Changes

# kappa_score is an alias for `sklearn.metrics.cohen_kappa_score`

kappa_score = cohen_kappa_score

#def kappa_score(y_true, y_pred):

#  """Calculate Cohen's kappa for classification tasks.

#

#  See https://en.wikipedia.org/wiki/Cohen%27s_kappa

#

#  Note that this implementation of Cohen's kappa expects binary labels.

#

#  Parameters

#  ----------

#  y_true: np.ndarray

#    Numpy array containing true values of shape `(N,)`

#  y_pred: np.ndarray

#    Numpy array containing predicted values of shape `(N,)`

#

#  Returns

#  -------

#  kappa: np.ndarray

#    Numpy array containing kappa for each classification task.

#

#  Raises

#  ------

#  AssertionError: If y_true and y_pred are not the same size, or if

#  class labels are not in [0, 1].

#  """

#  assert len(y_true) == len(y_pred), 'Number of examples does not match.'

#  yt = np.asarray(y_true, dtype=int)

#  yp = np.asarray(y_pred, dtype=int)

#  if not set(np.unique(yt)).issubset(set([0, 1])):

#    raise ValueError("Class labels must be binary 0, 1")

#  assert np.array_equal(

#      np.unique(yt),

#      [0, 1]), ('Class labels must be binary: %s' % np.unique(yt))

#  observed_agreement = np.true_divide(

#      np.count_nonzero(np.equal(yt, yp)), len(yt))

#  expected_agreement = np.true_divide(

#      np.count_nonzero(yt == 1) * np.count_nonzero(yp == 1) +

#      np.count_nonzero(yt == 0) * np.count_nonzero(yp == 0),

#      len(yt)**2)

#  kappa = np.true_divide(observed_agreement - expected_agreement,

#                         1.0 - expected_agreement)

#  return kappa

def bedroc_score(y_true, y_pred, alpha=20.0):

  """BEDROC metric implemented according to Truchon and Bayley that modifies