# Licensed under a 3-clause BSD style license - see LICENSE.rst
import logging
import numpy as np
from astropy.utils import lazyproperty
from .covariance import Covariance
from .iminuit import confidence_iminuit, covariance_iminuit, mncontour, optimize_iminuit
from .scipy import confidence_scipy, optimize_scipy
from .sherpa import optimize_sherpa
__all__ = ["Fit"]
log = logging.getLogger(__name__)
class Registry:
"""Registry of available backends for given tasks.
Gives users the power to extend from their scripts.
Used by `Fit` below.
Not sure if we should call it "backend" or "method" or something else.
Probably we will code up some methods, e.g. for profile analysis ourselves,
using scipy or even just Python / Numpy?
"""
register = {
"optimize": {
"minuit": optimize_iminuit,
"sherpa": optimize_sherpa,
"scipy": optimize_scipy,
},
"covariance": {
"minuit": covariance_iminuit,
# "sherpa": covariance_sherpa,
# "scipy": covariance_scipy,
},
"confidence": {
"minuit": confidence_iminuit,
# "sherpa": confidence_sherpa,
"scipy": confidence_scipy,
},
}
@classmethod
def get(cls, task, backend):
if task not in cls.register:
raise ValueError(f"Unknown task {task!r}")
backend_options = cls.register[task]
if backend not in backend_options:
raise ValueError(f"Unknown backend {backend!r} for task {task!r}")
return backend_options[backend]
registry = Registry()
[docs]class Fit:
"""Fit class.
The fit class provides a uniform interface to multiple fitting backends.
Currently available: "minuit", "sherpa" and "scipy"
Parameters
----------
datasets : `Datasets`
Datasets
"""
def __init__(self, datasets):
from gammapy.datasets import Datasets
self.datasets = Datasets(datasets)
@lazyproperty
def _parameters(self):
return self.datasets.parameters
@lazyproperty
def _models(self):
return self.datasets.models
[docs] def run(self, backend="minuit", optimize_opts=None, covariance_opts=None):
"""
Run all fitting steps.
Parameters
----------
backend : str
Backend used for fitting, default : minuit
optimize_opts : dict
Options passed to `Fit.optimize`.
covariance_opts : dict
Options passed to `Fit.covariance`.
Returns
-------
fit_result : `FitResult`
Results
"""
if optimize_opts is None:
optimize_opts = {}
optimize_result = self.optimize(backend, **optimize_opts)
if covariance_opts is None:
covariance_opts = {}
if backend not in registry.register["covariance"]:
log.warning("No covariance estimate - not supported by this backend.")
return optimize_result
covariance_result = self.covariance(backend, **covariance_opts)
# TODO: not sure how best to report the results
# back or how to form the FitResult object.
optimize_result._success = optimize_result.success and covariance_result.success
return optimize_result
[docs] def optimize(self, backend="minuit", **kwargs):
"""Run the optimization.
Parameters
----------
backend : str
Which backend to use (see ``gammapy.modeling.registry``)
**kwargs : dict
Keyword arguments passed to the optimizer. For the `"minuit"` backend
see https://iminuit.readthedocs.io/en/latest/api.html#iminuit.Minuit
for a detailed description of the available options. If there is an entry
'migrad_opts', those options will be passed to `iminuit.Minuit.migrad()`.
For the `"sherpa"` backend you can from the options `method = {"simplex", "levmar", "moncar", "gridsearch"}`
Those methods are described and compared in detail on
http://cxc.cfa.harvard.edu/sherpa/methods/index.html. The available
options of the optimization methods are described on the following
pages in detail:
* http://cxc.cfa.harvard.edu/sherpa/ahelp/neldermead.html
* http://cxc.cfa.harvard.edu/sherpa/ahelp/montecarlo.html
* http://cxc.cfa.harvard.edu/sherpa/ahelp/gridsearch.html
* http://cxc.cfa.harvard.edu/sherpa/ahelp/levmar.html
For the `"scipy"` backend the available options are desribed in detail here:
https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
Returns
-------
fit_result : `FitResult`
Results
"""
parameters = self._parameters
# TODO: expose options if / when to scale? On the Fit class?
if np.all(self._models.covariance.data == 0):
parameters.autoscale()
compute = registry.get("optimize", backend)
