class Experiment:
def __init__(self, folder: Path, um: str) -> None:
train = str(folder / "train.txt")
test = str(folder / "test.txt")
self.n_rankers = 5
self.num_features = 45
self.train = load_queries(train, self.num_features)
self.test = load_queries(test, self.num_features)
print("Found {} train queries and {} test queries.".format(
len(self.train), len(self.test)))
self.ml = TeamDraftMultileave()
self.rankers = np.array([
ProbabilisticRankingFunction("1", "random", self.num_features)
for _ in range(self.n_rankers)
])
self.eval = NdcgEval()
self.um = CascadeUserModel(um)
self.cutoff = 10
print("OK")
def run(self, n_impressions):
results = {"offline_ndcg": [], "online_ndcg": []}
for i in range(n_impressions):
print("Impression #", i)
res = self.impression()
for eval in results.keys():
results[eval].append(res[eval])
def impression(self):
query = self.train[np.random.choice(self.train.get_qids())]
td_results = self.impression_tdm(query)
print(td_results)
return td_results
def impression_tdm(self, query):
ranking, a = self.ml.interleave(self.rankers, query, self.cutoff)
clicks = self.um.get_clicks(ranking, query.get_labels())
creds = np.array(self.ml.infer_outcome(ranking, a, clicks, query))
online_ndcg = self.eval.evaluate_ranking(ranking, query, 10)
offline_ndcg = self.evaluate_offline(creds)
return {"online_ndcg": online_ndcg, "offline_ndcg": offline_ndcg}
def evaluate_offline(self, creds):
# todo: evaluate only for best ranker
winners_idx = np.argwhere(creds == np.amax(creds)).flatten()
winners = self.rankers[winners_idx]
result = []
for query in self.test:
off_ndcg = []
for ranker in winners:
ranker.init_ranking(query)
off_ndcg.append(
self.eval.evaluate_ranking(ranker.get_ranking(), query,
10))
result.append(np.mean(off_ndcg))
return np.mean(result)
def testEvaluateRankingLastZeroWithCutoff(self):
ev = NdcgEval()
dot_prod = np.dot(self.query.get_feature_vectors(),
self.zero_weights.transpose())
ranking = ev._sort_docids_by_score(self.query.get_docids(),
dot_prod, ties="last")
self.assertAlmostEquals(0.4452805, ev.evaluate_ranking(ranking,
self.query, cutoff=10))
def testEvaluateRankingLastZeroWithCutoff(self):
ev = NdcgEval()
dot_prod = np.dot(self.query.get_feature_vectors(),
self.zero_weights.transpose())
ranking = ev._sort_docids_by_score(self.query.get_docids(),
dot_prod,
ties="last")
self.assertAlmostEquals(
0.4452805, ev.evaluate_ranking(ranking, self.query, cutoff=10))
def __init__(self, folder: Path, um: str) -> None:
train = str(folder / "train.txt")
test = str(folder / "test.txt")
self.n_rankers = 5
self.num_features = 45
self.train = load_queries(train, self.num_features)
self.test = load_queries(test, self.num_features)
print("Found {} train queries and {} test queries.".format(
len(self.train), len(self.test)))
self.ml = TeamDraftMultileave()
self.rankers = np.array([
ProbabilisticRankingFunction("1", "random", self.num_features)
for _ in range(self.n_rankers)
])
self.eval = NdcgEval()
self.um = CascadeUserModel(um)
self.cutoff = 10
print("OK")
def testGetNdcg(self):
ev = NdcgEval()
self.assertAlmostEqual(ev.get_ndcg([0, 0, 1, 0], [1, 0, 0, 0]), 0.5)
self.assertAlmostEqual(ev.get_ndcg([0, 1, 0, 0], [1, 0, 0, 0]),
0.6309298)
self.assertAlmostEqual(ev.get_ndcg([0, 0, 0, 0], [1, 0, 0, 0]), 0)
def testGetNdcg(self):
ev = NdcgEval()
self.assertAlmostEqual(ev.get_ndcg([0, 0, 1, 0], [1, 0, 0, 0]), 0.5)
self.assertAlmostEqual(ev.get_ndcg([0, 1, 0, 0], [1, 0, 0, 0]),
0.6309298)
self.assertAlmostEqual(ev.get_ndcg([0, 0, 0, 0], [1, 0, 0, 0]), 0)