Class HashingVectorizerScikitsLearnNode

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

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

It turns a collection of text documents into a scipy.sparse matrix holding
token occurrence counts (or binary occurrence information), possibly
normalized as token frequencies if norm='l1' or projected on the euclidean
unit sphere if norm='l2'.

This text vectorizer implementation uses the hashing trick to find the
token string name to feature integer index mapping.

This strategy has several advantages:


- it is very low memory scalable to large datasets as there is no need to
  store a vocabulary dictionary in memory

- it is fast to pickle and un-pickle as it holds no state besides the
  constructor parameters

- it can be used in a streaming (partial fit) or parallel pipeline as there
  is no state computed during fit.

There are also a couple of cons (vs using a CountVectorizer with an
in-memory vocabulary):


- there is no way to compute the inverse transform (from feature indices to
  string feature names) which can be a problem when trying to introspect
  which features are most important to a model.

- there can be collisions: distinct tokens can be mapped to the same
  feature index. However in practice this is rarely an issue if n_features
  is large enough (e.g. 2 ** 18 for text classification problems).

- no IDF weighting as this would render the transformer stateful.

The hash function employed is the signed 32-bit version of Murmurhash3.

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, default='utf-8'
    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.

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), default=(1, 1)
    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'``.

lowercase : boolean, default=True
    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 selects tokens of 2
    or more alphanumeric characters (punctuation is completely ignored
    and always treated as a token separator).

n_features : integer, default=(2 ** 20)
    The number of features (columns) in the output matrices. Small numbers
    of features are likely to cause hash collisions, but large numbers
    will cause larger coefficient dimensions in linear learners.

norm : 'l1', 'l2' or None, optional
    Norm used to normalize term vectors. None for no normalization.

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().

non_negative : boolean, default=False
    Whether output matrices should contain non-negative values only;
    effectively calls abs on the matrix prior to returning it.
    When True, output values can be interpreted as frequencies.
    When False, output values will have expected value zero.

See also

CountVectorizer, TfidfVectorizer

Overrides: object.__init__

Transform a sequence of documents to a document-term matrix.

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

Parameters

X : iterable over raw text documents, length = n_samples

Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed.

y : (ignored)

Returns

X : scipy.sparse matrix, shape = (n_samples, self.n_features)

Document-term matrix.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Overrides: Node.stop_training