torch.nanquantile — PyTorch 2.7 documentation (original) (raw)

torch.nanquantile(input, q, dim=None, keepdim=False, *, interpolation='linear', out=None) → Tensor¶

This is a variant of torch.quantile() that “ignores” NaN values, computing the quantiles q as if NaN values in input did not exist. If all values in a reduced row are NaN then the quantiles for that reduction will be NaN. See the documentation for torch.quantile().

Parameters

• input (Tensor) – the input tensor.
• q (float or Tensor) – a scalar or 1D tensor of quantile values in the range [0, 1]
• dim (int) – the dimension to reduce.
• keepdim (bool) – whether the output tensor has dim retained or not.

Keyword Arguments

• interpolation (str) – interpolation method to use when the desired quantile lies between two data points. Can be linear, lower, higher, midpoint and nearest. Default is linear.
• out (Tensor, optional) – the output tensor.

Example:

t = torch.tensor([float('nan'), 1, 2]) t.quantile(0.5) tensor(nan) t.nanquantile(0.5) tensor(1.5000) t = torch.tensor([[float('nan'), float('nan')], [1, 2]]) t tensor([[nan, nan], [1., 2.]]) t.nanquantile(0.5, dim=0) tensor([1., 2.]) t.nanquantile(0.5, dim=1) tensor([ nan, 1.5000])