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

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Equivalent to CountVectorizer followed byTfidfTransformer.

Read more in the User Guide.

Parameters
input{‘filename’, ‘file’, ‘content’}, default=’content’

If ‘filename’, the sequence passed as an argument to fit isexpected to be a list of filenames that need reading to fetchthe raw content to analyze.

If ‘file’, the sequence items must have a ‘read’ method (file-likeobject) that is called to fetch the bytes in memory.

Otherwise the input is expected to be a sequence of items thatcan be of type string or byte.

encodingstr, default=’utf-8’

If bytes or files are given to analyze, this encoding is used todecode.

decode_error{‘strict’, ‘ignore’, ‘replace’}, default=’strict’

Instruction on what to do if a byte sequence is given to analyze thatcontains characters not of the given encoding. By default, it is‘strict’, meaning that a UnicodeDecodeError will be raised. Othervalues are ‘ignore’ and ‘replace’.

strip_accents{‘ascii’, ‘unicode’}, default=None

Remove accents and perform other character normalizationduring the preprocessing step.‘ascii’ is a fast method that only works on characters that havean 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 fromunicodedata.normalize.

lowercasebool, default=True

Convert all characters to lowercase before tokenizing.

preprocessorcallable, default=None

Override the preprocessing (string transformation) stage whilepreserving the tokenizing and n-grams generation steps.Only applies if analyzerisnotcallable.

tokenizercallable, default=None

Override the string tokenization step while preserving thepreprocessing and n-grams generation steps.Only applies if analyzer'word'.

analyzer{‘word’, ‘char’, ‘char_wb’} or callable, default=’word’

Whether the feature should be made of word or character n-grams.Option ‘char_wb’ creates character n-grams only from text insideword 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 featuresout of the raw, unprocessed input.

Since v0.21, if input is filename or file, the data isfirst read from the file and then passed to the given callableanalyzer.

stop_words{‘english’}, list, default=None

If a string, it is passed to _check_stop_list and the appropriate stoplist is returned. ‘english’ is currently the only supported stringvalue.There are several known issues with ‘english’ and you shouldconsider an alternative (see Using stop words).

If a list, that list is assumed to contain stop words, all of whichwill 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 valuein the range [0.7, 1.0) to automatically detect and filter stopwords based on intra corpus document frequency of terms.

token_patternstr

Regular expression denoting what constitutes a “token”, only usedif analyzer'word'. The default regexp selects tokens of 2or more alphanumeric characters (punctuation is completely ignoredand always treated as a token separator).

ngram_rangetuple (min_n, max_n), default=(1, 1)

The lower and upper boundary of the range of n-values for differentn-grams to be extracted. All values of n such that min_n <= n <= max_nwill be used. For example an ngram_range of (1,1) means onlyunigrams, (1,2) means unigrams and bigrams, and (2,2) meansonly bigrams.Only applies if analyzerisnotcallable.

max_dffloat or int, default=1.0

When building the vocabulary ignore terms that have a documentfrequency strictly higher than the given threshold (corpus-specificstop words).If float in range [0.0, 1.0], the parameter represents a proportion ofdocuments, integer absolute counts.This parameter is ignored if vocabulary is not None.

min_dffloat or int, default=1

When building the vocabulary ignore terms that have a documentfrequency strictly lower than the given threshold. This value is alsocalled cut-off in the literature.If float in range of [0.0, 1.0], the parameter represents a proportionof documents, integer absolute counts.This parameter is ignored if vocabulary is not None.

max_featuresint, default=None

If not None, build a vocabulary that only consider the topmax_features ordered by term frequency across the corpus.

This parameter is ignored if vocabulary is not None.

vocabularyMapping or iterable, default=None

Either a Mapping (e.g., a dict) where keys are terms and values areindices in the feature matrix, or an iterable over terms. If notgiven, a vocabulary is determined from the input documents.

binarybool, default=False

If True, all non-zero term counts are set to 1. This does not meanoutputs will have only 0/1 values, only that the tf term in tf-idfis binary. (Set idf and normalization to False to get 0/1 outputs).

dtypedtype, default=float64

Type of the matrix returned by fit_transform() or transform().

norm{‘l1’, ‘l2’}, default=’l2’

Each output row will have unit norm, either:* ‘l2’: Sum of squares of vector elements is 1. The cosinesimilarity between two vectors is their dot product when l2 norm hasbeen applied.* ‘l1’: Sum of absolute values of vector elements is 1.See preprocessing.normalize.

use_idfbool, default=True

Enable inverse-document-frequency reweighting.

smooth_idfbool, default=True

Smooth idf weights by adding one to document frequencies, as if anextra document was seen containing every term in the collectionexactly once. Prevents zero divisions.

sublinear_tfbool, default=False

Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).

Attributes
vocabulary_dict

A mapping of terms to feature indices.

fixed_vocabulary_: bool

True if a fixed vocabulary of term to indices mappingis provided by the user

idf_array of shape (n_features,)

The inverse document frequency (IDF) vector; only definedif use_idf is True.

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

CountVectorizer

Transforms text into a sparse matrix of n-gram counts.

