大作业

一.

#导入os包加载数据目录
import os
path = r'F:\迅雷下载\258'
#停词库
with open(r'F:\迅雷下载\stopsCN.txt', encoding='utf-8') as f:
stopwords = f.read().split('\n')

 

#对数据进行标准编码处理(encoding='utf-8')
import codecs
import jieba
#存放文件名
filePaths = []
#存放读取的数据
fileContents = []
#存放文件类型
fileClasses = []
#进行遍历实现转码读取处理并对每条新闻进行切分
for root, dirs, files in os.walk(path):
for name in files:
filePath = os.path.join(root, name)
filePaths.append(filePath)
fileClasses.append(filePath.split('\\')[2])
f = codecs.open(filePath, 'r', 'utf-8')
fileContent = f.read()
fileContent = fileContent.replace('\n','')
tokens = [token for token in jieba.cut(fileContent)]
tokens = " ".join([token for token in tokens if token not in stopwords])
f.close()
fileContents.append(tokens)

import pandas
all_datas = pandas.DataFrame({
'fileClass': fileClasses,
'fileContent': fileContents
})
print(all_datas)

 

二.

#回归模型预测波士顿房价
#导入load_boston数据
from sklearn.datasets import load_boston
data = load_boston()
#多元线性回归模型
from sklearn.model_selection import train_test_split

# 训练集与测试集划分为7:3
x_train,x_test,y_train,y_test = train_test_split(data.data,data.target,test_size=0.3)
print(x_train.shape,y_train.shape)

#线性回归模型：建立13个变量与房价之间的预测模型，并检测模型好
#线性回归模型公式：y=^bx+^a
from sklearn.linear_model import LinearRegression
mlr = LinearRegression()
mlr.fit(x_train,y_train)
print('系数b',mlr.coef_,"\n截距a",mlr.intercept_)

#检测模型的好坏
from sklearn.metrics import regression
y_predict = mlr.predict(x_test)
#计算模型的预测指标
print('线性回归模型判断指数')
print("预测的均方误差：",regression.mean_squared_error(y_test,y_predict))
print("预测的平均绝对误差：",regression.mean_absolute_error(y_test,y_predict))
#打印模型分数
print("模型的分数：",mlr.score(x_test,y_test))

#多项式回归模型：建立13个变量与房价之间的预测模型，并检测模型好坏。
# 多项式回归模型公式y = a0 + a1 * x + a2 * (x**2) + ... + an * (x ** n) + e
from sklearn.preprocessing import PolynomialFeatures
#多项式的训练集与测试集
poly2 =PolynomialFeatures(degree=2)
x_poly_train = poly2.fit_transform(x_train)
x_poly_test = poly2.transform(x_test)

#多项回归模型
mlrp=LinearRegression()
mlrp.fit(x_poly_train,y_train)

#预测值
y_predict2 = mlrp.predict(x_poly_test)
#检测模型预测指数的好坏
print("多项式回归模型判断指数")
print("预测的均方误差：",regression.mean_squared_error(y_test,y_predict2))
print("预测平均绝对误差：",regression.mean_absolute_error(y_test,y_predict2))
#打印模型分数
print("模型的分数：",mlrp.score(x_poly_test,y_test))

 

三.

import os
#读取文件
all_txt=[]
all_target=[]
path = r'F:\\迅雷下载\\258'
files = os.listdir(path)
import jieba
with open(r'F:\迅雷下载\stopsCN.txt', encoding='utf-8') as f:
stopwords = f.read().split('\n')
def processing(tokens):
# 去掉非字母汉字的字符
tokens = "".join([char for char in tokens if char.isalpha()])
# 结巴分词
tokens = [token for token in jieba.cut(tokens,cut_all=True) if len(token) >=2]
# 去掉停用词
tokens = " ".join([token for token in tokens if token not in stopwords])
return tokens
for root,dirs,files in os.walk(path):
for file in files:
filepath = os.path.join(root, file) # 文件路径
tokens=open(filepath,'r',encoding='utf-8').read()
tokens=processing(tokens)
all_txt.append(tokens)
target = filepath.split('\\')[-2]#按文件夹获取特征名
all_target.append(target)
#按0.7:0.3比例分为训练集和测试集
from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test=train_test_split(all_txt,all_target,test_size=0.3,stratify=all_target)
#将其向量化
from sklearn.feature_extraction.text import TfidfVectorizer
vectorizer=TfidfVectorizer()
X_train=vectorizer.fit_transform(x_train)
X_test=vectorizer.transform(x_test)
#分类结果显示
from sklearn.naive_bayes import MultinomialNB
mnb=MultinomialNB()
clf=mnb.fit(X_train,y_train)
#进行预测
y_predict = clf.predict(X_test)
# 输出模型精确度
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
scores=cross_val_score(mnb,X_test,y_test,cv=4)
print("Accuracy:%.3f"%scores.mean())
# 输出模型评估报告
print("classification_report:\n",classification_report(y_predict,y_test))

# #分类结果显示
# from sklearn.metrics import confusion_matrix
# from sklearn.metrics import classification_report
# #x_test预测结果
# y_nb_pred = clf.predict(X_test)
# 将预测结果和实际结果进行对比
import collections
import matplotlib.pyplot as plt
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题

# 统计测试集和预测集的各类新闻个数
testCount = collections.Counter(y_test)
predCount = collections.Counter(y_predict)
print('实际：',testCount,'\n', '预测', predCount)

# 建立标签列表，实际结果列表，预测结果列表，
nameList = list(testCount.keys())
testList = list(testCount.values())
predictList = list(predCount.values())
x = list(range(len(nameList)))
print("新闻类别：",nameList,'\n',"实际：",testList,'\n',"预测：",predictList)

# 画图
plt.figure(figsize=(7,5))
total_width, n = 0.6, 2
width = total_width / n
plt.bar(x, testList, width=width,label='实际',fc = 'blue')
for i in range(len(x)):
x[i] = x[i] + width
plt.bar(x, predictList,width=width,label='预测',tick_label = nameList,fc='pink')
plt.grid()
plt.title('实际和预测对比图',fontsize=17)
plt.xlabel('新闻类别',fontsize=17)
plt.ylabel('频数',fontsize=17)
plt.legend(fontsize =17)
plt.tick_params(labelsize=15)
plt.show()