from sklearn.naive_bayes import BernoulliNB,MultinomialNB from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split import pandas as pd path = 'E:/Python_file/zuoye/SMSSpamCollection.txt' Cnames=['labels','messages'] data = pd.read_csv(path,sep='\t', header=None, names=Cnames) #读取数据集，分隔符是\t data=data.replace({'ham':0,'spam':1}) #替换标签值 print('数据集展示：') print(data) print('\n----------------------------------\n') X=data['messages'] y=data['labels'] x_train,x_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=123) vector_nomial=CountVectorizer() #实现词袋模型 vector_bernou=CountVectorizer() #多项式模型分类垃圾短信 train_matrix=vector_nomial.fit_transform(x_train) test_matrix=vector_nomial.transform(x_test) polynomial=MultinomialNB() clm_nomial=polynomial.fit(train_matrix,y_train) result_nomial=clm_nomial.predict(test_matrix) #伯努利模型分类垃圾短信 train_matrix=vector_bernou.fit_transform(x_train) test_matrix=vector_bernou.transform(x_test) Bernoulli=BernoulliNB() clm_bernoulli=Bernoulli.fit(train_matrix,y_train) result_bernou=clm_bernoulli.predict(test_matrix) print('多项式模型的预测结果,类型，长度：') print(result_nomial,type(result_nomial),result_nomial.shape) print('多项式模型的前一百个预测结果：') print(result_nomial[0:100]) print('多项式模型模型R²评分：'+ str(clm_nomial.score(test_matrix,y_test))) print('\n----------------------------------\n') print('伯努利模型的预测结果,类型，长度：') print(result_bernou,type(result_bernou),result_bernou.shape) print('伯努利模型的前一百个预测结果：') print(result_bernou[0:100]) print('伯努利模型R²评分：'+ str(clm_bernoulli.score(test_matrix,y_test)))

Bayes2.rar_bayes2_matlab naive bayes_朴素贝叶斯_贝叶斯_贝叶斯算法

Na_ve.rar_JAVA NAIVE bayes_NA_bayes

ApMl.rar_CRAWL_classify Bayes_naive bayes_naive bayes for web

ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning ... Classification Algorithms include Naive Bayes, KNN

import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import GaussianNB from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer # df1是经过预处理后的数据集，包含content_clean和category两列 X = df1['content_clean'] y = df1['category'] # 将数据集划分为训练集和测试集 train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2,) # 向量化文本 vectorizer = TfidfVectorizer() train_vectors = vectorizer.fit_transform(train_X) test_vectors = vectorizer.transform(test_X) # X_dense = X.toarray() classifier=GaussianNB().fit(train_vectors, train_labels)

from sklearn.model_selection import train_test_split # df1是经过预处理后的数据集，包含content_clean和category两列 X = df1['content_clean'] y = df1['category'] # 将数据集划分为训练集和测试集 ...

能否优化以下程序，使用test.csv和train.csv,程序如下:import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 df = pd.read_csv('news.csv', encoding='utf-8', error_bad_lines=False) # 删除无效行 df.dropna(inplace=True) # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['content'], df['category'], test_size=0.2) # 特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) # 训练模型 model = MultinomialNB() model.fit(X_train, y_train) # 测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 train_df = pd.read_csv('...

能否优化以下程序import pandas as pd from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # 读取数据集 df = pd.read_csv('news_dataset.csv', error_bad_lines=False) # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['label'], test_size=0.2) # 将文本转换为数字向量 vectorizer = CountVectorizer() X_train_vec = vectorizer.fit_transform(X_train) X_test_vec = vectorizer.transform(X_test) # 训练朴素贝叶斯分类器 classifier = MultinomialNB() classifier.fit(X_train_vec, y_train) # 预测测试集 y_pred = classifier.predict(X_test_vec) # 计算准确率 accuracy = (y_pred == y_test).sum() / y_test.shape[0] print(f'Accuracy: {accuracy}')

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB # 读取数据集，指定数据类型 df = pd.read_csv('news_dataset.csv', error_bad_lines=False, dtype...

