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Facing AttributeError: 'int' object has no attribute 'toarray' in topic modelling

I am try to do topic modelling using LSA, with the following code:
and in the next line I have this:
top_n_words_lsa = get_top_n_words(10,
lsa_keys,
small_document_term_matrix,
small_count_vectorizer)
for i in range(len(top_n_words_lsa)):
print("Topic {}: ".format(i+1), top_n_words_lsa[i])
But I am facing this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_15164/599799419.py in <module>
----> 1 top_n_words_lsa = get_top_n_words(10,
2 lsa_keys,
3 small_document_term_matrix,
4 small_count_vectorizer)
5
~\AppData\Local\Temp/ipykernel_15164/2401631730.py in get_top_n_words(n, keys,
document_term_matrix, count_vectorizer)
11 if keys[i] == topic:
12 temp_vector_sum += document_term_matrix[i]
---> 13 temp_vector_sum = temp_vector_sum.toarray()
14 top_n_word_indices = np.flip(np.argsort(temp_vector_sum)[0][-n:],0)
15 top_word_indices.append(top_n_word_indices)
AttributeError: 'int' object has no attribute 'toarray'
The related helper functions are defined below:
# Define helper functions
def get_top_n_words(n, keys, document_term_matrix, count_vectorizer):
'''
returns a list of n_topic strings, where each string contains the n most common
words in a predicted category, in order
'''
top_word_indices = []
for topic in range(n_topics):
temp_vector_sum = 0
for i in range(len(keys)):
if keys[i] == topic:
temp_vector_sum += document_term_matrix[i]
temp_vector_sum = temp_vector_sum.toarray()
top_n_word_indices = np.flip(np.argsort(temp_vector_sum)[0][-n:],0)
top_word_indices.append(top_n_word_indices)
top_words = []
for topic in top_word_indices:
topic_words = []
for index in topic:
temp_word_vector = np.zeros((1,document_term_matrix.shape[1]))
temp_word_vector[:,index] = 1
the_word = count_vectorizer.inverse_transform(temp_word_vector)[0][0]
topic_words.append(the_word.encode('ascii').decode('utf-8'))
top_words.append(" ".join(topic_words))
return top_words
Can you please tell me what I am missing here?

You define temp_vector_sum as 0 and then add to it. So it's an object of type int. That class doesn't define a function toarray. You could do something like: np.array([temp_vector_sum]).

Is there a specified way to transform int to str in nltk module?

My 1st time here!
I think I got a trouble in my code, could you help me?
The point is, I created a function with nltk module and I don't know where I'm going wrong when it reports me that errors:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-94-7fe386d50c39> in <module>
----> 1 frasescomstemming = fazstemmer(base)
<ipython-input-93-e5451b452b6b> in fazstemmer(texto)
7 for (palavras, emocao) in texto:
8 comstemming = [str(stemmer.stem(p))
----> 9 for p in palavras.split() if p not in stopwords]
10 frasessstemming.append((comstemming, emocao))
11 return frasessstemming
AttributeError: 'int' object has no attribute 'split'
Here is my code:
def fazstemmer(texto):
stemmer = nltk.stem.RSLPStemmer()
frasessstemming = []
for (palavras, emocao) in texto:
comstemming = [str(stemmer.stem(p))
for p in palavras.split() if p not in stopwords]
frasessstemming.append((comstemming, emocao))
return frasessstemming

As for AttributeError: 'int' object has no attribute 'split' This should be caused by for p in palavras.split() if p not in stopwords] You can change the type of palavras in the last loop.
def fazstemmer(texto):
stemmer = nltk.stem.RSLPStemmer()
frasessstemming = []
for (palavras, emocao) in texto:
comstemming = str(stemmer.stem(p))
palavras = str(palavras)
for p in palavras.split():
if p not in stopwords:
frasessstemming.append((comstemming, emocao))
return frasessstemming

Without an example input texto and desired output, it's hard to guess what's the data structure / type of the variables, so here's a guess.
Try this:
stemmer = nltk.stem.RSLPStemmer()
def fazstemmer(text):
for words, emotion in text:
words, emotion = str(words), str(emotion)
for word in words.split():
if word not in stopwords:
yield stemmer.stem(word), emotion
stemmed_text = list(fazstemmer(text))

