def testExploitativeProbabilisticInterleaveInterleave(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(l, (r1_ret, r2_ret)) = epi.interleave(r1, r2, self.query, 4)
self.assertEqual(r1, r1_ret, "r1 is just passed through.")
self.assertEqual(r2, r2_ret, "r2 is just passed through.")
self.assertEqual(len(l), 4, "interleave produces a list of length 4.")
self.assertTrue(0 in l, "document 0 is in l.")
self.assertTrue(1 in l, "document 0 is in l.")
self.assertTrue(2 in l, "document 0 is in l.")
self.assertTrue(3 in l, "document 0 is in l.")
observed_l = {}
for _ in range(0, 100):
(l, (r1_ret, r2_ret)) = epi.interleave(r1, r2, self.query, 4)
l_str = " ".join([str(docid) for docid in l])
if not l_str in observed_l:
observed_l[l_str] = 1
else:
observed_l[l_str] += 1
self.assertIn("0 1 2 3", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("1 0 3 2", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("3 1 2 0", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("2 1 0 3", observed_l, "List was observed: 0 1 2 3.")
def testExploitativeProbabilisticInterleaveInterleave(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(l, (r1_ret, r2_ret)) = epi.interleave(r1, r2, self.query, 4)
self.assertEqual(r1, r1_ret, "r1 is just passed through.")
self.assertEqual(r2, r2_ret, "r2 is just passed through.")
self.assertEqual(len(l), 4, "interleave produces a list of length 4.")
self.assertTrue(0 in l, "document 0 is in l.")
self.assertTrue(1 in l, "document 0 is in l.")
self.assertTrue(2 in l, "document 0 is in l.")
self.assertTrue(3 in l, "document 0 is in l.")
observed_l = {}
for _ in range(0, 100):
(l, (r1_ret, r2_ret)) = epi.interleave(r1, r2, self.query, 4)
l_str = " ".join([str(docid) for docid in l])
if not l_str in observed_l:
observed_l[l_str] = 1
else:
observed_l[l_str] += 1
self.assertIn("0 1 2 3", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("1 0 3 2", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("3 1 2 0", observed_l, "List was observed: 0 1 2 3.")
self.assertIn("2 1 0 3", observed_l, "List was observed: 0 1 2 3.")
def testInferOutcomeUnbiased(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
outcome = epi.infer_outcome([1, 0, 3, 2], (None, r1, r2), [0, 1, 0, 0],
self.query)
self.assertAlmostEquals(0.03581, outcome, 8,
"Obtained outcome = %.8f" % outcome)
def testInferOutcomeUnbiased(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
outcome = epi.infer_outcome([1, 0, 3, 2], (None, r1, r2), [0, 1, 0, 0],
self.query)
self.assertAlmostEquals(0.03581, outcome, 8,
"Obtained outcome = %.8f" % outcome)
def testExploitativeProbabilisticInterleave(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 0, 3, 2]
exp_probs = [0.36, 0.3, 0.2, 0.14]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testExploitativeProbabilisticInterleave(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 0, 3, 2]
exp_probs = [0.36, 0.3, 0.2, 0.14]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testGetSourceProbabilityOfList(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
# with exploration rate 0.5
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
p = epi._get_source_probability_of_list([1, 0, 3, 2], (None, r1, r2),
self.query)
self.assertAlmostEquals(0.090916137, p, 8, "Obtained p = %.g" % p)
# with exploration rate 0.1
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
p = epi._get_source_probability_of_list([1, 0, 3, 2], (None, r1, r2),
self.query)
self.assertAlmostEquals(0.073751736, p, 8, "Obtained p = %.g" % p)
def testExploitativeProbabilisticInterleaveTwoDocs(self):
# prepare rankers
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(1)
r2.rm_document(1)
r1.rm_document(3)
r2.rm_document(3)
# test after 1 and 3 were removed
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [0, 2]
exp_probs = [0.61428571, 0.38571429]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testExploitativeProbabilisticInterleaveTwoDocs(self):
# prepare rankers
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(1)
r2.rm_document(1)
r1.rm_document(3)
r2.rm_document(3)
# test after 1 and 3 were removed
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [0, 2]
exp_probs = [0.61428571, 0.38571429]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testExploitativeProbabilisticInterleaveExploit(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
# exploration rate = 0.1
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2, 0]
exp_probs = [0.456, 0.232, 0.156, 0.156]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
# exploration rate = 0.0
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.0")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2, 0]
exp_probs = [0.48, 0.24, 0.16, 0.12]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testExploitativeProbabilisticInterleaveThreeDocs(self):
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
# prepare rankers
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(0)
r2.rm_document(0)
# test after document 0 was removed
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2]
exp_probs = [0.5034965, 0.29020979, 0.20629371]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
# prepare rankers
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(3)
r2.rm_document(3)
# test after document 3 was removed
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 0, 2]
exp_probs = [0.45864662, 0.36466165, 0.17669173]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testExploitativeProbabilisticInterleaveThreeDocs(self):
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
# prepare rankers
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(0)
r2.rm_document(0)
# test after document 0 was removed
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2]
exp_probs = [0.5034965, 0.29020979, 0.20629371]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
# prepare rankers
r1.init_ranking(self.query)
r2.init_ranking(self.query)
r1.rm_document(3)
r2.rm_document(3)
# test after document 3 was removed
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 0, 2]
exp_probs = [0.45864662, 0.36466165, 0.17669173]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
def testGetSourceProbabilityOfList(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
# with exploration rate 0.5
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.5")
p = epi._get_source_probability_of_list([1, 0, 3, 2], (None, r1, r2),
self.query)
self.assertAlmostEquals(0.090916137, p, 8, "Obtained p = %.g" % p)
# with exploration rate 0.1
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
p = epi._get_source_probability_of_list([1, 0, 3, 2], (None, r1, r2),
self.query)
self.assertAlmostEquals(0.073751736, p, 8, "Obtained p = %.g" % p)
def testExploitativeProbabilisticInterleaveExploit(self):
r1 = ProbabilisticRankingFunction(1, self.weights_1)
r2 = ProbabilisticRankingFunction(1, self.weights_2)
# exploration rate = 0.1
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.1")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2, 0]
exp_probs = [0.456, 0.232, 0.156, 0.156]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)
# exploration rate = 0.0
epi = ExploitativeProbabilisticInterleave("--exploration_rate=0.0")
r1.init_ranking(self.query)
r2.init_ranking(self.query)
(docids, probs) = epi._get_document_distribution(r1, r2)
exp_docids = [1, 3, 2, 0]
exp_probs = [0.48, 0.24, 0.16, 0.12]
self._prob_doc_test_helper(docids, exp_docids, probs, exp_probs)