Question

我正在尝试使用NLTK将电子邮件分类为垃圾邮件/火腿

下面是遵循的步骤：

尝试提取所有令牌
获取所有功能
从所有唯一单词和映射的语料库中提取特征正确/错误
训练朴素贝叶斯分类器中的数据

from nltk.classify.util import apply_features
from nltk import NaiveBayesClassifier
import pandas as pd
import collections
from sklearn.model_selection import train_test_split
from collections import Counter
data = pd.read_csv('https://raw.githubusercontent.com/venkat1017/Data/master/emails.csv')

"""fetch array of tuples where each tuple is defined by (tokenized_text, label)
"""

processed_tokens=data['text'].apply(lambda x:([x for x in x.split() if x.isalpha()]))
processed_tokens=processed_tokens.apply(lambda x:([x for x in x if len(x)>3]))

processed_tokens = [(i,j) for i,j in zip(processed_tokens,data['spam'])]



"""
 dictword return a Set of unique words in complete corpus.
"""

list = zip(*processed_tokens)

dictionary = Counter(word for i, j in processed_tokens for word in i)

dictword = [word for word, count in dictionary.items() if count == 1]


"""maps each input text into feature vector"""

y_dict = ( [ (word, True) for word in dictword] )
feature_vec=dict(y_dict)

"""Training"""

training_set, testing_set = train_test_split(y_dict, train_size=0.7)

classifier = NaiveBayesClassifier.train(training_set)

    ~\AppData\Local\Continuum\anaconda3\lib\site-packages\nltk\classify\naivebayes.py in train(cls, labeled_featuresets, estimator)
    197         for featureset, label in labeled_featuresets:
    198             label_freqdist[label] += 1
--> 199             for fname, fval in featureset.items():
    200                 # Increment freq(fval|label, fname)
    201                 feature_freqdist[label, fname][fval] += 1

AttributeError: 'str' object has no attribute 'items'

在尝试训练唯一单词的语料库时，我面临以下错误

Answer 1

首先，我希望您知道y_dict只是一个字典，它将在语料库中仅出现过一次的单词（字符串）映射为值True的键。您要将其作为训练集传递给分类器，而应该传递tuple的{{1}}（每个文本行的特征字典）和（相应的标签）。当您的分类器应该接收[({'feat1': 'value1', ... }, label_value), ...]作为输入时，您正在传递[ ('word1', True), ... ]。 string类型没有items属性，只有dict有。因此是错误。

第二，您的数据建模错误。您的训练集应包含从data['text']映射到data['spam']值的功能字典（因为这是您的标签）。请在第here节1.3中了解如何使用nltk的分类器执行文档分类。

无法在朴素贝叶斯训练模型

1 个答案: