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Add name argument to all cost functions #1017

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Aug 1, 2024
Merged

Add name argument to all cost functions #1017

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058392c
add name argument to BinnedNLL and ExtendedBinnedNLL
HDembinski Aug 1, 2024
20404e2
support LeastSquares, UnbinnedNLL, ExtendedUnbinnedNLL
HDembinski Aug 1, 2024
288276c
fix coverage
HDembinski Aug 1, 2024
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13 changes: 8 additions & 5 deletions noxfile.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,26 +25,29 @@
nox.options.sessions = ["test", "mintest", "maxtest"]


@nox.session()
@nox.session(reuse_venv=True)
def test(session: nox.Session) -> None:
"""Run all tests."""
session.install("-e.[test]")
session.run("pytest", "-n=auto", *session.posargs, env=ENV)
extra_args = session.posargs if session.posargs else ("-n=auto",)
session.run("pytest", *extra_args, env=ENV)


@nox.session(python=MINIMUM_PYTHON, venv_backend="uv")
def mintest(session: nox.Session) -> None:
"""Run tests on the minimum python version."""
session.install("-e.", "--resolution=lowest-direct")
session.install("pytest", "pytest-xdist")
session.run("pytest", "-n=auto", *session.posargs)
extra_args = session.posargs if session.posargs else ("-n=auto",)
session.run("pytest", *extra_args)


@nox.session(python=LATEST_PYTHON)
def maxtest(session: nox.Session) -> None:
"""Run the unit and regular tests."""
session.install("-e.", "scipy", "matplotlib", "pytest", "pytest-xdist", "--pre")
session.run("pytest", "-n=auto", *session.posargs, env=ENV)
extra_args = session.posargs if session.posargs else ("-n=auto",)
session.run("pytest", *extra_args, env=ENV)


@nox.session(python="pypy3.9")
Expand All @@ -56,7 +59,7 @@ def pypy(session: nox.Session) -> None:


# Python-3.12 provides coverage info faster
@nox.session(python="3.12", venv_backend="uv")
@nox.session(python="3.12", venv_backend="uv", reuse_venv=True)
def cov(session: nox.Session) -> None:
"""Run covage and place in 'htmlcov' directory."""
session.install("-e.[test,doc]")
Expand Down
5 changes: 1 addition & 4 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -33,10 +33,7 @@ classifiers = [
"Operating System :: Unix",
"Operating System :: MacOS",
]
dependencies = [
"numpy >=1.21",
"typing_extensions >=3.7.4; python_version<'3.9'",
]
dependencies = ["numpy >=1.21"]

[project.urls]
repository = "http://github.com/scikit-hep/iminuit"
Expand Down
100 changes: 75 additions & 25 deletions src/iminuit/cost.py
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Original file line number Diff line number Diff line change
Expand Up @@ -834,15 +834,15 @@ class MaskedCost(Cost):

def __init__(
self,
args: Dict[str, Optional[Tuple[float, float]]],
parameters: Dict[str, Optional[Tuple[float, float]]],
data: NDArray,
verbose: int,
):
"""For internal use."""
self._data = data
self._mask = None
self._update_cache()
Cost.__init__(self, args, verbose)
Cost.__init__(self, parameters, verbose)

@property
def mask(self):
Expand Down Expand Up @@ -938,12 +938,13 @@ def __init__(
verbose: int,
log: bool,
grad: Optional[ModelGradient],
name: Optional[Sequence[str]],
):
"""For internal use."""
self._model = model
self._log = log
self._model_grad = grad
super().__init__(_model_parameters(model), _norm(data), verbose)
super().__init__(_model_parameters(model, name), _norm(data), verbose)

