def tearDown(self): session.stop() try: session.cleanup("*", self.job_id, True) except EnvironmentError: pass try: session.cleanup("*", self.job_id, False) except EnvironmentError: pass
def tearDown(self): session.stop()
def tearDown(self): # for table in self.table_list: # table.destroy() session.stop()
def tearDown(self): # self.table.destroy() session.stop()
def tearDownClass(self): session.stop()
fore_gradient = self.en_wx.join(self.data_inst, lambda wx, d: 0.25 * wx - 0.5 * d.label) sparse_data = self._make_sparse_data() gradient_computer = hetero_linear_model_gradient.HeteroGradientBase() for fit_intercept in [True, False]: dense_result = gradient_computer.compute_gradient(self.data_inst, fore_gradient, fit_intercept) dense_result = [self.paillier_encrypt.decrypt(iterator) for iterator in dense_result] if fit_intercept: self.assertListEqual(dense_result, self.gradient_fit_intercept) else: self.assertListEqual(dense_result, self.gradient) sparse_result = gradient_computer.compute_gradient(sparse_data, fore_gradient, fit_intercept) sparse_result = [self.paillier_encrypt.decrypt(iterator) for iterator in sparse_result] self.assertListEqual(dense_result, sparse_result) def _make_sparse_data(self): def trans_sparse(instance): dense_features = instance.features indices = [i for i in range(len(dense_features))] sparse_features = SparseVector(indices=indices, data=dense_features, shape=len(dense_features)) return Instance(inst_id=None, features=sparse_features, label=instance.label) return self.data_inst.mapValues(trans_sparse) if __name__ == "__main__": session.init("1111") unittest.main() session.stop()