This model is an example to show how to use STOs to train, evaluate and score micro models (individual models per data partition in Teradata).
A sample notebook is located here.
The dataset required to train or evaluate this model is the PIMA Indians Diabetes dataset available here.
CREATE TABLE PIMA_PATIENT_FEATURES AS
(SELECT
patientid,
numtimesprg,
plglcconc,
bloodp,
skinthick,
twohourserins,
bmi,
dipedfunc,
age
FROM PIMA
) WITH DATA;
CREATE TABLE PIMA_PATIENT_DIAGNOSES AS
(SELECT
patientid,
hasdiabetes
FROM PIMA
) WITH DATA;
The micro models are stored in Teradata in the following schema.
CREATE MULTISET TABLE vmo_sto_models, FALLBACK ,
NO BEFORE JOURNAL,
NO AFTER JOURNAL,
CHECKSUM = DEFAULT,
DEFAULT MERGEBLOCKRATIO,
MAP = TD_MAP1
(
partition_id VARCHAR(255) CHARACTER SET LATIN CASESPECIFIC,
model_version VARCHAR(255) CHARACTER SET LATIN CASESPECIFIC,
num_rows BIGINT,
partition_metadata JSON CHARACTER SET UNICODE,
model_artefact CLOB
)
UNIQUE PRIMARY INDEX (partition_id, model_version);