ScopeRX

Neural Network Explainability and Interpretability Library

ScopeRX is a comprehensive, production-grade Python library for explaining and interpreting neural network predictions. It provides state-of-the-art attribution methods, evaluation metrics, and visualization tools - all unified under a simple, intuitive API.

Version 2.0.0 Updates

• Type Safety: Fully typed codebase with mypy compliance.
• Production Grade: Enhanced stability, error handling, and performance optimizations.
• CI/CD: Automated testing and linting pipelines.
• Improved Methods: Refactored KernelSHAP, RISE, and Attention methods for better accuracy and speed.

Features

• 15+ Explanation Methods: From classic GradCAM to cutting-edge RISE and attention methods
• Unified API: One interface to rule them all - switch between methods with a single parameter
• Evaluation Metrics: Faithfulness, sensitivity, and stability metrics to quantify explanation quality
• Beautiful Visualizations: Publication-ready plots with minimal code
• Model Agnostic: Works with any PyTorch model architecture
• Transformer Support: Dedicated attention visualization for Vision Transformers
• CLI Tool: Generate explanations from the command line

Installation

pip install scope-rx

With optional dependencies:

# For interactive Plotly visualizations
pip install scope-rx[interactive]

# For development
pip install scope-rx[dev]

# Full installation with all extras
pip install scope-rx[full]

Quick Start

from scope_rx import ScopeRX
import torch
import torchvision.models as models

# Load your model
model = models.resnet50(pretrained=True)
model.eval()

# Create explainer
explainer = ScopeRX(model)

# Generate explanation
result = explainer.explain(
    input_tensor,
    method='gradcam',
    target_class=predicted_class
)

# Visualize
result.visualize()

# Or save to file
result.save("explanation.png")

Available Methods

Gradient-Based Methods

Perturbation-Based Methods

Model-Agnostic Methods

Attention-Based Methods (for Transformers)

Compare Methods

from scope_rx import ScopeRX

explainer = ScopeRX(model)

# Compare multiple methods at once
results = explainer.compare_methods(
    input_tensor,
    methods=['gradcam', 'smoothgrad', 'integrated_gradients', 'rise'],
    target_class=predicted_class
)

# Visualize comparison
from scope_rx.visualization import plot_comparison
plot_comparison({name: r.attribution for name, r in results.items()})

Evaluate Explanations

from scope_rx.metrics import (
    faithfulness_score,
    insertion_deletion_auc,
    sensitivity_score,
    stability_score
)

# Faithfulness: Does the explanation reflect model behavior?
faith = faithfulness_score(model, input_tensor, attribution, target_class=0)

# Insertion/Deletion: How does model output change as we add/remove important features?
scores = insertion_deletion_auc(model, input_tensor, attribution, target_class=0)
print(f"Insertion AUC: {scores['insertion_auc']:.3f}")
print(f"Deletion AUC: {scores['deletion_auc']:.3f}")

# Sensitivity: Are explanations sensitive to meaningful changes?
sens = sensitivity_score(explainer, input_tensor, target_class=0)

# Stability: Are explanations stable across similar inputs?
stab = stability_score(explainer, input_tensor, target_class=0)

Visualization

from scope_rx.visualization import (
    plot_attribution,
    plot_comparison,
    overlay_attribution,
    create_interactive_plot,
    export_visualization
)

# Simple plot
plot_attribution(attribution, image=original_image)

# Interactive Plotly plot
fig = create_interactive_plot(attribution, image=original_image)
fig.show()

# Export to various formats
export_visualization(attribution, "output.png", colormap="jet")
export_visualization(attribution, "output.npy")  # Raw numpy array

Command Line Interface

# Generate explanation
scope-rx explain image.jpg --model resnet50 --method gradcam --output heatmap.png

# Compare methods
scope-rx compare image.jpg --model resnet50 --methods gradcam,smoothgrad,rise

# List available methods
scope-rx list-methods

# Show model layers (for layer selection)
scope-rx show-layers --model resnet50

Advanced Usage

Custom Target Layers

from scope_rx import GradCAM

# Specify exact layer
explainer = GradCAM(model, target_layer="layer4.1.conv2")

Custom Baselines for Integrated Gradients

from scope_rx import IntegratedGradients

# Use different baselines
explainer = IntegratedGradients(
    model,
    n_steps=50,
    baseline="blur"  # Options: "zero", "random", "blur"
)

Batch Processing

from scope_rx import ScopeRX
from scope_rx.utils import preprocess_image
from pathlib import Path

explainer = ScopeRX(model)

# Process multiple images
for image_path in image_paths:
    input_tensor = preprocess_image(image_path)
    result = explainer.explain(input_tensor, method='gradcam')
    result.save(f"explanations/{Path(image_path).stem}.png")

Using Individual Explainers

from scope_rx import GradCAM, SmoothGrad, RISE

# Use specific explainer directly
gradcam = GradCAM(model, target_layer="layer4")
result = gradcam.explain(input_tensor, target_class=0)

# SmoothGrad with custom parameters
smoothgrad = SmoothGrad(model, n_samples=50, noise_level=0.2)
result = smoothgrad.explain(input_tensor, target_class=0)

Testing

# Run all tests
pytest tests/

# Run with coverage
pytest tests/ --cov=scope_rx --cov-report=html

# Run specific test module
pytest tests/test_gradient_methods.py -v

Documentation

For full documentation, visit our documentation site.

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Citation

If you use ScopeRX in your research, please cite:

@software{scoperx2024,
  title = {ScopeRX: Neural Network Explainability Library},
  author = {XCALEN},
  year = {2024},
  url = {https://github.com/xcalen/scope-rx}
}

Acknowledgments

ScopeRX builds upon the excellent work of the interpretability research community. Special thanks to the authors of:

• GradCAM, GradCAM++, ScoreCAM, LayerCAM
• SHAP and LIME
• Integrated Gradients
• RISE
• And many others who have contributed to the field of explainable AI

Made with love by Desenyon