bce6052bd84a421a44bc590b1eda23923f63b951,test/examples/kissgp_kronecker_product_regression_test.py,,test_kissgp_gp_mean_abs_error,#,53

Before Change

def test_kissgp_gp_mean_abs_error():
    gp_model = GPRegressionModel()

    // Optimize the model
    gp_model.train()
    optimizer = optim.Adam(gp_model.parameters(), lr=0.2)
    optimizer.n_iter = 0
    for i in range(20):
        optimizer.zero_grad()
        output = gp_model(train_x)
        loss = -gp_model.marginal_log_likelihood(output, train_y)
        loss.backward()
        optimizer.n_iter += 1
        optimizer.step()

    // Test the model
    gp_model.eval()
    gp_model.condition(train_x, train_y)
    test_preds = gp_model(test_x).mean()
    mean_abs_error = torch.mean(torch.abs(test_y - test_preds))
    assert(mean_abs_error.data.squeeze()[0] < 0.1)

After Change

def test_kissgp_gp_mean_abs_error():
    likelihood = GaussianLikelihood()
    gp_model = GPRegressionModel(train_x.data, train_y.data, likelihood)

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

    optimizer = optim.Adam(list(gp_model.parameters()) + list(likelihood.parameters()), lr=0.2)
    optimizer.n_iter = 0
    for i in range(20):
        optimizer.zero_grad()
        output = gp_model(train_x)
        loss = -gp_model.marginal_log_likelihood(likelihood, output, train_y)
        loss.backward()
        optimizer.n_iter += 1
        optimizer.step()

    // 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))
    assert(mean_abs_error.data.squeeze()[0] < 0.1)

Instances