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- SelectionCriteria(data=None, data_filter_expr=None, var_count=None, constant=False, use_likelihood=False, **generic_arguments)
- DESCRIPTION:
The SelectionCriteria() function calculates metrics to help
determine the users for an forecast modeling project.
PARAMETERS:
data:
Required Argument.
Specifies a single multivariate series or a TDAnalyticResult object
created on the residual results from a previously run regression
operation. A single multivariate series has the following fields:
* First field is the original series value.
* Second field is the calculated series value from the model.
* Third field is the calculated residual, which is the original
value minus the calculated value.
When TDAnalyticResult is used, make sure TDAnalyticResult is created without
passing the "layer" argument.
Types: TDSeries, TDAnalyticResult
data_filter_expr:
Optional Argument.
Specifies the filter expression for "data".
Types: ColumnExpression
var_count:
Required Argument.
Specifies the total number of arguments present in the model.
Types: int
constant:
Optional Argument.
Specifies whether model has a constant. When set to False,
model has no constant, otherwise has constant.
Default Value: False
Types: bool
use_likelihood:
Optional Argument.
Specifies whether the selection criteria use residual sum squares
(RSS) or log-likelihood. When set to False, indicates RSS, otherwise
indicates log-likelihood.
Note:
* Applicable only when input is from the ArimaEstimate() function.
Default Value: False
Types: bool
**generic_arguments:
Specifies the generic keyword arguments of UAF functions.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to True,
results are persisted in a table; otherwise,
results are garbage collected at the end of the
session.
Note that, when UAF function is executed, an
analytic result table (ART) is created.
Default Value: False
Types: bool
volatile:
Optional Argument.
Specifies whether to put the results of the
function in a volatile ART or not. When set to
True, results are stored in a volatile ART,
otherwise not.
Default Value: False
Types: bool
output_table_name:
Optional Argument.
Specifies the name of the table to store results.
If not specified, a unique table name is internally
generated.
Types: str
output_db_name:
Optional Argument.
Specifies the name of the database to create output
table into. If not specified, table is created into
database specified by the user at the time of context
creation or configuration parameter. Argument is ignored,
if "output_table_name" is not specified.
Types: str
RETURNS:
Instance of SelectionCriteria.
Output teradataml DataFrames can be accessed using attribute
references, such as SelectionCriteria_obj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Check the list of available UAF analytic functions.
display_analytic_functions(type="UAF")
# Load the example data.
load_example_data("uaf", ["blood2ageandweight"])
# Create teradataml DataFrame object.
df = DataFrame.from_table("blood2ageandweight")
# Create teradataml TDSeries object.
series_arimaestimate = TDSeries(data=df,
id="PatientID",
row_index="SeqNo",
row_index_style="SEQUENCE",
payload_field="Age",
payload_content="REAL")
# Function outputs a result set that contains the estimated
# coefficients with accompanying per-coefficient statistical ratings.
arima_estimate = ArimaEstimate(data1=series_arimaestimate,
nonseasonal_model_order=[1,0,2],
constant=True,
algorithm='MLE',
fit_percentage=70,
coeff_stats=True,
fit_metrics=True,
residuals=True
)
# Example 1 : Calculate the metrics on the series created on the output
# of ArimaEstimate() function.
# Create teradataml TDSeries object.
selectioncriteria_series = TDSeries(data=arima_estimate.fitresiduals,
id="PatientID",
row_index="ROW_I",
row_index_style= "SEQUENCE",
payload_field=["ACTUAL_VALUE", "CALC_VALUE","RESIDUAL"],
payload_content="MULTIVAR_REAL")
uaf_out=SelectionCriteria(data=selectioncriteria_series,
var_count=4,
constant=True,
use_likelihood=False)
# Print the result DataFrame.
print(uaf_out.result)
# Example 2 : Calculate the metrics on the the output of ArimaEstimate()
# function. Note that output of ArimaEstimate() is encapsulated
# in TDAnalyticResult while passing as input.
# Create teradataml TDAnalyticResult object.
art_SelectionCriteria = TDAnalyticResult(data=arima_estimate.result)
uaf_out=SelectionCriteria(data=art_SelectionCriteria,var_count=4,
constant=True,
use_likelihood=True)
# Print the result DataFrame.
print(uaf_out.result)
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