The Imputer Predict component uses trained models by Imputer Training component to generate batch predictions. The following imputation policies are supported: MEAN, MIN, MAX, and VALUE.
Limits
The supported compute engines are MaxCompute and Realtime Compute for Apache Flink.
Introduction
This Imputer Predict component uses trained models by Imputer Training component to generate batch predictions. You must specify a model trained by the Imputer Train component when you run the Imputer Predict component. The following imputation policies are supported: MEAN, MIN, MAX, and VALUE. You can specify a policy when you configure the Imputer Train component to generate models.
Configure the component in Machine Learning Designer
Input ports
Input port (from left to right) | Data type | Recommended upstream component | Required |
Input model of the prediction | None | Yes | |
Input data of the prediction | Integer | Yes |
Component parameters
Tab | Parameter | Description |
Parameter Setting | outputCols | The number of generated columns must be the same as the number of columns that you specify when you configure the Imputer Train component. If you do not specify this parameter, the generated columns replace the original columns. |
numThreads | The number of threads used by the component. Default value: 1. | |
Execution Tuning | Number of Workers | The number of workers. This parameter must be used together with the Memory per worker, unit MB parameter. The value of this parameter must be a positive integer. Valid values: [1,9999]. |
Memory per worker, unit MB | The memory size of each worker. Valid values: 1024 to 65536. Unit: MB. |
Output ports
Output port (from left to right) | Storage location | Recommended downstream component | Model type |
Output result | N/A | None | None |
Example
You can copy the following code to the code editor of the PyAlink Script component. This allows the PyAlink Script component to function like the Imputer Predict component.
from pyalink.alink import *
def main(sources, sinks, parameter):
model = sources[0]
data = sources[1]
predictOp = ImputerPredictBatchOp()
result = predictOp.linkFrom(model, data)
result.link(sinks[0])
BatchOperator.execute()