def testBalancedInterleave(self):
bi = BalancedInterleave()
r1 = DeterministicRankingFunction(None, self.weights_1)
r2 = DeterministicRankingFunction(None, self.weights_2)
(interleaved_list, assignments) = bi.interleave(r1, r2, self.query, 10)
self.assertIn(interleaved_list.tolist(),
[[0, 1, 2, 3], [1, 0, 2, 3], [0, 1, 3, 2], [1, 0, 3, 2]])
self.assertEqual(assignments[0].tolist(), [1, 3, 2, 0])
self.assertEqual(assignments[1].tolist(), [0, 1, 3, 2])
o = bi.infer_outcome([1, 0, 3, 2], [[1, 3, 2, 0], [0, 1, 3, 2]],
[1, 0, 0, 0], self.query)
self.assertEqual(o, -1, "l1 should win (1), o = %g" % o)
o = bi.infer_outcome([1, 0, 3, 2], [[1, 3, 2, 0], [0, 1, 3, 2]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (2), o = %g" % o)
o = bi.infer_outcome([1, 0, 3, 2], [[1, 2, 3, 0], [0, 1, 3, 2]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, 0, "The rankers should tie (1), o = %g" % o)
o = bi.infer_outcome([0, 1, 2, 3], [[0, 1, 2, 3], [1, 2, 3, 0]],
[0, 1, 0, 1], self.query)
self.assertEqual(o, 1, "l1 should win, o = %g" % o)
o = bi.infer_outcome([1, 0, 2, 3], [[0, 1, 2, 3], [1, 2, 3, 0]],
[0, 1, 0, 1], self.query)
self.assertEqual(o, 0, "The rankers should tie (2), o = %g" % o)
o = bi.infer_outcome([0, 2, 1, 3], [[3, 0, 1, 2], [1, 3, 2, 0]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (3), o = %g" % o)
o = bi.infer_outcome([0, 2, 1, 3], [[3, 0, 1, 2], [4, 3, 2, 0]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (4), o = %g" % o)
def testBalancedInterleave(self):
bi = BalancedInterleave()
r1 = DeterministicRankingFunction(None, self.weights_1)
r2 = DeterministicRankingFunction(None, self.weights_2)
(interleaved_list, assignments) = bi.interleave(r1, r2, self.query, 10)
self.assertIn(interleaved_list.tolist(), [[0, 1, 2, 3], [1, 0, 2, 3],
[0, 1, 3, 2], [1, 0, 3, 2]])
self.assertEqual(assignments[0].tolist(), [1, 3, 2, 0])
self.assertEqual(assignments[1].tolist(), [0, 1, 3, 2])
o = bi.infer_outcome([1, 0, 3, 2], [[1, 3, 2, 0], [0, 1, 3, 2]],
[1, 0, 0, 0], self.query)
self.assertEqual(o, -1, "l1 should win (1), o = %g" % o)
o = bi.infer_outcome([1, 0, 3, 2], [[1, 3, 2, 0], [0, 1, 3, 2]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (2), o = %g" % o)
o = bi.infer_outcome([1, 0, 3, 2], [[1, 2, 3, 0], [0, 1, 3, 2]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, 0, "The rankers should tie (1), o = %g" % o)
o = bi.infer_outcome([0, 1, 2, 3], [[0, 1, 2, 3], [1, 2, 3, 0]],
[0, 1, 0, 1], self.query)
self.assertEqual(o, 1, "l1 should win, o = %g" % o)
o = bi.infer_outcome([1, 0, 2, 3], [[0, 1, 2, 3], [1, 2, 3, 0]],
[0, 1, 0, 1], self.query)
self.assertEqual(o, 0, "The rankers should tie (2), o = %g" % o)
o = bi.infer_outcome([0, 2, 1, 3], [[3, 0, 1, 2], [1, 3, 2, 0]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (3), o = %g" % o)
o = bi.infer_outcome([0, 2, 1, 3], [[3, 0, 1, 2], [4, 3, 2, 0]],
[1, 0, 1, 0], self.query)
self.assertEqual(o, -1, "l1 should win (4), o = %g" % o)
class HistBalancedInterleave(AbstractHistInterleavedComparison):
"""Balanced interleave method, applied to historical data."""
def __init__(self, arg_str=None):
self.bi = BalancedInterleave()
def _get_assignment(self, r1, r2, query, length):
r1.init_ranking(query)
r2.init_ranking(query)
length = min(r1.document_count(), r2.document_count(), length)
# get ranked list for each ranker
l1, l2 = [], []
for _ in range(length):
l1.append(r1.next())
l2.append(r2.next())
return (asarray(l1), asarray(l2))
def infer_outcome(self, l, a, c, target_r1, target_r2, query):
"""count clicks within the top-k interleaved list"""
return self.bi.infer_outcome(l, self._get_assignment(target_r1, target_r2, query, len(l)), c, query)
class HistBalancedInterleave(AbstractHistInterleavedComparison):
"""Balanced interleave method, applied to historical data."""
def __init__(self, arg_str=None):
self.bi = BalancedInterleave()
def _get_assignment(self, r1, r2, query, length):
r1.init_ranking(query)
r2.init_ranking(query)
length = min(r1.document_count(), r2.document_count(), length)
# get ranked list for each ranker
l1, l2 = [], []
for _ in range(length):
l1.append(r1.next())
l2.append(r2.next())
return (asarray(l1), asarray(l2))
def infer_outcome(self, l, a, c, target_r1, target_r2, query):
"""count clicks within the top-k interleaved list"""
return self.bi.infer_outcome(
l, self._get_assignment(target_r1, target_r2, query, len(l)), c,
query)
def __init__(self, arg_str=None):
self.bi = BalancedInterleave()
def __init__(self, arg_str=None):
self.bi = BalancedInterleave()
