Atlas
Getting Started
Introduction to Atlas
Executive Summary
Why Atlas?
Core Philosophy
1.
Model Agnostic Architecture
2.
Business-First Design
3.
Enterprise-Ready Standards
4.
Open and Extensible
Key Capabilities
Unified Model Integration
Advanced Optimization Engine
Multi-Dimensional Data Management
Business Intelligence Integration
Target Users
Business Value Proposition
Getting Started
Framework Architecture
Next Steps
Quick Start Guide
Prerequisites
Installation
Install from PyPI
Install from Source
Your First Optimization
Step 1: Import Required Components
Step 2: Create a Simple Model
Step 3: Define Optimization Constraints
Step 4: Run Optimization
Expected Output
Real-World Example: Multi-KPI Optimization
Using Docker Models
Advanced Features Quick Examples
1. Scenario Analysis
2. Real-Time Optimization
3. Model Registry Usage
Configuration Management
Next Steps
Getting Help
Common Issues
Issue: ImportError
Issue: Model Not Found
Issue: Optimization Not Converging
Core Concepts
4 Core Concepts
Overview
Data
Data Architecture
Core Data Structures
Budget Allocations
Model Outputs
Historical Data
Data Operations
Budget Aggregation
Data Alignment
Resampling and Interpolation
Data Validation
Data Transformation
Unit Conversion
Time Aggregation
Feature Engineering
Data Import/Export
File Formats
Database Connections
API Integration
Data Performance
Lazy Loading
Efficient Storage
Models
Model Types
Machine Learning Models
Statistical Models
External Services
Custom Models
Model Integration
1. Direct Python Integration
2. Docker Container Integration
3. API Integration
Model Requirements
Required Methods
Optional Methods
Model Validation
Optimization
Optimization Backends
SciPy Optimizer
Optuna Optimizer
CVXPY Optimizer
Multi-Objective Optimization
Optimization Strategies
Single-Point Optimization
Multi-Start Optimization
Scenario Optimization
Performance Optimization
Caching
Parallel Execution
Warm Starting
Constraints
Constraint Types
Budget Constraints
Business Rule Constraints
Temporal Constraints
Performance Constraints
Custom Constraints
Constraint Validation
Constraint Relaxation
Integration Example
Data
Overview
Why Data Matters in Optimization
Key Concepts
Multi-Dimensional Nature
Data Sources and Integration
Data Quality and Validation
Real-World Applications
Marketing Mix Modeling
Hospital Resource Planning
Supply Chain Optimization
Benefits of Atlas’s Data Approach
Reduced Complexity
Improved Accuracy
Faster Insights
Enhanced Flexibility
Common Data Patterns
Time Series Alignment
Hierarchical Structures
Sparse Data
External Data Integration
Getting Started with Data
Best Practices
Start Simple
Document Everything
Validate Continuously
Plan for Growth
Next Steps
Models
Overview
Understanding Models in Optimization
The Power of Model Agnosticism
Why It Matters
Integration Without Disruption
Types of Models
Statistical Models
Machine Learning Models
Business Rule Models
Hybrid Approaches
Real-World Model Applications
Marketing Mix Modeling
Healthcare Capacity Planning
Supply Chain Optimization
Dynamic Pricing
Model Requirements in Atlas
Answer Prediction Queries
Handle Valid Inputs
Provide Timely Responses
Maintain Reliability
Model Validation and Trust
Why Validation Matters
Building Trust
Common Model Patterns
Diminishing Returns
Interaction Effects
Temporal Dynamics
Uncertainty Quantification
Getting Started with Models
For Business Users
For Technical Teams
The Future of Models in Atlas
Next Steps
Optimization
Overview
What is Optimization?
