Source code for alibi_detect.utils.pytorch.misc

import logging
from typing import Type
from alibi_detect.utils._types import TorchDeviceType

import torch

logger = logging.getLogger(__name__)

[docs]def zero_diag(mat: torch.Tensor) -> torch.Tensor: """ Set the diagonal of a matrix to 0 Parameters ---------- mat A 2D square matrix Returns ------- A 2D square matrix with zeros along the diagonal """ return mat - torch.diag(mat.diag())
[docs]def quantile(sample: torch.Tensor, p: float, type: int = 7, sorted: bool = False) -> float: """ Estimate a desired quantile of a univariate distribution from a vector of samples Parameters ---------- sample A 1D vector of values p The desired quantile in (0,1) type The method for computing the quantile. See sorted Whether or not the vector is already sorted into ascending order Returns ------- An estimate of the quantile """ N = len(sample) if len(sample.shape) != 1: raise ValueError("Quantile estimation only supports vectors of univariate samples.") if not 1/N <= p <= (N-1)/N: raise ValueError(f"The {p}-quantile should not be estimated using only {N} samples.") sorted_sample = sample if sorted else sample.sort().values if type == 6: h = (N+1)*p elif type == 7: h = (N-1)*p + 1 elif type == 8: h = (N+1/3)*p + 1/3 h_floor = int(h) quantile = sorted_sample[h_floor-1] if h_floor != h: quantile += (h - h_floor)*(sorted_sample[h_floor]-sorted_sample[h_floor-1]) return float(quantile)
[docs]def get_device(device: TorchDeviceType = None) -> torch.device: """ Instantiates a PyTorch device object. Parameters ---------- device Either `None`, a str ('gpu', 'cuda' or 'cpu') indicating the device to choose, or an already instantiated device object. If `None`, the GPU is selected if it is detected, otherwise the CPU is used as a fallback. Returns ------- The instantiated device object. """ if isinstance(device, torch.device): # Already a torch device return device else: # Instantiate device if device is None or device.lower() in ['gpu', 'cuda']: torch_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if torch_device.type == 'cpu': logger.warning('No GPU detected, fall back on CPU.') else: torch_device = torch.device('cpu') if device.lower() != 'cpu': logger.warning('Requested device not recognised, fall back on CPU.') return torch_device
[docs]def get_optimizer(name: str = 'Adam') -> Type[torch.optim.Optimizer]: """ Get an optimizer class from its name. Parameters ---------- name Name of the optimizer. Returns ------- The optimizer class. """ optimizer = getattr(torch.optim, name, None) if optimizer is None: raise NotImplementedError(f"Optimizer {name} not implemented.") return optimizer