ray.data.preprocessors.MaxAbsScaler#

class ray.data.preprocessors.MaxAbsScaler(columns: List[str])[source]#

Bases: Preprocessor

Scale each column by its absolute max value.

The general formula is given by

\[x' = \frac{x}{\max{\vert x \vert}}\]

where \(x\) is the column and \(x'\) is the transformed column. If \(\max{\vert x \vert} = 0\) (i.e., the column contains all zeros), then the column is unmodified.

Tip

This is the recommended way to scale sparse data. If you data isn’t sparse, you can use MinMaxScaler or StandardScaler instead.

Examples

>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import MaxAbsScaler
>>>
>>> df = pd.DataFrame({"X1": [-6, 3], "X2": [2, -4], "X3": [0, 0]})   # noqa: E501
>>> ds = ray.data.from_pandas(df)  
>>> ds.to_pandas()  
   X1  X2  X3
0  -6   2   0
1   3  -4   0

Columns are scaled separately.

>>> preprocessor = MaxAbsScaler(columns=["X1", "X2"])
>>> preprocessor.fit_transform(ds).to_pandas()  
    X1   X2  X3
0 -1.0  0.5   0
1  0.5 -1.0   0

Zero-valued columns aren’t scaled.

>>> preprocessor = MaxAbsScaler(columns=["X3"])
>>> preprocessor.fit_transform(ds).to_pandas()  
   X1  X2   X3
0  -6   2  0.0
1   3  -4  0.0
Parameters:

columns – The columns to separately scale.

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

Methods

deserialize

Load the original preprocessor serialized via self.serialize().

fit

Fit this Preprocessor to the Dataset.

fit_transform

Fit this Preprocessor to the Dataset and then transform the Dataset.

preferred_batch_format

Batch format hint for upstream producers to try yielding best block format.

serialize

Return this preprocessor serialized as a string.

transform

Transform the given dataset.

transform_batch

Transform a single batch of data.

transform_stats

Return Dataset stats for the most recent transform call, if any.