gam-problem 0.3.156

//! Analytic diagnostic helpers for LAML/REML optimization.
//!
//! Production diagnostics inspect analytic invariants only. Runtime fitting,
//! prediction, and diagnostic APIs must consume quantities the optimizer
//! already computes. This module implements diagnostic strategies that identify
//! root causes of gradient pathologies from those analytic quantities:
//!
//! 1. KKT Audit (Envelope Theorem Check): Detects violations of the stationarity
//!    assumption used in implicit differentiation.
//!
//! 2. Spectral Bleed Trace: Detects when truncated eigenspace corrections are
//!    inconsistent with the penalty's energy in that subspace.
//!
//! 3. Dual-Ridge Consistency Check: Verifies that the ridge used by the inner
//!    solver (PIRLS) matches what the outer gradient calculation assumes.

use ndarray::Array1;
use std::collections::BTreeMap;
use std::fmt;
use std::sync::atomic::{AtomicI32, AtomicUsize, Ordering};

// =============================================================================
// Rate-Limited Diagnostic Output
// =============================================================================
// These helpers prevent diagnostic spam while ensuring important messages are seen.
// Pattern: show first occurrence, then every Nth occurrence, with count indicator.

/// Rate-limited diagnostic for Hessian minimum eigenvalue warnings
pub static H_MIN_EIG_LOG_BUCKET: AtomicI32 = AtomicI32::new(i32::MIN);
/// Count of `should_emit_h_min_eig_diag` invocations that have ever been
/// considered for emission; used together with `H_MIN_EIG_LOG_BUCKET` to
/// rate-limit one diagnostic per decade-magnitude bucket and per
/// `MIN_EIG_DIAG_EVERY` repeats within the same bucket.
pub static H_MIN_EIG_LOG_COUNT: AtomicUsize = AtomicUsize::new(0);
/// Repeat period within a magnitude bucket for the Hessian-minimum-eigenvalue
/// diagnostic: after the first emission for a bucket, every Nth subsequent
/// invocation also emits.
pub const MIN_EIG_DIAG_EVERY: usize = 200;
/// Threshold below which a positive Hessian minimum eigenvalue is treated as
/// nearly-singular and routed through the rate-limited diagnostic.
pub const MIN_EIG_DIAG_THRESHOLD: f64 = 1e-4;

/// Diagnostic formatter shared across the outer optimizer and the custom-family
/// fitter: shows the `max_items` entries of `values` with largest absolute
/// value, formatted as `label=[i:value, ...]`.
pub fn format_top_abs(values: &Array1<f64>, label: &str, max_items: usize) -> String {
    if values.is_empty() {
        return format!("{label}=<empty>");
    }
    let mut ranked: Vec<(usize, f64)> = values.iter().copied().enumerate().collect();
    ranked.sort_by(|(_, left), (_, right)| {
        right
            .abs()
            .partial_cmp(&left.abs())
            .unwrap_or(std::cmp::Ordering::Equal)
    });
    let parts: Vec<String> = ranked
        .into_iter()
        .take(max_items)
        .map(|(idx, value)| format!("{idx}:{value:.3e}"))
        .collect();
    format!("{label}=[{}]", parts.join(", "))
}

/// Rate-limited check for Hessian minimum eigenvalue diagnostics.
/// Returns true if this eigenvalue warrants a diagnostic message.
pub fn should_emit_h_min_eig_diag(min_eig: f64) -> bool {
    if !min_eig.is_finite() || min_eig <= 0.0 {
        return true;
    }
    if min_eig >= MIN_EIG_DIAG_THRESHOLD {
        return false;
    }
    let bucket = if min_eig.is_finite() && min_eig > 0.0 {
        min_eig.log10().floor() as i32
    } else {
        i32::MIN
    };
    let last = H_MIN_EIG_LOG_BUCKET.load(Ordering::Relaxed);
    let count = H_MIN_EIG_LOG_COUNT.fetch_add(1, Ordering::Relaxed);
    if bucket != last || count.is_multiple_of(MIN_EIG_DIAG_EVERY) {
        H_MIN_EIG_LOG_BUCKET.store(bucket, Ordering::Relaxed);
        true
    } else {
        false
    }
}

// =============================================================================
// Formatting Utilities for Diagnostic Output
// =============================================================================

/// Configuration for gradient diagnostics
#[derive(Clone, Debug)]
pub struct DiagnosticConfig {
    /// Tolerance for KKT residual norm (envelope theorem violation)
    pub kkt_tolerance: f64,
    /// Relative error threshold for flagging issues
    pub rel_error_threshold: f64,
    /// Whether to emit warnings to stderr
    pub emitwarnings: bool,
}

impl Default for DiagnosticConfig {
    fn default() -> Self {
        Self {
            kkt_tolerance: 1e-4,
            rel_error_threshold: 0.1,
            emitwarnings: true,
        }
    }
}

