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hhl_14_python学习之KNN算法.py
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from sklearn.datasets import load_iris, fetch_20newsgroups
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.tree import DecisionTreeClassifier,export_graphviz
from pandas import pandas as pd
def knn_ieis():
"""
用KNN算法对鸢尾花进行分类
return None
"""
# 1 获取数据
iris = load_iris()
# 2 划分数据集 特征值和目标值
x_train,x_test,y_train,y_test =train_test_split(iris.data,iris.target,random_state=22)
# 3特征工程 标准化
# 实例化一个转换器
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
# 为什么这里只要transform
x_test = transfer.transform(x_test)
# KNN 算法预估器
# 找到最近的3个坐标来确定自己的坐标
estimator = KNeighborsClassifier (n_neighbors=3)
estimator.fit(x_train,y_train)
# 5）模型评估
# 方法1：直接比对真实值和预测值
# 用评估器进行预测
y_predict = estimator.predict(x_test)
print("输出预测值", y_predict)
print("直接比对真实值和预测值:\n", y_test == y_predict)
# 方法2：计算准确率
# 利用评估器对特征检验值和目标检验值进行打分,得出来打分率
score = estimator.score(x_test,y_test)
print("准确率为：\n", score)
return None
def knn_iris_gscv():
"""
用KNN算法对鸢尾花进行分类，添加网格搜索和交叉验证
:return:
"""
# 1 获取数据
iris = load_iris()
# 2 进行数据划分
# 训练集和测试集划分
x_train, x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state=22)
# 3 特征工程标准化
# 实例化一个转化器
transfer = StandardScaler()
# 对特征值的训练集进行标准化处理
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4 KNN算法预估器
estimator = KNeighborsClassifier()
# 加入网格搜索与交叉验证
# 参数准备
param_dict = {"n_neighbors": [1, 3, 5, 7, 9, 11]}
# 加入网格搜索
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=10)
# 拟合数据
estimator.fit(x_train, y_train)
# 5 模型评估
# 方法1 ： 直接跟真实值对比
y_pridect = estimator.predict(x_test)
print(y_pridect==y_test)
# 方法2 计算准确率
score = estimator.score(x_test,y_test)
print(score)
# 最佳参数：best_params_
print("最佳参数：\n", estimator.best_params_)
# 最佳结果：best_score_
print("最佳结果：\n", estimator.best_score_)
# 最佳估计器：best_estimator_
print("最佳估计器:\n", estimator.best_estimator_)
# 交叉验证结果：cv_results_
print("交叉验证结果:\n", estimator.cv_results_)
return None
def nb_news():
"""
用朴素贝叶斯算法对新闻进行分类
:return:
"""
# 1 获取数据
news = fetch_20newsgroups(subset="all")
# 2 数据集的划分
x_train, x_test, y_train, y_test = train_test_split(news.data,news.target)
# 3 特征工程：文本特征抽取-tfidf
# 实例化一个转换器类
transfer = TfidfVectorizer()
# 调用fit_transform
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
# 4朴素贝叶斯算法预估器流程
estimator = MultinomialNB()
estimator.fit(x_train,y_train)
# 5 模型评估
# 方法一 预测值和真实值进行对比
y_predict = estimator.predict(x_test)
print("打印预测值", y_predict)
print("打印比较结果", y_predict==y_test)
# 方法2 计算准确率
score = estimator.score(x_test, y_test)
print("打印准确率",score)
return None
def decision_iris():
"""
用决策树对鸢尾花进行分类
:return:None
"""
# 1 获取数据集
iris = load_iris()
# 2 划分数据集
x_train, x_test, y_train, y_test = train_test_split(iris.data,iris.target, random_state= 22)
# 3 决策树预估器
estimator = DecisionTreeClassifier(criterion="entropy")
estimator.fit(x_train,y_train)
# 4 模型评估
# 方法一比较预测值和真实值
y_predict = estimator.predict(x_test)
print("打印预测值",y_predict)
print("对比真实值和预测值",y_predict==y_test)
# 方法2 计算准确率
score = estimator.score(x_test,y_test)
print("打印准确率",score)
# 可视化决策树
export_graphviz(estimator,out_file = "iris_tree.dot",feature_names = iris.feature_names)
return None
if __name__ == "__main__":
# 用KNN算法对鸢尾花进行分类
# knn_ieis()
# 用KNN算法对鸢尾花进行分类,添加网格搜索和交叉验证
# knn_iris_gscv()
# 用朴素贝叶斯算法对新闻进行分类
# nb_news()
# 用决策树对鸢尾花进行分类
decision_iris()