torch_mist.utils.estimation
Module Contents
Functions
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Attributes
- torch_mist.utils.estimation.DEFAULTS
- torch_mist.utils.estimation._instantiate_estimator(estimator: str | torch_mist.estimators.base.MIEstimator | Callable[[Any], torch_mist.estimators.base.MIEstimator], data: torch_mist.utils.data.utils.TensorDictLike, x_key: str | None = None, y_key: str | None = None, verbose: bool = True, **estimator_params) torch_mist.estimators.base.MIEstimator
- torch_mist.utils.estimation._determine_train_duration(max_iterations: int | None, max_epochs: int | None, data: torch_mist.utils.data.utils.TensorDictLike) Tuple[int | None, int | None]
- torch_mist.utils.estimation.estimate_mi(data: torch_mist.utils.data.utils.TensorDictLike, estimator: torch_mist.estimators.base.MIEstimator | str = DEFAULTS['estimator_name'], valid_data: torch_mist.utils.data.utils.TensorDictLike | None = None, test_data: torch_mist.utils.data.utils.TensorDictLike | bool | None = None, valid_percentage: float = 0.1, test_percentage: float = 0.0, device: torch.device | str = torch.device('cpu'), max_epochs: int | None = None, max_iterations: int | None = None, optimizer_class: Type[torch.optim.Optimizer] = Adam, optimizer_params: Dict[str, Any] | None = None, verbose: bool = True, logger: torch_mist.utils.logging.logger.base.Logger | bool | None = None, lr_annealing: bool = False, warmup_percentage: float = 0, batch_size: int | None = DEFAULTS['batch_size'], eval_batch_size: int | None = None, num_workers: int = 0, early_stopping: bool = True, patience: int | None = None, tolerance: float = 0.001, return_estimator: bool = False, fast_train: bool = False, x_key: str = 'x', y_key: str = 'y', train_logged_methods: List[str | Tuple[str, Callable]] | None = None, eval_logged_methods: List[str | Tuple[str, Callable]] | None = None, trained_model_save_path: str | None = None, save_train_log: bool = True, **estimator_params) Dict[str, float] | float | Tuple[Dict[str, float] | float, pandas.DataFrame] | Tuple[Dict[str, float] | float, torch_mist.estimators.base.MIEstimator] | Tuple[Dict[str, float] | float, torch_mist.estimators.base.MIEstimator, pandas.DataFrame]
- torch_mist.utils.estimation.estimate_temporal_mi(data: numpy.ndarray | torch.Tensor, lagtimes: List[int] | numpy.ndarray | torch.Tensor, estimator: torch_mist.estimators.base.MIEstimator | str = DEFAULTS['estimator_name'], valid_data: torch_mist.utils.data.utils.TensorDictLike | None = None, test_data: torch_mist.utils.data.utils.TensorDictLike | bool | None = None, valid_percentage: float = 0.1, device: torch.device | str = torch.device('cpu'), max_epochs: int | None = None, max_iterations: int | None = None, optimizer_class: Type[torch.optim.Optimizer] = Adam, optimizer_params: Dict[str, Any] | None = None, verbose: bool = True, logger: torch_mist.utils.logging.logger.base.Logger | bool | None = None, lr_annealing: bool = False, warmup_percentage: float = 0, batch_size: int | None = DEFAULTS['batch_size'], evaluation_batch_size: int | None = None, num_workers: int = 0, early_stopping: bool = True, patience: int | None = None, tolerance: float = 0.001, return_estimator: bool = False, fast_train: bool = False, train_logged_methods: List[str | Tuple[str, Callable]] | None = None, eval_logged_methods: List[str | Tuple[str, Callable]] | None = None, **estimator_params) Dict[str, float] | Tuple[Dict[str, float], pandas.DataFrame] | Tuple[Dict[str, float], torch_mist.estimators.base.MIEstimator] | Tuple[Dict[str, float], torch_mist.estimators.base.MIEstimator, pandas.DataFrame]
- torch_mist.utils.estimation._train_on_fold(chunks: List[torch.utils.data.Dataset], fold: int, device: str | torch.device, verbose: bool, batch_size: int, evaluation_batch_size: int | None = None, num_workers: int = 0, **train_params) Tuple[Dict[str, float], int, int]
- torch_mist.utils.estimation._prepare_k_fold_data(data: torch_mist.utils.data.utils.TensorDictLike, folds: int, seed: int | None, verbose: bool) List[torch.utils.data.Dataset]
- torch_mist.utils.estimation.k_fold_mi_estimate(data: torch_mist.utils.data.utils.TensorDictLike, estimator: torch_mist.estimators.base.MIEstimator | str = DEFAULTS['estimator_name'], verbose: bool = True, verbose_train: bool = False, logger: torch_mist.utils.logging.logger.base.Logger | bool | None = None, seed: int | None = None, folds: int = 10, batch_size: int = DEFAULTS['batch_size'], device: str | torch.device = torch.device('cpu'), n_estimations: int | None = None, save_log: bool = True, **kwargs) Tuple[float, Any]