vak.predict.parametric_umap.predict_with_parametric_umap_model

vak.predict.parametric_umap.predict_with_parametric_umap_model(model_name: str, model_config: dict, dataset_path, checkpoint_path, num_workers=2, transform_params: dict | None = None, dataset_params: dict | None = None, timebins_key='t', device=None, output_dir=None)

Make predictions on a dataset with a trained model.

model_namestr

Model name, must be one of vak.models.registry.MODEL_NAMES.

model_configdict

Model configuration in a dict, as loaded from a .toml file, and used by the model method from_config.

dataset_pathstr

Path to dataset, e.g., a csv file generated by running vak prep.

checkpoint_pathstr

path to directory with checkpoint files saved by Torch, to reload model

num_workersint

Number of processes to use for parallel loading of data. Argument to torch.DataLoader. Default is 2.

transform_params: dict, optional

Parameters for data transform. Passed as keyword arguments. Optional, default is None.

dataset_params: dict, optional

Parameters for dataset. Passed as keyword arguments. Optional, default is None.

timebins_keystr

key for accessing vector of time bins in files. Default is ‘t’.

devicestr

Device on which to work with model + data. Defaults to ‘cuda’ if torch.cuda.is_available is True.

annot_csv_filenamestr

name of .csv file containing predicted annotations. Default is None, in which case the name of the dataset .csv is used, with ‘.annot.csv’ appended to it.

output_dirstr, Path

path to location where .csv containing predicted annotation should be saved. Defaults to current working directory.