Nikhil Raghavendra Nikhil Raghavendra - 9 days ago 7
Python Question

TypeError: list object is not callable

I have the following function,

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


When I run the code, it gives me the following error.


TypeError: 'list' object is not callable


Is there any overshadowing going on here? I followed many threads on SO dealing with this error but could not solve my problem.

This is my full code.

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)


Please help me. I really have no idea how to debug this.

Answer

It would have been more helpful had you printed the full stack-trace. As this is a relatively simple error, the problem is easily identifiable in this case. It's this line,

for word in current_words():

You needn't call a list while looping it. Simply this will do,

for word in current_words: