Teradata R Package Function Reference - HMMDecoder - Teradata R Package - Look here for syntax, methods and examples for the functions included in the Teradata R Package.

Description

The HMMDecoder function finds the state sequence with the highest probability, given the learned model and observed sequences.

Usage

  td_hmm_decoder_mle (
      init.state.prob = NULL,
      state.transition.prob = NULL,
      emission.prob = NULL,
      observation = NULL,
      state.model.key = NULL,
      state.key = NULL,
      state.prob.key = NULL,
      trans.model.key = NULL,
      trans.from.key = NULL,
      trans.to.key = NULL,
      trans.prob.key = NULL,
      emit.model.key = NULL,
      emit.state.key = NULL,
      emit.observed.key = NULL,
      emit.prob.key = NULL,
      model.key = NULL,
      sequence.key = NULL,
      observed.key = NULL,
      sequence.max.size = 2147483647,
      skip.key = NULL,
      accumulate = NULL,
      observation.sequence.column = NULL,
      init.state.prob.sequence.column = NULL,
      state.transition.prob.sequence.column = NULL,
      emission.prob.sequence.column = NULL,
      observation.partition.column = NULL,
      init.state.prob.partition.column = NULL,
      state.transition.prob.partition.column = NULL,
      emission.prob.partition.column = NULL,
      observation.order.column = NULL
  )

Arguments

`init.state.prob`	Required Argument. Specifies a tbl_teradata that contains the initial state table.
`init.state.prob.partition.column`	Required Argument. Specifies the Partition By columns for argument "init.state.prob". Values to this argument can be provided as vector, if multiple columns are used for ordering.
`state.transition.prob`	Required Argument. Specifies a tbl_teradata that contains the state transition table.
`state.transition.prob.partition.column`	Required Argument. Specifies the Partition By columns for argument "state.transition.prob". Values to this argument can be provided as vector, if multiple columns are used for ordering.
`emission.prob`	Required Argument. Specifies a tbl_teradata that contains the emission probability table.
`emission.prob.partition.column`	Required Argument. Specifies the Partition By columns for argument "emission.prob". Values to this argument can be provided as vector, if multiple columns are used for ordering.
`observation`	Required Argument. Specifies a tbl_teradata that contains the observation table.
`observation.partition.column`	Required Argument. Specifies the Partition By columns for argument "observation". Values to this argument can be provided as vector, if multiple columns are used for ordering.
`observation.order.column`	Required Argument. Specifies the Order By columns for argument "observation". Values to this argument can be provided as vector, if multiple columns are used for ordering.
`state.model.key`	Required Argument. Specifies the name of the model attribute column in the "init.state.prob" table.
`state.key`	Required Argument. Specifies the name of the state attribute column in the "init.state.prob" table.
`state.prob.key`	Required Argument. Specifies the name of the initial probability column in the "init.state.prob" table.
`trans.model.key`	Required Argument. Specifies the name of the model attribute column in the "state.transition.prob" table.
`trans.from.key`	Required Argument. Specifies the name of the source state transition column in the "state.transition.prob" table.
`trans.to.key`	Required Argument. Specifies the name of the target state transition column in the "state.transition.prob" table.
`trans.prob.key`	Required Argument. Specifies the name of the state transition probability column in the "state.transition.prob" table.
`emit.model.key`	Required Argument. Specifies the name of the model attribute column in the "emission.prob" table.
`emit.state.key`	Required Argument. Specifies the name of the state attribute column in the "emission.prob" table.
`emit.observed.key`	Required Argument. Specifies the name of the observation attribute column in the "emission.prob" table.
`emit.prob.key`	Required Argument. Specifies the name of the emission probability column in the "emission.prob" table.
`model.key`	Required Argument. Specifies the name of the column that contains the model attribute. It must match one of the column names specified in the "observation.partition.column" argument.
`sequence.key`	Required Argument. Specifies the name of the column that contains the sequence attribute. It must match one of the column names specified in the "observation.partition.column" argument.
`observed.key`	Required Argument. Specifies the name of the column that contains the observed symbols. Note: Observed symbols are case-sensitive.
`sequence.max.size`	Optional Argument. Specifies the maximum length (in rows), of a sequence in the "observation" table. Default Value: 2147483647
`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.
`accumulate`	Optional Argument. Specifies the names of the columns in the input argument "observation" that the function copies to the output table.
`observation.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "observation". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.
`init.state.prob.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "init.state.prob". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.
`state.transition.prob.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "state.transition.prob". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.
`emission.prob.sequence.column`	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "emission.prob". The argument is used to ensure deterministic results for functions which produce results that vary from run to run.

Value

Function returns an object of class "td_hmm_decoder_mle" which is a named list containing Teradata tbl object.
Named list member can be referenced directly with the "$" operator using name: result.

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("hmmsupervised_example", "customer_loyalty")
    loadExampleData("hmmunsupervised_example", "loan_prediction")
    
