# Fitting Types¶

## Fit (Least Squares)¶

The default fitting object does least-squares fitting:

```
from symfit import parameters, variables, Fit
import numpy as np
# Define a model to fit to.
a, b = parameters('a, b')
x = variables('x')
model = a * x + b
# Generate some data
xdata = np.linspace(0, 100, 100) # From 0 to 100 in 100 steps
a_vec = np.random.normal(15.0, scale=2.0, size=(100,))
b_vec = np.random.normal(100.0, scale=2.0, size=(100,))
# Point scattered around the line 5 * x + 105
ydata = a_vec * xdata + b_vec
fit = Fit(model, xdata, ydata)
fit_result = fit.execute()
```

The `Fit`

object also supports standard deviations. In
order to provide these, it’s nicer to use a named model:

```
a, b = parameters('a, b')
x, y = variables('x, y')
model = {y: a * x + b}
fit = Fit(model, x=xdata, y=ydata, sigma_y=sigma)
```

Warning

`symfit`

assumes these sigma to be from measurement errors by
default, and not just as a relative weight. This means the standard
deviations on parameters are calculated assuming the absolute size of sigma
is significant. This is the case for measurement errors and therefore for
most use cases `symfit`

was designed for. If you only want to use the
sigma for relative weights, then you can use `absolute_sigma=False`

as a
keyword argument.

Please note that this is the opposite of the convention used by scipy’s
`curve_fit()`

. Looking through their mailing list this
seems to have been implemented the opposite way for historical reasons, and
was understandably never changed so as not to loose backwards compatibility.
Since this is a new project, we don’t have that problem.

## Constrained Least Squares Fit¶

The `Fit`

takes a `constraints`

keyword; a list of
relationships between the parameters that has to be respected. As an example of
fitting with constraints, we could imagine fitting the angles of a triangle:

```
a, b, c = parameters('a, b, c')
a_i, b_i, c_i = variables('a_i, b_i, c_i')
model = {a_i: a, b_i: b, c_i: c}
data = np.array([
[10.1, 9., 10.5, 11.2, 9.5, 9.6, 10.],
[102.1, 101., 100.4, 100.8, 99.2, 100., 100.8],
[71.6, 73.2, 69.5, 70.2, 70.8, 70.6, 70.1],
])
fit = Fit(
model=model,
a_i=data[0],
b_i=data[1],
c_i=data[2],
constraints=[Equality(a + b + c, 180)]
)
fit_result = fit.execute()
```

The line `constraints=[Equality(a + b + c, 180)]`

ensures the our basic
knowledge of geometry is respected despite my sloppy measurements.

## (Non)LinearLeastSquares¶

The `LinearLeastSquares`

implements the analytical
solution to Least Squares fitting. When your model is linear in it’s parameters,
consider using this rather than the default
`Fit`

since this gives the exact
solution in one step, no iteration and no guesses needed.

`NonLinearLeastSquares`

is the generalization to
non-linear models. It works by approximating the model by a linear one around
the value of your guesses and repeating that process iteratively. This process
is therefore very sensitive to getting good initial guesses.

Notes on these objects:

- Use
`NonLinearLeastSquares`

instead of`LinearLeastSquares`

unless you have a reason not to.`NonLinearLeastSquares`

will behave exactly the same as`LinearLeastSquares`

when the model is linear. - Bounds are currently ignored by both. This is because for linear models there can only be one solution. For non-linear models it simply hasn’t been considered yet.
- When performance matters, use
`Fit`

instead of`NonLinearLeastSquares`

. These analytical objects are implemented in pure python and are therefore massively outgunned by`Fit`

which is ultimately a wrapper to efficient numerical methods such as MINPACK of BFGS implemented in Fortran.

## Likelihood¶

Given a dataset and a model, what values should the model’s parameters have to make the observed data most likely? This is the principle of maximum likelihood and the question the Likelihood object can answer for you.

Example:

```
from symfit import Parameter, Variable, exp
from symfit.core.objectives import LogLikelihood
import numpy as np
# Define the model for an exponential distribution (numpy style)
beta = Parameter('beta')
x = Variable('x')
model = (1 / beta) * exp(-x / beta)
# Draw 100 samples from an exponential distribution with beta=5.5
data = np.random.exponential(5.5, 100)
# Do the fitting!
fit = Fit(model, data, objective=LogLikelihood)
fit_result = fit.execute()
```

`fit_result`

is a normal `FitResults`

object.
As always, bounds on parameters and even constraints are supported.

