{"id":1169561,"date":"2025-01-15T16:12:12","date_gmt":"2025-01-15T08:12:12","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1169561.html"},"modified":"2025-01-15T16:12:14","modified_gmt":"2025-01-15T08:12:14","slug":"python%e5%a6%82%e4%bd%95%e5%af%b9%e6%96%87%e6%9c%ac%e5%88%86%e7%b1%bb","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1169561.html","title":{"rendered":"python\u5982\u4f55\u5bf9\u6587\u672c\u5206\u7c7b"},"content":{"rendered":"

\"python\u5982\u4f55\u5bf9\u6587\u672c\u5206\u7c7b\"<\/p>\n

Python\u5bf9\u6587\u672c\u5206\u7c7b\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u5305\u62ec\uff1a\u4f7f\u7528NLP\u5e93\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u3001\u5e94\u7528\u673a\u5668\u5b66\u4e60<\/a>\u7b97\u6cd5\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3001\u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b\u7b49\u3002<\/strong>\u5176\u4e2d\uff0c\u5229\u7528NLP\u5e93\u8fdb\u884c\u7279\u5f81\u63d0\u53d6\u662f\u975e\u5e38\u91cd\u8981\u7684\u6b65\u9aa4\uff0c\u5e38\u7528\u7684\u5e93\u6709NLTK\u3001spaCy\u3001TextBlob\u7b49\u3002\u672c\u6587\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\u3002<\/p>\n<\/p>\n

\u4e00\u3001NLP\u5e93\u8fdb\u884c\u7279\u5f81\u63d0\u53d6<\/h2>\n<\/p>\n

1\u3001\u4f7f\u7528NLTK\u8fdb\u884c\u7279\u5f81\u63d0\u53d6<\/h3>\n<\/p>\n

NLTK\uff08Natural Language Toolkit\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684Python\u5e93\uff0c\u7528\u4e8e\u5904\u7406\u548c\u5206\u6790\u4eba\u7c7b\u8bed\u8a00\u6570\u636e\u3002\u5b83\u5305\u542b\u4e86\u6587\u672c\u5904\u7406\u7684\u5404\u4e2a\u65b9\u9762\uff0c\u5305\u62ec\u6807\u8bb0\u5316\u3001\u8bcd\u5e72\u5316\u3001\u8bcd\u888b\u6a21\u578b\u7b49\u3002<\/p>\n<\/p>\n

\u6807\u8bb0\u5316<\/h4>\n<\/p>\n

\u6807\u8bb0\u5316\u662f\u5c06\u6587\u672c\u5206\u5272\u6210\u5355\u72ec\u7684\u5355\u8bcd\u6216\u77ed\u8bed\u7684\u8fc7\u7a0b\u3002NLTK\u63d0\u4f9b\u4e86\u8bb8\u591a\u5de5\u5177\u6765\u6267\u884c\u8fd9\u4e00\u64cd\u4f5c\u3002<\/p>\n<\/p>\n

