Platform For AI:SVD

You can configure the component by using one of the following methods:

• Use the Machine Learning Platform for AI console

  Tab	Parameter	Description
  Fields Setting	Feature Columns	The columns that are used to store key-value pairs. The keys and values are separated by colons (:), and multiple key-value pairs are separated by commas (,).
  Parameters Setting	Number of Reserved Singular Values	The top N singular groups that you want to decompose. All singular groups are decomposed by default.
  	Accuracy Error	The error precision that is allowed.
  Tuning	Memory Size per Node	The memory size of each node. Unit: MB. This parameter must be used with the Number of Nodes parameter. The value of this parameter must be a positive integer. Valid values: [1,9999].
  	Number of Nodes	The value of this parameter must be a positive integer. Valid values: [1024,64 × 1024].
  	Lifetime	The lifecycle of the output table.

• Use commands

  PAI -name svd
      -project algo_public
      -DinputTableName=bank_data
      -DselectedColNames=col0
      -DenableSparse=true
      -Dk=5
      -DoutputUTableName=u_table
      -DoutputVTableName=v_table
      -DoutputSTableName=s_table;

  Parameter	Required	Description	Default value
  inputTableName	Yes	The input table that is used for training.	N/A
  selectedColNames	No	The columns that are selected from the input table for training. Separate the columns with commas (,). If a sparse matrix is used, the columns of the STRING data type are supported. If a data table is used, the columns of the INT and DOUBLE types are supported.	All columns
  inputTablePartitions	No	The partitions that are selected from the input table for training. Set this parameter in the Partition_name=value format. To specify multi-level partitions, set this parameter in the name1=value1/name2=value2; format. If you specify multiple partitions, separate them with commas (,).	All partitions
  outputUTableName	Yes	The output table of the unitary matrix. The output table is generated from the m * sgNum dimension. m represents the number of rows of the data table, and sgNum represents the number of calculated singular values.	N/A
  outputSTableName	Yes	The output table of the scattering matrix (S-matrix). The output table is generated from the sgNum * sgNum dimension. sgNum represents the number of calculated singular values.	N/A
  outputVTableName	Yes	The output table of the V matrix. The output table is generated from the n * sgNum dimension. n represents the number of columns of the matrix, and sgNum represents the number of calculated singular values.	N/A
  k	Yes	The number of expected singular values. The number of generated singular values may be a positive integer less than the value specified by the k parameter.	N/A
  tol	No	The convergence error.	1.0e~06
  enableSparse	No	Specifies whether data in the input table is in the sparse format. Valid values: true false	false
  itemDelimiter	No	The delimiter that is used to separate key-value pairs when data in the input table is in the sparse format.	Space
  kvDelimiter	No	The delimiter that is used to separate keys and values when data in the input table is in the sparse format.	:
  coreNum	No	The number of cores. This parameter must be used with the memSizePerCore parameter. The value of this parameter must be a positive integer. Valid values: [1,9999].	Determined by the system
  memSizePerCore	No	The memory size of each core. Unit: MB. The value of this parameter must be a positive integer. Valid values: [1024,64 × 1024].	Determined by the system
  lifecycle	No	The lifecycle of the output table. The value must be a positive integer.	N/A