BETTER-NMA 1.1.1

NMA: Dendrogram-based model analysis, white-box testing, and adversarial detection

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NMA – Near Misses Analysis

NMA (Near Misses Analysis) is a Python package for analyzing machine learning models through dendrogram-based hierarchical clustering, white-box testing, and adversarial attack detection.

It provides visualization, explanation, and diagnostic tools to help developers and researchers understand their models’ decision boundaries, identify vulnerabilities, and detect adversarial inputs.

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✨ Features

• 📊 Dendrogram construction & visualization

  • Build hierarchical trees from model predictions.
  • Plot full dendrograms or sub-dendrograms for specific labels.

• 🧪 White-box testing

  • Identify problematic training samples likely to cause misclassification.
  • Run structured analysis across source/target label pairs.

• 🛡 Adversarial attack detection

  • Train a logistic regression adversarial detector.
  • Detect adversarial images and compute adversarial scores.

• 🔎 Model querying & explanations

  • Query images for predictions with hierarchical context.
  • Generate verbal explanations of model predictions.

• 🧩 Cluster analysis tools

  • Find lowest common ancestors (LCA) in the dendrogram.
  • Rename clusters for more meaningful interpretation.

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📦 Installation

pip install BETTER_NMA

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🚀 Quickstart

from BETTER_NMA import NMA
import numpy as np

# Example data (replace with your dataset/model)
x_train = np.random.rand(100, 32, 32, 3)
y_train = np.random.randint(0, 2, size=100)
labels = ["cat", "dog"]

# Your pre-trained model (e.g., Keras, PyTorch wrapper with predict)
model = my_model  

# Initialize NMA
nma = NMA(
    x_train=x_train,
    y_train=y_train,
    labels=labels,
    model=model,
    explanation_method="similarity", 
    save_connections=True
)

# Plot dendrogram
nma.plot(title="Model Decision Hierarchy")

# Run white-box testing
issues = nma.white_box_testing(["cat"], ["dog"], analyze_results=True)

# Train adversarial detector
nma.train_adversarial_detector(authentic_images, adversarial_images)

# Detect if a new image is adversarial
result = nma.detect_attack(test_image)

# Get verbal explanation of an image
explanation = nma.verbal_explanation(test_image)
print(explanation)

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📚 API Overview

Dendrogram & Visualization

• plot(sub_labels=None, ...) – plot full or partial dendrogram.
• plot_sub_dendrogram(sub_labels, ...) – zoom into specific classes.

White-box Testing

• white_box_testing(source_labels, target_labels, ...) – find problematic images.
• get_white_box_analysis(source_labels, target_labels, ...) – detailed analysis.

Adversarial Detection

• train_adversarial_detector(authentic_images, attacked_images) – train detector.
• detect_attack(image, plot_result=False) – detect adversarial samples.
• adversarial_score(image, top_k=5) – compute adversarial score.

Query & Explanation

• query_image(image, top_k=5) – get predictions & explanation.
• verbal_explanation(image) – generate natural language explanation.

Cluster Analysis

• find_lca(label1, label2) – lowest common ancestor.
• change_cluster_name(cluster_id, new_name) – rename clusters.

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🛠 Requirements

• Python ≥ 3.8
• NumPy, Pandas, Matplotlib, Scikit-learn
• (Optional) PyTorch / TensorFlow for model support

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📖 Use Cases

• Research – interpret model predictions via hierarchical clustering.
• Robustness testing – identify adversarial vulnerabilities.
• Explainability – provide visual + verbal explanations.
• Debugging – detect mislabeled or problematic training samples.

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📜 License

MIT License – free to use and modify.

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