Source code for pygrinder.utils

"""
Utility functions for pygrinder.
"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

from typing import Union, Tuple

import numpy as np
import torch


[docs] def calc_missing_rate(X: Union[np.ndarray, torch.Tensor]) -> float: """Calculate the originally missing rate of the raw data. Parameters ---------- X: Data array that may contain missing values. Returns ------- originally_missing_rate, The originally missing rate of the raw data. Its value should be in the range [0,1]. """ if isinstance(X, list): X = np.asarray(X) if isinstance(X, np.ndarray): originally_missing_rate = np.sum(np.isnan(X)) / np.prod(X.shape) elif isinstance(X, torch.Tensor): originally_missing_rate = torch.sum(torch.isnan(X)) / np.prod(X.shape) originally_missing_rate = originally_missing_rate.item() else: raise TypeError( "X must be type of list/numpy.ndarray/torch.Tensor, " f"but got {type(X)}" ) return originally_missing_rate
[docs] def masked_fill( X: Union[np.ndarray, torch.Tensor], mask: Union[np.ndarray, torch.Tensor], val: float, ) -> Union[np.ndarray, torch.Tensor]: """Like torch.Tensor.masked_fill(), fill elements in given `X` with `val` where `mask` is True. Parameters ---------- X: The data vector. mask: The boolean mask. val: The value to fill in with. Returns ------- filled_X: Mask filled X. """ if isinstance(X, list): X = np.asarray(X) mask = np.asarray(mask) assert X.shape == mask.shape, ( "Shapes of X and mask must match, " f"but X.shape={X.shape}, mask.shape={mask.shape}" ) assert isinstance(X, type(mask)), ( "Data types of X and mask must match, " f"but got {type(X)} and {type(mask)}" ) if isinstance(X, np.ndarray): filled_X = X.copy() mask = mask.copy() mask = mask.astype(bool) filled_X[mask] = val elif isinstance(X, torch.Tensor): filled_X = torch.clone(X) mask = torch.clone(mask) mask = mask.type(torch.bool) filled_X[mask] = val else: raise TypeError( "X must be type of list/numpy.ndarray/torch.Tensor, " f"but got {type(X)}" ) return filled_X
[docs] def fill_and_get_mask_numpy( X: np.ndarray, nan: Union[float, int] = 0, ) -> Tuple[np.ndarray, ...]: """Fill missing values in numpy array X with `nan` and return the missing mask. Parameters ---------- X : np.ndarray Time series data generated from X_intact, with artificially missing values added. nan : int/float, optional, default=0 Value used to fill NaN values. Only valid when return_masks is True. If return_masks is False, the NaN values will be kept as NaN. Returns ------- X : Original X with artificial missing values. X is for model input. Both originally-missing and artificially-missing values are filled with given parameter `nan`. missing_mask : The mask indicates all missing values in X. In it, 1 indicates observed values, and 0 indicates missing values. """ X_missing_mask = (~np.isnan(X)).astype(np.float32) X = np.nan_to_num(X, nan=nan) return X, X_missing_mask
[docs] def fill_and_get_mask_torch( X: torch.Tensor, nan: Union[float, int] = 0, ) -> Tuple[torch.Tensor, ...]: """Fill missing values in torch tensor X with `nan` and return the missing mask. Parameters ---------- X : Time series data generated from X_intact, with artificially missing values added. nan : int/float, optional, default=0 Value used to fill NaN values. Only valid when return_masks is True. If return_masks is False, the NaN values will be kept as NaN. Returns ------- X : Original X with artificial missing values. X is for model input. Both originally-missing and artificially-missing values are filled with given parameter `nan`. missing_mask : The mask indicates all missing values in X. In it, 1 indicates observed values, and 0 indicates missing values. """ missing_mask = (~torch.isnan(X)).type(torch.float32) X = torch.nan_to_num(X, nan=nan) return X, missing_mask
[docs] def fill_and_get_mask( X: Union[torch.Tensor, np.ndarray], nan: Union[float, int] = 0, ) -> Union[Tuple[np.ndarray, ...], Tuple[torch.Tensor, ...]]: """Fill missing values in X with `nan` and return the missing mask. Parameters ---------- X : Data with missing values nan : int/float, optional, default=0 Value used to fill NaN values. Only valid when return_masks is True. If return_masks is False, the NaN values will be kept as NaN. Returns ------- X : Original X with artificial missing values. X is for model input. Both originally-missing and artificially-missing values are filled with given parameter `nan`. missing_mask : The mask indicates all missing values in X. In it, 1 indicates observed values, and 0 indicates missing values. """ if isinstance(X, list): X = np.asarray(X) if isinstance(X, np.ndarray): X, missing_mask = fill_and_get_mask_numpy(X, nan) elif isinstance(X, torch.Tensor): X, missing_mask = fill_and_get_mask_torch(X, nan) else: raise TypeError( "X must be type of list/numpy.ndarray/torch.Tensor, " f"but got {type(X)}" ) return X, missing_mask