axonml_optim/

health.rs

//! Training Health Monitor — a novel AxonML feature for real-time diagnostics.
//!
//! Attaches to any optimizer and monitors gradient norms, loss trends, and
//! parameter statistics each step. Detects: NaN/Inf gradients, gradient
//! explosion (norm > threshold), vanishing gradients (norm < epsilon), dead
//! neurons (zero activation percentage), loss trend classification
//! (decreasing/stable/increasing/oscillating), and convergence scoring.
//! Provides automatic learning rate suggestions based on the detected issues.
//!
//! # File
//! `crates/axonml-optim/src/health.rs`
//!
//! # Author
//! Andrew Jewell Sr. — AutomataNexus LLC
//! ORCID: 0009-0005-2158-7060
//!
//! # Updated
//! April 14, 2026 11:15 PM EST
//!
//! # Disclaimer
//! Use at own risk. This software is provided "as is", without warranty of any
//! kind, express or implied. The author and AutomataNexus shall not be held
//! liable for any damages arising from the use of this software.

use std::collections::HashMap;
use std::fmt;

// =============================================================================
// Configuration
// =============================================================================

/// Configuration for the training monitor.
#[derive(Debug, Clone)]
pub struct MonitorConfig {
    /// Rolling window size for statistics (default 100).
    pub window_size: usize,
    /// Alert if gradient norm exceeds this value (default 100.0).
    pub grad_norm_threshold: f32,
    /// Alert if loss exceeds factor * moving average (default 10.0).
    pub loss_divergence_factor: f32,
    /// Steps with zero gradient before alerting on dead neurons (default 50).
    pub dead_neuron_threshold: usize,
    /// Whether to check for NaN/Inf values (default true).
    pub nan_check: bool,
    /// Loss change below this threshold indicates convergence (default 1e-6).
    pub convergence_threshold: f32,
    /// Maximum history entries to retain in memory (default 1000).
    pub max_history: usize,
}

impl Default for MonitorConfig {
    fn default() -> Self {
        Self {
            window_size: 100,
            grad_norm_threshold: 100.0,
            loss_divergence_factor: 10.0,
            dead_neuron_threshold: 50,
            nan_check: true,
            convergence_threshold: 1e-6,
            max_history: 1000,
        }
    }
}

// =============================================================================
// Alert Types
// =============================================================================

/// A training alert generated by the monitor.
#[derive(Debug, Clone)]
pub struct TrainingAlert {
    /// The training step at which this alert was generated.
    pub step: usize,
    /// Severity level of the alert.
    pub severity: AlertSeverity,
    /// Category of the alert.
    pub kind: AlertKind,
    /// Human-readable description of the issue.
    pub message: String,
}

impl fmt::Display for TrainingAlert {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        write!(
            f,
            "[step {}] {:?} {:?}: {}",
            self.step, self.severity, self.kind, self.message
        )
    }
}

/// Severity level for training alerts.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AlertSeverity {
    /// Informational -- no action required.
    Info,
    /// Warning -- training may be suboptimal.
    Warning,
    /// Critical -- training is likely broken, immediate action needed.
    Critical,
}

/// Category of training alert.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum AlertKind {
    /// NaN detected in loss or gradients.
    NaNDetected,
    /// Infinity detected in loss or gradients.
    InfDetected,
    /// Gradient norms are exploding (exceeding threshold).
    GradientExplosion,
    /// Gradient norms are vanishing (near zero for many steps).
    GradientVanishing,
    /// Loss is diverging from its moving average.
    LossDivergence,
    /// Loss has stagnated (not improving).
    LossStagnation,
    /// A parameter has had zero gradient for many steps (dead neuron).
    DeadNeuron,
    /// Learning rate appears too high based on loss behavior.
    LearningRateTooHigh,
    /// Learning rate appears too low based on loss behavior.
    LearningRateTooLow,
    /// Training has converged (loss is no longer changing).
    Converged,
}

// =============================================================================
// Health Report
// =============================================================================

/// Snapshot of current training health.
#[derive(Debug, Clone)]
pub struct HealthReport {
    /// Whether training appears healthy overall.
    pub is_healthy: bool,
    /// Current training step.
    pub step: usize,
    /// Most recent loss value.
    pub current_loss: f32,
    /// Current loss trajectory.
    pub loss_trend: LossTrend,
    /// Mean gradient norm over the recent window.
    pub mean_grad_norm: f32,
    /// Maximum gradient norm over the recent window.
    pub max_grad_norm: f32,
    /// Convergence score from 0.0 (actively changing) to 1.0 (fully converged).
    pub convergence_score: f32,
    /// Currently active alerts.
    pub active_alerts: Vec<TrainingAlert>,
    /// Number of parameters detected as dead neurons.
    pub dead_neurons: usize,
}

/// Loss trajectory classification.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LossTrend {
    /// Loss is decreasing -- training is progressing.
    Decreasing,
    /// Loss is stable -- may be converged or stuck.
    Stable,
    /// Loss is increasing -- training may be diverging.
    Increasing,
    /// Loss is oscillating -- learning rate may be too high.
    Oscillating,
    /// Not enough data to determine trend.
    Unknown,
}