修改代码，使用其他方式跳过错误行，import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB #加载 CSV 文件并忽略错误行 train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False) test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False) #删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) #划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df #特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) #训练模型 model = MultinomialNB() model.fit(X_train, y_train) #测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB #加载 CSV 文件并忽略错误行 try: ...

import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False) test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False) # 删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) # 划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df['category'] # 特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) # 训练模型 model = MultinomialNB() model.fit(X_train, y_train) # 测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)修改以上代码，使其使用error_bad_lines=False

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件并忽略错误行 train_df ...

修改下列代码，替换参数 error_bad_lines，代码为：import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False) test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False) # 删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) # 划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df['category'] # 特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) # 训练模型 model = MultinomialNB() model.fit(X_train, y_train) # 测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 train_df = pd.read_...

优化import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB #加载 CSV 文件并忽略错误行 try: train_df = pd.read_csv('train.csv', encoding='utf-8') except: pass try: test_df = pd.read_csv('test.csv', encoding='utf-8') except: pass #删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) #划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df #特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) #训练模型 model = MultinomialNB() model.fit(X_train, y_train) #测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件并忽略错误行 with ...

详细分析代码”import re from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB class TextClassifier(): def init(self, classifier=MultinomialNB()): self.classifier = classifier self.vectorizer = CountVectorizer(analyzer='word' ,ngram_range=(1,4) ,max_features=20000) def features(self, X): return self.vectorizer.transform(X) def fit(self, X, y): self.vectorizer.fit(X) self.classifier.fit(self.features(X), y) def predict(self, x): return self.classifier.predict(self.features([x])) def score(self, X, y): return self.classifier.score(self.features(X), y)“并添加注释，每段代码的作用及蚕食代表什么

from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 定义文本分类器类 class ...

修改代码，使用skiprows参数，跳过包含错误行的行：import pandas as pdfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.model_selection import train_test_splitfrom sklearn.naive_bayes import MultinomialNB# 加载 CSV 文件train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False)test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False)# 删除无效行train_df.dropna(inplace=True)test_df.dropna(inplace=True)# 划分训练集和测试集X_train = train_df['content']y_train = train_df['category']X_test = test_df['content']y_test = test_df['category']# 特征提取vectorizer = TfidfVectorizer()X_train = vectorizer.fit_transform(X_train)X_test = vectorizer.transform(X_test)# 训练模型model = MultinomialNB()model.fit(X_train, y_train)# 测试模型score = model.score(X_test, y_test)print('Accuracy:', score)

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件，跳过错误行 train_df = ...

详细分析代码“from sklearn.cross_validation import StratifiedKFold from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score,precision_score #from sklearn.model_selection import train_test_split x,y=zip(*sentences) from sklearn.feature_extraction.text import CountVectorizer vec = CountVectorizer( analyzer='word', # tokenise by character ngrams ngram_range=(1,4), # use ngrams of size 1 and 2 max_features=20000, # keep the most common 1000 ngrams ) vec.fit(x) def stratifiedkfold_cv(x,y,clf_class,shuffle=True,n_folds=5,kwargs): stratifiedk_fold = StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle) y_pred = y[:] for train_index, test_index in stratifiedk_fold: X_train, X_test = x[train_index], x[test_index] y_train = y[train_index] clf = clf_class(kwargs) clf.fit(X_train,y_train) y_pred[test_index] = clf.predict(X_test) return y_pred NB = MultinomialNB print(precision_score(y ,stratifiedkfold_cv(vec.transform(x) ,np.array(y),NB) , average='macro'))”并添加注释，每段代码的作用，参数代表什么

from sklearn.feature_extraction.text import CountVectorizer vec = CountVectorizer( analyzer='word', # 按照单词进行分割 ngram_range=(1,4), # 使用1~4个单词的组合方式 max_features=20000, # 保留最...

