我有以下功能:类型错误:列表对象是不可调用的
def sample_handling(sample, lexicon, classification):
featureset = []
with open(sample, 'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
current_words = word_tokenize(l.lower())
current_words = [lemmatizer.lemmatize(i) for i in current_words]
features = np.zeros(len(lexicon))
for word in current_words():
if word.lower() in lexicon:
index_value = lexicon.index(word.lower())
features[index_value] += 1
features = list(features)
featureset.append([features, classification])
return featureset
当我运行的代码,它给了我下面的错误:
TypeError: 'list' object is not callable
是否有任何掩盖回事?我跟着很多线程处理这个错误,但无法解决我的问题。
这是我的全码:
import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import numpy as np
import random
import pickle
from collections import Counter
lemmatizer = WordNetLemmatizer()
hm_lines = 10000000
def create_lexicon(pos, neg):
lexicon = []
for fi in [pos, neg]:
with open(fi, 'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
all_words = word_tokenize(l.lower())
lexicon += list(all_words)
lexicon = [lemmatizer.lemmatize(i) for i in lexicon]
w_counts = Counter(lexicon)
#w_counts = {'the': 52521, 'and': 25242}
l2 = []
for w in w_counts:
if 1000 > w_counts[w] > 50:
l2.append(w)
print(l2)
return l2
def sample_handling(sample, lexicon, classification):
featureset = []
with open(sample, 'r') as f:
contents = f.readlines()
for l in contents[:hm_lines]:
current_words = word_tokenize(l.lower())
current_words = [lemmatizer.lemmatize(i) for i in current_words]
features = np.zeros(len(lexicon))
for word in current_words():
if word.lower() in lexicon:
index_value = lexicon.index(word.lower())
features[index_value] += 1
features = list(features)
featureset.append([features, classification])
return featureset
def create_feature_sets_and_lables(pos, neg, test_size = 0.1):
lexicon = create_lexicon(pos, neg)
features = []
features += sample_handling('pos.txt', lexicon,[1,0])
features += sample_handling('neg.txt', lexicon,[0,1])
random.shuffle(features)
features = np.array(features)
testing_size = int(test_size * len(features))
train_x = list(features[:,0][:-testing_size])
train_y = list(features[:,1][:-testing_size])
test_x = list(features[:,0][-testing_size:])
test_y = list(features[:,1][-testing_size:])
return train_x, train_y, test_x, test_y
if __name__ == '__main__':
train_x, train_y, test_x, test_y = create_feature_sets_and_lables('pos.txt', 'neg.txt')
with open('sentiment_set.pickle', 'wb') as f:
pickle.dump([train_x, train_y, test_x, test_y], f)
谢谢!这是一个愚蠢的错误... –