朴素贝叶斯（Naive Bayes）：鸢尾花分类项目（不调库，手工推）

一、数据来源

1.数据来源：kaggle

2.数据样式

通过对数据“萼片、花瓣的长度、宽度（sepal_length、sepal_width、petal_length、petal_width）”搭建模型进行计算，判断鸢尾花的种类（species）。

二、使用方法

朴素贝叶斯（Naive Bayes）

方法说明：

贝叶斯公式：

实际运用时，需要判断在已知数据的情况下，属于该组的概率，公式变形为：

在建立模型时，依次对比是每一组数据的概率大小，而每一组数据的全概率是相同的，所以，公式再次变形为：

三、代码实现

从数据读取开始，不调取三方库，纯手工推。

1.导入基础库

#1.导入基础库
from csv import reader
from math import exp,pi,sqrt
from random import randrange,seed
import copy

2.读取csv文件和转换数据类型

#2.读取csv文件和转换数据类型
#读取csv文件
def csv_loader(file):dataset=list()with open(file,'r') as f:csv_reader=reader(f)    for row in csv_reader:if not row:continuedataset.append(row)return dataset#指标数据转换为浮点型
def str_to_float_converter(dataset):dataset=dataset[1:]for i in range(len(dataset[0])-1):for row in dataset:row[i]= float(row[i].strip())#分类数据转换为整型
def str_to_int_converter(dataset):class_values= [row[-1] for row in dataset]unique_values= set(class_values)converter_dict=dict()for i,value in enumerate(unique_values):converter_dict[value] = ifor row in dataset:row[-1] = converter_dict[row[-1] ]

3.K折交叉验证拆分数据

#3.K折交叉验证拆分数据
def k_fold_cross_validation(dataset,n_folds):dataset_splitted=list()fold_size= int(len(dataset)/n_folds)dataset_copy = list(dataset)for i in range(n_folds):fold_data = list()while len(fold_data) < fold_size:index = randrange(len(dataset_copy))fold_data.append(dataset_copy.pop(index))dataset_splitted.append(fold_data)return dataset_splitted

4.计算准确性

#4.计算准确性
def calculate_accuracy(actual,predicted):correct_num = 0for i in range(len(actual)):if actual[i] == predicted[i]:correct_num +=1accuracy = correct_num/float(len(actual)) *100.0return accuracy

5.模型测试

#5.模型测试
def mode_test(dataset,algo,n_folds,*args):dataset_splitted = k_fold_cross_validation(dataset,n_folds)scores  = list() for fold in dataset_splitted:train = copy.deepcopy(dataset_splitted)train.remove(fold)train = sum(train, [])test =list()test = copy.deepcopy(fold)predicted = algo(train, test, *args)actual = [row[-1] for row in fold]accuracy= calculate_accuracy(actual,predicted)scores.append(accuracy)return scores

6.数据按字典分类和描述

首先，将数据按分类数据作为key，每一行作为value,进行字典转换；

然后，计算每一列的均值、标准差、长度，并通过字典进行描述：{class:[(mean，std，len)])}。

#6.数据按字典分类和描述
#数据组合成按每一种类进行分类
def split_class(dataset):splitted = dict()for i in range(len(dataset)):vector = dataset[i]class_value = vector[-1]if class_value not in splitted:splitted[class_value]=list()splitted[class_value].append(vector)return splitted#计算每一列（x_i)的均值
def calculate_mean(column):mean = sum(column)/len(column)return mean#计算每一列（x_i)的标准差
def calculate_std(column):mean = calculate_mean(column)var = sum([(x - mean )**2  for x in column])/float((len(column)-1))std = sqrt(var)return std#描述数据[(mean，std，len)]
def describe_data(dataset):description = [(calculate_mean(column), calculate_std(column),len(column)) for column in zip(*dataset)]del description[-1]return description#数据按字典分类描述{class:[(mean，std，len)])}
def describe_class(dataset):splitted = split_class(dataset)descriptions = dict()for class_value, rows in splitted.items():descriptions[class_value] = describe_data(rows)return descriptions

7.设置计算概率的基础模型

正态分布概率计算：

如果随机变量X服从

#7.计算概率的基础模型
def calculate_probability(x,mean,std):exponent = exp(-((x - mean)**2)/(2 *(std**2)))probability = (1/(sqrt(2* pi) * std)) *exponentreturn probability

8.计算每一行数据的概率

#8.计算每一行数据的概率
def calculate_class_probabilities(dataset,row):descriptions= describe_class(dataset)total = sum([descriptions[label][0][-1] for label in descriptions])pribabilities = dict()for class_key, class_value  in descriptions.items():pribabilities [class_key] = class_value[0][-1]/float(total)for i in range(len(class_value)):mean,std,count = class_value[i]pribabilities [class_key] *= calculate_probability(row[i],mean,std)return pribabilities

9.每一行数据中找出最好的标签

#9.每一行数据中找出最好的标签
def predict(dataset,row):pribabilities=calculate_class_probabilities(dataset,row)best_label,best_probability =None, -1for class_value, probability in pribabilities.items():if best_label is None or probability >best_probability:best_probability = probabilitybest_label= class_valuereturn best_label

