The table to vector component converts multiple columns of data to vector data.
Limits
The supported compute engines are MaxCompute and Realtime Compute for Apache Flink.
Introduction
This columns to vector component converts multiple numeric column data to vector data.
Configure the component in Machine Learning Designer
Input ports
Input port (from left to right) | Data type | Recommended upstream component | Required |
data | Integer | Yes |
Component parameters
Tab | Parameter | Description |
Field Setting | reservedCols | The names of the generated columns that you want to reserve. By default, all columns are reserved. |
selectedCols | The names of the numeric columns whose data that you want to convert to vectors. | |
Parameter Setting | vectorCol | The name of the generated column that contains vector data. |
handleInvalid | The policy that is used to handle exceptions. Default value: ERROR. Valid values:
| |
vectorSize | The number of elements in a vector. 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 table to vector component.
from pyalink.alink import *
def main(sources, sinks, parameter):
data = sources[0]
op = ColumnsToVectorBatchOp()\
.setSelectedCols(["f0", "f1"])\
.setReservedCols(["row"])\
.setVectorCol("vec")\
.linkFrom(data)
result = op.linkFrom(data)
result.link(sinks[0])
BatchOperator.execute()