/// Result of envelope theorem (KKT) audit
#[derive(Clone, Debug)]
pub struct EnvelopeAudit {
    /// Norm of the inner KKT residual ∇_β L(β*, ρ)
    pub kkt_residual_norm: f64,
    /// Ridge used by the inner solver
    pub innerridge: f64,
    /// Ridge assumed by the outer gradient calculation
    pub outerridge: f64,
    /// Whether the envelope theorem is violated
    pub isviolated: bool,
    /// Human-readable diagnostic message
    pub message: String,
}

impl fmt::Display for EnvelopeAudit {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(f, "{}", self.message)
    }
}

/// Result of spectral bleed trace diagnostic
#[derive(Clone, Debug)]
pub struct SpectralBleedResult {
    pub penalty_k: usize,
    /// Energy of penalty S_k in the truncated subspace: trace(U_⊥' S_k U_⊥)
    pub truncated_energy: f64,
    /// Correction term actually applied in the gradient
    pub applied_correction: f64,
    /// Whether there's a spectral bleed issue
    pub has_bleed: bool,
    /// Human-readable diagnostic message
    pub message: String,
}

impl fmt::Display for SpectralBleedResult {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(f, "{}", self.message)
    }
}

/// Result of dual-ridge consistency check
#[derive(Clone, Debug)]
pub struct DualRidgeResult {
    /// Ridge used during P-IRLS optimization
    pub pirlsridge: f64,
    /// Ridge used in LAML cost function
    pub costridge: f64,
    /// Ridge used in gradient calculation
    pub gradientridge: f64,
    /// Effective ridge impact: ||ridge * β||
    pub ridge_impact: f64,
    /// Phantom penalty contribution: 0.5 * ridge * ||β||²
    pub phantom_penalty: f64,
    /// Whether there's a ridge mismatch
    pub has_mismatch: bool,
    /// Human-readable diagnostic message
    pub message: String,
}

impl fmt::Display for DualRidgeResult {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(f, "{}", self.message)
    }
}

/// Residual diagnostics for observed values and predicted means.
#[derive(Clone, Debug, PartialEq)]
pub struct PredictionDiagnostics {
    pub n_obs: usize,
    pub mae: f64,
    pub rmse: f64,
    pub bias: f64,
    pub r_squared: Option<f64>,
    pub residuals: Vec<f64>,
}

/// Probability clipping used by the bundled classification diagnostic panel.
/// Individual log-loss and Nagelkerke APIs accept an explicit clipping value;
/// this named policy keeps the combined Rust/Python diagnostic contract in one
/// core location.
pub const DEFAULT_PROBABILITY_CLIP: f64 = 1.0e-12;

/// Smallest standard deviation used by the bundled Gaussian score panel.
pub const DEFAULT_GAUSSIAN_SCALE_FLOOR: f64 = 1.0e-12;

/// Number of equal-width probability bins in the bundled expected-calibration
/// error diagnostic.
pub const DEFAULT_CALIBRATION_BINS: usize = 20;

/// Production classification scores computed from one prediction vector.
#[derive(Clone, Debug, PartialEq)]
pub struct ClassificationPredictionMetrics {
    pub auc: f64,
    pub precision_recall_auc: f64,
    pub brier: f64,
    pub log_loss: f64,
    pub nagelkerke_r_squared: Option<f64>,
    pub expected_calibration_error: f64,
}

fn validate_metric_inputs(
    metric: &str,
    observed: &[f64],
    predicted_mean: &[f64],
) -> Result<(), String> {
    if observed.is_empty() {
        return Err(format!("{metric} requires at least one observation"));
    }
    if observed.len() != predicted_mean.len() {
        return Err(format!(
            "{metric} length mismatch: observed={} predicted={}",
            observed.len(),
            predicted_mean.len()
        ));
    }
    if let Some((index, value)) = observed
        .iter()
        .copied()
        .enumerate()
        .find(|(_, value)| !value.is_finite())
    {
        return Err(format!(
            "{metric}: observed[{index}] must be finite; got {value}"
        ));
    }
    if let Some((index, value)) = predicted_mean
        .iter()
        .copied()
        .enumerate()
        .find(|(_, value)| !value.is_finite())
    {
        return Err(format!(
            "{metric}: predicted_mean[{index}] must be finite; got {value}"
        ));
    }
    Ok(())
}

fn validate_probability_clip(metric: &str, probability_clip: f64) -> Result<(), String> {
    if !(probability_clip.is_finite() && probability_clip > 0.0 && probability_clip < 0.5) {
        return Err(format!(
            "{metric}: probability_clip must be finite and in (0, 0.5); got {probability_clip}"
        ));
    }
    Ok(())
}

fn validate_probability_inputs(
    metric: &str,
    observed: &[f64],
    predicted_mean: &[f64],
) -> Result<(), String> {
    validate_metric_inputs(metric, observed, predicted_mean)?;
    if let Some((index, value)) = observed
        .iter()
        .copied()
        .enumerate()
        .find(|(_, value)| *value < 0.0 || *value > 1.0)
    {
        return Err(format!(
            "{metric}: observed[{index}] must be in [0, 1]; got {value}"
        ));
    }
    if let Some((index, value)) = predicted_mean
        .iter()
        .copied()
        .enumerate()
        .find(|(_, value)| *value < 0.0 || *value > 1.0)
    {
        return Err(format!(
            "{metric}: predicted_mean[{index}] must be in [0, 1]; got {value}"
        ));
    }
    Ok(())
}

