nan_euclidean_distances

cuml.metrics.nan_euclidean_distances(

X,

Y=None,

*,

squared=False,

missing_values=cp.nan,

copy=True,

convert_dtype='deprecated',

)

Calculate the euclidean distances in the presence of missing values.

Compute the euclidean distance between each pair of samples in X and Y, where Y=X is assumed if Y=None. When calculating the distance between a pair of samples, this formulation ignores feature coordinates with a missing value in either sample and scales up the weight of the remaining coordinates:

dist(x,y) = sqrt(weight * sq. distance from present coordinates) where, weight = Total # of coordinates / # of present coordinates

For example, the distance between [3, na, na, 6] and [1, na, 4, 5] is:

\[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]

If all the coordinates are missing or if there are no common present coordinates then NaN is returned for that pair.

Parameters:

Xarray-like (device or host) of shape (n_samples_X, n_features)

Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy.

Yarray-like (device or host) of shape (n_samples_Y, n_features), default=None

A second feature array. If None, Y is assumed to be X. Acceptable formats: cuDF DataFrame, NumPy ndarray, Numba device ndarray, cuda array interface compliant array like CuPy.

squaredbool, default=False

Return squared Euclidean distances.

missing_valuesnp.nan or int, default=np.nan

Representation of missing value.

copybool, default=True,

Whether to make a copy of X and Y when necessary. Setting to False can reduce memory usage, but may result in mutation of X and Y.

convert_dtypebool, default=”deprecated”

Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.

Returns:

distancesarray of shape (n_samples_X, n_samples_Y)

Returns the distances between the row vectors of X and the row vectors of Y.