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)
