def runMLKR(X_train, X_test, y_train, y_test): transformer = MLKR(verbose=True) transformer.fit(X_train, y_train) X_train_proj = transformer.transform(X_train) X_test_proj = transformer.transform(X_test) np.save('X_train_MLKR', X_train_proj) np.save('X_test_MLKR', X_test_proj) return X_train_proj, X_test_proj
def test_mlkr(self): mlkr = MLKR(n_components=2) mlkr.fit(self.X, self.y) res_1 = mlkr.transform(self.X) mlkr = MLKR(n_components=2) res_2 = mlkr.fit_transform(self.X, self.y) assert_array_almost_equal(res_1, res_2)
def test_mlkr(self): mlkr = MLKR(num_dims=2) mlkr.fit(self.X, self.y) res_1 = mlkr.transform(self.X) mlkr = MLKR(num_dims=2) res_2 = mlkr.fit_transform(self.X, self.y) assert_array_almost_equal(res_1, res_2)
def get_embedding(args, rescaled_domain_lst, domain_name_lst, eval_lst): if (args.embedding == "origin") or (args.embedding == "mds" and args.embedding_distance == "heuristic"): return rescaled_domain_lst elif (args.embedding == "bleu") or (args.embedding == "mds" and args.embedding_distance == "bleudif"): return [[e] for e in eval_lst] elif (args.embedding == "ml"): mlkr = MLKR() x = np.array(rescaled_domain_lst) y = np.array(eval_lst) mlkr.fit(x, y) return mlkr.transform(x)
def test_iris(self): mlkr = MLKR() mlkr.fit(self.iris_points, self.iris_labels) csep = class_separation(mlkr.transform(self.iris_points), self.iris_labels) self.assertLess(csep, 0.25)
def test_iris(self): mlkr = MLKR() mlkr.fit(self.iris_points, self.iris_labels) csep = class_separation(mlkr.transform(), self.iris_labels) self.assertLess(csep, 0.25)
def mlkr(data, label, dim): mlkr = MLKR(num_dims=dim) mlkr.fit(data, label) result = mlkr.transform(data) return result