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 precisionAtK(positiveArray, orderedItems, k, verbose=False):
"""
Compute the average precision@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 precision 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)
if verbose:
return precisions, orderedItems
else:
return precisions.mean()
def precisionAtK(positiveArray, orderedItems, k, verbose=False):
"""
Compute the average precision@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 precision 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)
if verbose:
return precisions, orderedItems
else:
return precisions.mean()