面对 AttributeError: 'list' 对象没有属性 'lower' [英] Facing AttributeError: 'list' object has no attribute 'lower'

问题描述

我已经发布了我的样本训练数据和测试数据以及我的代码.我正在尝试使用朴素贝叶斯算法来训练模型.

I have posted my sample train data as well as test data along with my code. I'm trying to use Naive Bayes algorithm to train the model.

但是，在评论中我得到了列表.所以，我认为我的代码失败并出现以下错误:

But, in the reviews I'm getting list of list. So, I think my code is failing with the following error:

return lambda x: strip_accents(x.lower())
AttributeError: 'list' object has no attribute 'lower'

你们中的任何人都可以帮助我解决问题，因为我是 Python 新手....

Can anyone of you please help me out regarding the same as I'm new to python ....

review,label
Colors & clarity is superb,positive
Sadly the picture is not nearly as clear or bright as my 40 inch Samsung,negative

test.txt:

review,label
The picture is clear and beautiful,positive
Picture is not clear,negative

我的代码:

from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import BernoulliNB
from sklearn.metrics import confusion_matrix
from sklearn.feature_extraction.text import CountVectorizer

def load_data(filename):

    reviews = list()
    labels = list()
    with open(filename) as file:
        file.readline()
        for line in file:
            line = line.strip().split(',')
            labels.append(line[1])
            reviews.append(line[0].split())


    return reviews, labels

X_train, y_train = load_data('/Users/7000015504/Desktop/Sep_10/sample_train.csv')
X_test, y_test = load_data('/Users/7000015504/Desktop/Sep_10/sample_test.csv')


clf = CountVectorizer()
X_train_one_hot =  clf.fit(X_train)
X_test_one_hot = clf.transform(X_test)

bnbc = BernoulliNB(binarize=None)
bnbc.fit(X_train_one_hot, y_train)

score = bnbc.score(X_test_one_hot, y_test)
print("score of Naive Bayes algo is :" , score)

推荐答案

我对您的代码进行了一些修改.下面发布的一个作品；我添加了关于如何调试您在上面发布的评论的评论.

I have applied a few modifications to your code. The one posted below works; I added comments on how to debug the one you posted above.

# These three will not used, do not import them
# from sklearn.preprocessing import MultiLabelBinarizer 
# from sklearn.model_selection import train_test_split 
# from sklearn.metrics import confusion_matrix

# This performs the classification task that you want with your input data in the format provided
from sklearn.naive_bayes import MultinomialNB 

from sklearn.feature_extraction.text import CountVectorizer

def load_data(filename):
    """ This function works, but you have to modify the second-to-last line from
    reviews.append(line[0].split()) to reviews.append(line[0]).
    CountVectorizer will perform the splits by itself as it sees fit, trust him :)"""
    reviews = list()
    labels = list()
    with open(filename) as file:
        file.readline()
        for line in file:
            line = line.strip().split(',')
            labels.append(line[1])
            reviews.append(line[0])

    return reviews, labels

X_train, y_train = load_data('train.txt')
X_test, y_test = load_data('test.txt')

vec = CountVectorizer() 
# Notice: clf means classifier, not vectorizer. 
# While it is syntactically correct, it's bad practice to give misleading names to your objects. 
# Replace "clf" with "vec" or something similar.

# Important! you called only the fit method, but did not transform the data 
# afterwards. The fit method does not return the transformed data by itself. You 
# either have to call .fit() and then .transform() on your training data, or just fit_transform() once.

X_train_transformed =  vec.fit_transform(X_train) 

X_test_transformed = vec.transform(X_test)

clf= MultinomialNB()
clf.fit(X_train_transformed, y_train)

score = clf.score(X_test_transformed, y_test)
print("score of Naive Bayes algo is :" , score)

这段代码的输出是:

score of Naive Bayes algo is : 0.5

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