AttributeError: 'list' object has no attribute 'lower' with CountVectorizer

Question

I am trying to make a prediction on a pandas dataframe in Python. Somehow the CountVectorizer can’t convert the data. Does anyone know what’s causing the problem?

This is my code:

filename = 'final_model.sav'
print(response.status_code)
data = response.json()
print(data)

dictionary = pd.read_json('rating_company_small.json', lines=True)

dictionary_df = pd.DataFrame()
dictionary_df["comment text"] = dictionary["comment"]

data = pd.DataFrame.from_dict(json_normalize(data), orient='columns')
print(data)

df = pd.DataFrame()

df["comment text"] = data["Text"]
df["status"] = data["Status"]

print(df)
Processing.dataframe_cleaning(df)

comment_data = df['comment text']

tfidf = CountVectorizer()
tfidf.fit(dictionary_df["comment text"])
Test_X_Tfidf = tfidf.transform(df["comment text"])


print(comment_data)
print(Test_X_Tfidf)
loaded_model = pickle.load(open(filename, 'rb'))
predictions_NB = loaded_model.predict(Test_X_Tfidf)

This is the dataframe:

                         comment text    status
0                   [slecht, bedrijf]    string
1  [leuk, bedrijfje, goed, behandeld]  Approved
2  [leuk, bedrijfje, goed, behandeld]  Approved
3                   [leuk, bedrijfje]  Approved

full error message:

Traceback (most recent call last):
  File "Request.py", line 36, in <module>
    Test_X_Tfidf = tfidf.transform(df["comment text"])
  File "C:UsersjunioAnaconda3libsite-packagessklearnfeature_extractiontext.py", line 1112, in transform
    _, X = self._count_vocab(raw_documents, fixed_vocab=True)
  File "C:UsersjunioAnaconda3libsite-packagessklearnfeature_extractiontext.py", line 970, in _count_vocab
    for feature in analyze(doc):
  File "C:UsersjunioAnaconda3libsite-packagessklearnfeature_extractiontext.py", line 352, in <lambda>
    tokenize(preprocess(self.decode(doc))), stop_words)
  File "C:UsersjunioAnaconda3libsite-packagessklearnfeature_extractiontext.py", line 256, in <lambda>
    return lambda x: strip_accents(x.lower())
AttributeError: 'list' object has no attribute 'lower'

I’m expecting it to return the predictions on the dataframe.

Asked By: Lars Assen

||

Source

Answer 1

CountVectorizer cannot directly handle a Series of lists, which is why you’re getting that error (lower is a string method).
I looks like you want a MultiLabelBinarizer instead, which can handle this input structure:

from sklearn.preprocessing import MultiLabelBinarizer

count_vec = MultiLabelBinarizer()
mlb = count_vec.fit(df["comment text"])
pd.DataFrame(mlb.transform(df["comment text"]), columns=[mlb.classes_])

  bedrijf bedrijfje behandeld goed leuk slecht
0       1         0         0    0    0      1
1       0         1         1    1    1      0
2       0         1         1    1    1      0
3       0         1         0    0    1      0

However the above approach won’t account for duplicate elements in the lists, the output elements can either be 0 or 1. If that is the behavior you’re expecting instead, you could join the lists into strings and then use a CountVectorizer, since it is expecting strings:

text = df["comment text"].map(' '.join)
count_vec = CountVectorizer()
cv = count_vec.fit(text)

pd.DataFrame(cv.transform(text).toarray(), columns=[mlb.classes_])

  bedrijf bedrijfje behandeld goed leuk slecht
0       1         0         0    0    0      1
1       0         1         1    1    1      0
2       0         1         1    1    1      0
3       0         1         0    0    1      0

Note that this is not the same as a tf-idf of the input strings. Here you just have the actual counts. For that you have TfidfVectorizer, which for the same example would produce:

    bedrijf bedrijfje behandeld      goed      leuk    slecht
0  0.707107  0.000000  0.000000  0.000000  0.000000  0.707107
1  0.000000  0.444931  0.549578  0.549578  0.444931  0.000000
2  0.000000  0.444931  0.549578  0.549578  0.444931  0.000000
3  0.000000  0.707107  0.000000  0.000000  0.707107  0.000000

Answered By: yatu

Answer 2

An array of strings is what the CountVectorizer expects. Therefore, it will crash if you pass in a nested array of tokens.

Instead of

['ham', 'go', 'until', 'jurong', 'point', 'crazy', 'available', 'only', 'in', 'bugis', 'n', 'great', 'world', 'la', 'e', 'buffet', 'cine', 'there', 'got', 'amore', 'wat']

Send the data in the form of this to the CountVectorizer.

ham Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...

Answered By: jeffasante

AttributeError: 'list' object has no attribute 'lower' with CountVectorizer

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