The Optimization Landscape
Different Problems, Different Approaches
Single vs. Multi-Objective
How Optimization Works
The Search Process
Intelligent Exploration
Real-World Optimization Scenarios
Marketing Budget Allocation
Healthcare Staff Scheduling
Supply Chain Network Design
Product Portfolio Optimization
Advanced Optimization Features
Pareto Frontier Analysis
Scenario Optimization
Rolling Horizon Optimization
Warm Starting
Optimization Performance
Speed Matters
Scalability
Making Optimization Accessible
For Business Users
For Technical Users
Common Optimization Patterns
Budget Allocation
Scheduling
Network Flow
Portfolio Selection
Capacity Planning
Getting Started with Optimization
Define Clear Objectives
Identify Decision Variables
Understand Constraints
Start Simple
Iterate and Refine
The Future of Optimization
Next Steps
Constraints
Overview
Why Constraints Matter
Understanding Constraints
Hard Constraints
Soft Constraints
Dynamic Constraints
Types of Business Constraints
Budget and Financial Constraints
Operational Constraints
Strategic Constraints
Regulatory and Compliance Constraints
Real-World Constraint Examples
Marketing Mix Optimization
Hospital Staff Scheduling
Supply Chain Planning
How Atlas Handles Constraints
Intelligent Validation
Constraint Prioritization
Adaptive Relaxation
Benefits of Proper Constraint Management
Risk Mitigation
Stakeholder Alignment
Practical Implementation
Continuous Improvement
Common Constraint Patterns
Mutual Exclusivity
Conditional Logic
Capacity Sharing
Time Dependencies
Balance Requirements
Designing Effective Constraints
Start with Business Logic
Be Specific but Flexible
Document Reasoning
Plan for Change
Test Thoroughly
Managing Constraint Complexity
Hierarchical Organization
Version Control
Performance Impact
The Future of Constraints
Intelligent Constraint Learning
Natural Language Constraints
Constraint Explanation
Getting Started with Constraints
Next Steps
Features
Atlas Features
Overview
Core Features
1. Universal Model Integration
Supported Model Types
Integration Methods
2. Multi-Objective Optimization
Key Capabilities
Supported Objectives
3. Advanced Optimization Algorithms
SciPy Backend
Optuna Backend
CVXPY Backend
4. Constraint Management
Constraint Types
5. Multi-Dimensional Data Handling
Supported Dimensions
6. Real-Time Optimization
Features
7. Scenario Analysis & What-If Planning
Capabilities
8. Model Registry & Version Control
Features
9. Visualization & Reporting
Visualization Types
Report Generation
10. API & Integration Ecosystem
REST API
Python SDK
Integrations
Advanced Features
Model Chaining & Nesting
Custom Optimization Strategies
Distributed Optimization
Performance & Scalability
Getting Started with Features
Design Guides
Black Box Model Integration Guide
Overview
Key Components
1.
BlackBoxModelConfig
2.
BlackBoxModelConfigBuilder
3.
BlackBoxModel
Quick Start Examples
Example 1: Simple Linear Model
Example 2: Marketing Mix Model with Saturation
Example 3: Multi-Geography Model with Constraints
Advanced Features
Custom Contribution Calculation
Preprocessing and Transformations
Working with Time Series
Integration with Atlas Optimization
Using with Different Optimizers
Multi-Objective Optimization
Best Practices
1.
Validate Your Functions
2.
Use Meaningful Variable Names
3.
Document Metadata
4.
Set Reasonable Bounds
5.
Save and Version Configurations
Troubleshooting
Common Issues
Next Steps
Model Integration Design Guide
Overview
Model Integration Principles
Integration Approaches
Approach 1: Direct Python Integration
Step 1: Implement the AbstractModel Interface
Step 2: Create Model Configuration
Step 3: Register and Test the Model
Approach 2: Docker Container Integration
Step 1: Create Model Service
Step 2: Create Dockerfile
Step 3: Create Docker Wrapper
Approach 3: External API Integration
Step 1: Create API Wrapper
Best Practices
1. Input Validation
2. Error Handling
3. Performance Optimization
4. Monitoring and Logging
5. Testing Strategy
Model Lifecycle Management
Version Control
Model Registry Integration
Troubleshooting Guide
Common Issues and Solutions
Next Steps
Custom Optimization Strategy Guide
Overview
Business Problem - US Distribution Network Inventory Optimization
The Challenge
Business Objectives
Strategy Design
Core Components
Mathematical Framework
Implementation Guide
Step 1: Define the Strategy Interface
Step 2: Implement Core Optimization Logic
Step 3: Implement Demand Forecasting
Step 4: Optimize Base Stock Levels
Step 5: Optimize Network Transfers
Step 6: Handle Uncertainty and Risk
Step 7: Create Performance Monitoring
Integration with Atlas
Step 1: Register the Strategy
Step 2: Configure the Strategy
Step 3: Execute Optimization
Testing and Validation
Unit Testing
Integration Testing
Performance Testing
Deployment Considerations
Configuration Management
Monitoring and Alerts
Continuous Improvement
Results and Benefits
Expected Outcomes
Key Success Factors
Conclusion
Performance Tuning Guide
Table of Contents
Performance Profiling
Identifying Bottlenecks
Using Line Profiler
Memory Profiling
Model Optimization
Optimize Model Predictions
1. Vectorized Operations
2. Model Simplification
3. Model Caching
Batch Processing
Optimizer Performance
Algorithm Selection
Optimizer Configuration
SciPy Optimization
Optuna Optimization
Early Stopping
Data Handling
Efficient Data Structures
Use Xarray Efficiently
Lazy Loading
Data Preprocessing
Parallel Processing
Multi-threaded Optimization
GPU Acceleration
Distributed Optimization
Caching Strategies
Multi-level Caching
Smart Cache Invalidation
Memory Management
Memory-Efficient Data Loading
Garbage Collection
Memory Monitoring
Infrastructure Optimization
Docker Optimization
Kubernetes Scaling
Monitoring and Benchmarking
Performance Metrics
Continuous Benchmarking
Best Practices Summary
Quick Wins
Architecture Patterns
Monitoring Checklist
Performance Troubleshooting
Common Issues and Solutions
Debug Performance Issues
Production Deployment Guide
Table of Contents
Pre-Deployment Checklist
Code Readiness
Infrastructure Requirements
Operational Readiness
Deployment Architectures
Single Server Deployment
Microservices Architecture
Docker Deployment
Production Dockerfile
Docker Compose Production
Production Nginx Configuration
Kubernetes Deployment
Deployment Manifest
Service and Ingress
Horizontal Pod Autoscaler
Cloud Deployments
AWS Deployment
ECS Task Definition
CloudFormation Template
Google Cloud Deployment
Azure Deployment
API Gateway Setup
Kong Configuration
Security Considerations
Security Checklist
Security Configuration
Monitoring and Observability
Prometheus Metrics
Logging Configuration
Grafana Dashboard
Backup and Recovery
Backup Strategy
Recovery Procedures
Troubleshooting Production Issues
Common Issues and Solutions
High Memory Usage
Slow Response Times
Database Connection Issues
Production Debugging
Post-Deployment
Health Checks
Smoke Tests
Maintenance
Rolling Updates
Maintenance Mode
Configuration Guide
Table of Contents
Configuration Overview
Configuration Loading Order
Configuration File Formats
YAML Configuration (Recommended)
JSON Configuration
TOML Configuration
Environment Variables
Standard Environment Variables
Loading Environment Variables
Model Configuration
Model Registry Configuration
Model-Specific Configuration
Optimizer Configuration
Optimizer Profiles
Dynamic Optimizer Selection
Constraint Configuration
Constraint Templates
Dynamic Constraints
Logging Configuration
Structured Logging
Log Aggregation
API Configuration
Rate Limiting Configuration
CORS Configuration
Performance Tuning
