Struct ClassificationPipeline 

pub struct ClassificationPipeline<T, M> { /* private fields */ }

A pipeline combining a transformer and a binary classifier.

Implementations

impl<T, M> ClassificationPipeline<T, M>

where T: Transformer, M: OnlineBinaryClassifier,

pub fn new(transformer: T, model: M) -> Result<Self, RillError>

Create a new classification pipeline.

pub fn transformer(&self) -> &T

Borrow the transformer.

pub fn model(&self) -> &M

Borrow the model.

pub fn learn_transactional( &mut self, features: &[f64], target: bool, ) -> Result<(), RillError>

where T: Clone, M: Clone,

Learn one classification sample with all-or-nothing state changes.

Trait Implementations

impl<T: Clone, M: Clone> Clone for ClassificationPipeline<T, M>

fn clone(&self) -> ClassificationPipeline<T, M>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T: Debug, M: Debug> Debug for ClassificationPipeline<T, M>

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

Formats the value using the given formatter.

impl<T, M> OnlineBinaryClassifier for ClassificationPipeline<T, M>

where T: Transformer, M: OnlineBinaryClassifier,

fn feature_count(&self) -> usize

The number of features the model expects.

fn samples_seen(&self) -> u64

How many training samples the model has seen so far.

fn predict_proba(&self, features: &[f64]) -> Result<f64, RillError>

Predict the probability of the positive class.

fn learn(&mut self, features: &[f64], target: bool) -> Result<(), RillError>

Update the model using a single labeled sample.

fn reset(&mut self)

Reset the model to its initial state.

fn predict(&self, features: &[f64]) -> Result<bool, RillError>

Predict the boolean class label using a 0.5 threshold.