{"id":1048388,"date":"2024-12-31T13:50:46","date_gmt":"2024-12-31T05:50:46","guid":{"rendered":""},"modified":"2024-12-31T13:50:48","modified_gmt":"2024-12-31T05:50:48","slug":"python%e5%a6%82%e4%bd%95%e5%81%9a%e6%90%9c%e7%b4%a2%e5%bc%95%e6%93%8e","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1048388.html","title":{"rendered":"Python\u5982\u4f55\u505a\u641c\u7d22\u5f15\u64ce"},"content":{"rendered":"

\"Python\u5982\u4f55\u505a\u641c\u7d22\u5f15\u64ce\"<\/p>\n

Python\u5982\u4f55\u505a\u641c\u7d22\u5f15\u64ce<\/strong><\/p>\n<\/p>\n

\u5728\u4f7f\u7528Python\u521b\u5efa\u641c\u7d22\u5f15\u64ce\u65f6\uff0c\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u3001\u4f7f\u7528\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u3001\u5b9e\u73b0\u722c\u866b\u529f\u80fd\u3001\u8fdb\u884c\u6587\u672c\u5904\u7406\u548c\u5206\u6790\u3001\u5efa\u7acb\u7d22\u5f15\u3001\u63d0\u4f9b\u67e5\u8be2\u63a5\u53e3\u3001\u8bc4\u4f30\u548c\u4f18\u5316\u6027\u80fd<\/strong>\u662f\u5173\u952e\u6b65\u9aa4\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u6b65\u9aa4\uff0c\u5e76\u63d0\u4f9b\u4e00\u4e9b\u5b9e\u7528\u7684\u5efa\u8bae\u548c\u6280\u5de7\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5<\/h3>\n<\/p>\n

\u641c\u7d22\u5f15\u64ce\u7684\u6838\u5fc3\u5728\u4e8e\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\uff0c\u8fd9\u76f4\u63a5\u5f71\u54cd\u641c\u7d22\u7ed3\u679c\u7684\u51c6\u786e\u6027\u548c\u6027\u80fd\u3002\u5e38\u89c1\u7684\u7b97\u6cd5\u5305\u62ec\u5e03\u5c14\u641c\u7d22\u3001\u5411\u91cf\u7a7a\u95f4\u6a21\u578b\u548cPageRank\u7b97\u6cd5\u7b49\u3002<\/p>\n<\/p>\n

\u5e03\u5c14\u641c\u7d22<\/h4>\n<\/p>\n

\u5e03\u5c14\u641c\u7d22\u4f7f\u7528\u5e03\u5c14\u903b\u8f91\uff08AND, OR, NOT\uff09\u6765\u7ec4\u5408\u67e5\u8be2\u8bcd\uff0c\u4ece\u800c\u5b9e\u73b0\u7b80\u5355\u7684\u641c\u7d22\u529f\u80fd\u3002\u5b83\u7684\u4f18\u70b9\u662f\u5b9e\u73b0\u7b80\u5355\uff0c\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u7684\u6570\u636e\u96c6\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u7b80\u5355\u7684\u5e03\u5c14\u641c\u7d22\u5b9e\u73b0\u4f8b\u5b50\uff1a<\/p>\n<\/p>\n

def boolean_search(documents, query):<\/p>\n
    query_words = query.lower().split()<\/p>\n
    results = []<\/p>\n
    for doc in documents:<\/p>\n
        if all(word in doc.lower() for word in query_words):<\/p>\n
            results.append(doc)<\/p>\n
    return results<\/p>\n
documents = ["Python is great", "Python is a snake", "I love programming in Python"]<\/p>\n
query = "Python is"<\/p>\n
print(boolean_search(documents, query))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5411\u91cf\u7a7a\u95f4\u6a21\u578b<\/h4>\n<\/p>\n
