Teradata R Package Function Reference | 17.00 - 17.00 - HMMSupervised - Teradata R Package

Teradata® R Package Function Reference
prodname
Teradata Package for R
vrm_release
17.00
created_date
September 2020
category
Programming Reference
featnum
B700-4007-090K

Description

The HMMSupervisedLearner function is available on SQL-Graph platform. The function can produce multiple HMM models simultaneously, where each model is learned from a set of sequences and where each sequence represents a vertex.

Usage

  td_hmm_supervised_mle (
      vertices = NULL,
      model.key = NULL,
      sequence.key = NULL,
      observed.key = NULL,
      state.key = NULL,
      skip.key = NULL,
      batch.size = NULL,
      vertices.sequence.column = NULL,
      vertices.partition.column = NULL,
      vertices.order.column = NULL
  )

Arguments

vertices

Required Argument.
Specifies a tbl_teradata that contains the input vertex information.

vertices.partition.column

Required Argument.
Specifies the Partition By columns for "vertices".
Values to this argument can be provided as vector, if multiple columns are used for partition.
Note:

1. This argument must contain the name of the column specified in "sequence.key" argument.

2. This argument should contain the name of the column specified in "model.key", if "model.key" argument is used, and it must be the first column followed by the name of the column specified in "sequence.key".
Types: character OR vector of Strings (character)

vertices.order.column

Required Argument.
Specifies the Order By columns for "vertices".
Values to this argument can be provided as vector, if multiple columns are used for ordering.
Note: This argument must contain the name of the column, containing time ordered sequence, as one of its columns.
Types: character OR vector of Strings (character)

model.key

Optional Argument.
Specifies the name of the column that contains the model attribute. The values in the column can be integers or strings.
Note: The "vertices.partition.column" argument should contain the name of the column specified in this argument.
Types: character

sequence.key

Required Argument.
Specifies the name of the column that contains the sequence attribute. It must match one of the columns specified in the "vertices.partition.column" argument. A sequence (value in this column) must contain more than two observation symbols. Each sequence represent a vertex.
Types: character

observed.key

Required Argument.
Specifies the name of the column that contains the observed symbols. The function scans the input tbl_teradata to find all possible observed symbols.
Note: Observed symbols are case-sensitive.
Types: character

state.key

Required Argument.
Specifies the state attributes. You can specify multiple states. The states are case-sensitive.
Types: character

skip.key

Optional Argument.
Specifies the name of the column whose values determine whether the function skips the row. The function skips the row if the value is "true", "yes", "y", or "1". The function does not skip the row if the value is "false", "f", "no", "n", "0", or NULL.
Types: character

batch.size

Optional Argument.
Specifies the number of models to process. The size must be positive. If the batch size is not specified, the function avoids out-of-memory errors by determining the appropriate size. If the batch size is specified and there is insufficient free memory, the function reduces the batch size. The function determines the batch size dynamically based on the memory conditions. For example, the batch size is set to 1000, at time T1, it might be adjusted to 980, and at time T2, it might be adjusted to 800.
Types: integer

vertices.sequence.column

Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row of the input argument "vertices". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.
Types: character OR vector of Strings (character)

Value

Function returns an object of class "td_hmm_supervised_mle" which is a named list containing objects of class "tbl_teradata".
Named list members can be referenced directly with the "$" operator using following names:

  1. output.initialstate.table

  2. output.statetransition.table

  3. output.emission.table

  4. output

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("hmmsupervised_example", "customer_loyalty")

    # Create object(s) of class "tbl_teradata".
    customer_loyalty <- tbl(con, "customer_loyalty")

    # Example 1 - Train a td_hmm_supervised_mle() function on the customer loyalty dataset
    td_hmm_supervised_out <- td_hmm_supervised_mle(vertices = customer_loyalty,
                                               vertices.partition.column = c("user_id", "seq_id"),
                                               vertices.order.column = c("user_id", "seq_id",
                                               "purchase_date"),
                                               model.key = "user_id",
                                               sequence.key = "seq_id",
                                               observed.key = "observation",
                                               state.key = "loyalty_level"
                                               )