figure2.py

# This code is supporting material for the book
# Building Machine Learning Systems with Python
# by Willi Richert and Luis Pedro Coelho
# published by PACKT Publishing
#
# It is made available under the MIT License

COLOUR_FIGURE = False

from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
data = load_iris()
features = data['data']
feature_names = data['feature_names']
species = data['target_names'][data['target']]

setosa = (species == 'setosa')
features = features[~setosa]
species = species[~setosa]
virginica = species == 'virginica'

t = 1.75
p0, p1 = 3, 2

if COLOUR_FIGURE:
    area1c = (1., .8, .8)
    area2c = (.8, .8, 1.)
else:
    area1c = (1., 1, 1)
    area2c = (.7, .7, .7)

x0, x1 = [features[:, p0].min() * .9, features[:, p0].max() * 1.1]
y0, y1 = [features[:, p1].min() * .9, features[:, p1].max() * 1.1]

plt.fill_between([t, x1], [y0, y0], [y1, y1], color=area2c)
plt.fill_between([x0, t], [y0, y0], [y1, y1], color=area1c)
plt.plot([t, t], [y0, y1], 'k--', lw=2)
plt.plot([t - .1, t - .1], [y0, y1], 'k:', lw=2)
plt.scatter(features[virginica, p0],
            features[virginica, p1], c='b', marker='o')
plt.scatter(features[~virginica, p0],
            features[~virginica, p1], c='r', marker='x')
plt.ylim(y0, y1)
plt.xlim(x0, x1)
plt.xlabel(feature_names[p0])
plt.ylabel(feature_names[p1])
plt.savefig('figure2.png')