\u5411\u91cf\u7a7a\u95f4\u6a21\u578b\uff08Vector Space Model, VSM\uff09\u5c06\u6587\u6863\u548c\u67e5\u8be2\u8868\u793a\u4e3a\u5411\u91cf\uff0c\u901a\u8fc7\u8ba1\u7b97\u5411\u91cf\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\uff08\u5982\u4f59\u5f26\u76f8\u4f3c\u5ea6\uff09\u6765\u5224\u65ad\u6587\u6863\u7684\u76f8\u5173\u6027\u3002\u5b9e\u73b0\u5411\u91cf\u7a7a\u95f4\u6a21\u578b\u65f6\uff0c\u9700\u8981\u8fdb\u884c\u6587\u672c\u9884\u5904\u7406\uff08\u5982\u5206\u8bcd\u3001\u53bb\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\uff09\u548cTF-IDF\u8ba1\u7b97\u3002<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
from sklearn.metrics.pAI<\/a>rwise import cosine_similarity<\/p>\n
def vector_space_search(documents, query):<\/p>\n
    vectorizer = TfidfVectorizer()<\/p>\n
    tfidf_matrix = vectorizer.fit_transform(documents + [query])<\/p>\n
    cosine_similarities = cosine_similarity(tfidf_matrix[-1], tfidf_matrix[:-1])<\/p>\n
    return cosine_similarities.argsort()[0][::-1]<\/p>\n
documents = ["Python is great", "Python is a snake", "I love programming in Python"]<\/p>\n
query = "Python programming"<\/p>\n
print(vector_space_search(documents, query))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784<\/h3>\n<\/p>\n
\u9009\u62e9\u5408\u9002\u7684\u6570\u636e\u7ed3\u6784\u53ef\u4ee5\u63d0\u9ad8\u641c\u7d22\u5f15\u64ce\u7684\u6027\u80fd\u3002\u5e38\u89c1\u7684\u6570\u636e\u7ed3\u6784\u5305\u62ec\u5012\u6392\u7d22\u5f15\u3001B\u6811\u548c\u54c8\u5e0c\u8868\u7b49\u3002<\/p>\n<\/p>\n
\u5012\u6392\u7d22\u5f15<\/h4>\n<\/p>\n
\u5012\u6392\u7d22\u5f15\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6570\u636e\u7ed3\u6784\uff0c\u7528\u4e8e\u5feb\u901f\u67e5\u627e\u5305\u542b\u67d0\u4e2a\u8bcd\u7684\u6587\u6863\u3002\u5b83\u7531\u4e00\u4e2a\u8bcd\u5178\u548c\u4e00\u4e2a\u6587\u6863\u5217\u8868\u7ec4\u6210\uff0c\u8bcd\u5178\u5b58\u50a8\u8bcd\u548c\u5bf9\u5e94\u7684\u6587\u6863\u5217\u8868\u3002<\/p>\n<\/p>\n
from collections import defaultdict<\/p>\n
def build_inverted_index(documents):<\/p>\n
    inverted_index = defaultdict(list)<\/p>\n
    for doc_id, doc in enumerate(documents):<\/p>\n
        for word in doc.lower().split():<\/p>\n
            inverted_index[word].append(doc_id)<\/p>\n
    return inverted_index<\/p>\n
documents = ["Python is great", "Python is a snake", "I love programming in Python"]<\/p>\n
inverted_index = build_inverted_index(documents)<\/p>\n
print(inverted_index)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5b9e\u73b0\u722c\u866b\u529f\u80fd<\/h3>\n<\/p>\n
\u722c\u866b\uff08Crawler\uff09\u662f\u641c\u7d22\u5f15\u64ce\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u8d1f\u8d23\u4ece\u4e92\u8054\u7f51\u4e0a\u6536\u96c6\u6570\u636e\u3002Python\u7684requests<\/code>\u548cBeautifulSoup<\/code>\u5e93\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u722c\u866b\u529f\u80fd\u3002<\/p>\n<\/p>\n
