Source code for botorch.utils.testing

#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

from __future__ import annotations

import math
import warnings
from abc import abstractproperty
from collections import OrderedDict
from typing import Any, List, Optional, Tuple, Union
from unittest import mock, TestCase

import torch
from botorch import settings
from botorch.acquisition.objective import PosteriorTransform
from botorch.exceptions.warnings import BotorchTensorDimensionWarning, InputDataWarning
from botorch.models.model import FantasizeMixin, Model
from botorch.posteriors.gpytorch import GPyTorchPosterior
from botorch.posteriors.posterior import Posterior
from botorch.sampling.base import MCSampler
from botorch.sampling.get_sampler import GetSampler
from botorch.sampling.stochastic_samplers import StochasticSampler
from botorch.test_functions.base import BaseTestProblem
from botorch.utils.transforms import unnormalize
from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
from linear_operator.operators import AddedDiagLinearOperator, DiagLinearOperator
from torch import Tensor

EMPTY_SIZE = torch.Size()

[docs] class BotorchTestCase(TestCase): r"""Basic test case for Botorch. This 1. sets the default device to be `torch.device("cpu")` 2. ensures that no warnings are suppressed by default. """ device = torch.device("cpu")
[docs] def setUp(self, suppress_input_warnings: bool = True) -> None: warnings.resetwarnings() settings.debug._set_state(False) warnings.simplefilter("always", append=True) if suppress_input_warnings: warnings.filterwarnings( "ignore", message="The model inputs are of type", category=UserWarning, ) warnings.filterwarnings( "ignore", message="Non-strict enforcement of botorch tensor conventions.", category=BotorchTensorDimensionWarning, ) warnings.filterwarnings( "ignore", message="Input data is not standardized.", category=InputDataWarning, ) warnings.filterwarnings( "ignore", message="Input data is not contained to the unit cube.", category=InputDataWarning, )
[docs] def assertAllClose( self, input: torch.Tensor, other: Union[torch.Tensor, float], rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False, ) -> None: r""" Calls torch.testing.assert_close, using the signature and default behavior of torch.allclose. Example output: AssertionError: Scalars are not close! Absolute difference: 1.0000034868717194 (up to 0.0001 allowed) Relative difference: 0.8348668001940709 (up to 1e-05 allowed) """ # Why not just use the signature and behavior of `torch.testing.assert_close`? # Because we used `torch.allclose` for testing in the past, and the two don't # behave exactly the same. In particular, `assert_close` requires both `atol` # and `rtol` to be set if either one is. torch.testing.assert_close( input, other, rtol=rtol, atol=atol, equal_nan=equal_nan, )
[docs] class BaseTestProblemTestCaseMixIn:
[docs] def test_forward(self): for dtype in (torch.float, torch.double): for batch_shape in (torch.Size(), torch.Size([2]), torch.Size([2, 3])): for f in self.functions:, dtype=dtype) X = torch.rand(*batch_shape, f.dim, device=self.device, dtype=dtype) X = f.bounds[0] + X * (f.bounds[1] - f.bounds[0]) res = f(X) f(X, noise=False) self.assertEqual(res.dtype, dtype) self.assertEqual(res.device.type, self.device.type) tail_shape = torch.Size( [f.num_objectives] if f.num_objectives > 1 else [] ) self.assertEqual(res.shape, batch_shape + tail_shape)
@abstractproperty def functions(self) -> List[BaseTestProblem]: # The functions that should be tested. Typically defined as a class # attribute on the test case subclassing this class. pass # pragma: no cover
[docs] class SyntheticTestFunctionTestCaseMixin:
[docs] def test_optimal_value(self): for dtype in (torch.float, torch.double): for f in self.functions:, dtype=dtype) try: optval = f.optimal_value optval_exp = -f._optimal_value if f.negate else f._optimal_value self.assertEqual(optval, optval_exp) except NotImplementedError: pass
[docs] def test_optimizer(self): for dtype in (torch.float, torch.double): for f in self.functions:, dtype=dtype) try: Xopt = f.optimizers.clone().requires_grad_(True) except NotImplementedError: continue res = f(Xopt, noise=False) # if we have optimizers, we have the optimal value res_exp = torch.full_like(res, f.optimal_value) self.assertAllClose(res, res_exp, atol=1e-3, rtol=1e-3) if f._check_grad_at_opt: grad = torch.autograd.grad([*res], Xopt)[0] self.assertLess(grad.abs().max().item(), 1e-3)
[docs] class MultiObjectiveTestProblemTestCaseMixin:
[docs] def test_attributes(self): for f in self.functions: self.assertTrue(hasattr(f, "dim")) self.assertTrue(hasattr(f, "num_objectives")) self.assertEqual(f.bounds.shape, torch.Size([2, f.dim]))
