如何使用python更有效地组合不同类型的功能

我正在做一个文本分类任务，由10个标签平均分布7000个文本。我将字符、单词和POS设置为列名称，并将每个文本的所有字符、单词和POS标记存储到Sqlite3数据库中。我想做的是将字符2-gram、单词2-gram和词性2-gram组合成支持向量机的训练向量。我的代码如下：

def dbConnect(db_name,sql):
    conn = sqlite3.connect(db_name)
    cursor = conn.cursor()
    n = cursor.execute(sql)
    res1 = cursor.fetchall()
    return res1

def createtrainingVector(item,n,count1,res1):
        ''' append all items(character/word/POS n-gram) that occurs at least count1 times in the text into a training Vector used for SVM'''

def featureComb(item,count1,res1):
    '''combine features into a vector'''
    if item == "word":
        train_vector1 = createtrainingVector(item,1,count1,res1)
        train_vector2 = createtrainingVector(item,2,count1,res1)
    elif item == "character":
        train_vector1 = createtrainingVector(item,1,count1,res1)
        train_vector2 = createtrainingVector(item,2,count1,res1)
    elif item == "pos":
        train_vector1 = createtrainingVector(item,1,count1,res1)
        train_vector2 = createtrainingVector(item,2,count1,res1)
    return sorted(train_vector1+train_vector2)

def svmParaComb(item,train_vector,label_vector,X,y,res1):
    matrix = []
    for res in res1:            
        y.append(label_vector[res[0]])

        if item == "word":
            word_dic1 = wordNgrams(res[1], 1)
            word_dic2 = wordNgrams(res[1], 2)
        elif lexical == "pos":
            word_dic1 = lemmaNgrams(json.loads(res[1]), 1)
            word_dic2 = lemmaNgrams(json.loads(res[1]), 2)
        elif lexical == "character":
            word_dic1 = characterNgrams(res[1], 1)
            word_dic2 = characterNgrams(res[1], 2)
        X.append([1 if gram in (word_dic1 or word_dic2) else 0 for gram in train_vecter)

db = 'text.db'
'''Access to database and Extract label and essay'''
sql_dict1 = {"word": "select label,word from main.training_data",
        "pos" : "select label,pos from main.training_data",
        "character" : "select label,character from main.training_data",
        }
sql_dict2 = {"word": "select label,word from main.test_data",
        "pos" : "select label,pos from main.test_data", 
        "character" : "select label,character from main.test_data",
        }

if i == 1:
        sql1 = sql_dict1["word"]
        sql2 = sql_dict2["word"]
elif i == 2:
        sql1 = sql_dict1["character"]
        sql2 = sql_dict2["character"]
elif i == 3:
        sql1 = sql_dict1["pos"]
        sql2 = sql_dict2["pos"]
res1 = dbConnect(db, sql1)
    X = []
    y = []
    train_vector_combine = featureComb(item, 2,res1)
    svmParaComb(item, train_vector_combine, label_vector, X, y, res1)

    clf1 = svm.LinearSVC()
    clf1.fit(X, y)

    res2 = dbConnect(db, sql2)

    '''Parameter used for predicting'''
    X_test = []
    y_true = []
    svmCombinePredict(item, train_vector, label_vector, X_test, y_true, res2)  

    clf1.predict(X_test)
    print clf1.score(X_test,y_true)

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但是，它只能组合相同类型的特征。我不知道如何处理不同类型特征的组合，因为res1只表示一个colume单词/字符/词性。因此，如果我想将字符2-gram、单词2-gram和词性2-gram组合成一个训练向量，我应该怎么做？有人能帮我写代码吗？

如果我理解正确，你想让SVM区分1克、2克等？如果这是您唯一的问题，为什么不在您的令牌前面加上允许您区分它们的前缀呢？你的代币是字符串，对吗？@pi。谢谢你的回复。然而，我想做的是结合不同类型的特征，例如字符2-gram、单词2-gram和词性2-gram。我的代码只适用于相同类型的功能组合，例如单词1-gram和单词2-gram。因此，我的问题是如何更正我的代码以处理不同类型的功能组合？在scikit中使用featureunion