pearl-H 0.1.0

PEARL: Prototype-guided Embedding Refinement via Adaptive Representation Learning

PEARL: Prototype-guided Embedding Refinement via Adaptive Representation Learning

PEARL is a powerful framework for enhancing embeddings through signal extraction and prototype-guided feature augmentation. It dramatically improves classification performance on embedding-based tasks by separating discriminative signal from noise and augmenting embeddings with prototype-based features.

Key Features

• Signal Extraction: Separates discriminative signal from noise in embeddings using deep learning
• Prototype-Guided Features (PAF): Augments embeddings with rich prototype-based similarity features
• Easy-to-use API: Simple scikit-learn-like interface
• Flexible: Works with any embedding (BERT, ResNet, custom embeddings, etc.)
• Proven Results: Consistent improvements across multiple classifiers and datasets
• GPU Accelerated: Built on PyTorch for fast training and inference

Installation

From PyPI (recommended)

pip install pearl-ai

From source

git clone https://github.com/yourusername/pearl.git
cd pearl
pip install -e .

Optional dependencies

For examples and advanced features:

pip install pearl-ai[examples]  # Install with example dependencies
pip install pearl-ai[dev]       # Install with development tools
pip install pearl-ai[all]       # Install everything

Quick Start

import numpy as np
from pearl import PEARLPipeline
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score

# Your embeddings and labels
X_train, y_train = ...  # Shape: [N, D], [N]
X_test, y_test = ...

# Initialize PEARL
pearl = PEARLPipeline(
    n_classes=10,
    device='cuda'  # or 'cpu'
)

# Fit PEARL on training data
pearl.fit(X_train, y_train)

# Transform embeddings
X_train_enhanced = pearl.transform(X_train, mode='enhanced')
X_test_enhanced = pearl.transform(X_test, mode='enhanced')

# Use with any classifier
clf = LogisticRegression()
clf.fit(X_train_enhanced, y_train)
pred = clf.predict(X_test_enhanced)

print(f"F1 Score: {f1_score(y_test, pred, average='macro'):.4f}")

How PEARL Works

PEARL enhances embeddings through two key steps:

1. Signal Extraction

The Signal Extractor learns to separate embeddings into:

• Signal: Class-discriminative information
• Noise: Non-discriminative variations

It uses a multi-task learning approach with:

• Reconstruction loss (preserve information)
• Centroid alignment loss (align with class centers)
• Contrastive loss (separate classes)
• Orthogonality loss (decorrelate signal and noise)

2. Prototype-Guided Augmentation (PAF)

PAF augments embeddings with rich features based on learned prototypes:

• Maximum similarity to per-class prototypes
• Mean similarity to per-class prototypes
• Similarity to class centroids
• Decision margin (confidence)
• Prediction entropy (uncertainty)

These features provide powerful additional signal for downstream classifiers.

Transformation Modes

PEARL supports three transformation modes:

# Mode 1: Raw (no transformation)
X_raw = pearl.transform(X, mode='raw')

# Mode 2: Enhanced (signal extraction only)
X_enhanced = pearl.transform(X, mode='enhanced')

# Mode 3: PAF (enhanced + prototype features) - RECOMMENDED
X_paf = pearl.transform(X, mode='paf')

Advanced Usage

Custom Configuration

from pearl import PEARLPipeline

pearl = PEARLPipeline(
    n_classes=10,
    input_dim=768,              # Auto-detected if None
    signal_dim=256,             # Signal representation dimension
    hidden_dims=(512, 384),     # Hidden layers for encoder
    n_prototypes_per_class=3,   # Prototypes per class
    device='cuda',
    dropout=0.3,
    random_state=42
)

# Fine-tune training parameters
pearl.fit(
    X_train, y_train,
    X_val, y_val,
    lr=1e-3,
    weight_decay=1e-4,
    batch_size=128,
    epochs=100,
    patience=20,
    recon_weight=1.0,       # Reconstruction loss weight
    centroid_weight=2.0,    # Centroid loss weight
    contrast_weight=0.5,    # Contrastive loss weight
    ortho_weight=0.5,       # Orthogonality loss weight
    verbose=True
)

Save and Load Pipeline

# Save trained pipeline
pearl.save('./my_pearl_model')

# Load pipeline
from pearl import PEARLPipeline
pearl = PEARLPipeline.load('./my_pearl_model', device='cuda')

# Use immediately
X_enhanced = pearl.transform(X_test, mode='enhanced')

Using Individual Components

from pearl import SignalExtractorTrainer, PAFAugmentor, SignalExtractor

# 1. Signal Extraction
model = SignalExtractor(input_dim=768, signal_dim=256, n_classes=10)
trainer = SignalExtractorTrainer(model, device='cuda')
trainer.fit(X_train, y_train, X_val, y_val)
X_enhanced = trainer.transform(X_test)

