FIX multiply by random number < 0.5 for BorderlineSMOTE-2 (#1027)

Co-authored-by: Soledad Galli <solegalli@protonmail.com>

Author: glemaitre

imblearn/over_sampling/_smote/base.py

@@ -68,7 +68,7 @@ def _validate_estimator(self):
  )
 
  def _make_samples(
- self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0
+ self, X, y_dtype, y_type, nn_data, nn_num, n_samples, step_size=1.0, y=None
  ):
  """A support function that returns artificial samples constructed along
  the line connecting nearest neighbours.
@@ -98,6 +98,10 @@ def _make_samples(
  step_size : float, default=1.0
  The step size to create samples.
 
+ y : ndarray of shape (n_samples_all,), default=None
+ The true target associated with `nn_data`. Used by Borderline SMOTE-2 to
+ weight the distances in the sample generation process.
+
  Returns
  -------
  X_new : {ndarray, sparse matrix} of shape (n_samples_new, n_features)
@@ -114,11 +118,13 @@ def _make_samples(
  rows = np.floor_divide(samples_indices, nn_num.shape[1])
  cols = np.mod(samples_indices, nn_num.shape[1])
 
- X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps, y_type)
+ X_new = self._generate_samples(X, nn_data, nn_num, rows, cols, steps, y_type, y)
  y_new = np.full(n_samples, fill_value=y_type, dtype=y_dtype)
  return X_new, y_new
 
- def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type=None):
+ def _generate_samples(
+ self, X, nn_data, nn_num, rows, cols, steps, y_type=None, y=None
+ ):
  r"""Generate a synthetic sample.
 
  The rule for the generation is:
@@ -153,15 +159,26 @@ def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type=None):
  steps : ndarray of shape (n_samples,), dtype=float
  Step sizes for new samples.
 
- y_type : None
- Unused parameter. Only for compatibility reason with SMOTE-NC.
+ y_type : str, int or None, default=None
+ Class label of the current target classes for which we want to generate
+ samples.
+
+ y : ndarray of shape (n_samples_all,), default=None
+ The true target associated with `nn_data`. Used by Borderline SMOTE-2 to
+ weight the distances in the sample generation process.
 
  Returns
  -------
  X_new : {ndarray, sparse matrix} of shape (n_samples, n_features)
  Synthetically generated samples.
  """
  diffs = nn_data[nn_num[rows, cols]] - X[rows]
+ if y is not None: # only entering for BorderlineSMOTE-2
+ random_state = check_random_state(self.random_state)
+ mask_pair_samples = y[nn_num[rows, cols]] != y_type
+ diffs[mask_pair_samples] *= random_state.uniform(
+ low=0.0, high=0.5, size=(mask_pair_samples.sum(), 1)
+ )
 
  if sparse.issparse(X):
  sparse_func = type(X).__name__
@@ -736,7 +753,7 @@ def _fit_resample(self, X, y):
 
  return X_resampled, y_resampled
 
- def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type):
+ def _generate_samples(self, X, nn_data, nn_num, rows, cols, steps, y_type, y=None):
  """Generate a synthetic sample with an additional steps for the
  categorical features.
 

imblearn/over_sampling/_smote/filter.py

@@ -224,8 +224,10 @@ def _fit_resample(self, X, y):
 
  if self.kind == "borderline-1":
  X_to_sample_from = X_class # consider the positive class only
+ y_to_check_neighbors = None
  else: # self.kind == "borderline-2"
  X_to_sample_from = X # consider the whole dataset
+ y_to_check_neighbors = y
 
  self.nn_k_.fit(X_to_sample_from)
  nns = self.nn_k_.kneighbors(X_danger, return_distance=False)[:, 1:]
@@ -236,6 +238,7 @@ def _fit_resample(self, X, y):
  X_to_sample_from,
  nns,
  n_samples,
+ y=y_to_check_neighbors,
  )
  if sparse.issparse(X_new):
  X_resampled = sparse.vstack([X_resampled, X_new])