def f1AtK(positiveArray, orderedItems, k, verbose=False):
"""
Return the F1@k measure for each row of the predicted matrix UV.T
using real values in positiveArray. positiveArray is a tuple (indPtr, colInds)
:param orderedItems: The ordered items for each user (users are rows, items are cols)
:param verbose: If true return recall and first k recommendation for each row, otherwise just precisions
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
orderedItems = orderedItems[:, 0:k]
indPtr, colInds = positiveArray
precisions = MCEvaluatorCython.precisionAtk(indPtr, colInds, orderedItems)
recalls = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
denominator = precisions+recalls
denominator += denominator == 0
f1s = 2*precisions*recalls/denominator
if verbose:
return f1s, orderedItems
else:
return f1s.mean()
def f1AtK(positiveArray, orderedItems, k, verbose=False):
"""
Return the F1@k measure for each row of the predicted matrix UV.T
using real values in positiveArray. positiveArray is a tuple (indPtr, colInds)
:param orderedItems: The ordered items for each user (users are rows, items are cols)
:param verbose: If true return recall and first k recommendation for each row, otherwise just precisions
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
orderedItems = orderedItems[:, 0:k]
indPtr, colInds = positiveArray
precisions = MCEvaluatorCython.precisionAtk(indPtr, colInds,
orderedItems)
recalls = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
denominator = precisions + recalls
denominator += denominator == 0
f1s = 2 * precisions * recalls / denominator
if verbose:
return f1s, orderedItems
else:
return f1s.mean()
def testStratifiedRecallAtk(self):
m = 20
n = 50
r = 3
alpha = 1
X, U, V = SparseUtilsCython.generateSparseBinaryMatrixPL((m, n),
r,
density=0.2,
alpha=alpha,
csarray=True)
itemCounts = numpy.array(X.sum(0) + 1, numpy.int32)
(indPtr, colInds) = X.nonzeroRowsPtr()
indPtr = numpy.array(indPtr, numpy.uint32)
colInds = numpy.array(colInds, numpy.uint32)
k = 5
orderedItems = numpy.random.randint(0, n, m * k)
orderedItems = numpy.reshape(orderedItems, (m, k))
orderedItems = numpy.array(orderedItems, numpy.int32)
beta = 0.5
recalls, denominators = MCEvaluatorCython.stratifiedRecallAtk(
indPtr, colInds, orderedItems, itemCounts, beta)
recalls2 = numpy.zeros(m)
#Now compute recalls from scratch
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i + 1]]
numerator = 0
for j in range(k):
if orderedItems[i, j] in omegai:
numerator += 1 / itemCounts[orderedItems[i, j]]**beta
denominator = 0
for j in omegai:
denominator += 1 / itemCounts[j]**beta
recalls2[i] = numerator / denominator
nptst.assert_array_equal(recalls, recalls2)
#Now try to match with normal recall
itemCounts = numpy.ones(n, numpy.int32)
recalls, denominators = MCEvaluatorCython.stratifiedRecallAtk(
indPtr, colInds, orderedItems, itemCounts, beta)
recalls2 = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
nptst.assert_array_equal(recalls, recalls2)
def testStratifiedRecallAtk(self):
m = 20
n = 50
r = 3
alpha = 1
X, U, V = SparseUtilsCython.generateSparseBinaryMatrixPL((m,n), r, density=0.2, alpha=alpha, csarray=True)
itemCounts = numpy.array(X.sum(0)+1, numpy.int32)
(indPtr, colInds) = X.nonzeroRowsPtr()
indPtr = numpy.array(indPtr, numpy.uint32)
colInds = numpy.array(colInds, numpy.uint32)
k = 5
orderedItems = numpy.random.randint(0, n, m*k)
orderedItems = numpy.reshape(orderedItems, (m, k))
orderedItems = numpy.array(orderedItems, numpy.int32)
beta = 0.5
recalls, denominators = MCEvaluatorCython.stratifiedRecallAtk(indPtr, colInds, orderedItems, itemCounts, beta)
recalls2 = numpy.zeros(m)
#Now compute recalls from scratch
for i in range(m):
omegai = colInds[indPtr[i]:indPtr[i+1]]
numerator = 0
for j in range(k):
if orderedItems[i, j] in omegai:
numerator += 1/itemCounts[orderedItems[i, j]]**beta
denominator = 0
for j in omegai:
denominator += 1/itemCounts[j]**beta
recalls2[i] = numerator/denominator
nptst.assert_array_equal(recalls, recalls2)
#Now try to match with normal recall
itemCounts = numpy.ones(n, numpy.int32)
recalls, denominators = MCEvaluatorCython.stratifiedRecallAtk(indPtr, colInds, orderedItems, itemCounts, beta)
recalls2 = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
nptst.assert_array_equal(recalls, recalls2)
def recallAtK(positiveArray, orderedItems, k, verbose=False):
"""
Compute the average recall@k score for each row of the predicted matrix UV.T
using real values in positiveArray. positiveArray is a tuple (indPtr, colInds)
:param orderedItems: The ordered items for each user (users are rows, items are cols)
:param verbose: If true return recall and first k recommendation for each row, otherwise just precisions
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
orderedItems = orderedItems[:, 0:k]
indPtr, colInds = positiveArray
recalls = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
if verbose:
return recalls, orderedItems
else:
return recalls.mean()
def recallAtK(positiveArray, orderedItems, k, verbose=False):
"""
Compute the average recall@k score for each row of the predicted matrix UV.T
using real values in positiveArray. positiveArray is a tuple (indPtr, colInds)
:param orderedItems: The ordered items for each user (users are rows, items are cols)
:param verbose: If true return recall and first k recommendation for each row, otherwise just precisions
"""
if type(positiveArray) != tuple:
positiveArray = SparseUtils.getOmegaListPtr(positiveArray)
orderedItems = orderedItems[:, 0:k]
indPtr, colInds = positiveArray
recalls = MCEvaluatorCython.recallAtk(indPtr, colInds, orderedItems)
if verbose:
return recalls, orderedItems
else:
return recalls.mean()
