robust_scale

cuml.preprocessing.robust_scale(X, *, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True)

Standardize a dataset along any axis

Center to the median and component wise scale according to the interquartile range.

Parameters:

X{array-like, sparse matrix}

The data to center and scale.

axisint (0 by default)

axis used to compute the medians and IQR along. If 0, independently scale each feature, otherwise (if 1) scale each sample.

with_centeringboolean, True by default

If True, center the data before scaling.

with_scalingboolean, True by default

If True, scale the data to unit variance (or equivalently, unit standard deviation).

quantile_rangetuple (q_min, q_max), 0.0 < q_min < q_max < 100.0

Default: (25.0, 75.0) = (1st quantile, 3rd quantile) = IQR Quantile range used to calculate scale_.

copyboolean, optional, default is True

Whether a forced copy will be triggered. If copy=False, a copy might be triggered by a conversion.

See also

RobustScaler

Performs centering and scaling using the Transformer API

Notes

This implementation will refuse to center sparse matrices since it would make them non-sparse and would potentially crash the program with memory exhaustion problems.

Instead the caller is expected to either set explicitly with_centering=False (in that case, only variance scaling will be performed on the features of the CSR matrix) or to densify the matrix if he/she expects the materialized dense array to fit in memory.

To avoid memory copy the caller should pass a CSR matrix.