// =============================================================================
// Training Monitor
// =============================================================================

/// Real-time training health monitor.
///
/// Attaches to any optimizer to detect pathological training behavior
/// including gradient explosion/vanishing, loss divergence, dead neurons,
/// and convergence. Generates alerts with severity levels and provides
/// actionable suggestions.
pub struct TrainingMonitor {
    /// Current training step count.
    step_count: usize,
    /// History of loss values.
    loss_history: Vec<f32>,
    /// History of overall gradient norms (max across all parameters per step).
    grad_norm_history: Vec<f32>,
    /// History of learning rate values.
    lr_history: Vec<f32>,
    /// Accumulated training alerts.
    alerts: Vec<TrainingAlert>,
    /// Monitor configuration.
    config: MonitorConfig,
    /// Per-parameter count of consecutive steps with zero gradient.
    zero_grad_counts: HashMap<String, usize>,
    /// Count of consecutive steps with vanishing gradients.
    vanishing_streak: usize,
}

impl TrainingMonitor {
    /// Creates a new training monitor with default configuration.
    pub fn new() -> Self {
        Self::with_config(MonitorConfig::default())
    }

    /// Creates a new training monitor with the given configuration.
    pub fn with_config(config: MonitorConfig) -> Self {
        Self {
            step_count: 0,
            loss_history: Vec::new(),
            grad_norm_history: Vec::new(),
            lr_history: Vec::new(),
            alerts: Vec::new(),
            config,
            zero_grad_counts: HashMap::new(),
            vanishing_streak: 0,
        }
    }

    /// Records a single training step.
    ///
    /// # Arguments
    ///
    /// * `loss` - The loss value for this step.
    /// * `grad_norms` - Slice of (parameter_name, gradient_norm) pairs.
    /// * `lr` - The current learning rate.
    pub fn record_step(&mut self, loss: f32, grad_norms: &[(&str, f32)], lr: f32) {
        self.step_count += 1;
        let step = self.step_count;

        // Append to histories, respecting max_history
        self.push_bounded(&mut self.loss_history.clone(), loss);
        self.loss_history.push(loss);
        if self.loss_history.len() > self.config.max_history {
            self.loss_history.remove(0);
        }

        // Compute max gradient norm across all parameters
        let max_grad_norm = grad_norms.iter().map(|(_, n)| *n).fold(0.0_f32, f32::max);

        self.grad_norm_history.push(max_grad_norm);
        if self.grad_norm_history.len() > self.config.max_history {
            self.grad_norm_history.remove(0);
        }

        self.lr_history.push(lr);
        if self.lr_history.len() > self.config.max_history {
            self.lr_history.remove(0);
        }

        // --- NaN/Inf checks ---
        if self.config.nan_check {
            if loss.is_nan() {
                self.emit_alert(
                    step,
                    AlertSeverity::Critical,
                    AlertKind::NaNDetected,
                    "NaN detected in loss value".to_string(),
                );
            } else if loss.is_infinite() {
                self.emit_alert(
                    step,
                    AlertSeverity::Critical,
                    AlertKind::InfDetected,
                    "Infinity detected in loss value".to_string(),
                );
            }

            for (name, norm) in grad_norms {
                if norm.is_nan() {
                    self.emit_alert(
                        step,
                        AlertSeverity::Critical,
                        AlertKind::NaNDetected,
                        format!("NaN detected in gradient norm for '{}'", name),
                    );
                } else if norm.is_infinite() {
                    self.emit_alert(
                        step,
                        AlertSeverity::Critical,
                        AlertKind::InfDetected,
                        format!("Infinity detected in gradient norm for '{}'", name),
                    );
                }
            }
        }

        // --- Gradient explosion ---
        if max_grad_norm > self.config.grad_norm_threshold && max_grad_norm.is_finite() {
            self.emit_alert(
                step,
                AlertSeverity::Warning,
                AlertKind::GradientExplosion,
                format!(
                    "Gradient norm {:.4} exceeds threshold {:.4}",
                    max_grad_norm, self.config.grad_norm_threshold
                ),
            );
        }

        // --- Gradient vanishing ---
        if max_grad_norm < 1e-8 && max_grad_norm.is_finite() {
            self.vanishing_streak += 1;
            if self.vanishing_streak >= 10 {
                self.emit_alert(
                    step,
                    AlertSeverity::Warning,
                    AlertKind::GradientVanishing,
                    format!(
                        "Gradient norms near zero for {} consecutive steps",
                        self.vanishing_streak
                    ),
                );
            }
        } else {
            self.vanishing_streak = 0;
        }

