from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\nfrom sklearn.naive_bayes import MultinomialNB<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
texts = ["\u8fd9\u662f\u4e00\u6761\u6b63\u9762\u8bc4\u8bba\u3002", "\u8fd9\u662f\u4e00\u6761\u8d1f\u9762\u8bc4\u8bba\u3002", "\u8fd9\u662f\u4e00\u6761\u4e2d\u6027\u8bc4\u8bba\u3002"]<\/p>\n
labels = ["\u6b63\u9762", "\u8d1f\u9762", "\u4e2d\u6027"]<\/p>\n
vectorizer = TfidfVectorizer()<\/p>\n
X = vectorizer.fit_transform(texts)<\/p>\n
y = labels<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
clf = MultinomialNB()<\/p>\n
clf.fit(X_train, y_train)<\/p>\n
y_pred = clf.predict(X_test)<\/p>\n
print(f"\u5206\u7c7b\u51c6\u786e\u7387\uff1a{accuracy_score(y_test, y_pred)}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cTfidfVectorizer<\/code>\u5c06\u6587\u672c\u8f6c\u5316\u4e3aTF-IDF\u7279\u5f81\u77e9\u9635\uff0ctrain_test_split<\/code>\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0cMultinomialNB<\/code>\u8fdb\u884c\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\uff0caccuracy_score<\/code>\u8ba1\u7b97\u5206\u7c7b\u51c6\u786e\u7387\u3002<\/p>\n<\/p>\n2\u3001\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b<\/h4>\n<\/p>\n
\u652f\u6301\u5411\u91cf\u673a\uff08SVM\uff09\u662f\u4e00\u79cd\u5f3a\u5927\u7684\u5206\u7c7b\u7b97\u6cd5\uff0c\u5e38\u7528\u4e8e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u3002\u53ef\u4ee5\u4f7f\u7528scikit-learn\u5e93\u5b9e\u73b0\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b\uff1a<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\nfrom sklearn.svm import SVC<\/p>\n
from sklearn.model_selection import train_test_split<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
texts = ["\u8fd9\u662f\u4e00\u6761\u6b63\u9762\u8bc4\u8bba\u3002", "\u8fd9\u662f\u4e00\u6761\u8d1f\u9762\u8bc4\u8bba\u3002", "\u8fd9\u662f\u4e00\u6761\u4e2d\u6027\u8bc4\u8bba\u3002"]<\/p>\n
labels = ["\u6b63\u9762", "\u8d1f\u9762", "\u4e2d\u6027"]<\/p>\n
vectorizer = TfidfVectorizer()<\/p>\n
X = vectorizer.fit_transform(texts)<\/p>\n
y = labels<\/p>\n
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
clf = SVC(kernel='linear')<\/p>\n
clf.fit(X_train, y_train)<\/p>\n
y_pred = clf.predict(X_test)<\/p>\n
print(f"\u5206\u7c7b\u51c6\u786e\u7387\uff1a{accuracy_score(y_test, y_pred)}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cTfidfVectorizer<\/code>\u5c06\u6587\u672c\u8f6c\u5316\u4e3aTF-IDF\u7279\u5f81\u77e9\u9635\uff0ctrain_test_split<\/code>\u5c06\u6570\u636e\u96c6\u5212\u5206\u4e3a\u8bad\u7ec3\u96c6\u548c\u6d4b\u8bd5\u96c6\uff0cSVC<\/code>\u8fdb\u884c\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b\uff0caccuracy_score<\/code>\u8ba1\u7b97\u5206\u7c7b\u51c6\u786e\u7387\u3002<\/p>\n<\/p>\n\u516b\u3001\u60c5\u611f\u5206\u6790<\/h3>\n<\/p>\n
\u60c5\u611f\u5206\u6790\u662f\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u7684\u4e00\u79cd\u4efb\u52a1\uff0c\u7528\u4e8e\u5224\u65ad\u6587\u672c\u7684\u60c5\u611f\u503e\u5411\uff08\u5982\u6b63\u9762\u3001\u8d1f\u9762\u3001\u4e2d\u6027\uff09\u3002\u53ef\u4ee5\u4f7f\u7528TextBlob\u5e93\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff1a<\/p>\n<\/p>\n
