FITTER documentation¶
fitter package provides a simple class to figure out from whih distribution your data comes from. It uses scipy package to try 80 distributions and allows you to plot the results to check what is the most probable distribution.
Installation¶
pip install fitter
Usage¶
Nothing complicated since there is just one class provided. First, we will need to create some data samples. Let us create a sequence of 100000 samples from a gamma distribution:
from scipy import stats
data = stats.gamma.rvs(2, loc=1.5, scale=2, size=100000)
Now, we may ask ourself (without any knowledge about the distribution or its parameter) what is a probable distributions that fit the data best ? scipy has 80 distributions, each has a method called fit that will help us here. The Fitter will scan all the scipy distributions, call the fit function for you, ignoring those that fail or run forever and finally it will give you a summary of the best distributions in the sense of sum of the square errors. The best is to give an example:
from scipy import stats
data = stats.gamma.rvs(2, loc=1.5, scale=2, size=100000)
from fitter import Fitter
f = Fitter(data, distributions=['gamma', 'rayleigh', 'uniform'])
f.fit()
f.summary()
(Source code, png, hires.png, pdf)
Here, we restrict the analysis to only 3 distributions by providing the list of distributions to considered. If you do not provide that parameter, 80 distributions will be considered (the analysis will be longer).
The fitter.fitter.Fitter.summary() method shows the first best distributions (in
terms of fitting).
Once the fitting is performed, one may want to get the parameters
corresponding to the best distribution. The
parameters are stored in fitted_param. For instance in the example
above, the summary told us that the Gamma distribution has the best fit. You
would retrieve the parameters of the Gamma distribution as follows:
>>> f.fitted_param['gamma']
(1.9870244799532322, 1.5026555566189543, 2.0174462493492964)
Here, you will need to look at scipy documentation to figure out what are those parameters (mean, sigma, shape, ...). For convenience, we do provide the corresponding PDF:
f.fitted_pdf['gamma']
but you may want to plot the gamma distribution yourself. In that case, you will need to use Scipy package itself. Here is an example
from pylab import linspace, plot
import scipy.stats
dist = scipy.stats.gamma
param = (1.9870, 1.5026, 2.0174)
X = linspace(0,10, 10)
pdf_fitted = dist.pdf(X, *param)
plot(X, pdf_fitted, 'o')
(Source code, png, hires.png, pdf)
