juniper-cascor 0.3.17

Cascade Correlation Neural Network implementation for the Juniper project

Juniper: Dynamic Neural Network Research Platform

Version: 0.6.3 | Changelog | Full Documentation

Juniper is an AI/ML research platform for investigating dynamic neural network architectures and novel learning paradigms. The project emphasizes ground-up implementations from primary literature, enabling a more transparent exploration of fundamental algorithms.

Ecosystem Compatibility

This service is part of the Juniper ecosystem. Verified compatible versions:

juniper-data	juniper-cascor	juniper-canopy	data-client	cascor-client	cascor-worker
0.4.x	0.3.x	0.2.x	>=0.3.1	>=0.1.0	>=0.1.0

For full-stack Docker deployment and integration tests, see juniper-deploy.

Architecture

JuniperCascor is the training service of the Juniper ecosystem. It depends on JuniperData for datasets and is monitored by juniper-canopy in real-time.

┌─────────────────────┐     REST+WS      ┌──────────────────────┐
│   juniper-canopy     │ ◄──────────────► │  JuniperCascor       │
│   Dashboard         │                  │  Training Svc        │
│   Port 8050         │                  │  Port 8200  ◄── here │
└──────────┬──────────┘                  └──────────┬───────────┘
           │ REST                                    │ REST
           ▼                                         ▼
┌──────────────────────────────────────────────────────────────┐
│                      JuniperData                              │
│                   Dataset Service  ·  Port 8100               │
└──────────────────────────────────────────────────────────────┘

API: REST + WebSocket (/ws/training, /ws/control). All responses use a {status, data, meta} envelope.

Related Services

Service	Relationship	Notes
juniper-data	JuniperCascor fetches datasets from here	Set JUNIPER_DATA_URL
juniper-canopy	Monitors CasCor training in real-time	Connects to /ws/training
juniper-cascor-client	PyPI REST+WS client library	pip install juniper-cascor-client

Service Configuration

Variable	Required	Default	Description
JUNIPER_DATA_URL	Yes	http://localhost:8100	JuniperData service URL
CASCOR_HOST	No	0.0.0.0	Listen address
CASCOR_PORT	No	8200	Service port
CASCOR_LOG_LEVEL	No	INFO	Log verbosity (DEBUG, INFO, WARNING)

Docker Deployment

# Standalone:
docker build -t juniper-cascor:latest .
docker run -p 8200:8200 -e JUNIPER_DATA_URL=http://host.docker.internal:8100 juniper-cascor:latest

# Full stack:
git clone https://github.com/pcalnon/juniper-deploy.git
cd juniper-deploy && docker compose up --build

Quick Start

Prerequisites

• Python 3.11 or later (3.14 recommended)
• Conda package manager

Installation

# Clone the repository
git clone https://github.com/pcalnon/juniper-cascor.git
cd juniper-cascor

# Create and activate conda environment
conda env create -f conf/conda_environment.yaml
conda activate juniper_cascor

# Run the spiral problem evaluation
cd src && python main.py

Run Tests

# Fast tests (recommended for development)
cd src/tests && bash scripts/run_tests.bash

# Or using pytest directly
pytest -m "not slow" -v

For detailed installation instructions, see the Quick Start Guide.

Active Research Components

juniper_cascor: Cascade Correlation Neural Network

• Reference implementation from foundational research (Fahlman & Lebiere, 1990)
• Designed for flexibility, modularity, and scalability
• Enables investigation of constructive learning algorithms

juniper_canopy: Interactive Research Interface

• Research-driven monitoring and visualization environment
• Delivers novel observations through real-time network introspection
• Transforms metrics into insights, accelerating experimental iteration

Documentation

Category	Description
Documentation Overview	Complete navigation guide to all docs
Documentation Index	Quick documentation index
Quick Start	Get up and running quickly
User Manual	Comprehensive usage guide
API Reference	Complete API documentation
Testing Guide	Testing instructions
CI/CD Guide	Continuous integration
Source Code Guide	Contributor documentation
Constants Reference	Configuration constants

Basic Usage

from cascade_correlation.cascade_correlation import CascadeCorrelationNetwork
from cascade_correlation.cascade_correlation_config.cascade_correlation_config import CascadeCorrelationConfig
import torch

# Create configuration
config = CascadeCorrelationConfig(
    input_size=2,
    output_size=2,
    random_seed=42
)

# Create and train network
network = CascadeCorrelationNetwork(config=config)
history = network.fit(x_train, y_train, epochs=100)

# Evaluate
accuracy = network.get_accuracy(x_test, y_test)
print(f"Test accuracy: {accuracy:.2%}")

# Save/Load
network.save_to_hdf5("model.h5")
loaded = CascadeCorrelationNetwork.load_from_hdf5("model.h5")

Research Philosophy

Juniper prioritizes transparency over convenience and understanding over abstraction. By implementing algorithms from first principles, the platform provides researchers with increased visibility into network behavior, enabling a more rigorous and more controlled investigation of learning dynamics and architectural innovations.

Important Notices

Thread Safety Warning

The CascadeCorrelationNetwork class is NOT thread-safe. Do not share network instances between threads without proper synchronization. For concurrent training scenarios, create separate network instances per thread. The internal multiprocessing for candidate training is handled within the class and does not require external synchronization.

Juniper Ecosystem

Repository	Description
juniper-cascor	CasCor neural network training service (this repo)
juniper-canopy	Real-time monitoring dashboard
juniper-data	Dataset generation service
juniper-data-client	PyPI: juniper-data-client
juniper-cascor-client	PyPI: juniper-cascor-client
juniper-cascor-worker	PyPI: juniper-cascor-worker

License

MIT License - see LICENSE for details.

Contributing

Contributions are welcome. Please see the Source Code Guide for development setup and coding conventions.