        // --- Dead neuron tracking ---
        let dead_threshold = self.config.dead_neuron_threshold;
        let mut new_dead_alerts: Vec<(String, usize)> = Vec::new();
        for (name, norm) in grad_norms {
            let count = self
                .zero_grad_counts
                .entry((*name).to_string())
                .or_insert(0);
            if *norm == 0.0 {
                *count += 1;
                if *count == dead_threshold {
                    new_dead_alerts.push(((*name).to_string(), *count));
                }
            } else {
                *count = 0;
            }
        }
        for (name, count) in new_dead_alerts {
            self.emit_alert(
                step,
                AlertSeverity::Warning,
                AlertKind::DeadNeuron,
                format!(
                    "Parameter '{}' has had zero gradient for {} steps (dead neuron)",
                    name, count
                ),
            );
        }

        // --- Loss divergence ---
        if self.loss_history.len() >= self.config.window_size && loss.is_finite() {
            let window_start = self
                .loss_history
                .len()
                .saturating_sub(self.config.window_size);
            let window = &self.loss_history[window_start..self.loss_history.len() - 1];
            let finite_vals: Vec<f32> = window.iter().copied().filter(|v| v.is_finite()).collect();
            if !finite_vals.is_empty() {
                let avg: f32 = finite_vals.iter().sum::<f32>() / finite_vals.len() as f32;
                if avg > 0.0 && loss > avg * self.config.loss_divergence_factor {
                    self.emit_alert(
                        step,
                        AlertSeverity::Warning,
                        AlertKind::LossDivergence,
                        format!(
                            "Loss {:.6} diverged from moving average {:.6} (factor {:.1}x)",
                            loss,
                            avg,
                            loss / avg
                        ),
                    );
                }
            }
        }

        // --- Convergence check ---
        if self.loss_history.len() >= self.config.window_size {
            let window_start = self.loss_history.len() - self.config.window_size;
            let window = &self.loss_history[window_start..];
            let finite_vals: Vec<f32> = window.iter().copied().filter(|v| v.is_finite()).collect();
            if finite_vals.len() >= 2 {
                let max_val = finite_vals
                    .iter()
                    .copied()
                    .fold(f32::NEG_INFINITY, f32::max);
                let min_val = finite_vals.iter().copied().fold(f32::INFINITY, f32::min);
                let range = max_val - min_val;
                if range < self.config.convergence_threshold {
                    self.emit_alert(
                        step,
                        AlertSeverity::Info,
                        AlertKind::Converged,
                        format!(
                            "Training converged: loss range {:.2e} over last {} steps",
                            range, self.config.window_size
                        ),
                    );
                }
            }
        }
    }

    /// Returns a full health report for the current training state.
    pub fn check_health(&self) -> HealthReport {
        let (mean_gn, _std_gn, max_gn) = self.grad_norm_stats();
        let trend = self.loss_trend();
        let conv_score = self.convergence_score();

        let current_loss = self.loss_history.last().copied().unwrap_or(f32::NAN);

        // Count dead neurons
        let dead_neurons = self
            .zero_grad_counts
            .values()
            .filter(|c| **c >= self.config.dead_neuron_threshold)
            .count();

        // Determine overall health
        let has_critical = self
            .alerts
            .iter()
            .any(|a| a.severity == AlertSeverity::Critical);
        let is_healthy = !has_critical
            && trend != LossTrend::Increasing
            && !current_loss.is_nan()
            && !current_loss.is_infinite();

        // Collect recent alerts (last window_size steps)
        let min_step = self.step_count.saturating_sub(self.config.window_size);
        let active_alerts: Vec<TrainingAlert> = self
            .alerts
            .iter()
            .filter(|a| a.step > min_step)
            .cloned()
            .collect();

        HealthReport {
            is_healthy,
            step: self.step_count,
            current_loss,
            loss_trend: trend,
            mean_grad_norm: mean_gn,
            max_grad_norm: max_gn,
            convergence_score: conv_score,
            active_alerts,
            dead_neurons,
        }
    }

    /// Returns true if training appears healthy (no critical alerts, loss not diverging).
    pub fn is_healthy(&self) -> bool {
        self.check_health().is_healthy
    }

    /// Returns the accumulated alerts.
    pub fn alerts(&self) -> &[TrainingAlert] {
        &self.alerts
    }

    /// Clears all accumulated alerts.
    pub fn clear_alerts(&mut self) {
        self.alerts.clear();
    }

    /// Analyzes the loss trajectory over the recent window.
    ///
    /// Compares the rolling average of the most recent window to the
    /// rolling average of the previous window to classify the trend.
    pub fn loss_trend(&self) -> LossTrend {
        let w = self.config.window_size;
        if self.loss_history.len() < w * 2 {
            return LossTrend::Unknown;
        }

        let len = self.loss_history.len();
        let recent = &self.loss_history[len - w..];
        let previous = &self.loss_history[len - 2 * w..len - w];

        let recent_finite: Vec<f32> = recent.iter().copied().filter(|v| v.is_finite()).collect();
        let prev_finite: Vec<f32> = previous.iter().copied().filter(|v| v.is_finite()).collect();

        if recent_finite.is_empty() || prev_finite.is_empty() {
            return LossTrend::Unknown;
        }

        let recent_avg = recent_finite.iter().sum::<f32>() / recent_finite.len() as f32;
        let prev_avg = prev_finite.iter().sum::<f32>() / prev_finite.len() as f32;

        if prev_avg == 0.0 {
            return LossTrend::Unknown;
        }

        let ratio = recent_avg / prev_avg;

