从 UCI 上下载数据集
实现方法一
调用 scikit-learn 库来完成分类
from sklearn.naive_bayes import GaussianNB # 高斯朴素贝叶斯
from sklearn.model_selection import train_test_split # 训练集测试集划分
from sklearn.metrics import accuracy_score # 计算准确率
# 导入数据集 <鸢尾花>
from sklearn import datasets
def sk_learn():
"""
方式一，直接调用 sk_learn类库
:return:
"""
iris = datasets.load_iris()
# 分类数据
# print(iris.data)
# 标签
# print(iris.target)
# 切分数据集
trainSet, testSet, trainTarget, testTarget = train_test_split(iris.data, iris.target, random_state=37)
# 建模
clf = GaussianNB()
# 传入数据和标签
clf.fit(trainSet, trainTarget)
# 在测试集上进行预测，proba导出的是每个样本属于某类的概率
predict = clf.predict(testSet)
clf.predict_proba(testSet)
target_name = iris.target_names
# 测试准确率
print("预测结果准确率：{}".format(accuracy_score(testTarget, predict)))
for i in range(len(testSet)):
print("{}===>{}".format(testSet[i], target_name[predict[i]]))
if __name__ == '__main__':
sk_learn()
pass
D:\Anaconda3\envs\TF2.1\python.exe naive_bayes.py
预测结果准确率：1.0
[6.4 2.9 4.3 1.3]===>versicolor
[5.2 3.5 1.5 0.2]===>setosa
[6.5 3. 5.8 2.2]===>virginica
[5.8 2.7 5.1 1.9]===>virginica
[4.6 3.4 1.4 0.3]===>setosa
[5.6 2.7 4.2 1.3]===>versicolor
[4.7 3.2 1.3 0.2]===>setosa
[5.7 2.5 5. 2. ]===>virginica
[6.9 3.2 5.7 2.3]===>virginica
[6. 3. 4.8 1.8]===>virginica
[5.4 3.9 1.7 0.4]===>setosa
[5.9 3. 5.1 1.8]===>virginica
[6.7 3.1 5.6 2.4]===>virginica
[5.3 3.7 1.5 0.2]===>setosa
[4.3 3. 1.1 0.1]===>setosa
[6.5 3. 5.5 1.8]===>virginica
[5.6 2.5 3.9 1.1]===>versicolor
[6.4 3.1 5.5 1.8]===>virginica
[6.8 3. 5.5 2.1]===>virginica
[6.9 3.1 5.4 2.1]===>virginica
[5.6 3. 4.5 1.5]===>versicolor
[5.4 3.7 1.5 0.2]===>setosa
[6.7 3.3 5.7 2.1]===>virginica
[6.3 2.5 4.9 1.5]===>versicolor
[4.9 3.6 1.4 0.1]===>setosa
[5.7 2.9 4.2 1.3]===>versicolor
[7.2 3.2 6. 1.8]===>virginica
[5.2 2.7 3.9 1.4]===>versicolor
[5.1 3.3 1.7 0.5]===>setosa
[5.7 3. 4.2 1.2]===>versicolor
[4.6 3.1 1.5 0.2]===>setosa
[6.3 2.5 5. 1.9]===>virginica
[5.7 2.6 3.5 1. ]===>versicolor
[5. 3.5 1.3 0.3]===>setosa
[6.3 3.4 5.6 2.4]===>virginica
[7.1 3. 5.9 2.1]===>virginica
[6.3 2.3 4.4 1.3]===>versicolor
[6.6 3. 4.4 1.4]===>versicolor
Process finished with exit code 0
实现方式二
手写
# 方式二：
import pandas as pd
import numpy as np
import random
def load_dataSet():
csv = pd.read_csv('iris.data', header=None)
return csv
pass
def randSplit(dataSet, rate):
"""
随机打乱数据集
:param dataSet: 数据集
:param rate: 提取训练集和测试集的比率
:return: train test
"""
l_index = list(dataSet.index) # 提取出索引
random.shuffle(l_index) # 随机打乱索引
dataSet.index = l_index # 将打乱后的索引重新赋值给原数据集
n = dataSet.shape[0] # 总行数
m = int(n * rate) # 训练集的数量
train = dataSet.loc[range(m), :] # 提取前m个记录作为训练集
test = dataSet.loc[range(m, n), :] # 剩下的作为测试集
dataSet.index = range(dataSet.shape[0]) # 更新原数据集的索引
