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想改进这个问题吗更新问题，使其只关注一个问题editing this post。
我有两个文本文件（1）坏单词样本和（2）好单词样本现在我进行了最近邻分类，其中新发现的单词将被归类为好的或坏的。我想了解如何用我现有的代码来处理这个问题。谢谢
class Words_Works():
def __init__(self):
self.all_texts = {}
self.categories = {}
self.knn_results = {}
self.stop_words = ['and','the','i','am','he','she','his',
'me','my','a','at','in','you','your',
'of','to','this','that','him','her',
'they','is','it','can','for','into',
'as','with','we','us','them','a',
'it', 'on', 'so', 'too','k','the',
'but', 'are','though'
'very', 'here', 'even', 'from',
'then', 'than']
self.leaf_words = ['s', 'es', 'ed', 'er', 'ly', 'ing']
def add_category(self,f,cat_name):
f_in = open(f)
self.text = f_in.read().lower()
f_in.close()
self.wordify()
self.unstopify()
self.unleafify()
self.categories[cat_name] = {}
for item in self.unleaf:
if self.categories[cat_name].has_key(item):
self.categories[cat_name][item] += 1
else:
self.categories[cat_name][item] = 1
def load_categories(self):
try:
cat_db = open('tweetCategory.txt','rb')
self.categories = cPickle.load(cat_db)
cat_db.close()
print 'File successfully loaded from categories db'
except:
print 'File not loaded from categories_db'
# Finds the levenshtein's distance
def levenshtein_distance(first, second):
"""Find the Levenshtein distance between two strings."""
if len(first) > len(second):
first, second = second, first
if len(second) == 0:
return len(first)
first_length = len(first) + 1
second_length = len(second) + 1
distance_matrix = [[0] * second_length for x in range(first_length)]
for i in range(first_length):
distance_matrix[i][0] = i
for j in range(second_length):
distance_matrix[0][j]=j
for i in xrange(1, first_length):
for j in range(1, second_length):
deletion = distance_matrix[i-1][j] + 1
insertion = distance_matrix[i][j-1] + 1
substitution = distance_matrix[i-1][j-1]
if first[i-1] != second[j-1]:
substitution += 1
distance_matrix[i][j] = min(insertion, deletion, substitution)
return distance_matrix[first_length-1][second_length-1]
def add_text(self,f):
f_in = open(f)
self.text = f_in.read().lower()
f_in.close()
self.wordify()
self.unstopify()
self.unleafify()
self.all_texts[f] = {}
for item in self.unleaf:
if self.all_texts[f].has_key(item):
self.all_texts[f][item] += 1
else:
self.all_texts[f][item] = 1
def save_categories(self):
cat_db = open('tweetCategory.txt','wb')
cPickle.dump(cat_db,self.categories,-1)
cat_db.close()
def unstopify(self):
self.unstop = [item for item in self.words if item not in self.stop_words]
def unleafify(self):
self.unleaf = self.unstop[:]
for leaf in self.leaf_words:
leaf_len = len(leaf)
leaf_pattern = re.compile('%s$' % leaf)
for i in range(len(self.unleaf)):
if leaf_pattern.findall(self.unleaf[i]):
self.unleaf[i] = self.unleaf[i][:-leaf_len]
def wordify(self):
words_pattern = re.compile('//w+')
self.words = words_pattern.findall(self.text)
def knn_calc(self):
for text in self.all_texts.keys():
self.knn_results[text] = {}
for category in self.categories.keys():
self.knn_results[text][category] = {}
iterations = 0
distance = 0
for word in self.all_texts[text].keys():
if word in self.categories[text].keys():
distance = levenshtein_distance(text,category)
self.knn_results[text][category]['Knn Distance'] = distance
self.knn_results [text][category]['Knn Iterations'] = iterations
def knn(self):
for text in self.all_texts.keys():
Result = None
for category in self.categories.keys():
if not result or self.knn_results[text][category]['Knn Distance'] < result:
knn = category
distance = self.knn_results[text][category]['Knn Distance']
iterations = self.knn_results[text][category]['Knn Iterations']
print 'File:',text
print 'Knn:',category
print 'Distance :', distance
print 'Iterations :', iterations
print 'End of nearest neighbour search'
以及测试用例：
mywork = Words_Works()
positive = 'positive.txt'
mywork.add_category(positive, 'Positive Tweets') # Adding as category
negative = 'negative.txt'
mywork.add_category(negative, 'Negative Tweets')
neutral = 'neutral.txt'
mywork.add_category(neutral, 'Neutral Tweets')
for category in mywork.categories.keys(): # Print categories
print category
print mywork.categories[category]
print
print
txts = ('samplegood.txt', 'samplebad.txt') # Creating list of files to
for text in txts: # Adding them
mywork.add_text(text)
for text in mywork.all_texts.keys(): # Print texts
print text
print mywork.all_texts[text]
print
print
mywork.knn_calc() # calculate knn
for files in mywork.knn_results.keys(): # print detailed results
print files
for category in mywork.knn_results[files].keys():
print category
print mywork.knn_results[files][category]
print
print
mywork.knn() # Display results
最佳答案
两条建议：首先，正如@YvesDaoust所指出的，您应该使用编辑距离，也称为Levenshtein距离您可以在python-Levenshtein package中找到它。
其次，使用标准库中的unittest或doctest库来测试代码使用保存在外部文件中的示例来测试代码是一个坏主意，因为没有访问这些文件的权限的第三方（例如，我们）无法知道输入是什么；避免打印输出并手动检查它，因为这是一个缓慢的、易出错的过程，而且其他人也无法复查。
关于python - 最近邻居文本分类，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/21781382/
10-10 18:00