This lab on Splines and GAMs is a python adaptation of p. 293-297 of "Introduction to Statistical Learning with Applications in R" by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. It was originally written by Jordi Warmenhoven, and was adapted by R. Jordan Crouser at Smith College in Spring 2016.

import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt

from sklearn.preprocessing import PolynomialFeatures
import statsmodels.api as sm
import statsmodels.formula.api as smf

%matplotlib inline

# Generate a sequence of age values spanning the range
age_grid = np.arange(df.age.min(), df.age.max()).reshape(-1,1)


# 7.8.2 Splines¶

In order to fit regression splines in python, we use the ${\tt dmatrix}$ module from the ${\tt patsy}$ library. In lecture, we saw that regression splines can be fit by constructing an appropriate matrix of basis functions. The ${\tt bs()}$ function generates the entire matrix of basis functions for splines with the specified set of knots. Fitting ${\tt wage}$ to ${\tt age}$ using a regression spline is simple:

from patsy import dmatrix

# Specifying 3 knots
transformed_x1 = dmatrix("bs(df.age, knots=(25,40,60), degree=3, include_intercept=False)",
{"df.age": df.age}, return_type='dataframe')

# Build a regular linear model from the splines
fit1 = sm.GLM(df.wage, transformed_x1).fit()
fit1.params


Here we have prespecified knots at ages 25, 40, and 60. This produces a spline with six basis functions. (Recall that a cubic spline with three knots has seven degrees of freedom; these degrees of freedom are used up by an intercept, plus six basis functions.) We could also use the ${\tt df}$ option to produce a spline with knots at uniform quantiles of the data:

# Specifying 6 degrees of freedom
transformed_x2 = dmatrix("bs(df.age, df=6, include_intercept=False)",
{"df.age": df.age}, return_type='dataframe')
fit2 = sm.GLM(df.wage, transformed_x2).fit()
fit2.params


In this case python chooses knots which correspond to the 25th, 50th, and 75th percentiles of ${\tt age}$. The function ${\tt bs()}$ also has a ${\tt degree}$ argument, so we can fit splines of any degree, rather than the default degree of 3 (which yields a cubic spline).

In order to instead fit a natural spline, we use the ${\tt cr()}$ function. Here we fit a natural spline with four degrees of freedom:

# Specifying 4 degrees of freedom
transformed_x3 = dmatrix("cr(df.age, df=4)", {"df.age": df.age}, return_type='dataframe')
fit3 = sm.GLM(df.wage, transformed_x3).fit()
fit3.params


As with the ${\tt bs()}$ function, we could instead specify the knots directly using the ${\tt knots}$ option.

Let's see how these three models stack up:

# Generate a sequence of age values spanning the range
age_grid = np.arange(df.age.min(), df.age.max()).reshape(-1,1)

# Make some predictions
pred1 = fit1.predict(dmatrix("bs(age_grid, knots=(25,40,60), include_intercept=False)",
{"age_grid": age_grid}, return_type='dataframe'))
pred2 = fit2.predict(dmatrix("bs(age_grid, df=6, include_intercept=False)",
{"age_grid": age_grid}, return_type='dataframe'))
pred3 = fit3.predict(dmatrix("cr(age_grid, df=4)", {"age_grid": age_grid}, return_type='dataframe'))

# Plot the splines and error bands
plt.scatter(df.age, df.wage, facecolor='None', edgecolor='k', alpha=0.1)
plt.plot(age_grid, pred1, color='b', label='Specifying three knots')
plt.plot(age_grid, pred2, color='r', label='Specifying df=6')
plt.plot(age_grid, pred3, color='g', label='Natural spline df=4')
[plt.vlines(i , 0, 350, linestyles='dashed', lw=2, colors='b') for i in [25,40,60]]
plt.legend()
plt.xlim(15,85)
plt.ylim(0,350)
plt.xlabel('age')
plt.ylabel('wage')


To get credit for this lab, post your answer to thew following question:

• How would you choose whether to use a polynomial, step, or spline function for each predictor when building a GAM?