import nltk<\/p>\n
from nltk.tokenize import word_tokenize<\/p>\n
text = "Python is an amazing programming language!"<\/p>\n
tokens = word_tokenize(text)<\/p>\n
print(tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bcd\u5e72\u5316<\/h4>\n<\/p>\n
\u8bcd\u5e72\u5316\u662f\u5c06\u5355\u8bcd\u8fd8\u539f\u4e3a\u5176\u57fa\u672c\u5f62\u5f0f\u7684\u8fc7\u7a0b\u3002NLTK\u63d0\u4f9b\u4e86\u591a\u79cd\u8bcd\u5e72\u5316\u7b97\u6cd5\u3002<\/p>\n<\/p>\n
from nltk.stem import PorterStemmer<\/p>\n
stemmer = PorterStemmer()<\/p>\n
stemmed_tokens = [stemmer.stem(token) for token in tokens]<\/p>\n
print(stemmed_tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bcd\u888b\u6a21\u578b<\/h4>\n<\/p>\n
\u8bcd\u888b\u6a21\u578b\u5c06\u6587\u672c\u8f6c\u6362\u4e3a\u56fa\u5b9a\u957f\u5ea6\u7684\u5411\u91cf\uff0c\u5411\u91cf\u7684\u6bcf\u4e2a\u5143\u7d20\u8868\u793a\u4e00\u4e2a\u7279\u5b9a\u5355\u8bcd\u5728\u6587\u672c\u4e2d\u51fa\u73b0\u7684\u6b21\u6570\u3002<\/p>\n<\/p>\n
from sklearn.feature_extraction.text import CountVectorizer<\/p>\n
vectorizer = CountVectorizer()<\/p>\n
X = vectorizer.fit_transform([text])<\/p>\n
print(X.toarray())<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528spaCy\u8fdb\u884c\u7279\u5f81\u63d0\u53d6<\/h3>\n<\/p>\n
spaCy\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u9ad8\u7ea7\u529f\u80fd\uff0c\u5982\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u3001\u8bcd\u6027\u6807\u6ce8\u3001\u4f9d\u5b58\u5173\u7cfb\u89e3\u6790\u7b49\u3002<\/p>\n<\/p>\n
\u6807\u8bb0\u5316<\/h4>\n<\/p>\n
import spacy<\/p>\n
nlp = spacy.load("en_core_web_sm")<\/p>\n
doc = nlp(text)<\/p>\n
tokens = [token.text for token in doc]<\/p>\n
print(tokens)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bcd\u6027\u6807\u6ce8<\/h4>\n<\/p>\n
pos_tags = [(token.text, token.pos_) for token in doc]<\/p>\n
print(pos_tags)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u547d\u540d\u5b9e\u4f53\u8bc6\u522b<\/h4>\n<\/p>\n
entities = [(ent.text, ent.label_) for ent in doc.ents]<\/p>\n
print(entities)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001\u4f7f\u7528TextBlob\u8fdb\u884c\u7279\u5f81\u63d0\u53d6<\/h3>\n<\/p>\n
TextBlob\u662f\u4e00\u4e2a\u7b80\u5355\u6613\u7528\u7684\u5e93\uff0c\u9002\u5408\u8fdb\u884c\u5feb\u901f\u7684\u6587\u672c\u5904\u7406\u548c\u5206\u6790\u3002<\/p>\n<\/p>\n
\u60c5\u611f\u5206\u6790<\/h4>\n<\/p>\n
from textblob import TextBlob<\/p>\n
blob = TextBlob(text)<\/p>\n
print(blob.sentiment)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u8bcd\u6027\u6807\u6ce8<\/h4>\n<\/p>\n
print(blob.tags)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u5e94\u7528\u673a\u5668\u5b66\u4e60\u7b97\u6cd5<\/h2>\n<\/p>\n
1\u3001\u4f7f\u7528Scikit-learn\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
Scikit-learn\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u673a\u5668\u5b66\u4e60\u5e93\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u5206\u7c7b\u7b97\u6cd5\uff0c\u5982\u6734\u7d20\u8d1d\u53f6\u65af\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u903b\u8f91\u56de\u5f52\u7b49\u3002<\/p>\n<\/p>\n
\u6734\u7d20\u8d1d\u53f6\u65af\u5206\u7c7b\u5668<\/h4>\n<\/p>\n
from sklearn.feature_extraction.text import TfidfVectorizer<\/p>\n
from sklearn.nAI<\/a>ve_bayes import MultinomialNB<\/p>\n
from sklearn.pipeline import make_pipeline<\/p>\n
model = make_pipeline(TfidfVectorizer(), MultinomialNB())<\/p>\n
texts = ["Python is great", "I love programming", "Coding is fun"]<\/p>\n
labels = [1, 1, 0]<\/p>\n
model.fit(texts, labels)<\/p>\n
predicted_labels = model.predict(["I enjoy coding"])<\/p>\n
print(predicted_labels)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u652f\u6301\u5411\u91cf\u673a\u5206\u7c7b\u5668<\/h4>\n<\/p>\n
from sklearn.svm import SVC<\/p>\n
model = make_pipeline(TfidfVectorizer(), SVC())<\/p>\n
model.fit(texts, labels)<\/p>\n
predicted_labels = model.predict(["I enjoy coding"])<\/p>\n
print(predicted_labels)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528Keras\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