# TODO: change this calling interface!
# probably should pass a fit statistic, which has a model, which has parameters
# and return something simpler, not a tuple of three things
factors, info, optimizer = compute(
parameters=parameters, function=self.datasets.stat_sum, **kwargs
)
# TODO: Change to a stateless interface for minuit also, or if we must support
# stateful backends, put a proper, backend-agnostic solution for this.
# As preliminary solution would like to provide a possibility that the user
# can access the Minuit object, because it features a lot useful functionality
if backend == "minuit":
self.minuit = optimizer
# Copy final results into the parameters object
parameters.set_parameter_factors(factors)
return OptimizeResult(
parameters=parameters,
total_stat=self.datasets.stat_sum(),
backend=backend,
method=kwargs.get("method", backend),
**info,
)
[docs] def covariance(self, backend="minuit", **kwargs):
"""Estimate the covariance matrix.
Assumes that the model parameters are already optimised.
Parameters
----------
backend : str
Which backend to use (see ``gammapy.modeling.registry``)
Returns
-------
result : `CovarianceResult`
Results
"""
compute = registry.get("covariance", backend)
parameters = self._parameters
# TODO: wrap MINUIT in a stateless backend
with parameters.restore_values:
if backend == "minuit":
method = "hesse"
if hasattr(self, "minuit"):
factor_matrix, info = compute(self.minuit)
else:
raise RuntimeError("To use minuit, you must first optimize.")
else:
method = ""
factor_matrix, info = compute(
parameters, self.datasets.stat_sum, **kwargs
)
covariance = Covariance.from_factor_matrix(
parameters=self._models.parameters, matrix=factor_matrix
)
self._models.covariance = covariance
# TODO: decide what to return, and fill the info correctly!
return CovarianceResult(
backend=backend,
method=method,
parameters=parameters,
success=info["success"],
message=info["message"],
)
[docs] def confidence(
self, parameter, backend="minuit", sigma=1, reoptimize=True, **kwargs
):
"""Estimate confidence interval.
Extra ``kwargs`` are passed to the backend.
E.g. `iminuit.Minuit.minos` supports a ``maxcall`` option.
For the scipy backend ``kwargs`` are forwarded to `~scipy.optimize.brentq`. If the
confidence estimation fails, the bracketing interval can be adapted by modifying the
the upper bound of the interval (``b``) value.
Parameters
----------
backend : str
Which backend to use (see ``gammapy.modeling.registry``)
parameter : `~gammapy.modeling.Parameter`
Parameter of interest
sigma : float
Number of standard deviations for the confidence level
reoptimize : bool
Re-optimize other parameters, when computing the confidence region.
**kwargs : dict
Keyword argument passed ot the confidence estimation method.
Returns
-------
result : dict
Dictionary with keys "errp", 'errn", "success" and "nfev".
"""
compute = registry.get("confidence", backend)
parameters = self._parameters
parameter = parameters[parameter]
# TODO: wrap MINUIT in a stateless backend
with parameters.restore_values:
if backend == "minuit":
if hasattr(self, "minuit"):
# This is ugly. We will access parameters and make a copy
# from the backend, to avoid modifying the state
result = compute(
self.minuit, parameters, parameter, sigma, **kwargs
)
else:
raise RuntimeError("To use minuit, you must first optimize.")
else:
result = compute(
parameters,
parameter,
self.datasets.stat_sum,
sigma,
reoptimize,
**kwargs,
)
result["errp"] *= parameter.scale
result["errn"] *= parameter.scale
return result
[docs] def stat_profile(
self,
parameter,
values=None,
bounds=2,
nvalues=11,
reoptimize=False,
optimize_opts=None,
):
"""Compute fit statistic profile.
The method used is to vary one parameter, keeping all others fixed.
So this is taking a "slice" or "scan" of the fit statistic.
See also: `Fit.minos_profile`.
