<|file_name|>test_sgpr_regression.py<|end_file_name|><｜fim▁begin｜>#!/usr/bin/env python3

import os
import random
import unittest
import warnings
from math import exp, pi

import gpytorch
import torch
from gpytorch.distributions import MultivariateNormal
from gpytorch.kernels import InducingPointKernel, RBFKernel, ScaleKernel
from gpytorch.likelihoods import GaussianLikelihood
from gpytorch.means import ConstantMean
from gpytorch.priors import SmoothedBoxPrior
from gpytorch.test.utils import least_used_cuda_device
from gpytorch.utils.warnings import NumericalWarning
from torch import optim


# Simple training data: let's try to learn a sine function,
# but with SGPR
# let's use 100 training examples.
def make_data(cuda=False):
    train_x = torch.linspace(0, 1, 100)
    train_y = torch.sin(train_x * (2 * pi))
    train_y.add_(torch.randn_like(train_y), alpha=1e-2)
    test_x = torch.rand(51)
    test_y = torch.sin(test_x * (2 * pi))
    if cuda:
        train_x = train_x.cuda()
        train_y = train_y.cuda()
        test_x = test_x.cuda()
        test_y = test_y.cuda()
    return train_x, train_y, test_x, test_y


class GPRegressionModel(gpytorch.models.ExactGP):
    def __init__(self, train_x, train_y, likelihood):
        super(GPRegressionModel, self).__init__(train_x, train_y, likelihood)
        self.mean_module = ConstantMean(prior=SmoothedBoxPrior(-1e-5, 1e-5))
        self.base_covar_module = ScaleKernel(RBFKernel(lengthscale_prior=SmoothedBoxPrior(exp(-5), exp(6), sigma=0.1)))
        self.covar_module = InducingPointKernel(
            self.base_covar_module, inducing_points=torch.linspace(0, 1, 32), likelihood=likelihood
        )

    def forward(self, x):
        mean_x = self.mean_module(x)
        covar_x = self.covar_module(x)
        return MultivariateNormal(mean_x, covar_x)


class TestSGPRRegression(unittest.TestCase):
    <|fim_middle|>



if __name__ == "__main__":
    unittest.main()
<｜fim▁end｜>
    def setUp(self):
        if os.getenv("UNLOCK_SEED") is None or os.getenv("UNLOCK_SEED").lower() == "false":
            self.rng_state = torch.get_rng_state()
            torch.manual_seed(0)
            if torch.cuda.is_available():
                torch.cuda.manual_seed_all(0)
            random.seed(0)

    def tearDown(self):
        if hasattr(self, "rng_state"):
            torch.set_rng_state(self.rng_state)

    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)

    def test_sgpr_fast_pred_var(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(50):
            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()

        with gpytorch.settings.max_preconditioner_size(5), gpytorch.settings.max_cg_iterations(50):
            with gpytorch.settings.fast_pred_var(True):
                fast_var = gp_model(test_x).variance
                fast_var_cache = gp_model(test_x).variance
                self.assertLess(torch.max((fast_var_cache - fast_var).abs()), 1e-3)

            with gpytorch.settings.fast_pred_var(False):
                slow_var = gp_model(test_x).variance

        self.assertLess(torch.max((fast_var_cache - slow_var).abs()), 1e-3)

    def test_sgpr_mean_abs_error_cuda(self):
        # Suppress numerical warnings
        warnings.simplefilter("ignore", NumericalWarning)

        if not torch.cuda.is_available():
            return
        with least_used_cuda_device():
            train_x, train_y, test_x, test_y = make_data(cuda=True)
            likelihood = GaussianLikelihood().cuda()
            gp_model = GPRegressionModel(train_x, train_y, likelihood).cuda()
            mll = gpytorch.mlls.ExactMarginalLogLikelihood(likelihood, gp_model)

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

            optimizer = optim.Adam(gp_model.parameters(), lr=0.1)
            optimizer.n_iter = 0
            for _ in range(25):
                optimizer.zero_grad()
                output = gp_model(train_x)
                loss = -mll(output, train_y)
                loss.backward()
                optimizer.n_iter += 1
                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(), 0.02)