[数据集]Iris

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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个

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

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# -*- 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

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# -*- 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：

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(75, 4)
(75, 4)
(75,)
(75,)

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