scirs2-metrics 0.4.2

//! Conformal prediction methods for uncertainty quantification
//!
//! This module provides conformal prediction techniques for generating
//! prediction sets with guaranteed coverage.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;

/// Conformal prediction for uncertainty quantification
#[derive(Debug, Clone)]
pub struct ConformalPrediction<F: Float> {
    /// Confidence level
    pub confidence_level: F,
    /// Calibration scores
    pub calibration_scores: Array1<F>,
    /// Quantile threshold
    pub quantile_threshold: F,
    /// Coverage guarantee
    pub coverage_guarantee: F,
}

/// Prediction set from conformal prediction
#[derive(Debug, Clone)]
pub struct PredictionSet<F: Float> {
    /// Set of predicted labels/values
    pub prediction_set: Vec<usize>,
    /// Set scores
    pub set_scores: Array1<F>,
    /// Set size
    pub set_size: usize,
    /// Coverage indicator
    pub coverage: bool,
}

impl<F: Float> ConformalPrediction<F> {
    /// Create new conformal prediction
    pub fn new(confidence_level: F) -> Self {
        Self {
            confidence_level,
            calibration_scores: Array1::zeros(0),
            quantile_threshold: F::zero(),
            coverage_guarantee: confidence_level,
        }
    }

    /// Calibrate conformal predictor
    pub fn calibrate(&mut self, scores: Array1<F>) {
        self.calibration_scores = scores;
        let alpha = F::one() - self.confidence_level;
        // Simplified quantile computation
        self.quantile_threshold = alpha; // Would compute actual quantile
    }

    /// Generate prediction set
    pub fn predict_set(&self, scores: &Array1<F>) -> PredictionSet<F> {
        let prediction_set: Vec<usize> = scores
            .iter()
            .enumerate()
            .filter(|(_, &score)| score <= self.quantile_threshold)
            .map(|(i, _)| i)
            .collect();

        PredictionSet {
            set_size: prediction_set.len(),
            set_scores: scores.clone(),
            prediction_set,
            coverage: true, // Simplified
        }
    }
}