[docs] def test_max_hv(self): for dtype in (torch.float, torch.double): for f in self.functions:, dtype=dtype) if not hasattr(f, "_max_hv"): with self.assertRaises(NotImplementedError): f.max_hv else: self.assertEqual(f.max_hv, f._max_hv)
[docs] def test_ref_point(self): for dtype in (torch.float, torch.double): for f in self.functions:, device=self.device) self.assertTrue( torch.allclose( f.ref_point, torch.tensor(f._ref_point, dtype=dtype, device=self.device), ) )
[docs] class ConstrainedTestProblemTestCaseMixin:
[docs] def test_num_constraints(self): for f in self.functions: self.assertTrue(hasattr(f, "num_constraints"))
[docs] def test_evaluate_slack(self): for dtype in (torch.float, torch.double): for f in self.functions:, dtype=dtype) X = unnormalize( torch.rand(1, f.dim, device=self.device, dtype=dtype), bounds=f.bounds, ) slack_true = f.evaluate_slack_true(X) # Mock out the random generator to ensure that noise realizations are # sizable so we don't run into any floating point comparison issues. with mock.patch( "botorch.test_functions.base.torch.randn_like", side_effect=lambda y: y, ): slack_observed = f.evaluate_slack(X) self.assertEqual(slack_true.shape, torch.Size([1, f.num_constraints])) self.assertEqual( slack_observed.shape, torch.Size([1, f.num_constraints]) ) is_equal = (slack_observed == slack_true).bool() if isinstance(f.constraint_noise_std, float): self.assertEqual( is_equal.all().item(), f.constraint_noise_std == 0.0 ) elif isinstance(f.constraint_noise_std, list): for i, noise_std in enumerate(f.constraint_noise_std): self.assertEqual( is_equal[:, i].item(), noise_std in (0.0, None) ) else: self.assertTrue(is_equal.all().item())
[docs] class MockPosterior(Posterior): r"""Mock object that implements dummy methods and feeds through specified outputs""" def __init__( self, mean=None, variance=None, samples=None, base_shape=None, batch_range=None ) -> None: r""" Args: mean: The mean of the posterior. variance: The variance of the posterior. samples: Samples to return from `rsample`, unless `base_samples` is provided. base_shape: If given, this is returned as `base_sample_shape`, and also used as the base of the `_extended_shape`. batch_range: If given, this is returned as `batch_range`. Defaults to (0, -2). """ self._mean = mean self._variance = variance self._samples = samples self._base_shape = base_shape self._batch_range = batch_range or (0, -2) @property def device(self) -> torch.device: for t in (self._mean, self._variance, self._samples): if torch.is_tensor(t): return t.device return torch.device("cpu") @property def dtype(self) -> torch.dtype: for t in (self._mean, self._variance, self._samples): if torch.is_tensor(t): return t.dtype return torch.float32 @property def batch_shape(self) -> torch.Size: for t in (self._mean, self._variance, self._samples): if torch.is_tensor(t): return t.shape[:-2] raise NotImplementedError # pragma: no cover def _extended_shape( self, sample_shape: torch.Size = torch.Size() # noqa: B008 ) -> torch.Size: return sample_shape + self.base_sample_shape @property def base_sample_shape(self) -> torch.Size: if self._base_shape is not None: return self._base_shape if self._samples is not None: return self._samples.shape if self._mean is not None: return self._mean.shape if self._variance is not None: return self._variance.shape return torch.Size() @property def batch_range(self) -> Tuple[int, int]: return self._batch_range @property def mean(self): return self._mean @property def variance(self): return self._variance
[docs] def rsample( self, sample_shape: Optional[torch.Size] = None, base_samples: Optional[Tensor] = None, ) -> Tensor: """Mock sample by repeating self._samples. If base_samples is provided, do a shape check but return the same mock samples.""" if sample_shape is None: sample_shape = torch.Size() if sample_shape is not None and base_samples is not None: # check the base_samples shape is consistent with the sample_shape if base_samples.shape[: len(sample_shape)] != sample_shape: raise RuntimeError("sample_shape disagrees with base_samples.") return self._samples.expand(sample_shape + self._samples.shape)
[docs] def rsample_from_base_samples( self, sample_shape: torch.Size, base_samples: Tensor, ) -> Tensor: return self.rsample(sample_shape, base_samples)
@GetSampler.register(MockPosterior) def _get_sampler_mock( posterior: MockPosterior, sample_shape: torch.Size, **kwargs: Any ) -> MCSampler: r"""Get the dummy `StochasticSampler` for `MockPosterior`.""" return StochasticSampler(sample_shape=sample_shape, **kwargs)
[docs] class MockModel(Model, FantasizeMixin): r"""Mock object that implements dummy methods and feeds through specified outputs""" def __init__(self, posterior: MockPosterior) -> None: # noqa: D107 super(Model, self).__init__() self._posterior = posterior
