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)