        // Check for oscillation: high variance in recent window
        let recent_mean = recent_avg;
        let recent_var = recent_finite
            .iter()
            .map(|v| (v - recent_mean).powi(2))
            .sum::<f32>()
            / recent_finite.len() as f32;
        let recent_std = recent_var.sqrt();
        let cv = if recent_mean.abs() > 1e-12 {
            recent_std / recent_mean.abs()
        } else {
            0.0
        };

        if ratio < 0.95 {
            LossTrend::Decreasing
        } else if ratio > 1.05 {
            LossTrend::Increasing
        } else if cv > 0.1 {
            LossTrend::Oscillating
        } else {
            LossTrend::Stable
        }
    }

    /// Suggests a learning rate adjustment based on current training dynamics.
    ///
    /// Returns `None` if no adjustment is needed or training has converged.
    pub fn suggest_lr(&self) -> Option<f32> {
        let current_lr = self.lr_history.last().copied()?;
        let trend = self.loss_trend();

        // Check for gradient explosion
        let (_, _, max_gn) = self.grad_norm_stats();
        if max_gn > self.config.grad_norm_threshold && max_gn.is_finite() {
            return Some(current_lr * 0.1);
        }

        match trend {
            LossTrend::Oscillating => Some(current_lr * 0.5),
            LossTrend::Stable => {
                // Check if truly converged or just stagnating
                let conv = self.convergence_score();
                if conv > 0.99 {
                    None // Converged, no adjustment needed
                } else {
                    Some(current_lr * 2.0) // Stagnating, try higher LR
                }
            }
            LossTrend::Increasing => Some(current_lr * 0.1),
            _ => None,
        }
    }

    /// Returns (mean, std, max) of gradient norms over the recent window.
    pub fn grad_norm_stats(&self) -> (f32, f32, f32) {
        if self.grad_norm_history.is_empty() {
            return (0.0, 0.0, 0.0);
        }

        let w = self.config.window_size.min(self.grad_norm_history.len());
        let start = self.grad_norm_history.len() - w;
        let window = &self.grad_norm_history[start..];

        let finite: Vec<f32> = window.iter().copied().filter(|v| v.is_finite()).collect();
        if finite.is_empty() {
            return (0.0, 0.0, 0.0);
        }

        let n = finite.len() as f32;
        let mean = finite.iter().sum::<f32>() / n;
        let variance = finite.iter().map(|v| (v - mean).powi(2)).sum::<f32>() / n;
        let std = variance.sqrt();
        let max = finite.iter().copied().fold(f32::NEG_INFINITY, f32::max);

        (mean, std, max)
    }

    /// Returns a convergence score between 0.0 and 1.0.
    ///
    /// 1.0 indicates full convergence (no loss change over the window).
    /// 0.0 indicates the loss is still actively changing.
    pub fn convergence_score(&self) -> f32 {
        let w = self.config.window_size;
        if self.loss_history.len() < w {
            return 0.0;
        }

        let start = self.loss_history.len() - w;
        let window = &self.loss_history[start..];

        let finite: Vec<f32> = window.iter().copied().filter(|v| v.is_finite()).collect();
        if finite.len() < 2 {
            return 0.0;
        }

        let max_val = finite.iter().copied().fold(f32::NEG_INFINITY, f32::max);
        let min_val = finite.iter().copied().fold(f32::INFINITY, f32::min);
        let range = max_val - min_val;

        let mean = finite.iter().sum::<f32>() / finite.len() as f32;
        if mean.abs() < 1e-12 {
            // If mean is essentially zero, check absolute range
            if range < self.config.convergence_threshold {
                return 1.0;
            }
            return 0.0;
        }

        // Relative range (normalized by mean)
        let relative_range = range / mean.abs();

        // Map relative_range to [0, 1] score: 0 range -> 1.0, large range -> 0.0
        // Use exponential decay for smooth mapping
        let score = (-relative_range * 100.0).exp();
        score.clamp(0.0, 1.0)
    }

