python - 如何训练大型数据集进行分类

Question

我有一个包含 1600000 条推文的训练数据集。我该如何训练这种庞大的数据。

我尝试过使用 nltk.NaiveBayesClassifier。如果我运行，训练需要5天以上。

def extract_features(tweet):

    tweet_words = set(tweet)
    features = {}
    for word in featureList:
        features['contains(%s)' % word] = (word in tweet_words)

    return features


training_set = nltk.classify.util.apply_features(extract_features, tweets)

NBClassifier = nltk.NaiveBayesClassifier.train(training_set)  # This takes lots of time  

我该怎么办？

我需要使用 SVM 和朴素贝叶斯对我的数据集进行分类。

我想使用的数据集:Link

样本(训练数据集):

Label     Tweet
0         url aww bummer you shoulda got david carr third day
4         thankyou for your reply are you coming england again anytime soon

样本(测试数据集):

Label     Tweet
4         love lebron url
0         lebron beast but still cheering the til the end
^
I have to predict Label 0/4 only

如何有效地训练这个庞大的数据集？

Answer 1

根据关于特征提取的出色建议，您可以使用 scikit 库中的 tfidvectorizer 从推文中提取重要单词。使用默认配置，再加上一个简单的 LogisticRegression，它给了我 0.8 的准确度。希望能有所帮助。这是一个关于如何使用它来解决你的问题的例子:

    train_df_raw = pd.read_csv('train.csv',header=None, names=['label','tweet'])
test_df_raw = pd.read_csv('test.csv',header=None, names=['label','tweet'])
train_df_raw =  train_df_raw[train_df_raw['tweet'].notnull()]
test_df_raw =  test_df_raw[test_df_raw['tweet'].notnull()]
test_df_raw =  test_df_raw[test_df_raw['label']!=2]

y_train = [x if x==0 else 1 for x in train_df_raw['label'].tolist()]
y_test = [x if x==0 else 1 for x in test_df_raw['label'].tolist()]
X_train = train_df_raw['tweet'].tolist()
X_test = test_df_raw['tweet'].tolist()

print('At vectorizer')
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)
print('At vectorizer for test data')
X_test = vectorizer.transform(X_test)

print('at Classifier')
classifier = LogisticRegression()
classifier.fit(X_train, y_train)

predictions = classifier.predict(X_test)
print 'Accuracy:', accuracy_score(y_test, predictions)

confusion_matrix = confusion_matrix(y_test, predictions)
print(confusion_matrix)

Accuracy: 0.8
[[135  42]
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