TfidfTransformer

Performs the TF-IDF transformation from a provided matrix of counts.

Notes

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

Examples

Methods

build_analyzer()

Return a callable that handles preprocessing, tokenization and n-grams generation.

build_preprocessor()

Return a function to preprocess the text before tokenization.

build_tokenizer()

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

decode(doc)

Decode the input into a string of unicode symbols.

fit(raw_documents[, y])

Learn vocabulary and idf from training set.

fit_transform(raw_documents[, y])

Learn vocabulary and idf, return document-term matrix.

get_feature_names()

Array mapping from feature integer indices to feature name.

get_params([deep])

Get parameters for this estimator.

get_stop_words()

Build or fetch the effective stop words list.

inverse_transform(X)

Return terms per document with nonzero entries in X.

set_params(**params)

Set the parameters of this estimator.

transform(raw_documents[, copy])

Transform documents to document-term matrix.

__init__(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern='(?u)bww+b', ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=<class 'numpy.float64'>, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)[source]

Initialize self. See help(type(self)) for accurate signature.

build_analyzer()[source]

Return a callable that handles preprocessing, tokenizationand n-grams generation.

Returns
analyzer: callable

A function to handle preprocessing, tokenizationand n-grams generation.

build_preprocessor()[source]

Return a function to preprocess the text before tokenization.

Returns
preprocessor: callable

A function to preprocess the text before tokenization.

build_tokenizer()[source]

Return 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(doc)[source]
Learn()

Decode the input into a string of unicode symbols.

The decoding strategy depends on the vectorizer parameters.

Parameters
docstr

The string to decode.

Returns
doc: str

A string of unicode symbols.

fit(raw_documents, y=None)[source]

Learn vocabulary and idf from training set.

Parameters
raw_documentsiterable

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

While True: Learn() Crack
yNone

This parameter is not needed to compute tfidf.

Returns
selfobject

Fitted vectorizer.

fit_transform(raw_documents, y=None)[source]

Learn vocabulary and idf, return document-term matrix.

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

Parameters
raw_documentsiterable

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

yNone

This parameter is ignored.

Returns
Xsparse matrix of (n_samples, n_features)

Tf-idf-weighted document-term matrix.

get_feature_names()[source]

Array mapping from feature integer indices to feature name.

Returns
feature_nameslist

A list of feature names.

get_params(deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

get_stop_words()[source]

Build or fetch the effective stop words list.

Returns
stop_words: list or None

A list of stop words.

inverse_transform(X)[source]

Return 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_invlist of arrays of shape (n_samples,)

List of arrays of terms.

set_params(**params)[source]

Set 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 eachcomponent of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfobject

Estimator instance.

transform(raw_documents, copy='deprecated')[source]

Transform documents to document-term matrix.

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

Parameters
raw_documentsiterable

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

copybool, default=True

Whether to copy X and operate on the copy or perform in-placeoperations.

Deprecated since version 0.22: The copy parameter is unused and was deprecated in version0.22 and will be removed in 0.24. This parameter will beignored.

Returns
Xsparse matrix of (n_samples, n_features)

Tf-idf-weighted document-term matrix.

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While True Learn Wiki


A while loop statement in Python programming language repeatedly executes a target statement as long as a given condition is true.

Syntax

The syntax of a while loop in Python programming language is −

Here, statement(s) may be a single statement or a block of statements. The condition may be any expression, and true is any non-zero value. The loop iterates while the condition is true.

When the condition becomes false, program control passes to the line immediately following the loop.

In Python, all the statements indented by the same number of character spaces after a programming construct are considered to be part of a single block of code. Python uses indentation as its method of grouping statements.

Flow Diagram

Here, key point of the while loop is that the loop might not ever run. When the condition is tested and the result is false, the loop body will be skipped and the first statement after the while loop will be executed.

Example

Live Demo

When the above code is executed, it produces the following result −

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The block here, consisting of the print and increment statements, is executed repeatedly until count is no longer less than 9. With each iteration, the current value of the index count is displayed and then increased by 1.

The Infinite Loop

A loop becomes infinite loop if a condition never becomes FALSE. You must use caution when using while loops because of the possibility that this condition never resolves to a FALSE value. This results in a loop that never ends. Such a loop is called an infinite loop.

An infinite loop might be useful in client/server programming where the server needs to run continuously so that client programs can communicate with it as and when required.

When the above code is executed, it produces the following result −

Above example goes in an infinite loop and you need to use CTRL+C to exit the program.

Using else Statement with While Loop

Python supports to have an else statement associated with a loop statement.

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  • If the else statement is used with a while loop, the else statement is executed when the condition becomes false.

The following example illustrates the combination of an else statement with a while statement that prints a number as long as it is less than 5, otherwise else statement gets executed.

Live DemoTrue:

When the above code is executed, it produces the following result −

Single Statement Suites

Similar to the if statement syntax, if your while clause consists only of a single statement, it may be placed on the same line as the while header.

While True Learn Walkthrough

Here is the syntax and example of a one-line while clause −

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It is better not try above example because it goes into infinite loop and you need to press CTRL+C keys to exit.