# Internal API Structure¶

Here we describe how the code is organized internally. This is only really relevant for advanced users and developers.

## Fitting 101¶

Fitting a model to data is, at it’s most basic, a parameter optimisation, and depending on whether you do a least-squares fit or a loglikelihood fit your objective function changes. This means we can split the process of fitting in three distinct, isolated parts: the Model, the Objective and the Minimizer.

In practice, Fit will choose an appropriate objective and minimizer on the basis of the model and the data, but you can also give it specific instances and classes; just in case you know better.

For both the minimizers and objectives there are abstract base classes, which describe the minimal API required. If a minimizer is more specific, e.g. it supports constraints, then there are corresponding abstract classes for that, e.g. ConstrainedMinimizer.

## Models¶

Models house the mathematical definition of the model we want to use to fit. For the typical usecase in symfit these are fully symbolical, and therefore a lot of their properties can be inspected automatically.

As a basic quality, all models are callable, i.e. they have implemented __call__. This is used to numerically evaluate the model given the parameters and independent variables. In order to make sure you get all the basic functionality, always inherit from BaseModel.

Next level up, if they inherit from GradientModel then they will have eval_jacobian, which will numerically evaluate the jacobian of the model. Lastly, if they inherit from HessianModel, they will also have eval_hessian to evaluate the hessian of the model. The standard Model is all of the above.

Odd ones out from the current library are CallableNumericalModel and ODEModel. They only inherit from BaseModel and are therefore callable, but their other behaviors are custom build.

Since symfit 0.5.0, the core of the model has been improved significantly. At the center of these improvements is connectivity_mapping. This mapping represent the connectivity matrix of the variables and parameters, and therefore encodes which variable depends on which. This is used in __call__ to evaluate the components in order. To help with this, models have ordered_symbols. This property is the topologically sorted connectivity_mapping, and dictates the order in which variables have to be evaluated.

## Objectives¶

Objectives wrap both the Model and the data supplied, and expose only the free parameters of the model to the outside world. When called they must return a scalar. This scalar will be minimized, so when you need something maximized, be sure to add a negation in the right place(s). They can be called by using the parameter names as keyword arguments, or with a list of parameter values in the same order as free_params (alphabetical). The latter is there because this is how scipy likes it. Be sure to inherit from the abstract base class(es) so you’re sure you define all the methods that are expected of an objective. Similar to the models, they come in three types: BaseObjective, GradientObjective and HessianObjective. These must implement __call__, eval_jacobian and eval_hessian respectively.

When defining a new objective, it is best to inherit from HessianObjective and to define all three if possible. When feeding a model that does not implement eval_hessian to a HessianObjective no puppies die, Fit is clever enough to prevent this.

## Minimizers¶

Last in the chain are the minimizers. They are provided with a function to minimize (the objective) and the Parameter s as a function of which the objective should be minimized. Note that once again there are different base classes for minimizers that take e.g. bounds or support gradients. Their execute() method takes the metaparameters for the minimization. Again, be sure to inherit from the appropriate base class(es) if you’re implementing your own minimizer to make sure all the expected methods are there. Fit depends on this to make its decisions. And if you’re wrapping Scipy style minimizers, have a look at ScipyMinimize to avoid a duplication of efforts.

Minimizers must always implement a method execute, which will return an instance of FitResults. Any *args and **kwargs given to execute must be passed to the underlying minimizer.

## Fit¶

Fit is responsible for stringing all of the above together intelligently. When not coached into the right direction, it will decide which minimizer and objective to use on the basis of the model and data.