from _pytest.fixtures import SubRequest
@@ -677,15 +677,15 @@ def mock_before_trial(study: Study, trial: FrozenTrial) -> None:
         assert study._thread_local.cached_all_trials is None
         original_before_trial(study, trial)
 
-    sampler.before_trial = Mock(side_effect=mock_before_trial)
-    study = optuna.study.create_study(sampler=sampler)
+    with patch.object(sampler, "before_trial", side_effect=mock_before_trial):
+        study = optuna.study.create_study(sampler=sampler)
 
-    def objective(trial: Trial) -> float:
-        assert trial._relative_params is None
+        def objective(trial: Trial) -> float:
+            assert trial._relative_params is None
 
-        trial.suggest_float("x", -10, 10)
-        trial.suggest_float("y", -10, 10)
-        assert trial._relative_params is not None
-        return -1
+            trial.suggest_float("x", -10, 10)
+            trial.suggest_float("y", -10, 10)
+            assert trial._relative_params is not None
+            return -1
 
-    study.optimize(objective, n_trials=10, n_jobs=n_jobs)
+        study.optimize(objective, n_trials=10, n_jobs=n_jobs)

From a5bc25eca548412c4a42924f9cc22a767e0b63c6 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 31 Oct 2024 05:30:17 +0100
Subject: [PATCH 48/51] Apply not's comment

---
 package/samplers/auto_sampler/_sampler.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index 85aa971d..28e52e4c 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -168,7 +168,7 @@ def _determine_single_objective_sampler(
             # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
             # 1D problems.
             return CmaEsSampler(
-                seed=seed, source_trials=warm_start_trials, warn_independent_sampling=False
+                seed=seed, source_trials=warm_start_trials, warn_independent_sampling=True
             )
 
         return self._sampler  # No update happens to self._sampler.

From ea89dd19d12d7b85b848717823b606d9895cb6ed Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 31 Oct 2024 08:44:44 +0100
Subject: [PATCH 49/51] Apply the feedback from the mob review

---
 package/samplers/auto_sampler/_sampler.py     | 27 ++++++++++++-------
 .../auto_sampler/tests/test_auto_sampler.py   | 20 ++++++++++++++
 2 files changed, 37 insertions(+), 10 deletions(-)

diff --git a/package/samplers/auto_sampler/_sampler.py b/package/samplers/auto_sampler/_sampler.py
index 28e52e4c..55669274 100644
--- a/package/samplers/auto_sampler/_sampler.py
+++ b/package/samplers/auto_sampler/_sampler.py
@@ -159,16 +159,23 @@ def _determine_single_objective_sampler(
             # len(complete_trials) < _N_COMPLETE_TRIALS_FOR_CMAES.
             if not isinstance(self._sampler, GPSampler):
                 return GPSampler(seed=seed)
-        elif not isinstance(self._sampler, CmaEsSampler):
-            # Use ``CmaEsSampler`` if search space is numerical and
-            # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
-            # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
-            complete_trials.sort(key=lambda trial: trial.datetime_complete)
-            warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
-            # NOTE(nabenabe): ``CmaEsSampler`` internally falls back to ``RandomSampler`` for
-            # 1D problems.
-            return CmaEsSampler(
-                seed=seed, source_trials=warm_start_trials, warn_independent_sampling=True
+        elif len(search_space) > 1:
+            if not isinstance(self._sampler, CmaEsSampler):
+                # Use ``CmaEsSampler`` if search space is numerical and
+                # len(complete_trials) > _N_COMPLETE_TRIALS_FOR_CMAES.
+                # Warm start CMA-ES with the first _N_COMPLETE_TRIALS_FOR_CMAES complete trials.
+                complete_trials.sort(key=lambda trial: trial.datetime_complete)
+                warm_start_trials = complete_trials[: self._N_COMPLETE_TRIALS_FOR_CMAES]
+                return CmaEsSampler(
+                    seed=seed, source_trials=warm_start_trials, warn_independent_sampling=True
+                )
+        else:
+            return TPESampler(
+                seed=seed,
+                multivariate=True,
+                warn_independent_sampling=False,
+                constraints_func=self._constraints_func,
+                constant_liar=True,
             )
 
         return self._sampler  # No update happens to self._sampler.
diff --git a/package/samplers/auto_sampler/tests/test_auto_sampler.py b/package/samplers/auto_sampler/tests/test_auto_sampler.py
index b5432636..130473cc 100644
--- a/package/samplers/auto_sampler/tests/test_auto_sampler.py
+++ b/package/samplers/auto_sampler/tests/test_auto_sampler.py
@@ -14,6 +14,11 @@
 parametrize_constraints = pytest.mark.parametrize("use_constraint", [True, False])
 
