Solve the bug for the SVM SMOTE when SVs are never in danger

unbalanced_dataset/over_sampling.py

@@ -419,34 +419,46 @@ def resample(self):
  fractions = betavariate(alpha=10, beta=10)
 
  # Interpolate samples in danger
- nns = self.nearest_neighbour_.kneighbors(support_vector[danger_bool],
- return_distance=False)[:, 1:]
+ if (np.count_nonzero(danger_bool) > 0):
+ nns = self.nearest_neighbour_.kneighbors(support_vector[danger_bool],
+ return_distance=False)[:, 1:]
 
- sx1, sy1 = self.make_samples(support_vector[danger_bool],
- minx,
- self.minc, nns,
- fractions * (int(self.ratio * len(minx)) + 1),
- step_size=1,
- random_state=self.rs,
- verbose=self.verbose)
+  sx1, sy1 = self.make_samples(support_vector[danger_bool],
+  minx,
+  self.minc, nns,
+  fractions * (int(self.ratio * len(minx)) + 1),
+  step_size=1,
+  random_state=self.rs,
+  verbose=self.verbose)
 
  # Extrapolate safe samples
- nns = self.nearest_neighbour_.kneighbors(support_vector[safety_bool],
- return_distance=False)[:, 1:]
-
- sx2, sy2 = self.make_samples(support_vector[safety_bool],
- minx,
- self.minc, nns,
- (1 - fractions) * int(self.ratio * len(minx)),
- step_size=-self.out_step,
- random_state=self.rs,
- verbose=self.verbose)
+ if (np.count_nonzero(safety_bool) > 0):
+ nns = self.nearest_neighbour_.kneighbors(support_vector[safety_bool],
+ return_distance=False)[:, 1:]
+
+ sx2, sy2 = self.make_samples(support_vector[safety_bool],
+ minx,
+ self.minc, nns,
+ (1 - fractions) * int(self.ratio * len(minx)),
+ step_size=-self.out_step,
+ random_state=self.rs,
+ verbose=self.verbose)
 
  if self.verbose:
  print("done!")
 
  # Concatenate the newly generated samples to the original data set
- ret_x = concatenate((self.x, sx1, sx2), axis=0)
- ret_y = concatenate((self.y, sy1, sy2), axis=0)
+ if ( (np.count_nonzero(danger_bool) > 0) and
+ (np.count_nonzero(safety_bool) > 0) ):
+ ret_x = concatenate((self.x, sx1, sx2), axis=0)
+ ret_y = concatenate((self.y, sy1, sy2), axis=0)
+ # not any support vectors in danger
+ elif np.count_nonzero(danger_bool) == 0:
+ ret_x = concatenate((self.x, sx2), axis=0)
+ ret_y = concatenate((self.y, sy2), axis=0)
+ # All the support vector in danger
+ elif np.count_nonzero(safety_bool) == 0:
+ ret_x = concatenate((self.x, sx1), axis=0)
+ ret_y = concatenate((self.y, sy1), axis=0)
 
  return ret_x, ret_y