import logging
from typing import Callable, Dict, List, Optional, Tuple
import numpy as np
from spey.base.model_config import ModelConfig
from .scipy_tools import minimize
# pylint: disable=W1203
log = logging.getLogger("Spey")
[docs]
def fit(
func: Callable[[np.ndarray], np.ndarray],
model_configuration: ModelConfig,
do_grad: bool = False,
hessian: Optional[Callable[[np.ndarray], np.ndarray]] = None,
initial_parameters: Optional[np.ndarray] = None,
bounds: Optional[List[Tuple[float, float]]] = None,
fixed_poi_value: Optional[float] = None,
logpdf: Optional[Callable[[List[float]], float]] = None,
constraints: Optional[List[Dict]] = None,
**options,
) -> Tuple[float, np.ndarray]:
"""
Operating the fitting of the given function.
Args:
func (``Callable[[np.ndarray], np.ndarray]``): Function to be optimised. If `do_grad=True`,
function is expected to return `Tuple[np.ndarray, np.ndarray]` where first is the value
of the function and the second is gradients with respect to the nuisance parameters.
model_configuration (``~spey.base.model_config.ModelConfig``): Model configuration.
do_grad (``bool``, default ``False``): boolean to set gradient on or off.
hessian (``Optional[Callable[[np.ndarray], np.ndarray]]``, default ``None``): hessian of the function
with respect to variational parameters. Currently not used.
initial_parameters (``Optional[np.ndarray]``, default ``None``): Initial guess for the variational
parameters.
bounds (``Optional[List[Tuple[float, float]]]``, default ``None``): Bounds for the variational parameters
each item has to have a lower and upper bound definition `(<lower>, <upper>)` in case of open boundary
input can be `None`. Number of boundaries has to match with the number of parameters. They will match
with respect to the index within the list.
fixed_poi_value (``Optional[float]``, default ``None``): If `float` the poi index will be fixed.
logpdf (``Optional[Callable[[List[float]], float]]``, default ``None``): If provided, log-probability
distribution will be computed once the parameters are fitted. If not, the result of the ``func`` input
will be returned.
constraints (``Optional[List[Dict]]``, default ``None``): Constraints of the model. see scipy for details.
options (``Dict``): extra options for the optimiser. see scipy minimiser for details.
Returns:
``Tuple[float, np.ndarray]``:
value and the parameters. log-probability value will be returned if `logpdf` argument is provided.
"""
init_pars = [*(initial_parameters or model_configuration.suggested_init)]
par_bounds = [*(bounds or model_configuration.fixed_poi_bounds(fixed_poi_value))]
def make_constraint(index: int, value: float) -> Callable[[np.ndarray], float]:
def func(vector: np.ndarray) -> float:
return vector[index] - value
return func
constraints = [] if constraints is None else constraints
if fixed_poi_value is not None:
init_pars[model_configuration.poi_index] = fixed_poi_value
log.debug(
f"Fixing POI index at: {model_configuration.poi_index} to value {fixed_poi_value}"
)
constraints.append(
{
"type": "eq",
"fun": make_constraint(model_configuration.poi_index, fixed_poi_value),
}
)
if model_configuration.suggested_fixed is not None:
for idx, isfixed in enumerate(model_configuration.suggested_fixed):
if isfixed:
log.debug(f"Constraining {idx} to value {init_pars[idx]}")
constraints.append(
{
"type": "eq",
"fun": make_constraint(idx, init_pars[idx]),
}
)
options.update({"poi_index": model_configuration.poi_index})
fun, x = minimize(
func=func,
init_pars=init_pars,
do_grad=do_grad,
hessian=hessian,
bounds=par_bounds,
constraints=constraints,
**options,
)
return fun if logpdf is None else logpdf(x), x