/// Tie-aware Mann-Whitney AUC. Observations greater than `0.5` are the
/// positive class, matching the package's binomial response convention.
pub fn auc_from_predictions(observed: &[f64], predicted_mean: &[f64]) -> Result<f64, String> {
    weighted_auc_from_predictions(observed, predicted_mean, None)
}

/// Weighted tie-aware Mann-Whitney AUC. Each positive/negative pair carries
/// weight `w_positive * w_negative`; `None` is exactly the unit-weight score.
pub fn weighted_auc_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    weights: Option<&[f64]>,
) -> Result<f64, String> {
    validate_metric_inputs("auc", observed, predicted_mean)?;
    if let Some(weights) = weights {
        if weights.len() != observed.len() {
            return Err(format!(
                "auc length mismatch: observed={} weights={}",
                observed.len(),
                weights.len()
            ));
        }
        if let Some((index, value)) = weights
            .iter()
            .copied()
            .enumerate()
            .find(|(_, value)| !value.is_finite() || *value < 0.0)
        {
            return Err(format!(
                "auc: weights[{index}] must be finite and non-negative; got {value}"
            ));
        }
    }

    let weight_at = |index: usize| weights.map_or(1.0, |values| values[index]);
    let mut pairs: Vec<(f64, bool, f64)> = observed
        .iter()
        .zip(predicted_mean)
        .enumerate()
        .map(|(index, (&y, &prediction))| (prediction, y > 0.5, weight_at(index)))
        .collect();
    // AUC is invariant to independent positive/negative class weight scales,
    // so divide each class by its own MAXIMUM weight. That is what keeps a
    // harmless common factor near `f64::MAX` from overflowing the pair
    // products: every scaled weight is then <= 1, so the concordant total is
    // bounded by `n_positive * n_negative` and the denominator by the same.
    //
    // The class TOTAL is deliberately NOT folded in here. It used to be, and
    // dividing before summing destroyed the statistic's exactness: with unit
    // weights each row contributed `1/n`, and n copies of `1/n` do not sum to
    // 1.0 unless n is a power of two. A perfectly separable ranking — whose
    // Mann-Whitney value is exactly 1 — came back as 0.9999999999999986,
    // failing `test_gamclassifier_score_is_auc_and_metrics_panel_is_sane`'s
    // `assert perfect == 1.0`. Dividing ONCE at the end instead keeps the
    // unit-weight accumulation in exact integer-and-half arithmetic (every
    // term is a whole number or a whole number plus 0.5, all exact in f64 well
    // past any realistic row count), so the exact rational answer is returned
    // exactly.
    let positive_scale = pairs
        .iter()
        .filter(|(_, positive, _)| *positive)
        .map(|(_, _, weight)| weight)
        .copied()
        .fold(0.0_f64, f64::max);
    let negative_scale = pairs
        .iter()
        .filter(|(_, positive, _)| !*positive)
        .map(|(_, _, weight)| weight)
        .copied()
        .fold(0.0_f64, f64::max);
    if positive_scale <= 0.0 || negative_scale <= 0.0 {
        return Ok(0.5);
    }
    let positive_weight: f64 = pairs
        .iter()
        .filter(|(_, positive, _)| *positive)
        .map(|(_, _, weight)| weight / positive_scale)
        .sum();
    let negative_weight: f64 = pairs
        .iter()
        .filter(|(_, positive, _)| !*positive)
        .map(|(_, _, weight)| weight / negative_scale)
        .sum();
    pairs.sort_by(|(left, _, _), (right, _, _)| left.total_cmp(right));

    let mut concordant = 0.0_f64;
    let mut negative_weight_below = 0.0_f64;
    let mut start = 0usize;
    while start < pairs.len() {
        let mut end = start + 1;
        while end < pairs.len() && pairs[end].0 == pairs[start].0 {
            end += 1;
        }
        let positive_in_group: f64 = pairs[start..end]
            .iter()
            .filter(|(_, positive, _)| *positive)
            .map(|(_, _, weight)| weight / positive_scale)
            .sum();
        let negative_in_group: f64 = pairs[start..end]
            .iter()
            .filter(|(_, positive, _)| !*positive)
            .map(|(_, _, weight)| weight / negative_scale)
            .sum();
        concordant += positive_in_group * negative_weight_below;
        concordant += 0.5 * positive_in_group * negative_in_group;
        negative_weight_below += negative_in_group;
        start = end;
    }
    // One division, at the end. `positive_weight` and `negative_weight` are
    // both strictly positive here: each class maximum is > 0 (checked above), so
    // the row achieving it contributes exactly 1.0 to its class total.
    let auc = concordant / (positive_weight * negative_weight);
    if auc.is_finite() {
        Ok(auc)
    } else {
        Err("auc: weighted pair total is not representable in f64".to_string())
    }
}

/// Mean squared probability error.
pub fn brier_from_predictions(observed: &[f64], predicted_mean: &[f64]) -> Result<f64, String> {
    validate_probability_inputs("brier", observed, predicted_mean)?;
    let diagnostics = diagnostics_from_predictions(observed, predicted_mean)?;
    Ok(diagnostics.rmse * diagnostics.rmse)
}

