Platform For AI:MTable Expander

Limits

This component supports the following compute engines: MaxCompute, Flink, and Deep Learning Containers (DLC).

Configure the component in the Platform for AI (PAI) console

• Input ports

  Input port (left-to-right)	Data type	Recommended upstream component	Required
  data	None	MTable Assembler Prophet	Yes

• Component parameters

  Tab	Parameter	Description
  Field Setting	selectedCol	The name of the computed column. The value of this parameter is of the STRING type in the MTABLE format.
  	reservedCols	The columns to be reserved by the algorithm.
  Parameters Setting	Schema	The name and type of the expanded column. The value of this parameter is in the colname coltype[, colname2, coltype2[, ...]] format. For example, the value is f0 string, f1 bigint, f2 double.
  	handleInvalidMethod	The method used to handle invalid values. Valid values: ERROR: An error is returned. This is the default value. SKIP: Invalid values are skipped.
  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 64 × 1024. Unit: MB.

Configure the component by coding

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 MTable Expander component.

import numpy as np
import pandas as pd
from pyalink.alink import *
 
df_data = pd.DataFrame([
      ["a1", "11L", 2.2],
      ["a1", "12L", 2.0],
      ["a2", "11L", 2.0],
      ["a2", "12L", 2.0],
      ["a3", "12L", 2.0],
      ["a3", "13L", 2.0],
      ["a4", "13L", 2.0],
      ["a4", "14L", 2.0],
      ["a5", "14L", 2.0],
      ["a5", "15L", 2.0],
      ["a6", "15L", 2.0],
      ["a6", "16L", 2.0]
])
 
input = BatchOperator.fromDataframe(df_data, schemaStr='id string, f0 string, f1 double')
 
zip = GroupByBatchOp()\
	.setGroupByPredicate("id")\
	.setSelectClause("id, mtable_agg(f0, f1) as m_table_col")
 
flatten = FlattenMTableBatchOp()\
	.setReservedCols(["id"])\
	.setSelectedCol("m_table_col")\
	.setSchemaStr('f0 string, f1 int')
 
zip.linkFrom(input).link(flatten).print()