API updates, more robust model fitting

Breaking changes

• rename botorch.qmc to botorch.sampling, move MC samplers from
  acquisition.sampler to botorch.sampling.samplers (#172)

New Features

• Add condition_on_observations and fantasize to the Model level API (#173)
• Support pending observations generically for all MCAcqusitionFunctions (#176)
• Add fidelity kernel for training iterations/training data points (#178)
• Support for optimization constraints across q-batches (to support things like
  sample budget constraints) (2a95a6c)
• Add ModelList <-> Batched Model converter (#187)
• New test functions
  • basic: neg_ackley, cosine8, neg_levy, neg_rosenbrock, neg_shekel
    (e26dc75)
  • for multi-fidelity BO: neg_aug_branin, neg_aug_hartmann6,
    neg_aug_rosenbrock (ec4aca7)

Improved functionality:

• More robust model fitting
  • Catch gpytorch numerical issues and return NaN to the optimizer (#184)
  • Restart optimization upon failure by sampling hyperparameters from their prior (#188)
  • Sequentially fit batched and ModelListGP models by default (#189)
  • Change minimum inferred noise level (e2c64fe)
• Introduce optional batch limit in joint_optimize to increases scalability of
  parallel optimization (baab578)
• Change constructor of ModelListGP to comply with GPyTorch’s IndependentModelList
  constructor (a6cf739)
• Use torch.random to set default seed for samplers (rather than random) to
  making sampling reproducible when setting torch.manual_seed
  (ae507ad)

Performance Improvements

• Use einsum in LinearMCObjective (22ca295)
• Change default Sobol sample size for MCAquisitionFunctions to be base-2 for
  better MC integration performance (5d8e818)
• Add ability to fit models in SumMarginalLogLikelihood sequentially (and make
  that the default setting) (#183)
• Do not construct the full covariance matrix when computing posterior of
  single-output BatchedMultiOutputGPyTorchModel (#185)

Bug fixes

• Properly handle observation_noise kwarg for BatchedMultiOutputGPyTorchModels (#182)
• Fix a issue where f_best was always max for NoisyExpectedImprovement
  (410de58)
• Fix bug and numerical issues in initialize_q_batch
  (844dcd1)
• Fix numerical issues with inv_transform for qMC sampling (#162)

Other

• Bump GPyTorch minimum requirement to 0.3.3