/// Mean Bernoulli log loss with a caller-selected probability clip.
pub fn binary_log_loss_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    probability_clip: f64,
) -> Result<f64, String> {
    validate_probability_inputs("log_loss", observed, predicted_mean)?;
    validate_probability_clip("log_loss", probability_clip)?;
    let loss = observed
        .iter()
        .zip(predicted_mean)
        .map(|(&y, &prediction)| {
            let probability = prediction.clamp(probability_clip, 1.0 - probability_clip);
            -(y * probability.ln() + (1.0 - y) * (1.0 - probability).ln())
        })
        .sum::<f64>()
        / observed.len() as f64;
    if loss.is_finite() {
        Ok(loss)
    } else {
        Err("log_loss: result is not representable in f64".to_string())
    }
}

/// Nagelkerke's rescaling of Cox-Snell R² from model/null log likelihoods.
/// Returns `None` when the null normalization is undefined.
pub fn nagelkerke_r_squared_from_log_likelihoods(
    model_log_likelihood: f64,
    null_log_likelihood: f64,
    n_observations: usize,
) -> Option<f64> {
    if n_observations == 0 || !model_log_likelihood.is_finite() || !null_log_likelihood.is_finite()
    {
        return None;
    }
    let scale = 2.0 / n_observations as f64;
    let cox_snell = -(scale * (null_log_likelihood - model_log_likelihood)).exp_m1();
    let maximum_cox_snell = -(scale * null_log_likelihood).exp_m1();
    if cox_snell.is_finite() && maximum_cox_snell.is_finite() && maximum_cox_snell > 0.0 {
        Some(cox_snell / maximum_cox_snell)
    } else {
        None
    }
}

/// Nagelkerke R² for binomial predictions against an explicit null mean.
pub fn nagelkerke_r_squared_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    null_mean: f64,
    probability_clip: f64,
) -> Result<Option<f64>, String> {
    if observed.is_empty() {
        return Ok(None);
    }
    validate_probability_inputs("nagelkerke_r_squared", observed, predicted_mean)?;
    validate_probability_clip("nagelkerke_r_squared", probability_clip)?;
    if !null_mean.is_finite() || null_mean <= 0.0 || null_mean >= 1.0 {
        return Ok(None);
    }

    let log_null = null_mean.ln();
    let log_not_null = (1.0 - null_mean).ln();
    let null_log_likelihood = observed
        .iter()
        .map(|&y| y * log_null + (1.0 - y) * log_not_null)
        .sum::<f64>();
    let model_log_likelihood = observed
        .iter()
        .zip(predicted_mean)
        .map(|(&y, &prediction)| {
            let probability = prediction.clamp(probability_clip, 1.0 - probability_clip);
            y * probability.ln() + (1.0 - y) * (1.0 - probability).ln()
        })
        .sum::<f64>();
    Ok(nagelkerke_r_squared_from_log_likelihoods(
        model_log_likelihood,
        null_log_likelihood,
        observed.len(),
    ))
}

/// Trapezoidal area under the precision-recall curve. Equal predicted scores
/// enter as one threshold group, so row order within a tie cannot change the
/// score.
pub fn precision_recall_auc_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
) -> Result<f64, String> {
    validate_metric_inputs("precision_recall_auc", observed, predicted_mean)?;
    let mut pairs: Vec<(f64, bool)> = observed
        .iter()
        .zip(predicted_mean)
        .map(|(&y, &prediction)| (prediction, y > 0.5))
        .collect();
    let positives = pairs.iter().filter(|(_, positive)| *positive).count();
    if positives == 0 {
        return Ok(0.0);
    }
    pairs.sort_by(|(left, _), (right, _)| right.total_cmp(left));
    let mut true_positives = 0usize;
    let mut false_positives = 0usize;
    let mut previous_precision = 1.0_f64;
    let mut previous_recall = 0.0_f64;
    let mut area = 0.0_f64;
    let mut start = 0usize;
    while start < pairs.len() {
        let score = pairs[start].0;
        let mut end = start;
        while end < pairs.len() && pairs[end].0 == score {
            if pairs[end].1 {
                true_positives += 1;
            } else {
                false_positives += 1;
            }
            end += 1;
        }
        let precision = true_positives as f64 / (true_positives + false_positives) as f64;
        let recall = true_positives as f64 / positives as f64;
        area += 0.5 * (precision + previous_precision) * (recall - previous_recall);
        previous_precision = precision;
        previous_recall = recall;
        start = end;
    }
    Ok(area)
}