import requests<\/p>\n
from bs4 import BeautifulSoup<\/p>\n
def crawl(url):<\/p>\n
    response = requests.get(url)<\/p>\n
    if response.status_code == 200:<\/p>\n
        soup = BeautifulSoup(response.text, 'html.parser')<\/p>\n
        return soup.get_text()<\/p>\n
    return ""<\/p>\n
url = "https:\/\/www.example.com"<\/p>\n
print(crawl(url))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u8fdb\u884c\u6587\u672c\u5904\u7406\u548c\u5206\u6790<\/h3>\n<\/p>\n
\u6587\u672c\u5904\u7406\u548c\u5206\u6790\u662f\u641c\u7d22\u5f15\u64ce\u7684\u57fa\u7840\uff0c\u5305\u62ec\u5206\u8bcd\u3001\u53bb\u505c\u7528\u8bcd\u3001\u8bcd\u5e72\u63d0\u53d6\u7b49\u3002<\/p>\n<\/p>\n
\u5206\u8bcd<\/h4>\n<\/p>\n
\u5206\u8bcd\u662f\u5c06\u6587\u672c\u5206\u89e3\u4e3a\u5355\u8bcd\u6216\u77ed\u8bed\u7684\u8fc7\u7a0b\u3002Python\u7684nltk<\/code>\u5e93\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5206\u8bcd\u5de5\u5177\u3002<\/p>\n<\/p>\n
import nltk<\/p>\n
nltk.download('punkt')<\/p>\n
def tokenize(text):<\/p>\n
    return nltk.word_tokenize(text)<\/p>\n
text = "I love programming in Python"<\/p>\n
print(tokenize(text))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u53bb\u505c\u7528\u8bcd<\/h4>\n<\/p>\n
\u505c\u7528\u8bcd\u662f\u6307\u5bf9\u6587\u672c\u5206\u6790\u65e0\u5173\u7d27\u8981\u7684\u9ad8\u9891\u8bcd\uff0c\u5982\u201cthe\u201d\u3001\u201cis\u201d\u7b49\u3002\u53bb\u505c\u7528\u8bcd\u53ef\u4ee5\u63d0\u9ad8\u641c\u7d22\u5f15\u64ce\u7684\u6027\u80fd\u3002<\/p>\n<\/p>\n
from nltk.corpus import stopwords<\/p>\n
nltk.download('stopwords')<\/p>\n
def remove_stopwords(words):<\/p>\n
    stop_words = set(stopwords.words('english'))<\/p>\n
    return [word for word in words if word not in stop_words]<\/p>\n
words = tokenize("I love programming in Python")<\/p>\n
print(remove_stopwords(words))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u5efa\u7acb\u7d22\u5f15<\/h3>\n<\/p>\n
\u5efa\u7acb\u7d22\u5f15\u662f\u641c\u7d22\u5f15\u64ce\u7684\u91cd\u8981\u6b65\u9aa4\uff0c\u5012\u6392\u7d22\u5f15\u662f\u5e38\u7528\u7684\u7d22\u5f15\u7ed3\u6784\u3002\u901a\u8fc7\u5012\u6392\u7d22\u5f15\uff0c\u53ef\u4ee5\u5feb\u901f\u67e5\u627e\u5305\u542b\u67e5\u8be2\u8bcd\u7684\u6587\u6863\u3002<\/p>\n<\/p>\n
def build_index(documents):<\/p>\n
    index = defaultdict(list)<\/p>\n
    for doc_id, doc in enumerate(documents):<\/p>\n
        words = remove_stopwords(tokenize(doc))<\/p>\n
        for word in words:<\/p>\n
            index[word].append(doc_id)<\/p>\n
    return index<\/p>\n
documents = ["Python is great", "Python is a snake", "I love programming in Python"]<\/p>\n
