iter

imblearn/metrics/pairwise.py

@@ -90,30 +90,19 @@ def fit(self, X, y):
 
  # list of length n_features of ndarray (n_categories, n_classes)
  # compute the counts
- # self.proba_per_class_ = [
- # np.array(
- # [
- # np.bincount(
- # X[y == klass, feature_idx],
- # minlength=len(self.categories[feature_idx]),
- # )
- # for klass in self.classes
- # ],
- # dtype=np.float64,
- # ).T
- # for feature_idx in range(n_features)
- # ]
- self.proba_per_class_ = []
- for feature_idx in range(n_features):
- arr = []
- for klass in self.classes:
- tmp = np.bincount(
- X[y == klass, feature_idx],
- minlength=len(self.categories[feature_idx]),
- )
- tmp = np.array(tmp, dtype=np.float64).T
- arr.append(tmp)
- self.proba_per_class_.append(arr)
+ self.proba_per_class_ = [
+ np.array(
+ [
+ np.bincount(
+ X[y == klass, feature_idx],
+ minlength=len(self.categories[feature_idx]),
+ )
+ for klass in self.classes
+ ],
+ dtype=np.float64,
+ ).T
+ for feature_idx in range(n_features)
+ ]
  # normalize by the summing over the classes
  for feature_idx in range(n_features):
  self.proba_per_class_[feature_idx] /= (