Description
The SVMSparsePredict function takes the model output by the function
SVMSparse (td_svm_sparse_mle
) and a set of test samples (in sparse format)
and outputs a prediction for each sample.
Note: This function is only available when tdplyr is connected to Vantage 1.1
or later versions.
Usage
td_svm_sparse_predict_mle (
object = NULL,
newdata = NULL,
sample.id.column = NULL,
attribute.column = NULL,
value.column = NULL,
accumulate.label = NULL,
output.class.num = NULL,
output.response.probdist = TRUE,
output.responses = NULL,
newdata.sequence.column = NULL,
object.sequence.column = NULL,
newdata.partition.column = NULL,
newdata.order.column = NULL,
object.order.column = NULL
)
Arguments
object |
Required Argument.
Specifies the model tbl_teradata generated by td_svm_sparse_mle .
This argument can accept either a tbl_teradata or an object of
"td_svm_sparse_mle" class.
|
object.order.column |
Optional Argument.
Specifies Order By columns for "object".
Values to this argument can be provided as a vector, if multiple
columns are used for ordering.
Types: character OR vector of Strings (character)
|
newdata |
Required Argument.
Specifies the tbl_teradata containing the input test data.
|
newdata.partition.column |
Required Argument.
Specifies Partition By columns for "newdata".
Values to this argument can be provided as a vector, if multiple
columns are used for partition.
Types: character OR vector of Strings (character)
|
newdata.order.column |
Optional Argument.
Specifies Order By columns for "newdata".
Values to this argument can be provided as a vector, if multiple
columns are used for ordering.
Types: character OR vector of Strings (character)
|
sample.id.column |
Required Argument.
Specifies the name of the input tbl_teradata column that contains the
identifiers of the test samples. The input tbl_teradata 'newdata' must
be partitioned by this column.
Types: character
|
attribute.column |
Required Argument.
Specifies the name of the input tbl_teradata column that contains the
attributes of the test samples.
Types: character
|
value.column |
Optional Argument.
Specifies the name of the input tbl_teradata column that contains the
attribute values. By default, each attribute has the value 1.
Types: character
|
accumulate.label |
Optional Argument.
Specifies the names of the input tbl_teradata columns to copy to the
output tbl_teradata.
Types: character OR vector of Strings (character)
|
output.class.num |
Optional Argument.
Valid only for multiple-class models. Specifies the number of class
labels to appear in the output tbl_teradata, with its corresponding
prediction confidence.
Note:
With Vantage version prior to 1.1.1, the argument defaults to
the value 1.
"output.class.num" cannot be specified along with
"output.responses".
Types: integer
|
output.response.probdist |
Optional Argument.
Specifies whether to display output probability for the predicted
category.
Note: "output.response.probdist" argument support is only available when
tdplyr is connected to Vantage 1.1.1 or later versions.
Default Value: TRUE
Types: logical
|
output.responses |
Optional Argument.
Specifies responses in the input tbl_teradata object.
Note:
"output.responses" argument support is only available
when tdplyr is connected to Vantage 1.1.1 or later versions.
"output.responses" cannot be specified along with
"output.class.num".
The argument "output.response.probdist" must be set to TRUE to
use this argument.
Types: character OR vector of characters
|
newdata.sequence.column |
Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row
of the input argument "newdata". 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)
|
object.sequence.column |
Optional Argument.
Specifies the vector of column(s) that uniquely identifies each row
of the input argument "object". 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_svm_sparse_predict_mle" which
is a named list containing object of class "tbl_teradata".
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("svmsparsepredict_example", "svm_iris_input_test", "svm_iris_input_train")
# Create object(s) of class "tbl_teradata".
svm_iris_input_train <- tbl(con, "svm_iris_input_train")
svm_iris_input_test <- tbl(con, "svm_iris_input_test")
# Example 1 -
# Generate SparseSVMTrainer model based on train data "svm_iris_input_train".
svm_iris_model <- td_svm_sparse_mle(data = svm_iris_input_train,
sample.id.column = "id",
attribute.column = "attribute",
value.column = "value1",
label.column = "species",
max.step = 150,
seed = 0
)
# Use the generated model to predict the 'species' on the test data "svm_iris_input_test".
td_svm_sparse_predict_mle_out <- td_svm_sparse_predict_mle(object = svm_iris_model,
newdata = svm_iris_input_test,
newdata.partition.column = c("id"),
attribute.column = "attribute",
sample.id.column = "id",
value.column = "value1",
accumulate.label = "species"
)