Tutorial

Simple Example

The example below shows how easy it is to define a model that we could fit to.

from symfit import Parameter, Variable

a = Parameter()
b = Parameter()
x = Variable()
model = a * x + b

Lets fit this model to some generated data.

from symfit import Fit
import numpy as np

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,))
ydata = a_vec * xdata + b_vec  # Point scattered around the line 5 * x + 105

fit = Fit(model, xdata, ydata)
fit_result = fit.execute()

Printing fit_result will give a full report on the values for every parameter, including the uncertainty, and quality of the fit.

Initial Guess

For fitting to work as desired you should always give a good initial guess for a parameter. The Parameter object can therefore be initiated with the following keywords:

• value the initial guess value. Defaults to 1.
• min Minimal value for the parameter.
• max Maximal value for the parameter.
• fixed Whether the parameter’s value can vary during fitting.

In the example above, we might change our Parameter‘s to the following after looking at a plot of the data:

k = Parameter(value=4, min=3, max=6)

a, b = parameters('a, b')
a.value = 60
a.fixed = True

Accessing the Results

A call to Fit.execute returns a FitResults instance. This object holds all information about the fit. The fitting process does not modify the Parameter objects. In the above example, a.value will still be 60 and not the value we obtain after fitting. To get the value of fit parameters we can do:

>>> print(fit_result.value(a))
>>> 14.66946...
>>> print(fit_result.stdev(a))
>>> 0.3367571...
>>> print(fit_result.value(b))
>>> 104.6558...
>>> print(fit_result.stdev(b))
>>> 19.49172...
>>> print(fit_result.r_squared)
>>> 0.950890866472

For more FitResults, see the Module Documentation.

Evaluating the Model

With these parameters, we could now evaluate the model with these parameters so we can make a plot of it. In order to do this, we simply call the model with these values:

import matplotlib.pyplot as plt

y = model(x=xdata, a=fit_result.value(a), b=fit_result.value(b))
plt.plot(xdata, y)
plt.show()

The model has to be called by keyword arguments to prevent any ambiguity. So the following does not work:

y = model(xdata, fit_result.value(a), fit_result.value(b))

To make life easier, there is a nice shorthand notation to immediately use a fit result:

y = model(x=xdata, **fit_result.params)

This unpacks the params object as a dict. For more info view ParameterDict in the Module Documentation.

Named Models

More complicated models are also relatively easy to deal with by using named models. Let’s try our luck with a bivariate normal distribution:

from symfit import parameters, variables, exp, pi, sqrt

x, y, p = variables('x, y, p')
mu_x, mu_y, sig_x, sig_y, rho = parameters('mu_x, mu_y, sig_x, sig_y, rho')

z = (
    (x - mu_x)**2/sig_x**2
    + (y - mu_y)**2/sig_y**2
    - 2 * rho * (x - mu_x) * (y - mu_y)/(sig_x * sig_y)
)
model = {
    p: exp(
        - z / (2 * (1 - rho**2)))
        / (2 * pi * sig_x * sig_y * sqrt(1 - rho**2)
    )
}

fit = Fit(model, x=xdata, y=ydata, p=pdata)

By using the magic of named models, the flow of information is still relatively clear, even with such a complicated function.

This syntax also supports vector valued functions:

model = {y_1: a * x**2, y_2: 2 * x * b}

One thing to note about such models is that now model(x=xdata) obviously no longer works as type(model) == dict. There is a preferred way to resolve this. If any kind of fitting object has been initiated, it will have a model atribute containing an instance of Model. This can again be called:

a, b = parameters('a, b')
y_1, y_2, x = variables('y_1, y_2, x')

model = {y_1: a * x**2, y_2: 2 * x * b}
fit = Fit(model, x=xdata, y_1=y_data1, y_2=y_data2)
fit_result = fit.execute()

y_1_result, y_2_result = fit.model(x=xdata, **fit_result.params)

This returns a namedtuple(), with the components evaluated. So through the magic of tuple unpacking, y_1 and y_2 contain the evaluated fit. The dependent variables will be ordered alphabetically in the returned namedtuple(). Alternatively, the unpacking can be performed explicitly.

If for some reason no Fit is initiated you can make a Model object yourself:

  model = Model(model_dict)
  y_1_result, y_2_result = model(x=xdata, a=2.4, b=0.1)

or equivalently::

  outcome = model(x=xdata, a=2.4, b=0.1)
  y_1_result = outcome.y_1
  y_2_result = outcome.y_2

symfit exposes sympy.api

symfit exposes the sympy api as well, so mathematical expressions such as exp, sin and Pi are importable from symfit as well. For more, read the sympy docs.