    /// Returns a human-readable summary of the current training state.
    pub fn summary(&self) -> String {
        let report = self.check_health();
        let (mean_gn, std_gn, max_gn) = self.grad_norm_stats();

        let mut s = String::new();
        s.push_str(&format!(
            "=== Training Health Report (step {}) ===\n",
            report.step
        ));
        s.push_str(&format!(
            "Status: {}\n",
            if report.is_healthy {
                "HEALTHY"
            } else {
                "UNHEALTHY"
            }
        ));
        s.push_str(&format!(
            "Loss: {:.6} (trend: {:?})\n",
            report.current_loss, report.loss_trend
        ));
        s.push_str(&format!(
            "Grad norms: mean={:.4}, std={:.4}, max={:.4}\n",
            mean_gn, std_gn, max_gn
        ));
        s.push_str(&format!(
            "Convergence: {:.2}%\n",
            report.convergence_score * 100.0
        ));
        s.push_str(&format!("Dead neurons: {}\n", report.dead_neurons));

        if !report.active_alerts.is_empty() {
            s.push_str(&format!(
                "Active alerts ({}):\n",
                report.active_alerts.len()
            ));
            for alert in &report.active_alerts {
                s.push_str(&format!("  {}\n", alert));
            }
        }

        if let Some(lr) = self.suggest_lr() {
            s.push_str(&format!("Suggested LR: {:.6}\n", lr));
        }

        s
    }

    // =========================================================================
    // Private helpers
    // =========================================================================

    fn push_bounded(&self, _history: &mut Vec<f32>, _value: f32) {
        // Intentionally left as no-op: actual push + trim is done inline
        // in record_step for each history vector individually.
    }

    fn emit_alert(
        &mut self,
        step: usize,
        severity: AlertSeverity,
        kind: AlertKind,
        message: String,
    ) {
        self.alerts.push(TrainingAlert {
            step,
            severity,
            kind,
            message,
        });
    }
}

impl Default for TrainingMonitor {
    fn default() -> Self {
        Self::new()
    }
}

// =============================================================================
// Tests
// =============================================================================

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

    // -------------------------------------------------------------------------
    // Construction
    // -------------------------------------------------------------------------

    #[test]
    fn test_monitor_creation_defaults() {
        let monitor = TrainingMonitor::new();
        assert_eq!(monitor.step_count, 0);
        assert!(monitor.loss_history.is_empty());
        assert!(monitor.alerts.is_empty());
        assert_eq!(monitor.config.window_size, 100);
        assert!((monitor.config.grad_norm_threshold - 100.0).abs() < 1e-6);
        assert!(monitor.config.nan_check);
    }

    #[test]
    fn test_monitor_with_custom_config() {
        let config = MonitorConfig {
            window_size: 50,
            grad_norm_threshold: 50.0,
            loss_divergence_factor: 5.0,
            dead_neuron_threshold: 20,
            nan_check: false,
            convergence_threshold: 1e-5,
            max_history: 500,
        };
        let monitor = TrainingMonitor::with_config(config);
        assert_eq!(monitor.config.window_size, 50);
        assert!((monitor.config.grad_norm_threshold - 50.0).abs() < 1e-6);
        assert!(!monitor.config.nan_check);
        assert_eq!(monitor.config.max_history, 500);
    }

    // -------------------------------------------------------------------------
    // Recording steps
    // -------------------------------------------------------------------------

    #[test]
    fn test_record_step_updates_state() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(0.5, &[("w1", 1.0)], 0.001);

        assert_eq!(monitor.step_count, 1);
        assert_eq!(monitor.loss_history.len(), 1);
        assert_eq!(monitor.grad_norm_history.len(), 1);
        assert_eq!(monitor.lr_history.len(), 1);
        assert!((monitor.loss_history[0] - 0.5).abs() < 1e-6);
    }

    // -------------------------------------------------------------------------
    // NaN/Inf detection
    // -------------------------------------------------------------------------

    #[test]
    fn test_nan_detection_generates_critical_alert() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(f32::NAN, &[("w1", 1.0)], 0.001);

        assert_eq!(monitor.alerts.len(), 1);
        assert_eq!(monitor.alerts[0].severity, AlertSeverity::Critical);
        assert_eq!(monitor.alerts[0].kind, AlertKind::NaNDetected);
        assert!(monitor.alerts[0].message.contains("NaN"));
    }

    #[test]
    fn test_inf_detection_generates_critical_alert() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(f32::INFINITY, &[("w1", 1.0)], 0.001);

        assert_eq!(monitor.alerts.len(), 1);
        assert_eq!(monitor.alerts[0].severity, AlertSeverity::Critical);
        assert_eq!(monitor.alerts[0].kind, AlertKind::InfDetected);
        assert!(monitor.alerts[0].message.contains("Infinity"));
    }

    #[test]
    fn test_nan_in_grad_norm_detected() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(0.5, &[("w1", f32::NAN)], 0.001);

        let nan_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::NaNDetected)
            .collect();
        assert_eq!(nan_alerts.len(), 1);
        assert!(nan_alerts[0].message.contains("w1"));
    }

    #[test]
    fn test_nan_check_disabled() {
        let config = MonitorConfig {
            nan_check: false,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);
        monitor.record_step(f32::NAN, &[("w1", f32::NAN)], 0.001);

        assert!(monitor.alerts.is_empty());
    }

    // -------------------------------------------------------------------------
    // Gradient explosion/vanishing
    // -------------------------------------------------------------------------

