dilax 0.1.3

Differentiable (binned) likelihoods in JAX.

dilax

Differentiable (binned) likelihoods in JAX.

Installation

python -m pip install dilax

From source:

git clone https://github.com/pfackeldey/dilax
cd dilax
python -m pip install .

Usage - Model definition and fitting

See more in examples/

dilax in a nutshell:

import jax
import jax.numpy as jnp
import equinox as eqx

from dilax.likelihood import NLL
from dilax.model import Model, Result
from dilax.optimizer import JaxOptimizer
from dilax.parameter import Parameter, gauss, modifier, unconstrained
from dilax.util import HistDB


jax.config.update("jax_enable_x64", True)


# define a simple model with two processes and two parameters
class MyModel(Model):
    def __call__(self, processes: HistDB, parameters: dict[str, Parameter]) -> Result:
        res = Result()

        # signal
        mu_mod = modifier(name="mu", parameter=parameters["mu"], effect=unconstrained())
        res.add(process="signal", expectation=mu_mod(processes["signal"]))

        # background
        bkg_mod = modifier(name="sigma", parameter=parameters["sigma"], effect=gauss(0.2))
        res.add(process="background", expectation=bkg_mod(processes["background"]))
        return res


# setup model
processes = HistDB({"signal": jnp.array([10.0]), "background": jnp.array([50.0])})
parameters = {
    "mu": Parameter(value=jnp.array([1.0]), bounds=(0.0, jnp.inf)),
    "sigma": Parameter(value=jnp.array([0.0])),
}
model = MyModel(processes=processes, parameters=parameters)

# define negative log-likelihood with data (observation)
nll = NLL(model=model, observation=jnp.array([64.0]))
# jit it!
fast_nll = eqx.filter_jit(nll)

# setup fit: initial values of parameters and a suitable optimizer
init_values = model.parameter_values
optimizer = JaxOptimizer.make(name="ScipyMinimize", settings={"method": "trust-constr"})

# fit
values, state = optimizer.fit(fun=fast_nll, init_values=init_values)

print(values)
# -> {'mu': Array([1.4], dtype=float64),
#     'sigma': Array([4.04723836e-14], dtype=float64)}

# eval model with fitted values/parameters
print(model.update(values=values).evaluate().expectation())
# -> Array([64.], dtype=float64)


# gradients of "prefit" model:
fast_grad_nll_prefit = eqx.filter_grad(nll)
print(fast_grad_nll_prefit({"sigma": jnp.array([0.2])}))
# -> {'sigma': Array([-0.12258065], dtype=float64)}

# gradients of "postfit" model:
postfit_nll = NLL(model=model.update(values=values), observation=jnp.array([64.0]))
fast_grad_nll_postfit = eqx.filter_grad(eqx.filter_jit(postfit_nll))
print(fast_grad_nll_postfit({"sigma": jnp.array([0.2])}))
# -> {'sigma': Array([0.5030303], dtype=float64)}

Contributing

See CONTRIBUTING.md for instructions on how to contribute.

License

Distributed under the terms of the BSD license.