model-eval-framework-spec.md

Model Evaluation Framework Specification

Issue: #73 Version: 1.1.1 Status: Implemented Target: aprender 0.10.0 + entrenar 0.2.0 PMAT Work Item: APR-0073 Author: Aprender Team Date: 2026-01-21

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Abstract

This specification defines the integrated model evaluation and drift detection system for the aprender ecosystem. Adhering to the Toyota Way, it transforms model evaluation from a manual, ad-hoc process into an automated, continuous control loop (Jidoka).

Crucially, this framework is not isolated. It integrates directly with:

1. Renacer: To trace evaluation overhead and ensure "Science" (metrics) doesn't kill "Systems" (latency).
2. TruenoDB: To store historical performance data for longitudinal drift analysis.
3. Entrenar: To trigger automated retraining loops when drift is detected (Andon Cord).
4. Brick Architecture: To provide standardized, zero-JS visualizations for evaluation reports.

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1. Overview

1.1 Problem Statement (Muda)

Currently, aprender evaluation is manual and fragmented:

• No Standardization: Users write custom loops for accuracy/F1.
• No History: "It worked yesterday" is anecdotal, not data.
• No Automation: Retraining is a manual decision, leading to stale models.

1.2 Solution (Kaizen)

Add aprender::eval module with:

1. Standardized Metrics: Classification, Regression, Clustering (Parity with sklearn).
2. Drift Detection: Statistical tests (KS, Chi-Square, PSI) to detect Data Drift and Concept Drift.
3. Renacer Integration: Every evaluation is a Span; every metric is a Tag.
4. Entrenar Hooks: "If drift > threshold, trigger entrenar::retrain."

1.3 Mandatory Adoption (Standardization)

To ensure ecosystem consistency, the following "Gurus" (Workflows) MUST use aprender::eval:

1. Fine-Tuning Guru (LoRA/QLoRA): Must use ModelEvaluator to compare Base vs. Fine-Tuned performance (e.g., "Win Rate" or specific task metrics).
2. Quantization Guru (PTQ/QAT): Must use ModelEvaluator to quantify accuracy degradation (e.g., FP16 vs INT4). Relative accuracy drop > 1% triggers a failure.
3. Distillation Guru: Must use ModelEvaluator to verify Student vs. Teacher parity.

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2. Architecture

┌─────────────────────────────────────────────────────────────────────────┐
│                          aprender::eval Module                          │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│  ┌─────────────────┐  ┌──────────────────┐  ┌───────────────────────┐   │
│  │    metrics/     │  │    evaluator/    │  │        drift/         │   │
│  │                 │  │                  │  │                       │   │
│  │ - accuracy      │  │ - ModelEvaluator │  │ - DriftDetector       │   │
│  │ - f1_score      │  │ - CrossValidate  │  │ - KSTest / PSI        │   │
│  │ - confusion_mat │  │ - Leaderboard    │  │ - AndonCallback       │   │
│  └────────┬────────┘  └────────┬─────────┘  └──────────┬────────────┘   │
│           │                    │                       │                │
│           └────────────────────┼───────────────────────┘                │
│                                │                                        │
│                                ▼                                        │
│                    ┌───────────────────────┐                            │
│                    │  renacer::Trace       │  (System Observability)    │
│                    │  - span: "eval"       │                            │
│                    │  - tag: "acc=0.95"    │                            │
│                    └───────────────────────┘                            │
│                                │                                        │
│                                ▼                                        │
│                    ┌───────────────────────┐                            │
│                    │  entrenar::Trainer    │  (Correction Loop)         │
│                    │  - trigger_retrain()  │                            │
│                    └───────────────────────┘                            │
└─────────────────────────────────────────────────────────────────────────┘

---

3. API Specification

3.1 Classification Metrics (src/metrics/classification.rs)

Standardized metrics ensuring consistent calculation across the ecosystem.

/// Averaging strategy for multi-class metrics
#[derive(Clone, Copy, Debug)]
pub enum Average {
    /// Calculate metrics for each label, return unweighted mean (Macro)
    Macro,
    /// Calculate metrics globally by counting total TP, FP, FN (Micro)
    Micro,
    /// Weighted mean by support (number of true instances per label)
    Weighted,
    /// Return array of metrics per class (no averaging)
    None,
}

/// Compute classification accuracy
/// accuracy = (TP + TN) / (TP + TN + FP + FN)
pub fn accuracy(y_pred: &[usize], y_true: &[usize]) -> f32;

/// Compute precision score
/// precision = TP / (TP + FP)
pub fn precision(y_pred: &[usize], y_true: &[usize], average: Average) -> f32;

/// Compute recall score
/// recall = TP / (TP + FN)
pub fn recall(y_pred: &[usize], y_true: &[usize], average: Average) -> f32;

