Struct ClassificationDataSet 

pub struct ClassificationDataSet<Features, Label> {
    pub dataset: Vec<ClassificationRecord<Features, Label>>,
}

Dataset to feed into classification model for training task

Fields

dataset: Vec<ClassificationRecord<Features, Label>>

dataset of classification records on which to train

Implementations

impl<Features, Label> ClassificationDataSet<Features, Label>

pub fn get_labels(&self) -> Vec<&Label>

get labels for record

pub fn get_features(&self) -> Vec<&Features>

get features

pub fn get_records(&self) -> &Vec<ClassificationRecord<Features, Label>>

get records

pub fn consume_records(self) -> Vec<ClassificationRecord<Features, Label>>

consume records of dataset

pub fn from_struct<'a, I, S: 'a>( it: I, feature_extraction: fn(&S) -> Features, label_extraction: fn(&S) -> Label, ) -> Self

where I: Iterator<Item = &'a S>,

Create dataset from iterator of structs

Trait Implementations

impl<Features: Debug, Label: Debug> Debug for ClassificationDataSet<Features, Label>

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

Formats the value using the given formatter.

impl<Input: Estimatable, Label: PartialEq + Clone> Estimator<ClassificationDataSet<Input, Label>> for GaussianNBEstimator

type Estimator = GaussianNB<Input, Label>

Output model or transformer fitted to input data.

fn fit( &self, input: &ClassificationDataSet<Input, Label>, ) -> Option<Self::Estimator>

Fit model or transformer based on given inputs, or None if the estimator was not able to fit to the input data as expected.

impl<Itr, Record, Features, Label> From<Itr> for ClassificationDataSet<Features, Label>

where Itr: IntoIterator<Item = Record>, Record: Into<ClassificationRecord<Features, Label>>,

fn from(value: Itr) -> Self

Converts to this type from the input type.