sk-autod 0.1.1

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.

Website For Tutorial and documentation

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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-ML-Library-for-Training-Curve-Auto-Diagnostician.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-ML-Library-for-Training-Curve-Auto-Diagnostician.git
cd SK-AutoD-ML-Library-for-Training-Curve-Auto-Diagnostician
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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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-ML-Library-for-Training-Curve-Auto-Diagnostician}
}

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

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