Metadata-Version: 2.4
Name: meta-learning-toolkit
Version: 2.5.0
Summary: Production-ready meta-learning algorithms with research-accurate implementations of MAML, Prototypical Networks, and test-time compute scaling
Project-URL: Homepage, https://github.com/benedictchen/meta-learning
Project-URL: Documentation, https://github.com/benedictchen/meta-learning/blob/main/README.md
Project-URL: Repository, https://github.com/benedictchen/meta-learning
Project-URL: Issues, https://github.com/benedictchen/meta-learning/issues
Project-URL: Funding, https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=WXQKYYKPHWXHS
Project-URL: Sponsor, https://github.com/sponsors/benedictchen
Author-email: Benedict Chen <benedict@benedictchen.com>
Maintainer-email: Benedict Chen <benedict@benedictchen.com>
License: Custom Non-Commercial License
License-File: LICENSE
Keywords: MAML,few-shot,meta-learning,prototypical-networks,test-time-compute
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.9
Requires-Dist: numpy>=1.21.0
Requires-Dist: pyyaml>=6.0
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Description-Content-Type: text/markdown

# 💰 Meta-Learning Toolkit - PLEASE DONATE! 💰

<div align="center">

🚨 **THIS RESEARCH NEEDS YOUR FINANCIAL SUPPORT TO SURVIVE!** 🚨

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**💡 If this toolkit saves you months of research time, please donate! 💡**

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**Production-ready meta-learning algorithms with research-accurate implementations**

*Based on foundational research in meta-learning and few-shot learning*

[📚 Documentation](#-documentation) •
[🚀 Quick Start](#-60-second-quickstart) •
[💻 CLI Tool](#-cli-tool) •
[🎯 Algorithms](#-algorithms-implemented) •
[❤️ Support](#️-support-this-research)

</div>

---

## 🧠 What is Meta-Learning?

Meta-learning, or "learning to learn," enables AI systems to rapidly adapt to new tasks with minimal examples. Instead of training from scratch on each task, meta-learning algorithms develop learning strategies that generalize across tasks.

**Key Insight**: Train on many tasks → Learn to learn → Rapidly adapt to new tasks

## 🚀 60-Second Quickstart

### Installation

```bash
pip install meta-learning-toolkit
```

### High-Level API: MetaLearningToolkit (Recommended)

```python
import torch
from meta_learning import MetaLearningToolkit, create_meta_learning_toolkit
from meta_learning import Episode, Conv4

# 1. Quick setup with convenience function
model = Conv4(out_dim=64)
toolkit = create_meta_learning_toolkit(
    model=model,
    algorithm='test_time_compute',  # or 'maml'
    seed=42  # for reproducible research
)

# 2. Create episode (support and query sets)
support_x = torch.randn(6, 3, 32, 32)  # 6 examples, 3 classes, 2 shots each
support_y = torch.tensor([0, 0, 1, 1, 2, 2])
query_x = torch.randn(9, 3, 32, 32)    # 9 queries, 3 per class  
query_y = torch.tensor([0, 0, 0, 1, 1, 1, 2, 2, 2])
episode = Episode(support_x, support_y, query_x, query_y)

# 3. Train on episode (one-liner!)
results = toolkit.train_episode(episode)
print(f"Query accuracy: {results['query_accuracy']:.3f}")

# 4. Advanced: Manual toolkit creation with full control
toolkit = MetaLearningToolkit()
toolkit.setup_deterministic_training(seed=42)  # Research reproducibility
model = toolkit.apply_batch_norm_fixes(model)  # Few-shot learning fixes
ttc_scaler = toolkit.create_test_time_compute_scaler(model)
eval_harness = toolkit.create_evaluation_harness()  # 95% CI evaluation
```

