Hello,
I must be missing something obvious because I can't find the "actual"
coefficients of the polynomial fitted using LassoCV. That is, for a 3rd
degree polynomial
p = a0 + a1 * x + a2 * x^2 + a3 * x^3
I want the a0, a1, a2 and a3 coefficients (as those returned by
numpy.polyfit()). Here is an example code of what I'm after
import numpy as np
import matplotlib.pyplot as plt
from pandas import *
from math import *
from patsy import dmatrix
from sklearn.linear_model import LassoCV
sin_data = DataFrame({'x' : np.linspace(0, 1, 101)})
sin_data['y'] = np.sin(2 * pi * sin_data['x']) + np.random.normal(0, 0.1,
101)
x = sin_data['x']
y = sin_data['y']
Xpoly = dmatrix('C(x, Poly)')
n = 3
lasso_model = LassoCV(cv=15, copy_X=True, normalize=True)
lasso_fit = lasso_model.fit(Xpoly[:,1:n+1], y)
lasso_predict = lasso_model.predict(Xpoly[:,1:n+1])
a = np.r_[lasso_fit.intercept_, lasso_fit.coef_]
b = np.polyfit(x, y, n)[::-1]
p_lasso = a[0] + a[1] * x + a[2] * x**2 + a[3] * x**3
p_polyfit = b[0] + b[1] * x + b[2] * x**2 + b[3] * x**3
print 'coef. lasso:', a
print 'coef. polyfit:', b
The returned coefficients 'a' and 'b' are completely different, and while
'p_polyfit' is indeed the fitted polynomial of degree 3, 'p_lasso' makes no
sense (plot to see). Unless 'b' is something else... If so, what actually
are the coefficients returned by fit()? And how can I get the coefficients
that reconstruct the fitted polynomial?
Thank you,
-fernando
PS: If this is mentioned in any documentation, I couldn't find it.
--
Fernando Paolo
Institute of Geophysics & Planetary Physics
Scripps Institution of Oceanography
University of California, San Diego
9500 Gilman Drive
La Jolla, CA 92093-0225
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