CountVectorizer: Vocabolario non era montato

Ho istanziato un sklearn.feature_extraction.di testo.CountVectorizer oggetto passando un vocabolario attraverso il vocabulary argomento, ma ho un sklearn.utils.validation.NotFittedError: CountVectorizer - Vocabulary wasn't fitted. messaggio di errore. Perché?

Esempio:

import sklearn.feature_extraction
import numpy as np
import pickle

# Save the vocabulary
ngram_size = 1
dictionary_filepath = 'my_unigram_dictionary'
vectorizer = sklearn.feature_extraction.text.CountVectorizer(ngram_range=(ngram_size,ngram_size), min_df=1)

corpus = ['This is the first document.',
        'This is the second second document.',
        'And the third one.',
        'Is this the first document? This is right.',]

vect = vectorizer.fit(corpus)
print('vect.get_feature_names(): {0}'.format(vect.get_feature_names()))
pickle.dump(vect.vocabulary_, open(dictionary_filepath, 'w'))

# Load the vocabulary
vocabulary_to_load = pickle.load(open(dictionary_filepath, 'r'))
loaded_vectorizer = sklearn.feature_extraction.text.CountVectorizer(ngram_range=(ngram_size,ngram_size), min_df=1, vocabulary=vocabulary_to_load)
print('loaded_vectorizer.get_feature_names(): {0}'.format(loaded_vectorizer.get_feature_names()))

Di uscita:

vect.get_feature_names(): [u'and', u'document', u'first', u'is', u'one', u'right', u'second', u'the', u'third', u'this']
Traceback (most recent call last):
  File "C:\Users\Francky\Documents\GitHub\adobe\dstc4\test\CountVectorizerSaveDic.py", line 22, in <module>
    print('loaded_vectorizer.get_feature_names(): {0}'.format(loaded_vectorizer.get_feature_names()))
  File "C:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py", line 890, in get_feature_names
    self._check_vocabulary()
  File "C:\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py", line 271, in _check_vocabulary
    check_is_fitted(self, 'vocabulary_', msg=msg),
  File "C:\Anaconda\lib\site-packages\sklearn\utils\validation.py", line 627, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})
sklearn.utils.validation.NotFittedError: CountVectorizer - Vocabulary wasn't fitted.

OriginaleL’autore Franck Dernoncourt | 2015-09-19

One Reply
  1. 12

    Per qualche motivo, anche se è passato vocabulary=vocabulary_to_load come argomento per sklearn.feature_extraction.text.CountVectorizer(), è comunque necessario chiamare loaded_vectorizer._validate_vocabulary() prima di essere in grado di chiamare loaded_vectorizer.get_feature_names().

    Nel tuo esempio, si dovrebbe quindi effettuare le seguenti operazioni durante la creazione di un CountVectorizer oggetto con il vostro vocabolario:

    vocabulary_to_load = pickle.load(open(dictionary_filepath, 'r'))
    loaded_vectorizer = sklearn.feature_extraction.text.CountVectorizer(ngram_range=(ngram_size,
                                            ngram_size), min_df=1, vocabulary=vocabulary_to_load)
    loaded_vectorizer._validate_vocabulary()
    print('loaded_vectorizer.get_feature_names(): {0}'.
      format(loaded_vectorizer.get_feature_names()))

    OriginaleL’autore Franck Dernoncourt

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