    # Example 1 - This example uses loan status updates to build a Unupservised HMM
    # model and then predict loan defaults.
    loadExampleData("hmmdecoder_example", "test_loan_prediction")
    loan_prediction = tbl(con, "loan_prediction")
    td_hmm_unsup_out <- td_hmm_unsupervised_mle(vertices=loan_prediction,
                                  vertices.partition.column=c('model_id','seq_id'),
                                  vertices.order.column='seq_vertex_id',
                                  sequence.key='seq_id',
                                  observed.key='observed_id',
                                  model.key='model_id',
                                  hidden.states.num=3
    )
    
    test_loan_prediction <- tbl(con, "test_loan_prediction")
  
    td_hmm_decoder_out1 <- td_hmm_decoder_mle(init.state.prob = td_hmm_unsup_out$output.initialstate.table,
                             init.state.prob.partition.column = c("model_id"),
                             state.transition.prob = td_hmm_unsup_out$output.statetransition.table,
                             state.transition.prob.partition.column = c("model_id"),
                             emission.prob = td_hmm_unsup_out$output.emission.table,
                             emission.prob.partition.column = c("model_id"),
                             observation = test_loan_prediction,
                             observation.partition.column = c("model_id "),
                             observation.order.column = c("model_id", "seq_id", "seq_vertex_id"),
                             state.model.key = "model_id",
                             state.key = "state",
                             state.prob.key = "probability",
                             trans.model.key = "model_id",
                             trans.from.key = "from_state",
                             trans.to.key = "to_state",
                             trans.prob.key = "probability",
                             emit.model.key = "model_id",
                             emit.state.key = "state",
                             emit.observed.key = "observed",
                             emit.prob.key = "probability",
                             model.key = "model_id",
                             sequence.key = "seq_id",
                             observed.key = "observed_id",
                             accumulate = c("seq_vertex_id")
                            )
    
    # Example 2 - This example uses the output of a HMM Supervised model with the input
    # to determine the loyalty levels of customers from the new sequence of purchases.
    loadExampleData("hmmdecoder_example", "customer_loyalty_newseq")
    customer_loyalty = tbl(con, "customer_loyalty")
  
    td_hmm_sup_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'),
                                        state.key='loyalty_level',
                                        observed.key='observation',
                                        sequence.key='seq_id',
                                        model.key='user_id'
                                       )
    
    customer_loyalty_newseq = tbl(con, "customer_loyalty_newseq")
  
    td_hmm_decoder_out2 <- td_hmm_decoder_mle(init.state.prob = td_hmm_sup_out$output.initialstate.table,
                             init.state.prob.partition.column = c("user_id"),
                             state.transition.prob = td_hmm_sup_out$output.statetransition.table,
                             state.transition.prob.partition.column = c("user_id"),
                             emission.prob = td_hmm_sup_out$output.emission.table,
                             emission.prob.partition.column = c("user_id"),
                             observation = customer_loyalty_newseq,
                             observation.partition.column = c("user_id"),
                             observation.order.column = c("user_id", "seq_id", "purchase_date"),
                             state.model.key = "user_id",
                             state.key = "state",
                             state.prob.key = "probability",
                             trans.model.key = "user_id",
                             trans.from.key = "from_state",
                             trans.to.key = "to_state",
                             trans.prob.key = "probability",
                             emit.model.key = "user_id",
                             emit.state.key = "state",
                             emit.observed.key = "observed",
                             emit.prob.key = "probability",
                             model.key = "user_id",
                             sequence.key = "seq_id",
                             observed.key = "observation",
                             accumulate = "purchase_date"
                            )
    
    # Example 3 - Part of Speech Tagging example
    loadExampleData("hmmdecoder_example", "initial", "state_transition", "emission", "phrases")
    initial <- tbl(con, "initial")
    state_transition <- tbl(con, "state_transition")
    emission <- tbl(con, "emission")
    phrases <- tbl(con, "phrases")
    
    td_hmm_decoder_out3 <- td_hmm_decoder_mle(init.state.prob = initial,
                                         init.state.prob.partition.column = c("model"),
                                         state.transition.prob = state_transition,
                                         state.transition.prob.partition.column = c("model"),
                                         emission.prob = emission,
                                         emission.prob.partition.column = c("model"),
                                         observation = phrases,
                                         observation.partition.column = c("model"),
                                         observation.order.column = c("model", "phrase_id"),
                                         state.model.key = "model",
                                         state.key = "tag",
                                         state.prob.key = "probability",
                                         trans.model.key = "model",
                                         trans.from.key = "from_tag",
                                         trans.to.key = "to_tag",
                                         trans.prob.key = "probability",
                                         emit.model.key = "model",
                                         emit.state.key = "tag",
                                         emit.observed.key = "word",
                                         emit.prob.key = "probability",
                                         model.key = "model",
                                         sequence.key = "phrase_id",
                                         observed.key = "word"
                                         )
    
    # Example 4 - This example uses HMMDecoder to find the propensity of customer churn, 
    # given the actions or transactions of a bank customer.
    loadExampleData("hmmdecoder_example", "churn_initial", "churn_state_transition", "churn_emission", "churn_data")
    churn_initial <- tbl(con, "churn_initial")
    churn_state_transition <- tbl(con, "churn_state_transition")
    churn_emission <- tbl(con, "churn_emission")
    churn_data <- tbl(con, "churn_data")
    
    td_hmm_decoder_out4 <- td_hmm_decoder_mle(init.state.prob = churn_initial,
                                         init.state.prob.partition.column = c("model"),
                                         state.transition.prob = churn_state_transition,
                                         state.transition.prob.partition.column = c("model"),
                                         emission.prob = churn_emission,
                                         emission.prob.partition.column = c("model"),
                                         observation = churn_data,
                                         observation.partition.column = c("model"),
                                         observation.order.column = c("model","id", "path_id"),
                                         state.model.key = "model",
                                         state.key = "tag",
                                         state.prob.key = "probability",
                                         trans.model.key = "model",
                                         trans.from.key = "from_tag",
                                         trans.to.key = "to_tag",
                                         trans.prob.key = "probability",
                                         emit.model.key = "model",
                                         emit.state.key = "state",
                                         emit.observed.key = "observed",
                                         emit.prob.key = "probability",
                                         model.key = "model",
                                         sequence.key = "id",
                                         observed.key = "action",
                                         accumulate = c("path_id")
                                         )