期末大作业
# 多元线性回归模型 from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.metrics import regression # 波士顿房价数据集 data = load_boston() # 划分数据集 x_train, x_test, y_train, y_test = train_test_split(data.data,data.target,test_size=0.3) # 建立多元线性回归模型 mlr = LinearRegression() mlr.fit(x_train,y_train)#学习 print('系数',mlr.coef_,"\n截距",mlr.intercept_) # 检测模型好坏 y_predict = mlr.predict(x_test)#预测 # 计算模型的预测指标 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)) # 打印模型的分数 print("模型的分数:",mlr.score(x_test, y_test)) # 多元多项式回归模型 # 多项式化 poly2 = PolynomialFeatures(degree=2) x_poly_train = poly2.fit_transform(x_train)#先拟合数据,然后转化它将其转化为标准形式 x_poly_test = poly2.transform(x_test)#通过找中心和缩放等实现标准化 # 建立模型 mlrp = LinearRegression() mlrp.fit(x_poly_train, y_train) # 预测 y_predict2 = mlrp.predict(x_poly_test) # 检测模型好坏 # 计算模型的预测指标 print("预测的均方误差:", regression.mean_squared_error(y_test,y_predict2)) print("预测的平均绝对误差:", regression.mean_absolute_error(y_test,y_predict2)) # 打印模型的分数 print("模型的分数:",mlrp.score(x_poly_test, y_test))
#导入os包加载数据目录 import os path = r'D:\shuju' #停词库 with open(r'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):#用os.walk获取需要的变量,并拼接文件路径再打开每一个文件 for name in files: filePath = os.path.join(root, name)#将路径和文件串起来 filePaths.append(filePath)#添加数据到外部容器 fileClasses.append(filePath.split('\\')[2]) f = codecs.open(filePath, 'r', 'utf-8')#获取新闻类别标签,并处理该新闻 fileContent = f.read() fileContent = fileContent.replace('\n','')#去除转行符 tokens = [token for token in jieba.cut(fileContent)] tokens = " ".join([token for token in tokens if token not in stopwords])#去除停用词 f.close() fileContents.append(tokens)#添加关键字
import pandas; all_datas = pandas.DataFrame({ 'fileClass': fileClasses, 'fileContent': fileContents }) print(all_datas)
str='' for i in range(len(fileContents)): str+=fileContents[i] #TF-IDF算法 #统计词频 import jieba.analyse keywords = jieba.analyse.extract_tags(str, topK=20, withWeight=True, allowPOS=('n','nr','ns')) print(keywords )
from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer x_train,x_test,y_train,y_test = train_test_split(fileContents,fileClasses,test_size=0.3,random_state=0,stratify=fileClasses) vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(x_train) X_test = vectorizer.transform(x_test) from sklearn.naive_bayes import MultinomialNB clf= MultinomialNB().fit(X_train,y_train) y_nb_pred=clf.predict(X_test) #分类结果显示 from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report print('nb_confusion_matrix:') print(y_nb_pred.shape,y_nb_pred)#x_test预测结果 cm=confusion_matrix(y_test,y_nb_pred)#混淆矩阵 print('nb_classification_report:') print(cm)
from sklearn.feature_extraction.text import TfidfVectorizer 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(fileContents,fileClasses,test_size=0.2,stratify=fileClasses) # 转化为特征向量,这里选择TfidfVectorizer的方式建立特征向量。不同新闻的词语使用会有较大不同。 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(x_train) X_test = vectorizer.transform(x_test) # 建立模型,这里用多项式朴素贝叶斯,因为样本特征的a分布大部分是多元离散值 mnb = MultinomialNB() module = mnb.fit(X_train, y_train) #进行预测 y_predict = module.predict(X_test) # 输出模型精确度 scores=cross_val_score(mnb,X_test,y_test,cv=5) print("Accuracy:%.3f"%scores.mean()) # 输出模型评估报告 print("classification_report:\n",classification_report(y_predict,y_test))