文本分类之朴素贝叶斯算法

数据集如下，有两个特征共250条数据。数据集暂不提供下载。

代码如下，人工智能小白，大佬轻喷。

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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