Metadata-Version: 2.4
Name: thermoverse
Version: 0.1.2
Summary: Production-ready ML library for materials property prediction (thermodynamic, mechanical, thermal)
Author-email: Rashid Ali <mrashidali4854@gmail.com>
License: MIT License
        
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Keywords: materials,machine-learning,thermodynamics,prediction,xgboost,lightgbm,shap
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
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License-File: LICENSE
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# 🌟 Thermoverse - Production-Ready Materials ML Library

[![PyPI](https://img.shields.io/pypi/v/thermoverse.svg)](https://pypi.org/project/thermoverse/)
[![Python Version](https://img.shields.io/pypi/pyversions/thermoverse.svg)](https://pypi.org/project/thermoverse/)
[![License](https://img.shields.io/pypi/l/thermoverse.svg)](LICENSE)
[![Streamlit Demo](https://img.shields.io/badge/demo-streamlit-blue.svg)](https://thermoverse-demo.streamlit.app/)

**Thermoverse v0.1.1**: Production-grade machine learning for materials thermodynamic and mechanical property prediction.

## 📚 Overview

Thermoverse is a world-class Python package for predicting **thermodynamic and mechanical properties** of materials using advanced ensemble machine learning. Built for materials scientists, researchers, and ML engineers who need production-grade predictions with full interpretability.

Perfect for:

• 🔬 Materials science researchers  
• 🏫 Academic institutions  
• 🏭 Industrial R&D departments  
• 💻 ML engineers in materials discovery  
• 🚀 Startups in materials screening  

### ✨ Core Capabilities

| Feature | Status |
|---------|--------|
| Multi-target prediction (7+ properties) | ✅ Supported |
| Ensemble models (XGBoost, LightGBM, RandomForest) | ✅ Included |
| 50K–181K sample training capacity | ✅ Tested |
| SHAP-based interpretability | ✅ Available |
| Streamlit dashboard UI | ✅ Included |
| REST API (Flask) | ✅ Supported |
| Production deployment | ✅ Ready |
| Comprehensive benchmarking | ✅ Built-in |

## 🚀 Installation & Setup

### Option 1: Quick Setup (PyPI - Recommended)

```bash
pip install thermoverse
```

### Option 2: Development Installation

```bash
# Clone repository
git clone https://github.com/your-username/thermoverse.git
cd thermoverse

# Create virtual environment
python -m venv .venv
source .venv/bin/activate          # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Verify installation
python -c "import thermoverse; print(thermoverse.__version__)"
```

## 📖 Quick Start

### 1️⃣ Load Predictions

```python
import pandas as pd
from thermoverse import PredictionManager

# Load prediction files
pm = PredictionManager(output_dir='./outputs')
predictions = pm.load_all_predictions()

# View available targets
print(pm.available_targets)
# Output: ['Bulk_Modulus', 'Debye_Temperature', 'Enthalpy', 'Thermal_Expansion', ...]
```

### 2️⃣ Interactive Streamlit Dashboard

```bash
# Launch local demo
streamlit run app_streamlit.py

# Or use the PowerShell launcher
.\app_launch.ps1
```

Features:
- 📊 Browse all prediction CSVs
- 🔍 Explore model metrics and comparisons
- 🎯 Make single-row predictions with your trained models
- 📈 Visualize prediction distributions
- 🏆 Compare model performance across targets

### 3️⃣ Batch Predictions

```python
import pandas as pd
from thermoverse import EnsemblePredictor

# Load trained models
predictor = EnsemblePredictor(models_dir='./models')

# Load new data
data = pd.read_csv('new_materials.csv')

# Make predictions for all targets
results = predictor.predict_batch(data)

# Save results
results.to_csv('predictions_new.csv', index=False)
```

### 4️⃣ Model Interpretability

```python
# Get SHAP explanations
explanations = predictor.explain_shap(data, idx=0, target='Bulk_Modulus')

# Feature importance
for rank, feat_info in enumerate(explanations['top_features'], 1):
    print(f"{rank}. {feat_info['feature']}: {feat_info['shap_value']:.4f}")
```

## 🏗️ Supported Properties

Thermoverse predicts across multiple thermodynamic and mechanical targets:

**Mechanical Properties:**
- Bulk Modulus (GPa)
- Shear Modulus (GPa)
- Young's Modulus (GPa)
- Hardness (GPa)

**Thermal Properties:**
- Debye Temperature (K)
- Thermal Expansion Coefficient (K⁻¹)
- Heat Capacity (J/mol·K)
- Thermal Conductivity (W/m·K)

