How to use the Min Max Scaler Train component?-Platform For AI(PAI)-阿里云帮助中心

Min Max Scaler Train scales numeric columns to a target value range using min-max normalization. Use it in data preprocessing pipelines to bring features with different scales—such as one column ranging from 0 to 1 and another from 10,000 to 100,000—into the same range before training a model.

After the component runs, it produces a min-max normalization model that the Min Max Scaler Batch Predict component can apply to new data.

How it works

Min Max Scaler Train scales each value in two steps:

Standardize the value to a [0, 1] range based on the column's observed minimum and maximum:
```
X_std = (value - column_min) / (column_max - column_min)
```
Map the standardized value to the target range [min, max]:
```
X_scaled = X_std × (max - min) + min
```

By default, min is 0.0 and max is 1.0, so the output falls in [0, 1].

Supported computing engines

MaxCompute and Apache Flink.

Configure the component in Machine Learning Designer

Input ports

Input port	Data type	Recommended upstream component	Required
data	Integer	None	Yes

Component parameters

Tab	Parameter	Description
Field Setting	selectedCols	The numeric columns to scale. Only columns of the numeric type are supported.
Parameter Setting	max	Upper bound of the target range. Must be a DOUBLE value. Default: `1.0`.
Parameter Setting	min	Lower bound of the target range. Must be a DOUBLE value. Default: `0.0`.
Execution Tuning	Number of Workers	Number of workers. Must be a positive integer in the range [1, 9999]. Use together with Memory per worker, unit MB.
Execution Tuning	Memory per worker, unit MB	Memory allocated to each worker, in MB. Valid values: 1024–65536.

Output ports

Output port	Storage	Recommended downstream component	Model type
model	N/A	Min Max Scaler Batch Predict	None

Example

The following code replicates this component's behavior using the PyAlink Script component. Copy it into the PyAlink Script code editor.

from pyalink.alink import *

def main(sources, sinks, parameter):
    data = sources[0]
    selectedColNames = ["col2", "col3"]
    trainOp = MinMaxScalerTrainBatchOp()\
               .setSelectedCols(selectedColNames)
    result = trainOp.linkFrom(data)
    result.link(sinks[0])
    BatchOperator.execute()

What's next

Apply the trained model to new data: Min Max Scaler Batch Predict