Cross Validation Examples: Part 4

Figure 8.15

The learning curves for the data given by eq. 8.75, with d = 2 and d = 3. Both models have high variance for a few data points, visible in the spread between training and validation error. As the number of points increases, it is clear that d = 2 is a high-bias model which cannot be improved simply by adding training points.

Code output:

Python source code:

# Author: Jake VanderPlas
# License: BSD
#   The figure produced by this code is published in the textbook
#   "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
#   For more information, see http://astroML.github.com
#   To report a bug or issue, use the following forum:
#    https://groups.google.com/forum/#!forum/astroml-general
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import ticker
from matplotlib.patches import FancyArrow

#----------------------------------------------------------------------
# This function adjusts matplotlib settings for a uniform feel in the textbook.
# Note that with usetex=True, fonts are rendered with LaTeX.  This may
# result in an error if LaTeX is not installed on your system.  In that case,
# you can set usetex to False.
from astroML.plotting import setup_text_plots
setup_text_plots(fontsize=8, usetex=True)


#------------------------------------------------------------
# Define our functional form
def func(x, dy=0.1):
    return np.random.normal(np.sin(x) * x, dy)

#------------------------------------------------------------
# select the (noisy) data
np.random.seed(0)
x = np.linspace(0, 3, 22)[1:-1]
dy = 0.1
y = func(x, dy)

#------------------------------------------------------------
# Select the cross-validation points
np.random.seed(1)
x_cv = 3 * np.random.random(20)
y_cv = func(x_cv)

x_fit = np.linspace(0, 3, 1000)

#------------------------------------------------------------
# Fourth figure: plot errors as a function of training set size
np.random.seed(0)
x = 3 * np.random.random(100)
y = func(x)

np.random.seed(1)
x_cv = 3 * np.random.random(100)
y_cv = func(x_cv)

Nrange = np.arange(10, 101, 2)

fig = plt.figure(figsize=(5, 5))
fig.subplots_adjust(left=0.15, top=0.95)

for subplot, d in zip([211, 212], [2, 3]):
    ax = fig.add_subplot(subplot)
    training_err = np.zeros(Nrange.shape)
    crossval_err = np.zeros(Nrange.shape)

    for j, N in enumerate(Nrange):
        p = np.polyfit(x[:N], y[:N], d)
        training_err[j] = np.sqrt(np.sum((np.polyval(p, x[:N])
                                          - y[:N]) ** 2) / len(y))
        crossval_err[j] = np.sqrt(np.sum((np.polyval(p, x_cv)
                                          - y_cv) ** 2) / len(y_cv))

    ax.plot(Nrange, crossval_err, '--k', label='cross-validation')
    ax.plot(Nrange, training_err, '-k', label='training')
    ax.plot(Nrange, 0.1 * np.ones(Nrange.shape), ':k')
    ax.legend(loc=1)
    ax.text(0.03, 0.94, "d = %i" % d, transform=ax.transAxes,
            ha='left', va='top', bbox=dict(ec='k', fc='w', pad=10))

    ax.set_ylim(0, 0.4)

    ax.set_xlabel('Number of training points')
    ax.set_ylabel('rms error')

plt.show()

[download source: fig_cross_val_D.py]