/// Equal-width-bin expected calibration error.
pub fn expected_calibration_error_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    n_bins: usize,
) -> Result<f64, String> {
    validate_probability_inputs("expected_calibration_error", observed, predicted_mean)?;
    if n_bins == 0 {
        return Err("expected_calibration_error requires at least one bin".to_string());
    }
    // Only occupied bins need storage. A dense `vec![..; n_bins]` lets an
    // otherwise valid diagnostic request allocate independently of the data
    // size, whereas at most `observed.len()` bins can be occupied.
    let mut bins: BTreeMap<usize, (usize, f64, f64)> = BTreeMap::new();
    for (&y, &prediction) in observed.iter().zip(predicted_mean) {
        let index = ((prediction.clamp(0.0, 1.0) * n_bins as f64).floor() as usize).min(n_bins - 1);
        let bin = bins.entry(index).or_insert((0, 0.0, 0.0));
        bin.0 += 1;
        bin.1 += y;
        bin.2 += prediction;
    }
    let n = observed.len() as f64;
    Ok(bins
        .into_values()
        .map(|(count, observed_sum, predicted_sum)| {
            let count = count as f64;
            (count / n) * ((observed_sum / count) - (predicted_sum / count)).abs()
        })
        .sum())
}

/// Gaussian negative log predictive density, allowing either one shared
/// standard deviation or one per observation.
pub fn gaussian_log_loss_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    sigma: &[f64],
    sigma_floor: f64,
) -> Result<f64, String> {
    validate_metric_inputs("gaussian_log_loss", observed, predicted_mean)?;
    if sigma.len() != 1 && sigma.len() != observed.len() {
        return Err(format!(
            "gaussian_log_loss: sigma length must be 1 or {}; got {}",
            observed.len(),
            sigma.len()
        ));
    }
    if !(sigma_floor.is_finite() && sigma_floor > 0.0) {
        return Err(format!(
            "gaussian_log_loss: sigma_floor must be finite and positive; got {sigma_floor}"
        ));
    }
    let shared_sigma = sigma.len() == 1;
    let mut total = 0.0_f64;
    for (index, (&y, &mean)) in observed.iter().zip(predicted_mean).enumerate() {
        let raw_sigma = if shared_sigma { sigma[0] } else { sigma[index] };
        if !raw_sigma.is_finite() || raw_sigma <= 0.0 {
            return Err(format!(
                "gaussian_log_loss: sigma[{}] must be finite and positive; got {raw_sigma}",
                if shared_sigma { 0 } else { index }
            ));
        }
        let sigma = raw_sigma.max(sigma_floor);
        let standardized_residual = (y - mean) / sigma;
        total += 0.5 * std::f64::consts::TAU.ln()
            + sigma.ln()
            + 0.5 * standardized_residual * standardized_residual;
    }
    let loss = total / observed.len() as f64;
    if loss.is_finite() {
        Ok(loss)
    } else {
        Err("gaussian_log_loss: result is not representable in f64".to_string())
    }
}

/// The package's standard classification diagnostic panel.
pub fn classification_metrics_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
    null_mean: f64,
) -> Result<ClassificationPredictionMetrics, String> {
    validate_probability_inputs("classification_metrics", observed, predicted_mean)?;
    Ok(ClassificationPredictionMetrics {
        auc: auc_from_predictions(observed, predicted_mean)?,
        precision_recall_auc: precision_recall_auc_from_predictions(observed, predicted_mean)?,
        brier: brier_from_predictions(observed, predicted_mean)?,
        log_loss: binary_log_loss_from_predictions(
            observed,
            predicted_mean,
            DEFAULT_PROBABILITY_CLIP,
        )?,
        nagelkerke_r_squared: nagelkerke_r_squared_from_predictions(
            observed,
            predicted_mean,
            null_mean,
            DEFAULT_PROBABILITY_CLIP,
        )?,
        expected_calibration_error: expected_calibration_error_from_predictions(
            observed,
            predicted_mean,
            DEFAULT_CALIBRATION_BINS,
        )?,
    })
}

/// Compute prediction residual diagnostics from observed values and predicted means.
pub fn diagnostics_from_predictions(
    observed: &[f64],
    predicted_mean: &[f64],
) -> Result<PredictionDiagnostics, String> {
    if observed.is_empty() {
        return Err("diagnostics_from_predictions requires at least one observation".to_string());
    }
    if observed.len() != predicted_mean.len() {
        return Err(format!(
            "diagnostics_from_predictions length mismatch: observed has {} values but predicted mean has {}",
            observed.len(),
            predicted_mean.len()
        ));
    }
    if observed.iter().any(|value| !value.is_finite()) {
        return Err("observed values must contain only finite numbers".to_string());
    }
    if predicted_mean.iter().any(|value| !value.is_finite()) {
        return Err("predicted mean values must contain only finite numbers".to_string());
    }

    let n_obs = observed.len();
    let n_obs_f = n_obs as f64;
    let mut residuals = Vec::with_capacity(n_obs);
    let mut abs_sum = 0.0_f64;
    let mut residual_sum = 0.0_f64;
    let mut residual_sum_squares = 0.0_f64;
    let mut observed_sum = 0.0_f64;
    for (obs, pred) in observed.iter().zip(predicted_mean.iter()) {
        let residual = obs - pred;
        residuals.push(residual);
        abs_sum += residual.abs();
        residual_sum += residual;
        residual_sum_squares += residual * residual;
        observed_sum += obs;
    }

    let observed_mean = observed_sum / n_obs_f;
    let total_sum_squares = observed
        .iter()
        .map(|value| {
            let centered = value - observed_mean;
            centered * centered
        })
        .sum::<f64>();
    let r_squared = if total_sum_squares > 0.0 {
        Some(1.0 - residual_sum_squares / total_sum_squares)
    } else {
        None
    };

