Untitled

```
python examples/model_selection/grid_search_text_feature_extraction.py

==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================

The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the document
classification example.

You can adjust the number of categories by giving their names to the dataset
loader or setting them to None to get the 20 of them.

Here is a sample output of a run on a quad-core machine::

  Loading 20 newsgroups dataset for categories:
  ['alt.atheism', 'talk.religion.misc']
  1427 documents
  2 categories

  Performing grid search...
  pipeline: ['vect', 'tfidf', 'clf']
  parameters:
  {'clf__alpha': (1.0000000000000001e-05, 9.9999999999999995e-07),
   'clf__n_iter': (10, 50, 80),
   'clf__penalty': ('l2', 'elasticnet'),
   'tfidf__use_idf': (True, False),
   'vect__max_n': (1, 2),
   'vect__max_df': (0.5, 0.75, 1.0),
   'vect__max_features': (None, 5000, 10000, 50000)}
  done in 1737.030s

  Best score: 0.940
  Best parameters set:
      clf__alpha: 9.9999999999999995e-07
      clf__n_iter: 50
      clf__penalty: 'elasticnet'
      tfidf__use_idf: True
      vect__max_n: 2
      vect__max_df: 0.75
      vect__max_features: 50000


Loading 20 newsgroups dataset for categories:
['alt.atheism', 'talk.religion.misc']
857 documents
2 categories

Performing grid search...
pipeline: ['vect', 'tfidf', 'clf']
parameters:
{'clf__alpha': (1e-05, 1e-06),
 'clf__penalty': ('l2', 'elasticnet'),
 'vect__max_df': (0.5, 0.75, 1.0),
 'vect__ngram_range': ((1, 1), (1, 2))}
Fitting 3 folds for each of 24 candidates, totalling 72 fits
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x14964 sparse matrix of type '<type 'numpy.int64'>'
	with 87583 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 5.6s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x107094 sparse matrix of type '<type 'numpy.int64'>'
	with 267319 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x14987 sparse matrix of type '<type 'numpy.int64'>'
	with 95383 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 7.7s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x107119 sparse matrix of type '<type 'numpy.int64'>'
	with 275778 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x15002 sparse matrix of type '<type 'numpy.int64'>'
	with 103163 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: kilroy@gboro.rowan.edu (Dr Nancy\'s Sweetie)\nSubject: Re: Food For Thought On Tyre\nSummary: Another Inerrantist rewrites the Bible.\nKeywords: Scripture, implication, prophesy, `Woof!\'\nOrganization: Rowan College of New Jersey\nDisclaimer: Brandy the WonderDog hopes his doghouse will be rebuilt.\nLines: 93\n\n\nThere has been a lot of discussion about Tyre.  In sum, Ezekiel prophesied\nthat the place would be mashed and never rebuilt; as there are a lot of\npeople living there, it would appear that Ezekiel was not literally correct.\n\nThis doesn\'t bother me at all, because I understand the language Ezekiel used\ndifferently than do so-called Biblical literalists.  For example...,
array([1, ..., 0]))
________________________________________________fit_transform_one - 5.6s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x107135 sparse matrix of type '<type 'numpy.int64'>'
	with 284068 stored elements in Compressed Sparse Row format>,
array([1, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/30e9604ce8c0ea1f20d7038af017990d
___________________________________fit_transform_one cache loaded - 0.3s, 0.0min
[Memory]    0.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/49317c0a7afb6d03bf83fcf596db15cd
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/4646f5b8465c587e5b92d0a6177a7594
___________________________________fit_transform_one cache loaded - 1.6s, 0.0min
[Memory]    1.6s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/fc9b38e84471ec3df93c042e4ea18155
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/6a0196bc192932f1bb651a3a962868b4
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d8e4013451121baa9ba0d2c226edbf0b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/ae384682370b31c8aee9250ccaecdce8
___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b35050845bcdae4e0e834cc7a5eee484
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.7s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.5s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.8s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.6s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/69146ce4c22e01d84d210b66b4f433c6
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
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___________________________________fit_transform_one cache loaded - 1.6s, 0.0min
[Memory]    1.6s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/2398745deaef3ec60bb9aff3f24d81ff
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x14932 sparse matrix of type '<type 'numpy.int64'>'
	with 85564 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 6.2s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x101997 sparse matrix of type '<type 'numpy.int64'>'
	with 257123 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x14955 sparse matrix of type '<type 'numpy.int64'>'
	with 93371 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 5.1s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x102022 sparse matrix of type '<type 'numpy.int64'>'
	with 265583 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x14969 sparse matrix of type '<type 'numpy.int64'>'
	with 100664 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 5.1s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <571x102037 sparse matrix of type '<type 'numpy.int64'>'
	with 273380 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/6f78b312e03abfd7cf33f748816b1af5
