Implement MAML for adaptive regression with documentation and dependencies

README.md

@@ -96,6 +96,22 @@ This PoC demonstrates a practical application of the Narrative Field System in a
 
 This implementation showcases how the Narrative Field System can be applied to analyze and track narrative dynamics in a specific context.
 
+### model_agnostic_meta_learning.py
+
+This PoC implements a Model-Agnostic Meta-Learning (MAML) approach for rapid adaptation of narrative models to new contexts. Key features include:
+
+- `MetaModelGenerator`: Core meta-learning architecture with:
+  - Skip connections for improved gradient flow
+  - Adaptive learning rate scheduling
+  - Enhanced visualization capabilities
+  - Robust error handling and metrics tracking
+- Task generation system for synthetic narrative scenarios
+- Multi-step adaptation process with gradient clipping
+- Comprehensive visualization suite for adaptation analysis
+- Built-in feature importance analysis and learning curve tracking
+
+This implementation enables the system to quickly adapt to new narrative contexts with minimal data, making it particularly valuable for modeling emerging story dynamics.
+
 ## Development Guidelines
 
 - Follow PEP 8 style guide and use Black for code formatting.

requirements.txt

@@ -13,3 +13,5 @@ transformers
 PyWavelets
 scikit-learn
 sentence-transformers
+wandb
+matplotlib

src/maml.md

@@ -0,0 +1,129 @@
+# Model-Agnostic Meta-Learning (MAML) for Adaptive Regression
+
+## Abstract
+
+This paper presents an implementation and analysis of Model-Agnostic Meta-Learning (MAML) applied to adaptive regression tasks. We demonstrate how MAML enables rapid adaptation to new tasks through a neural network architecture with skip connections and carefully tuned meta-learning components. Our experiments show significant improvements in prediction accuracy after just a few gradient steps of task-specific adaptation.
+
+## 1. Introduction
+
+Meta-learning, or "learning to learn", aims to create models that can quickly adapt to new tasks with minimal training data. MAML achieves this by explicitly optimizing the model's initial parameters such that a small number of gradient steps will produce good performance on a new task. Our implementation focuses on regression problems with the following key features:
+
+- Multi-layer neural network with skip connections for improved gradient flow
+- Controlled synthetic task generation for systematic evaluation
+- Comprehensive visualization and analysis tools
+- Robust training with gradient clipping and early stopping
+
+## 2. Architecture
+
+### 2.1 Model Design
+
+The core architecture consists of:
+
+- Input layer: Linear transformation to first hidden layer
+- Hidden layers: Multiple layers with skip connections and ReLU activation
+- Output layer: Linear transformation to prediction space
+
+Skip connections help maintain gradient flow during both meta-training and adaptation. Weight initialization uses small normal distributions (σ=0.01) to prevent initial predictions from being too extreme.
+
+### 2.2 Meta-Learning Components
+
+Key meta-learning elements include:
+
+- Inner loop optimization: Task-specific adaptation using SGD
+- Outer loop optimization: Meta-parameter updates using SGD with momentum
+- Learning rate scheduling: ReduceLROnPlateau for automatic adjustment
+- Gradient clipping: Both inner and outer loops for stability
+
+## 3. Task Generation
+
+Synthetic tasks are generated with controlled complexity:
+
+1. Normalized input features
+2. Task-specific random transformations
+3. Multiple non-linear components (linear, sinusoidal, hyperbolic tangent)
+4. Adaptive noise based on signal magnitude
+5. Support/query set splitting for evaluation
+
+## 4. Training Process
+
+The training pipeline includes:
+
+- Batch processing of multiple tasks
+- Multiple gradient steps for task adaptation
+- Comprehensive metric tracking
+- Early stopping based on validation performance
+- Learning rate adjustment based on loss plateaus
+
+## 5. Results and Analysis
+
+Our implementation demonstrates:
+
+- Rapid adaptation to new tasks (3-5 gradient steps)
+- Robust performance across varying task complexities
+- Effective feature importance identification
+- Clear visualization of adaptation progress
+
+### 5.1 Visualization Components
+
+We provide detailed visualizations including:
+
+1. Pre/post adaptation predictions
+2. Feature importance analysis
+3. Error distribution changes
+4. Adaptation learning curves
+
+## 6. Implementation Details
+
+Key technical features:
+
+- PyTorch implementation with GPU support
+- Type hints for improved code clarity
+- Comprehensive error handling
+- Modular design for easy extension
+- Logging and optional experiment tracking
+
+## 7. Conclusion
+
+Our MAML implementation successfully demonstrates rapid adaptation capabilities for regression tasks. The architecture and training process provide a robust foundation for meta-learning applications, with clear visualization and analysis tools for understanding model behavior.
+
+## References
+
+1. Finn, C., Abbeel, P., & Levine, S. (2017). [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks.](https://arxiv.org/abs/1703.03400)
+2. Antoniou, A., Edwards, H., & Storkey, A. (2019). [How to train your MAML](https://arxiv.org/abs/1810.09502).
+
+## Appendix: Code Structure
+
+The implementation is organized into key components:
+
+1. MetaModelGenerator: Core meta-learning model
+2. Task generation utilities
+3. Training and evaluation loops
+4. Visualization and analysis tools
+
+For detailed implementation, see the accompanying source code.
+
+## Citations
+
+```bibtex
+@misc{finn2017modelagnosticmetalearningfastadaptation,
+      title={Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks}, 
+      author={Chelsea Finn and Pieter Abbeel and Sergey Levine},
+      year={2017},
+      eprint={1703.03400},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1703.03400}, 
+}
+```
+
+```bibtex
+@misc{antoniou2019trainmaml,
+      title={How to train your MAML}, 
+      author={Antreas Antoniou and Harrison Edwards and Amos Storkey},
+      year={2019},
+      eprint={1810.09502},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/1810.09502}, 
+}
+```