    Ok(PredictionDiagnostics {
        n_obs,
        mae: abs_sum / n_obs_f,
        rmse: (residual_sum_squares / n_obs_f).sqrt(),
        bias: residual_sum / n_obs_f,
        r_squared,
        residuals,
    })
}

/// Complete diagnostic report for a gradient evaluation
#[derive(Clone, Debug, Default)]
pub struct GradientDiagnosticReport {
    /// Envelope theorem audit results
    pub envelopeaudit: Option<EnvelopeAudit>,
    /// Spectral bleed results for each penalty
    pub spectral_bleed: Vec<SpectralBleedResult>,
    /// Dual-ridge consistency result
    pub dualridge: Option<DualRidgeResult>,
}

impl GradientDiagnosticReport {
    /// Create an empty report
    pub fn new() -> Self {
        Self::default()
    }

    /// Generate a summary string of all issues found
    pub fn summary(&self) -> String {
        let mut lines = Vec::new();

        if let Some(ref audit) = self.envelopeaudit
            && audit.isviolated
        {
            lines.push(format!("[DIAG] {}", audit));
        }

        for bleed in &self.spectral_bleed {
            if bleed.has_bleed {
                lines.push(format!("[DIAG] {}", bleed));
            }
        }

        if let Some(ref ridge) = self.dualridge
            && ridge.has_mismatch
        {
            lines.push(format!("[DIAG] {}", ridge));
        }

        if lines.is_empty() {
            "No gradient diagnostic issues detected.".to_string()
        } else {
            lines.join("\n")
        }
    }
}

// =============================================================================
// Strategy 1: Envelope Theorem (KKT) Audit
// =============================================================================

// =============================================================================
// Strategy 4: Dual-Ridge Consistency Check
// =============================================================================

/// Three-way classification of why the cert refused, computed from the
/// H_pen spectrum and the projected residual at the refusing iterate.
/// `RankDeficientHPen` is the regression canary the nullspace lead's
/// smooth-construction rework is intended to eliminate; keep this variant
/// intact when extending — it doubles as the user-facing signal for
/// "an unconstrained polynomial null space slipped past absorption."
///
/// Relocated from `gam-solve`'s `custom_family/joint_newton.rs` (issue #1521
/// crate carve): this is the neutral diagnostic carrier that `gam-solve`'s
/// REML/PIRLS core consumes when classifying a custom-family cert refusal,
/// so it must live BELOW both the core and the (extracted) custom-family
/// subsystem.
#[derive(Clone, Copy, Debug, PartialEq, Eq)]
pub enum KktRefusalDiagnosis {
    RankDeficientHPen,
    PhantomMultiplierWithWellConditionedH,
    ActiveSetIncomplete,
    /// Cross-block identifiability aliasing surfaced mid-inner-solve
    /// (e.g., a binding active set materialised a 2-way alias that
    /// the pre-fit audit could not see at the cold design). The fix
    /// is structural — drop or reparameterise the aliased block;
    /// rho-anneal will not recover.
    AliasingDetectedAtFit,
}

impl KktRefusalDiagnosis {
    pub fn as_str(&self) -> &'static str {
        match self {
            KktRefusalDiagnosis::RankDeficientHPen => "rank_deficient_H_pen",
            KktRefusalDiagnosis::PhantomMultiplierWithWellConditionedH => {
                "phantom_multiplier_with_well_conditioned_H"
            }
            KktRefusalDiagnosis::ActiveSetIncomplete => "active_set_incomplete",
            KktRefusalDiagnosis::AliasingDetectedAtFit => "aliasing_detected_at_fit",
        }
    }

    /// Parse the textual `diagnosis:` field embedded in the structured
    /// bubbled error string. Returns `None` when no recognised label is
    /// present (legacy / non-cert-refusal error strings).
    pub fn parse_from_error(message: &str) -> Option<Self> {
        let marker = "diagnosis: ";
        let start = message.rfind(marker)? + marker.len();
        let tail = &message[start..];
        let end = tail
            .find(|c: char| c == ';' || c == '\n' || c == ' ')
            .unwrap_or(tail.len());
        match &tail[..end] {
            "rank_deficient_H_pen" => Some(KktRefusalDiagnosis::RankDeficientHPen),
            "phantom_multiplier_with_well_conditioned_H" => {
                Some(KktRefusalDiagnosis::PhantomMultiplierWithWellConditionedH)
            }
            "active_set_incomplete" => Some(KktRefusalDiagnosis::ActiveSetIncomplete),
            "aliasing_detected_at_fit" => Some(KktRefusalDiagnosis::AliasingDetectedAtFit),
            _ => None,
        }
    }