## Minimize/Maximize¶

Minimize or Maximize a model subject to bounds and/or constraints. As an example I present an example from the scipy docs.

Suppose we want to maximize the following function:

Subject to the following constraints:

In SciPy code the following lines are needed:

```
def func(x, sign=1.0):
""" Objective function """
return sign*(2*x[0]*x[1] + 2*x[0] - x[0]**2 - 2*x[1]**2)
def func_deriv(x, sign=1.0):
""" Derivative of objective function """
dfdx0 = sign*(-2*x[0] + 2*x[1] + 2)
dfdx1 = sign*(2*x[0] - 4*x[1])
return np.array([ dfdx0, dfdx1 ])
cons = ({'type': 'eq',
'fun' : lambda x: np.array([x[0]**3 - x[1]]),
'jac' : lambda x: np.array([3.0*(x[0]**2.0), -1.0])},
{'type': 'ineq',
'fun' : lambda x: np.array([x[1] - 1]),
'jac' : lambda x: np.array([0.0, 1.0])})
res = minimize(func, [-1.0,1.0], args=(-1.0,), jac=func_deriv,
constraints=cons, method='SLSQP', options={'disp': True})
```

Takes a couple of read-throughs to make sense, doesn’t it? Let’s do the same
problem in `symfit`

:

```
from symfit import parameters, Maximize, Eq, Ge
x, y = parameters('x, y')
model = 2*x*y + 2*x - x**2 -2*y**2
constraints = [
Eq(x**3 - y, 0),
Ge(y - 1, 0),
]
fit = Fit(- model, constraints=constraints)
fit_result = fit.execute()
```

Done! `symfit`

will determine all derivatives automatically, no need for
you to think about it. Notice the minus sign in the call to Fit. This is
because Fit will always minimize, so in order to achieve maximization we should
minimize - model.

Warning

You might have noticed that `x`

and `y`

are
`Parameter`

’s in the above problem, which may
strike you as weird. However, it makes perfect sense because in this problem
they are parameters to be optimised, not independent variables. Furthermore,
this way of defining it is consistent with the treatment of
`Variable`

’s and
`Parameter`

’s in `symfit`

. Be aware of this
when minimizing such problems, as the whole process won’t work otherwise.

## ODE Fitting¶

Fitting to a system of ordinary differential equations (ODEs) is also
remarkedly simple with `symfit`

. Let’s do a simple example from reaction
kinetics. Suppose we have a reaction A + A -> B with rate constant \(k\).
We then need the following system of rate equations:

In `symfit`

, this becomes:

```
model_dict = {
D(a, t): - k * a**2,
D(b, t): k * a**2,
}
```

We see that the `symfit`

code is already very readable. Let’s do a fit to
this:

```
tdata = np.array([10, 26, 44, 70, 120])
adata = 10e-4 * np.array([44, 34, 27, 20, 14])
a, b, t = variables('a, b, t')
k = Parameter('k', 0.1)
a0 = 54 * 10e-4
model_dict = {
D(a, t): - k * a**2,
D(b, t): k * a**2,
}
ode_model = ODEModel(model_dict, initial={t: 0.0, a: a0, b: 0.0})
fit = Fit(ode_model, t=tdata, a=adata, b=None)
fit_result = fit.execute()
```

That’s it! An `ODEModel`

behaves just like any other
model object, so `Fit`

knows how to deal with it! Note
that since we don’t know the concentration of B, we explicitly set `b=None`

when calling `Fit`

so it will be ignored.

Warning

Fitting to ODEs is extremely difficult from an algorithmic point of view, since these systems are usually very sensitive to the parameters. Using (very) good initial guesses for the parameters and initial values is critical.

Upon every iteration of performing the fit the ODEModel is integrated again from the initial point using the new guesses for the parameters.

We can plot it just like always:

```
# Generate some data
tvec = np.linspace(0, 500, 1000)
A, B = ode_model(t=tvec, **fit_result.params)
plt.plot(tvec, A, label='[A]')
plt.plot(tvec, B, label='[B]')
plt.scatter(tdata, adata)
plt.legend()
plt.show()
```

As an example of the power of `symfit`

’s ODE syntax, let’s have a look at
a system with 2 equilibria: compound AA + B <-> AAB and AAB + B <-> BAAB.

