)
if self.add_indicator:
self.indicator_ = MissingIndicator(
missing_values=self.missing_values, error_on_new=False)X_trans_indicator = self.indicator_.fit_transform(X)
else:
self.indicator_ = None
if self.estimator is None:
from ..linear_model import BayesianRidge
self._estimator = BayesianRidge()
else:
self._estimator = clone(self.estimator)
self.imputation_sequence_ = []
if hasattr(self._estimator, "random_state"):
self._estimator.random_state = self.random_state_
self._min_value = -np.inf if self.min_value is None else self.min_value
self._max_value = np.inf if self.max_value is None else self.max_value
self.initial_imputer_ = None
X, Xt, mask_missing_values = self._initial_imputation(X)
if self.max_iter == 0 or np.all(mask_missing_values):
self.n_iter_ = 0
return Xt
// Edge case: a single feature. We return the initial ...
if Xt.shape[1] == 1:
self.n_iter_ = 0
return Xt
// order in which to impute
// note this is probably too slow for large feature data (d > 100000)
// and a better way would be good.
// see: https://goo.gl/KyCNwj and subsequent comments
ordered_idx = self._get_ordered_idx(mask_missing_values)
self.n_features_with_missing_ = len(ordered_idx)
abs_corr_mat = self._get_abs_corr_mat(Xt)
n_samples, n_features = Xt.shape
if self.verbose > 0:
print("[IterativeImputer] Completing matrix with shape %s"
% (X.shape,))
start_t = time()
if not self.sample_posterior:
Xt_previous = Xt.copy()
normalized_tol = self.tol * np.max(np.abs(X[~mask_missing_values]))
for self.n_iter_ in range(1, self.max_iter + 1):
if self.imputation_order == "random":
ordered_idx = self._get_ordered_idx(mask_missing_values)
for feat_idx in ordered_idx:
neighbor_feat_idx = self._get_neighbor_feat_idx(n_features,
feat_idx,
abs_corr_mat)
Xt, estimator = self._impute_one_feature(
Xt, mask_missing_values, feat_idx, neighbor_feat_idx,
estimator=None, fit_mode=True)
estimator_triplet = _ImputerTriplet(feat_idx,
neighbor_feat_idx,
estimator)
self.imputation_sequence_.append(estimator_triplet)
if self.verbose > 1:
print("[IterativeImputer] Ending imputation round "
"%d/%d, elapsed time %0.2f"
% (self.n_iter_, self.max_iter, time() - start_t))
if not self.sample_posterior:
inf_norm = np.linalg.norm(Xt - Xt_previous, ord=np.inf,
axis=None)
if self.verbose > 0:
print("[IterativeImputer] "
"Change: {}, scaled tolerance: {} ".format(
inf_norm, normalized_tol))
if inf_norm < normalized_tol:
if self.verbose > 0:
print("[IterativeImputer] Early stopping criterion "
"reached.")
break
Xt_previous = Xt.copy()
else:
if not self.sample_posterior:
warnings.warn("[IterativeImputer] Early stopping criterion not"
" reached.", ConvergenceWarning)
Xt[~mask_missing_values] = X[~mask_missing_values]
if self.add_indicator:
Xt = np.hstack((Xt, X_trans_indicator))
return Xt
def transform(self, X):
Imputes all missing values in X.
After Change
// Edge case: a single feature. We return the initial ...
if Xt.shape[1] == 1:
self.n_iter_ = 0
return super()._concatenate_indicator(Xt, X_indicator)
// order in which to impute
// note this is probably too slow for large feature data (d > 100000)
// and a better way would be good.
