Class CountVectorizerScikitsLearnNode

Convert a collection of text documents to a matrix of token counts

This node has been automatically generated by wrapping the ``sklearn.feature_extraction.text.CountVectorizer`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

This implementation produces a sparse representation of the counts using
scipy.sparse.coo_matrix.

If you do not provide an a-priori dictionary and you do not use an analyzer
that does some kind of feature selection then the number of features will
be equal to the vocabulary size found by analyzing the data.

Read more in the :ref:`User Guide <text_feature_extraction>`.

**Parameters**

input : string {'filename', 'file', 'content'}
    If 'filename', the sequence passed as an argument to fit is
    expected to be a list of filenames that need reading to fetch
    the raw content to analyze.

    If 'file', the sequence items must have a 'read' method (file-like
    object) that is called to fetch the bytes in memory.

    Otherwise the input is expected to be the sequence strings or
    bytes items are expected to be analyzed directly.

encoding : string, 'utf-8' by default.
    If bytes or files are given to analyze, this encoding is used to
    decode.

decode_error : {'strict', 'ignore', 'replace'}
    Instruction on what to do if a byte sequence is given to analyze that
    contains characters not of the given `encoding`. By default, it is
    'strict', meaning that a UnicodeDecodeError will be raised. Other
    values are 'ignore' and 'replace'.

strip_accents : {'ascii', 'unicode', None}
    Remove accents during the preprocessing step.
    'ascii' is a fast method that only works on characters that have
    an direct ASCII mapping.
    'unicode' is a slightly slower method that works on any characters.
    None (default) does nothing.

analyzer : string, {'word', 'char', 'char_wb'} or callable
    Whether the feature should be made of word or character n-grams.
    Option 'char_wb' creates character n-grams only from text inside
    word boundaries.

    If a callable is passed it is used to extract the sequence of features
    out of the raw, unprocessed input.
    Only applies if ``analyzer == 'word'``.

preprocessor : callable or None (default)
    Override the preprocessing (string transformation) stage while
    preserving the tokenizing and n-grams generation steps.

tokenizer : callable or None (default)
    Override the string tokenization step while preserving the
    preprocessing and n-grams generation steps.
    Only applies if ``analyzer == 'word'``.

ngram_range : tuple (min_n, max_n)
    The lower and upper boundary of the range of n-values for different
    n-grams to be extracted. All values of n such that min_n <= n <= max_n
    will be used.

stop_words : string {'english'}, list, or None (default)
    If 'english', a built-in stop word list for English is used.

    If a list, that list is assumed to contain stop words, all of which
    will be removed from the resulting tokens.
    Only applies if ``analyzer == 'word'``.

    If None, no stop words will be used. max_df can be set to a value
    in the range [0.7, 1.0) to automatically detect and filter stop
    words based on intra corpus document frequency of terms.

lowercase : boolean, True by default
    Convert all characters to lowercase before tokenizing.

token_pattern : string
    Regular expression denoting what constitutes a "token", only used
    if ``analyzer == 'word'``. The default regexp select tokens of 2
    or more alphanumeric characters (punctuation is completely ignored
    and always treated as a token separator).

max_df : float in range [0.0, 1.0] or int, default=1.0
    When building the vocabulary ignore terms that have a document
    frequency strictly higher than the given threshold (corpus-specific
    stop words).
    If float, the parameter represents a proportion of documents, integer
    absolute counts.
    This parameter is ignored if vocabulary is not None.

min_df : float in range [0.0, 1.0] or int, default=1
    When building the vocabulary ignore terms that have a document
    frequency strictly lower than the given threshold. This value is also
    called cut-off in the literature.
    If float, the parameter represents a proportion of documents, integer
    absolute counts.
    This parameter is ignored if vocabulary is not None.

max_features : int or None, default=None
    If not None, build a vocabulary that only consider the top
    max_features ordered by term frequency across the corpus.

    This parameter is ignored if vocabulary is not None.

vocabulary : Mapping or iterable, optional
    Either a Mapping (e.g., a dict) where keys are terms and values are
    indices in the feature matrix, or an iterable over terms. If not
    given, a vocabulary is determined from the input documents. Indices
    in the mapping should not be repeated and should not have any gap
    between 0 and the largest index.

binary : boolean, default=False
    If True, all non zero counts are set to 1. This is useful for discrete
    probabilistic models that model binary events rather than integer
    counts.

dtype : type, optional
    Type of the matrix returned by fit_transform() or transform().

**Attributes**

``vocabulary_`` : dict
    A mapping of terms to feature indices.

``stop_words_`` : set
    Terms that were ignored because they either:


      - occurred in too many documents (`max_df`)
      - occurred in too few documents (`min_df`)
      - were cut off by feature selection (`max_features`).

    This is only available if no vocabulary was given.

See also

HashingVectorizer, TfidfVectorizer

**Notes**

The ``stop_words_`` attribute can get large and increase the model size
when pickling. This attribute is provided only for introspection and can
be safely removed using delattr or set to None before pickling.

Overrides: object.__init__

Transform documents to document-term matrix.

This node has been automatically generated by wrapping the sklearn.feature_extraction.text.CountVectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor.

Parameters

raw_documents : iterable

An iterable which yields either str, unicode or file objects.

Returns

X : sparse matrix, [n_samples, n_features]

Document-term matrix.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Learn a vocabulary dictionary of all tokens in the raw documents.

This node has been automatically generated by wrapping the sklearn.feature_extraction.text.CountVectorizer class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

raw_documents : iterable

An iterable which yields either str, unicode or file objects.

Returns

self

Overrides: Node.stop_training