Remove unused file optuna_multi_objective.py

.github/workflows/ci.yml

@@ -8,7 +8,7 @@ jobs:
 
     strategy:
       matrix:
-        python-version: [3.5, 3.6, 3.7, 3.8]
+        python-version: [3.6, 3.7, 3.8, 3.9]
 
     steps:
     - uses: actions/checkout@v1
@@ -31,6 +31,7 @@ jobs:
       if: matrix.python-version == 3.7
       run: |
         pip install --progress-bar off $(ls dist/*.tar.gz)[checking]
+        pip install --progress-bar off types-setuptools
 
     - name: Run checks
       if: matrix.python-version == 3.7

examples/quadratic_problem.py

@@ -38,7 +38,7 @@ def current_step(self) -> int:
 
     def evaluate(self, next_step: int) -> List[float]:
         self._current_step = 1
-        return [self._x ** 2 + self._y]
+        return [self._x**2 + self._y]
 
 
 if __name__ == "__main__":

examples/random_solver.py

@@ -1,3 +1,6 @@
+from typing import List
+from typing import Optional
+
 import numpy as np
 
 from kurobako import problem
@@ -18,7 +21,7 @@ def __init__(self, seed: int, problem: problem.ProblemSpec):
         self._problem = problem
 
     def ask(self, idg: solver.TrialIdGenerator) -> solver.NextTrial:
-        params = []
+        params: List[Optional[float]] = []
         for p in self._problem.params:
             if p.distribution == problem.Distribution.UNIFORM:
                 params.append(self._rng.uniform(p.range.low, p.range.high))

kurobako/solver/__init__.py

@@ -132,7 +132,11 @@ def __init__(self, trial_id: int, values: List[float], current_step: int):
         self.current_step = current_step
 
     def to_dict(self) -> Dict[str, Any]:
-        return {"id": self.trial_id, "values": self.values, "current_step": self.current_step}
+        return {
+            "id": self.trial_id,
+            "values": self.values,
+            "current_step": self.current_step,
+        }
 
     @staticmethod
     def from_dict(d: Dict[str, Any]) -> Any:
@@ -221,7 +225,11 @@ def _handle_ask_call(self, message: Dict[str, Any]):
         solver = self._solvers[solver_id]
         trial = solver.ask(idg)
 
-        message = {"type": "ASK_REPLY", "trial": trial.to_dict(), "next_trial_id": idg.next_id}
+        message = {
+            "type": "ASK_REPLY",
+            "trial": trial.to_dict(),
+            "next_trial_id": idg.next_id,
+        }
         self._send_message(message)
 
     def _handle_tell_call(self, message: Dict[str, Any]):

kurobako/solver/optuna.py

@@ -1,6 +1,6 @@
 import optuna
-from pkg_resources import get_distribution
 from pkg_resources import DistributionNotFound
+from pkg_resources import get_distribution
 import queue
 from typing import Callable
 from typing import Dict  # NOQA
@@ -28,11 +28,11 @@ def __init__(
         self._warm_starting_trials = warm_starting_trials
 
     def specification(self) -> solver.SolverSpec:
-        try: 
+        try:
             optuna_version = get_distribution("optuna").version
         except DistributionNotFound:
-            optuna_version = "unknown" 
-        
+            optuna_version = "unknown"
+
         try:
             kurobako_version = get_distribution("kurobako").version
         except DistributionNotFound:
@@ -41,9 +41,7 @@ def specification(self) -> solver.SolverSpec:
         return solver.SolverSpec(
             name=self._name,
             attrs={
-                "version": "optuna={}, kurobako-py={}".format(
-                    optuna_version, kurobako_version
-                ),
+                "version": "optuna={}, kurobako-py={}".format(optuna_version, kurobako_version),
                 "github": "https://github.com/optuna/optuna",
                 "paper": 'Akiba, Takuya, et al. "Optuna: A next-generation hyperparameter '
                 'optimization framework." Proceedings of the 25th ACM SIGKDD International '
@@ -101,7 +99,7 @@ def _next_step(self, current_step: int) -> int:
                 rung = 0
                 while True:
                     n = pruner._min_early_stopping_rate + rung
-                    step = pruner._min_resource * (pruner._reduction_factor ** n)
+                    step = pruner._min_resource * (pruner._reduction_factor**n)
                     if step > current_step:
                         return min(step, self._problem.last_step)
                     rung += 1