Mukul - 6 months ago 29
Python Question

# how to run non linear regression in python

i am having the following information(dataframe) in python

product baskets scaling_factor
12345   475     95.5
12345   108     57.7
12345   2       1.4
12345   38      21.9
12345   320     88.8


and I want to run the following non-linear regression and estimate the parameters.

a ,b and c

Equation that i want to fit:

scaling_factor = a - (b*np.exp(c*baskets))


In sas we usually run the following model:(uses gauss newton method )

proc nlin data=scaling_factors;
parms a=100 b=100 c=-0.09;
model scaling_factor = a - (b * (exp(c*baskets)));
output out=scaling_equation_parms
parms=a b c;


is there a similar way to estimate the parameters in Python using non linear regression, how can i see the plot in python.

Agreeing with Chris Mueller, I'd also use scipy but scipy.optimize.curve_fit. The code looks like:

###the top two lines are required on my linux machine
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
from matplotlib.pyplot import cm
import numpy as np
from scipy.optimize import curve_fit #we could import more, but this is what we need
def func(x, a, b, c):
return a - b* np.exp(c * x)
###OP's data
baskets = np.array([475, 108, 2, 38, 320])
scaling_factor = np.array([95.5, 57.7, 1.4, 21.9, 88.8])
###let us guess some start values
initialGuess=[100, 100,-.01]
guessedFactors=[func(x,*initialGuess ) for x in baskets]
###making the actual fit
#one may want to
print popt
print pcov
###preparing data for showing the fit
fittedData=[func(x, *popt) for x in basketCont]
###preparing the figure
fig1 = plt.figure(1)
###the three sets of data to plot
###beautification
ax.legend(loc=0, title="graphs", fontsize=12)
ax.set_ylabel("factor")
ax.grid()
ax.set_title("$\mathrm{curve}_\mathrm{fit}$")
###putting the covariance matrix nicely
tab= [['{:.2g}'.format(j) for j in i] for i in pcov]
the_table = plt.table(cellText=tab,
colWidths = [0.2]*3,
loc='upper right', bbox=[0.483, 0.35, 0.5, 0.25] )
plt.text(250,65,'covariance:',size=12)
###putting the plot
plt.show()
###done


Eventually, giving you: