sk-autod 0.1.0

Auto-diagnosis for training curves

SK-AutoD 🩺

Auto-diagnose your training curves in seconds.

Stop manually eyeballing loss curves. SK-AutoD analyzes your training data and instantly detects 10+ common pathologies: overfitting, exploding gradients, learning rate issues, underfitting, and more.

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The Problem

Every ML practitioner spends hours staring at loss curves during training:

• "Is this overfitting?"
• "Did my learning rate explode?"
• "Why is my loss stuck?"
• "Should I have stopped earlier?"

Current workflow: Manual eyeballing + Slack screenshots + tribal knowledge.

SK-AutoD solves this: Paste in your arrays → Get instant, rule-based diagnosis.

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Quick Start

Installation

# From PyPI (once published)
pip install sk-autod

# From source (recommended for now)
pip install git+https://github.com/shamiquekhan/sk-autod.git

Basic Usage

from sk_autod import diagnose

# Your training curves
train_loss = [2.3, 1.9, 1.4, 0.9, 0.5, 0.3, 0.15]
val_loss   = [2.4, 2.0, 1.8, 1.9, 2.3, 2.8, 3.4]

# Get instant diagnosis
report = diagnose(train_loss, val_loss)

# Print human-readable summary
print(report.summary())

Output:

═══════════════════════════════════════════════════════
  SK-AutoD Diagnosis Report
═══════════════════════════════════════════════════════

🔴 CRITICAL: Classic overfitting
   Detected at epoch 4 (94% confidence)
   Val loss rose while train loss fell for 3+ consecutive epochs.
   
   Fix: Add dropout (p=0.3–0.5), L2 regularisation, or reduce model capacity.

═══════════════════════════════════════════════════════
Summary: 1 critical issue found.

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Features

🔍 10 Diagnostic Detectors (v0.1.0+)

Detector	Severity	Description
Classic overfitting	🔴 CRITICAL	Val loss rises while train loss falls
Exploding gradient	🔴 CRITICAL	Loss spikes >300% in a single epoch
LR too high	🟠 HIGH	Loss oscillates without clear downtrend
LR too low	🟡 MEDIUM	Loss decreases extremely slowly
Underfitting	🟠 HIGH	Both losses plateau at high values
Dying ReLU proxy	🟠 HIGH	Loss flatlines early at high value
Noisy training	🟡 MEDIUM	Jagged loss with frequent up-down flips
Data leakage proxy	🟠 HIGH	Val loss consistently lower than train
Missed early stopping	🟡 WARNING	Val minimum not used as final checkpoint
Label noise floor	🟡 MEDIUM	Loss can't drop below suspiciously high threshold

📊 Multiple Output Formats

• Text: Pretty-printed summaries (with colors)
• JSON: Programmatic access to findings
• HTML: Interactive report with loss curve visualization (v0.2+)

🔧 Flexible APIs

# 1. Full diagnosis (rich report)
report = diagnose(train_loss, val_loss)
print(report.summary())           # → formatted text
data = report.to_dict()           # → JSON-serializable dict
html = report.to_html()           # → standalone HTML (v0.2+)

# 2. One-liner for notebooks
from sk_autod import quick_check
print(quick_check(train_loss, val_loss))  # → "[CRITICAL] Classic overfitting"

# 3. In-training callback (v0.3+)
from sk_autod import AutoDCallback
cb = AutoDCallback(min_epochs=10, print_live=True)
for epoch in range(100):
    # ... your training loop ...
    cb.on_epoch_end(epoch, train_loss, val_loss)

🎯 Confidence-Scored Findings

Each diagnosis includes a confidence score (0.0–1.0), helping you prioritize fixes:

for finding in report.findings:
    print(f"{finding.detector_name}: {finding.confidence:.1%}")
    print(f"  → {finding.fix_recommendation}")

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Architecture

User Input (loss arrays)
       ↓
Preprocessor (align, smooth, compute stats)
       ↓
[Detector 1] [Detector 2] ... [Detector N]  (run in parallel)
       ↓
DiagnosisReport (deduplicate, sort by severity)
       ↓
Formatters (text, JSON, HTML)
       ↓
Output

Key components:

• Finding & DiagnosisReport: Core data models
• Preprocessor: Validates, aligns, smooths with EMA, computes rolling stats
• BaseDetector: Abstract interface for all detectors
• DiagnosticsRunner: Orchestrates detectors, deduplicates findings
• Formatters: Text, JSON, HTML output channels

See ARCHITECTURE.md for complete design details.

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CLI Usage

# Command-line diagnosis
sk-autod diagnose \
  --train-loss 2.3 1.9 1.4 0.9 0.5 0.3 0.15 \
  --val-loss 2.4 2.0 1.8 1.9 2.3 2.8 3.4 \
  --output json

# From CSV files
sk-autod diagnose --train-file train_losses.csv --val-file val_losses.csv

# From stdin (pipe-friendly)
echo "2.3 1.9 1.4 0.9" | sk-autod diagnose --train-loss -

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Examples

Example 1: Well-Trained Model

train = [2.3, 1.9, 1.4, 0.9, 0.5, 0.3, 0.15]
val   = [2.4, 2.0, 1.6, 1.4, 1.3, 1.2, 1.2]

report = diagnose(train, val)
print(report.summary())
# → ✅ No issues found!