    #[test]
    fn test_gradient_explosion_detection() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(0.5, &[("w1", 200.0)], 0.001);

        let explosion_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::GradientExplosion)
            .collect();
        assert_eq!(explosion_alerts.len(), 1);
        assert_eq!(explosion_alerts[0].severity, AlertSeverity::Warning);
    }

    #[test]
    fn test_gradient_vanishing_detection() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Record 10 steps with near-zero gradients to trigger vanishing alert
        for _ in 0..10 {
            monitor.record_step(0.5, &[("w1", 1e-10)], 0.001);
        }

        let vanishing_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::GradientVanishing)
            .collect();
        assert!(!vanishing_alerts.is_empty());
    }

    #[test]
    fn test_gradient_vanishing_resets_on_normal_grad() {
        let mut monitor = TrainingMonitor::new();

        // 5 vanishing steps
        for _ in 0..5 {
            monitor.record_step(0.5, &[("w1", 1e-10)], 0.001);
        }
        // Normal gradient resets the streak
        monitor.record_step(0.5, &[("w1", 1.0)], 0.001);
        assert_eq!(monitor.vanishing_streak, 0);
    }

    // -------------------------------------------------------------------------
    // Loss divergence
    // -------------------------------------------------------------------------

    #[test]
    fn test_loss_divergence_detection() {
        let config = MonitorConfig {
            window_size: 10,
            loss_divergence_factor: 2.0,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Build up a history of stable loss
        for _ in 0..10 {
            monitor.record_step(1.0, &[("w1", 0.5)], 0.001);
        }

        // Spike the loss to trigger divergence
        monitor.record_step(100.0, &[("w1", 0.5)], 0.001);

        let divergence_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::LossDivergence)
            .collect();
        assert!(!divergence_alerts.is_empty());
    }

    // -------------------------------------------------------------------------
    // Dead neuron tracking
    // -------------------------------------------------------------------------

    #[test]
    fn test_dead_neuron_tracking() {
        let config = MonitorConfig {
            dead_neuron_threshold: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for _ in 0..5 {
            monitor.record_step(0.5, &[("dead_layer", 0.0), ("alive_layer", 0.5)], 0.001);
        }

        let dead_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::DeadNeuron)
            .collect();
        assert_eq!(dead_alerts.len(), 1);
        assert!(dead_alerts[0].message.contains("dead_layer"));
    }

    #[test]
    fn test_dead_neuron_resets_on_nonzero_grad() {
        let config = MonitorConfig {
            dead_neuron_threshold: 10,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // 5 steps with zero grad
        for _ in 0..5 {
            monitor.record_step(0.5, &[("layer", 0.0)], 0.001);
        }
        // One step with nonzero grad resets the counter
        monitor.record_step(0.5, &[("layer", 1.0)], 0.001);

        assert_eq!(*monitor.zero_grad_counts.get("layer").unwrap(), 0);
    }

    // -------------------------------------------------------------------------
    // Convergence detection
    // -------------------------------------------------------------------------

    #[test]
    fn test_convergence_detection() {
        let config = MonitorConfig {
            window_size: 10,
            convergence_threshold: 1e-4,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Record identical losses
        for _ in 0..10 {
            monitor.record_step(0.001, &[("w1", 0.1)], 0.001);
        }

        let converged_alerts: Vec<_> = monitor
            .alerts
            .iter()
            .filter(|a| a.kind == AlertKind::Converged)
            .collect();
        assert!(!converged_alerts.is_empty());
    }

    // -------------------------------------------------------------------------
    // Loss trend analysis
    // -------------------------------------------------------------------------

    #[test]
    fn test_loss_trend_decreasing() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // First window: higher losses
        for i in 0..5 {
            monitor.record_step(2.0 - i as f32 * 0.01, &[("w1", 0.5)], 0.001);
        }
        // Second window: much lower losses
        for i in 0..5 {
            monitor.record_step(1.0 - i as f32 * 0.01, &[("w1", 0.5)], 0.001);
        }

        assert_eq!(monitor.loss_trend(), LossTrend::Decreasing);
    }

    #[test]
    fn test_loss_trend_increasing() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // First window: lower losses
        for i in 0..5 {
            monitor.record_step(1.0 + i as f32 * 0.01, &[("w1", 0.5)], 0.001);
        }
        // Second window: much higher losses
        for i in 0..5 {
            monitor.record_step(2.0 + i as f32 * 0.01, &[("w1", 0.5)], 0.001);
        }

        assert_eq!(monitor.loss_trend(), LossTrend::Increasing);
    }

    #[test]
    fn test_loss_trend_oscillating() {
        let config = MonitorConfig {
            window_size: 10,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // First window: stable
        for _ in 0..10 {
            monitor.record_step(1.0, &[("w1", 0.5)], 0.001);
        }
        // Second window: oscillating around same mean but with high variance
        for i in 0..10 {
            let loss = if i % 2 == 0 { 1.3 } else { 0.7 };
            monitor.record_step(loss, &[("w1", 0.5)], 0.001);
        }

        assert_eq!(monitor.loss_trend(), LossTrend::Oscillating);
    }

    #[test]
    fn test_loss_trend_stable() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Two windows with nearly identical means and low variance
        for _ in 0..10 {
            monitor.record_step(1.0, &[("w1", 0.5)], 0.001);
        }

        assert_eq!(monitor.loss_trend(), LossTrend::Stable);
    }

    #[test]
    fn test_loss_trend_unknown_insufficient_data() {
        let config = MonitorConfig {
            window_size: 100,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        monitor.record_step(1.0, &[("w1", 0.5)], 0.001);

        assert_eq!(monitor.loss_trend(), LossTrend::Unknown);
    }

    // -------------------------------------------------------------------------
    // Health report
    // -------------------------------------------------------------------------

