朴素贝叶斯算法
输入：样本集合D=｛(x_1,y_1),(x_2,y_2)
(x_m,y_m); 待预测样本x; 样本标记的所有可能取值｛c_1,c_2,c_3
c_k｝; 样本输入变量X的每个属性变量X^i的所有可能取值｛a_i1,a_i2,~,a_iAi｝； 输出：待预测样本x所属的类别
1.计算标记为c_k的样本出现概率。
2.计算标记c_k的样本，其X^i分量的属性值为a_ip的概率。
3.根据上面的估计值计算x属于y_k的概率值，并选择概率最大的作为输出。
1.使用sklearn的朴素贝叶斯算法对垃圾短信数据集进行分类
要求：
(1)划分训练集和测试集（测试集占20%） (2)对测试集的预测类别标签和真实标签进行对比 (3)掌握特征提取方法 (4)输出分类的准确率
代码：
from sklearn.feature_extraction.text import CountVectorizer as CVfrom sklearn.model_selection import train_test_splitfrom sklearn.naive_bayes import MultinomialNB as NBimport pandas as pd# 加载SMS垃圾短息数据集with open('SMSSpamCollection.txt', 'r', encoding='utf8') as f:sms = [line.split('\t') for line in f]y, x = zip(*sms)# SMS垃圾短息数据集的特征提取y = [label == 'spam' for label in y]x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)counter = CV(token_pattern='[a-zA-Z]{2,}')x_train = counter.fit_transform(x_train)x_test = counter.transform(x_test)# 朴素贝叶斯分类器的构造与训练model = NB()model.fit(x_train, y_train)train_score = model.score(x_train, y_train)test_score = model.score(x_test, y_test)print('train score:', train_score)print('test score:', test_score)# 对测试集的预测类别标签和真实标签进行对比y_predict = model.predict(x_test)print('测试集的预测类别标签与真实标签的对比：\n', pd.concat([pd.DataFrame(x_test), pd.DataFrame(y_test), pd.DataFrame(y_predict)], axis=1))复制代码
结果：
2.自己写朴素贝叶斯算法对垃圾短信数据集进行分类
代码：
# coding = utf-8import pandas as pdimport numpy as npimport randomimport mathclass bayesianClassifier(object):def __init__(self, ratio=0.7):self.trainset = []self.testset = []self.ratio = ratiodef loadData(self, filepath):""":param filepath: csv:return: list"""data_df = pd.read_csv(filepath)data_list = np.array(data_df).tolist()print("Loaded {0} samples secessfully.".format(len(data_list)))self.trainset, self.testset = self.splitData(data_list)return data_listdef splitData(self, data_list):""":param data_list:all data with list type:param ratio: train date's ratio:return: list type of trainset and testset"""train_size = int(len(data_list) * self.ratio)random.shuffle(data_list)self.trainset = data_list[:train_size]self.testset = data_list[train_size:]return self.trainset, self.testsetdef seprateByClass(self, dataset):""":param dataset: train data with list type:return: seprate_dict:separated data by class;info_dict:Number of samples per class(category)"""seprate_dict = {}info_dict = {}for vector in dataset:if vector[-1] not in seprate_dict:seprate_dict[vector[-1]] = []info_dict[vector[-1]] = 0seprate_dict[vector[-1]].append(vector)info_dict[vector[-1]] += 1return seprate_dict, info_dictdef mean(self, number_list):number_list = [float(x) for x in number_list] # str to numberreturn sum(number_list) / float(len(number_list))def var(self, number_list):number_list = [float(x) for x in number_list]avg = self.mean(number_list)var = sum([math.pow((x - avg), 2) for x in number_list]) / float(len(number_list) - 1)return vardef summarizeAttribute(self, dataset):"""calculate mean and var of per attribution in one class:param dataset: train data with list type:return: len(attribution)'s tuple ,that's (mean,var) with per attribution"""dataset = np.delete(dataset, -1, axis=1) # delete labelsummaries = [(self.mean(attr), self.var(attr)) for attr in zip(*dataset)]return summariesdef summarizeByClass(self, dataset):"""calculate all class with per attribution:param dataset: train data with list type:return: num:len(class)*len(attribution){class1:[(mean1,var1),(),...],class2:[(),(),...]...}"""dataset_separated, dataset_info = self.seprateByClass(dataset)summarize_by_class = {}for classValue, vector in dataset_separated.items():summarize_by_class[classValue] = self.summarizeAttribute(vector)return summarize_by_classdef calulateClassPriorProb(self, dataset, dataset_info):"""calculate every class's prior probability:param dataset: train data with list type:param dataset_info: Number of samples per class(category):return: dict type with every class's prior probability"""dataset_prior_prob = {}sample_sum = len(dataset)for class_value, sample_nums in dataset_info.items():dataset_prior_prob[class_value] = sample_nums / float(sample_sum)return dataset_prior_probdef calculateProb(self, x, mean, var):"""Continuous value using probability density function as class conditional probability:param x: one sample's one attribution:param mean: trainset's one attribution's mean:param var: trainset's one attribution's var:return: one sample's one attribution's class conditional probability"""exponent = math.exp(math.pow((x - mean), 2) / (-2 * var))p = (1 / math.sqrt(2 * math.pi * var)) * exponentreturn pdef calculateClassProb(self, input_data, train_Summary_by_class):"""calculate class conditional probability through multiplyevery attribution's class conditional probability per class:param input_data: one sample vectors:param train_Summary_by_class: every class with every attribution's (mean,var):return: dict type , class conditional probability per class of this input data belongs to which class"""prob = {}p = 1for class_value, summary in train_Summary_by_class.items():prob[class_value] = 1for i in range(len(summary)):mean, var = summary[i]x = input_data[i]p = self.calculateProb(x, mean, var)prob[class_value] *= preturn probdef bayesianPredictOneSample(self, input_data):""":param input_data: one sample without label:return: predicted class"""train_separated, train_info = self.seprateByClass(self.trainset)prior_prob = self.calulateClassPriorProb(self.trainset, train_info)train_Summary_by_class = self.summarizeByClass(self.trainset)classprob_dict = self.calculateClassProb(input_data, train_Summary_by_class)result = {}for class_value, class_prob in classprob_dict.items():p = class_prob * prior_prob[class_value]result[class_value] = preturn max(result, key=result.get)def calculateAccByBeyesian(self, ratio=0.7):""":param dataset: list type,test data:return: acc"""self.ratio = ratiocorrect = 0for vector in self.testset:input_data = vector[:-1]label = vector[-1]result = self.bayesianPredictOneSample(input_data)if result == label:correct += 1return correct / len(self.testset)if __name__ == "__main__":bys = bayesianClassifier()data_samples = bys.loadData('IrisData.csv')print("Accuracy is:", bys.calculateAccByBeyesian(ratio=0.7))复制代码
结果：
【人工智能】机器学习及与智能数据处理Python使用朴素贝叶斯算法对垃圾短信数据集进行分类
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