AutoML for scientific research tasks
autosai is a Python library that simplifies the process of AutoML model selection and hyperparameter tuning using Bayesian optimization. It supports various deep learning architectures including Dense, CNN, LSTM, Transformer, and TabNet.
To install autosai, you can use Poetry, a dependency management and packaging tool for Python projects.
poetry add autosai
This will add autosai to your Poetry project.
poetry run pytest
from autosai import DataPreprocessor
data_path = 'your_dataset.csv'
target_column = 'target_column'
X_train, X_val, y_train, y_val = DataPreprocessor.prepare_data(data_path, target_column)
from autosai import ModelSelector
model_selector = ModelSelector()
best_params = model_selector.optimize_hyperparameters(X_train, y_train, X_val, y_val, n_trials=50)
print(f"Best hyperparameters: {best_params}")
best_model_type = best_params.pop('model_type')
best_model = model_selector.create_model(best_model_type, best_params, X_train.shape[1])
best_model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=50, batch_size=32, verbose=0)
from autosai import ModelSaver
ModelSaver.save_model(best_model, best_model_type, 'best_model')
Refer to the examples/example_usage.py file for a complete example.
This version of the autosai library uses classes to organize the code in a more extensible and maintainable manner. Each model type is encapsulated in its own class, and the optimization process is handled by the BayesianOptimization class. The data preprocessing and model saving functionalities are also encapsulated in their respective classes.