hyperphoenixcv 0.3.0

Hyper-parameter search that rises from the ashes of interrupted experiments

HyperPhoenixCV 🐦‍🔥

"Rise from the ashes of interrupted experiments"

HyperPhoenixCV is a smart hyperparameter tuning library that, like the mythical phoenix, resumes after interruptions and continues searching for optimal solutions. Never lose hours of computation due to unexpected stops again!

Other languages: Русский

✨ Features

• 🔄 Resumable searches – Continue from the last checkpoint after any interruption.
• 🧠 Bayesian optimization – Find better parameters faster with intelligent search.
• 🎯 Multiple search strategies – Exhaustive grid search, random search, or predictive optimization.
• 📊 Multi‑metric evaluation – Score using multiple metrics (F1, accuracy, precision, etc.) simultaneously.
• 💾 Automatic checkpointing – Results are saved automatically to pickle files and CSV.
• 🔌 Scikit‑learn compatible – Seamlessly integrates with the scikit‑learn ecosystem.

🚀 Installation

Install from PyPI:

pip install hyperphoenixcv

Or install the latest development version from source:

git clone https://github.com/valeksan/hyperphoenixcv.git
cd hyperphoenixcv
pip install -e .

📖 Why HyperPhoenixCV?

The name HyperPhoenixCV refers to the mythical phoenix – a bird that rises from its ashes. In the same way, your hyperparameter search can "rise again" after an interruption, continuing from the last saved checkpoint instead of starting over from scratch.

The "CV" in the name highlights the library's focus on cross‑validation and machine‑learning workflows.

How It Differs from Plain GridSearchCV

Feature	GridSearchCV	HyperPhoenixCV
Resumability	Starts over after interruption	✅ Continues from checkpoint
Optimization	Exhaustive search only	✅ Bayesian, random, or exhaustive
Multi‑metric	Single metric at a time	✅ Multiple metrics simultaneously
Checkpointing	Manual saving required	✅ Automatic pickle & CSV export
Progress tracking	Limited	✅ Verbose logs & intermediate results

🛠️ Quick Start

Here’s a minimal example that shows the core workflow:

from hyperphoenixcv import HyperPhoenixCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

# Create a simple dataset
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)

# Define the model and parameter grid
model = RandomForestClassifier()
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20],
    'min_samples_split': [2, 5, 10]
}

# Create a HyperPhoenixCV instance with checkpointing
hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    scoring='accuracy',
    cv=5,
    checkpoint_path='my_experiment.pkl',
    verbose=True
)

# Run the search (resumes automatically if interrupted)
hp.fit(X, y)

print("Best parameters:", hp.best_params_)
print("Best score:", hp.best_score_)

# Get top‑5 results
top_results = hp.get_top_results(5)
print(top_results)

🔁 Resuming an Interrupted Search

If the process is stopped (e.g., due to time limits), simply run the same script again – it will load the checkpoint and continue where it left off:

hp.fit(X, y)  # Automatically resumes from 'my_experiment.pkl'

📚 Advanced Usage

Bayesian Optimization

Enable Bayesian optimization to reduce the number of evaluations:

hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    use_bayesian_optimization=True,
    n_iter=30,          # Number of Bayesian iterations
    verbose=True
)

Random Search

Perform a random search over the parameter space:

hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    random_search=True,
    n_iter=50           # Number of random combinations
)

Multiple Metrics

Evaluate using several metrics at once:

hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    scoring=['f1', 'accuracy', 'precision']
)

Exporting Results

Save all results to a CSV file for further analysis:

hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    results_csv='experiment_results.csv'
)

Custom Cross‑Validation Splitter

HyperPhoenixCV supports any cross‑validation splitter that follows the scikit‑learn interface (e.g., TimeSeriesSplit, GroupKFold, StratifiedKFold). You can pass a splitter object directly to the cv parameter:

from sklearn.model_selection import TimeSeriesSplit, GroupKFold

# Time‑series cross‑validation
ts_cv = TimeSeriesSplit(n_splits=5)
hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    cv=ts_cv,          # Use the splitter object
    scoring='accuracy'
)

# Group‑aware cross‑validation
group_cv = GroupKFold(n_splits=5)
hp = HyperPhoenixCV(
    estimator=model,
    param_grid=param_grid,
    cv=group_cv,
    scoring='accuracy'
)
# Then call fit with groups parameter
hp.fit(X, y, groups=groups)

See the full example: examples/custom_cv_example.py

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License – see the LICENSE file for details.

🙏 Acknowledgments

Thanks to the scikit‑learn community for the foundation on which this library is built.