pydruglogics.execution.Executor
Provides training and prediction functions for Boolean models.
- pydruglogics.execution.Executor.train(*args, **kwargs)
Train a Boolean Model using genetic algorithm and evolution strategy. Finds the models with the best fitness score.
Parameters
boolean_model (BooleanModel)
: The model to be trained.model_outputs (ModelOutputs)
: The outputs to be optimized.ga_args (dict)
: Genetic algorithm parameters.ev_args (dict)
: Evolution strategy parameters.training_data (TrainingData, optional)
: Training data for the model.save_best_models (bool)
: Whether to save the best models.save_path (str)
: Path to save models.
Returns
List[BooleanModel]
: List of models with the best fitness.
- pydruglogics.execution.Executor.predict(*args, **kwargs)
Predict model outcomes and plot the results.
Parameters
best_boolean_models (list of BooleanModel, optional)
: Models to use for prediction.model_outputs (ModelOutputs)
: The outputs to be predicted.perturbations (Perturbation)
: Perturbation scenarios.observed_synergy_scores (list of str)
: Observed synergy scores.synergy_method (str)
: Method for calculating synergy (‘bliss’, etc.).run_parallel (bool)
: Whether to run in parallel.plot_roc_pr_curves (bool)
: Whether to plot ROC and PR curves.save_predictions (bool)
: Whether to save predictions.save_path (str)
: Path to save predictions.model_directory (str)
: Directory to load models from if not provided.attractor_tool (str)
: Tool for attractor analysis.attractor_type (str)
: Type of attractor analysis.cores (int)
: Number of CPU cores to use.