/// Compute F1 score (harmonic mean of precision and recall)
/// F1 = 2 * (precision * recall) / (precision + recall)
pub fn f1_score(y_pred: &[usize], y_true: &[usize], average: Average) -> f32;

/// Compute confusion matrix
/// Returns Matrix<usize> where element [i,j] is count of samples
/// with true label i and predicted label j
pub fn confusion_matrix(y_pred: &[usize], y_true: &[usize]) -> Matrix<usize>;

/// Generate text classification report (sklearn-style)
/// *Mieruka* (Visual Control) for CLI usage.
pub fn classification_report(y_pred: &[usize], y_true: &[usize]) -> String;

3.2 Model Evaluator (src/eval/evaluator.rs)

The ModelEvaluator manages the lifecycle of testing and comparison. It is responsible for reporting metrics to Renacer.

/// Configuration for model evaluation
#[derive(Clone, Debug)]
pub struct EvalConfig {
    /// Metrics to compute
    pub metrics: Vec<Metric>,
    /// Number of cross-validation folds (0 = no CV)
    pub cv_folds: usize,
    /// Random seed for reproducibility
    pub seed: u64,
    /// Parallel evaluation (requires rayon feature)
    pub parallel: bool,
    /// Enable Renacer tracing (defaults to true if feature enabled)
    pub trace_enabled: bool,
}

/// Available evaluation metrics
#[derive(Clone, Copy, Debug, PartialEq, Eq, Hash)]
pub enum Metric {
    // Classification
    Accuracy,
    Precision(Average),
    Recall(Average),
    F1(Average),
    // Regression
    R2,
    MSE,
    MAE,
    RMSE,
    // Clustering
    Silhouette,
    Inertia,
}

/// Model evaluation results
#[derive(Clone, Debug)]
pub struct EvalResult {
    pub model_name: String,
    pub scores: HashMap<Metric, f32>,
    pub cv_scores: Option<Vec<f32>>,
    pub cv_mean: Option<f32>,
    pub cv_std: Option<f32>,
    pub inference_time_ms: f64,
    /// Link to Renacer trace ID
    pub trace_id: Option<String>,
}

/// Leaderboard for comparing multiple models
#[derive(Clone, Debug)]
pub struct Leaderboard {
    pub results: Vec<EvalResult>,
    pub primary_metric: Metric,
}

impl Leaderboard {
    /// Print formatted leaderboard to stdout (Mieruka)
    pub fn print(&self);

    /// Export as markdown table for documentation
    pub fn to_markdown(&self) -> String;

    /// Get best model by primary metric
    pub fn best(&self) -> &EvalResult;
}

/// Main evaluator struct
pub struct ModelEvaluator {
    config: EvalConfig,
}

impl ModelEvaluator {
    pub fn new(config: EvalConfig) -> Self;

    /// Evaluate single model
    pub fn evaluate<M: Estimator>(
        &self,
        model: &M,
        x: &Matrix<f32>,
        y: &[usize],
    ) -> Result<EvalResult>;

    /// Compare multiple models, return leaderboard
    pub fn compare<M: Estimator>(
        &self,
        models: &[(&str, &M)],
        x: &Matrix<f32>,
        y: &[usize],
    ) -> Result<Leaderboard>;
}

3.3 Drift Detection (src/eval/drift.rs)

Implements Jidoka (Automation with a Human Touch). Detects when the process is out of control and signals for help (Retraining).

/// Statistical test for drift detection
#[derive(Clone, Copy, Debug)]
pub enum DriftTest {
    /// Kolmogorov-Smirnov test (continuous features)
    KS { threshold: f64 },
    /// Chi-square test (categorical features)
    ChiSquare { threshold: f64 },
    /// Population Stability Index (Standard industry metric)
    PSI { threshold: f64 },
    /// Wasserstein Distance (Earth Mover's Distance)
    Wasserstein { threshold: f64 },
}

/// Drift detection result
#[derive(Clone, Debug)]
pub struct DriftResult {
    pub feature: String,
    pub test: DriftTest,
    pub statistic: f64,
    pub p_value: f64,
    pub drifted: bool,
    pub severity: Severity,
}

#[derive(Clone, Copy, Debug)]
pub enum Severity {
    None,
    Warning, // Log warning, continue
    Critical, // Stop inference or trigger retrain
}

/// Callback type for drift events
pub type DriftCallback = Box<dyn Fn(&[DriftResult]) -> Result<()> + Send + Sync>;

/// Drift detector with retraining hooks
pub struct DriftDetector {
    tests: Vec<DriftTest>,
    baseline: Option<Matrix<f32>>,
    callbacks: Vec<DriftCallback>,
}

impl DriftDetector {
    pub fn new(tests: Vec<DriftTest>) -> Self;

    /// Set baseline distribution (e.g., training data)
    pub fn set_baseline(&mut self, data: &Matrix<f32>);

    /// Check new data for drift
    pub fn check(&self, current: &Matrix<f32>) -> Vec<DriftResult>;

    /// Register callback for drift events (Andon Cord)
    pub fn on_drift<F>(&mut self, callback: F)
    where
        F: Fn(&[DriftResult]) -> Result<()> + Send + Sync + 'static;

    /// Check and trigger callbacks if drift detected
    pub fn check_and_trigger(&self, current: &Matrix<f32>) -> Result<Vec<DriftResult>>;
}

3.4 Entrenar Integration (src/eval/retrain.rs)

The bridge to the training loop.

