Class: TfidfVectorizer

Convert a collection of raw documents to a matrix of TF-IDF features.

Equivalent to CountVectorizer followed by TfidfTransformer.

For an example of usage, see Classification of text documents using sparse features.

For an efficiency comparison of the different feature extractors, see FeatureHasher and DictVectorizer Comparison.

For an example of document clustering and comparison with HashingVectorizer, see Clustering text documents using k-means.

Constructors

new TfidfVectorizer()

new TfidfVectorizer(opts?): TfidfVectorizer

Parameters

Parameter	Type	Description
`opts`?	`object`	-
`opts.analyzer`?	`"word"` \| `"char"` \| `"char_wb"`	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; 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.
`opts.binary`?	`boolean`	If `true`, all non-zero term counts are set to 1. This does not mean outputs will have only 0/1 values, only that the tf term in tf-idf is binary. (Set `binary` to `true`, `use_idf` to `false` and `norm` to `undefined` to get 0/1 outputs).
`opts.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’.
`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.
`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.
`opts.lowercase`?	`boolean`	Convert all characters to lowercase before tokenizing.
`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 in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not `undefined`.
`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 in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not `undefined`.
`opts.ngram_range`?	`any`	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. 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.norm`?	`"l1"` \| `"l2"`	Each output row will have unit norm, either:
`opts.preprocessor`?	`any`	Override the preprocessing (string transformation) stage while preserving the tokenizing and n-grams generation steps. Only applies if `analyzer` is not callable.
`opts.smooth_idf`?	`boolean`	Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.
`opts.stop_words`?	`any`[] \| `"english"`	If a string, it is passed to _check_stop_list and the appropriate stop list is returned. ‘english’ is currently the only supported string value. 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) means no character normalization is performed. Both ‘ascii’ and ‘unicode’ use NFKD normalization from `unicodedata.normalize`.
`opts.sublinear_tf`?	`boolean`	Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).
`opts.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). 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.use_idf`?	`boolean`	Enable inverse-document-frequency reweighting. If `false`, idf(t) = 1.
`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.

Returns TfidfVectorizer

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:31

Properties

Property	Type	Default value	Defined in
`_isDisposed`	`boolean`	`false`	generated/feature_extraction/text/TfidfVectorizer.ts:29
`_isInitialized`	`boolean`	`false`	generated/feature_extraction/text/TfidfVectorizer.ts:28
`_py`	`PythonBridge`	`undefined`	generated/feature_extraction/text/TfidfVectorizer.ts:27
`id`	`string`	`undefined`	generated/feature_extraction/text/TfidfVectorizer.ts:24
`opts`	`any`	`undefined`	generated/feature_extraction/text/TfidfVectorizer.ts:25

Accessors

fixed_vocabulary_

Get Signature

get fixed_vocabulary_(): Promise<boolean>

True if a fixed vocabulary of term to indices mapping is provided by the user.

Returns Promise<boolean>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:709

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

Parameter	Type
`pythonBridge`	`PythonBridge`

Returns void

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:177

vocabulary_

Get Signature

get vocabulary_(): Promise<any>

A mapping of terms to feature indices.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:684

Methods

build_analyzer()

build_analyzer(opts): Promise<any>

Return a callable to process input data.

The callable handles preprocessing, tokenization, and n-grams generation.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:248

build_preprocessor()

build_preprocessor(opts): Promise<any>

Return a function to preprocess the text before tokenization.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:276

build_tokenizer()

build_tokenizer(opts): Promise<any>

Return a function that splits a string into a sequence of tokens.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:304

decode()

decode(opts): Promise<any>

Decode the input into a string of unicode symbols.

The decoding strategy depends on the vectorizer parameters.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.doc`?	`string`	The string to decode.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:334

dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:229

fit()

fit(opts): Promise<any>

Learn vocabulary and idf from training set.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.raw_documents`?	`any`	An iterable which generates either str, unicode or file objects.
`opts.y`?	`any`	This parameter is not needed to compute tfidf.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:366

fit_transform()

fit_transform(opts): Promise<any>

Learn vocabulary and idf, return document-term matrix.

This is equivalent to fit followed by transform, but more efficiently implemented.

Parameters

Parameter	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/TfidfVectorizer.ts:405

get_feature_names_out()

get_feature_names_out(opts): Promise<any>

Get output feature names for transformation.

Parameters

Parameter	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/TfidfVectorizer.ts:442

get_metadata_routing()

get_metadata_routing(opts): Promise<any>

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.routing`?	`any`	A `MetadataRequest` encapsulating routing information.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:478

get_stop_words()

get_stop_words(opts): Promise<any>

Build or fetch the effective stop words list.

Parameters

Parameter	Type
`opts`	`object`

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:512

init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

Parameter	Type
`py`	`PythonBridge`

Returns Promise<void>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:190

inverse_transform()

inverse_transform(opts): Promise<any[]>

Return terms per document with nonzero entries in X.

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.X`?	`ArrayLike`	Document-term matrix.

Returns Promise<any[]>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:540

set_fit_request()

set_fit_request(opts): Promise<any>

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.raw_documents`?	`string` \| `boolean`	Metadata routing for `raw_documents` parameter in `fit`.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:578

set_transform_request()

set_transform_request(opts): Promise<any>

Request metadata passed to the transform method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

Parameters

Parameter	Type	Description
`opts`	`object`	-
`opts.raw_documents`?	`string` \| `boolean`	Metadata routing for `raw_documents` parameter in `transform`.

Returns Promise<any>

Defined in generated/feature_extraction/text/TfidfVectorizer.ts:616

transform()

transform(opts): Promise<any>

Transform documents to document-term matrix.

Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform).

Parameters

Parameter	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/TfidfVectorizer.ts:652

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