    #[test]
    fn test_health_report_healthy_normal_training() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for i in 0..10 {
            monitor.record_step(1.0 - i as f32 * 0.05, &[("w1", 0.5)], 0.001);
        }

        let report = monitor.check_health();
        assert!(report.is_healthy);
        assert_eq!(report.step, 10);
        assert_eq!(report.dead_neurons, 0);
    }

    #[test]
    fn test_health_report_not_healthy_with_nan() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(f32::NAN, &[("w1", 1.0)], 0.001);

        let report = monitor.check_health();
        assert!(!report.is_healthy);
    }

    // -------------------------------------------------------------------------
    // Learning rate suggestions
    // -------------------------------------------------------------------------

    #[test]
    fn test_suggest_lr_exploding_gradients() {
        let config = MonitorConfig {
            window_size: 5,
            grad_norm_threshold: 10.0,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Record steps with exploding gradients
        for _ in 0..5 {
            monitor.record_step(1.0, &[("w1", 50.0)], 0.01);
        }

        let suggested = monitor.suggest_lr();
        assert!(suggested.is_some());
        assert!((suggested.unwrap() - 0.001).abs() < 1e-6); // 0.01 * 0.1
    }

    #[test]
    fn test_suggest_lr_oscillating_loss() {
        let config = MonitorConfig {
            window_size: 10,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // First window stable
        for _ in 0..10 {
            monitor.record_step(1.0, &[("w1", 0.5)], 0.01);
        }
        // Second window oscillating
        for i in 0..10 {
            let loss = if i % 2 == 0 { 1.3 } else { 0.7 };
            monitor.record_step(loss, &[("w1", 0.5)], 0.01);
        }

        let suggested = monitor.suggest_lr();
        assert!(suggested.is_some());
        assert!((suggested.unwrap() - 0.005).abs() < 1e-6); // 0.01 * 0.5
    }

    #[test]
    fn test_suggest_lr_converged_returns_none() {
        let config = MonitorConfig {
            window_size: 5,
            convergence_threshold: 1e-4,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Two windows of identical loss (converged + stable)
        for _ in 0..10 {
            monitor.record_step(0.001, &[("w1", 0.01)], 0.001);
        }

        // Stable trend with high convergence score -> should return None
        let trend = monitor.loss_trend();
        let conv = monitor.convergence_score();
        assert_eq!(trend, LossTrend::Stable);
        assert!(conv > 0.99);
        assert!(monitor.suggest_lr().is_none());
    }

    // -------------------------------------------------------------------------
    // Convergence score
    // -------------------------------------------------------------------------

    #[test]
    fn test_convergence_score_fully_converged() {
        let config = MonitorConfig {
            window_size: 10,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for _ in 0..10 {
            monitor.record_step(0.5, &[("w1", 0.1)], 0.001);
        }

        let score = monitor.convergence_score();
        assert!((score - 1.0).abs() < 1e-3, "Expected ~1.0, got {}", score);
    }

    #[test]
    fn test_convergence_score_actively_changing() {
        let config = MonitorConfig {
            window_size: 10,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for i in 0..10 {
            monitor.record_step(10.0 - i as f32 * 1.0, &[("w1", 0.5)], 0.001);
        }

        let score = monitor.convergence_score();
        assert!(score < 0.5, "Expected low score, got {}", score);
    }

    #[test]
    fn test_convergence_score_insufficient_data() {
        let monitor = TrainingMonitor::new();
        assert!((monitor.convergence_score() - 0.0).abs() < 1e-6);
    }

    // -------------------------------------------------------------------------
    // Summary output
    // -------------------------------------------------------------------------

    #[test]
    fn test_summary_contains_key_metrics() {
        let config = MonitorConfig {
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for _ in 0..5 {
            monitor.record_step(0.5, &[("w1", 1.0)], 0.001);
        }

        let summary = monitor.summary();
        assert!(summary.contains("Training Health Report"));
        assert!(summary.contains("HEALTHY"));
        assert!(summary.contains("Loss:"));
        assert!(summary.contains("Grad norms:"));
        assert!(summary.contains("Convergence:"));
        assert!(summary.contains("Dead neurons:"));
    }

    // -------------------------------------------------------------------------
    // Alert management
    // -------------------------------------------------------------------------

