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 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 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)