Struct CountVectorizer 

pub struct CountVectorizer { /* private fields */ }

Complete Count Vectorizer implementation for text feature extraction

Converts a collection of text documents to a matrix of token counts. Supports n-grams, stop word filtering, and various preprocessing options.

Implementations

impl CountVectorizer

pub fn new() -> Self

Create a new CountVectorizer with default parameters

pub fn max_features(self, max_features: Option<usize>) -> Self

Set maximum number of features to select

pub fn min_df(self, min_df: usize) -> Self

Set minimum document frequency for feature inclusion

pub fn max_df(self, max_df: f64) -> Self

Set maximum document frequency for feature inclusion

pub fn ngram_range(self, ngram_range: (usize, usize)) -> Self

Set n-gram range (min_n, max_n)

pub fn binary(self, binary: bool) -> Self

Set binary mode (presence/absence instead of counts)

pub fn lowercase(self, lowercase: bool) -> Self

Set whether to convert to lowercase

pub fn stop_words(self, stop_words: Option<Vec<String>>) -> Self

Set stop words to filter out

pub fn fit_instance_method(&mut self, documents: &[String]) -> SklResult<()>

Fit the vectorizer to a collection of documents

pub fn transform(&self, documents: &[String]) -> SklResult<Array2<Float>>

Transform documents to feature vectors

pub fn fit_transform( &mut self, documents: &[String], ) -> SklResult<Array2<Float>>

Fit and transform in one step

pub fn get_feature_names(&self) -> &[String]

Get feature names

pub fn get_vocabulary(&self) -> Option<&HashMap<String, usize>>

Get vocabulary

Trait Implementations

impl Clone for CountVectorizer

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CountVectorizer

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for CountVectorizer

fn default() -> Self

Returns the “default value” for a type.

impl Fit<Vec<String>, ()> for CountVectorizer

type Fitted = FittedCountVectorizer

The fitted model type

fn fit(self, x: &Vec<String>, _y: &()) -> SklResult<Self::Fitted>

Fit the model to the provided data with validation

fn fit_with_validation( self, x: &X, y: &Y, _x_val: Option<&X>, _y_val: Option<&Y>, ) -> Result<(Self::Fitted, FitMetrics), SklearsError>

where Self: Sized,

Fit with custom validation and early stopping