In `symfit`

these can be implemented as:

```
AA, B, AAB, BAAB, t = variables('AA, B, AAB, BAAB, t')
k, p, l, m = parameters('k, p, l, m')
AA_0 = 10 # Some made up initial amound of [AA]
B = AA_0 - BAAB + AA # [B] is not independent.
model_dict = {
D(BAAB, t): l * AAB * B - m * BAAB,
D(AAB, t): k * A * B - p * AAB - l * AAB * B + m * BAAB,
D(A, t): - k * A * B + p * AAB,
}
```

The result is as readable as one can reasonably expect from a multicomponent system (and while using chemical notation). Let’s plot the model for some kinetics constants:

```
model = ODEModel(model_dict, initial={t: 0.0, AA: AA_0, AAB: 0.0, BAAB: 0.0})
# Generate some data
tdata = np.linspace(0, 3, 1000)
# Eval the normal way.
AA, AAB, BAAB = model(t=tdata, k=0.1, l=0.2, m=0.3, p=0.3)
plt.plot(tdata, AA, color='red', label='[AA]')
plt.plot(tdata, AAB, color='blue', label='[AAB]')
plt.plot(tdata, BAAB, color='green', label='[BAAB]')
plt.plot(tdata, B(BAAB=BAAB, AA=AA), color='pink', label='[B]')
# plt.plot(tdata, AA + AAB + BAAB, color='black', label='total')
plt.legend()
plt.show()
```

More common examples, such as dampened harmonic oscillators also work as expected:

```
# Oscillator strength
k = Parameter('k')
# Mass, just there for the physics
m = 1
# Dampening factor
gamma = Parameter('gamma')
x, v, t = symfit.variables('x, v, t')
# Define the force based on Hooke's law, and dampening
a = (-k * x - gamma * v)/m
model_dict = {
D(x, t): v,
D(v, t): a,
}
ode_model = ODEModel(model_dict, initial={t: 0, v: 0, x: 1})
# Let's create some data...
times = np.linspace(0, 15, 150)
data = ode_model(times, k=11, gamma=0.9, m=m.value).x
# ... and add some noise to it.
noise = np.random.normal(1, 0.1, data.shape) # 10% error
data *= noise
fit = Fit(ode_model, t=times, x=data)
fit_result = fit.execute()
```

Note

Evaluating the model above will produce a named tuple with values for
both `x`

and `v`

. Since we are only interested in the values for `x`

,
we immediately select it with `.x`

.

## Fitting multiple datasets¶

A common fitting problem is to fit to multiple datasets. This is sometimes
referred to as global fitting. In such fits parameters might be shared
between the fits to the different datasets. The same syntax used for ODE
fitting makes this problem very easy to solve in `symfit`

.

As a simple example, suppose we have two datasets measuring exponential decay, with the same background, but different amplitude and decay rate.

In order to fit to this, we define the following model:

```
x_1, x_2, y_1, y_2 = variables('x_1, x_2, y_1, y_2')
y0, a_1, a_2, b_1, b_2 = parameters('y0, a_1, a_2, b_1, b_2')
model = Model({
y_1: y0 + a_1 * exp(- b_1 * x_1),
y_2: y0 + a_2 * exp(- b_2 * x_2),
})
```

Note that `y0`

is shared between the components. Fitting is then done in the
normal way:

```
fit = Fit(model, x_1=xdata1, x_2=xdata2, y_1=ydata1, y_2=ydata2)
fit_result = fit.execute()
```

Any `Model`

that comes to mind is fair game. Behind the scenes `symfit`

will build a least squares function where the residues of all the components
are added squared, ready to be minimized. Unlike in the above example, the
x-axis does not even have to be shared between the components.

Warning

The regression coefficient is not properly defined for vector-valued models, but it is still listed! Until this is fixed, please recalculate it on your own for every component using the bestfit parameters.

Do not cite the overall \(R^2\) given by `symfit`

.

## Global Minimization¶

Very often, there are multiple solutions to a fitting (or minimisation) problem. These are local minima of the objective function. The best solution of course is the global minimum, but most minimization algorithms will only find a local minimum, and thus the answer you get will depend on the initial values of your parameters. This can be incredibly annoying if you have no further knowledge about your system.