    #[test]
    fn test_clear_alerts_empties_list() {
        let mut monitor = TrainingMonitor::new();
        monitor.record_step(f32::NAN, &[("w1", 1.0)], 0.001);
        assert!(!monitor.alerts().is_empty());

        monitor.clear_alerts();
        assert!(monitor.alerts().is_empty());
    }

    // -------------------------------------------------------------------------
    // Memory bounds
    // -------------------------------------------------------------------------

    #[test]
    fn test_max_history_bounds_memory() {
        let config = MonitorConfig {
            max_history: 20,
            window_size: 5,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        for i in 0..50 {
            monitor.record_step(i as f32, &[("w1", 0.5)], 0.001);
        }

        assert!(monitor.loss_history.len() <= 20);
        assert!(monitor.grad_norm_history.len() <= 20);
        assert!(monitor.lr_history.len() <= 20);
    }

    // -------------------------------------------------------------------------
    // Grad norm stats
    // -------------------------------------------------------------------------

    #[test]
    fn test_grad_norm_stats_computation() {
        let config = MonitorConfig {
            window_size: 4,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Record known gradient norms: 1, 2, 3, 4
        monitor.record_step(1.0, &[("w1", 1.0)], 0.001);
        monitor.record_step(1.0, &[("w1", 2.0)], 0.001);
        monitor.record_step(1.0, &[("w1", 3.0)], 0.001);
        monitor.record_step(1.0, &[("w1", 4.0)], 0.001);

        let (mean, std, max) = monitor.grad_norm_stats();
        assert!(
            (mean - 2.5).abs() < 1e-4,
            "Expected mean ~2.5, got {}",
            mean
        );
        assert!((max - 4.0).abs() < 1e-4, "Expected max 4.0, got {}", max);
        // std of [1,2,3,4] = sqrt(1.25) ~= 1.118
        assert!(
            (std - 1.118).abs() < 0.01,
            "Expected std ~1.118, got {}",
            std
        );
    }

    #[test]
    fn test_grad_norm_stats_empty() {
        let monitor = TrainingMonitor::new();
        let (mean, std, max) = monitor.grad_norm_stats();
        assert!((mean - 0.0).abs() < 1e-6);
        assert!((std - 0.0).abs() < 1e-6);
        assert!((max - 0.0).abs() < 1e-6);
    }

    // -------------------------------------------------------------------------
    // Integration test
    // -------------------------------------------------------------------------

    #[test]
    fn test_integration_100_step_improving_training() {
        let config = MonitorConfig {
            window_size: 20,
            ..MonitorConfig::default()
        };
        let mut monitor = TrainingMonitor::with_config(config);

        // Simulate 100 steps of steadily improving loss
        for i in 0..100 {
            let loss = 2.0 * (-0.03 * i as f32).exp(); // Exponential decay
            let grad_norm = 1.0 * (-0.01 * i as f32).exp();
            let lr = 0.001;
            monitor.record_step(
                loss,
                &[
                    ("layer1.weight", grad_norm),
                    ("layer2.weight", grad_norm * 0.5),
                ],
                lr,
            );
        }

        assert_eq!(monitor.step_count, 100);
        assert!(monitor.is_healthy());

        let report = monitor.check_health();
        assert!(report.is_healthy);
        assert_eq!(report.step, 100);
        assert!(report.current_loss < 0.2); // Should have decayed significantly
        assert_eq!(report.dead_neurons, 0);

        // Should detect decreasing trend
        let trend = monitor.loss_trend();
        assert_eq!(trend, LossTrend::Decreasing);

        // No critical alerts should exist
        let critical_count = monitor
            .alerts
            .iter()
            .filter(|a| a.severity == AlertSeverity::Critical)
            .count();
        assert_eq!(critical_count, 0);

        // Summary should be valid
        let summary = monitor.summary();
        assert!(summary.contains("HEALTHY"));
        assert!(summary.contains("step 100"));
    }

    #[test]
    fn test_default_trait() {
        let monitor = TrainingMonitor::default();
        assert_eq!(monitor.step_count, 0);
        assert_eq!(monitor.config.window_size, 100);
    }

    #[test]
    fn test_alert_display() {
        let alert = TrainingAlert {
            step: 42,
            severity: AlertSeverity::Critical,
            kind: AlertKind::NaNDetected,
            message: "NaN detected".to_string(),
        };
        let display = format!("{}", alert);
        assert!(display.contains("42"));
        assert!(display.contains("Critical"));
        assert!(display.contains("NaN"));
    }

    #[test]
    fn test_multiple_parameters_grad_norms() {
        let mut monitor = TrainingMonitor::new();

        // The max grad norm should be tracked (not mean)
        monitor.record_step(1.0, &[("w1", 5.0), ("w2", 10.0), ("w3", 3.0)], 0.001);

        assert_eq!(monitor.grad_norm_history.len(), 1);
        assert!((monitor.grad_norm_history[0] - 10.0).abs() < 1e-6);
    }
}