def permute(self, x):
prmt = range(len(self.Y)) # increasing order
prmt = np.array(prmt)
counter = 0
for pocketList in margins(self.Y):
pocketlen = len(pocketList)
if pocketlen > 1:
pocket = np.arange(counter, counter + pocketlen)
sortedInds = np.argsort(x[pocket] * self.direction)
prmt[pocket] = pocket[sortedInds]
counter += pocketlen
return prmt
def __init__(self,Y,cost,order="partial",margin="soft"):
if order != "partial" and order != "total":
raise ValueError("order must be 'partial' or 'total'")
if margin != "hard" and margin != "soft":
raise ValueError("margin must be 'hard' or 'soft'")
if np.all(np.diff(Y)>=0):
self.direction = 1 # increasing order
elif np.all(np.diff(Y)<=0):
self.direction = -1 # decreasing order
else:
raise ValueError("Y must be fully sorted")
self.Y = Y
self.order = order
self.margin = margin
if order == "partial":
Y = margins(Y)
partialBins = list((len(y) for y in Y)) # size of each partially-ordered group
costVector = []
for k in range(len(partialBins)):
if k == 0:
value = -partialBins[k+1]
elif k == len(partialBins)-1:
value = partialBins[k-1]
else:
value = partialBins[k-1] - partialBins[k+1]
costVector += [value]*partialBins[k]
else:
# total order: [-1 0 0 0 0 0 1] or similar
costVector = np.array([0]*(len(Y)))
costVector[0] = -1
costVector[-1] = 1
self.costVector = np.array(costVector) * cost * self.direction
if margin == 'soft':
self.costVector = self.costVector / 2.
if self.direction < 0:
self.IR = sklearn.isotonic.IsotonicRegression(increasing=False)
else:
self.IR = sklearn.isotonic.IsotonicRegression()