Add READM

package/samplers/ctpe/README.md

@@ -19,6 +19,10 @@ The default parameter set of this sampler is the recommended setup from the pape
 
 - cTPESampler
 
+Although most arguments in [`TPESampler`](https://optuna.readthedocs.io/en/stable/reference/samplers/generated/optuna.samplers.TPESampler.html) are also available for `cTPESampler`, the original paper uses the default arguments of this sampler.
+However, `constraints_func` must be provided to use this sampler.
+For the reproducibility purpose, please set a fixed `seed`.
+
 ## Installation
 
 The version constraint of this package is Optuna v4.0.0 or later.
@@ -33,62 +37,36 @@ $ pip install -r https://hub.optuna.org/samplers/ctpe/requirements.txt
 
 ## Example
 
-TODO: Change here.
-
-This sampler supports the arguments discussed in [the original paper](https://arxiv.org/abs/2304.11127) and can be used in this way.
+This sampler is the official implementation of [the c-TPE paper](https://arxiv.org/abs/2211.14411).
 
 ```python
-import numpy as np
+from __future__ import annotations
 
+import numpy as np
 import optuna
-
 import optunahub
 
 
-def objective(trial):
-    x = trial.suggest_float("x", -5, 5)
-    y = trial.suggest_int("y", -5, 5)
-    z = trial.suggest_categorical("z", ["a", "aa", "aaa"])
-    return len(z) * (x**2 + y**2)
-
-
-module = optunahub.load_module(package="samplers/tpe_tutorial")
-optuna.logging.set_verbosity(optuna.logging.CRITICAL)
-arg_choices = {
-    "consider_prior": [True, False],
-    "consider_magic_clip": [True, False],
-    "multivariate": [True, False],
-    "b_magic_exponent": [0.5, 1.0, 2.0, np.inf],
-    "min_bandwidth_factor": [0.01, 0.1],
-    "gamma_strategy": ["linear", "sqrt"],
-    "weight_strategy": ["uniform", "old-decay", "old-drop", "EI"],
-    "bandwidth_strategy": ["optuna", "hyperopt", "scott"],
-    "categorical_prior_weight": [0.1, None],
-}
-for arg_name, choices in arg_choices.items():
-    results = []
-    for choice in choices:
-        print(arg_name, choice)
-        sampler = module.CustomizableTPESampler(seed=0, **{arg_name: choice})
-        study = optuna.create_study(sampler=sampler)
-        study.optimize(objective, n_trials=100 if arg_name != "b_magic_exponent" else 200)
-        results.append(study.trials[-1].value)
+def objective(trial: optuna.Trial) -> float:
+    x = trial.suggest_float("x", 0.0, 2 * np.pi)
+    y = trial.suggest_float("y", 0.0, 2 * np.pi)
+    return float(np.sin(x) + y)
 
-    print(f"Did every setup yield different results for {arg_name}?: {len(set(results)) == len(results)}")
 
-```
+def constraints(trial: optuna.trial.FrozenTrial) -> tuple[float]:
+    x = trial.params["x"]
+    y = trial.params["y"]
+    c = float(np.sin(x) * np.sin(y) + 0.95)
+    trial.set_user_attr("c", c)
+    return (c, )
 
-In the paper, the following arguments, which do not exist in Optuna, were researched:
 
-- `gamma_strategy`: The splitting algorithm in Table 3. The choices are `linear` and `sqrt`.
-- `gamma_beta`: The beta parameter for the splitting algorithm in Table 3. This value must be positive.
-- `weight_strategy`: The weighting algorithm in Table 3. The choices are `uniform`, `old-decay`, `old-drop`, and `EI`.
-- `categorical_prior_weight`: The categorical bandwidth in Table 3. If `None`, the Optuna default algorithm will be used.
-- `bandwidth_strategy`: The bandwidth selection heuristic in Table 6. The choices are `optuna`, `hyperopt`, and `scott`.
-- `min_bandwidth_factor`: The minimum bandwidth factor in Table 6. This value must be positive.
-- `b_magic_exponent`: The exponent alpha in Table 6. Optuna takes 1.0 by default.
+sampler = optunahub.load_module(package="samplers/ctpe").cTPESampler(constraints_func=constraints)
+study = optuna.create_study(sampler=sampler)
+study.optimize(objective, n_trials=30)
+print(study.best_trials)
 
-For more details, please check [the paper](https://arxiv.org/abs/2304.11127).
+```
 
 ### Bibtex