Trait Profiler 

pub trait Profiler<Model, Opt, Input, Output> {
    type EvalMetrics;

    // Required methods
    fn train(
        &self,
        model: &mut Model,
        optimizer: &mut Opt,
        x: &Input,
        y: &Output,
    ) -> Result<(TrainMetrics, Self::EvalMetrics), ProfilerError>;
    fn profile_evaluation(
        &self,
        model: &mut Model,
        x: &Input,
        y: &Output,
    ) -> Result<Self::EvalMetrics, ProfilerError>;
}

Trait for profiling and benchmarking machine learning models during training and evaluation.

This trait defines methods for collecting performance metrics during model training and evaluation. It allows for consistent measurement of training time and model performance metrics across different model types and optim strategies.

Type Parameters

• Model - The machine learning model type being profiled
• Opt - The optimizer type used for training
• Input - The input data type (features)
• Output - The output data type (targets/labels)

Required Associated Types

type EvalMetrics

The type of evaluation metrics returned by the profiler

Required Methods

fn train( &self, model: &mut Model, optimizer: &mut Opt, x: &Input, y: &Output, ) -> Result<(TrainMetrics, Self::EvalMetrics), ProfilerError>

Profiles the training process of a model, collecting training time and evaluation metrics.

This method measures the time taken for training while also computing performance metrics on the provided data.

Arguments

• model - Mutable reference to the model being trained
• optimizer - Mutable reference to the optimizer used for training
• x - Reference to input features
• y - Reference to output targets

Returns

A tuple containing training metrics (including training time) and evaluation metrics specific to the model type, or a ProfilerError if an error occurs during profiling.

fn profile_evaluation( &self, model: &mut Model, x: &Input, y: &Output, ) -> Result<Self::EvalMetrics, ProfilerError>

Profiles the evaluation process of a model, computing performance metrics.

This method evaluates the model on the provided data and returns metrics specific to the model type.

Arguments

• model - Mutable reference to the model being evaluated
• x - Reference to input features
• y - Reference to output targets

Returns

Evaluation metrics specific to the model type, or a ProfilerError if an error occurs during evaluation.

Implementors

impl<Model, Opt, Input, Output> Profiler<Model, Opt, Input, Output> for ClassificationProfiler<Model, Opt, Input, Output>

where Model: BaseModel<Input, Output> + ClassificationModel<Input, Output>, Opt: Optimizer<Input, Output, Model>,

type EvalMetrics = ClassificationMetrics

impl<Model, Opt, Input, Output> Profiler<Model, Opt, Input, Output> for RegressionProfiler<Model, Opt, Input, Output>

where Model: BaseModel<Input, Output> + RegressionModel<Input, Output>, Opt: Optimizer<Input, Output, Model>,

type EvalMetrics = RegressionMetrics