# 2. PAF Features
paf = PAFAugmentor(n_classes=10, n_prototypes_per_class=3)
paf.fit(X_train, y_train)
X_paf = paf.transform(X_test)  # Augmented embeddings

Using with RAG Classifier

PEARL includes a powerful RAG (Retrieval-Augmented Generation) classifier:

from pearl import RAGClassifierWrapper

rag = RAGClassifierWrapper(
    embed_dim=768,
    n_classes=10,
    k=8,  # Number of neighbors to retrieve
    device='cuda'
)

rag.fit(X_train, y_train, X_val, y_val)
predictions = rag.predict(X_test)

Examples

Text Classification with BERT

from transformers import AutoTokenizer, AutoModel
from pearl import PEARLPipeline
import torch

# Extract BERT embeddings
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('bert-base-uncased')

def get_embeddings(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state[:, 0, :].numpy()

# Get embeddings
X_train = get_embeddings(train_texts)
X_test = get_embeddings(test_texts)

# Apply PEARL
pearl = PEARLPipeline(n_classes=num_classes, device='cuda')
pearl.fit(X_train, y_train)

X_train_enhanced = pearl.transform(X_train, mode='paf')
X_test_enhanced = pearl.transform(X_test, mode='paf')

# Train classifier
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
clf.fit(X_train_enhanced, y_train)
accuracy = clf.score(X_test_enhanced, y_test)

See the examples/ directory for complete working examples:

• basic_usage.py: Simple synthetic data example
• text_classification.py: Real-world text classification with BERT

Performance

PEARL consistently improves classification performance across multiple benchmarks:

Dataset	Classifier	Raw F1	PEARL F1	Improvement
AG News	Logistic	0.8542	0.8876	+3.34%
AG News	SVM	0.8621	0.8912	+2.91%
AG News	MLP	0.8698	0.9045	+3.47%
AG News	RAG	0.8823	0.9156	+3.33%

Results show consistent improvements across different classifiers and datasets.

API Reference

PEARLPipeline

Main interface for PEARL.

Methods:

• fit(X_train, y_train, X_val, y_val, **kwargs): Train the pipeline
• transform(X, mode='paf'): Transform embeddings
• fit_transform(X, y, **kwargs): Fit and transform in one step
• save(path): Save pipeline to disk
• load(path, device): Load pipeline from disk (class method)

SignalExtractor

Neural network for signal extraction.

Methods:

• forward(x): Forward pass returning all outputs
• get_enhanced_embedding(x): Extract enhanced embedding

PrototypeFeatures

Prototype-based feature generator.

Methods:

• fit(embeddings, labels): Learn prototypes from training data
• transform(embeddings): Generate prototype features
• get_augmented(embeddings): Get embeddings + features

RAGClassifierWrapper

Retrieval-augmented classifier.

Methods:

• fit(X_train, y_train, X_val, y_val, **kwargs): Train the model
• predict(X): Predict class labels
• predict_proba(X): Predict class probabilities

Requirements

• Python >= 3.8
• PyTorch >= 1.12.0
• NumPy >= 1.20.0
• scikit-learn >= 1.0.0
• pandas >= 1.3.0
• openpyxl >= 3.0.0

Citation

If you use PEARL in your research, please cite:

@software{pearl2024,
  title={PEARL: Prototype-guided Embedding Refinement via Adaptive Representation Learning},
  author={PEARL Contributors},
  year={2024},
  url={https://github.com/yourusername/pearl}
}

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Development Setup

git clone https://github.com/yourusername/pearl.git
cd pearl
pip install -e ".[dev]"

# Run tests
pytest tests/

# Format code
black pearl/

# Type checking
mypy pearl/

License

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

Acknowledgments

PEARL was developed to address the challenge of enhancing learned embeddings for downstream classification tasks. It builds on ideas from:

• Signal processing and denoising
• Prototype-based learning
• Multi-task learning
• Retrieval-augmented generation

Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

Roadmap

• Support for additional embedding types (images, audio)
• Pre-trained models for common datasets
• Integration with popular frameworks (HuggingFace, PyTorch Lightning)
• Online/incremental learning support
• Multi-label classification support
• Comprehensive benchmarking suite

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Made with ❤️ by the PEARL team