Keras\u662f\u4e00\u4e2a\u9ad8\u5c42\u795e\u7ecf\u7f51\u7edcAPI\uff0c\u9002\u7528\u4e8e\u5feb\u901f\u6784\u5efa\u548c\u8bad\u7ec3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002<\/p>\n<\/p>\n
\u6784\u5efa\u548c\u8bad\u7ec3\u6a21\u578b<\/h4>\n<\/p>\n
from keras.preprocessing.text import Tokenizer<\/p>\n
from keras.preprocessing.sequence import pad_sequences<\/p>\n
from keras.models import Sequential<\/p>\n
from keras.layers import Embedding, LSTM, Dense<\/p>\n
tokenizer = Tokenizer(num_words=5000)<\/p>\n
tokenizer.fit_on_texts(texts)<\/p>\n
X = tokenizer.texts_to_sequences(texts)<\/p>\n
X = pad_sequences(X, maxlen=100)<\/p>\n
model = Sequential()<\/p>\n
model.add(Embedding(input_dim=5000, output_dim=64, input_length=100))<\/p>\n
model.add(LSTM(128))<\/p>\n
model.add(Dense(1, activation='sigmoid'))<\/p>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
model.fit(X, labels, epochs=10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u9884\u6d4b<\/h4>\n<\/p>\n
test_texts = ["I enjoy coding"]<\/p>\n
X_test = tokenizer.texts_to_sequences(test_texts)<\/p>\n
X_test = pad_sequences(X_test, maxlen=100)<\/p>\n
predicted_labels = model.predict(X_test)<\/p>\n
print(predicted_labels)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b<\/h2>\n<\/p>\n
1\u3001\u4f7f\u7528RNN\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
RNN\uff08Recurrent Neural Network\uff09\u662f\u5904\u7406\u5e8f\u5217\u6570\u636e\u7684\u6709\u6548\u5de5\u5177\uff0c\u9002\u7528\u4e8e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u6784\u5efa\u548c\u8bad\u7ec3RNN\u6a21\u578b<\/h4>\n<\/p>\n
from keras.layers import SimpleRNN<\/p>\n
model = Sequential()<\/p>\n
model.add(Embedding(input_dim=5000, output_dim=64, input_length=100))<\/p>\n
model.add(SimpleRNN(128))<\/p>\n
model.add(Dense(1, activation='sigmoid'))<\/p>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
model.fit(X, labels, epochs=10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528CNN\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
CNN\uff08Convolutional Neural Network\uff09\u4e5f\u53ef\u4ee5\u7528\u4e8e\u6587\u672c\u5206\u7c7b\uff0c\u901a\u8fc7\u63d0\u53d6\u5c40\u90e8\u7279\u5f81\u6765\u8fdb\u884c\u5206\u7c7b\u3002<\/p>\n<\/p>\n
\u6784\u5efa\u548c\u8bad\u7ec3CNN\u6a21\u578b<\/h4>\n<\/p>\n
from keras.layers import Conv1D, GlobalMaxPooling1D<\/p>\n
model = Sequential()<\/p>\n
model.add(Embedding(input_dim=5000, output_dim=64, input_length=100))<\/p>\n
model.add(Conv1D(128, 5, activation='relu'))<\/p>\n
model.add(GlobalMaxPooling1D())<\/p>\n
model.add(Dense(1, activation='sigmoid'))<\/p>\n
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
model.fit(X, labels, epochs=10)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u5229\u7528\u9884\u8bad\u7ec3\u6a21\u578b<\/h2>\n<\/p>\n
1\u3001\u4f7f\u7528BERT\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
BERT\uff08Bidirectional Encoder Representations from Transformers\uff09\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u5404\u79cdNLP\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u4f7f\u7528Transformers\u5e93\u52a0\u8f7dBERT\u6a21\u578b<\/h4>\n<\/p>\n
from transformers import BertTokenizer, BertForSequenceClassification<\/p>\n
from transformers import Trainer, TrainingArguments<\/p>\n
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')<\/p>\n
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)<\/p>\n
train_texts = ["Python is great", "I love programming", "Coding is fun"]<\/p>\n
train_labels = [1, 1, 0]<\/p>\n
train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=100)<\/p>\n
train_dataset = tf.data.Dataset.from_tensor_slices((<\/p>\n
    dict(train_encodings),<\/p>\n
    train_labels<\/p>\n
))<\/p>\n
training_args = TrainingArguments(<\/p>\n
    output_dir='.\/results',<\/p>\n
    num_train_epochs=3,<\/p>\n
    per_device_train_batch_size=16,<\/p>\n
    per_device_eval_batch_size=16,<\/p>\n
    warmup_steps=500,<\/p>\n
    weight_decay=0.01,<\/p>\n
