import logging
from typing import Type
from alibi_detect.utils._types import TorchDeviceType
import torch
logger = logging.getLogger(__name__)
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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())
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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 https://wikipedia.org/wiki/Quantile#Estimating_quantiles_from_a_sample
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)
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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
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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