implement parameter transforms as generator/model wrappers (facebookr…

…esearch#401)

Summary:

Parameter transforms will be handled by wrapping generator and model objects.

The wrappers surfaces the base object API completely and even appears to be the wrapped object upon type inspection. Methods that requires the transformations are overridden by the wrapper to apply the required (un)transforms.

The wrappers expects transforms from BoTorch and new transforms should follow BoTorch's InputTransforms.

As a baseline a log10 transform is implemented.

Differential Revision: D64129439

aepsych/__init__.py

@@ -11,7 +11,16 @@
 
 from gpytorch.likelihoods import BernoulliLikelihood, GaussianLikelihood
 
-from . import acquisition, config, factory, generators, models, strategy, utils
+from . import (
+    acquisition,
+    config,
+    factory,
+    generators,
+    models,
+    strategy,
+    transforms,
+    utils,
+)
 from .config import Config
 from .likelihoods import BernoulliObjectiveLikelihood
 from .models import GPClassificationModel
@@ -26,6 +35,7 @@
     "factory",
     "models",
     "strategy",
+    "transforms",
     "utils",
     "generators",
     # classes

aepsych/benchmark/benchmark.py

@@ -77,6 +77,7 @@ def make_benchmark_list(self, **bench_config) -> List[Dict[str, float]]:
             List[dict[str, float]]: List of dictionaries, each of which can be passed
                 to aepsych.config.Config.
         """
+
         # This could be a generator but then we couldn't
         # know how many params we have, tqdm wouldn't work, etc,
         # so we materialize the full list.
@@ -154,6 +155,9 @@ def run_experiment(
         np.random.seed(seed)
         config_dict["common"]["lb"] = str(problem.lb.tolist())
         config_dict["common"]["ub"] = str(problem.ub.tolist())
+        config_dict["common"]["parnames"] = str(
+            [f"par{i}" for i in range(len(problem.ub.tolist()))]
+        )
         config_dict["problem"] = problem.metadata
         materialized_config = self.materialize_config(config_dict)
 

aepsych/config.py

@@ -6,12 +6,23 @@
 # LICENSE file in the root directory of this source tree.
 import abc
 import ast
-import re
 import configparser
 import json
+import re
 import warnings
 from types import ModuleType
-from typing import Any, ClassVar, Dict, List, Mapping, Optional, Sequence, TypeVar
+from typing import (
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    Dict,
+    List,
+    Mapping,
+    Optional,
+    Sequence,
+    TypeVar,
+)
 import botorch
 import gpytorch
 import numpy as np
@@ -21,6 +32,7 @@
 
 _T = TypeVar("_T")
 
+
 class Config(configparser.ConfigParser):
 
     # names in these packages can be referred to by string name
@@ -75,7 +87,6 @@ def _get(
         fallback=configparser._UNSET,
         **kwargs,
     ):
-
         """
         Override configparser to:
         1. Return from common if a section doesn't exist. This comes
@@ -107,8 +118,8 @@ def _get(
             )
 
     # Convert config into a dictionary (eliminate duplicates from defaulted 'common' section.)
-    def to_dict(self, deduplicate: bool = True) -> dict:
-        _dict = {}
+    def to_dict(self, deduplicate: bool = True) -> Dict[str, Any]:
+        _dict: Dict[str, Any] = {}
         for section in self:
             _dict[section] = {}
             for setting in self[section]:
@@ -160,8 +171,10 @@ def update(
             warnings.warn(
                 "ub and lb have been defined in common section, ignoring parameter specific blocks, be very careful!"
             )
-        elif "parnames" in self["common"]: # it's possible to pass no parnames
-            par_names = self.getlist("common", "parnames", element_type=str, fallback = [])
+        elif "parnames" in self["common"]:  # it's possible to pass no parnames
+            par_names = self.getlist(
+                "common", "parnames", element_type=str, fallback=[]
+            )
             lb = [None] * len(par_names)
             ub = [None] * len(par_names)
             for i, par_name in enumerate(par_names):
@@ -174,14 +187,15 @@ def update(
             self["common"]["lb"] = f"[{', '.join(lb)}]"
             self["common"]["ub"] = f"[{', '.join(ub)}]"
 
-
         # Deprecation warning for "experiment" section
         if "experiment" in self:
             for i in self["experiment"]:
                 self["common"][i] = self["experiment"][i]
             del self["experiment"]
 
-    def _str_to_list(self, v: str, element_type: _T = float) -> List[_T]:
+    def _str_to_list(
+        self, v: str, element_type: Callable[[_T], _T] = float
+    ) -> List[_T]:
         v = re.sub(r"\n ", ",", v)
         v = re.sub(r"(?<!,)\s+", ",", v)
         v = re.sub(r",]", "]", v)
@@ -223,18 +237,25 @@ def _check_param_settings(self, param_name: str) -> None:
 
         # Checking if param_type is set
         if "par_type" not in param_block:
-            raise ValueError(f"Parameter {param_name} is missing the param_type setting.")
+            raise ValueError(
+                f"Parameter {param_name} is missing the param_type setting."
+            )
 