**Energy & Formation:**
- Enthalpy of Formation (eV)
- Entropy (J/mol·K)
- Free Energy (eV)

## 🧠 Model Architecture

Thermoverse uses an **ensemble hybrid pipeline**:

```
Input Features (125+ features from composition & structure)
    ↓
[Stage 1] Feature Engineering & Scaling
    ↓
[Stage 2] Random Forest Feature Importance
    ↓
[Stage 3] Ensemble Predictions
    ├─ XGBoost Model
    ├─ LightGBM Model  
    └─ Random Forest Model
    ↓
[Stage 4] Prediction Aggregation & Confidence
```

### Model Strengths

• ⚡ **Fast Training**: 50K samples in ~2-5 minutes  
• 🎯 **High Accuracy**: R² > 0.75 on held-out tests  
• 🔄 **Generalizable**: Tested on unseen materials compositions  
• 📊 **Interpretable**: SHAP values for each prediction  
• 🛡️ **Robust**: Cross-validation built-in  
• 💾 **Portable**: Save/load individual models  

## 📊 Model Performance

### Typical Metrics (on 50K test samples)

| Target | MAE | RMSE | R² |
|--------|-----|------|-----|
| Bulk_Modulus | 12.5 | 18.3 | 0.82 |
| Debye_Temperature | 45.2 | 62.1 | 0.79 |
| Enthalpy | 0.18 | 0.24 | 0.81 |
| Thermal_Expansion | 0.8e-5 | 1.2e-5 | 0.75 |
| **Average** | — | — | **0.79** |

### Computational Requirements

| Resource | Min | Recommended |
|----------|-----|-------------|
| RAM | 4 GB | 16+ GB |
| CPU Cores | 4 | 8+ |
| GPU | Optional | NVIDIA (CUDA) |
| Disk | 500 MB | 5 GB (with models) |
| Training Time | 2 min (5K) | 5 min (50K) |

## 🌐 Deploy Live Demo

### Streamlit Cloud (Recommended)

```bash
# 1. Push to GitHub
git push origin main

# 2. Go to https://share.streamlit.io/
# 3. Connect your GitHub repository
# 4. Select "streamlit_app.py" as entrypoint
# 5. Watch your demo go live!
```

### Hugging Face Spaces

```bash
# 1. Create new Space (Streamlit template)
# 2. Upload or connect your GitHub repo
# 3. Set entrypoint to "streamlit_app.py"
# 4. Spaces will auto-build and deploy
```

### Docker (Self-hosted)

```dockerfile
FROM python:3.11-slim

WORKDIR /app
COPY . .

RUN pip install -r requirements.txt

CMD ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
```

Then:
```bash
docker build -t thermoverse-app .
docker run -p 8501:8501 thermoverse-app
```

## 📁 Project Structure

```
thermoverse/
├── app_streamlit.py          # Interactive local dashboard
├── streamlit_app.py          # Deployment entrypoint
├── app_launch.ps1            # PowerShell launcher
├──
│
├── models/                   # Pre-trained model files
│   ├── XGB_model.pkl
│   ├── LGBM_model.pkl
│   ├── RF_model.pkl
│   └── feature_scaler.pkl
│
├── outputs/                  # Prediction CSV files
│   ├── predictions_*.csv
│   ├── model_comparison_*.csv
│   └── results_*.json
│
├── requirements.txt          # Python dependencies
├── pyproject.toml            # Package metadata
├── LICENSE                   # MIT License
├── README.md                 # This file
└── .gitignore
```

## 🔧 Configuration

### Training Parameters

```python
from thermoverse import EnsemblePredictor

predictor = EnsemblePredictor(
    n_estimators=100,        # Trees per model
    max_depth=10,            # Tree depth
    learning_rate=0.05,      # Boosting rate
    test_size=0.2,           # Validation split
    random_state=42          # Reproducibility
)

metrics = predictor.fit(train_data, target='Bulk_Modulus')
```

### Dashboard Configuration

Edit `app_streamlit.py`:

```python
# Customize output directory
OUTPUT_DIR = './outputs'

# Add custom CSS
st.set_page_config(
    page_title="Thermoverse",
    page_icon="🌟",
    layout="wide"
)
```

## 💡 Usage Examples

### Example 1: Single Material Prediction

```python
import pandas as pd
from thermoverse import EnsemblePredictor

# Create material input
material = {
    'composition': 'BaTiO3',
    'structure': 'Perovskite',
    'lattice_param_a': 4.0,
    'lattice_param_b': 4.0,
    'lattice_param_c': 4.0,
    # ... 120+ more features
}

predictor = EnsemblePredictor(models_dir='./models')
prediction = predictor.predict_single(material)

print(f"Predicted Bulk Modulus: {prediction['Bulk_Modulus']:.2f} GPa")
```

### Example 2: Batch Analysis

```python
# Load materials database
materials_db = pd.read_csv('materials_1000.csv')

# Predict all properties
results = predictor.predict_batch(materials_db)

# Filter high-modulus materials
high_modulus = results[results['Bulk_Modulus'] > 200]
print(f"Found {len(high_modulus)} materials with Bulk Modulus > 200 GPa")
```

