Module ml4opf.formulations.model
Abstract base class for ML models for OPF.
Each formulation should inherit from OPFModel and implement the following methods:
evaluate_model: Evaluate the model on the test data.
User models should inherit from the formulation's OPFModel class (e.g., ACPModel) and implement the methods:
-
save_checkpoint: Save the model's checkpoint to a file. -
load_from_checkpoint: Load the model's checkpoint from a file. -
predict: Predict the solution for a given set of inputs.
Classes
class OPFModel-
An abstract base class for ACOPF models.
Ancestors
- abc.ABC
Subclasses
Static methods
def load_from_checkpoint(path_to_folder: str,
problem: OPFProblem)-
Load the model's checkpoint from a file.
Args
path:str- Path to load the checkpoint from.
Methods
def evaluate_model(self, reduction: str | None = None, inner_reduction: str | None = None) ‑> dict[str, torch.Tensor]-
Evaluate the model on the test data.
Args
reduction:str, optional- Reduction method for the metrics. Defaults to None. Must be one of "mean", "sum", "none". If specified, each value in the returned dictionary will be a scalar. Otherwise, they are arrays of shape (n_test_samples,)
Returns
dict[str, Tensor]- Dictionary containing Tensor metrics of the model's performance.
def predict(self, *inputs: torch.Tensor) ‑> dict[str, torch.Tensor]-
Predict the solution for a given set of inputs.
Args
*inputs:Tensor- Input tensors to the model.
Returns
dict[str, Tensor]- Dictionary containing the solution.
def save_checkpoint(self, path_to_folder: str)-
Save the model's checkpoint to a file.
Args
path:str- Path to save the checkpoint.