def test_views():
    X = np.array(X_list)
    adata = ad.AnnData(X, obs=obs_dict, var=var_dict, uns=uns_dict, dtype="int32")

    assert adata[:, 0].is_view
    assert adata[:, 0].X.tolist() == np.reshape([1, 4, 7], (3, 1)).tolist()

    adata[:2, 0].X = [0, 0]

    assert adata[:, 0].X.tolist() == np.reshape([0, 0, 7], (3, 1)).tolist()

    adata_subset = adata[:2, [0, 1]]

    assert adata_subset.is_view
    # now transition to actual object
    adata_subset.obs["foo"] = range(2)
    assert not adata_subset.is_view

    assert adata_subset.obs["foo"].tolist() == list(range(2))
def test_sgpr_mean_abs_error(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        train_x, train_y, test_x, test_y = make_data()
        likelihood = GaussianLikelihood()
        gp_model = GPRegressionModel(train_x, train_y, likelihood)
        mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

        # Optimize the model
        gp_model.train()
        likelihood.train()

        optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
        for _ in range(30):
            optimizer.zero_grad()
            output = gp_model(train_x)
            loss = -mll(output, train_y)
            loss.backward()
            optimizer.step()

        for param in gp_model.parameters():
            self.assertTrue(param.grad is not None)
            self.assertGreater(param.grad.norm().item(), 0)

        # Test the model
        gp_model.eval()
        likelihood.eval()

        test_preds = likelihood(gp_model(test_x)).mean
        mean_abs_error = torch.mean(torch.abs(test_y - test_preds))

        self.assertLess(mean_abs_error.squeeze().item(), 0.05)