Pairwise GP for Preference Learning, Sampling Strategies

Introduces a new Pairwise GP model for Preference Learning with pair-wise preferential feedback, as well as a Sampling Strategies abstraction for generating candidates from a discrete candidate set.

Compatibility

• Require PyTorch >=1.5 (#423).
• Require GPyTorch >=1.1.1 (#425).

New Features

• Add PairwiseGP for preference learning with pair-wise comparison data (#388).
• Add SamplingStrategy abstraction for sampling-based generation strategies, including
  MaxPosteriorSampling (i.e. Thompson Sampling) and BoltzmannSampling (#218, #407).

Deprecations

• The existing botorch.gen module is moved to botorch.generation.gen and imports
  from botorch.gen will raise a warning (an error in the next release) (#218).

Bug fixes

• Fix & update a number of tutorials (#394, #398, #393, #399, #403).
• Fix CUDA tests (#404).
• Fix sobol maxdim limitation in prune_baseline (#419).

Other changes

• Better stopping criteria for stochastic optimization (#392).
• Improve numerical stability of LinearTruncatedFidelityKernel (#409).
• Allow batched best_f in qExpectedImprovement and qProbabilityOfImprovement
  (#411).
• Introduce new logger framework (#412).
• Faster indexing in some situations (#414).
• More generic BaseTestProblem (9e604fe).