构建n-grams用于标记级文本分类

Question

我正在尝试使用scikit-learn在token层面对多类数据进行分类。 我已经有了一个 train 和 test 拆分。代币分批出现在同一类的代币中，如前10个代币属于 class0剩余的20个属于 class4 诸如此类。的数据。\t 分隔的格式。

-----------------
token       tag
-----------------
way          6
to           6
reduce       6
the          6
amount       6
of           6
traffic      6
   ....
public       2
transport    5
is           5
a            5
key          5
factor       5
to           5 
minimize     5
   ....

数据分布如下：

                              Training Data                    Test Data
# Total:                        119490                          29699
# Class 0:                      52631                           13490
# Class 1:                      35116                           8625
# Class 2:                      17968                           4161
# Class 3:                      8658                            2088
# Class 4:                      3002                            800
# Class 5:                      1201                            302
# Class 6:                      592                             153

我正在尝试的代码是：

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import f1_score
from sklearn.model_selection import KFold
from imblearn.over_sampling import SMOTE

if __name__ == '__main__':
    # reading Files
    train_df = pd.read_csv(TRAINING_DATA_PATH, names=['token', 'tag'], sep='\t').dropna().reset_index(drop=True)
    test_df = pd.read_csv(TEST_DATA_PATH, names=['token', 'tag'], sep='\t')

    # getting training and testing data
    train_X = train_df['token']
    test_X = test_df['token'].astype('U')
    train_y = train_df['tag']
    test_y = test_df['tag'].astype('U')

    # Naive-Bayes
    nb_pipeline = Pipeline([('vect', CountVectorizer()),        # Counts occurrences of each word
                            ('tfidf', TfidfTransformer()),      # Normalize the counts based on document length
                            ])
    f1_list = []
    cv = KFold(n_splits=5)
    for train_index, test_index in cv.split(train_X):
        train_text = train_X[train_index]
        train_label = train_y[train_index]
        val_text = train_X[test_index]
        val_y = train_y[test_index]
        vectorized_text = nb_pipeline.fit_transform(train_text)
        sm = SMOTE(random_state=42)
        train_text_res, train_y_res = sm.fit_sample(vectorized_text, train_label)
        print("\nTraining Data Class Distribution:")
        print(train_label.value_counts())
        print("\nRe-sampled Training Data Class Distribution:")
        print(train_y_res.value_counts())
        # clf = SVC(kernel='rbf', max_iter=1000, class_weight='balanced', verbose=1)
        clf = MultinomialNB()
        # clf = SGDClassifier(loss='log', penalty='l2', alpha=1e-3, max_iter=100, tol=None,
        #                    n_jobs=-1, verbose=1)
        clf.fit(train_text_res, train_y_res)
        predictions = clf.predict(nb_pipeline.transform(val_text))
        f1 = f1_score(val_y, predictions, average='macro')
        f1_list.append(f1)
    print(f1_list)
    pred = clf.predict(nb_pipeline.transform(test_X))
    print('F1-macro: %s' % f1_score(pred, test_y, average='macro'))

我想建立一个 n-grams 并将其作为一项功能添加到模型中，以便模型能够更好地理解上下文，但不知道如何操作，因为测试将再次在令牌级别进行。我如何建立n-grams并将其输入到模型中，然后再在token级别对测试数据进行预测？

Answer 1

而不是。

nb_pipeline = Pipeline([('vect', CountVectorizer()),
                        ('tfidf', TfidfTransformer())])

而不是： 一次性的计数，tfidf用于unigrams和bigrams。

from sklearn.feature_extraction.text import TfidfVectorizer
nb_pipeline = Pipeline([('tfidf', TfidfVectorizer(ngram_range=(1, 2)))])

参见 文件 更多。