@abc.abstractproperty
def pdf(self):
Expand Down Expand Up @@ -1093,6 +1094,7 @@ def __init__(
verbose: int = 0,
log: bool = False,
grad: Optional[ModelGradient] = None,
name: Optional[Sequence[str]] = None,
):
"""
Initialize UnbinnedNLL with data and model.
Expand Down Expand Up @@ -1126,8 +1128,11 @@ def __init__(
gradient can be used by Minuit to improve or speed up convergence and to
compute the sandwich estimator for the variance of the parameter estimates.
Default is None.
name : sequence of str or None, optional
Optional names for each parameter of the model (in order). Must have the
same length as there are model parameters. Default is None.
"""
super().__init__(data, pdf, verbose, log, grad)
super().__init__(data, pdf, verbose, log, grad, name)

def _value(self, args: Sequence[float]) -> float:
f = self._eval_model(args)
Expand Down Expand Up @@ -1201,6 +1206,7 @@ def __init__(
verbose: int = 0,
log: bool = False,
grad: Optional[ModelGradient] = None,
name: Optional[Sequence[str]] = None,
):
"""
Initialize cost function with data and model.
Expand Down Expand Up @@ -1239,8 +1245,11 @@ def __init__(
of the log-density instead. The gradient can be used by Minuit to improve or
speed up convergence and to compute the sandwich estimator for the variance
of the parameter estimates. Default is None.
name : sequence of str or None, optional
Optional names for each parameter of the model (in order). Must have the
same length as there are model parameters. Default is None.
"""
super().__init__(data, scaled_pdf, verbose, log, grad)
super().__init__(data, scaled_pdf, verbose, log, grad, name)

def _value(self, args: Sequence[float]) -> float:
fint, f = self._eval_model(args)
Expand Down Expand Up @@ -1346,7 +1355,7 @@ def prediction(
self, args: Sequence[float]
) -> Union[NDArray, Tuple[NDArray, NDArray]]:
"""
Return the bin expectation for the fitted model.
Return the bin-wise expectation for the fitted model.

Parameters
----------
Expand Down Expand Up @@ -1488,12 +1497,13 @@ class BinnedCostWithModel(BinnedCost):
"_model_dx",
"_model_len",
"_model_grad",
"_pred_impl",
)

_model_xe: np.ndarray
_xe_shape: Union[Tuple[int], Tuple[int, ...]]

def __init__(self, n, xe, model, verbose, grad, use_pdf):
def __init__(self, n, xe, model, verbose, grad, use_pdf, name):
"""For internal use."""
self._model = model
self._model_grad = grad
Expand All @@ -1510,11 +1520,11 @@ def __init__(self, n, xe, model, verbose, grad, use_pdf):
else:
msg = (
f"use_pdf={use_pdf} is not understood, "
"allowed values are 'approximate' and 'numerical'"
"allowed values are '', 'approximate', or 'numerical'"
)
raise ValueError(msg)

super().__init__(_model_parameters(model), n, xe, verbose)
super().__init__(_model_parameters(model, name), n, xe, verbose)

if self._ndim == 1:
self._xe_shape = (len(self.xe),)
Expand Down Expand Up @@ -1678,10 +1688,10 @@ def __init__(
where D is the number of histogram axes, then the last dimension must have
two elements and is interpreted as pairs of sum of weights and sum of
weights squared. Callables must return the model cdf evaluated as xe.
name : collection of str, optional
name : sequence of str or None, optional
Optional name for the yield of each template and the parameter of each model
(in order). Must have the same length as there are templates and model
parameters in templates_or_model.
parameters in templates_or_model. Default is None.
verbose : int, optional
Verbosity level. 0: is no output (default). 1: print current args and
negative log-likelihood value.
Expand Down Expand Up @@ -1723,7 +1733,7 @@ def __init__(
self._model_data.append((t1, t2))
annotated[f"x{i}"] = (0.0, np.inf)
elif isinstance(t, Model):
ann = _model_parameters(t)
ann = _model_parameters(t, None)
npar = len(ann)
self._model_data.append((t, npar))
for k in ann:
Expand Down Expand Up @@ -1900,6 +1910,7 @@ def __init__(
verbose: int = 0,
grad: Optional[ModelGradient] = None,
use_pdf: str = "",
name: Optional[Sequence[str]] = None,
):
"""
Initialize cost function with data and model.
Expand All @@ -1924,12 +1935,26 @@ def __init__(
verbose : int, optional
Verbosity level. 0: is no output (default).
1: print current args and negative log-likelihood value.
grad: callable
TODO
use_pdf: str
TODO
grad: callable or None, optional
Optionally pass the gradient of the cdf (Default is None). Has the same
calling signature like the cdf, but must return an array with the shape (K,
N), where N is the number of data points and K is the number of parameters.
The gradient can be used by Minuit to improve or speed up convergence.
use_pdf: str, optional
Either "", "numerical", or "approximate" (Default is ""). If the model cdf
is not available, but the model pdf is, this option can be set to
"numerical" or "approximate" to compute the integral of the pdf over the bin
patch. The option "numerical" uses numerical integration, which is accurate
but computationally expensive and only supported for 1D histograms. The
option "approximate" uses the zero-order approximation of evaluating the pdf
at the bin center, multiplied with the bin area. This is fast and works in
higher dimensions, but can lead to biased results if the curvature of the
pdf inside the bin is significant.
name : sequence of str or None, optional
Optional names for each parameter of the model (in order). Must have the
same length as there are model parameters. Default is None.
"""
super().__init__(n, xe, cdf, verbose, grad, use_pdf)
super().__init__(n, xe, cdf, verbose, grad, use_pdf, name)

