machinelearningclassification/irisplot.py at master · cambridgecoding/machinelearningclassification · GitHub

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import matplotlib.pyplot as plt

from sklearn.datasets import load_iris

iris = load_iris()

X = iris.data

labels = iris.target_names

#Symbols to represent the points for the three classes on the graph.

gMarkers = ["+", "_", "x"]

#Colours to represent the points for the three classes on the graph

gColours = ["blue", "magenta", "cyan"]

#The index of the class in target_names

gIndices = [0, 1, 2]

#Column indices for the two features you want to plot against each other:

f1 = 0

f2 = 1

for mark, col, i, iris.target_name in zip(gMarkers, gColours, gIndices, labels):

plt.scatter(x = X[iris.target == i, f1], y = X[iris.target == i, f2], marker = mark, c = col, label=iris.target_name)

plt.legend(loc='upper right')

plt.xlabel(iris.feature_names[f1])

plt.ylabel(iris.feature_names[f2])

plt.show()

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