Absolute imports (#443)

Summary:
Let's be explicit!

Pull Request resolved: #443

Test Plan: contbuild

Reviewed By: ldworkin

Differential Revision: D21558619

Pulled By: Balandat

fbshipit-source-id: a1c4f0abbd7c370a4c92e6df7b83eaa0ddc6decd

Author: Balandat

botorch/__init__.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from . import (
+from botorch import (
     acquisition,
     exceptions,
     models,
@@ -13,14 +13,14 @@
     settings,
     test_functions,
 )
-from .cross_validation import batch_cross_validation
-from .fit import fit_gpytorch_model
-from .generation.gen import (
+from botorch.cross_validation import batch_cross_validation
+from botorch.fit import fit_gpytorch_model
+from botorch.generation.gen import (
     gen_candidates_scipy,
     gen_candidates_torch,
     get_best_candidates,
 )
-from .utils import manual_seed
+from botorch.utils import manual_seed
 
 
 __version__ = "0.2.4"

botorch/acquisition/__init__.py

@@ -4,9 +4,12 @@
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
 
-from .acquisition import AcquisitionFunction, OneShotAcquisitionFunction
-from .active_learning import qNegIntegratedPosteriorVariance
-from .analytic import (
+from botorch.acquisition.acquisition import (
+    AcquisitionFunction,
+    OneShotAcquisitionFunction,
+)
+from botorch.acquisition.active_learning import qNegIntegratedPosteriorVariance
+from botorch.acquisition.analytic import (
     AnalyticAcquisitionFunction,
     ConstrainedExpectedImprovement,
     ExpectedImprovement,
@@ -15,27 +18,33 @@
     ProbabilityOfImprovement,
     UpperConfidenceBound,
 )
-from .cost_aware import GenericCostAwareUtility, InverseCostWeightedUtility
-from .fixed_feature import FixedFeatureAcquisitionFunction
-from .knowledge_gradient import qKnowledgeGradient
-from .max_value_entropy_search import qMaxValueEntropy, qMultiFidelityMaxValueEntropy
-from .monte_carlo import (
+from botorch.acquisition.cost_aware import (
+    GenericCostAwareUtility,
+    InverseCostWeightedUtility,
+)
+from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
+from botorch.acquisition.knowledge_gradient import qKnowledgeGradient
+from botorch.acquisition.max_value_entropy_search import (
+    qMaxValueEntropy,
+    qMultiFidelityMaxValueEntropy,
+)
+from botorch.acquisition.monte_carlo import (
     MCAcquisitionFunction,
     qExpectedImprovement,
     qNoisyExpectedImprovement,
     qProbabilityOfImprovement,
     qSimpleRegret,
     qUpperConfidenceBound,
 )
-from .objective import (
+from botorch.acquisition.objective import (
     ConstrainedMCObjective,
     GenericMCObjective,
     IdentityMCObjective,
     LinearMCObjective,
     MCAcquisitionObjective,
     ScalarizedObjective,
 )
-from .utils import get_acquisition_function
+from botorch.acquisition.utils import get_acquisition_function
 
 
 __all__ = [

botorch/acquisition/acquisition.py

@@ -14,12 +14,11 @@
 from abc import ABC, abstractmethod
 from typing import Optional
 
+from botorch.exceptions import BotorchWarning
+from botorch.models.model import Model
 from torch import Tensor
 from torch.nn import Module
 
-from ..exceptions import BotorchWarning
-from ..models.model import Model
-
 
 class AcquisitionFunction(Module, ABC):
     r"""Abstract base class for acquisition functions."""

botorch/acquisition/active_learning.py

@@ -26,14 +26,13 @@
 from typing import Optional
 
 from botorch import settings
+from botorch.acquisition.analytic import AnalyticAcquisitionFunction
+from botorch.acquisition.objective import ScalarizedObjective
+from botorch.models.model import Model
+from botorch.sampling.samplers import MCSampler, SobolQMCNormalSampler
+from botorch.utils.transforms import concatenate_pending_points, t_batch_mode_transform
 from torch import Tensor
 
-from ..models.model import Model
-from ..sampling.samplers import MCSampler, SobolQMCNormalSampler
-from ..utils.transforms import concatenate_pending_points, t_batch_mode_transform
-from .analytic import AnalyticAcquisitionFunction
-from .objective import ScalarizedObjective
-
 
 class qNegIntegratedPosteriorVariance(AnalyticAcquisitionFunction):
     r"""Batch Integrated Negative Posterior Variance for Active Learning.

botorch/acquisition/analytic.py

@@ -16,19 +16,18 @@
 from typing import Dict, Optional, Tuple, Union
 
 import torch
+from botorch.acquisition.acquisition import AcquisitionFunction
+from botorch.acquisition.objective import ScalarizedObjective
+from botorch.exceptions import UnsupportedError
+from botorch.models.gp_regression import FixedNoiseGP
+from botorch.models.gpytorch import GPyTorchModel
+from botorch.models.model import Model
+from botorch.posteriors.posterior import Posterior
+from botorch.sampling.samplers import SobolQMCNormalSampler
+from botorch.utils.transforms import convert_to_target_pre_hook, t_batch_mode_transform
 from torch import Tensor
 from torch.distributions import Normal
 
