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model.evaluate

logger_config​

logger​

use_gpu​

create_reconstruction_plot​

def create_reconstruction_plot(filepath: str,
                               test_loader: Data.DataLoader,
                               seq_len_half: int,
                               model: RNN_VAE,
                               model_name: str,
                               FUTURE_DECODER: bool,
                               FUTURE_STEPS: int,
                               suffix: Optional[str] = None) -> None

Plot the reconstruction and future prediction of the input sequence. Saves the plot to:

  • project_name/
    • model/
      • evaluate/
        • Reconstruction_model_name.png

Parameters

  • filepath (str): Path to save the plot.
  • test_loader (Data.DataLoader): DataLoader for the test dataset.
  • seq_len_half (int): Half of the temporal window size.
  • model (RNN_VAE): Trained VAE model.
  • model_name (str): Name of the model.
  • FUTURE_DECODER (bool): Flag indicating whether the model has a future prediction decoder.
  • FUTURE_STEPS (int): Number of future steps to predict.
  • suffix (str, optional): Suffix for the saved plot filename. Defaults to None.

Returns

  • None

eval_temporal​

def eval_temporal(config: dict,
                  use_gpu: bool,
                  model_name: str,
                  fixed: bool,
                  snapshot: Optional[str] = None,
                  suffix: Optional[str] = None) -> None

Evaluate the temporal aspects of the trained model.

Parameters

  • config (dict): Configuration dictionary.
  • use_gpu (bool): Flag indicating whether to use GPU for evaluation.
  • model_name (str): Name of the model.
  • fixed (bool): Flag indicating whether the data is fixed or not.
  • snapshot (str, optional): Path to the model snapshot. Defaults to None.
  • suffix (str, optional): Suffix for the saved plot filename. Defaults to None.

Returns

  • None

evaluate_model​

@save_state(model=EvaluateModelFunctionSchema)
def evaluate_model(config: dict,
                   use_snapshots: bool = False,
                   save_logs: bool = True) -> None

Evaluate the trained model. Fills in the values in the "evaluate_model" key of the states.json file. Saves the evaluation results to:

  • project_name/
    • model/
      • evaluate/

Parameters

  • config (dict): Configuration dictionary.
  • use_snapshots (bool, optional): Whether to plot for all snapshots or only the best model. Defaults to False.
  • save_logs (bool, optional): Whether to save logs. Defaults to True.

Returns

  • None