Minimizer objects are the last key element in the API framework of zfit. In particular, these are connected to the loss function and have an internal state that can be queried at any moment.

The zfit library is designed such that it is trivial to introduce new sets of minimizers. The only requirement in its initialisation is that a loss function must be given. Additionally, the parameters to be minimize, the tolerance, its name, as well as any other argument needed to configure the particular algorithm may be given.

Baseline minimizers

There are three minimizers currently included in the package: Minuit, Scipy and Adam TensorFlow optimiser. Let’s show how these can be initialised:

>>> # Minuit minimizer
>>> minimizer_minuit = zfit.minimize.Minuit()
>>> # Scipy minimizer
>>> minimizer_scipy = zfit.minimize.Scipy()
>>> # Adam's Tensorflow minimizer
>>> minimizer_adam = zfit.minimize.Adam()

A wrapper for TensorFlow optimisers is also available to allow to easily integrate new ideas in the framework. For instance, the Adam minimizer could have been initialised by

>>> # Adam's TensorFlor optimiser using a wrapper
>>> minimizer_wrapper = zfit.minimize.WrapOptimizer(tf.keras.optimizer.Adam())

Any of these minimizers can then be used to minimize the loss function we created in previous section, e.g.

>>> result = minimizer_minuit.minimize(loss=my_loss)

The choice of which parameters of your model should be floating in the fit can also be made at this stage

>>> # In the case of a Gaussian (e.g.)
>>> result = minimizer_minuit.minimize(loss=my_loss, params=[mu, sigma])

Only the parameters given in params are floated in the optimisation process. If this argument is not provided or params=None, all the floating parameters in the loss function are floated in the minimization process.

The result of the fit is return as a FitResult object, which provides access the minimiser state. zfit separates the minimisation of the loss function with respect to the error calculation in order to give the freedom of calculating this error whenever needed. The error() method can be used to perform the CPU-intensive error calculation. It returns two objects, the first are the parameter errors and the second is a new FitResult in case a new minimum was found during the profiling; this will also render the original result invalid as can be check with result.valid.

>>> param_errors, _ = result.errors()
>>> for var, errors in param_errors.items():
...   print('{}: ^{{+{}}}_{{-{}}}'.format(, errors['upper'], errors['lower']))
mu: ^{+0.00998104141841555}_{--0.009981515893414316}
sigma: ^{+0.007099472590970696}_{--0.0070162654764939734}

The result object also provides access the minimiser state:

>>> print("Function minimum:", result.fmin)
Function minimum: 14170.396450111948
>>> print("Converged:", result.converged)
Converged: True
>>> print("Full minimizer information:",
Full minimizer information: {'n_eval': 56, 'original': {'fval': 14170.396450111948, 'edm': 2.8519671693442587e-10,
'nfcn': 56, 'up': 0.5, 'is_valid': True, 'has_valid_parameters': True, 'has_accurate_covar': True, 'has_posdef_covar': True,
'has_made_posdef_covar': False, 'hesse_failed': False, 'has_covariance': True, 'is_above_max_edm': False, 'has_reached_call_limit': False}}

and the fitted parameters

>>> # Information on all the parameters in the fit
>>> params = result.params

>>> # Printing information on specific parameters, e.g. mu
>>> print("mu={}".format(params[mu]['value']))