Parameters
----------
parameter : `~gammapy.modeling.Parameter`
Parameter of interest
values : `~astropy.units.Quantity` (optional)
Parameter values to evaluate the fit statistic for.
bounds : int or tuple of float
When an `int` is passed the bounds are computed from `bounds * sigma`
from the best fit value of the parameter, where `sigma` corresponds to
the one sigma error on the parameter. If a tuple of floats is given
those are taken as the min and max values and ``nvalues`` are linearly
spaced between those.
nvalues : int
Number of parameter grid points to use.
reoptimize : bool
Re-optimize other parameters, when computing the fit statistic profile.
Returns
-------
results : dict
Dictionary with keys "values" and "stat".
"""
parameters = self._parameters
parameter = parameters[parameter]
optimize_opts = optimize_opts or {}
if values is None:
if isinstance(bounds, tuple):
parmin, parmax = bounds
else:
if np.isnan(parameter.error):
raise ValueError("Parameter error is not properly set.")
parerr = parameter.error
parval = parameter.value
parmin, parmax = parval - bounds * parerr, parval + bounds * parerr
values = np.linspace(parmin, parmax, nvalues)
stats = []
with parameters.restore_values:
for value in values:
parameter.value = value
if reoptimize:
parameter.frozen = True
result = self.optimize(**optimize_opts)
stat = result.total_stat
else:
stat = self.datasets.stat_sum()
stats.append(stat)
return {"values": values, "stat": np.array(stats)}
[docs] def stat_contour(self):
"""Compute fit statistic contour.
The method used is to vary two parameters, keeping all others fixed.
So this is taking a "slice" or "scan" of the fit statistic.
See also: `Fit.minos_contour`
Parameters
----------
TODO
Returns
-------
TODO
"""
raise NotImplementedError
[docs] def minos_contour(self, x, y, numpoints=10, sigma=1.0):
"""Compute MINOS contour.
Calls ``iminuit.Minuit.mncontour``.
This is a contouring algorithm for a 2D function
which is not simply the fit statistic function.
That 2D function is given at each point ``(par_1, par_2)``
by re-optimising all other free parameters,
and taking the fit statistic at that point.
Very compute-intensive and slow.
Parameters
----------
x, y : `~gammapy.modeling.Parameter`
Parameters of interest
numpoints : int
Number of contour points
sigma : float
Number of standard deviations for the confidence level
Returns
-------
result : dict
Dictionary with keys "x", "y" (Numpy arrays with contour points)
and a boolean flag "success".
The result objects from ``mncontour`` are in the additional
keys "x_info" and "y_info".
"""
parameters = self._parameters
x = parameters[x]
y = parameters[y]
with parameters.restore_values:
result = mncontour(self.minuit, parameters, x, y, numpoints, sigma)
x = result["x"] * x.scale
y = result["y"] * y.scale
return {
"x": x,
"y": y,
"success": result["success"],
"x_info": result["x_info"],
"y_info": result["y_info"],
}
class FitResult:
"""Fit result base class"""
def __init__(self, parameters, backend, method, success, message):
self._parameters = parameters
self._success = success
self._message = message
self._backend = backend
self._method = method
@property
def parameters(self):
"""Optimizer backend used for the fit."""
return self._parameters
@property
def backend(self):
"""Optimizer backend used for the fit."""
return self._backend
@property
def method(self):
"""Optimizer method used for the fit."""
return self._method
@property
def success(self):
"""Fit success status flag."""
return self._success
@property
def message(self):
"""Optimizer status message."""
return self._message
def __repr__(self):
return (
f"{self.__class__.__name__}\n\n"
f"\tbackend : {self.backend}\n"
f"\tmethod : {self.method}\n"
f"\tsuccess : {self.success}\n"
f"\tmessage : {self.message}\n"
)
class CovarianceResult(FitResult):
"""Covariance result object."""
pass
class OptimizeResult(FitResult):
"""Optimize result object."""
def __init__(self, nfev, total_stat, **kwargs):
self._nfev = nfev
self._total_stat = total_stat
super().__init__(**kwargs)
@property
def nfev(self):
"""Number of function evaluations."""
return self._nfev
@property
def total_stat(self):
"""Value of the fit statistic at minimum."""
return self._total_stat
def __repr__(self):
str_ = super().__repr__()
str_ += f"\tnfev : {self.nfev}\n"
str_ += f"\ttotal stat : {self.total_stat:.2f}\n"
return str_