#[cfg(feature = "entrenar")]
pub mod retrain {
    use entrenar::Trainer;

    /// Retraining trigger policy
    #[derive(Clone, Debug)]
    pub enum RetrainPolicy {
        /// Retrain if > N% of features drift
        FeatureCount { count: usize },
        /// Retrain if any critical feature drifts
        CriticalFeature { names: Vec<String> },
        /// Retrain if prediction confidence drops below threshold
        ConfidenceDrop { threshold: f32 },
    }

    /// Auto-retrainer with drift detection
    pub struct AutoRetrainer {
        drift_detector: DriftDetector,
        policy: RetrainPolicy,
        config: RetrainConfig,
    }

    impl AutoRetrainer {
        /// Ingest batch, check drift, trigger retrain if policy met
        pub fn process_batch(&mut self, batch: &Batch) -> Result<Action>;
    }
    
    pub enum Action {
        None,
        WarningLogged,
        RetrainTriggered(String), // Job ID
    }
}

---

4. File Structure

aprender/src/
├── eval/
│   ├── mod.rs              # Module exports
│   ├── evaluator.rs        # ModelEvaluator, Leaderboard
│   ├── drift.rs            # DriftDetector, statistical tests
│   └── retrain.rs          # AutoRetrainer (feature-gated)
├── metrics/
│   ├── mod.rs              # Re-exports
│   ├── regression.rs       # Existing: r_squared, mse, mae, rmse
│   ├── clustering.rs       # Existing: silhouette_score, inertia
│   └── classification.rs   # NEW: accuracy, f1, precision, recall

---

5. Feature Flags

[features]
default = []
eval = ["dep:statrs"]
# Enable eval module
entrenar = ["eval", "dep:entrenar"]
# Enable retraining integration
renacer = ["dep:renacer"]           # Enable tracing integration

---

6. Dependencies

Crate	Version	Purpose	Feature
aprender	0.9.x	Base library	-
entrenar	0.2.x	Retraining	entrenar
statrs	0.16	Statistical tests	eval
renacer	0.8.x	Tracing/Observability	renacer
rayon	1.8	Parallel evaluation	parallel

---

7. Implementation Tickets

ID	Task	Hours	Priority
APR-073-1	Classification metrics (Accuracy, F1, Matrix)	8	P0
APR-073-2	ModelEvaluator + Leaderboard + Renacer Trace	16	P0
APR-073-3	Cross-validation integration	8	P1
APR-073-4	Drift detection (KS, Chi-sq, PSI)	16	P1
APR-073-5	Entrenar integration (Andon loop)	16	P2
APR-073-6	Property tests (Proptest) + Documentation	12	P0

Total: 76 hours

---

8. Quality Requirements

• Test Coverage: ≥95% via make coverage (Strict).
  • Performance Constraint: Coverage suite must run in < 5 minutes to ensure rapid feedback loops (Toyota Way).
• Mutation Score: ≥85% (cargo-mutants).
• Property Tests: 1000+ iterations per metric.
• No Panics: All public APIs must return Result.
• WASM Compatible: Core logic must compile to WASM (excluding entrenar feature).
• Compliance: Must pass pmat comply (full strict mode).

---

9. Example Usage

use aprender::prelude::*;
use aprender::eval::{ModelEvaluator, EvalConfig, Metric, DriftDetector, DriftTest};

fn main() -> Result<()> {
    // 1. Configure Evaluator
    let evaluator = ModelEvaluator::new(EvalConfig {
        metrics: vec![
            Metric::Accuracy,
            Metric::F1(Average::Weighted),
        ],
        cv_folds: 5,
        seed: 42,
        parallel: true,
        trace_enabled: true,
    });

    // 2. Compare Models (Leaderboard)
    let leaderboard = evaluator.compare(&[
        ("RandomForest", &rf_model),
        ("GradientBoosting", &gb_model),
    ], &x_test, &y_test)?;

    leaderboard.print(); 
    // Output:
    // ┌─────────────────┬──────────┬─────────┐
    // │ Model           │ Accuracy │ F1      │
    // ├─────────────────┼──────────┼─────────┤
    // │ GradientBoosting│ 0.9423   │ 0.9401  │
    // │ RandomForest    │ 0.9318   │ 0.9295  │
    // └─────────────────┴──────────┴─────────┘

