iris



In [1]:
import os
import pandas as pd

s = os.path.join('https://archive.ics.uci.edu', 'ml', 'machine-learning-databases', 'iris', 'iris.data')
print(f'URL: {s}')

df = pd.read_csv(s, header=None, encoding='utf-8')

df.describe()


URL: https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
Out[1]:
0 1 2 3
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000

In [2]:
import matplotlib.pyplot as plt
import numpy as np

# select setosa and versicolor
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', 1, -1)

x = df.iloc[0:100, [0, 2]].values

plt.scatter(x[:50, 0], x[:50, 1], color='red', marker='o', label='setosa')
plt.scatter(x[50:100, 0], x[50:100, 1], color='blue', marker='x', label='versicolor')
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()



In [3]:
from perceptron import Perceptron

ppn = Perceptron(eta=0.1, n_iter=10)
ppn.fit(x, y)

plt.plot(range(1, len(ppn._errors) + 1), ppn._errors, marker='o')
plt.xlabel('Epochs')
plt.ylabel('Number of updates')
plt.show()



In [4]:
from helper import plot_decision_regions

plot_decision_regions(x, y, classifier=ppn)
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.legend(loc='upper left')
plt.show()