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a8511daa8257a914e072861e7039118d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/557820d50cc9304a4df42739dc0fd968
___________________________________fit_transform_one cache loaded - 1.5s, 0.0min
[Memory]    1.5s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b0dd8ebfd7f09b09a3e33a3ef48429e3
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/8e48ce63700af8325e781d514daabaab
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/0c487e206c8b7ce39989305d5917d439
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c9f6eccdff5b2f3f68800b92ccbe0687
___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/e114e933ee2b58beb8870baeec23cb48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/79ad57d52b94bd0dc5e2fed2b56c753f
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/736b93d0c6d4c23283689166bf663acc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b9f648a8a46cf7fd451a3aa32cb82d45
___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/7ebcab4deb1250f8a0dd82742eba573c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/6f78b312e03abfd7cf33f748816b1af5
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a8511daa8257a914e072861e7039118d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/557820d50cc9304a4df42739dc0fd968
___________________________________fit_transform_one cache loaded - 2.3s, 0.0min
[Memory]    2.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b0dd8ebfd7f09b09a3e33a3ef48429e3
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/8e48ce63700af8325e781d514daabaab
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/0c487e206c8b7ce39989305d5917d439
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c9f6eccdff5b2f3f68800b92ccbe0687
___________________________________fit_transform_one cache loaded - 1.3s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/e114e933ee2b58beb8870baeec23cb48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/79ad57d52b94bd0dc5e2fed2b56c753f
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/736b93d0c6d4c23283689166bf663acc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b9f648a8a46cf7fd451a3aa32cb82d45
___________________________________fit_transform_one cache loaded - 1.8s, 0.0min
[Memory]    1.8s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/7ebcab4deb1250f8a0dd82742eba573c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/6f78b312e03abfd7cf33f748816b1af5
___________________________________fit_transform_one cache loaded - 0.3s, 0.0min
[Memory]    0.3s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a8511daa8257a914e072861e7039118d
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/557820d50cc9304a4df42739dc0fd968
___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
[Memory]    1.5s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b0dd8ebfd7f09b09a3e33a3ef48429e3
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/8e48ce63700af8325e781d514daabaab
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/0c487e206c8b7ce39989305d5917d439
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c9f6eccdff5b2f3f68800b92ccbe0687
___________________________________fit_transform_one cache loaded - 1.5s, 0.0min
[Memory]    1.5s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/e114e933ee2b58beb8870baeec23cb48
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/79ad57d52b94bd0dc5e2fed2b56c753f
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/736b93d0c6d4c23283689166bf663acc
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/b9f648a8a46cf7fd451a3aa32cb82d45
___________________________________fit_transform_one cache loaded - 1.5s, 0.0min
[Memory]    1.5s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/7ebcab4deb1250f8a0dd82742eba573c
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x14645 sparse matrix of type '<type 'numpy.int64'>'
	with 83859 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.5, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 5.3s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x99921 sparse matrix of type '<type 'numpy.int64'>'
	with 249018 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x14667 sparse matrix of type '<type 'numpy.int64'>'
	with 91562 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 5.5s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x99945 sparse matrix of type '<type 'numpy.int64'>'
	with 257381 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 1), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 1.5s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x14681 sparse matrix of type '<type 'numpy.int64'>'
	with 98931 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=1.0, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
________________________________________________fit_transform_one - 5.3s, 0.1min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <572x99960 sparse matrix of type '<type 'numpy.int64'>'
	with 265258 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/fbd1cfef435063653817f440b7b3dfa6
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/ccb7cb423acd78f451d666d7a1db3530
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/2e25862bbdefd7076aca0f2b3d97328f
___________________________________fit_transform_one cache loaded - 2.3s, 0.0min
[Memory]    2.3s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/36c906b265de7005da416d5ccf4b2907
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c6f3f1d86c7e3c13e8cc1caf123c5e9a
___________________________________fit_transform_one cache loaded - 0.4s, 0.0min
[Memory]    0.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/445a4a85996fb7485772bfd8d0cfa249
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a468d807ef8a58e527acbf916a0c63d7