10.预测测试数据的分类

#10.预测测试数据的分类
def naive_bayes(train,test):pedictions = list()for row in test:prediction = predict(train,row)pedictions.append(prediction)return pedictions

11.运行和参数调整

#11.运行和参数调整
seed(5)
file='./download_datas/IRIS.csv'
dataset= csv_loader(file)
str_to_float_converter(dataset)
dataset=dataset[1:]
str_to_int_converter(dataset)
n_folds=3
algo=naive_bayes
scores = mode_test(dataset,algo,n_folds)print('The scores of our model are : %s' % scores)
print('The average score of our model is : %.3f%%' % (sum(scores)/float(len(scores))))

代码运行结果：

#结果输出
The scores of our model is : [94.0, 98.0, 96.0]
The average score of our model is : 96.000%

四、完整代码

#1.导入基础库
from csv import reader
from math import exp,pi,sqrt
from random import randrange,seed
import copy#2.读取csv文件和转换数据类型
#读取csv文件
def csv_loader(file):dataset=list()with open(file,'r') as f:csv_reader=reader(f)    for row in csv_reader:if not row:continuedataset.append(row)return dataset#指标数据转换为浮点型
def str_to_float_converter(dataset):dataset=dataset[1:]for i in range(len(dataset[0])-1):for row in dataset:row[i]= float(row[i].strip())#分类数据转换为整型
def str_to_int_converter(dataset):class_values= [row[-1] for row in dataset]unique_values= set(class_values)converter_dict=dict()for i,value in enumerate(unique_values):converter_dict[value] = ifor row in dataset:row[-1] = converter_dict[row[-1] ]#3.K折交叉验证拆分数据
def k_fold_cross_validation(dataset,n_folds):dataset_splitted=list()fold_size= int(len(dataset)/n_folds)dataset_copy = list(dataset)for i in range(n_folds):fold_data = list()while len(fold_data) < fold_size:index = randrange(len(dataset_copy))fold_data.append(dataset_copy.pop(index))dataset_splitted.append(fold_data)return dataset_splitted#4.计算准确性
def calculate_accuracy(actual,predicted):correct_num = 0for i in range(len(actual)):if actual[i] == predicted[i]:correct_num +=1accuracy = correct_num/float(len(actual)) *100.0return accuracy#5.模型测试
def mode_test(dataset,algo,n_folds,*args):dataset_splitted = k_fold_cross_validation(dataset,n_folds)scores  = list() for fold in dataset_splitted:train = copy.deepcopy(dataset_splitted)train.remove(fold)train = sum(train, [])test =list()test = copy.deepcopy(fold)predicted = algo(train, test, *args)actual = [row[-1] for row in fold]accuracy= calculate_accuracy(actual,predicted)scores.append(accuracy)return scores#6.数据按字典分类和描述
#数据组合成按每一种类进行分类
def split_class(dataset):splitted = dict()for i in range(len(dataset)):vector = dataset[i]class_value = vector[-1]if class_value not in splitted:splitted[class_value]=list()splitted[class_value].append(vector)return splitted#计算每一列（x_i)的均值
def calculate_mean(column):mean = sum(column)/len(column)return mean#计算每一列（x_i)的标准差
def calculate_std(column):mean = calculate_mean(column)var = sum([(x - mean )**2  for x in column])/float((len(column)-1))std = sqrt(var)return std#描述数据[(mean，std，len)]
def describe_data(dataset):description = [(calculate_mean(column), calculate_std(column),len(column)) for column in zip(*dataset)]del description[-1]return description#数据按字典分类描述{class:[(mean，std，len)])}
def describe_class(dataset):splitted = split_class(dataset)descriptions = dict()for class_value, rows in splitted.items():descriptions[class_value] = describe_data(rows)return descriptions#7.计算概率的基础模型
def calculate_probability(x,mean,std):exponent = exp(-((x - mean)**2)/(2 *(std**2)))probability = (1/(sqrt(2* pi) * std)) *exponentreturn probability#8.计算每一行数据的概率
def calculate_class_probabilities(dataset,row):descriptions= describe_class(dataset)total = sum([descriptions[label][0][-1] for label in descriptions])pribabilities = dict()for class_key, class_value  in descriptions.items():pribabilities [class_key] = class_value[0][-1]/float(total)for i in range(len(class_value)):mean,std,count = class_value[i]pribabilities [class_key] *= calculate_probability(row[i],mean,std)return pribabilities#9.每一行数据中找出最好的标签
def predict(dataset,row):pribabilities=calculate_class_probabilities(dataset,row)best_label,best_probability =None, -1for class_value, probability in pribabilities.items():if best_label is None or probability >best_probability:best_probability = probabilitybest_label= class_valuereturn best_label#10.预测测试数据的分类
def naive_bayes(train,test):pedictions = list()for row in test:prediction = predict(train,row)pedictions.append(prediction)return pedictions#11.运行和参数调整
seed(5)
file='./download_datas/IRIS.csv'
dataset= csv_loader(file)
str_to_float_converter(dataset)
dataset=dataset[1:]
str_to_int_converter(dataset)
n_folds=3
algo=naive_bayes
scores = mode_test(dataset,algo,n_folds)print('The scores of our model are : %s' % scores)
print('The average score of our model is : %.3f%%' % (sum(scores)/float(len(scores))))

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