ray.data.preprocessors.Normalizer#

class ray.data.preprocessors.Normalizer(columns: List[str], norm='l2')[source]#

Bases: ray.data.preprocessor.Preprocessor

Scales each sample to have unit norm.

This preprocessor works by dividing each sample (i.e., row) by the sample’s norm. The general formula is given by

\[s' = \frac{s}{\lVert s \rVert_p}\]

where \(s\) is the sample, \(s'\) is the transformed sample, :math:lVert s rVert`, and \(p\) is the norm type.

The following norms are supported:

  • "l1" (\(L^1\)): Sum of the absolute values.

  • "l2" (\(L^2\)): Square root of the sum of the squared values.

  • "max" (\(L^\infty\)): Maximum value.

Examples

>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import Normalizer
>>>
>>> df = pd.DataFrame({"X1": [1, 1], "X2": [1, 0], "X3": [0, 1]})
>>> ds = ray.data.from_pandas(df)  
>>> ds.to_pandas()  
   X1  X2  X3
0   1   1   0
1   1   0   1

The \(L^2\)-norm of the first sample is \(\sqrt{2}\), and the \(L^2\)-norm of the second sample is \(1\).

>>> preprocessor = Normalizer(columns=["X1", "X2"])
>>> preprocessor.fit_transform(ds).to_pandas()  
         X1        X2  X3
0  0.707107  0.707107   0
1  1.000000  0.000000   1

The \(L^1\)-norm of the first sample is \(2\), and the \(L^1\)-norm of the second sample is \(1\).

>>> preprocessor = Normalizer(columns=["X1", "X2"], norm="l1")
>>> preprocessor.fit_transform(ds).to_pandas()  
    X1   X2  X3
0  0.5  0.5   0
1  1.0  0.0   1

The \(L^\infty\)-norm of the both samples is \(1\).

>>> preprocessor = Normalizer(columns=["X1", "X2"], norm="max")
>>> preprocessor.fit_transform(ds).to_pandas()  
    X1   X2  X3
0  1.0  1.0   0
1  1.0  0.0   1
Parameters

  • columns – The columns to scale. For each row, these colmumns are scaled to unit-norm.

  • norm – The norm to use. The supported values are "l1", "l2", or "max". Defaults to "l2".

Raises

ValueError – if norm is not "l1", "l2", or "max".

PublicAPI (alpha): This API is in alpha and may change before becoming stable.