def _pred(self, args: Sequence[float]) -> NDArray:
# must return array of full length, mask not applied yet
Expand Down Expand Up @@ -2003,6 +2028,7 @@ def __init__(
verbose: int = 0,
grad: Optional[ModelGradient] = None,
use_pdf: str = "",
name: Optional[Sequence[str]] = None,
):
"""
Initialize cost function with data and model.
Expand All @@ -2025,12 +2051,26 @@ def __init__(
verbose : int, optional
Verbosity level. 0: is no output (default). 1: print current args and
negative log-likelihood value.
grad : callable, optional
TODO
use_pdf : str, optional
TODO
grad: callable or None, optional
Optionally pass the gradient of the cdf (Default is None). Has the same
calling signature like the cdf, but must return an array with the shape (K,
N), where N is the number of data points and K is the number of parameters.
The gradient can be used by Minuit to improve or speed up convergence.
use_pdf: str, optional
Either "", "numerical", or "approximate". If the model cdf is not available,
but the model pdf is, this option can be set to "numerical" or "approximate"
to compute the integral of the pdf over the bin patch. The option
"numerical" uses numerical integration, which is accurate but
computationally expensive and only supported for 1D histograms. The option
"approximate" uses the zero-order approximation of evaluating the pdf at the
bin center, multiplied with the bin area. This is fast and works in higher
dimensions, but can lead to biased results if the curvature of the pdf
inside the bin is significant.
name : sequence of str or None, optional
Optional names for each parameter of the model (in order). Must have the
same length as there are model parameters. Default is None.
"""
super().__init__(n, xe, scaled_cdf, verbose, grad, use_pdf)
super().__init__(n, xe, scaled_cdf, verbose, grad, use_pdf, name)

def _value(self, args: Sequence[float]) -> float:
mu = self._pred(args)
Expand Down Expand Up @@ -2146,9 +2186,11 @@ def __init__(
y: ArrayLike,
yerror: ArrayLike,
model: Model,
*,
loss: Union[str, LossFunction] = "linear",
verbose: int = 0,
grad: Optional[ModelGradient] = None,
name: Optional[Sequence[str]] = None,
):
"""
Initialize cost function with data and model.
Expand Down Expand Up @@ -2202,7 +2244,7 @@ def __init__(

x = np.atleast_2d(x)
data = np.column_stack(np.broadcast_arrays(*x, y, yerror))
super().__init__(_model_parameters(self._model), data, verbose)
super().__init__(_model_parameters(model, name), data, verbose)

def _ndata(self):
return len(self._masked)
Expand Down Expand Up @@ -2492,12 +2534,20 @@ def _shape_from_xe(xe):
return (len(xe) - 1,)


def _model_parameters(model):
def _model_parameters(model, name):
# strip first argument from model
ann = describe(model, annotations=True)
args = iter(ann)
next(args)
return {k: ann[k] for k in args}
params = {k: ann[k] for k in args}
if name:
if len(params) == len(name):
params = {n: att for (n, att) in zip(name, params.values())}
elif len(params) > 0:
raise ValueError("length of name does not match number of model parameters")
else:
params = {n: None for n in name}
return params


_deprecated_content = {
Expand Down
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