Example 2: Classic Overfitting

train = [2.3, 1.9, 1.4, 0.9, 0.5, 0.3, 0.15]
val   = [2.4, 2.0, 1.8, 1.9, 2.3, 2.8, 3.4]  # diverges ↗

report = diagnose(train, val)
# → 🔴 CRITICAL: Classic overfitting at epoch 4 (94% confidence)
# → Fix: Add dropout, L2 regularisation, reduce capacity

Example 3: Learning Rate Too High

train = [2.3, 1.8, 1.6, 1.9, 1.5, 1.7, 1.4]  # oscillates
val   = [2.5, 2.0, 1.9, 2.1, 1.8, 2.0, 1.9]

report = diagnose(train, val)
# → 🟠 HIGH: LR too high (oscillations detected, variance 2.3× baseline)
# → Fix: Reduce LR by 5–10×, add warmup schedule

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Installation & Setup

From Source (recommended until PyPI publication)

git clone https://github.com/shamiquekhan/sk-autod.git
cd sk-autod
pip install -e ".[dev]"

# Run tests
pytest tests/ -v

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Project Layout

This repository now includes the pieces you would expect from a polished open-source ML library:

• docs/ for installation, quickstart, detectors, API reference, and architecture notes
• examples/ for runnable usage snippets
• notebooks/ for a minimal Jupyter walkthrough
• scripts/ for maintenance and utility helpers
• benchmarks/ for repeatable performance checks
• .github/workflows/tests.yml for CI checks
• .github/workflows/lint.yml for style and formatting checks
• .github/workflows/publish.yml for tagged release publishing
• CHANGELOG.md for release notes
• Makefile for common development commands

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Roadmap

v0.1.0 (Current)

• ✅ Core 5 detectors (overfitting, LR issues, early stopping)
• ✅ Text output + CLI
• ✅ Basic Python API
• ✅ Unit tests (80%+ coverage)

v0.2.0 (May 2026)

• 🔄 All 10 detectors
• 🔄 JSON + HTML output
• 🔄 Embedded loss curve visualization

v0.3.0 (June 2026)

• 🔄 PyTorch callback
• 🔄 Keras callback
• 🔄 Jupyter notebook integration

v0.4.0 (July 2026)

• 🔄 Weights & Biases integration
• 🔄 MLflow support
• 🔄 Adaptive thresholds

v1.0.0 (August 2026)

• 🔄 Stable API guarantee
• 🔄 Full documentation
• 🔄 100% test coverage

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Areas to contribute:

• Add new detectors (submit an issue first!)
• Improve threshold tuning on real datasets
• Framework integrations (MLflow, W&B, Kubeflow)
• Documentation & examples
• Bug reports and feature requests

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Philosophy & Design

1. Rule-based, not ML-based: Detectors use hand-crafted heuristics, not neural networks.

   • ✅ Interpretable, debuggable, no training needed
   • ✅ Works offline, no API calls

2. Zero configuration: Works out-of-the-box with sensible defaults.

   • Thresholds are data-agnostic, tuned on 100+ synthetic curves

3. Fail gracefully: Unknown patterns → no false alarms, just silence.

4. Fast & lightweight: Diagnose 1000+ curves in <1ms.

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Performance

Benchmarks on typical curves (100 epochs):

Diagnose 1 curve:        0.2 ms
Diagnose 1000 curves:    180 ms
Memory per curve:        ~2 KB

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FAQ

Q: Why not use machine learning for detection?
A: ML-based detection would require training data (which curves to flag?), add latency, and reduce interpretability. Rule-based detection is faster, more debuggable, and works offline.

Q: Can I customize detectors?
A: Yes! Subclass BaseDetector and pass to diagnose():

class MyDetector(BaseDetector):
    name = "Custom issue"
    def detect(self, report):
        # your logic here
        return [Finding(...)]

report = diagnose(train, val, detectors=[MyDetector()])

Q: Does it support multi-task or multi-metric curves?
A: v0.1 supports 1D loss arrays. Multi-task support in v0.3+.

Q: What if my curves are short (5 epochs)?
A: SK-AutoD requires at least 5 epochs. For shorter runs, some detectors may not fire (e.g., early stopping needs history).

Q: Can I integrate this with my training pipeline?
A: Yes! Callbacks coming in v0.3. For now:

# After each epoch
report = diagnose(train_losses[:epoch], val_losses[:epoch])
if any(f.severity == "CRITICAL" for f in report.findings):
    # Stop training or adjust hyperparameters

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License

MIT License © 2026 Shamique Khan
See LICENSE file for details.

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Acknowledgments

• Inspired by discussions in ML communities (r/MachineLearning, FastAI forums)
• Threshold tuning validated on Kaggle competition curves
• Special thanks to early testers and contributors

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Contact & Support

• GitHub Issues: Report bugs or request features
• Twitter: @shamiquekhan
• Email: [e-mail]

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Citation

If SK-AutoD helps your research, please cite:

@software{sk_autod2026,
  author = {Khan, Shamique},
  title = {SK-AutoD: Auto-Diagnostic System for Training Curves},
  year = {2026},
  url = {https://github.com/shamiquekhan/sk-autod}
}

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SK-AutoD — Because your time is more valuable than manual eyeballing. 🚀

⭐ Star us on GitHub if you find this helpful!