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model.create_training

logger_config​

logger​

traindata_aligned​

def traindata_aligned(config: dict,
                      sessions: List[str] | None = None,
                      test_fraction: float | None = None,
                      read_from_variable: str = "position_processed") -> None

Create training dataset for aligned data. Save numpy arrays with the test/train info to the project folder.

Parameters

  • config (dict): Configuration parameters dictionary.
  • sessions (List[str], optional): List of session names. If None, all sessions will be used. Defaults to None.
  • test_fraction (float, optional): Fraction of data to use as test data. Defaults to 0.1.

Returns

  • None

create_trainset​

@save_state(model=CreateTrainsetFunctionSchema)
def create_trainset(config: dict, save_logs: bool = False) -> None

Creates a training and test datasets for the VAME model. Fills in the values in the "create_trainset" key of the states.json file. Creates the training dataset for VAME at:

  • project_name/
    • data/
      • session00/
        • session00-PE-seq-clean.npy
      • session01/
        • session01-PE-seq-clean.npy
      • train/
        • test_seq.npy
        • train_seq.npy

The produced -clean.npy files contain the aligned time series data in the shape of (num_dlc_features - 2, num_video_frames).

The produced test_seq.npy contains the combined data in the shape of (num_dlc_features - 2, num_video_frames * test_fraction).

The produced train_seq.npy contains the combined data in the shape of (num_dlc_features - 2, num_video_frames * (1 - test_fraction)).

Parameters

  • config (dict): Configuration parameters dictionary.
  • save_logs (bool, optional): If True, the function will save logs to the project folder. Defaults to False.

Returns

  • None