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# 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

from matplotlib import pyplot as plt

# We load the data with load_iris from sklearn

from sklearn.datasets import load_iris

# load_iris returns an object with several fields

data = load_iris()

features = data.data

feature_names = data.feature_names

target = data.target

target_names = data.target_names

fig,axes = plt.subplots(2, 3)

pairs = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]

# Set up 3 different pairs of (color, marker)

color_markers = [

('r', '>'),

('g', 'o'),

('b', 'x'),

]

for i, (p0, p1) in enumerate(pairs):

ax = axes.flat[i]

for t in range(3):

# Use a different color/marker for each class `t`

c,marker = color_markers[t]

ax.scatter(features[target == t, p0], features[

target == t, p1], marker=marker, c=c)

ax.set_xlabel(feature_names[p0])

ax.set_ylabel(feature_names[p1])

ax.set_xticks([])

ax.set_yticks([])

fig.tight_layout()

fig.savefig('figure1.png')