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⚡️ Speed up function _build_parzen_estimator by 19% #168

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⚡️ Speed up function _build_parzen_estimator by 19% #168

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12 changes: 5 additions & 7 deletions optuna/importance/_ped_anova/scott_parzen_estimator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,19 +120,17 @@ def _count_numerical_param_in_grid(
n_grids, grid_indices_of_trials = _get_grids_and_grid_indices_of_trials(
param_name, dist, trials, n_steps
)
unique_vals, counts_in_unique = np.unique(grid_indices_of_trials, return_counts=True)
counts = np.zeros(n_grids, dtype=np.int32)
counts[unique_vals] += counts_in_unique
counts = np.bincount(grid_indices_of_trials, minlength=n_grids)[:n_grids].astype(np.int32)
return counts


def _count_categorical_param_in_grid(
param_name: str, dist: CategoricalDistribution, trials: list[FrozenTrial]
) -> np.ndarray:
cat_indices = [int(dist.to_internal_repr(t.params[param_name])) for t in trials]
unique_vals, counts_in_unique = np.unique(cat_indices, return_counts=True)
counts = np.zeros(len(dist.choices), dtype=np.int32)
counts[unique_vals] += counts_in_unique
cat_indices = np.empty(len(trials), dtype=np.intp)
for i, t in enumerate(trials):
cat_indices[i] = int(dist.to_internal_repr(t.params[param_name]))
counts = np.bincount(cat_indices, minlength=len(dist.choices)).astype(np.int32)
return counts


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