[数据集]Iris

参考:Iris Data Set

Iris数据集包含3个类别4个属性,共150个实例

解析

4个属性分别表示(单位cm):

  1. 萼片(sepal)长度
  2. 萼片宽度
  3. 花瓣(petal)长度
  4. 花瓣宽度

每个类别有50个实例,整个数据集共150

下载

下载地址:machine-learning-databases/iris

文件iris.data中共150行,每行5个值,前4个表示属性,最后一个表示类别,每列用逗号隔开

Kaggle也提供了Iris数据集,其数据集格式略有差别,具体参考鸢尾数据集

解析

通过pandas库解析csv文件,通过sklearn库分离训练集和测试集,通过matplotlib绘制图像

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
# -*- coding: utf-8 -*-

"""
@author: zj
@file: iris.py
@time: 2019-12-14
"""

import pandas as pd
import sklearn.utils as utils
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt


def iris_str_to_int(x):
if 'Iris-setosa'.__eq__(x):
return 0
elif 'Iris-versicolor'.__eq__(x):
return 1
else:
return 2


def load_iris_data(data_path, shuffle=True, tsize=0.8):
data_list = pd.read_csv(data_path, header=None, sep=',')

data_array = data_list.values
height, width = data_array.shape[:2]
data_x = data_array[:, :(width - 1)].astype(np.float)
data_y = data_array[:, (width - 1)]

x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, train_size=tsize, test_size=(1 - tsize),
shuffle=shuffle)
y_train = np.array(list(map(lambda x: iris_str_to_int(x), y_train)))
y_test = np.array(list(map(lambda x: iris_str_to_int(x), y_test)))

return x_train, x_test, y_train, y_test


def draw_iris(x_data, y_data, title, xlabel, ylabel):
fig = plt.figure()

x = x_data[y_data == 0]
plt.scatter(x[:, 0], x[:, 1], c='r', marker='<', label='Iris-setosa')

x = x_data[y_data == 1]
plt.scatter(x[:, 0], x[:, 1], c='g', marker='8', label='Iris-versicolor')

x = x_data[y_data == 2]
plt.scatter(x[:, 0], x[:, 1], c='y', marker='*', label='Iris-virginica')

plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend()
plt.show()


if __name__ == '__main__':
iris_path = '/home/zj/data/iris-species/iris.data'
x_train, x_test, y_train, y_test = load_iris_data(iris_path, shuffle=True, tsize=0.8)

x_train = x_train.astype(np.double)
x_test = x_test.astype(np.double)
# 计算训练集每个属性的均值和方差
mu = np.mean(x_train, axis=0)
var = np.var(x_train, axis=0)
eps = 1e-8
# 将数据变换为均值为0,方差为1的标准正态分布
x_train = (x_train - mu) / np.sqrt(var + eps)
x_test = (x_test - mu) / np.sqrt(var + eps)

draw_iris(x_train[:, :2], y_train, 'sepal height/width', 'height', 'width')
draw_iris(x_train[:, 2:], y_train, 'petal height/width', 'height', 'width')

sklearn

sklearn库提供了iris数据集的封装

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
# -*- coding: utf-8 -*-

from sklearn import datasets
from sklearn.model_selection import train_test_split

def load_data():
# Import some data to play with
iris = datasets.load_iris()
X = iris.data
y = iris.target

# shuffle and split training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5)

return X_train, X_test, y_train, y_test

if __name__ == '__main__':
X_train, X_test, y_train, y_test = load_data()

print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape)

输出如下:

1
2
3
4
(75, 4)
(75, 4)
(75,)
(75,)
坚持原创技术分享,您的支持将鼓励我继续创作!