### Low-Level API: Test-Time Compute Scaling (2024 Breakthrough)

```python
import torch
import torch.nn as nn
from meta_learning import Episode, Conv4
from meta_learning.algos.ttcs import TestTimeComputeScaler, auto_ttcs
from meta_learning.algos.protonet import ProtoHead

# 1. Create your model and episode
encoder = Conv4(3, 64)  # 3 channels, 64 output features
head = ProtoHead()
episode = Episode(support_x, support_y, query_x, query_y)

# 2. Simple one-liner TTCS (auto-detects optimal settings)
log_probs = auto_ttcs(encoder, head, episode)

# 3. Advanced TTCS with full control and monitoring
scaler = TestTimeComputeScaler(
    encoder, head,
    passes=16,
    uncertainty_estimation=True,
    compute_budget="adaptive",
    performance_monitoring=True
)
predictions, metrics = scaler(episode)

print(f"Prediction confidence: {metrics['confidence_evolution']['final_confidence']:.3f}")
print(f"Uncertainty (entropy): {metrics['uncertainty']['entropy'].mean():.3f}")
```

### Prototypical Networks: Temperature Scaling & Distance Metrics

```python
import torch
from meta_learning import Conv4, Episode
from meta_learning.algos.protonet import ProtoHead

# Create encoder and different ProtoNet configurations
encoder = Conv4(3, 64)

# Distance metrics with unified temperature semantics
head_euclidean = ProtoHead(distance="sqeuclidean", tau=1.0)  # Standard
head_cosine = ProtoHead(distance="cosine", tau=2.0)         # Softer predictions

# Temperature effects (unified across both distance metrics):
# - Higher tau → Higher entropy → Less confident predictions
# - Lower tau → Lower entropy → More confident predictions

# Forward pass
episode = Episode(support_x, support_y, query_x, query_y)
z_support = encoder(episode.support_x)
z_query = encoder(episode.query_x)

# Both use same temperature semantics: logits = distance / tau
logits_euclidean = head_euclidean(z_support, episode.support_y, z_query)
logits_cosine = head_cosine(z_support, episode.support_y, z_query)

# Convert to probabilities
probs_euclidean = torch.softmax(logits_euclidean, dim=1)
probs_cosine = torch.softmax(logits_cosine, dim=1)

print(f"Euclidean entropy: {-(probs_euclidean * probs_euclidean.log()).sum(1).mean():.3f}")
print(f"Cosine entropy: {-(probs_cosine * probs_cosine.log()).sum(1).mean():.3f}")
```

### MAML with Toolkit API (Recommended)

```python
import torch
from meta_learning import create_meta_learning_toolkit
from meta_learning import Conv4, Episode

# 1. Quick MAML setup with toolkit
model = Conv4(out_dim=64)
maml_toolkit = create_meta_learning_toolkit(
    model=model,
    algorithm='maml',
    inner_lr=0.01,
    outer_lr=0.001,
    inner_steps=5,
    first_order=False,  # True for FOMAML
    seed=42
)

# 2. Train on episode - handles all MAML complexity internally
episode = Episode(support_x, support_y, query_x, query_y)
results = maml_toolkit.train_episode(episode, algorithm='maml')

print(f"Query accuracy: {results['query_accuracy']:.3f}")
print(f"Meta loss: {results['meta_loss']:.3f}")
print(f"Support loss: {results['support_loss']:.3f}")
```

### MAML: Low-Level Research-Accurate Implementation

```python
import torch
import torch.nn as nn
from meta_learning import Conv4, Episode
from meta_learning.algos.maml import inner_adapt_and_eval, meta_outer_step, ContinualMAML

# 1. Create your model and optimizer
model = Conv4(3, 64)  # CNN for image classification
outer_optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

# 2. Single episode adaptation (research-accurate MAML)
adapted_params = inner_adapt_and_eval(
    model, episode,
    inner_lr=0.01,
    inner_steps=5,
    first_order=False  # True for FOMAML
)

# 3. Meta-learning outer step with second-order gradients
meta_loss = meta_outer_step(
    model, [episode], outer_optimizer,
    inner_lr=0.01, inner_steps=5
)

# 4. Continual MAML for online meta-learning
continual_maml = ContinualMAML(model, ewc_lambda=0.4)
for episode in task_stream:
    loss = continual_maml.meta_update(episode, outer_optimizer)
    print(f"Meta-loss: {loss:.4f}")
```

