fixed linting errors.

imblearn/over_sampling/_rose.py

@@ -13,7 +13,14 @@ class ROSE(BaseOverSampler):
 
  """Oversample using Random OverSampling Examples (ROSE) algorithm.
 
+ The algorithm generates new samples by a smoothed bootstrap approach.
+ The generation of new examples corresponds to the generation of data from
+ the kernel density estimate of f(x|Y_i), with a smoothing matrix H_j.
+ A shrinking matrix can be provided, to set the bandwidth of the gaussian
+ kernel.
+
  Read more in the :ref:`User Guide <rose>`.
+
  Parameters
  ----------
  {sampling_strategy}
@@ -74,9 +81,6 @@ def _make_samples(self,
  Target values for synthetic samples.
 
  """
-
- # pdb.set_trace()
-
  p = X.shape[1]
 
  random_state = check_random_state(self.random_state)
@@ -94,12 +98,13 @@ def _make_samples(self,
 
  def _fit_resample(self, X, y):
 
- #random_state = check_random_state(self.random_state)
  X_resampled = np.empty((0, X.shape[1]), dtype=X.dtype)
  y_resampled = np.empty((0), dtype=X.dtype)
 
  if self.shrink_factors is None:
- self.shrink_factors = {key: 1 for key in self.sampling_strategy_.keys()}
+ self.shrink_factors = {
+ key: 1 for key in self.sampling_strategy_.keys()
+ }
 
  for class_sample, n_samples in self.sampling_strategy_.items():
  class_indices = np.flatnonzero(y == class_sample)
@@ -118,4 +123,3 @@ def _fit_resample(self, X, y):
  y_resampled = np.hstack((y_resampled, y_new))
 
  return X_resampled.astype(X.dtype), y_resampled.astype(y.dtype)
-

imblearn/over_sampling/tests/test_rose.py

@@ -7,53 +7,36 @@
 from imblearn.over_sampling import ROSE
 
 
-def test_testunit():
- """Check test unit"""
- return True
-
-
-def test_random_state():
- """Check randomState()"""
- assert np.random.RandomState(42)
-
-
-def test_instance():
- """Check ROSE instantiation"""
- rose = ROSE()
- assert id(rose)
-
-
-RND_SEED = 0
-X = np.array(
- [
- [0.11622591, -0.0317206],
- [0.77481731, 0.60935141],
- [1.25192108, -0.22367336],
- [0.53366841, -0.30312976],
- [1.52091956, -0.49283504],
- [-0.28162401, -2.10400981],
- [0.83680821, 1.72827342],
- [0.3084254, 0.33299982],
- [0.70472253, -0.73309052],
- [0.28893132, -0.38761769],
- [1.15514042, 0.0129463],
- [0.88407872, 0.35454207],
- [1.31301027, -0.92648734],
- [-1.11515198, -0.93689695],
- [-0.18410027, -0.45194484],
- [0.9281014, 0.53085498],
- [-0.14374509, 0.27370049],
- [-0.41635887, -0.38299653],
- [0.08711622, 0.93259929],
- [1.70580611, -0.11219234],
- ]
-)
-Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
-R_TOL = 1e-4
-
-
 def test_rose():
+
  """Check ROSE use"""
- X_res, y_res = ROSE().fit_resample(X, Y)
+
+ RND_SEED = 0
+ X = np.array(
+ [
+ [0.11622591, -0.0317206],
+ [0.77481731, 0.60935141],
+ [1.25192108, -0.22367336],
+ [0.53366841, -0.30312976],
+ [1.52091956, -0.49283504],
+ [-0.28162401, -2.10400981],
+ [0.83680821, 1.72827342],
+ [0.3084254, 0.33299982],
+ [0.70472253, -0.73309052],
+ [0.28893132, -0.38761769],
+ [1.15514042, 0.0129463],
+ [0.88407872, 0.35454207],
+ [1.31301027, -0.92648734],
+ [-1.11515198, -0.93689695],
+ [-0.18410027, -0.45194484],
+ [0.9281014, 0.53085498],
+ [-0.14374509, 0.27370049],
+ [-0.41635887, -0.38299653],
+ [0.08711622, 0.93259929],
+ [1.70580611, -0.11219234],
+ ]
+ )
+ Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
+ X_res, y_res = ROSE(random_state=RND_SEED).fit_resample(X, Y)
  assert np.unique(Y.all()) == np.unique(y_res.all())
  assert X_res.shape[1] == X.shape[1]