从文档集中提取TF-IDF值的降序排位前200的词汇。

我可以为您提供从文档集中提取TF-IDF值的降序排位前200的词汇，您需要使用Python中的sklearn库来实现代码。以下是实现代码片段：

from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

docs = ["This is the first document", "This document is the second document", "And this is the third one", "Is this the first document"]

tfidf = TfidfVectorizer()
tfidf.fit(docs)

feature_names = tfidf.get_feature_names()
dense = tfidf.transform(docs).todense()

denselist = dense.tolist()
df = pd.DataFrame(denselist, columns=feature_names)

top_200 = list(df.mean().nlargest(200).index)

print(top_200)

以上代码可以输出从文档集中提取TF-IDF值的降序排位前200的词汇列表。

相关问题

从文档集中提取TF-IDF值的降序排位前200的词汇

好的，以下是提取TF-IDF值降序排位前200的词汇的Python代码：

import os
import math
from collections import Counter

# 计算TF值
def compute_tf(word_freq, doc_words):
    tf_dict = {}
    doc_word_count = len(doc_words)
    for word, freq in word_freq.items():
        tf_dict[word] = freq / float(doc_word_count)
    return tf_dict

# 计算IDF值
def compute_idf(doc_list):
    idf_dict = {}
    n = len(doc_list)

    # 统计每个词在多少篇文档中出现过
    idf_dict = dict.fromkeys(doc_list[0].keys(), 0)
    for doc in doc_list:
        for word, count in doc.items():
            if count > 0:
                idf_dict[word] += 1

    # 计算每个词的IDF值
    for word, count in idf_dict.items():
        idf_dict[word] = math.log(n / float(count))

    return idf_dict

# 计算TF-IDF值
def compute_tfidf(tf_dict, idf_dict):
    tfidf_dict = {}
    for word, tf in tf_dict.items():
        tfidf_dict[word] = tf * idf_dict[word]
    return tfidf_dict

# 读取文档集
doc_list = []
for filename in os.listdir('./docs'):
    with open(os.path.join('./docs', filename), 'r') as f:
        doc_words = f.read().lower().split()
        word_freq = dict(Counter(doc_words))
        doc_list.append(word_freq)

# 计算每个文档中每个词的TF-IDF值
tfidf_list = []
for doc in doc_list:
    tf_dict = compute_tf(doc, doc_words)
    idf_dict = compute_idf(doc_list)
    tfidf_dict = compute_tfidf(tf_dict, idf_dict)
    tfidf_list.append(tfidf_dict)

# 统计每个词在整个文档集中的TF-IDF值
word_tfidf = {}
for tfidf_dict in tfidf_list:
    for word, tfidf in tfidf_dict.items():
        if word in word_tfidf:
            word_tfidf[word] += tfidf
        else:
            word_tfidf[word] = tfidf

# 将TF-IDF值降序排列并输出前200个词汇
sorted_word_tfidf = sorted(word_tfidf.items(), key=lambda x: x[1], reverse=True)
for word, tfidf in sorted_word_tfidf[:200]:
    print(word, tfidf)

需要注意的是，这段代码假设文档集都存储在`./docs`目录下，每个文档都是纯文本文件。你需要根据自己的情况修改相应的路径和文件格式。

从文档集中提取TF-IDF值的降序排位前200的词汇。用python代码如何实现

可以使用Python的sklearn库中的TfidfVectorizer实现。具体代码如下：

from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd

# 文档集合
docs = [
    "This is the first document",
    "This is the second document",
    "And this is the third one",
    "Is this the first document?"
]

# 使用TfidfVectorizer得到文档中词汇的TF-IDF值
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(docs)

# 得到所有单词对应的tf-idf值，并将其存为列表
features = vectorizer.get_feature_names()
df = pd.DataFrame(X.toarray(), columns=features)

# 将各单词的tf-idf值求和排序，得到前200个单词
top_words = df.sum(axis=0).sort_values(ascending=False)[:200].index.tolist()
print("Top 200 words by TF-IDF:")
print(', '.join(top_words))

此代码将得到文档集中提取TF-IDF值的降序排位前200的词汇。