python中tfidf = TfidfVectorizer() features = tfidf.fit_transform(df[1])什么意思
时间: 2024-05-24 22:15:50 浏览: 183
这段代码使用了Python中的scikit-learn库中的TfidfVectorizer类,用于将文本数据转换成TF-IDF特征向量。TF-IDF(Term Frequency-Inverse Document Frequency)是一种常用的文本特征提取方法,用于表示文本中每个词的重要程度。具体来说,TF-IDF特征向量表示每个词在文档中的频率乘以该词在整个语料库中的逆文档频率。这里的df[1]是一个包含多个文本字符串的DataFrame列,features是一个稀疏矩阵,表示每个文本的TF-IDF特征向量。
相关问题
import pandas as pd import matplotlib import numpy as np import matplotlib.pyplot as plt import jieba as jb import re from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.feature_selection import chi2 import numpy as np from sklearn.model_selection import train_test_split from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.naive_bayes import MultinomialNB def sigmoid(x): return 1 / (1 + np.exp(-x)) import numpy as np #定义删除除字母,数字,汉字以外的所有符号的函数 def remove_punctuation(line): line = str(line) if line.strip()=='': return '' rule = re.compile(u"[^a-zA-Z0-9\u4E00-\u9FA5]") line = rule.sub('',line) return line def stopwordslist(filepath): stopwords = [line.strip() for line in open(filepath, 'r', encoding='utf-8').readlines()] return stopwords df = pd.read_csv('./online_shopping_10_cats/online_shopping_10_cats.csv') df=df[['cat','review']] df = df[pd.notnull(df['review'])] d = {'cat':df['cat'].value_counts().index, 'count': df['cat'].value_counts()} df_cat = pd.DataFrame(data=d).reset_index(drop=True) df['cat_id'] = df['cat'].factorize()[0] cat_id_df = df[['cat', 'cat_id']].drop_duplicates().sort_values('cat_id').reset_index(drop=True) cat_to_id = dict(cat_id_df.values) id_to_cat = dict(cat_id_df[['cat_id', 'cat']].values) #加载停用词 stopwords = stopwordslist("./online_shopping_10_cats/chineseStopWords.txt") #删除除字母,数字,汉字以外的所有符号 df['clean_review'] = df['review'].apply(remove_punctuation) #分词,并过滤停用词 df['cut_review'] = df['clean_review'].apply(lambda x: " ".join([w for w in list(jb.cut(x)) if w not in stopwords])) tfidf = TfidfVectorizer(norm='l2', ngram_range=(1, 2)) features = tfidf.fit_transform(df.cut_review) labels = df.cat_id X_train, X_test, y_train, y_test = train_test_split(df['cut_review'], df['cat_id'], random_state = 0) count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) 已经写好以上代码,请补全train和test函数
以下是train和test函数的代码:
```python
def train(X_train_tfidf, y_train):
clf = MultinomialNB().fit(X_train_tfidf, y_train)
return clf
def test(clf, X_test):
X_test_counts = count_vect.transform(X_test)
X_test_tfidf = tfidf_transformer.transform(X_test_counts)
y_pred = clf.predict(X_test_tfidf)
return y_pred
```
train函数使用MultinomialNB()方法来拟合特征矩阵和标签,返回训练后的分类器clf。
test函数使用训练好的分类器clf来预测X_test的标签,返回预测结果y_pred。在预测之前,需要使用之前定义的count_vect和tfidf_transformer对X_test进行向量化和tf-idf转换。
英文文本词汇tfidf特征值的完整代码
以下是一个简单的Python代码示例,用于计算英文文本数据中每个单词的tfidf特征值:
```
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
# 读取文本数据
text_data = pd.read_csv('text_data.csv')
# 创建TfidfVectorizer对象
tfidf_vectorizer = TfidfVectorizer()
# 计算tfidf特征值
tfidf_features = tfidf_vectorizer.fit_transform(text_data['text'])
# 将tfidf特征值转换为DataFrame格式
tfidf_df = pd.DataFrame(tfidf_features.todense(), columns=tfidf_vectorizer.get_feature_names())
# 输出tfidf特征值
print(tfidf_df)
```
在此示例中,我们首先使用pandas库读取存储英文文本数据的CSV文件。然后,我们创建一个TfidfVectorizer对象并使用它来计算文本数据中每个单词的tfidf特征值。最后,我们将tfidf特征值转换为DataFrame格式,并将其打印出来。
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