Luckily, global minimizers exist which are not influenced by the initial
guesses for your parameters. In symfit, two such algorithms from `scipy`

have been wrapped for this pourpose. Firstly, the
`differential_evolution()`

algorithm from `scipy`

is
wrapped as `DifferentialEvolution`

. Secondly,
the `basinhopping()`

algorithm is available as
`BasinHopping`

. To use these minimizers,
just tell `Fit`

:

```
from symfit import Parameter, Variable, Model, Fit
from symfit.core.minimizers import DifferentialEvolution
x = Parameter('x')
x.min, x.max = -100, 100
x.value = -2.5
y = Variable('y')
model = Model({y: x**4 - 10 * x**2 - x}) # Skewed Mexican hat
fit = Fit(model, minimizer=DifferentialEvolution)
fit_result = fit.execute()
```

However, due to how this algorithm works, it’s not great at finding the exact minimum (but it will find it if given enough time). You can work around this by “chaining” minimizers: first run a global minimization to (hopefully) get close to your answer, and then polish it off using a local minimizer:

```
fit = Fit(model, minimizer=[DifferentialEvolution, BFGS])
```

Note

Global minimizers such as differential evolution and basin-hopping are rather sensitive to their hyperparameters. You might need to play with those to get appropriate results, e.g.:

```
fit.execute(DifferentialEvolution={'popsize': 20, 'recombination': 0.9})
```

Note

There is no way to guarantee that the minimum found is actually the global minimum. Unfortunately there is no way around this. Therefore, you should always critically inspect the results.

## Constrained Basin-Hopping¶

Worthy of special mention is the ease with which constraints or bounds can be
added to `symfit.core.minimizers.BasinHopping`

when used through the
`symfit.core.fit.Fit`

interface. As a very simple example, we shall
compare to an example from the `scipy`

docs:

```
import numpy as np
from scipy.optimize import basinhopping
def func2d(x):
f = np.cos(14.5 * x[0] - 0.3) + (x[1] + 0.2) * x[1] + (x[0] + 0.2) * x[0]
df = np.zeros(2)
df[0] = -14.5 * np.sin(14.5 * x[0] - 0.3) + 2. * x[0] + 0.2
df[1] = 2. * x[1] + 0.2
return f, df
minimizer_kwargs = {"method":"L-BFGS-B", "jac":True}
x0 = [1.0, 1.0]
ret = basinhopping(func2d, x0, minimizer_kwargs=minimizer_kwargs, niter=200)
```

Let’s compare to the same functionality in `symfit`

:

```
import numpy as np
from symfit.core.minimizers import BasinHopping
from symfit import parameters, Fit, cos
x0 = [1.0, 1.0]
x1, x2 = parameters('x1, x2', value=x0)
model = cos(14.5 * x1 - 0.3) + (x2 + 0.2) * x2 + (x1 + 0.2) * x1
fit = Fit(model, minimizer=BasinHopping)
fit_result = fit.execute(niter=200)
```

No minimizer_kwargs have to be provided, as `symfit`

will automatically
compute and provide the jacobian and select a minimizer. In this case, symfit
will choose BFGS. When bounds are provided, symfit will switch to
using L-BFGS-B instead. Setting bounds is as simple as:

```
x1.min = 0.0
x1.max = 100.0
```

However, the real strength of the symfit syntax lies in providing constraints:

```
constraints = [Eq(x1, x2)]
fit = Fit(model, minimizer=BasinHopping, constraints=constraints)
```

This artificial example will make sure x1 == x2 after fitting. If you have
read the Minimize/Maximize section, you will know how much work this
would be in pure `scipy`

.

## Advanced usage¶

In general, the separate components of the model can be whatever you need them to be. You can mix and match which variables and parameters should be coupled and decoupled ad lib. Some examples are given below.

Same parameters and same function, different (in)dependent variables:

```
datasets = [data_1, data_2, data_3, data_4, data_5, data_6]
xs = variables('x_1, x_2, x_3, x_4, x_5, x_6')
ys = variables('y_1, y_2, y_3, y_4, y_5, y_6')
zs = variables(', '.join('z_{}'.format(i) for i in range(1, 7)))
a, b = parameters('a, b')
model_dict = {
z: a/(y * b) * exp(- a * x)
for x, y, z in zip(xs, ys, zs)
}
```

## What if the model is unnamed?¶

Then you’ll have to use the ordering. Variables throughout `symfit`

’s
objects are internally ordered in the following way: first independent
variables, then dependent variables, then sigma variables, and lastly
parameters when applicable. Within each group alphabetical ordering applies.

It is therefore always possible to assign data to variables in an unambiguous way using this ordering. For example:

```
fit = Fit(model, x_data, y_data, sigma_y_data)
```