def localAUCApprox(positiveArray,
U,
V,
w,
numAucSamples=50,
r=None,
allArray=None):
"""
Compute the estimated local AUC for the score functions UV^T relative to X with
quantile w. The AUC is computed using positiveArray which is a tuple (indPtr, colInds)
assuming allArray is None. If allArray is not None then positive items are chosen
from positiveArray and negative ones are chosen to complement allArray.
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
indPtr, colInds = positiveArray
U = numpy.ascontiguousarray(U)
V = numpy.ascontiguousarray(V)
if r is None:
r = SparseUtilsCython.computeR(U, V, w, numAucSamples)
if allArray is None:
return MCEvaluatorCython.localAUCApprox(indPtr, colInds, indPtr,
colInds, U, V,
numAucSamples, r)
else:
allIndPtr, allColInd = allArray
return MCEvaluatorCython.localAUCApprox(indPtr, colInds, allIndPtr,
allColInd, U, V,
numAucSamples, r)
def localAUCApprox(positiveArray, U, V, w, numAucSamples=50, r=None, allArray=None):
"""
Compute the estimated local AUC for the score functions UV^T relative to X with
quantile w. The AUC is computed using positiveArray which is a tuple (indPtr, colInds)
assuming allArray is None. If allArray is not None then positive items are chosen
from positiveArray and negative ones are chosen to complement allArray.
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
indPtr, colInds = positiveArray
U = numpy.ascontiguousarray(U)
V = numpy.ascontiguousarray(V)
if r is None:
r = SparseUtilsCython.computeR(U, V, w, numAucSamples)
if allArray is None:
return MCEvaluatorCython.localAUCApprox(indPtr, colInds, indPtr, colInds, U, V, numAucSamples, r)
else:
allIndPtr, allColInd = allArray
return MCEvaluatorCython.localAUCApprox(indPtr, colInds, allIndPtr, allColInd, U, V, numAucSamples, r)
