#MoonMetricLab 🌙📊

#✨ Key Highlights

• 🎯 Complete Domain Coverage: Out-of-the-box support for Classification (binary, multiclass, ROC/PR curves), Regression (continuous and robust losses), Clustering (internal separation indices and external agreement metrics), and Ranking (information retrieval DCG/NDCG, MRR, MAP).
• 🚀 Zero-Copy Data Handling: Lightweight VectorView and MatrixView abstractions enable zero-copy slicing, windowing, and high-speed statistical operations over contiguous double and integer buffers.
• 🛡️ Checked Numerical Stability boundaries: Strict division-by-zero protection (EPSILON guards), underflow/overflow prevention (log_cosh and log-loss clipping), and granular structured errors (raise @core.MetricError) instead of ungraceful panics.
• 📈 Benchmark Comparison & Report Engine: Automated candidate vs. baseline comparison reports computing absolute deltas, percentage shifts, and automatic improvement/degradation detection.
• ✅ Release-Ready Experiment Workflow: Aggregate cross-fold metrics, rank models, enforce minimum/maximum/stability gates, and export deployment evidence as Markdown or CSV.
• 🎨 Multi-Format Visualization Suite: Built-in ASCII terminal bar charts, GitHub-flavored Markdown tables, self-contained SVG curve generators (to_svg_roc_curve), and dependency-free JSON/CSV exporters.

#🏗️ Architecture & Package Sitemap

myc1234567/moon_metric_lab
├── core            # Base numerical primitives, zero-copy views, constants, and structured errors
├── classification  # Binary & multi-class metrics, confusion matrix, threshold scanner, ROC/PR curves
├── regression      # Continuous error metrics (MAE/MSE/RMSE/R2/MAPE) and robust losses (Huber/Pinball/Log-Cosh)
├── clustering      # Internal (Silhouette, CH, DBI) and external (ARI, NMI, FMI) clustering evaluations
├── ranking         # Information retrieval ranking metrics (DCG@K, NDCG@K, MRR, MAP)
├── report          # Evaluation containers and automated candidate vs. baseline comparison engine
├── visualization   # ASCII bar charts, Markdown tables, SVG curve plots, and JSON/CSV exporters
├── workflow        # Cross-fold aggregation, model ranking, quality gates, and deployment reports
├── cmd/main        # Standalone CLI showcase executable demonstrator
└── examples        # Runnable demo projects (binary_demo, comparison_demo)

#📦 Installation & Dependency Integration

[deps]
"myc1234567/moon_metric_lab" = "0.1.3"

import {
  "myc1234567/moon_metric_lab/classification"
  "myc1234567/moon_metric_lab/hypothesis_testing"
  "myc1234567/moon_metric_lab/workflow"
}

moon add myc1234567/moon_metric_lab@0.1.3

#💡 Quick Start & Usage Examples

#1. Binary & Multi-Class Classification

test "README classification demo" {
  let y_true = [0, 0, 1, 1]
  let y_pred = [0, 1, 1, 1]
  let y_prob = [0.1, 0.6, 0.8, 0.9]

  let acc = @classification.accuracy(y_true, y_pred)
  inspect!(acc, content="0.75")

  let f1 = @classification.f1_score(y_true, y_pred, pos_label=1)
  inspect!(f1 > 0.79 && f1 < 0.81, content="true")

  let roc = @classification.roc_curve(y_true, y_prob, pos_label=1)
  inspect!(roc.auc, content="1")
}

#2. Regression & Robust Loss Functions

test "README regression demo" {
  let y_true = [3.0, -0.5, 2.0, 7.0]
  let y_pred = [2.5, 0.0, 2.0, 8.0]

  let rmse_val = @regression.rmse(y_true, y_pred)
  inspect!(rmse_val > 0.61 && rmse_val < 0.62, content="true")

  let r2 = @regression.r2_score(y_true, y_pred)
  inspect!(r2 > 0.94, content="true")

  let huber = @regression.huber_loss(y_true, y_pred, delta=1.0)
  inspect!(huber, content="0.1875")
}