[docs] def posterior( self, X: Tensor, output_indices: Optional[List[int]] = None, posterior_transform: Optional[PosteriorTransform] = None, observation_noise: bool = False, ) -> MockPosterior: if posterior_transform is not None: return posterior_transform(self._posterior) else: return self._posterior
@property def num_outputs(self) -> int: extended_shape = self._posterior._extended_shape() return extended_shape[-1] if len(extended_shape) > 0 else 0 @property def batch_shape(self) -> torch.Size: extended_shape = self._posterior._extended_shape() return extended_shape[:-2]
[docs] def state_dict(self) -> None: pass
[docs] def load_state_dict( self, state_dict: Optional[OrderedDict] = None, strict: bool = False ) -> None: pass
[docs] class MockAcquisitionFunction: r"""Mock acquisition function object that implements dummy methods.""" def __init__(self): # noqa: D107 self.model = None self.X_pending = None def __call__(self, X): return X[..., 0].max(dim=-1).values
[docs] def set_X_pending(self, X_pending: Optional[Tensor] = None): self.X_pending = X_pending
def _get_random_data( batch_shape: torch.Size, m: int, d: int = 1, n: int = 10, **tkwargs ) -> Tuple[Tensor, Tensor]: r"""Generate random data for testing purposes. Args: batch_shape: The batch shape of the data. m: The number of outputs. d: The dimension of the input. n: The number of data points. tkwargs: `device` and `dtype` tensor constructor kwargs. Returns: A tuple `(train_X, train_Y)` with randomly generated training data. """ rep_shape = batch_shape + torch.Size([1, 1]) train_x = torch.stack( [torch.linspace(0, 0.95, n, **tkwargs) for _ in range(d)], dim=-1 ) train_x = train_x + 0.05 * torch.rand_like(train_x).repeat(rep_shape) train_x[0] += 0.02 # modify the first batch train_y = torch.sin(train_x[..., :1] * (2 * math.pi)) train_y = train_y + 0.2 * torch.randn(n, m, **tkwargs).repeat(rep_shape) return train_x, train_y def _get_test_posterior( batch_shape: torch.Size, q: int = 1, m: int = 1, interleaved: bool = True, lazy: bool = False, independent: bool = False, **tkwargs, ) -> GPyTorchPosterior: r"""Generate a Posterior for testing purposes. Args: batch_shape: The batch shape of the data. q: The number of candidates m: The number of outputs. interleaved: A boolean indicating the format of the MultitaskMultivariateNormal lazy: A boolean indicating if the posterior should be lazy independent: A boolean indicating whether the outputs are independent tkwargs: `device` and `dtype` tensor constructor kwargs. """ if independent: mvns = [] for _ in range(m): mean = torch.rand(*batch_shape, q, **tkwargs) a = torch.rand(*batch_shape, q, q, **tkwargs) covar = a @ a.transpose(-1, -2) flat_diag = torch.rand(*batch_shape, q, **tkwargs) covar = covar + torch.diag_embed(flat_diag) mvns.append(MultivariateNormal(mean, covar)) mtmvn = MultitaskMultivariateNormal.from_independent_mvns(mvns) else: mean = torch.rand(*batch_shape, q, m, **tkwargs) a = torch.rand(*batch_shape, q * m, q * m, **tkwargs) covar = a @ a.transpose(-1, -2) flat_diag = torch.rand(*batch_shape, q * m, **tkwargs) if lazy: covar = AddedDiagLinearOperator(covar, DiagLinearOperator(flat_diag)) else: covar = covar + torch.diag_embed(flat_diag) mtmvn = MultitaskMultivariateNormal(mean, covar, interleaved=interleaved) return GPyTorchPosterior(mtmvn) def _get_max_violation_of_bounds(samples: torch.Tensor, bounds: torch.Tensor) -> float: """ The maximum value by which samples lie outside bounds. A negative value indicates that all samples lie within bounds. Args: samples: An `n x q x d` - dimension tensor, as might be returned from `sample_q_batches_from_polytope`. bounds: A `2 x d` tensor of lower and upper bounds for each column. """ n, q, d = samples.shape samples = samples.reshape((n * q, d)) lower = samples.min(0).values upper = samples.max(0).values lower_dist = (bounds[0, :] - lower).max().item() upper_dist = (upper - bounds[1, :]).max().item() return max(lower_dist, upper_dist) def _get_max_violation_of_constraints( samples: torch.Tensor, constraints: Optional[List[Tuple[Tensor, Tensor, float]]], equality: bool, ) -> float: r""" Amount by which equality constraints are not obeyed. Args: samples: An `n x q x d` - dimension tensor, as might be returned from `sample_q_batches_from_polytope`. constraints: A list of tuples (indices, coefficients, rhs), with each tuple encoding an inequality constraint of the form `\sum_i (X[indices[i]] * coefficients[i]) = rhs`, or `>=` if `equality` is False. equality: Whether these are equality constraints (not inequality). """ n, q, d = samples.shape max_error = 0 if constraints is not None: for (ind, coef, rhs) in constraints: if ind.ndim == 1: constr = samples[:, :, ind] @ coef else: constr = samples[:, ind[:, 0], ind[:, 1]] @ coef if equality: error = (constr - rhs).abs().max() else: error = (rhs - constr).max() max_error = max(max_error, error) return max_error