Support black 23.1.0

benchmarks/bayesmark/optuna_optimizer.py

@@ -43,11 +43,9 @@
 
 
 class OptunaOptimizer(AbstractOptimizer):
-
     primary_import = "optuna"
 
     def __init__(self, api_config: ApiConfig, **kwargs: Any) -> None:
-
         super().__init__(api_config, **kwargs)
 
         try:
@@ -74,7 +72,6 @@ def __init__(self, api_config: ApiConfig, **kwargs: Any) -> None:
         self.current_trials: Dict[int, int] = dict()
 
     def _suggest(self, trial: optuna.trial.Trial) -> Suggestion:
-
         suggestions: Suggestion = dict()
         for name, config in self.api_config.items():
             low, high = config["range"]
@@ -100,7 +97,6 @@ def _suggest(self, trial: optuna.trial.Trial) -> Suggestion:
         return suggestions
 
     def suggest(self, n_suggestions: int) -> List[Suggestion]:
-
         suggestions: List[Suggestion] = list()
         for _ in range(n_suggestions):
             trial = self.study.ask()
@@ -112,7 +108,6 @@ def suggest(self, n_suggestions: int) -> List[Suggestion]:
         return suggestions
 
     def observe(self, X: List[Suggestion], y: List[float]) -> None:
-
         for params, objective_value in zip(X, y):
             sid = hash(frozenset(params.items()))
             trial = self.current_trials.pop(sid)

benchmarks/bayesmark/report_bayesmark.py

@@ -49,7 +49,6 @@ class BestValueMetric(BaseMetric):
     precision = 6
 
     def calculate(self, data: pd.DataFrame) -> List[float]:
-
         return data.groupby("uuid").generalization.min().values
 
 
@@ -58,7 +57,6 @@ class AUCMetric(BaseMetric):
     precision = 3
 
     def calculate(self, data: pd.DataFrame) -> List[float]:
-
         aucs: List[float] = list()
         for _, grp in data.groupby("uuid"):
             auc = np.sum(grp.generalization.cummin())
@@ -71,7 +69,6 @@ class ElapsedMetric(BaseMetric):
     precision = 3
 
     def calculate(self, data: pd.DataFrame) -> List[float]:
-
         # Total time does not include evaluation of bayesmark
         # objective function (no Optuna APIs are called there).
         time_cols = ["suggest", "observe"]
@@ -80,22 +77,18 @@ def calculate(self, data: pd.DataFrame) -> List[float]:
 
 class PartialReport:
     def __init__(self, data: pd.DataFrame) -> None:
-
         self._data = data
 
     @property
     def optimizers(self) -> List[str]:
-
         return list(self._data.opt.unique())
 
     @classmethod
     def from_json(cls, path: str) -> "PartialReport":
-
         data = pd.read_json(path)
         return cls(data)
 
     def summarize_solver(self, solver: str, metric: BaseMetric) -> Moments:
-
         solver_data = self._data[self._data.opt == solver]
         if solver_data.shape[0] == 0:
             raise ValueError(f"{solver} not found in report.")
@@ -104,7 +97,6 @@ def summarize_solver(self, solver: str, metric: BaseMetric) -> Moments:
         return np.mean(run_metrics).item(), np.var(run_metrics).item()
 
     def sample_performance(self, metric: BaseMetric) -> Samples:
-
         performance: Dict[str, List[float]] = {}
         for solver, data in self._data.groupby("opt"):
             run_metrics = metric.calculate(data)
@@ -119,26 +111,22 @@ def __init__(self, metrics: List[BaseMetric]) -> None:
         self._borda: Optional[np.ndarray] = None
 
     def __iter__(self) -> Generator[Tuple[str, int], None, None]:
-
         yield from zip(self.solvers, self.borda)
 
     @property
     def solvers(self) -> List[str]:
-
         if self._ranking is None:
             raise ValueError("Call rank first.")
         return self._ranking
 
     @property
     def borda(self) -> np.ndarray:
-
         if self._borda is None:
             raise ValueError("Call rank first.")
         return self._borda
 
     @staticmethod
     def pick_alpha(report: PartialReport) -> float:
-
         # https://github.com/optuna/kurobako/blob/788dd4cf618965a4a5158aa4e13607a5803dea9d/src/report.rs#L412-L424  # noqa E501
         num_optimizers = len(report.optimizers)
         candidates = [0.075, 0.05, 0.025, 0.01] * 4 / np.repeat([1, 10, 100, 1000], 4)
@@ -149,19 +137,16 @@ def pick_alpha(report: PartialReport) -> float:
         return candidates[-1]
 
     def _set_ranking(self, wins: Dict[str, int]) -> None:
-
         sorted_wins = [k for k, _ in sorted(wins.items(), key=lambda x: x[1])]
         self._ranking = sorted_wins[::-1]
 
     def _set_borda(self, wins: Dict[str, int]) -> None:
-
         sorted_wins = np.array(sorted(wins.values()))
         num_wins, num_ties = np.unique(sorted_wins, return_counts=True)
         points = np.searchsorted(sorted_wins, num_wins)
         self._borda = np.repeat(points, num_ties)[::-1]
 