    pub fn guidance(self) -> &'static str {
        match self {
            KktRefusalDiagnosis::RankDeficientHPen => {
                "check whether the named block has a structural or numerical null direction \
                 not identified by the likelihood/penalty combination; for Duchon-style \
                 smooths this may be a polynomial null space, while marginal-slope fits can \
                 also expose callback-owned weak directions"
            }
            KktRefusalDiagnosis::PhantomMultiplierWithWellConditionedH => {
                "check whether the named block has a near-separated or weakly identified \
                 direction despite a well-conditioned penalized Hessian; in marginal-slope \
                 fits this often indicates marginal/slope coupling rather than a \
                 Matérn/Duchon polynomial-nullspace failure"
            }
            KktRefusalDiagnosis::ActiveSetIncomplete => {
                "check whether the named block's linear constraints need an additional \
                 active row or a tighter constrained re-solve; this is an active-set \
                 certification failure, not a polynomial-nullspace diagnosis"
            }
            KktRefusalDiagnosis::AliasingDetectedAtFit => {
                "check whether the named block aliases another block after runtime \
                 constraints or callbacks materialize; drop or reparameterize the aliased \
                 direction before fitting"
            }
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn diagnostics_from_predictions_computes_residual_metrics() {
        let observed = [1.0, 2.0, 4.0];
        let predicted = [1.5, 1.5, 3.0];

        let result = diagnostics_from_predictions(&observed, &predicted).unwrap();

        assert_eq!(result.residuals, vec![-0.5, 0.5, 1.0]);
        assert_eq!(result.n_obs, 3);
        assert_eq!(result.mae, 2.0 / 3.0);
        assert_eq!(result.bias, 1.0 / 3.0);
        assert_eq!(result.rmse, (1.5_f64 / 3.0).sqrt());
        assert_eq!(result.r_squared, Some(1.0 - 1.5 / (14.0 / 3.0)));
    }

    #[test]
    fn diagnostics_from_predictions_omits_r_squared_for_constant_observed() {
        let observed = [2.0, 2.0];
        let predicted = [1.0, 3.0];

        let result = diagnostics_from_predictions(&observed, &predicted).unwrap();

        assert_eq!(result.r_squared, None);
    }

    #[test]
    fn diagnostics_from_predictions_rejects_invalid_inputs() {
        assert_eq!(
            diagnostics_from_predictions(&[], &[]),
            Err("diagnostics_from_predictions requires at least one observation".to_string())
        );
        assert_eq!(
            diagnostics_from_predictions(&[1.0], &[1.0, 2.0]),
            Err(
                "diagnostics_from_predictions length mismatch: observed has 1 values but predicted mean has 2"
                    .to_string()
            )
        );
        assert_eq!(
            diagnostics_from_predictions(&[f64::NAN], &[1.0]),
            Err("observed values must contain only finite numbers".to_string())
        );
        assert_eq!(
            diagnostics_from_predictions(&[1.0], &[f64::INFINITY]),
            Err("predicted mean values must contain only finite numbers".to_string())
        );
    }

    #[test]
    fn auc_is_tie_aware_and_weighted_auc_reduces_to_unit_weights() {
        let observed = [0.0, 1.0, 0.0, 1.0];
        let predicted = [0.1, 0.8, 0.8, 0.9];
        let auc = auc_from_predictions(&observed, &predicted).unwrap();
        assert_eq!(auc, 0.875);
        assert_eq!(
            weighted_auc_from_predictions(&observed, &predicted, Some(&[1.0; 4])).unwrap(),
            auc
        );

        let weighted = weighted_auc_from_predictions(
            &[1.0, 0.0, 1.0],
            &[0.5, 0.5, 0.9],
            Some(&[2.0, 3.0, 1.0]),
        )
        .unwrap();
        assert_eq!(weighted, 2.0 / 3.0);
        assert!(
            weighted_auc_from_predictions(&[0.0, 1.0], &[0.2, 0.8], Some(&[1.0, -1.0])).is_err()
        );
        assert_eq!(
            weighted_auc_from_predictions(&[1.0, 0.0], &[0.5, 0.5], Some(&[f64::MAX, f64::MAX]),)
                .unwrap(),
            0.5
        );
    }

    /// A perfect ranking has Mann-Whitney AUC exactly 1, and the estimator must
    /// return exactly that — not 1 - 6ulp.
    ///
    /// The old accumulation divided each row by its class TOTAL before summing,
    /// so it added `n` copies of `1/n`; that equals 1.0 only when `n` is a power
    /// of two. `n = 100` (and 97, and 63) are the ordinary cases where it does
    /// not, and `test_gamclassifier_score_is_auc_and_metrics_panel_is_sane` had
    /// been failing on exactly this: `separable ranking must give AUC 1.0; got
    /// 0.9999999999999986`.
    ///
    /// The class sizes below are deliberately NOT powers of two, and are
    /// unequal, so the test would have failed before the fix and cannot pass by
    /// accident of a representable denominator.
    #[test]
    fn a_perfectly_separable_ranking_scores_exactly_one() {
        for (negatives, positives) in [(100usize, 100usize), (97, 63), (13, 501)] {
            let mut observed = Vec::with_capacity(negatives + positives);
            let mut predicted = Vec::with_capacity(negatives + positives);
            for index in 0..negatives {
                observed.push(0.0);
                predicted.push(index as f64);
            }
            for index in 0..positives {
                observed.push(1.0);
                predicted.push((negatives + index) as f64);
            }
            let auc = auc_from_predictions(&observed, &predicted).unwrap();
            assert_eq!(
                auc, 1.0,
                "separable ranking with {negatives} negatives and {positives} positives \
                 must score exactly 1.0, got {auc:?}"
            );

