期末大作业

一、boston房价预测

import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

# 读取数据集
boston = load_boston()

print (boston.keys())
print (boston.target) # 房价数据
print (boston.feature_names) # 数据集特征

# 划分训练集与测试集
#随机擦痒25%的数据构建测试样本，剩余作为训练样本
X_train,X_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size = 0.3 ) #random_state：是随机数的种子
print (X_train.shape,y_train.shape)

# 建立模型
LineR = LinearRegression()
LineR.fit(X_train,y_train)

# 检查模型好坏
x_predict = LineR.predict(X_test)
print ( "各列权重" ,LineR.coef_)
print ( "测试集上的评分：" ,LineR.score(X_test, y_test))
print ( "训练集上的评分：" ,LineR.score(X_train, y_train))
print ( "预测的均方误差：" , np.mean(x_predict - y_test) * * 2 )
print ( "最小目标值:" ,np. min (boston.target))
print ( "平均目标值:" ,np.mean(boston.target))

# 画图
X = boston.data[:, 12 ].reshape( - 1 , 1 )
y = boston.target

plt.scatter(X,y)

LineR2 = LinearRegression()
LineR2.fit(X,y)
y_predict = LineR2.predict(X)
plt.plot(X,y_predict, 'r' )
plt.show()

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt

# 读取数据集
boston = load_boston()

# 划分训练集与测试集
#随机擦痒25%的数据构建测试样本，剩余作为训练样本
x_train,x_test,y_train,y_test = train_test_split(boston.data,boston.target,test_size = 0.3 ) #random_state：是随机数的种子

x = x_train[:, 12 ].reshape( - 1 , 1 )
poly = PolynomialFeatures(degree = 2 )
x_poly = poly.fit_transform(x)

# 建立多项式回归模型
lrp = LinearRegression()
lrp.fit(x_poly,y_train)

lr = LinearRegression()
lr.fit(x,y_train)
w = lr.coef_
b = lr.intercept_

# 预测
x_poly2 = poly.transform(x_test[:, 12 ].reshape( - 1 , 1 ))
y_ploy_predict = lrp.predict(x_poly2)

# 画图
plt.scatter(x_test[:, 12 ], y_test)
plt.plot(x, w * x + b, 'g' )
plt.scatter(x_test[:, 12 ], y_ploy_predict, c = 'r' )
plt.show()

二、中文文本分类

#新闻文本分类
import os
import jieba
#读取文件内容

leirong = [] #存放新闻的内容
kinds = [] #存放新闻的类别
def read_txt(path):
    folder_list = os.listdir(path) #遍历data下的文件名
    for file in folder_list:
        new_path = os.path.join(path, file ) #读取文件夹的名称，生成新的路径
        files = os.listdir(new_path) #存放文件的内容
        #遍历每个txt文件
        for f in files:
            with open (os.path.join(new_path,f), 'r' ,encoding = 'UTF-8' )as f: #打开txt文件
                word = f.read()
            leirong.append(processing(word))
            kinds.append( file )
#             print(content)
#             print(label)

#对数据进行预处理
with open (r 'D:/stopsCN.txt' , encoding = 'utf-8' ) as f:
    stopwords = f.read().split( '\n' )
def processing(texts):
    # 去掉非法的字符
    texts = "".join([char for char in texts if char.isalpha()])
    # 用jieba分词
    texts = [text for text in jieba.cut(texts,cut_all = True ) if len (text) > = 2 ]
    # 去掉停用词
    texts = " " .join([text for text in texts if text not in stopwords])
    return texts
if __name__ = = '__main__' :
    path = r 'D:\147'
    read_txt(path)

#划分训练集和测试，用TF-IDF算法进行单词权值的计算
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
tfidf = TfidfVectorizer()
x_train,x_test,y_train,y_test = train_test_split(leirong,kinds,test_size = 0.2 )
X_train = tfidf.fit_transform(x_train) #转换成特征向量
X_test = tfidf.transform(x_test)
#构建贝叶斯模型
from sklearn.naive_bayes import MultinomialNB #用于离散特征分类，文本分类单词统计，以出现的次数作为特征值
mulp = MultinomialNB ()
mulp_NB = mulp.fit(X_train,y_train)
#对模型进行预测
y_predict = mulp.predict(X_test)
# # 从sklearn.metrics里导入classification_report做分类的性能报告
from sklearn.metrics import classification_report
print ( '模型的准确率为：' , mulp.score(X_test, y_test))
print ( 'classification_report:\n' ,classification_report(y_test, y_predict))