test.index = range(test.shape[0]) # 更新测试集的索引
return train, test
def gnb_classify(train, test):
"""
分类
:param train: 训练集
:param test: 测试集
:return:
"""
labels = train.iloc[:, -1].value_counts().index # 提取训练集的标签种类
mean = [] # 存放每个类别的均值
std = [] # 存放每个类别的方差
result = [] # 存放测试集的预测结果
for i in labels:
item = train.loc[train.iloc[:, -1] == i, :] # 分别提取出每一种类别
m = item.iloc[:, :-1].mean() # 当前类别的平均值
s = np.sum((item.iloc[:, :-1] - m) ** 2) / (item.shape[0]) # 方差
mean.append(m)
std.append(s)
means = pd.DataFrame(mean, index=labels)
stds = pd.DataFrame(std, index=labels)
for j in range(test.shape[0]):
iset = test.iloc[j, :-1].tolist() # 当前测试实例
iprob = np.exp(-1 * (iset - means) ** 2 / (stds * 2)) / (np.sqrt(2 * np.pi * stds)) # 正态分布公式
prob = 1 # 初始化当前实例总概率
for k in range(test.shape[-1] - 1):
prob *= iprob[k]
cla = prob.index[np.argmax(prob.values)] # 返回最大概率的类别
result.append(cla)
test['predict'] = result
acc = (test.iloc[:, -1] == test.iloc[:, -2]).mean() # 计算预测准确率
print("预测准确率: {}".format(acc))
# 输入预测的结果
print(test)
if __name__ == '__main__':
data_set = load_dataSet()
# 给 80% 的数据用来训练，20% 用来测试
_train, _test = randSplit(data_set, 0.8)
gnb_classify(_train, _test)
pass
D:\Anaconda3\envs\TF2.1\python.exe /naive_bayes.py
预测准确率: 0.9666666666666667
0 1 2 3 4 predict
0 6.8 2.8 4.8 1.4 Iris-versicolor Iris-versicolor
1 7.4 2.8 6.1 1.9 Iris-virginica Iris-virginica
2 4.6 3.1 1.5 0.2 Iris-setosa Iris-setosa
3 7.0 3.2 4.7 1.4 Iris-versicolor Iris-versicolor
4 6.3 2.3 4.4 1.3 Iris-versicolor Iris-versicolor
5 6.9 3.2 5.7 2.3 Iris-virginica Iris-virginica
6 6.0 2.2 5.0 1.5 Iris-virginica Iris-versicolor
7 6.3 3.3 4.7 1.6 Iris-versicolor Iris-versicolor
8 5.5 2.6 4.4 1.2 Iris-versicolor Iris-versicolor
9 5.4 3.9 1.7 0.4 Iris-setosa Iris-setosa
10 5.8 4.0 1.2 0.2 Iris-setosa Iris-setosa
11 6.5 3.0 5.2 2.0 Iris-virginica Iris-virginica
12 5.2 3.5 1.5 0.2 Iris-setosa Iris-setosa
13 6.3 2.5 4.9 1.5 Iris-versicolor Iris-versicolor
14 7.7 3.0 6.1 2.3 Iris-virginica Iris-virginica
15 5.4 3.4 1.5 0.4 Iris-setosa Iris-setosa
16 5.0 3.6 1.4 0.2 Iris-setosa Iris-setosa
17 5.1 3.8 1.9 0.4 Iris-setosa Iris-setosa
18 5.7 2.9 4.2 1.3 Iris-versicolor Iris-versicolor
19 7.2 3.2 6.0 1.8 Iris-virginica Iris-virginica
20 4.8 3.0 1.4 0.3 Iris-setosa Iris-setosa
21 4.3 3.0 1.1 0.1 Iris-setosa Iris-setosa
22 5.0 3.4 1.6 0.4 Iris-setosa Iris-setosa
23 5.1 3.8 1.5 0.3 Iris-setosa Iris-setosa
24 6.0 2.7 5.1 1.6 Iris-versicolor Iris-versicolor
25 5.5 2.3 4.0 1.3 Iris-versicolor Iris-versicolor
26 5.0 3.3 1.4 0.2 Iris-setosa Iris-setosa
27 5.5 2.5 4.0 1.3 Iris-versicolor Iris-versicolor
28 5.4 3.9 1.3 0.4 Iris-setosa Iris-setosa
29 6.4 2.8 5.6 2.1 Iris-virginica Iris-virginica
Process finished with exit code 0
问题：训练数是随机筛选的，每次运行的准确率不一样