贝叶斯算法

贝叶斯介绍

 

 

 

 

 

# 导入算法包以及数据集
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.naive_bayes import MultinomialNB,BernoulliNB,GaussianNB#朴素贝叶斯的三种模型
# 载入数据
iris = datasets.load_iris()
x_train,x_test,y_train,y_test = train_test_split(iris.data, iris.target)
mul_nb = GaussianNB()#适合连续性数据，所以模型效果很好，其他两种则很差
mul_nb.fit(x_train,y_train)
print(classification_report(mul_nb.predict(x_test),y_test))
print(confusion_matrix(mul_nb.predict(x_test),y_test))
mul_nb = MultinomialNB()
mul_nb.fit(x_train,y_train)
print(classification_report(mul_nb.predict(x_test),y_test))
print(confusion_matrix(mul_nb.predict(x_test),y_test))
mul_nb = BernoulliNB()
mul_nb.fit(x_train,y_train)
print(classification_report(mul_nb.predict(x_test),y_test))
print(confusion_matrix(mul_nb.predict(x_test),y_test))

词袋模型介绍

 

from sklearn.feature_extraction.text import CountVectorizer#向量化

texts=["dog cat fish","dog cat cat","fish bird", 'bird']
cv = CountVectorizer()
cv_fit=cv.fit_transform(texts)

#
print(cv.get_feature_names())
print(cv_fit.toarray())
print(cv_fit.toarray().sum(axis=0))
'''
['bird', 'cat', 'dog', 'fish']
[[0 1 1 1]
 [0 2 1 0]
 [1 0 0 1]
 [1 0 0 0]]
[2 3 2 2]
'''

TF-IDF算法介绍

 

 

from sklearn.feature_extraction.text import TfidfVectorizer
# 文本文档列表
text = ["The quick brown fox jumped over the lazy dog.",
"The dog.",
"The fox"]
# 创建变换函数
vectorizer = TfidfVectorizer()
# 词条化以及创建词汇表
vectorizer.fit(text)
# 总结
print(vectorizer.vocabulary_)
print(vectorizer.idf_)
# 编码文档
vector = vectorizer.transform([text[0]])
# 总结编码文档
print(vector.shape)
print(vector.toarray())