OpenModels is a flexible and extensible library for serializing and deserializing machine learning models. It's designed to support any serialization format through a plugin-based architecture, providing a safe and transparent solution for exporting and sharing predictive models.
- Format Agnostic: Supports any serialization format through a plugin-based system.
- Extensible: Easily add support for new model types and serialization formats.
- Safe: Provides alternatives to potentially unsafe serialization methods like Pickle.
- Transparent: Supports human-readable formats for easy inspection of serialized models.
pip install openmodelsfrom openmodels import SerializationManager, SklearnSerializer
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
# Create and train a scikit-learn model
X, y = make_classification(n_samples=1000, n_features=4, n_informative=2, n_redundant=0, random_state=0, shuffle=False)
model = RandomForestClassifier(n_estimators=10, max_depth=5, random_state=0)
model.fit(X, y)
# Create a SerializationManager
manager = SerializationManager(SklearnSerializer())
# Serialize the model (default format is JSON)
serialized_model = manager.serialize(model)
# Deserialize the model
deserialized_model = manager.deserialize(serialized_model)
# Use the deserialized model
predictions = deserialized_model.predict(X[:5])
print(predictions)OpenModels is designed to be easily extended with new serialization formats and model types.
To add a new serialization format, create a class that implements the FormatConverter protocol and register it with the FormatRegistry:
from openmodels.protocols import FormatConverter
from openmodels.format_registry import FormatRegistry
from typing import Dict, Any
class YAMLConverter(FormatConverter):
@staticmethod
def serialize_to_format(data: Dict[str, Any]) -> str:
import yaml
return yaml.dump(data)
@staticmethod
def deserialize_from_format(formatted_data: str) -> Dict[str, Any]:
import yaml
return yaml.safe_load(formatted_data)
FormatRegistry.register("yaml", YAMLConverter)To add support for a new type of model, create a class that implements the ModelSerializer protocol:
from openmodels.protocols import ModelSerializer
from typing import Any, Dict
class TensorFlowSerializer(ModelSerializer):
def serialize(self, model: Any) -> Dict[str, Any]:
# Implementation for serializing TensorFlow models
...
def deserialize(self, data: Dict[str, Any]) -> Any:
# Implementation for deserializing TensorFlow models
...OpenModels currently supports a wide range of scikit-learn models, including:
- Classification: LogisticRegression, RandomForestClassifier, SVC, etc.
- Regression: LinearRegression, RandomForestRegressor, SVR, etc.
- Clustering: KMeans
- Dimensionality Reduction: PCA
For a full list of supported models, please refer to the SUPPORTED_ESTIMATORS dictionary in serializers/sklearn_serializer.py.
We welcome contributions to OpenModels! Whether you want to add support for new models, implement new serialization formats, or improve the existing codebase, your help is appreciated.
Please refer to our Contributing Guidelines for more information on how to get started.
To run the tests:
-
Clone the repository:
git clone https://github.com/your-repo/openmodels.git cd openmodels -
Install the package and its dependencies:
pip install -e . -
Run the tests:
pytest
This project is licensed under the MIT License. See the LICENSE file for details.
For a detailed changelog, please see the CHANGELOG.md file.
If you encounter any issues or have questions, please file an issue on our GitHub repository.
We're always looking to improve OpenModels. If you have any suggestions or feature requests, please let us know!