#3. Clustering Separation & Agreement

test "README clustering demo" {
  let x = [[1.0, 1.0], [1.2, 0.8], [5.0, 5.0], [5.2, 4.8]]
  let labels = [0, 0, 1, 1]

  let sil = @clustering.silhouette_score(x, labels)
  inspect!(sil > 0.8, content="true")

  let ari = @clustering.adjusted_rand_index([0, 0, 1, 1], [1, 1, 0, 0])
  inspect!(ari, content="1")
}

#4. Ranking & Information Retrieval

test "README ranking demo" {
  let y_rels = [3.0, 2.0, 3.0, 0.0, 1.0]
  let y_scores = [0.9, 0.8, 0.7, 0.6, 0.5]

  let ndcg = @ranking.ndcg_at_k(y_rels, y_scores, k=3)
  inspect!(ndcg > 0.8, content="true")
}

#5. Benchmark Comparison & Visualization

test "README comparison demo" {
  let baseline = @report.ModelResult::new("LogReg", "classification")
  baseline.add_metric("accuracy", 0.80)
  baseline.add_metric("log_loss", 0.45)

  let candidate = @report.ModelResult::new("XGBoost", "classification")
  candidate.add_metric("accuracy", 0.88)
  candidate.add_metric("log_loss", 0.32)

  let comp = @report.compare_models(baseline, candidate)
  let table = comp.to_table_string()
  inspect!(table.contains("IMPROVED"), content="true")
}

#6. Multi-fold significance and effect size

test "README significance demo" {
  let before = @core.Vector::from_array([0.70, 0.75, 0.80, 0.85])
  let after = @core.Vector::from_array([0.75, 0.80, 0.85, 0.90])
  let difference = @hypothesis_testing.paired_mean_difference(before, after)
  inspect!(difference > 0.049 && difference < 0.051, content="true")
  let effect = @hypothesis_testing.cohens_d(before, after)
  inspect!(effect < 0.0, content="true")
}

#7. Cross-fold evaluation to a deployment decision

test "README release workflow demo" {
  let candidate = @workflow.Experiment::new("calibrated-model", "classification")
  candidate.add_metadata("dataset", "UCI Breast Cancer Wisconsin")
  for i, auc in [0.94, 0.95, 0.93, 0.94] {
    let fold = @workflow.FoldObservation::new(i + 1)
    fold.add_metric("roc_auc", auc)
    fold.add_metric("log_loss", 1.0 - auc)
    candidate.add_fold(fold)
  }
  let summary = @workflow.summarize(candidate)
  let gate = @workflow.evaluate_gate(summary, "production-v1", [
    @workflow.GateRule::new("roc_auc").with_minimum(0.92),
    @workflow.GateRule::new("log_loss").with_maximum(0.10),
  ])
  inspect!(gate.is_passed(), content="true")
  inspect!(@workflow.summary_to_markdown(summary).contains("roc_auc"), content="true")
}

#🛠️ Tooling & Commands

# Check compilation across all targets (Wasm-GC, Wasm, JS, Native)
moon check --target all

# Run the complete test suite on every available backend
moon test --target all --deny-warn

# Generate updated package interfaces
moon info

# Verify that generated public interfaces are already up to date
git diff --exit-code -- '**/pkg.generated.mbti'

# Run the full CLI evaluation showcase
moon run --target wasm-gc cmd/main

#Publishing to mooncakes.io

#📄 License & OSC 2026 Originality Statement

#🛡️ Third-Party Source Boundary & Originality Declaration (第三方来源边界与原创代码声明)

• Original implementation scope: The repository currently contains 4,400+ effective lines of MoonBit source (excluding generated interfaces and build artifacts), including the hypothesis_testing and workflow packages and their boundary/fixture tests. The implementation is authored in this repository; external dataset rights remain with their respective providers.
• Mathematical principle boundary: The metrics implemented here (ROC AUC, Huber Loss, Silhouette, NDCG@K, Confusion Matrix, MAE/RMSE and others) follow standard mathematical definitions. The repository's data structures, error handling, numerical validation and multi-format rendering are implemented as MoonBit engineering in this project. See THIRD_PARTY_NOTICES.md for the source and license boundary.