index = build_index(documents)<\/p>\n
print(index)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u63d0\u4f9b\u67e5\u8be2\u63a5\u53e3<\/h3>\n<\/p>\n
\u63d0\u4f9b\u67e5\u8be2\u63a5\u53e3\u662f\u641c\u7d22\u5f15\u64ce\u7684\u6700\u7ec8\u76ee\u6807\uff0c\u901a\u8fc7\u67e5\u8be2\u63a5\u53e3\uff0c\u7528\u6237\u53ef\u4ee5\u65b9\u4fbf\u5730\u641c\u7d22\u76f8\u5173\u6587\u6863\u3002<\/p>\n<\/p>\n
def search(index, query):<\/p>\n
    query_words = remove_stopwords(tokenize(query))<\/p>\n
    results = set(index[query_words[0]])<\/p>\n
    for word in query_words[1:]:<\/p>\n
        results &= set(index[word])<\/p>\n
    return results<\/p>\n
query = "Python programming"<\/p>\n
print(search(index, query))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e03\u3001\u8bc4\u4f30\u548c\u4f18\u5316\u6027\u80fd<\/h3>\n<\/p>\n
\u8bc4\u4f30\u548c\u4f18\u5316\u6027\u80fd\u662f\u641c\u7d22\u5f15\u64ce\u5f00\u53d1\u7684\u91cd\u8981\u73af\u8282\u3002\u5e38\u89c1\u7684\u8bc4\u4f30\u6307\u6807\u5305\u62ec\u7cbe\u51c6\u7387\u3001\u53ec\u56de\u7387\u548cF1\u503c\u3002\u4f18\u5316\u6027\u80fd\u7684\u65b9\u6cd5\u5305\u62ec\u4f7f\u7528\u7f13\u5b58\u3001\u5e76\u884c\u8ba1\u7b97\u548c\u5206\u5e03\u5f0f\u8ba1\u7b97\u7b49\u3002<\/p>\n<\/p>\n
\u7cbe\u51c6\u7387\u548c\u53ec\u56de\u7387<\/h4>\n<\/p>\n
\u7cbe\u51c6\u7387\u662f\u6307\u6b63\u786e\u68c0\u7d22\u5230\u7684\u6587\u6863\u6570\u5360\u68c0\u7d22\u5230\u7684\u6587\u6863\u603b\u6570\u7684\u6bd4\u4f8b\uff0c\u53ec\u56de\u7387\u662f\u6307\u6b63\u786e\u68c0\u7d22\u5230\u7684\u6587\u6863\u6570\u5360\u76f8\u5173\u6587\u6863\u603b\u6570\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n
def precision_recall(true_positive, false_positive, false_negative):<\/p>\n
    precision = true_positive \/ (true_positive + false_positive)<\/p>\n
    recall = true_positive \/ (true_positive + false_negative)<\/p>\n
    return precision, recall<\/p>\n
true_positive = 5<\/p>\n
false_positive = 2<\/p>\n
false_negative = 3<\/p>\n
print(precision_recall(true_positive, false_positive, false_negative))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4f7f\u7528\u7f13\u5b58<\/h4>\n<\/p>\n
\u7f13\u5b58\u53ef\u4ee5\u51cf\u5c11\u91cd\u590d\u8ba1\u7b97\uff0c\u63d0\u9ad8\u641c\u7d22\u5f15\u64ce\u7684\u6027\u80fd\u3002Python\u7684functools.lru_cache<\/code>\u88c5\u9970\u5668\u53ef\u4ee5\u65b9\u4fbf\u5730\u5b9e\u73b0\u7f13\u5b58\u529f\u80fd\u3002<\/p>\n<\/p>\n
from functools import lru_cache<\/p>\n
@lru_cache(maxsize=100)<\/p>\n
def expensive_function(x):<\/p>\n
    return x * x<\/p>\n
