qfeval_functions.functions.nancumsum

nancumsum(x, dim)[source]

Compute the cumulative sum along a dimension, treating NaN as 0.

This function calculates the cumulative sum of elements along the specified dimension, where NaN values are treated as 0 (additive identity) for the purpose of computing the cumulative sum. However, positions with NaN values in the original tensor remain NaN in the output.

Parameters:

x (Tensor) – The input tensor containing values.
dim (int) – The dimension along which to compute the cumulative sum.

Returns:

A tensor of the same shape as the input, containing the cumulative sum values. Original NaN positions remain NaN.

Return type:

Tensor

Example

>>> # Simple cumulative sum with NaN
>>> x = torch.tensor([1.0, nan, 3.0, 4.0])
>>> QF.nancumsum(x, dim=0)
tensor([1., nan, 4., 8.])

>>> # 2D tensor along columns
>>> x = torch.tensor([[1.0, 2.0, nan, 4.0],
...                   [2.0, nan, 3.0, 5.0]])
>>> QF.nancumsum(x, dim=1)
tensor([[ 1.,  3., nan,  7.],
        [ 2., nan,  5., 10.]])

>>> # Cumulative sum along rows
>>> x = torch.tensor([[1.0, 2.0],
...                   [nan, 3.0],
...                   [4.0, 5.0]])
>>> QF.nancumsum(x, dim=0)
tensor([[ 1.,  2.],
        [nan,  5.],
        [ 5., 10.]])

>>> # Multiple NaNs
>>> x = torch.tensor([1.0, nan, nan, 2.0, 3.0])
>>> QF.nancumsum(x, dim=0)
tensor([1., nan, nan, 3., 6.])

>>> # All NaNs
>>> x = torch.tensor([nan, nan, nan])
>>> QF.nancumsum(x, dim=0)
tensor([nan, nan, nan])

Note

This function treats NaN values as the additive identity (0) for computation purposes, allowing the cumulative sum to continue past NaN values. However, the original NaN positions are preserved in the output, maintaining data integrity while enabling meaningful cumulative operations on partially missing data.