我有一个1600000推文的训练数据集.我该如何训练这类巨大的数据.
我尝试过使用nltk.NaiveBayesClassifier.如果我跑步,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和朴素的bayes对我的数据集进行分类.
我想使用的数据集：Link
样本(培训数据集)：
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
示例(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？
解决方法
根据对特征提取的精心提出,您可以使用scikit库中的tfidvectorizer从推文中提取重要的单词.使用默认配置,再加上一个简单的LogisticRegression,它给我0.8精度.希望有所帮助.
以下是如何使用它来解决问题的示例：
以下是如何使用它来解决问题的示例：
train_df_raw = pd.read_csv('train.csv',header=None,names=['label','tweet'])
test_df_raw = pd.read_csv('test.csv','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]
[ 30 153]]