     def rank(self, report: PartialReport) -> None:
-
         # Implements Section 2.1.1
         # https://proceedings.mlr.press/v64/dewancker_strategy_2016.pdf
         wins: Dict[str, int] = defaultdict(int)
@@ -200,7 +185,6 @@ class Problem:
 
 class BayesmarkReportBuilder:
     def __init__(self) -> None:
-
         self.solvers: Set[str] = set()
         self.datasets: Set[str] = set()
         self.models: Set[str] = set()
@@ -210,7 +194,6 @@ def __init__(self) -> None:
         self.problems: List[Problem] = []
 
     def set_precedence(self, metrics: List[BaseMetric]) -> None:
-
         self.metric_precedence = " -> ".join([m.name for m in metrics])
 
     def add_problem(
@@ -220,7 +203,6 @@ def add_problem(
         ranking: DewanckerRanker,
         metrics: List[BaseMetric],
     ) -> "BayesmarkReportBuilder":
-
         solvers: List[Solver] = list()
         positions = np.abs(ranking.borda - (max(ranking.borda) + 1))
         for pos, solver in zip(positions, ranking.solvers):
@@ -238,33 +220,28 @@ def add_problem(
         return self
 
     def update_leaderboard(self, ranking: DewanckerRanker) -> "BayesmarkReportBuilder":
-
         for solver, borda in ranking:
             if borda == max(ranking.borda):
                 self.firsts[solver] += 1
             self.borda[solver] += borda
         return self
 
     def add_dataset(self, dataset: str) -> "BayesmarkReportBuilder":
-
         self.datasets.update(dataset)
         return self
 
     def add_model(self, model: str) -> "BayesmarkReportBuilder":
-
         self.models.update(model)
         return self
 
     def assemble_report(self) -> str:
-
         loader = FileSystemLoader(os.path.join("benchmarks", "bayesmark"))
         env = Environment(loader=loader)
         report_template = env.get_template("report_template.md")
         return report_template.render(report=self)
 
 
 def build_report() -> None:
-
     # Order of this list sets metric precedence.
     # https://proceedings.mlr.press/v64/dewancker_strategy_2016.pdf
     metrics = [BestValueMetric(), AUCMetric()]

benchmarks/kurobako/mo_create_study.py

@@ -38,7 +38,6 @@ def create_study(seed: int) -> optuna.Study:
 
 
 if __name__ == "__main__":
-
     factory = OptunaSolverFactory(create_study)
     runner = solver.SolverRunner(factory)
     runner.run()

benchmarks/kurobako/problems/wfg/problem.py

@@ -19,7 +19,6 @@ def __init__(
         shapes: List[shape_functions.BaseShapeFunction],
         transformations: List[List[transformation_functions.BaseTransformations]],
     ) -> None:
-
         assert all(S > 0)
         assert all((A == 0) + (A == 1))
         assert all(upper_bounds > 0)
@@ -31,7 +30,6 @@ def __init__(
         self._transformations = transformations
 
     def __call__(self, z: np.ndarray) -> np.ndarray:
-
         S = self._S
         A = self._A
         unit_z = z / self._upper_bounds
@@ -69,7 +67,6 @@ class WFG1:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -148,7 +145,6 @@ class WFG2:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments // 2
         assert (n_arguments - k) % 2 == 0
@@ -229,7 +225,6 @@ class WFG3:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments // 2
         assert (n_arguments - k) % 2 == 0
@@ -310,7 +305,6 @@ class WFG4:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -377,7 +371,6 @@ class WFG5:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -445,7 +438,6 @@ class WFG6:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -512,7 +504,6 @@ class WFG7:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -596,7 +587,6 @@ class WFG8:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 
@@ -679,7 +669,6 @@ class WFG9:
     """
 
     def __init__(self, n_arguments: int, n_objectives: int, k: int):
-
         assert k % (n_objectives - 1) == 0
         assert k + 1 <= n_arguments
 

benchmarks/kurobako/problems/wfg/shape_functions.py

@@ -5,7 +5,6 @@
 
 class BaseShapeFunction(object, metaclass=abc.ABCMeta):
     def __init__(self, n_objectives: int) -> None:
-
         self._n_objectives = n_objectives
 
     def __call__(self, m: int, x: np.ndarray) -> float:
@@ -19,13 +18,11 @@ def _call(self, m: int, x: np.ndarray) -> float:
 
     @property
     def n_objectives(self) -> int:
-
         return self._n_objectives
 
 
 class LinearShapeFunction(BaseShapeFunction):
     def _call(self, m: int, x: np.ndarray) -> float:
-
         if m == 1:
             return x[:-1].prod()
 
@@ -37,7 +34,6 @@ def _call(self, m: int, x: np.ndarray) -> float:
 
 class ConvexShapeFunction(BaseShapeFunction):
     def _call(self, m: int, x: np.ndarray) -> float:
-
         if m == 1:
             return (
                 1
@@ -56,7 +52,6 @@ def _call(self, m: int, x: np.ndarray) -> float:
 
 class ConcaveShapeFunction(BaseShapeFunction):
     def _call(self, m: int, x: np.ndarray) -> float:
-
         if m == 1:
             return np.sin(x * np.pi / 2.0)[:-1].prod()