Cache Configuration
Worker Configuration
Best Practices
Configuration Management
Security Considerations
Configuration Testing
API Reference
Core Classes
Table of Contents
Models
AbstractModel
Class Definition
Methods
ModelWrapper
Class Definition
Example Usage
DockerModelWrapper
Class Definition
Methods
Example Usage
Optimizers
BaseOptimizer
Class Definition
ScipyOptimizer
Class Definition
Configuration Options
Example Usage
OptunaOptimizer
Class Definition
Configuration Options
Example Usage
Strategies
BaseOptimizationStrategy
Class Definition
MultiObjectiveStrategy
Class Definition
Example Usage
Configuration
ConfigurationManager
Class Definition
Example Usage
ModelConfiguration
Class Definition
Results
OptimizationResult
Class Definition
Properties and Methods
Utilities
Factory Classes
OptimizerFactory
ModelFactory
Data Utilities
BudgetConverter
ConstraintValidator
Visualization Utilities
OptimizationVisualizer
Error Handling
Custom Exceptions
Best Practices
1. Model Integration
2. Error Handling
3. Configuration Management
Version Information
Examples
Marketing Mix Modeling (MMM) Example
Overview
Complete MMM Implementation
Step 1: Setup and Data Preparation
Step 2: Load and Prepare Marketing Data
Step 3: Create Marketing Mix Model
Step 4: Set Up Optimization
Step 5: Run Optimization for Different Scenarios
Step 6: Advanced Analysis
Step 7: Generate Executive Report
Step 8: Continuous Optimization
Key Takeaways
Next Steps
Scikit-learn Integration with Atlas
Overview
Installation
Quick Start
1. Loading an Existing Model
2. Using the Factory Pattern
3. Configuration-Based Setup
Supported Models
Registering Custom Models
Feature Contributions
Working with Time Series
Best Practices
1. Feature Scaling
2. Model Validation
3. Contribution Methods
Troubleshooting
Common Issues
Advanced Usage
Multi-Objective Optimization
Custom Preprocessing
Performance Tips
Next Steps
Additional Resources
Changelog
[0.0.1] - 2025-08-01
Added
Fixed
Changed
Deprecated
Removed
Security
Contributing to Atlas
Table of Contents
Code of Conduct
Getting Started
Types of Contributions
First-Time Contributors
Development Setup
Prerequisites
Setting Up Your Environment
Development Tools
Making Contributions
Finding Something to Work On
Workflow
Commit Message Convention
Coding Standards
Python Style Guide
Code Structure
Type Hints
Error Handling
Testing Guidelines
Test Structure
Writing Tests
Test Coverage
Integration Tests
Documentation
Docstring Format
Documentation Updates
Building Documentation
Pull Request Process
Before Submitting
PR Template
Review Process
After Merge
Community
Getting Help
Becoming a Maintainer
Recognition
Thank You!
Frequently Asked Questions (FAQ)
General Questions
What is Atlas?
Who should use Atlas?
How is this different from other optimization tools?
What are the system requirements?
Installation and Setup
How do I install the framework?
Can I use this with my existing models?
Do I need Docker?
How do I handle authentication for cloud models?
Model Integration
How do I integrate a scikit-learn model?
Can I use R models?
How do I handle models with preprocessing?
What about deep learning models?
Optimization
How do I choose the right optimizer?
Can I optimize over time periods?
How do I handle non-linear constraints?
What if optimization doesn’t converge?
Performance
How can I speed up optimization?
How much data can the framework handle?
Can I use GPU acceleration?
Deployment
How do I deploy to production?
How do I monitor optimization jobs?
Can I schedule regular optimizations?
Troubleshooting
Common Error Messages
“Model prediction shape mismatch”
“Constraint violation in optimal solution”
“Memory error during optimization”
How do I debug optimization issues?
Best Practices
Should I normalize my data?
How often should I retrain models?
What’s the best way to handle multiple currencies?
Advanced Topics
Can I implement custom optimization algorithms?
How do I handle uncertainty in predictions?
Can I use the framework for non-marketing applications?
Support
Where can I get help?
How do I report a bug?
Can I contribute to the project?
Glossary of Terms
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y
Z
Technical Acronyms
Mathematical Notation
Atlas
Index
Index