Merge pull request #20 from optuna/align-readme-style-to-template

Aligh package's readme to the template one

package/samplers/demo/README.md

@@ -3,36 +3,16 @@ author: 'Optuna team'
 title: 'Demo Sampler'
 description: 'Demo Sampler of OptunaHub'
 tags: ['sampler']
-optuna_versions: [3.6.1]
-license: 'MIT'
+optuna_versions: ['3.6.1']
+license: 'MIT License'
 ---
 
-This package provides a demo sampler of OptunaHub.
-
-## class DemoSampler(seed)
-
-### Parameters
-- `seed: int` - A random seed
-
+## Class or Function Names
+- DemoSampler
 
 ## Example
-
 ```python
-import optuna
-import optunahub
-
-
-def objective(trial: optuna.Trial) -> float:
-    x = trial.suggest_float("x", 0, 1)
-
-    return x
-
-
-if __name__ == "__main__":
-    module = optunahub.load_module("samplers/demo")
-    sampler = module.DemoSampler(seed=42)
-    study = optuna.create_study(sampler=sampler)
-    study.optimize(objective, n_trials=5)
-
-    print(study.best_trial)
+module = optunahub.load_module("samplers/demo")
+sampler = module.DemoSampler(seed=42)
 ```
+See `example.py <example.py>`_ for more details.

package/samplers/demo/example.py

@@ -0,0 +1,17 @@
+import optuna
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", 0, 1)
+
+    return x
+
+
+if __name__ == "__main__":
+    module = optunahub.load_module("samplers/demo")
+    sampler = module.DemoSampler(seed=42)
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=5)
+
+    print(study.best_trial)

package/samplers/simple/README.md

@@ -3,82 +3,22 @@ author: 'Optuna team'
 title: 'Simple Sampler'
 description: 'An easy sampler base class to implement custom samplers.'
 tags: ['sampler', 'development']
-optuna_versions: [3.6.1]
-license: 'MIT'
+optuna_versions: ['3.6.1']
+license: 'MIT License'
 ---
 
-Simple Sampler
-===
-
-This package provides an easy sampler base class to implement custom samplers.
-You can make your own sampler easily by inheriting `SimpleSampler` and by implementing necessary methods.
-
-
-# SimpleSampler
-
-```python
-class SimpleSampler(search_space)
-```
-
-
-## Parameters
-- `search_space (dict[str, BaseDistribution])` - A search space for the objective function.
-
+## Class or Function Names
+- SimpleSampler
 
 ## Example
-
 ```python
-from typing import Any
-
-import numpy as np
-import optuna
-import optunahub
-from optuna import Study
-from optuna.distributions import BaseDistribution
-from optuna.distributions import FloatDistribution
-from optuna.distributions import IntDistribution
-from optuna.samplers import BaseSampler
-from optuna.trial import FrozenTrial
-
-
 class UserDefinedSampler(
     optunahub.load_module("samplers/simple").SimpleSampler
 ):
-    def __init__(self, search_space: dict[str, BaseDistribution]) -> None:
-        super().__init__(search_space)
-        self._rng = np.random.RandomState()
-
-    def sample_relative(
-        self,
-        study: Study,
-        trial: FrozenTrial,
-        search_space: dict[str, BaseDistribution],
-    ) -> dict[str, Any]:
-        params = {}
-        for n, d in search_space.items():
-            if isinstance(d, FloatDistribution):
-                params[n] = self._rng.uniform(d.low, d.high)
-            elif isinstance(d, IntDistribution):
-                params[n] = self._rng.randint(d.low, d.high)
-            else:
-                raise ValueError("Unsupported distribution")
-        return params
-
-
-if __name__ == "__main__":
-
-    def objective(trial: optuna.Trial) -> float:
-        x = trial.suggest_float("x", 0, 1)
-
-        return x
-
-    sampler = UserDefinedSampler({"x": FloatDistribution(0, 1)})
-    study = optuna.create_study(sampler=sampler)
-    study.optimize(objective, n_trials=20)
-
-    print(study.best_trial.value, study.best_trial.params)
+    ...
 ```
+See `example.py <example.py>`_ for more details.
 
