type t = [ `BaseEstimator | `CountVectorizer | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?input:[ `Filename | `File | `Content ] ->
?encoding:string ->
?decode_error:[ `Strict | `Ignore | `Replace ] ->
?strip_accents:[ `Ascii | `Unicode ] ->
?lowercase:bool ->
?preprocessor:Py.Object.t ->
?tokenizer:Py.Object.t ->
?stop_words:[ `Arr of [> `ArrayLike ] Np.Obj.t | `English ] ->
?token_pattern:string ->
?ngram_range:Py.Object.t ->
?analyzer:
[ `Callable of Py.Object.t
| `S of string
| `Char
| `PyObject of Py.Object.t ] ->
?max_df:[ `I of int | `F of float ] ->
?min_df:[ `I of int | `F of float ] ->
?max_features:int ->
?vocabulary:[ `Arr of [> `ArrayLike ] Np.Obj.t | `Mapping of Py.Object.t ] ->
?binary:bool ->
?dtype:Np.Dtype.t ->
unit ->
tConvert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts using scipy.sparse.csr_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', default='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 a sequence of items that can be of type string or byte.
encoding : string, default='utf-8' If bytes or files are given to analyze, this encoding is used to decode.
decode_error : 'strict', 'ignore', 'replace', default='strict' 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', default=None Remove accents and perform other character normalization 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.
Both 'ascii' and 'unicode' use NFKD normalization from :func:`unicodedata.normalize`.
lowercase : bool, default=True Convert all characters to lowercase before tokenizing.
preprocessor : callable, default=None Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if ``analyzer is not callable``.
tokenizer : callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if ``analyzer == 'word'``.
stop_words : string 'english', list, default=None If 'english', a built-in stop word list for English is used. There are several known issues with 'english' and you should consider an alternative (see :ref:`stop_words`).
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.
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).
ngram_range : tuple (min_n, max_n), default=(1, 1)
The lower and upper boundary of the range of n-values for different
word n-grams or char n-grams to be extracted. All values of n such
such that min_n <= n <= max_n will be used. For example an
``ngram_range`` of ``(1, 1)`` means only unigrams, ``(1, 2)`` means
unigrams and bigrams, and ``(2, 2)`` means only bigrams.
Only applies if ``analyzer is not callable``.
analyzer : string, {'word', 'char', 'char_wb'} or callable, default='word'
Whether the feature should be made of word n-gram or character
n-grams.
Option 'char_wb' creates character n-grams only from text inside
word boundaries; n-grams at the edges of words are padded with space.
If a callable is passed it is used to extract the sequence of features
out of the raw, unprocessed input.
.. versionchanged:: 0.21
Since v0.21, if ``input`` is ``filename`` or ``file``, the data is
first read from the file and then passed to the given callable
analyzer.
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, 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, default=None
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 : bool, 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, default=np.int64
Type of the matrix returned by fit_transform() or transform().
Attributes
----------
vocabulary_ : dict
A mapping of terms to feature indices.
fixed_vocabulary_: boolean
True if a fixed vocabulary of term to indices mapping
is provided by the user
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.
Examples
--------
>>> from sklearn.feature_extraction.text import CountVectorizer
>>> corpus = [
... 'This is the first document.',
... 'This document is the second document.',
... 'And this is the third one.',
... 'Is this the first document?',
... ]
>>> vectorizer = CountVectorizer()
>>> X = vectorizer.fit_transform(corpus)
>>> print(vectorizer.get_feature_names())
['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
>>> print(X.toarray())
[[0 1 1 1 0 0 1 0 1]
[0 2 0 1 0 1 1 0 1]
[1 0 0 1 1 0 1 1 1]
[0 1 1 1 0 0 1 0 1]]
>>> vectorizer2 = CountVectorizer(analyzer='word', ngram_range=(2, 2))
>>> X2 = vectorizer2.fit_transform(corpus)
>>> print(vectorizer2.get_feature_names())
['and this', 'document is', 'first document', 'is the', 'is this',
'second document', 'the first', 'the second', 'the third', 'third one',
'this document', 'this is', 'this the']
>>> print(X2.toarray())
[[0 0 1 1 0 0 1 0 0 0 0 1 0]
[0 1 0 1 0 1 0 1 0 0 1 0 0]
[1 0 0 1 0 0 0 0 1 1 0 1 0]
[0 0 1 0 1 0 1 0 0 0 0 0 1]]
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.
val build_analyzer : [> tag ] Obj.t -> Py.Object.tReturn a callable that handles preprocessing, tokenization and n-grams generation.
Returns ------- analyzer: callable A function to handle preprocessing, tokenization and n-grams generation.
val build_preprocessor : [> tag ] Obj.t -> Py.Object.tReturn a function to preprocess the text before tokenization.
Returns ------- preprocessor: callable A function to preprocess the text before tokenization.
val build_tokenizer : [> tag ] Obj.t -> Py.Object.tReturn a function that splits a string into a sequence of tokens.
Returns ------- tokenizer: callable A function to split a string into a sequence of tokens.
Decode the input into a string of unicode symbols.
The decoding strategy depends on the vectorizer parameters.
Parameters ---------- doc : str The string to decode.
Returns ------- doc: str A string of unicode symbols.
val fit :
?y:Py.Object.t ->
raw_documents:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tLearn a vocabulary dictionary of all tokens in the raw documents.
Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects.
Returns ------- self
val fit_transform :
?y:Py.Object.t ->
raw_documents:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tLearn the vocabulary dictionary and return document-term matrix.
This is equivalent to fit followed by transform, but more efficiently implemented.
Parameters ---------- raw_documents : iterable An iterable which yields either str, unicode or file objects.
Returns ------- X : array of shape (n_samples, n_features) Document-term matrix.
Array mapping from feature integer indices to feature name.
Returns ------- feature_names : list A list of feature names.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
Build or fetch the effective stop words list.
Returns ------- stop_words: list or None A list of stop words.
val inverse_transform :
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
Np.Numpy.Ndarray.List.tReturn terms per document with nonzero entries in X.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Document-term matrix.
Returns ------- X_inv : list of arrays of shape (n_samples,) List of arrays of terms.
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
val transform :
raw_documents:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tTransform documents to document-term matrix.
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 of shape (n_samples, n_features) Document-term matrix.
val fixed_vocabulary_ : t -> boolAttribute fixed_vocabulary_: get value or raise Not_found if None.
val fixed_vocabulary_opt : t -> bool optionAttribute fixed_vocabulary_: get value as an option.
val stop_words_ : t -> Py.Object.tAttribute stop_words_: get value or raise Not_found if None.
val stop_words_opt : t -> Py.Object.t optionAttribute stop_words_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.