from textblob import TextBlob<\/p>\ntext = "I love this product! It's amazing."<\/p>\n
blob = TextBlob(text)<\/p>\n
sentiment = blob.sentiment<\/p>\n
print(f"\u60c5\u611f\u503e\u5411\uff1a{sentiment.polarity}")<\/p>\n
print(f"\u60c5\u611f\u4e3b\u89c2\u6027\uff1a{sentiment.subjectivity}")<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0cTextBlob<\/code>\u5bf9\u8c61\u5bf9\u6587\u672c\u8fdb\u884c\u60c5\u611f\u5206\u6790\uff0csentiment<\/code>\u5c5e\u6027\u8fd4\u56de\u60c5\u611f\u503e\u5411\uff08\u6781\u6027\uff09\u548c\u60c5\u611f\u4e3b\u89c2\u6027\u3002<\/p>\n<\/p>\n\u4e5d\u3001\u6587\u672c\u751f\u6210<\/h3>\n<\/p>\n
\u6587\u672c\u751f\u6210\u662f\u4e00\u79cd\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4efb\u52a1\uff0c\u7528\u4e8e\u751f\u6210\u7c7b\u4f3c\u4e8e\u7ed9\u5b9a\u6587\u672c\u7684\u5185\u5bb9\u3002\u53ef\u4ee5\u4f7f\u7528GPT-3\u7b49\u8bed\u8a00\u6a21\u578b\u8fdb\u884c\u6587\u672c\u751f\u6210\uff1a<\/p>\n<\/p>\n
import openai<\/p>\nopenai.api_key = 'your-api-key'<\/p>\n
response = openai.Completion.create(<\/p>\n
engine="davinci",<\/p>\n
prompt="Write a short story about a robot learning to love.",<\/p>\n
max_tokens=100<\/p>\n
)<\/p>\n
print(response.choices[0].text.strip())<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u8fd9\u4e2a\u4f8b\u5b50\u4e2d\uff0copenai.Completion.create<\/code>\u65b9\u6cd5\u4f7f\u7528GPT-3\u751f\u6210\u6587\u672c\uff0cresponse.choices[0].text<\/code>\u8fd4\u56de\u751f\u6210\u7684\u6587\u672c\u5185\u5bb9\u3002<\/p>\n<\/p>\n\u5341\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
Python\u5728\u6587\u672c\u5904\u7406\u65b9\u9762\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u548c\u5f3a\u5927\u7684\u529f\u80fd\u3002\u901a\u8fc7\u6b63\u5219\u8868\u8fbe\u5f0f\u3001\u5b57\u7b26\u4e32\u65b9\u6cd5\u3001\u6587\u672c\u5904\u7406\u5e93\u7b49\u5de5\u5177\uff0c\u53ef\u4ee5\u9ad8\u6548\u5730\u8fdb\u884c\u6587\u672c\u5339\u914d\u3001\u66ff\u6362\u3001\u62c6\u5206\u3001\u9884\u5904\u7406\u3001\u7279\u5f81\u63d0\u53d6\u3001\u5206\u7c7b\u3001\u60c5\u611f\u5206\u6790\u548c\u751f\u6210\u7b49\u64cd\u4f5c\u3002\u638c\u63e1\u8fd9\u4e9b\u6280\u672f\uff0c\u53ef\u4ee5\u5728\u81ea\u7136\u8bed\u8a00\u5904\u7406\u548c\u6587\u672c\u5206\u6790\u4e2d\u5927\u5c55\u8eab\u624b\u3002\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u4e3a\u60a8\u63d0\u4f9b\u6709\u4ef7\u503c\u7684\u53c2\u8003\uff0c\u5e2e\u52a9\u60a8\u66f4\u597d\u5730\u7406\u89e3\u548c\u5e94\u7528Python\u8fdb\u884c\u6587\u672c\u5904\u7406\u3002<\/p>\n<\/p>\n
\u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n \u5982\u4f55\u4f7f\u7528Python\u5904\u7406\u6587\u672c\u6570\u636e\uff1f<\/strong>
