使用python对字段issue进行贝叶斯分类并可视化的代码

首先，你需要安装以下库：`pandas`, `numpy`, `sklearn`, `matplotlib`, `seaborn`, `nltk`。

然后，你可以按照以下步骤进行贝叶斯分类并可视化：

1. 导入必要的库和数据集

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import confusion_matrix, classification_report

import nltk
nltk.download('stopwords')

df = pd.read_csv('your_dataset.csv')

2. 对数据进行预处理

# 删除缺失值和不需要的列
df.dropna(inplace=True)
df.drop(['column1', 'column2'], axis=1, inplace=True)

# 将issue字段转换为小写
df['issue'] = df['issue'].apply(lambda x: x.lower())

# 对issue字段进行分词
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
df['issue'] = df['issue'].apply(lambda x: ' '.join([word for word in x.split() if word not in stop_words]))

# 对issue字段进行词干提取
from nltk.stem import PorterStemmer
ps = PorterStemmer()
df['issue'] = df['issue'].apply(lambda x: ' '.join([ps.stem(word) for word in x.split()]))

3. 将issue字段转换为词袋向量

cv = CountVectorizer()
X = cv.fit_transform(df['issue']).toarray()
y = df['label']

4. 将数据集分割为训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

5. 训练朴素贝叶斯模型

nb = MultinomialNB()
nb.fit(X_train, y_train)

6. 对模型进行预测并评估性能

y_pred = nb.predict(X_test)

print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))

7. 可视化混淆矩阵

sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap="Blues")
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()

以上是使用Python进行字段issue的贝叶斯分类并可视化的代码。你需要根据自己的数据集进行调整。