Merge pull request #86 from zfit/pyhfcomp

add pyhf compatibility layer

CHANGELOG.rst

@@ -9,6 +9,7 @@ Major Features and Improvements
 -------------------------------
 - add a RooFit compatibility layer and automatically convert losses, also inside minimizers (through ``SimpleLoss.from_any``)
 - `TF-PWA <https://tf-pwa.readthedocs.io/en/latest/>`_ support for loss functions. Minimizer can directly minimize the loss function of a model.
+- `pyhf <https://pyhf.readthedocs.io/en/stable/>`_ support for loss functions. Minimizer can directly minimize the loss function of a model.
 
 Breaking changes
 ------------------

docs/api/static/zfit_physics.pyhf.rst

@@ -0,0 +1,32 @@
+pyhf
+=======================
+
+The pure Python package `pyhf <https://pyhf.readthedocs.io/en/stable/>`_ is a statistical fitting package for high energy physics for purely binned, templated fits. It is a Python implementation of the HistFactory schema.
+The connection to zfit is done via the loss function, which can be created from a pyhf model.
+
+Import the module with:
+
+.. code-block:: python
+
+    import zfit_physics.pyhf as zpyhf
+
+The loss function can be created from a ``data`` and a ``pdf`` model with
+
+.. code-block:: python
+
+    nll = zpyhf.loss.nll_from_pyhf(data, pdf)
+
+
+
+which optionally takes already created :py:class:~`zfit.core.interfaces.ZfitParameter` as arguments.
+
+
+
+
+Loss
+++++++++++++
+
+.. automodule:: zfit_physics.pyhf.loss
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/api/zfit_physics.models.pdf_argus.rst

@@ -0,0 +1,7 @@
+pdf\_argus
+==========
+
+.. automodule:: zfit_physics.models.pdf_argus
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_cmsshape.rst

@@ -0,0 +1,7 @@
+pdf\_cmsshape
+=============
+
+.. automodule:: zfit_physics.models.pdf_cmsshape
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_conv.rst

@@ -0,0 +1,7 @@
+pdf\_conv
+=========
+
+.. automodule:: zfit_physics.models.pdf_conv
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_cruijff.rst

@@ -0,0 +1,7 @@
+pdf\_cruijff
+============
+
+.. automodule:: zfit_physics.models.pdf_cruijff
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_erfexp.rst

@@ -0,0 +1,7 @@
+pdf\_erfexp
+===========
+
+.. automodule:: zfit_physics.models.pdf_erfexp
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_example.rst

@@ -0,0 +1,7 @@
+pdf\_example
+============
+
+.. automodule:: zfit_physics.models.pdf_example
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_kde.rst

@@ -0,0 +1,7 @@
+pdf\_kde
+========
+
+.. automodule:: zfit_physics.models.pdf_kde
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_novosibirsk.rst

@@ -0,0 +1,7 @@
+pdf\_novosibirsk
+================
+
+.. automodule:: zfit_physics.models.pdf_novosibirsk
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_relbw.rst

@@ -0,0 +1,7 @@
+pdf\_relbw
+==========
+
+.. automodule:: zfit_physics.models.pdf_relbw
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.pdf_tsallis.rst

@@ -0,0 +1,7 @@
+pdf\_tsallis
+============
+
+.. automodule:: zfit_physics.models.pdf_tsallis
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.models.rst

@@ -0,0 +1,24 @@
+models
+======
+
+.. automodule:: zfit_physics.models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 4
+
+   zfit_physics.models.pdf_argus
+   zfit_physics.models.pdf_cmsshape
+   zfit_physics.models.pdf_conv
+   zfit_physics.models.pdf_cruijff
+   zfit_physics.models.pdf_erfexp
+   zfit_physics.models.pdf_example
+   zfit_physics.models.pdf_kde
+   zfit_physics.models.pdf_novosibirsk
+   zfit_physics.models.pdf_relbw
+   zfit_physics.models.pdf_tsallis

docs/api/zfit_physics.pdf.rst

@@ -0,0 +1,7 @@
+pdf
+===
+
+.. automodule:: zfit_physics.pdf
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.rst

@@ -0,0 +1,24 @@
+zfit\_physics package
+=====================
+
+.. automodule:: zfit_physics
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   zfit_physics.models
+   zfit_physics.unstable
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 4
+
+   zfit_physics.pdf

docs/api/zfit_physics.unstable.pdf.rst

@@ -0,0 +1,7 @@
+pdf
+===
+
+.. automodule:: zfit_physics.unstable.pdf
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/api/zfit_physics.unstable.rst

@@ -0,0 +1,15 @@
+unstable
+========
+
+.. automodule:: zfit_physics.unstable
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Submodules
+----------
+
+.. toctree::
+   :maxdepth: 4
+
+   zfit_physics.unstable.pdf

pyproject.toml

@@ -38,15 +38,17 @@ dynamic = ["version"]
 [project.optional-dependencies]
 
 tfpwa = ["tfpwa@git+https://github.com/jiangyi15/tf-pwa"]
-
-all = ["zfit-physics[tfpwa]"]
+pyhf = [
+    "pyhf",
+]
+all = ["zfit-physics[tfpwa,pyhf]"]
 test = [
     "pytest",
     "pytest-cov",
     "pytest-rerunfailures",
     "pytest-xdist",
     "zfit-physics[all]",
-    "contextlib_chdir",  # backport of chdir from Python 3.11
+    "contextlib_chdir", # backport of chdir from Python 3.11
 ]
 dev = [
     "bumpversion>=0.5.3",

src/zfit_physics/__init__.py

@@ -18,6 +18,6 @@
     # TODO(release): add more, Anton etc
 ]
 
