"""Defines estimators which estimate a model's ability to represent the data.
All estimators have to implement the `.Estimator` interface.
"""
from typing import Callable, Dict, Mapping, Optional, Union
import numpy as np
from tensorwaves.interfaces import DataSample, Estimator, Model
from tensorwaves.model import LambdifiedFunction, get_backend_modules
[docs]def gradient_creator(
function: Callable[[Mapping[str, Union[float, complex]]], float],
backend: Union[str, tuple, dict],
) -> Callable[
[Mapping[str, Union[float, complex]]], Dict[str, Union[float, complex]]
]:
# pylint: disable=import-outside-toplevel
def not_implemented(
parameters: Mapping[str, Union[float, complex]]
) -> Dict[str, Union[float, complex]]:
raise NotImplementedError("Gradient not implemented.")
if isinstance(backend, str) and backend == "jax":
import jax
from jax.config import config
config.update("jax_enable_x64", True)
return jax.grad(function)
return not_implemented
[docs]class UnbinnedNLL(Estimator): # pylint: disable=too-many-instance-attributes
"""Unbinned negative log likelihood estimator.
Args:
model: A model that should be compared to the dataset.
dataset: The dataset used for the comparison. The model has to be
evaluateable with this dataset.
phsp_set: A phase space dataset, which is used for the normalization.
The model has to be evaluateable with this dataset. When correcting
for the detector efficiency use a phase space sample, that passed
the detector reconstruction.
"""
def __init__( # pylint: disable=too-many-arguments
self,
model: Model,
dataset: DataSample,
phsp_dataset: DataSample,
phsp_volume: float = 1.0,
backend: Union[str, tuple, dict] = "numpy",
use_caching: bool = False,
fixed_parameters: Optional[Dict[str, Union[float, complex]]] = None,
) -> None:
self.__use_caching = use_caching
self.__dataset = {k: np.array(v) for k, v in dataset.items()}
self.__phsp_dataset = {k: np.array(v) for k, v in phsp_dataset.items()}
if self.__use_caching:
fixed_data_inputs = dict(self.__dataset)
fixed_phsp_inputs = dict(self.__phsp_dataset)
if fixed_parameters:
fixed_data_inputs.update(fixed_parameters)
fixed_phsp_inputs.update(fixed_parameters)
self.__data_function = LambdifiedFunction(
model.performance_optimize(fix_inputs=fixed_data_inputs),
backend,
)
self.__phsp_function = LambdifiedFunction(
model.performance_optimize(fix_inputs=fixed_phsp_inputs),
backend,
)
else:
self.__data_function = LambdifiedFunction(model, backend)
self.__phsp_function = self.__data_function
self.__gradient = gradient_creator(self.__call__, backend)
backend_modules = get_backend_modules(backend)
def find_function_in_backend(name: str) -> Callable:
if isinstance(backend_modules, dict) and name in backend_modules:
return backend_modules[name]
if isinstance(backend_modules, (tuple, list)):
for module in backend_modules:
if name in module.__dict__:
return module.__dict__[name]
raise ValueError(f"Could not find function {name} in backend")
self.__mean_function = find_function_in_backend("mean")
self.__sum_function = find_function_in_backend("sum")
self.__log_function = find_function_in_backend("log")
self.__phsp_volume = phsp_volume
[docs] def __call__(
self, parameters: Mapping[str, Union[float, complex]]
) -> float:
self.__data_function.update_parameters(parameters)
if self.__use_caching:
self.__phsp_function.update_parameters(parameters)
bare_intensities = self.__data_function({})
phsp_intensities = self.__phsp_function({})
else:
bare_intensities = self.__data_function(self.__dataset)
phsp_intensities = self.__phsp_function(self.__phsp_dataset)
normalization_factor = 1.0 / (
self.__phsp_volume * self.__mean_function(phsp_intensities)
)
likelihoods = normalization_factor * bare_intensities
return -self.__sum_function(self.__log_function(likelihoods))
[docs] def gradient(
self, parameters: Mapping[str, Union[float, complex]]
) -> Dict[str, Union[float, complex]]:
return self.__gradient(parameters)