Calculation of gini coefficient metric

gini_coefficient_metric.py

"""
Calculation of gini coefficient metric
via https://www.kaggle.com/c/ClaimPredictionChallenge/forums/t/703/code-to-calculate-normalizedgini?forumMessageId=5897#post5897
I'm not the author, thant would be Kaggle user Patrick
See http://www.rhinorisk.com/Publications/Gini%20Coefficients.pdf 
"""
def gini(actual, pred, cmpcol = 0, sortcol = 1):
  assert( len(actual) == len(pred) )
  all = np.asarray(np.c_[ actual, pred, np.arange(len(actual)) ], dtype=np.float)
  all = all[ np.lexsort((all[:,2], -1*all[:,1])) ]
  totalLosses = all[:,0].sum()
  giniSum = all[:,0].cumsum().sum() / totalLosses
  giniSum -= (len(actual) + 1) / 2.
  return giniSum / len(actual)
 
def gini_normalized(a, p):
  return gini(a, p) / gini(a, a)
 
def test_gini():
  def fequ(a,b):
    return abs( a -b) < 1e-6
  def T(a, p, g, n):
    assert( fequ(gini(a,p), g) )
    assert( fequ(gini_normalized(a,p), n) )
  T([1, 2, 3], [10, 20, 30], 0.111111, 1)
  T([1, 2, 3], [30, 20, 10], -0.111111, -1)
  T([1, 2, 3], [0, 0, 0], -0.111111, -1)
  T([3, 2, 1], [0, 0, 0], 0.111111, 1)
  T([1, 2, 4, 3], [0, 0, 0, 0], -0.1, -0.8)
  T([2, 1, 4, 3], [0, 0, 2, 1], 0.125, 1)
  T([0, 20, 40, 0, 10], [40, 40, 10, 5, 5], 0, 0)
  T([40, 0, 20, 0, 10], [1000000, 40, 40, 5, 5], 0.171428, 0.6)
  T([40, 20, 10, 0, 0], [40, 20, 10, 0, 0], 0.285714, 1)
  T([1, 1, 0, 1], [0.86, 0.26, 0.52, 0.32], -0.041666, -0.333333)