### Research Patches and Evaluation

```python
# Apply research-accurate BatchNorm fixes
from meta_learning.core.bn_policy import freeze_batchnorm_running_stats
from meta_learning.core.seed import seed_all
from meta_learning.hardware_utils import setup_optimal_hardware

# Fix BatchNorm for few-shot learning (prevents query leakage)
freeze_batchnorm_running_stats(model)

# Ensure reproducible research
seed_all(42)

# Setup optimal hardware configuration
hardware_config = setup_optimal_hardware(
    device="cuda" if torch.cuda.is_available() else "cpu",
    deterministic=True,
    mixed_precision=True
)

# Professional evaluation
from meta_learning.eval import evaluate
accuracy, confidence_interval = evaluate(
    model, test_episodes,
    confidence_level=0.95
)
print(f"Accuracy: {accuracy:.3f} ± {confidence_interval:.3f}")
```

**That's it!** You now have access to 2024's most advanced meta-learning algorithms with research-grade accuracy.

## 💻 CLI Tool

The `mlfew` command provides benchmarking and evaluation:

```bash
# Check version
mlfew version

# Run benchmarks on few-shot tasks  
mlfew bench --dataset synthetic --n-way 5 --k-shot 1 --episodes 1000 --encoder conv4

# Evaluate with CIFAR-FS dataset
mlfew eval --dataset cifar_fs --n-way 5 --k-shot 5 --device auto --encoder conv4

# Quick synthetic evaluation
mlfew eval --dataset synthetic --n-way 5 --k-shot 1 --episodes 100 --encoder identity
```

## 📊 Supported Datasets

| Dataset | Classes | Samples/Class | Paper | Status |
|---------|---------|---------------|-------|---------|
| **CIFAR-FS** | 100 classes | 600 | Bertinetto et al. 2018 | ✅ Built-in |
| **MiniImageNet** | 100 classes | 600 | Vinyals et al. 2016 | ✅ Built-in |
| **Synthetic** | Configurable | Configurable | N/A | ✅ Built-in |

*Note: This package focuses on breakthrough algorithms. Additional datasets (Omniglot, tieredImageNet) can be easily integrated with torchvision or other dataset libraries.*

## 🧪 Algorithms Implemented

| Algorithm | Paper | Year | Implementation Status |
|-----------|--------|------|----------------------|
| **Test-Time Compute Scaling** | Snell et al. | 2024 | ✅ **World-first public implementation** |
| **MAML (All Variants)** | Finn et al. | 2017 | ✅ Research-accurate: MAML, FOMAML, ANIL, BOIL, Reptile |
| **BatchNorm Research Patches** | Various | 2017-2024 | ✅ Episode-aware policies for few-shot learning |
| **Evaluation Harness** | Research Standard | N/A | ✅ 95% confidence intervals, statistical rigor |

## 🔬 Research Accuracy

All implementations follow exact mathematical formulations from original papers:

### MAML (Research-Accurate)
```
Inner adaptation: θ'_i = θ - α * ∇_θ L_{T_i}^{train}(f_θ)
Meta-update: θ ← θ - β * ∇_θ Σ_i L_{T_i}^{test}(f_{θ'_i})
Second-order gradients: create_graph=True (preserved)
Functional updates: No in-place mutations
```

### Test-Time Compute Scaling
```
Compute allocation: C(t) = f(confidence, budget, time)
Process rewards: R_step = quality_estimation(step_output)
Solution selection: argmax_s Σ_i R_i * w_i
```

**Research-critical fixes**: Proper gradient computation, episodic BatchNorm, deterministic environments.

## 🚢 Installation Options

### Option 1: PyPI (Recommended)
```bash
pip install meta-learning-toolkit
```

### Option 2: Development Install
```bash
git clone https://github.com/benedictchen/meta-learning-toolkit
cd meta-learning-toolkit
pip install -e .[dev,test,datasets,visualization]
```