            // The mirror image is exactly 0 by the same argument; an estimator
            // that is exact at one end and not the other is still rounding.
            let reversed: Vec<f64> = observed.iter().map(|y| 1.0 - y).collect();
            let auc_reversed = auc_from_predictions(&reversed, &predicted).unwrap();
            assert_eq!(auc_reversed, 0.0, "reversed ranking must score exactly 0.0");
        }
    }

    /// Unit weights must remain the identity of the weighted path after the
    /// normalization change, at a size where the old per-row division rounded.
    #[test]
    fn unit_weights_match_the_unweighted_score_at_scale() {
        let observed: Vec<f64> = (0..200).map(|i| f64::from(i % 3 == 0)).collect();
        let predicted: Vec<f64> = (0..200).map(|i| ((i * 37) % 101) as f64).collect();
        let plain = auc_from_predictions(&observed, &predicted).unwrap();
        let unit =
            weighted_auc_from_predictions(&observed, &predicted, Some(&vec![1.0; 200])).unwrap();
        assert_eq!(plain, unit);
        // A common positive factor is a no-op: AUC is scale-invariant per class.
        let doubled =
            weighted_auc_from_predictions(&observed, &predicted, Some(&vec![2.0; 200])).unwrap();
        assert_eq!(plain, doubled);
    }

    #[test]
    fn precision_recall_auc_consumes_ties_as_one_threshold() {
        let first = precision_recall_auc_from_predictions(&[1.0, 0.0], &[0.5, 0.5]).unwrap();
        let reversed = precision_recall_auc_from_predictions(&[0.0, 1.0], &[0.5, 0.5]).unwrap();
        assert_eq!(first, 0.75);
        assert_eq!(first, reversed);
    }

    #[test]
    fn probability_scores_match_closed_forms() {
        let observed = [0.0, 1.0];
        let predicted = [0.5, 0.5];
        let log_loss =
            binary_log_loss_from_predictions(&observed, &predicted, DEFAULT_PROBABILITY_CLIP)
                .unwrap();
        assert!((log_loss - std::f64::consts::LN_2).abs() < 1.0e-15);
        assert_eq!(brier_from_predictions(&observed, &predicted).unwrap(), 0.25);
        assert_eq!(
            expected_calibration_error_from_predictions(&observed, &predicted, 2).unwrap(),
            0.0
        );
        assert!(
            binary_log_loss_from_predictions(&observed, &predicted, 0.5).is_err(),
            "a clip that collapses the probability interval must be rejected"
        );
        assert!(classification_metrics_from_predictions(&observed, &[0.5, 2.0], 0.5).is_err());
        assert!(classification_metrics_from_predictions(&[-0.1, 1.0], &predicted, 0.5).is_err());
    }

    #[test]
    fn nagelkerke_and_classification_panel_share_the_core_kernels() {
        let observed = [0.0, 0.0, 1.0, 1.0];
        let predicted = [0.1, 0.2, 0.8, 0.9];
        let metrics = classification_metrics_from_predictions(&observed, &predicted, 0.5).unwrap();
        assert_eq!(metrics.auc, 1.0);
        assert_eq!(metrics.precision_recall_auc, 1.0);
        assert_eq!(
            metrics.nagelkerke_r_squared,
            nagelkerke_r_squared_from_predictions(
                &observed,
                &predicted,
                0.5,
                DEFAULT_PROBABILITY_CLIP,
            )
            .unwrap()
        );
        assert!(metrics.nagelkerke_r_squared.unwrap() > 0.8);
        assert_eq!(
            nagelkerke_r_squared_from_predictions(
                &observed,
                &predicted,
                1.0,
                DEFAULT_PROBABILITY_CLIP,
            )
            .unwrap(),
            None
        );
    }

    #[test]
    fn gaussian_log_loss_matches_closed_form_and_rejects_invalid_sigma() {
        let observed = [1.0, 2.0, 3.0, 4.0];
        let predicted = observed;
        let sigma = [1.5];
        let got = gaussian_log_loss_from_predictions(
            &observed,
            &predicted,
            &sigma,
            DEFAULT_GAUSSIAN_SCALE_FLOOR,
        )
        .unwrap();
        let expected = 0.5 * (std::f64::consts::TAU * 1.5 * 1.5).ln();
        assert!((got - expected).abs() < 1.0e-12);
        assert!(
            gaussian_log_loss_from_predictions(
                &observed,
                &predicted,
                &[1.0, 2.0],
                DEFAULT_GAUSSIAN_SCALE_FLOOR,
            )
            .is_err()
        );
        assert!(
            gaussian_log_loss_from_predictions(
                &observed,
                &predicted,
                &[0.0],
                DEFAULT_GAUSSIAN_SCALE_FLOOR,
            )
            .is_err()
        );
    }
}