add CCR documentation

multi_imbalance/resampling/ccr.py

@@ -5,13 +5,38 @@
 
 
 class CCR(BaseSampler):
+ """
+ CCR is a combined cleaning and resampling energy-based algorithm.
+
+ Each minority example has an associated energy budget that is used to expand a sphere around it.
+ With each majority example within the sphere, the cost of further expansion increases.
+ When energy is used up, majority examples are pushed out of the spheres and synthetic minority examples are generated inside the spheres.
+ Synthetic examples are generated until the count of minority examples is equal to the count of majority examples.
+ Smaller spheres generate more synthetic examples than big ones to force the classification algorithm to focus on the most difficult examples.
+
+ Reference:
+ Koziarski, M., Wozniak, M.: CCR: A combined cleaning and resampling algorithm for imbalanced data classification.
+ International Journal of Applied Mathematics and Computer Science 2017
+ """
 
  def __init__(self, energy: float):
+ """
+ :param energy:
+ initial energy budget for each minority example to use for sphere expansion
+ """
  super().__init__()
  self.energy = energy
  self._sampling_type = "over-sampling"
 
  def _fit_resample(self, X: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ :param X:
+ two-dimensional numpy array (number of samples x number of features) with float numbers
+ :param y:
+ one-dimensional numpy array with labels for rows in X, assumes minority class = 1 and majority class = 0
+ :return:
+ resampled X, resampled y
+ """
  oversampled_X, oversampled_y = np.copy(X), np.copy(y)
 
  majority_examples = X[y == 0]