def test_nca(self):
n = self.X.shape[0]
nca = NCA(max_iter=(100000 // n))
nca.fit(self.X, self.y)
res_1 = nca.transform(self.X)
nca = NCA(max_iter=(100000 // n))
res_2 = nca.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def test_nca(self):
n = self.X.shape[0]
nca = NCA(max_iter=(100000//n))
nca.fit(self.X, self.y)
res_1 = nca.transform(self.X)
nca = NCA(max_iter=(100000//n))
res_2 = nca.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
print("Computing similarities")
# similarities = euclidean_distances(dataNew)
print("Preparing model")
model = NCA(
n_components=128,
max_iter=10,
eps=1e-9,
random_state=args.seed,
# dissimilarity="precomputed",
n_jobs=6)
print("Fitting model")
# dataNew = model.fit_transform(similarities)
dataNew = model.fit_transform(dataNew)
dataNew = {
"docs": dataNew[:len(data["docs"])].copy(),
"queries": dataNew[len(data["docs"]):].copy(),
}
print(len(dataNew["docs"]))
print(len(dataNew["queries"]))
val_ip_pca = rprec_a_ip(dataNew["queries"],
dataNew["docs"],
data["relevancy"],
data["relevancy_articles"],
data["docs_articles"],
fast=False)