帮我优化下面程序import pandas as pd from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB # 读取训练数据集 train_df = pd.read_csv('train.csv') # 读取测试数据集 test_df = pd.read_csv('test.csv') # 将文本数据转换成向量形式 vectorizer = CountVectorizer() train_vectors = vectorizer.fit_transform(train_df['text']) test_vectors = vectorizer.transform(test_df['text']) # 使用朴素贝叶斯分类器进行分类 classifier = MultinomialNB() classifier.fit(train_vectors, train_df['label']) # 对测试数据集进行预测 predictions = classifier.predict(test_vectors) # 输出预测结果 for i, prediction in enumerate(predictions): print(f"Prediction for news {i+1}: {prediction}")，让它复杂点

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.ensemble import VotingClassifier from sklearn.pipeline import Pipeline from ...

import pandas as pd import numpy as np from csv import reader from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_split filename = 'D:/111/20news-18828.tar/20newsgroups.srv' with open(filename, 'rt', encoding='UTF-8') as file: A = file.readlines() data = np.array(A) df = pd.DataFrame(data, columns=['text']) # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42) # 对文本进行特征提取 tfidf = TfidfVectorizer(stop_words='english') X_train_tfidf = tfidf.fit_transform(X_train.astype(str)) X_test_tfidf = tfidf.transform(X_test.astype(str)) # 使用多项式朴素贝叶斯模型进行训练和预测 clf = MultinomialNB() clf.fit(X_train_tfidf, y_train) y_pred = clf.predict(X_test_tfidf.toarray()) # 输出模型的准确率 print('Accuracy:', accuracy_score(y_test, y_pred))是否有错误

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_...

import pandas as pdimport numpy as npfrom csv import readerfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import accuracy_scorefrom sklearn.model_selection import train_test_splitfilename = input("请输入文件名：")with open(filename, 'rt', encoding='UTF-8') as file: A = file.readlines()data = np.array(A)df = pd.DataFrame(data, columns=['text'])# 分割数据集为训练集和测试集X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42)# 对文本进行特征提取tfidf = TfidfVectorizer(stop_words='english')X_train_tfidf = tfidf.fit_transform(X_train.astype(str))X_test_tfidf = tfidf.transform(X_test.astype(str))# 使用多项式朴素贝叶斯模型进行训练和预测clf = MultinomialNB()clf.fit(X_train_tfidf, y_train)y_pred = clf.predict(X_test_tfidf.toarray())# 输出模型的准确率print('Accuracy:', accuracy_score(y_test, y_pred))如何修改能导入我计算机上的文件

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_...

给出这段用于电影评论分类代码的详细思路“from sklearn.cross_validation import StratifiedKFold from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score,precision_score #from sklearn.model_selection import train_test_split x,y=zip(*sentences) from sklearn.feature_extraction.text import CountVectorizer vec = CountVectorizer( analyzer='word', # tokenise by character ngrams ngram_range=(1,4), # use ngrams of size 1 and 2 max_features=20000, # keep the most common 1000 ngrams ) vec.fit(x) def stratifiedkfold_cv(x,y,clf_class,shuffle=True,n_folds=5,kwargs): stratifiedk_fold = StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle) y_pred = y[:] for train_index, test_index in stratifiedk_fold: X_train, X_test = x[train_index], x[test_index] y_train = y[train_index] clf = clf_class(kwargs) clf.fit(X_train,y_train) y_pred[test_index] = clf.predict(X_test) return y_pred NB = MultinomialNB print(precision_score(y ,stratifiedkfold_cv(vec.transform(x) ,np.array(y),NB) , average='macro'))”分析每一部分代码的作用，给出整体思路框架

这段代码是用于电影评论分类的，主要使用了sklearn库中的StratifiedKFold、MultinomialNB和CountVectorizer等模块。具体思路如下： 1. 从sklearn中导入需要的模块，包括StratifiedKFold、MultinomialNB和...

