1，大作业
（1）读取清洗分词
import os
import numpy as np
import sys
from datetime import datetime
import gc
#倒入替换路径
path = r'C:\Users\WZZ\Desktop\147'
#倒入结巴
import jieba
#导入停用词
with open(r'F:\数据挖掘\stopsCN.txt', encoding='utf-8') as f:
stopwords = f.read().split('\n')
#print(stopwords.shape)#查看停用的字符数量
# for w in stopwords:#查看stopwords文件数据
# print(w)
#文本预处理
def processing(tokens):
tokens = "".join([char for char in tokens if char.isalpha()])# 去掉非字母汉字的字符
tokens = [token for token in jieba.cut(tokens, cut_all=True) if len(token) >= 2]#结巴分词
tokens = " ".join([token for token in tokens if token not in stopwords])# 去掉停用词
return tokens
tokenList = []
targetList = []
for root, dirs, files in os.walk(path):
# print(root)#地址
# print(dirs)#子目录
# print(files)#详细文件名
for f in files:
filePath = os.path.join(root, f)#地址拼接
with open(filePath, encoding='utf-8') as f:
content = f.read()
#获取类别标签
target = filePath.split('\\')[-2]
targetList.append(target)
tokenList.append(processing(content))
print( tokenList)
（2）预测评估
#建模
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
x_train, x_test, y_train, y_test = train_test_split(tokenList, targetList, test_size=0.3, stratify=targetList)
#转化特征向量
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)
from sklearn.naive_bayes import MultinomialNB
#贝叶斯预测种类
mnb = MultinomialNB()
module = mnb.fit(X_train, y_train)
y_predict = module.predict(X_test)
scores = cross_val_score(mnb, X_test, y_test, cv=5)
print("验证结果:%.3f" % scores.mean())
print("分类结果:\n", classification_report(y_predict, y_test))
（3）.对比
import collections
# 测试集和预测集的各类新闻数量
testCount = collections.Counter(y_test)
predCount = collections.Counter(y_predict)
print('实际：', testCount, '\n', '预测', predCount)
# 建立标签列表，实际结果与预测结果
nameList = list(testCount.keys())
testList = list(testCount.values())
predictList = list(predCount.values())
x = list(range(len(nameList)))
print("类别：", nameList, '\n', "实际：", testList, '\n', "预测：", predictList)
（4）.图像对比
# 画图
import matplotlib.pyplot as plt
from pylab import mpl
mpl.rcParams['font.sans-serif'] = ['FangSong'] # 指定字体
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='r')
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()
2-boston房价预测
# 1.读取数据集
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
data = load_boston
# 2.训练集与测试集划分
x_train, x_test, y_train, y_test = train_test_split(data.data,data.target,test_size=0.3)
#3.线性回归模型：建立13个变量与房价之间的预测模型，并检测模型好坏。
#建立模型
from sklearn.linear_model import LinearRegression
mlr = LinearRegression()
mlr.fit(x_train,y_train)
print('系数',mlr.coef_,"\n截距",mlr.intercept_)
复制代码
#检测模型好坏
from sklearn.metrics import regression
y_predict = mlr.predict(x_test)
print('线性回归模型：')
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))
print("模型的分数：",mlr.score(x_test,y_test))
#4.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
from sklearn.preprocessing import PolynomialFeatures
# 多项式化
poly2 =PolynomialFeatures(degree=2)
x_poly_train = poly2.fit_transform(x_train)
x_poly_test = poly2.transform(x_test)
# 建立模型
mlrp = LinearRegression()
mlrp.fit(x_poly_train, y_train)
# 测模型好坏
y_predict2 = mlrp.predict(x_poly_test)
print("多项式回归模型：")
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))
print("模型的分数：",mlrp.score(x_poly_test,y_test))
#5.比较线性与非线性性能
多项式回归模型误差比线性模型小，而且是一条平滑的曲线，对样本的拟合程度较高
