Naive Bayesian Algorithm for Text Classification

The data set is as follows, with two features and a total of 250 pieces of data. The dataset is not currently available for download.

The code is as follows, artificial intelligence novice, big brother light spray.

import pandas as pd
import jieba.analyse
import random

# 数据预处理
data = pd.read_excel('gastric.xlsx')
dataMap = {
    
    }
dataSum = ""
labelFrequency = {
    
    }  # 统计标签频率
for i in range(len(data.index.values)):
    dataSum += data.loc[i].values[1]
    if data.loc[i].values[0] in dataMap:
        dataMap[data.loc[i].values[0]] += data.loc[i].values[1]
        labelFrequency[data.loc[i].values[0]] += 1
    else:
        dataMap[data.loc[i].values[0]] = data.loc[i].values[1]
        labelFrequency[data.loc[i].values[0]] = 1

# 计算标签频率
dataLen = len(data.index.values)
for i in labelFrequency.keys():
    labelFrequency[i] /= dataLen

# 选取关键词
KWArray = jieba.analyse.extract_tags(dataSum)
KWSum = 0
for i in KWArray:
    KWSum += dataSum.count(i)

# 统计词频率
KWFrequency = {
    
    }
for (k, v) in dataMap.items():
    KWFrequency[k] = {
    
    }
    for i in KWArray:
        count = v.count(i)
        if count == 0:
            KWSum += 1
            KWFrequency[k][i] = 1 / KWSum  # 拉普拉斯平滑
        else:
            KWFrequency[k][i] = count / KWSum


# 构建朴素贝叶斯模型
def naiveBayes(test):
    resArr = []
    for ti in test:
        # 统计词频
        testKW = {
    
    }
        for kwValue in KWArray:
            kCount = ti.count(kwValue)
            if kCount != 0:
                testKW[kwValue] = kCount
        # 计算概率
        maxFreq = 0
        maxLabel = 0
        for (label, freq) in KWFrequency.items():
            tFreq = labelFrequency[label]
            for (tk, tq) in testKW.items():
                tFreq *= pow(freq[tk], tq)
            if tFreq > maxFreq:
                maxFreq = tFreq
                maxLabel = label
        resArr.append(maxLabel)
    return resArr


# 构建测试数据
testArray = []
testLabelArray = []
for i in range(len(data.index.values)):
    if random.random() < 0.2:
        testLabelArray.append(data.loc[i].values[0])
        testArray.append(data.loc[i].values[1])

# 计算准确率
resArr = naiveBayes(testArray)
trueNum = 0
for i in range(len(testArray)):
    if resArr[i] == testLabelArray[i]:
        trueNum += 1

print("准确率为：", 100 * trueNum / len(testArray), "%")

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