CountVectorizer
Convert 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 User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new CountVectorizer(opts?: object): CountVectorizer;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.analyzer? | "word" | "char" | "char_wb" | 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. Default Value 'word' |
opts.binary? | boolean | 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. Default Value false |
opts.decode_error? | "ignore" | "strict" | "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’. Default Value 'strict' |
opts.dtype? | any | Type of the matrix returned by fit_transform() or transform(). |
opts.encoding? | string | If bytes or files are given to analyze, this encoding is used to decode. Default Value 'utf-8' |
opts.input? | "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. Default Value 'content' |
opts.lowercase? | boolean | Convert all characters to lowercase before tokenizing. Default Value true |
opts.max_df? | number | 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 undefined. Default Value 1 |
opts.max_features? | number | If not undefined, build a vocabulary that only consider the top max\_features ordered by term frequency across the corpus. Otherwise, all features are used. This parameter is ignored if vocabulary is not undefined. |
opts.min_df? | number | 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 undefined. Default Value 1 |
opts.ngram_range? | any | 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. |
opts.preprocessor? | any | Override the preprocessing (strip_accents and lowercase) stage while preserving the tokenizing and n-grams generation steps. Only applies if analyzer is not callable. |
opts.stop_words? | any[] | "english" | 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 Using 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 undefined, no stop words will be used. In this case, setting max\_df to a higher value, such as in the range (0.7, 1.0), can automatically detect and filter stop words based on intra corpus document frequency of terms. |
opts.strip_accents? | "ascii" | "unicode" | Remove accents and perform other character normalization during the preprocessing step. ‘ascii’ is a fast method that only works on characters that have a direct ASCII mapping. ‘unicode’ is a slightly slower method that works on any characters. undefined (default) does nothing. Both ‘ascii’ and ‘unicode’ use NFKD normalization from unicodedata.normalize (opens in a new tab). |
opts.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). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted. |
opts.tokenizer? | any | Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if analyzer \== 'word'. |
opts.vocabulary? | any | 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. |
Returns
Defined in: generated/feature_extraction/text/CountVectorizer.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/feature_extraction/text/CountVectorizer.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/feature_extraction/text/CountVectorizer.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/feature_extraction/text/CountVectorizer.ts:23 (opens in a new tab)
id
string
Defined in: generated/feature_extraction/text/CountVectorizer.ts:20 (opens in a new tab)
opts
any
Defined in: generated/feature_extraction/text/CountVectorizer.ts:21 (opens in a new tab)
Accessors
fixed_vocabulary_
True if a fixed vocabulary of term to indices mapping is provided by the user.
Signature
fixed_vocabulary_(): Promise<boolean>;Returns
Promise<boolean>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:591 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/feature_extraction/text/CountVectorizer.ts:145 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/feature_extraction/text/CountVectorizer.ts:149 (opens in a new tab)
stop_words_
Terms that were ignored because they either:
Signature
stop_words_(): Promise<any>;Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:616 (opens in a new tab)
vocabulary_
A mapping of terms to feature indices.
Signature
vocabulary_(): Promise<any>;Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:566 (opens in a new tab)
Methods
build_analyzer()
Return a callable to process input data.
The callable handles preprocessing, tokenization, and n-grams generation.
Signature
build_analyzer(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:235 (opens in a new tab)
build_preprocessor()
Return a function to preprocess the text before tokenization.
Signature
build_preprocessor(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:263 (opens in a new tab)
build_tokenizer()
Return a function that splits a string into a sequence of tokens.
Signature
build_tokenizer(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:291 (opens in a new tab)
decode()
Decode the input into a string of unicode symbols.
The decoding strategy depends on the vectorizer parameters.
Signature
decode(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.doc? | string | The string to decode. |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:321 (opens in a new tab)
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:216 (opens in a new tab)
fit()
Learn a vocabulary dictionary of all tokens in the raw documents.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.raw_documents? | any | An iterable which generates either str, unicode or file objects. |
opts.y? | any | This parameter is ignored. |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:354 (opens in a new tab)
fit_transform()
Learn the vocabulary dictionary and return document-term matrix.
This is equivalent to fit followed by transform, but more efficiently implemented.
Signature
fit_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.raw_documents? | any | An iterable which generates either str, unicode or file objects. |
opts.y? | any | This parameter is ignored. |
Returns
Promise<any[]>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:394 (opens in a new tab)
get_feature_names_out()
Get output feature names for transformation.
Signature
get_feature_names_out(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.input_features? | any | Not used, present here for API consistency by convention. |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:432 (opens in a new tab)
get_stop_words()
Build or fetch the effective stop words list.
Signature
get_stop_words(opts: object): Promise<any>;Parameters
| Name | Type |
|---|---|
opts | object |
Returns
Promise<any>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:468 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:158 (opens in a new tab)
inverse_transform()
Return terms per document with nonzero entries in X.
Signature
inverse_transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Document-term matrix. |
Returns
Promise<any[]>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:496 (opens in a new tab)
transform()
Transform 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.
Signature
transform(opts: object): Promise<any[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.raw_documents? | any | An iterable which generates either str, unicode or file objects. |
Returns
Promise<any[]>
Defined in: generated/feature_extraction/text/CountVectorizer.ts:533 (opens in a new tab)