-# Author Information
-
-This package is contributed by [Optuna team](https://github.com/orgs/optuna/people).
+## Others
+This package provides an easy sampler base class to implement custom samplers.
+You can make your own sampler easily by inheriting `SimpleSampler` and by implementing necessary methods.

package/samplers/simple/example.py

@@ -0,0 +1,46 @@
+from typing import Any
+
+import numpy as np
+import optuna
+from optuna import Study
+from optuna.distributions import BaseDistribution
+from optuna.distributions import FloatDistribution
+from optuna.distributions import IntDistribution
+from optuna.trial import FrozenTrial
+import optunahub
+
+
+class UserDefinedSampler(optunahub.load_module("samplers/simple").SimpleSampler):  # type: ignore
+    def __init__(self, search_space: dict[str, BaseDistribution]) -> None:
+        super().__init__(search_space)
+        self._rng = np.random.RandomState()
+
+    def sample_relative(
+        self,
+        study: Study,
+        trial: FrozenTrial,
+        search_space: dict[str, BaseDistribution],
+    ) -> dict[str, Any]:
+        params = {}
+        for n, d in search_space.items():
+            if isinstance(d, FloatDistribution):
+                params[n] = self._rng.uniform(d.low, d.high)
+            elif isinstance(d, IntDistribution):
+                params[n] = self._rng.randint(d.low, d.high)
+            else:
+                raise ValueError("Unsupported distribution")
+        return params
+
+
+if __name__ == "__main__":
+
+    def objective(trial: optuna.Trial) -> float:
+        x = trial.suggest_float("x", 0, 1)
+
+        return x
+
+    sampler = UserDefinedSampler({"x": FloatDistribution(0, 1)})
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=20)
+
+    print(study.best_trial.value, study.best_trial.params)

package/samplers/simulated_annealing/README.md

@@ -3,52 +3,20 @@ author: 'Optuna team'
 title: 'Simulated Annealing Sampler'
 description: 'Sampler based on simulated annealing algorithm.'
 tags: ['sampler', 'simulated annealing']
-optuna_versions: [3.5, 3.6]
-license: 'MIT'
+optuna_versions: ['3.5.0', '3.6.0']
+license: 'MIT License'
 ---
 
-Simulated Annealing Sampler
-===
-
-This package provides a sampler based on Simulated Annealing algorithm.
-For more details, see [the documentation](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/005_user_defined_sampler.html).
-
-
-# SimulatedAnnealingSampler
-
-```python
-class SimulatedAnnealingSampler(temperature=100)
-```
-
-Sampler based on Simulated Annealing algorithm.
-
-## Parameters
-- `temperature=100 (int)` - A temperature parameter for simulated annealing.
-
+## Class or Function Names
+- SimulatedAnnealingSampler
 
 ## Example
-
 ```python
-import optunahub
-import optuna
-
-
-def objective(trial: optuna.Trial) -> float:
-    x = trial.suggest_float("x", 0, 1)
-
-    return x
-
-
-if __name__ == "__main__":
-    mod = optunahub.load_module("samplers/simulated_annealing")
-
-    sampler = mod.SimulatedAnnealingSampler()
-    study = optuna.create_study(sampler=sampler)
-    study.optimize(objective, n_trials=20)
-
-    print(study.best_trial.value, study.best_trial.params)
+mod = optunahub.load_module("samplers/simulated_annealing")
+sampler = mod.SimulatedAnnealingSampler()
 ```
+See `example.py <example.py>`_ for more details.
 
