CODEWITHSUNDEEP
pythonscikit-learnpipeline
Felicia.H
Felicia.H

Reputation: 361

TfidfVectorizer NotFittedError

I am using sklearn Pipeline and FeatureUnion to create features from text files and I want to print out the feature names.

First, I collect all transformations into a list.

In [225]:components
Out[225]: 
[TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
         dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
         lowercase=True, max_df=0.85, max_features=None, min_df=6,
         ngram_range=(1, 1), norm='l1', preprocessor=None, smooth_idf=True,
         stop_words='english', strip_accents=None, sublinear_tf=True,
         token_pattern=u'(?u)[#a-zA-Z0-9/\\-]{2,}',
         tokenizer=StemmingTokenizer(proc_type=stem, token_pattern=(?u)[a-zA-Z0-9/\-]{2,}),
         use_idf=True, vocabulary=None),
 TruncatedSVD(algorithm='randomized', n_components=150, n_iter=5,
        random_state=None, tol=0.0),
 TextStatsFeatures(),
 DictVectorizer(dtype=<type 'numpy.float64'>, separator='=', sort=True,
         sparse=True),
 DictVectorizer(dtype=<type 'numpy.float64'>, separator='=', sort=True,
         sparse=True),
 TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
         dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
         lowercase=True, max_df=0.85, max_features=None, min_df=6,
         ngram_range=(1, 2), norm='l1', preprocessor=None, smooth_idf=True,
         stop_words='english', strip_accents=None, sublinear_tf=True,
         token_pattern=u'(?u)[a-zA-Z0-9/\\-]{2,}',
         tokenizer=StemmingTokenizer(proc_type=stem, token_pattern=(?u)[a-zA-Z0-9/\-]{2,}),
         use_idf=True, vocabulary=None)]

For example the first component is a TfidfVectorizer() object.

components[0]
Out[226]: 
TfidfVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.85, max_features=None, min_df=6,
        ngram_range=(1, 1), norm='l1', preprocessor=None, smooth_idf=True,
        stop_words='english', strip_accents=None, sublinear_tf=True,
        token_pattern=u'(?u)[#a-zA-Z0-9/\\-]{2,}',
        tokenizer=StemmingTokenizer(proc_type=stem, token_pattern=(?u)[a-zA-Z0-9/\-]{2,}),
        use_idf=True, vocabulary=None)

type(components[0])
Out[227]: sklearn.feature_extraction.text.TfidfVectorizer

But when I try to use the TfidfVectorizer method get_feature_names, it throws a NotFittedError

components[0].get_feature_names()
Traceback (most recent call last):

  File "<ipython-input-228-0160deb904f5>", line 1, in <module>
    components[0].get_feature_names()

  File "C:\Users\fheng\AppData\Local\Continuum\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py", line 903, in get_feature_names
    self._check_vocabulary()

  File "C:\Users\fheng\AppData\Local\Continuum\Anaconda\lib\site-packages\sklearn\feature_extraction\text.py", line 275, in _check_vocabulary
    check_is_fitted(self, 'vocabulary_', msg=msg),

  File "C:\Users\fheng\AppData\Local\Continuum\Anaconda\lib\site-packages\sklearn\utils\validation.py", line 678, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})

**NotFittedError: TfidfVectorizer - Vocabulary wasn't fitted.**

Upvotes: 3

Views: 2869

Answers (1)

Vivek Kumar
Vivek Kumar

Reputation: 36599

Have u used this list in a pipeline or featureUnion ? And have you called fit() method on them?

This error is you have not called fit() (ie. trained the models) and direclty trying to access the values.

Upvotes: 7

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