Q : Error Function 'dict' object is not callable in python

Recently i starded learning python, this would be my first "useful" script (simple word learning script). I got a "TypeError: 'dict' object is not callable", but it's unrelevant in this case(alteast for me).
def discounted_price(total,discount,minprice=100):
fruits = ['Apple','Avocado','Banana','Blackberries','Blueberries','Cherries','Date Fruit','Grapes','Guava','Jackfruit','Kiwifruit']
prices = [6,5,3,10,12,7,14,15,8,7,9]
d1 = dict(zip(fruits,prices))
chart = ['Blueberries','Blueberries','Grapes','Apple','Apple','Apple','Blueberries','Guava','Jackfruit','Blueberries','Jackfruit']
fruit_price = None
counter_item = {}
for i in chart:
if i in counter_item:
counter_item[i] +=1
else:
counter_item[i] =1
counter_item
fruit_price = {}
for i, j in d1.items():
for x, y in counter_item.items():
if i == x:
fruit_price[i]=(j*y)
fruit_price
total_price = 0
for i in fruit_price.values():
total_price += i
total_price
minprice=100
discount=0
if total_price<minprice:
discount=total_price
else:
discount=total_price*(10/100)
discounted_price=total_price-discount
return discounted_price
print(discounted_price(total_price(counter_item(chart),fruit_price),10,minprice=100))
Type error
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-38-7dbc173506d2> in <module>
30 discounted_price=total_price-discount
31 return discounted_price
---> 32 print(discounted_price(total_price(counter_item(chart),fruit_price),10,minprice=100))
TypeError: 'dict' object is not callable
Thanks!

Gettig error: object of type...has no len()

I'm trying to get PMI count from work part file which is opened in NX software in Python, but getting error:
TypeError: object of type 'NXOpen.Annotations.PmiCollection' has no len()
Code:
lw = theSession.ListingWindow
lw.Open()
theSession = NXOpen.Session.GetSession()
theParts = theSession.Parts
theWorkPart = theParts.Work
allPMIObjects = theWorkPart.PmiManager.Pmis
count1 = len(allPMIObjects)
lw.WriteLine(count1)
lw.Close()
Documentation link: https://docs.plm.automation.siemens.com/data_services/resources/nx/11/nx_api/custom/en_US/nxopen_python_ref/NXOpen.Annotations.PmiCollection.html
Equivalent vb code: http://nxjournaling.com/content/find-out-if-part-has-any-pmi

Below is the code that gives count of PMIs from active part and assert if count is changed (here 3):
lw = theSession.ListingWindow
lw.Open()
# Custom code starts to get PMI count in part and check
theSession = NXOpen.Session.GetSession()
theParts = theSession.Parts
theWorkPart = theParts.Work
allPMIObjects = theWorkPart.PmiManager.Pmis
i = 0
for p in allPMIObjects:
i = i + 1
lw.WriteLine(str(i))
lw.Close()
#if PMI count is changed from 3, raise AssertionError:
assert i == 3

key error 0 sentiment analysis

I am trying to run sentiment analysis on a selection of a data set, but every time I do I get this error: KeyError: 0
For reference, this is the code I am working with:
OC = df[df["text"].str.contains("Obamacare")]
from textblob import TextBlob
import re
def clean_tweet(tweet):
return " ".join(re.sub("(#[A-Za-z0-9]+)|([^0-9A-Za-z \t])|(\w+:\/\/\S+)", " ", tweet).split())
def analize_sentiment(tweet):
analysis = TextBlob(clean_tweet(tweet))
if analysis.sentiment.polarity > 0:
return 1
elif analysis.sentiment.polarity == 0:
return 0
else:
return -1
df["sentiment"] = np.array([ analize_sentiment(tweet) for tweet in df["text"]])
pos_tweets = [tweet for index, tweet in enumerate(OC['text']) if OC['sentiment'][index] > 0]
neu_tweets = [ tweet for index, tweet in enumerate(OC['text']) if OC['sentiment'][index] == 0]
neg_tweets = [ tweet for index, tweet in enumerate(OC['text']) if OC['sentiment'][index] < 0]
It's after I try to run the pos_tweets, neu_tweets, neg_tweets that I keep getting Key Error: 0

I'm not sure what you're enumerating or why that is part of a sentiment analysis. This is how I did it...
def clean(tweet):
return " ".join(re.sub("(#[A-Za-z0-9]+) | ([^0-9A-Za-z\t]) | (w+:\/\/\s+)", " ", tweet).split())
def sentiment_analysis(tweet):
analysis = TextBlob(clean(tweet))
if analysis.sentiment.polarity > 0:
return 1
elif analysis.sentiment.polarity == 0:
return 0
else:
return -1
df["sentiment"] = np.array([sentiment_analysis(tweet) for tweet in
df["text"]])
df["OC"] = df.text.str.contains("obamacare", case = False)
df2 = df.loc[df["OC"] == True]
df2.sentiment.value_counts()
I basically just ran your list comprehension against the entire df, and then parsed.