print(expensive_function(10))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5e76\u884c\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5e76\u884c\u8ba1\u7b97\u53ef\u4ee5\u63d0\u9ad8\u641c\u7d22\u5f15\u64ce\u7684\u6027\u80fd\uff0cPython\u7684multiprocessing<\/code>\u5e93\u63d0\u4f9b\u4e86\u5e76\u884c\u8ba1\u7b97\u7684\u529f\u80fd\u3002<\/p>\n<\/p>\n
from multiprocessing import Pool<\/p>\n
def square(x):<\/p>\n
    return x * x<\/p>\n
with Pool(4) as p:<\/p>\n
    print(p.map(square, [1, 2, 3, 4]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5206\u5e03\u5f0f\u8ba1\u7b97<\/h4>\n<\/p>\n
\u5206\u5e03\u5f0f\u8ba1\u7b97\u53ef\u4ee5\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\uff0cHadoop\u548cSpark\u662f\u5e38\u7528\u7684\u5206\u5e03\u5f0f\u8ba1\u7b97\u6846\u67b6\u3002Python\u7684pyspark<\/code>\u5e93\u63d0\u4f9b\u4e86\u4e0eSpark\u7684\u63a5\u53e3\u3002<\/p>\n<\/p>\n
from pyspark import SparkContext<\/p>\n
sc = SparkContext("local", "Simple App")<\/p>\n
data = [1, 2, 3, 4, 5]<\/p>\n
dist_data = sc.parallelize(data)<\/p>\n
print(dist_data.map(lambda x: x * x).collect())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u7ed3\u8bba<\/h3>\n<\/p>\n
\u901a\u8fc7\u4ee5\u4e0a\u6b65\u9aa4\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528Python\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u4f46\u529f\u80fd\u5f3a\u5927\u7684\u641c\u7d22\u5f15\u64ce\u3002\u9009\u62e9\u5408\u9002\u7684\u7b97\u6cd5\u3001\u4f7f\u7528\u9ad8\u6548\u7684\u6570\u636e\u7ed3\u6784\u3001\u5b9e\u73b0\u722c\u866b\u529f\u80fd\u3001\u8fdb\u884c\u6587\u672c\u5904\u7406\u548c\u5206\u6790\u3001\u5efa\u7acb\u7d22\u5f15\u3001\u63d0\u4f9b\u67e5\u8be2\u63a5\u53e3\u3001\u8bc4\u4f30\u548c\u4f18\u5316\u6027\u80fd\u662f\u5173\u952e\u6b65\u9aa4\u3002\u5e0c\u671b\u672c\u6587\u5bf9\u60a8\u6709\u6240\u5e2e\u52a9\uff0c\u795d\u60a8\u5728\u641c\u7d22\u5f15\u64ce\u5f00\u53d1\u4e2d\u53d6\u5f97\u6210\u529f\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
 Python\u53ef\u4ee5\u7528\u4e8e\u6784\u5efa\u641c\u7d22\u5f15\u64ce\u5417\uff1f<\/strong>
\u662f\u7684\uff0cPython\u975e\u5e38\u9002\u5408\u6784\u5efa\u641c\u7d22\u5f15\u64ce\u3002\u5b83\u62e5\u6709\u5f3a\u5927\u7684\u5e93\u548c\u6846\u67b6\uff0c\u5982Scrapy\u7528\u4e8e\u6570\u636e\u6293\u53d6\uff0cWhoosh\u548cElasticsearch\u7528\u4e8e\u7d22\u5f15\u548c\u641c\u7d22\u529f\u80fd\u3002Python\u7684\u7b80\u5355\u8bed\u6cd5\u548c\u4e30\u5bcc\u7684\u793e\u533a\u652f\u6301\u4f7f\u5f97\u5f00\u53d1\u81ea\u5b9a\u4e49\u641c\u7d22\u5f15\u64ce\u53d8\u5f97\u76f8\u5bf9\u5bb9\u6613\uff0c\u5c24\u5176\u662f\u5728\u5904\u7406\u6587\u672c\u6570\u636e\u548c\u5b9e\u73b0\u81ea\u7136\u8bed\u8a00\u5904\u7406\u65f6\u3002<\/p>\n
\u4f7f\u7528Python\u6784\u5efa\u641c\u7d22\u5f15\u64ce\u9700\u8981\u54ea\u4e9b\u57fa\u672c\u7ec4\u4ef6\uff1f<\/strong>
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