-__all__ = ["pdf", "unstable"]
-
 from . import pdf, unstable
+
+__all__ = ["pdf", "unstable"]

src/zfit_physics/pyhf/__init__.py

@@ -0,0 +1,3 @@
+from .loss import nll_from_pyhf
+
+__all__ = ["nll_from_pyhf"]

src/zfit_physics/pyhf/loss.py

@@ -0,0 +1,47 @@
+from __future__ import annotations
+
+import numpy as np
+import zfit
+from zfit.util.container import convert_to_container
+
+
+def nll_from_pyhf(data, pdf, *, params=None, init_pars=None, par_bounds=None, fixed_params=None, errordef=None):
+    """Create a zfit loss function from a pyhf pdf and data.
+
+
+    Args:
+        data: Data to be used in the negative log-likelihood (NLL) calculation.
+        pdf: Probability density function model.
+        params: Optional; Model parameters for the fit. If None, initial parameters, bounds, and fixed status are recommended by the pdf.
+        init_pars: Optional; Initial parameter values. Ignored if params is provided.
+        par_bounds: Optional; Parameter bounds. Ignored if params is provided.
+        fixed_params: Optional; Boolean indicators of parameter fixed statuses. Ignored if params is provided.
+        errordef: Optional; Error definition, by default set to 0.5.
+    """
+    if params is None:
+        init_pars = init_pars or pdf.config.suggested_init()
+        par_bounds = par_bounds or pdf.config.suggested_bounds()
+        fixed_params = fixed_params or pdf.config.suggested_fixed()
+
+        from pyhf.infer import mle
+
+        mle._validate_fit_inputs(init_pars, par_bounds, fixed_params)
+
+        params = [
+            zfit.Parameter(f"param_{i}", init, bound[0], bound[1], floating=not is_fixed)
+            for i, (init, is_fixed, bound) in enumerate(zip(init_pars, fixed_params, par_bounds))
+        ]
+    else:
+        if init_pars is not None or par_bounds is not None or fixed_params is not None:
+            msg = "If `params` are given, `init_pars`, `par_bounds` and `fixed_params` must be None."
+            raise ValueError(msg)
+        params = convert_to_container(params)
+
+    if errordef is None:
+        errordef = 0.5
+
+    def nll_func(params, *, data=data, pdf=pdf, errordef=errordef):
+        params = np.asarray(params)
+        return mle.twice_nll(params, data, pdf) * errordef
+
+    return zfit.loss.SimpleLoss(func=nll_func, params=params, errordef=errordef, gradient="num", jit=False)

tests/pyhf/test_pyhf_loss.py

@@ -0,0 +1,53 @@
+import numpy as np
+import pyhf
+import zfit
+from pyhf.simplemodels import uncorrelated_background
+
+import zfit_physics.pyhf as zpyhf
+
+
+def generate_source_static(n_bins):
+    """Create the source structure for the given number of bins.
+
+    Args:
+        n_bins: `list` of number of bins
+
+    Returns:
+        source
+    """
+    scale = 10
+    binning = [n_bins, -0.5, n_bins + 0.5]
+    data = np.random.poisson(size=n_bins, lam=scale * 120).tolist()
+    bkg = np.random.poisson(size=n_bins, lam=scale * 100).tolist()
+    bkgerr = np.random.normal(size=n_bins, loc=scale * 10.0, scale=3).tolist()
+    sig = np.random.poisson(size=n_bins, lam=scale * 30).tolist()
+
+    source = {
+        "binning": binning,
+        "bindata": {"data": data, "bkg": bkg, "bkgerr": bkgerr, "sig": sig},
+    }
+    return source
+
+
+def test_nll_from_pyhf_simple():
+    nbins = 50
+    source = generate_source_static(nbins)
+
+    signp = source["bindata"]["sig"]
+    bkgnp = source["bindata"]["bkg"]
+    uncnp = source["bindata"]["bkgerr"]
+    datanp = source["bindata"]["data"]
+
+    pdf = uncorrelated_background(signp, bkgnp, uncnp)
+    data = datanp + pdf.config.auxdata
+
+    nll = zpyhf.loss.nll_from_pyhf(data, pdf)
+
+    minimizer = zfit.minimize.Minuit(verbosity=7)
+    resultz = minimizer.minimize(nll)
+
+    values, fmin = pyhf.infer.mle.fit(
+        data, pdf, pdf.config.suggested_init(), pdf.config.suggested_bounds(), return_fitted_val=True
+    )
+    assert np.allclose(resultz.fmin, fmin / 2, atol=1e-4)
+    np.testing.assert_allclose(resultz.values, values, atol = 5e-3)