spac.transformations.normalize_features_core(data, low_quantile=0.02, high_quantile=0.98, interpolation='linear')[source]

Normalize the features in a numpy array.

Any entry lower than the value corresponding to low_quantile of the column will be assigned a value of low_quantile, and entries that are greater than high_quantile value will be assigned as value of high_quantile. Other entries will be normalized with (values - quantile min)/(quantile max - quantile min). Resulting column will have values ranged between [0, 1].

Parameters:

  • data (np.ndarray) – The data to be normalized.

  • low_quantile (float, optional (default: 0.02)) – The lower quantile to use for normalization. Determines the minimum value after normalization. Must be a positive float between [0,1).

  • high_quantile (float, optional (default: 0.98)) – The higher quantile to use for normalization. Determines the maximum value after normalization. Must be a positive float between (0,1].

  • interpolation (str, optional (default: "linear")) – The interpolation method to use when selecting the value for low and high quantile. Values can be “nearest” or “linear”.

Returns:

The normalized data.

Return type:

np.ndarray

Raises:

  • TypeError – If low_quantile or high_quantile are not numeric.

  • ValueError – If low_quantile is not less than high_quantile, or if they are out of the range [0, 1] and (0, 1], respectively.

  • ValueError – If interpolation is not one of the allowed values.