Visual Studio Code能否import pandas as pdimport numpy as npfrom sklearn.feature_extraction.text import TfidfVectorizerfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import accuracy_scorefrom sklearn.model_selection import train_test_splitfilename = 'D:/111/20news-18828.tar/20newsgroups.srv'with open(filename, 'r', encoding='UTF-8') as file: A = file.readlines()data = []for line in A: category, text = line.split('\t') data.append((category, text))df = pd.DataFrame(data, columns=['category', 'text'])# 分割数据集为训练集和测试集X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42)# 对文本进行特征提取tfidf = TfidfVectorizer(stop_words='english')X_train_tfidf = tfidf.fit_transform(X_train.astype(str))X_test_tfidf = tfidf.transform(X_test.astype(str))# 使用多项式朴素贝叶斯模型进行训练和预测clf = MultinomialNB()clf.fit(X_train_tfidf, y_train)y_pred = clf.predict(X_test_tfidf)# 输出模型的准确率print('Accuracy:', accuracy_score(y_test, y_pred))显示结果

Visual Studio Code可以import pandas as pd、import numpy as np、from sklearn.feature_extraction.text import TfidfVectorizer、from sklearn.naive_bayes import MultinomialNB、from sklearn.metrics import ...

from sklearn.datasets import fetch_20news18828 from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB def bayesian_demo(): ''' 朴素贝叶斯-文本分类 :return: ''' # 1. 获取数据 news = fetch_20news18828(subset='all') # 2. 划分数据集 x_train, x_test, y_train, y_test = train_test_split(news.data, news.target, test_size=0.2) # 3. 特征工程 transfer = TfidfVectorizer() 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 # 5.1 直接对比真实值、预测值 y_predict = estimator.predict(x_test) print('y_predic:\n', y_predict) print('直接对比真实值与预测值：\n', y_test == y_predict) # 5.2 计算准确率 score = estimator.score(x_test,y_test) print('准确率为：\n', score) if name == 'main': bayesian_demo()

2. 使用train_test_split函数将数据集划分为训练集和测试集。 3. 使用TfidfVectorizer类将文本转换为TF-IDF特征向量。 4. 创建MultinomialNB类的实例，用fit函数对训练集进行拟合。 5. 对测试集进行预测，使用score...

import pandas as pd df = pd.read_csv(filename, header=None, names=['category', 'text']) from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score # 读取新闻文本数据 from csv import readerimport numpy as np filename = input("请输入文件名：") with open(filename,'rt',encoding = 'UTF-8') as file: readers = reader(raw_data,delimiter=',') A = list(file) data = np.array(A)print(data) df = pd.read_csv('20news-18828') # 分割数据集为训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(df['text'], df['category'], random_state=42) # 对文本进行特征提取 tfidf = TfidfVectorizer(stop_words='english') X_train = X_train.astype(str) X_test = X_test.astype(str) # 使用多项式朴素贝叶斯模型进行训练和预测 clf = MultinomialNB() clf.fit(X_train_tfidf, y_train) y_pred = clf.predict(X_test_tfidf.toarray()) # 输出模型的准确率 print('Accuracy:', accuracy_score(y_test, y_pred))是否有问题

from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import accuracy_score from sklearn.model_selection import train_test_...

相关推荐

Bayes2.rar_bayes2_matlab naive bayes_朴素贝叶斯_贝叶斯_贝叶斯算法

Na_ve.rar_JAVA NAIVE bayes_NA_bayes

ApMl.rar_CRAWL_classify Bayes_naive bayes_naive bayes for web

最新推荐

Java 员工管理系统项目源代码（可做毕设项目参考）

CAD实验报告：制药车间动力控制系统图、烘烤车间电气控制图、JSJ型晶体管式时间继电器原理图、液位控制器电路图

使用 Arduino 和 Python 实时数据绘图的温度监控系统源码（可做毕设项目参考）

软件测试-软件测试方案pdf

博物馆智能化系统的解决方案.pptx

zigbee-cluster-library-specification

管理建模和仿真的文件

优化MATLAB分段函数绘制：提升效率，绘制更快速

SDN如何实现简易防火墙

JSBSim Reference Manual