Python\u63d0\u4f9b\u4e86\u591a\u79cd\u5e93\u548c\u5de5\u5177\u7528\u4e8e\u6587\u672c\u5904\u7406\u3002\u5e38\u7528\u7684\u5e93\u5305\u62ecre<\/code>\u7528\u4e8e\u6b63\u5219\u8868\u8fbe\u5f0f\u5339\u914d\u3001nltk<\/code>\u7528\u4e8e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u4ee5\u53capandas<\/code>\u7528\u4e8e\u6570\u636e\u5206\u6790\u3002\u901a\u8fc7\u8fd9\u4e9b\u5e93\uff0c\u7528\u6237\u53ef\u4ee5\u8f7b\u677e\u8fdb\u884c\u6587\u672c\u6e05\u7406\u3001\u5206\u8bcd\u3001\u60c5\u611f\u5206\u6790\u7b49\u4efb\u52a1\u3002<\/p>\nPython\u4e2d\u6709\u54ea\u4e9b\u6587\u672c\u5904\u7406\u7684\u5e38\u7528\u65b9\u6cd5\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u5e38\u89c1\u7684\u6587\u672c\u5904\u7406\u65b9\u6cd5\u5305\u62ec\u5b57\u7b26\u4e32\u64cd\u4f5c\uff08\u5982\u5206\u5272\u3001\u66ff\u6362\u3001\u62fc\u63a5\uff09\u3001\u6b63\u5219\u8868\u8fbe\u5f0f\u5339\u914d\u3001\u6587\u672c\u6587\u4ef6\u8bfb\u5199\u3001\u4ee5\u53ca\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6280\u672f\uff08\u5982\u8bcd\u9891\u7edf\u8ba1\u3001TF-IDF\u8ba1\u7b97\u7b49\uff09\u3002\u8fd9\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u5e2e\u52a9\u7528\u6237\u9ad8\u6548\u5730\u5206\u6790\u548c\u5904\u7406\u6587\u672c\u6570\u636e\u3002<\/p>\n\u5982\u4f55\u5728Python\u4e2d\u8bfb\u53d6\u548c\u5199\u5165\u6587\u672c\u6587\u4ef6\uff1f<\/strong>
\u5728Python\u4e2d\uff0c\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684open()<\/code>\u51fd\u6570\u8bfb\u53d6\u548c\u5199\u5165\u6587\u672c\u6587\u4ef6\u3002\u4f7f\u7528'r'<\/code>\u6a21\u5f0f\u6253\u5f00\u6587\u4ef6\u53ef\u4ee5\u8bfb\u53d6\u5185\u5bb9\uff0c\u800c\u4f7f\u7528'w'<\/code>\u6216'a'<\/code>\u6a21\u5f0f\u53ef\u4ee5\u5199\u5165\u6216\u8ffd\u52a0\u5185\u5bb9\u3002\u901a\u8fc7with<\/code>\u8bed\u53e5\uff0c\u53ef\u4ee5\u786e\u4fdd\u6587\u4ef6\u5728\u64cd\u4f5c\u5b8c\u6210\u540e\u81ea\u52a8\u5173\u95ed\uff0c\u4ece\u800c\u907f\u514d\u8d44\u6e90\u6cc4\u6f0f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"Python\u53ef\u4ee5\u901a\u8fc7\u6b63\u5219\u8868\u8fbe\u5f0f\u3001\u5b57\u7b26\u4e32\u65b9\u6cd5\u3001\u6587\u672c\u5904\u7406\u5e93\u7b49\u591a\u79cd\u65b9\u5f0f\u5728\u6587\u672c\u4e0a\u5b9e\u73b0\u591a\u79cd\u64cd\u4f5c\u3002 \u4f8b\u5982\uff0c\u4f7f\u7528\u6b63\u5219\u8868\u8fbe\u5f0f\u53ef […]","protected":false},"author":3,"featured_media":1171122,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1171117"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1171117"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1171117\/revisions"}],"predecessor-version":[{"id":1171125,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1171117\/revisions\/1171125"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1171122"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1171117"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1171117"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1171117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"python\u5982\u4f55\u5728\u6587\u672c\u4e0a\u5b9e\u73b0\"](\"https:\/\/cdn-kb.worktile.com\/kb\/wp-content\/uploads\/2024\/04\/26073527\/4c311f8d-f5ef-4575-82d9-d8aea3e88b7a.webp\")
<\/p>\n