Merge pull request #53 from glemaitre/bug_make_samples

[MRG] Address issue #52

Author: glemaitre

unbalanced_dataset/over_sampling/smote.py

@@ -243,7 +243,7 @@ def fit(self, X, y):
 
  return self
 
- def in_danger_noise(self, samples, y, kind='danger'):
+ def _in_danger_noise(self, samples, y, kind='danger'):
  """Estimate if a set of sample are in danger or not.
 
  Parameters
@@ -288,7 +288,7 @@ def in_danger_noise(self, samples, y, kind='danger'):
  else:
  raise ValueError('Unknown string for parameter kind.')
 
- def make_samples(self, X, y_type, nn_data, nn_num, n_samples,
+ def _make_samples(self, X, y_type, nn_data, nn_num, n_samples,
  step_size=1.):
  """A support function that returns artificial samples constructed along
  the line connecting nearest neighbours.
@@ -303,11 +303,11 @@ def make_samples(self, X, y_type, nn_data, nn_num, n_samples,
  target values for the synthetic variables with correct length in
  a clear format.
 
- nn_data : ndarray, shape(n_samples_all, n_features)
+ nn_data : ndarray, shape (n_samples_all, n_features)
  Data set carrying all the neighbours to be used
 
- nn_num : int
- The number of nearest neighbours to be used.
+ nn_num : ndarray, shape (n_samples_all, k_nearest_neighbours)
+ The nearest neighbours of each sample in nn_data.
 
  n_samples : int
  The number of samples to generate.
@@ -429,7 +429,7 @@ def transform(self, X, y):
 
  # --- Generating synthetic samples
  # Use static method make_samples to generate minority samples
- X_new, y_new = self.make_samples(X_min,
+ X_new, y_new = self._make_samples(X_min,
  self.min_c_,
  X_min,
  nns,
@@ -457,7 +457,7 @@ def transform(self, X, y):
  print("done!")
 
  # Boolean array with True for minority samples in danger
- danger_index = self.in_danger_noise(X_min, y, kind='danger')
+ danger_index = self._in_danger_noise(X_min, y, kind='danger')
 
  # If all minority samples are safe, return the original data set.
  if not any(danger_index):
@@ -485,7 +485,7 @@ def transform(self, X, y):
  # B1 and B2 types diverge here!!!
  if self.kind == 'borderline1':
  # Create synthetic samples for borderline points.
- X_new, y_new = self.make_samples(X_min[danger_index],
+ X_new, y_new = self._make_samples(X_min[danger_index],
  self.min_c_,
  X_min,
  nns,
@@ -512,7 +512,7 @@ def transform(self, X, y):
  fractions = betavariate(alpha=10, beta=10)
 
  # Only minority
- X_new_1, y_new_1 = self.make_samples(X_min[danger_index],
+ X_new_1, y_new_1 = self._make_samples(X_min[danger_index],
  self.min_c_,
  X_min,
  nns,
@@ -521,7 +521,7 @@ def transform(self, X, y):
  step_size=1.)
 
  # Only majority with smaller step size
- X_new_2, y_new_2 = self.make_samples(X_min[danger_index],
+ X_new_2, y_new_2 = self._make_samples(X_min[danger_index],
  self.min_c_,
  X[y != self.min_c_],
  nns,
@@ -567,11 +567,11 @@ def transform(self, X, y):
 
  # Now, get rid of noisy support vectors
 
- noise_bool = self.in_danger_noise(support_vector, y, kind='noise')
+ noise_bool = self._in_danger_noise(support_vector, y, kind='noise')
 
  # Remove noisy support vectors
  support_vector = support_vector[np.logical_not(noise_bool)]
- danger_bool = self.in_danger_noise(support_vector, y,
+ danger_bool = self._in_danger_noise(support_vector, y,
  kind='danger')
  safety_bool = np.logical_not(danger_bool)
 
@@ -608,7 +608,7 @@ def transform(self, X, y):
  support_vector[danger_bool],
  return_distance=False)[:, 1:]
 
- X_new_1, y_new_1 = self.make_samples(
+ X_new_1, y_new_1 = self._make_samples(
  support_vector[danger_bool],
  self.min_c_,
  X_min,
@@ -622,7 +622,7 @@ def transform(self, X, y):
  support_vector[safety_bool],
  return_distance=False)[:, 1:]
 
- X_new_2, y_new_2 = self.make_samples(
+ X_new_2, y_new_2 = self._make_samples(
  support_vector[safety_bool],
  self.min_c_,
  X_min,