___________________________________fit_transform_one cache loaded - 1.8s, 0.0min
[Memory]    1.9s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/92f51e0af3e6be739fc0b11040c19186
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d4b333c2cfea194507b88100948e8f13
___________________________________fit_transform_one cache loaded - 0.4s, 0.0min
[Memory]    0.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/17512bc8fdd2d9c1d559af2cdb8c014b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d376e3adcbc6558246d678a851d444f8
___________________________________fit_transform_one cache loaded - 1.3s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/4fc5832a07ccd2a36210706882875b53
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/fbd1cfef435063653817f440b7b3dfa6
___________________________________fit_transform_one cache loaded - 0.3s, 0.0min
[Memory]    0.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/ccb7cb423acd78f451d666d7a1db3530
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/2e25862bbdefd7076aca0f2b3d97328f
___________________________________fit_transform_one cache loaded - 1.6s, 0.0min
[Memory]    1.6s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/36c906b265de7005da416d5ccf4b2907
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c6f3f1d86c7e3c13e8cc1caf123c5e9a
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/445a4a85996fb7485772bfd8d0cfa249
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a468d807ef8a58e527acbf916a0c63d7
___________________________________fit_transform_one cache loaded - 2.6s, 0.0min
[Memory]    2.6s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/92f51e0af3e6be739fc0b11040c19186
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d4b333c2cfea194507b88100948e8f13
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.3s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/17512bc8fdd2d9c1d559af2cdb8c014b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d376e3adcbc6558246d678a851d444f8
___________________________________fit_transform_one cache loaded - 1.7s, 0.0min
[Memory]    1.7s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/4fc5832a07ccd2a36210706882875b53
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/fbd1cfef435063653817f440b7b3dfa6
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.3s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/ccb7cb423acd78f451d666d7a1db3530
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/2e25862bbdefd7076aca0f2b3d97328f
___________________________________fit_transform_one cache loaded - 2.2s, 0.0min
[Memory]    2.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/36c906b265de7005da416d5ccf4b2907
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/c6f3f1d86c7e3c13e8cc1caf123c5e9a
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/445a4a85996fb7485772bfd8d0cfa249
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/a468d807ef8a58e527acbf916a0c63d7
___________________________________fit_transform_one cache loaded - 1.4s, 0.0min
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___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d4b333c2cfea194507b88100948e8f13
___________________________________fit_transform_one cache loaded - 0.2s, 0.0min
[Memory]    0.2s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/17512bc8fdd2d9c1d559af2cdb8c014b
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Memory]    0.0s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/d376e3adcbc6558246d678a851d444f8
___________________________________fit_transform_one cache loaded - 1.3s, 0.0min
[Memory]    1.4s, 0.0min: Loading _fit_transform_one from /tmp/joblib/joblib/sklearn/pipeline/_fit_transform_one/4fc5832a07ccd2a36210706882875b53
___________________________________fit_transform_one cache loaded - 0.0s, 0.0min
[Parallel(n_jobs=1)]: Done  72 out of  72 | elapsed:  2.7min finished
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(CountVectorizer(analyzer=u'word', binary=False, decode_error=u'strict',
        dtype=<type 'numpy.int64'>, encoding=u'utf-8', input=u'content',
        lowercase=True, max_df=0.75, max_features=None, min_df=1,
        ngram_range=(1, 2), preprocessor=None, stop_words=None,
        strip_accents=None, token_pattern=u'(?u)\\b\\w\\w+\\b',
        tokenizer=None, vocabulary=None),
None, [ u'From: mangoe@cs.umd.edu (Charley Wingate)\nSubject: Benediktine Metaphysics\nLines: 24\n\nBenedikt Rosenau writes, with great authority:\n\n>     IF IT IS CONTRADICTORY IT CANNOT EXIST.\n\n"Contradictory" is a property of language.  If I correct this to\n\n\n      THINGS DEFINED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nI will object to definitions as reality.  If you then amend it to\n\n      THINGS DESCRIBED BY CONTRADICTORY LANGUAGE DO NOT EXIST\n\nthen we\'ve come to something which is plainly false.  Failures in\ndescription are merely failures in description.\n\n(I\'m not an objectivist, remember.)\n\n\n-- \nC. Wingate        + "The peace of God, it is no peace,\n                  ...,
array([0, ..., 0]))
_______________________________________________fit_transform_one - 10.4s, 0.2min
________________________________________________________________________________
[Memory] Calling sklearn.pipeline._fit_transform_one...
_fit_transform_one(TfidfTransformer(norm=u'l2', smooth_idf=True, sublinear_tf=False,
         use_idf=True),
None, <857x135939 sparse matrix of type '<type 'numpy.int64'>'
	with 399586 stored elements in Compressed Sparse Row format>,
array([0, ..., 0]))
________________________________________________fit_transform_one - 0.1s, 0.0min
done in 173.933s

Best score: 0.945
Best parameters set:
	clf__alpha: 1e-05
	clf__penalty: 'elasticnet'
	vect__max_df: 0.75
	vect__ngram_range: (1, 2)
```