    logging_dir='.\/logs',<\/p>\n
)<\/p>\n
trainer = Trainer(<\/p>\n
    model=model,<\/p>\n
    args=training_args,<\/p>\n
    train_dataset=train_dataset<\/p>\n
)<\/p>\n
trainer.train()<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u4f7f\u7528GPT\u8fdb\u884c\u6587\u672c\u5206\u7c7b<\/h3>\n<\/p>\n
GPT\uff08Generative Pre-trained Transformer\uff09\u4e5f\u662f\u4e00\u4e2a\u5f3a\u5927\u7684\u9884\u8bad\u7ec3\u6a21\u578b\uff0c\u53ef\u4ee5\u7528\u4e8e\u6587\u672c\u5206\u7c7b\u4efb\u52a1\u3002<\/p>\n<\/p>\n
\u4f7f\u7528Transformers\u5e93\u52a0\u8f7dGPT\u6a21\u578b<\/h4>\n<\/p>\n
from transformers import GPT2Tokenizer, GPT2ForSequenceClassification<\/p>\n
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')<\/p>\n
model = GPT2ForSequenceClassification.from_pretrained('gpt2', num_labels=2)<\/p>\n
train_texts = ["Python is great", "I love programming", "Coding is fun"]<\/p>\n
train_labels = [1, 1, 0]<\/p>\n
train_encodings = tokenizer(train_texts, truncation=True, padding=True, max_length=100)<\/p>\n
train_dataset = tf.data.Dataset.from_tensor_slices((<\/p>\n
    dict(train_encodings),<\/p>\n
    train_labels<\/p>\n
))<\/p>\n
training_args = TrainingArguments(<\/p>\n
    output_dir='.\/results',<\/p>\n
    num_train_epochs=3,<\/p>\n
    per_device_train_batch_size=16,<\/p>\n
    per_device_eval_batch_size=16,<\/p>\n
    warmup_steps=500,<\/p>\n
    weight_decay=0.01,<\/p>\n
    logging_dir='.\/logs',<\/p>\n
)<\/p>\n
trainer = Trainer(<\/p>\n
    model=model,<\/p>\n
    args=training_args,<\/p>\n
    train_dataset=train_dataset<\/p>\n
)<\/p>\n
trainer.train()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u6587\u672c\u5206\u7c7b\u7684\u8bc4\u4f30\u65b9\u6cd5<\/h2>\n<\/p>\n
1\u3001\u51c6\u786e\u7387<\/h3>\n<\/p>\n
\u51c6\u786e\u7387\u662f\u5206\u7c7b\u6b63\u786e\u7684\u6837\u672c\u6570\u4e0e\u603b\u6837\u672c\u6570\u4e4b\u6bd4\u3002<\/p>\n<\/p>\n
from sklearn.metrics import accuracy_score<\/p>\n
predicted_labels = model.predict(X_test)<\/p>\n
accuracy = accuracy_score(test_labels, predicted_labels)<\/p>\n
print('Accuracy:', accuracy)<\/p>\n
<\/code><\/pre>\n<\/p>\n
2\u3001\u7cbe\u786e\u7387\u4e0e\u53ec\u56de\u7387<\/h3>\n<\/p>\n
\u7cbe\u786e\u7387\u662f\u5206\u7c7b\u4e3a\u6b63\u6837\u672c\u7684\u6837\u672c\u4e2d\u5b9e\u9645\u4e3a\u6b63\u6837\u672c\u7684\u6bd4\u4f8b\uff0c\u53ec\u56de\u7387\u662f\u5b9e\u9645\u4e3a\u6b63\u6837\u672c\u7684\u6837\u672c\u4e2d\u5206\u7c7b\u4e3a\u6b63\u6837\u672c\u7684\u6bd4\u4f8b\u3002<\/p>\n<\/p>\n
from sklearn.metrics import precision_score, recall_score<\/p>\n
precision = precision_score(test_labels, predicted_labels)<\/p>\n
recall = recall_score(test_labels, predicted_labels)<\/p>\n
print('Precision:', precision)<\/p>\n
print('Recall:', recall)<\/p>\n
<\/code><\/pre>\n<\/p>\n
3\u3001F1\u5206\u6570<\/h3>\n<\/p>\n
F1\u5206\u6570\u662f\u7cbe\u786e\u7387\u548c\u53ec\u56de\u7387\u7684\u8c03\u548c\u5e73\u5747\u6570\uff0c\u7efc\u5408\u4e86\u4e24\u8005\u7684\u4f18\u70b9\u3002<\/p>\n<\/p>\n
from sklearn.metrics import f1_score<\/p>\n
f1 = f1_score(test_labels, predicted_labels)<\/p>\n
print('F1 Score:', f1)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u6587\u672c\u5206\u7c7b\u7684\u5e94\u7528\u573a\u666f<\/h2>\n<\/p>\n
1\u3001\u60c5\u611f\u5206\u6790<\/h3>\n<\/p>\n
\u60c5\u611f\u5206\u6790\u662f\u6587\u672c\u5206\u7c7b\u7684\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\uff0c\u5e7f\u6cdb\u7528\u4e8e\u793e\u4ea4\u5a92\u4f53\u76d1\u63a7\u3001\u5e02\u573a\u8c03\u7814\u7b49\u9886\u57df\u3002<\/p>\n<\/p>\n
2\u3001\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b<\/h3>\n<\/p>\n
\u5783\u573e\u90ae\u4ef6\u68c0\u6d4b\u901a\u8fc7\u6587\u672c\u5206\u7c7b\u6280\u672f\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u8bc6\u522b\u548c\u8fc7\u6ee4\u5783\u573e\u90ae\u4ef6\uff0c\u63d0\u9ad8\u7535\u5b50\u90ae\u4ef6\u7cfb\u7edf\u7684\u5b89\u5168\u6027\u3002<\/p>\n<\/p>\n
3\u3001\u65b0\u95fb\u5206\u7c7b<\/h3>\n<\/p>\n
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