 
+def objective_1d(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", -5, 5)
+    return x**2
+
+
 def objective(trial: optuna.Trial) -> float:
     x = trial.suggest_float("x", -5, 5)
     y = trial.suggest_int("y", -5, 5)
@@ -105,6 +110,21 @@ def test_choose_cmaes() -> None:
     ] * n_trials_of_cmaes == sampler_names
 
 
+def test_choose_tpe_for_1d() -> None:
+    # This test must be performed with a numerical objective function.
+    # For 1d problems, TPESampler will be chosen instead of CmaEsSampler.
+    n_trials_of_tpe = 100
+    n_trials_before_tpe = 20
+    auto_sampler = AutoSampler()
+    auto_sampler._N_COMPLETE_TRIALS_FOR_CMAES = n_trials_before_tpe
+    study = optuna.create_study(sampler=auto_sampler)
+    study.optimize(objective_1d, n_trials=n_trials_of_tpe + n_trials_before_tpe)
+    sampler_names = _get_used_sampler_names(study)
+    assert ["RandomSampler"] + ["GPSampler"] * (n_trials_before_tpe - 1) + [
+        "TPESampler"
+    ] * n_trials_of_tpe == sampler_names
+
+
 def test_choose_tpe_in_single_with_constraints() -> None:
     n_trials = 30
     auto_sampler = AutoSampler(constraints_func=constraints_func)

From 5660f62340c1d34b4af93546ea61230a94bdcb15 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 31 Oct 2024 08:47:47 +0100
Subject: [PATCH 50/51] Add an Optuna version constraint for GPSampler with
 n_jobs>1

---
 package/samplers/auto_sampler/README.md        | 2 +-
 package/samplers/auto_sampler/requirements.txt | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index 96bddde9..efeefd6f 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -3,7 +3,7 @@ author: Optuna Team
 title: AutoSampler
 description: This sampler automatically chooses an appropriate built-in sampler for the provided objective function.
 tags: [sampler, automatic, automated]
-optuna_versions: [4.0.0]
+optuna_versions: [4.1.0]
 license: MIT License
 ---
 
diff --git a/package/samplers/auto_sampler/requirements.txt b/package/samplers/auto_sampler/requirements.txt
index 10fd7a0f..52b403bd 100644
--- a/package/samplers/auto_sampler/requirements.txt
+++ b/package/samplers/auto_sampler/requirements.txt
@@ -1,3 +1,4 @@
 scipy
 torch
 cmaes
+optuna>=4.1.0

From b0da1943f041e8a8a704821946fd7b391d0b6455 Mon Sep 17 00:00:00 2001
From: nabenabe0928 <shuhei.watanabe.utokyo@gmail.com>
Date: Thu, 31 Oct 2024 08:56:38 +0100
Subject: [PATCH 51/51] Add the note about the Optuna version constraint and
 remove constraint from requirements

---
 package/samplers/auto_sampler/README.md        | 2 ++
 package/samplers/auto_sampler/requirements.txt | 1 -
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/package/samplers/auto_sampler/README.md b/package/samplers/auto_sampler/README.md
index efeefd6f..78ffb581 100644
--- a/package/samplers/auto_sampler/README.md
+++ b/package/samplers/auto_sampler/README.md
@@ -27,6 +27,8 @@ This sampler requires optional dependencies of Optuna.
 $ pip install optunahub cmaes torch scipy
 ```
 
+Note that since we may update the implementation of `AutoSampler`, it is highly encouraged to use the latest version of Optuna.
+
 ## Example
 
 ```python
diff --git a/package/samplers/auto_sampler/requirements.txt b/package/samplers/auto_sampler/requirements.txt
index 52b403bd..10fd7a0f 100644
--- a/package/samplers/auto_sampler/requirements.txt
+++ b/package/samplers/auto_sampler/requirements.txt
@@ -1,4 +1,3 @@
 scipy
 torch
 cmaes
-optuna>=4.1.0