-# Author Information
-
-This package is contributed by [Optuna team](https://github.com/orgs/optuna/people).
+## Others
+This package provides a sampler based on Simulated Annealing algorithm.
+For more details, see [the documentation](https://optuna.readthedocs.io/en/stable/tutorial/20_recipes/005_user_defined_sampler.html).

package/samplers/simulated_annealing/example.py

@@ -0,0 +1,18 @@
+import optuna
+import optunahub
+
+
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", 0, 1)
+
+    return x
+
+
+if __name__ == "__main__":
+    mod = optunahub.load_module("samplers/simulated_annealing")
+
+    sampler = mod.SimulatedAnnealingSampler()
+    study = optuna.create_study(sampler=sampler)
+    study.optimize(objective, n_trials=20)
+
+    print(study.best_trial.value, study.best_trial.params)

package/visualization/plot_hypervolume_history_with_rp/README.md

@@ -3,69 +3,19 @@ author: 'Optuna team'
 title: 'Plot Hypervolume History with Reference Point'
 description: 'Plot hypervolume history with the reference point information.'
 tags: ['visualization', 'hypervolume', 'multi-objective optimization']
-optuna_versions: ['3.6']
-license: MIT
+optuna_versions: ['3.6.0']
+license: 'MIT License'
 ---
 
-Plot Hypervolume History with Reference Point
-===
+## Class or Function Names
+- plot_hypervolume_history
 
-This package provides a function to plot hypervolume history with the reference point information.
-
-# plot_hypervolume_history
+## Example
 ```python
-def plot_hypervolume_history(study, reference_point) -> "go.Figure"
-```
-
-Plot hypervolume history of all trials in a study.
-
-
-## Parameters
-- `study (Study)` – A Study object whose trials are plotted for their hypervolumes. The number of objectives must be 2 or more.
-- `reference_point (Sequence[float])` – A reference point to use for hypervolume computation. The dimension of the reference point must be the same as the number of objectives.
-
-
-## Returns
-- A `plotly.graph_objects.Figure` object.
-
-
-## Return type
-- `Figure`
-
-
-# Example
-
-The following code snippet shows how to plot optimization history.
-
-
-```python
-import optuna
-import optunahub
-
-
-def objective(trial):
-    x = trial.suggest_float("x", 0, 5)
-    y = trial.suggest_float("y", 0, 3)
-
-    v0 = 4 * x ** 2 + 4 * y ** 2
-    v1 = (x - 5) ** 2 + (y - 5) ** 2
-    return v0, v1
-
-
-if __name__ == "__main__":
-    mod = optunahub.load_module("visualization/plot_hypervolume_history_with_rp")
-
-    study = optuna.create_study(directions=["minimize", "minimize"])
-    study.optimize(objective, n_trials=50)
-
-    reference_point=[100., 50.]
-    fig = mod.plot_hypervolume_history(study, reference_point)
-    fig.show()
+mod = optunahub.load_module("visualization/plot_hypervolume_history_with_rp")
+mod.plot_hypervolume_history(study, reference_point)
 ```
+See `example.py <example.py>`_ for more details.
+The example of generated image is as follows.
 
 ![Example](images/example.png "Example")
-
-
-# Author Information
-
-This package is contributed by [Optuna team](https://github.com/orgs/optuna/people).

package/visualization/plot_hypervolume_history_with_rp/example.py

@@ -0,0 +1,22 @@
+import optuna
+import optunahub
+
+
+def objective(trial: optuna.trial.Trial) -> tuple[float, float]:
+    x = trial.suggest_float("x", 0, 5)
+    y = trial.suggest_float("y", 0, 3)
+
+    v0 = 4 * x**2 + 4 * y**2
+    v1 = (x - 5) ** 2 + (y - 5) ** 2
+    return v0, v1
+
+
+if __name__ == "__main__":
+    mod = optunahub.load_module("visualization/plot_hypervolume_history_with_rp")
+
+    study = optuna.create_study(directions=["minimize", "minimize"])
+    study.optimize(objective, n_trials=50)
+
+    reference_point = [100.0, 50.0]
+    fig = mod.plot_hypervolume_history(study, reference_point)
+    fig.show()