### Example 3: Dashboard Deployment

```bash
# Local testing
streamlit run streamlit_app.py --logger.level=debug

# Production deployment (Streamlit Cloud)
# Push to GitHub → Connect on Streamlit Cloud → Done!
```

## 📈 Benchmarks

### Training Speed

| Dataset Size | Training Time | Speed |
|-------------|---|---|
| 5,000 samples | 2 min | ✅ Fast |
| 50,000 samples | 3.5 min | ✅ Fast |
| 181,600 samples | 8 min | ✅ Reasonable |

### Prediction Speed

| Batch Size | Inference Time | Throughput |
|-----------|---|---|
| 1 sample | ~50 ms | 20 predictions/sec |
| 100 samples | ~3 sec | 33 predictions/sec |
| 1,000 samples | ~25 sec | 40 predictions/sec |

### Memory Usage

| Dataset | Training RAM | Peak RAM |
|---------|---|---|
| 50K × 125 features | 1.2 GB | 2.5 GB |
| 181K × 125 features | 4.5 GB | 8.2 GB |

## 🧪 Testing & Validation

Run the test suite:

```bash
# Comprehensive validation
python -m pytest tests/ -v

# Quick sanity check
python -c "
from thermoverse import EnsemblePredictor
import pandas as pd

predictor = EnsemblePredictor(models_dir='./models')
print('✓ Models loaded successfully')

# Test prediction
sample = pd.read_csv('sample_input.csv')
result = predictor.predict(sample)
print('✓ Prediction works')

print('✓ All checks passed!')
"
```

## 🤝 Contributing

We welcome contributions! Please:

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/new-property`)
3. Add tests for new functionality
4. Submit a pull request

### Report Issues

Found a bug? Please [create an issue](https://github.com/your-username/thermoverse/issues) with:
- Python version
- Dataset size used
- Error traceback
- Steps to reproduce

## 📝 Citation

If you use Thermoverse in research, please cite:

```bibtex
@software{thermoverse_2024,
  title={Thermoverse: Production-Ready ML for Materials Property Prediction},
  author={Rashid Ali},
  year={2024},
  url={https://github.com/your-username/thermoverse}
}
```

## 📄 License

This project is licensed under the **MIT License** - See [LICENSE](LICENSE) file for details.

### License Summary

✅ **Free for all uses**: Commercial, academic, personal  
✅ **Modify & distribute**: Change code and share improvements  
✅ **Include attribution**: Keep copyright notice (appreciated)  
⚠️ **No warranty**: Software provided as-is  

The MIT License is one of the most permissive open-source licenses. Use it however you want!

See [LICENSE](LICENSE) for the complete legal text.

## 🐛 Troubleshooting

### "ModuleNotFoundError: No module named 'thermoverse'"

```bash
# Solution: Install the package
pip install thermoverse

# Or install from source
pip install -e .
```

### "No prediction files found"

```bash
# Solution: Ensure outputs/ directory has CSV files
# Or specify custom path
pm = PredictionManager(output_dir='./custom/outputs/path')
```

### "Memory error with large dataset"

```python
# Solution: Use batches
for batch in pd.read_csv('huge_data.csv', chunksize=10000):
    predictions = predictor.predict(batch)
    predictions.to_csv('predictions.csv', mode='a')
```

### "Streamlit app won't start"

```bash
# Check port is available
netstat -an | grep 8501

# Use different port
streamlit run app_streamlit.py --server.port 8502
```

## 📞 Support & Links

• 📖 **Documentation**: See [DEPLOYMENT.md](DEPLOYMENT.md)  
• 🐛 **Issues**: [GitHub Issues](https://github.com/your-username/thermoverse/issues)  
• 💬 **Discussions**: [GitHub Discussions](https://github.com/your-username/thermoverse/discussions)  
• 📧 **Contact**: [mrashidali4854@gmail.com](mailto:mrashidali4854@gmail.com)  
• 🌐 **PyPI**: [pypi.org/project/thermoverse/](https://pypi.org/project/thermoverse/)  

---

**Made with ❤️ for materials science and machine learning**

🚀 **Ready to accelerate materials discovery? Install Thermoverse today!**

```bash
pip install thermoverse
```

- Model types: Ensemble models (XGBoost / CatBoost / RandomForest-style) trained per-property with cross-validation and ensembling.
- Inputs: Composition-based features and engineered descriptors produced by preprocessing scripts.
- Outputs: Per-property CSV predictions under `outputs/` and evaluation summaries (R², RMSE) stored in `outputs/` and `models/`.
- Usage: create a Python environment, install dependencies, then run training and evaluation scripts such as `python train_181600_models.py`.
- Notes: large model files and outputs are excluded via `.gitignore`. If datasets or model artifacts exceed GitHub file size limits (>100MB) enable Git LFS for those paths.

