This section describes how to set up the data model attributes based on
pyspark.sql.StructField.
spss.datamodel.Role Objects
This class enumerates valid roles for each field in a data model.
BOTH: Indicates that this field can be either an antecedent or a consequent.
FREQWEIGHT: Indicates that this field is to be used as a frequency weight; this
isn't displayed to the user.
INPUT: Indicates that this field is a predictor or an antecedent.
NONE: Indicates that this field is not used directly during modeling.
TARGET: Indicates that this field is predicted or a consequent.
PARTITION: Indicates that this field identifies the data partition.
RECORDID: Indicates that this field identifie the record id.
SPLIT: Indicates that this field splits the data.
spss.datamodel.Measure Objects
This class enumerates measurement levels for fields in a data model.
UNKNOWN: Indicates that the measure type is unknown.
CONTINUOUS: Indicates that the measure type is continuous.
NOMINAL: Indicates that the measure type is nominal.
FLAG: Indicates that the field value is one of two values.
DISCRETE: Indicates that the field value should be interpreted as a collection
of values.
ORDINAL: Indicates that the measure type is ordinal.
TYPELESS: Indicates that the field can have any value compatible with its
storage.
pyspark.sql.StructField Objects
StructType. A StructField object
comprises four fields:name (string): name of aStructFielddataType (pyspark.sql.DataType): specific data typenullable (bool): if the values of aStructFieldcan containNonevaluesmetadata (dictionary): a python dictionary that stores the option attributes
measure: the key word formeasureattributerole: the key word forroleattributedisplayLabel: the key word forlabelattribute
from spss.datamodel.Role import Role
from spss.datamodel.Measure import Measure
_metadata = {}
_metadata['measure'] = Measure.TYPELESS
_metadata['role'] = Role.NONE
_metadata['displayLabel'] = "field label description"
StructField("userName", StringType(), nullable=False,
metadata=_metadata)