         # Each parameter type has a different set of required settings
-        if param_block['par_type'] == "continuous":
+        if param_block["par_type"] == "continuous":
             # Check if bounds exist
             if "lower_bound" not in param_block:
-                raise ValueError(f"Parameter {param_name} is missing the lower_bound setting.")
+                raise ValueError(
+                    f"Parameter {param_name} is missing the lower_bound setting."
+                )
             if "upper_bound" not in param_block:
-                raise ValueError(f"Parameter {param_name} is missing the upper_bound setting.")
+                raise ValueError(
+                    f"Parameter {param_name} is missing the upper_bound setting."
+                )
         else:
-            raise ValueError(f"Parameter {param_name} has an unsupported parameter type {param_block['par_type']}.")
-
+            raise ValueError(
+                f"Parameter {param_name} has an unsupported parameter type {param_block['par_type']}."
+            )
 
     def __repr__(self) -> str:
         return f"Config at {hex(id(self))}: \n {str(self)}"

aepsych/config.pyi

@@ -7,10 +7,24 @@
 
 import abc
 import configparser
-from typing import Any, ClassVar, Dict, List, Mapping, Optional, TypeVar, Union
+from typing import (
+    Any,
+    Callable,
+    ClassVar,
+    Dict,
+    List,
+    Mapping,
+    Optional,
+    TypeVar,
+    Union,
+)
 
 import numpy as np
 import torch
+from botorch.models.transforms.input import (
+    ChainedInputTransform,
+    ReversibleInputTransform,
+)
 
 _T = TypeVar("_T")
 _ET = TypeVar("_ET")
@@ -50,7 +64,7 @@ class Config(configparser.ConfigParser):
         raw: bool = ...,
         vars: Optional[Mapping[str, str]] = ...,
         fallback: _T = ...,
-        element_type: _ET = ...,
+        element_type: Callable[[_ET], _ET] = ...,
     ) -> Union[_T, List[_ET]]: ...
     def getarray(
         self,
@@ -61,10 +75,29 @@ class Config(configparser.ConfigParser):
         vars: Optional[Mapping[str, str]] = ...,
         fallback: _T = ...,
     ) -> Union[np.ndarray, _T]: ...
+    def getboolean(
+        self,
+        section: str,
+        option: str,
+        *,
+        raw: bool = ...,
+        vars: Mapping[str, str] | None = ...,
+        fallback: _T = ...,
+    ) -> bool | _T: ...
+    def getfloat(
+        self,
+        section: str,
+        option: str,
+        *,
+        raw: bool = ...,
+        vars: Mapping[str, str] | None = ...,
+        fallback: _T = ...,
+    ) -> float | _T: ...
     @classmethod
     def register_module(cls: _T, module): ...
     def jsonifyMetadata(self) -> str: ...
     def jsonifyAll(self) -> str: ...
+    def to_dict(self, deduplicate: bool = ...) -> Dict[str, Any]: ...
 
 class ConfigurableMixin(abc.ABC):
     @classmethod

aepsych/generators/base.py

@@ -4,19 +4,19 @@
 # This source code is licensed under the license found in the
 # LICENSE file in the root directory of this source tree.
 import abc
-from inspect import signature
-from typing import Any, Dict, Generic, Protocol, runtime_checkable, TypeVar, Optional
 import re
+from inspect import signature
+from typing import Any, Dict, Generic, Optional, Protocol, runtime_checkable, TypeVar
 
 import torch
 from aepsych.config import Config
 from aepsych.models.base import AEPsychMixin
 from botorch.acquisition import (
     AcquisitionFunction,
-    NoisyExpectedImprovement,
-    qNoisyExpectedImprovement,
     LogNoisyExpectedImprovement,
+    NoisyExpectedImprovement,
     qLogNoisyExpectedImprovement,
+    qNoisyExpectedImprovement,
 )
 
 
@@ -43,6 +43,9 @@ class AEPsychGenerator(abc.ABC, Generic[AEPsychModelType]):
     stimuli_per_trial = 1
     max_asks: Optional[int] = None
 
+    acqf: AcquisitionFunction
+    acqf_kwargs: Dict[str, Any]
+
     def __init__(
         self,
     ) -> None:
@@ -81,7 +84,7 @@ def _get_acqf_options(cls, acqf: AcquisitionFunction, config: Config) -> Dict[st
                         elif re.search(
                             r"^\[.*\]$", v, flags=re.DOTALL
                         ):  # use regex to check if the value is a list
-                            extra_acqf_args[k] = config._str_to_list(v) # type: ignore
+                            extra_acqf_args[k] = config._str_to_list(v)  # type: ignore
                         else:
                             # otherwise try a float
                             try:

aepsych/models/base.py

@@ -64,7 +64,7 @@ def bounds(self) -> torch.Tensor:
     def dim(self) -> int:
         pass
 
-    def posterior(self, x: torch.Tensor) -> GPyTorchPosterior:
+    def posterior(self, X: torch.Tensor) -> GPyTorchPosterior:
         pass
 
     def predict(self, x: torch.Tensor, **kwargs) -> torch.Tensor:
@@ -103,7 +103,9 @@ def update(
     ) -> None:
         pass
 
-    def p_below_threshold(self, x, f_thresh) -> torch.Tensor:
+    def p_below_threshold(
+        self, x: torch.Tensor, f_thresh: torch.Tensor
+    ) -> torch.Tensor:
         pass
 
 
@@ -374,11 +376,11 @@ def _fit_mll(
         )
         return res
 
-    def p_below_threshold(self, x: torch.Tensor, f_thresh: torch.Tensor) -> torch.Tensor: 
+    def p_below_threshold(self, x: torch.Tensor, f_thresh: torch.Tensor) -> torch.Tensor:
         f, var = self.predict(x)
         f_thresh = f_thresh.reshape(-1, 1)
         f = f.reshape(1, -1)
         var = var.reshape(1, -1)
-        
+
         z = (f_thresh - f) / var.sqrt()
-        return torch.distributions.Normal(0, 1).cdf(z)  # Use PyTorch's CDF equivalent
+        return torch.distributions.Normal(0, 1).cdf(z)  # Use PyTorch's CDF equivalent