def _safe_indexing(
X: Union[OneDimArrayLikeType, TwoDimArrayLikeType], indices: OneDimArrayLikeType
) -> Union[OneDimArrayLikeType, TwoDimArrayLikeType]:
if X is None:
return X
return sklearn_safe_indexing(X, indices)
def _safe_indexing(
X, # type: Union[OneDimArrayLikeType, TwoDimArrayLikeType]
indices, # type: OneDimArrayLikeType
):
# type: (...) -> Union[OneDimArrayLikeType, TwoDimArrayLikeType]
if X is None:
return X
return sklearn_safe_indexing(X, indices)
def _check_fit_params(
X, # type: TwoDimArrayLikeType
fit_params, # type: Dict
indices, # type: OneDimArrayLikeType
):
# type: (...) -> Dict
fit_params_validated = {}
for key, value in fit_params.items():
# NOTE Original implementation:
# https://github.com/scikit-learn/scikit-learn/blob/ \
# 2467e1b84aeb493a22533fa15ff92e0d7c05ed1c/sklearn/utils/validation.py#L1324-L1328
# Scikit-learn does not accept non-iterable inputs.
# This line is for keeping backward compatibility.
# (See: https://github.com/scikit-learn/scikit-learn/issues/15805)
if not _is_arraylike(value) or _num_samples(value) != _num_samples(X):
fit_params_validated[key] = value
else:
fit_params_validated[key] = _make_indexable(value)
fit_params_validated[key] = sklearn_safe_indexing(fit_params_validated[key], indices)
return fit_params_validated
def fit(
self,
X, # type: TwoDimArrayLikeType
y=None, # type: Optional[Union[OneDimArrayLikeType, TwoDimArrayLikeType]]
groups=None, # type: Optional[OneDimArrayLikeType]
**fit_params # type: Any
):
# type: (...) -> 'OptunaSearchCV'
"""Run fit with all sets of parameters.
Args:
X:
Training data.
y:
Target variable.
groups:
Group labels for the samples used while splitting the dataset
into train/test set.
**fit_params:
Parameters passed to ``fit`` on the estimator.
Returns:
self:
Return self.
"""
self._check_params()
random_state = check_random_state(self.random_state)
max_samples = self.subsample
n_samples = _num_samples(X)
old_level = _logger.getEffectiveLevel()
if self.verbose > 1:
_logger.setLevel(DEBUG)
elif self.verbose > 0:
_logger.setLevel(INFO)
else:
_logger.setLevel(WARNING)
self.sample_indices_ = np.arange(n_samples)
if type(max_samples) is float:
max_samples = int(max_samples * n_samples)
if max_samples < n_samples:
self.sample_indices_ = random_state.choice(
self.sample_indices_, max_samples, replace=False
)
self.sample_indices_.sort()
X_res = sklearn_safe_indexing(X, self.sample_indices_)
y_res = sklearn_safe_indexing(y, self.sample_indices_)
groups_res = sklearn_safe_indexing(groups, self.sample_indices_)
fit_params_res = fit_params
if fit_params_res is not None:
fit_params_res = _check_fit_params(X, fit_params, self.sample_indices_)
classifier = is_classifier(self.estimator)
cv = check_cv(self.cv, y_res, classifier)
self.n_splits_ = cv.get_n_splits(X_res, y_res, groups=groups_res)
self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)
if self.study is None:
seed = random_state.randint(0, np.iinfo("int32").max)
sampler = samplers.TPESampler(seed=seed)
self.study_ = study_module.create_study(direction="maximize", sampler=sampler)
else:
self.study_ = self.study
objective = _Objective(
self.estimator,
self.param_distributions,
X_res,
y_res,
cv,
self.enable_pruning,
self.error_score,
fit_params_res,
groups_res,
self.max_iter,
self.return_train_score,
self.scorer_,
)
_logger.info(
"Searching the best hyperparameters using {} "
"samples...".format(_num_samples(self.sample_indices_))
)
self.study_.optimize(
objective, n_jobs=self.n_jobs, n_trials=self.n_trials, timeout=self.timeout
)
_logger.info("Finished hyperparemeter search!")
if self.refit:
self._refit(X, y, **fit_params)
_logger.setLevel(old_level)
return self
def safe_indexing(X, indices):
if X is None:
return X
else:
return sklearn_safe_indexing(X, indices)