    // 3. Setup Drift Detection (Jidoka)
    let mut detector = DriftDetector::new(vec![
        DriftTest::PSI { threshold: 0.1 }, // Population Stability Index
        DriftTest::KS { threshold: 0.05 },
    ]);
    detector.set_baseline(&x_train);

    // 4. Register Andon Cord (Retraining)
    detector.on_drift(|results| {
        println!("⚠️ DRIFT DETECTED: Triggering Retraining Protocol...");
        // In real app: entrenar::trigger_job(...)
        Ok(())
    });

    Ok(())
}

---

10. QA & Falsification (PMAT)

To ensure this specification is implemented correctly, the following 10-Point PMAT Checklist must be verified before release (v1.0.0).

1. Unit Tests: All metrics (accuracy, f1, etc.) match sklearn reference values to within 1e-6 precision.
2. Property Tests: proptest passes 100,000 iterations (up from 10k) for all metrics, verifying invariants (e.g., accuracy <= 1.0, F1 symmetry).
3. Mutation Testing: cargo mutants score > 90% (up from 85%) for metrics/ and drift/ modules, ensuring no "pseudo-tested" code.
4. Integration: entrenar callback successfully triggers a dummy job within 10ms when drift is simulated (Real-time Andon).
5. Drift Statistical Power: KS and Chi-Square tests demonstrate p-value calibration (uniform under null hypothesis) via examples/calibration_check.rs.
6. Renacer Tracing: ModelEvaluator produces a visible Span in jaeger with correct tags, adding < 2% overhead to inference.
7. Performance Complexity: Metric calculation complexity verified as O(N); overhead must be < 5ms for N=1M samples on Reference Hardware.
8. WASM: aprender-eval compiles to wasm32-unknown-unknown and runs in browser with zero allocations in the hot loop.
9. Documentation: All examples in mdbook use {{#include}} and compile; "Documentation as Code" principle enforced.
10. Compliance: pmat comply passes with 0 violations (Grade A, clean logs, no unwrap(), no panic!).

---

11. References

Peer-Reviewed Literature

1. Drift Detection: Gama, J., et al. (2014). "A Survey on Concept Drift Adaptation." ACM Computing Surveys. DOI:10.1145/2523813
2. Continuous Learning (Jidoka): Lesort, T., et al. (2020). "Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges." Information Fusion. DOI:10.1016/j.inffus.2019.12.004
3. LoRA Evaluation: Hu, E. J., et al. (2021). "LoRA: Low-Rank Adaptation of Large Language Models." ICLR 2022. arXiv:2106.09685
4. Quantization (QLoRA): Dettmers, T., et al. (2023). "QLoRA: Efficient Finetuning of Quantized LLMs." NeurIPS 2023. arXiv:2305.14314
5. MLOps Architecture: Kreuzberger, D., et al. (2023). "Machine Learning Operations (MLOps): Overview, Definition, and Architecture." IEEE Access.

Standard References

• Toyota Way: Jidoka (Automation), Mieruka (Visual Control).
• Vision Sync: entrenar/docs/specifications/paiml-sai-vision-sync.md
• Sklearn: Classification Report
• PSI: Population Stability Index Guide
• Brick Architecture: docs/specifications/brick-architecture.md (Zero-JS Visualization Standard)

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12. Post-Implementation Verification (2026-01-21)

The Model Evaluation Framework (APR-073) has been fully implemented and verified against this specification.

Verification Results

1. Classification Metrics: Verified with 8 sklearn parity tests (1e-6 precision).
2. Model Evaluator: Leaderboard and performance tracking verified.
3. Cross-Validation: KFold integration complete.
4. Drift Detection: KS, Chi-Square, and PSI verified via examples/calibration_check.rs and examples/drift_simulation.rs.
5. Entrenar Integration: AutoRetrainer and Andon loop verified with <10ms callback latency.
6. WASM Compatibility: Verified core logic compilation for wasm32-unknown-unknown.
7. Testing: 63 module tests + 17 property tests (100k iterations each) passing.

Falsification Status

• H0 Falsified? NO. All protocols passed as of Jan 21, 2026.

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Appendix D: Documentation Integration Strategy

To ensure "Documentation is Code" (The Toyota Way), all examples in the mdbook MUST be sourced directly from compiled, tested Rust files using the {{#include ...}} directive.

DO NOT write code blocks manually in markdown.

Incorrect (Bad):

\```rust
let acc = accuracy(&y_pred, &y_true); // Might not compile!
\```

Correct (Good):

\```rust
{{#include ../../examples/eval_metrics.rs:10:20}}
\```

Verification

Run mdbook test to verify that all included snippets compile and pass tests.