-from ..exceptions import UnsupportedError
-from ..models.gp_regression import FixedNoiseGP
-from ..models.gpytorch import GPyTorchModel
-from ..models.model import Model
-from ..posteriors.posterior import Posterior
-from ..sampling.samplers import SobolQMCNormalSampler
-from ..utils.transforms import convert_to_target_pre_hook, t_batch_mode_transform
-from .acquisition import AcquisitionFunction
-from .objective import ScalarizedObjective
-
 
 class AnalyticAcquisitionFunction(AcquisitionFunction, ABC):
     r"""Base class for analytic acquisition functions."""

botorch/acquisition/cost_aware.py

@@ -16,15 +16,14 @@
 from typing import Any, Callable, Optional
 
 import torch
+from botorch import settings
+from botorch.acquisition.objective import IdentityMCObjective, MCAcquisitionObjective
+from botorch.exceptions.warnings import CostAwareWarning
+from botorch.models.model import Model
+from botorch.sampling.samplers import MCSampler
 from torch import Tensor
 from torch.nn import Module
 
-from .. import settings
-from ..exceptions.warnings import CostAwareWarning
-from ..models.model import Model
-from ..sampling.samplers import MCSampler
-from .objective import IdentityMCObjective, MCAcquisitionObjective
-
 
 class CostAwareUtility(Module, ABC):
     r"""Abstract base class for cost-aware utilities."""

botorch/acquisition/fixed_feature.py

@@ -14,11 +14,10 @@
 from typing import List, Union
 
 import torch
+from botorch.acquisition.acquisition import AcquisitionFunction
 from torch import Tensor
 from torch.nn import Module
 
-from .acquisition import AcquisitionFunction
-
 
 class FixedFeatureAcquisitionFunction(AcquisitionFunction):
     """A wrapper around AquisitionFunctions to fix a subset of features.

botorch/acquisition/knowledge_gradient.py

@@ -30,19 +30,25 @@
 from typing import Callable, Optional, Tuple, Union
 
 import torch
+from botorch import settings
+from botorch.acquisition.acquisition import (
+    AcquisitionFunction,
+    OneShotAcquisitionFunction,
+)
+from botorch.acquisition.analytic import PosteriorMean
+from botorch.acquisition.cost_aware import CostAwareUtility
+from botorch.acquisition.monte_carlo import MCAcquisitionFunction, qSimpleRegret
+from botorch.acquisition.objective import (
+    AcquisitionObjective,
+    MCAcquisitionObjective,
+    ScalarizedObjective,
+)
+from botorch.exceptions.errors import UnsupportedError
+from botorch.models.model import Model
+from botorch.sampling.samplers import MCSampler, SobolQMCNormalSampler
+from botorch.utils.transforms import match_batch_shape, t_batch_mode_transform
 from torch import Tensor
 
-from .. import settings
-from ..exceptions.errors import UnsupportedError
-from ..models.model import Model
-from ..sampling.samplers import MCSampler, SobolQMCNormalSampler
-from ..utils.transforms import match_batch_shape, t_batch_mode_transform
-from .acquisition import AcquisitionFunction, OneShotAcquisitionFunction
-from .analytic import PosteriorMean
-from .cost_aware import CostAwareUtility
-from .monte_carlo import MCAcquisitionFunction, qSimpleRegret
-from .objective import AcquisitionObjective, MCAcquisitionObjective, ScalarizedObjective
-
 
 class qKnowledgeGradient(MCAcquisitionFunction, OneShotAcquisitionFunction):
     r"""Batch Knowledge Gradient using one-shot optimization.

botorch/acquisition/max_value_entropy_search.py

@@ -28,17 +28,16 @@
 from typing import Callable, Optional
 
 import torch
+from botorch.acquisition.cost_aware import CostAwareUtility, InverseCostWeightedUtility
+from botorch.acquisition.monte_carlo import MCAcquisitionFunction
+from botorch.models.cost import AffineFidelityCostModel
+from botorch.models.model import Model
+from botorch.models.utils import check_no_nans
+from botorch.sampling.samplers import SobolQMCNormalSampler
+from botorch.utils.transforms import match_batch_shape, t_batch_mode_transform
 from scipy.optimize import brentq
 from torch import Tensor
 
-from ..models.cost import AffineFidelityCostModel
-from ..models.model import Model
-from ..models.utils import check_no_nans
-from ..sampling.samplers import SobolQMCNormalSampler
-from ..utils.transforms import match_batch_shape, t_batch_mode_transform
-from .cost_aware import CostAwareUtility, InverseCostWeightedUtility
-from .monte_carlo import MCAcquisitionFunction
-
 
 CLAMP_LB = 1.0e-8
 

botorch/acquisition/monte_carlo.py

@@ -25,19 +25,18 @@
 from typing import Optional, Union
 
 import torch
-from torch import Tensor
-
-from ..exceptions.errors import UnsupportedError
-from ..models.model import Model
-from ..sampling.samplers import MCSampler, SobolQMCNormalSampler
-from ..utils.transforms import (
+from botorch.acquisition.acquisition import AcquisitionFunction
+from botorch.acquisition.objective import IdentityMCObjective, MCAcquisitionObjective
+from botorch.acquisition.utils import prune_inferior_points
+from botorch.exceptions.errors import UnsupportedError
+from botorch.models.model import Model
+from botorch.sampling.samplers import MCSampler, SobolQMCNormalSampler
+from botorch.utils.transforms import (
     concatenate_pending_points,
     match_batch_shape,
     t_batch_mode_transform,
 )
-from .acquisition import AcquisitionFunction
-from .objective import IdentityMCObjective, MCAcquisitionObjective
-from .utils import prune_inferior_points
+from torch import Tensor
 
 
 class MCAcquisitionFunction(AcquisitionFunction, ABC):