## 🧑‍💻 Requirements

- **Python**: 3.9+
- **PyTorch**: 2.0+  
- **Core**: `numpy`, `scipy`, `scikit-learn`, `tqdm`, `rich`, `pyyaml`
- **Optional**: `matplotlib`, `seaborn`, `wandb` (for visualization)
- **Development**: `pytest`, `ruff`, `mypy`, `pre-commit`

## 📚 Documentation

Complete documentation is included in the package:

- 🚀 **Quick Start**: Examples in this README
- 📖 **API Reference**: Comprehensive docstrings in all modules
- 💡 **Examples**: Working code examples throughout documentation
- 🔬 **Research**: Mathematical formulations and research foundations in docstrings

## 🧪 Testing

Test suite with expanding coverage:

```bash
# Run all tests
pytest

# Run specific test categories
pytest -m "not slow"          # Skip slow tests
pytest -m "regression"        # Mathematical correctness

# With coverage report
pytest --cov=src/meta_learning --cov-report=html
```

## 📄 License

Custom Non-Commercial License - See [LICENSE](LICENSE) for details.

**TL;DR**: Free for research and educational use. Commercial use requires permission.

## 🎓 Citation

If this toolkit helps your research, please cite:

```bibtex
@software{chen2025metalearning,
  title={Meta-Learning Toolkit: Production-Ready Few-Shot Learning},
  author={Chen, Benedict},
  year={2025},
  url={https://github.com/benedictchen/meta-learning-toolkit},
  version={2.3.0}
}
```

## 💰 URGENT: Support This Research - We Need Cash! 💰

🚨 **THIS PROJECT IS AT RISK OF ABANDONMENT WITHOUT FINANCIAL SUPPORT!** 🚨

This toolkit has saved researchers **millions of hours** and **hundreds of thousands of dollars** in development costs. If you've used this in your research, startup, or project - **it's time to pay it forward!**

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### 💰 **Donation Tiers - Help Keep This Research Alive!**

- ☕ **$5 - Coffee Tier**: Keeps me coding through the night
- 🍕 **$25 - Pizza Tier**: Fuels weekend debugging sessions  
- 🍺 **$100 - Beer Tier**: Celebrates breakthrough implementations
- 🎮 **$500 - Gaming Tier**: Funds GPU compute for testing
- 💻 **$2,500 - Workstation Tier**: Upgrades development hardware
- 🚗 **$25,000 - Tesla Tier**: Enables full-time research focus
- 🏎️ **$200,000 - Lamborghini Tier**: Creates the ultimate coding environment
- 🏝️ **$50,000,000 - Private Island Tier**: Establishes permanent AI research lab

### 🔥 **Why Donate? This Toolkit Has Given You:**

- 🎯 **Months of saved development time** (worth $10,000+ in labor)
- 📚 **Research-accurate implementations** (normally requiring PhD-level expertise)
- 🏭 **Production-ready code** (enterprise consulting would cost $50,000+)
- 🔬 **2024 breakthrough algorithms** unavailable anywhere else
- 💡 **Industrial engineering practices** (DevOps setup worth $25,000+)

### 💸 **Payment Options - Choose Your Method:**

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- ⭐ **Star the repository** (free but helps with visibility)
- 🐦 **Share on social media** (Twitter, LinkedIn, Reddit)
- 📝 **Cite in your papers** (academic citation)

### 🎯 **Be Honest - How Much Has This Saved You?**

- 📊 **Research paper accepted?** → Donate $500 (your success = our success)
- 🏢 **Used in commercial product?** → Donate $2,500 (fair compensation for value)
- 💰 **Got funding/promotion because of this?** → Donate $10,000 (pay it forward)
- 🚀 **Built a startup using this?** → Donate $25,000+ (you literally owe us)

**🔥 Don't be that person who uses open-source for free and never gives back! 🔥**

*Your donations directly fund continued development, new algorithm implementations, and breakthrough research that benefits the entire AI community!*

---

<div align="center">

**Built with ❤️ by [Benedict Chen](mailto:benedict@benedictchen.com)**

*Turning research papers into production-ready code*

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